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The Relation between Coagulation and Cancer affects Supportive Treatments

Posted in Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Disease Biology, Genome Biology, Personalized and Precision Medicine & Genomic Research, tagged , , , , , , on October 19, 2015| Leave a Comment »

The Relation between Coagulation and Cancer affects Supportive Treatments

Demet Sag, PhD

 

Coagulation and Cancer

There are several supportive therapies for cancer patients. One of the most important one is controlling the blood intake. This is sometimes observe keeping the blood cell count at certain levels, or providing safe blood/blood products to avoid any contaminations or infections,

The relation between cancer and coagulation was known for a long time but it was becoming clear recently.  Having coagulapathies also reduce the survival of patients since they can’t response to given treatments. Thus, it is necessary to give supportive therapies to control the coagulation. Problems in coagulation may develop from inherited (genetics), or acquired due to given therapies that cause varying abnormalities towards bleeding or thrombose at many levels.  The thrombotic events are important since they are the second leading cause of death in cancer patients (after cancer itself).  The presence of these coagulopathies determines the survival rate, length of survival either short-term or long-term, as well as relapses.

Cancer and Coagulation from start to finish:

Thrombotic risk factors in cancer patients

  1. Patient related
  2. Cancer related
  3. Treatment related

.

  1. Patient Related:
  • Older age
  • Bed rest
  • Obesity
  • Previous thrombosis
  • Prothrombotic mutations
  • High leukocyte and platelet counts
  • Comorbidities
  1. Cancer related:

a. Site of cancer:

  • brain,
  • pancreas,
  • kidney,
  • stomach,
  • lung,
  • bladder,
  • gynecologic,
  • hematologic malignancies

b. Stage of cancer:

  • advanced stage and
  • initial period after diagnosis
  1. Treatments:
  • Hospitalization
  • Surgery
  • Chemo- and
  • hormonal therapy
  • Anti-angiogenic therapy
  • Erythropoiesis stimulating agents
  • Blood transfusions
  • CVC, central venous catheters
  • Radiations

Thromboembolic events can be venous or arterial.

Venous events include

  • deep vein thrombosis (DVT),
  • pulmonary embolism (PE)

together categorized as venous thromboembolism (VTE).

Arterial events, include

  • stroke, myocardial infarction and
  • arterial embolism.

increase in the rate of VTEIncrease in the rate of venous thromboembolism (VTE) over time. Results are presented as annual rates of deep venous thrombosis (DVT), pulmonary embolism (PE) without deep venous thrombosis, and both between 1995 and 2003. Significant trends for increasing rates were observed for all 3 diagnoses (P < .0001). The rate of increase was found to be greater in the subgroup of patients who received chemotherapy. Error bars represent 95% confidence intervals.

There is an increase in both venous and arterial eventsrecently with “unacceptably high” event rates documented in the most contemporary studies:

There are significant consequences to the occurrence of thromboembolism in this setting:

  • requirement for long-term anticoagulation,
  • a 12% annual risk of bleeding complications,
  • an up to 21% annual risk of recurrent VTEand
  • potential impact on chemotherapy delivery and patient quality of life.

 

Therapeutic interventions enhance the risk of VTE in cancer.

  • Cancer patients undergoing surgery have a two-fold increased risk of postoperative VTE as compared to non-cancer patients, and this elevation in risk can persist for a period up to 7 weeks
  • Hospitalization also substantially increases the risk of developing VTE in cancer patients (OR 2.34, 95% CI 1.63 – 3.36)
  • The use of systemic chemotherapy is associated with a 2-to 6-fold increased risk of VTE compared to the general population.
  • Anti-angiogenic agents, particularly thalidomide and lenalidomide, have been associated with high rates of VTE when given in combination with dexamethasone or chemotherapy.
  • Bevacizumab-containing regimens have been associated with increased risk for an arterial thromboembolic event (hazard ratio [HR] 2.0, 95% CI 1.05- 3.75) but the data for risk of VTE are conflicting
  • Sunitinib and sorafenib, agents targeting the angiogenesis pathway, have also similarly been associated with elevated risk for arterial (but not venous) events [RR 3.03 (95% CI, 1.25 to 7.37)]

Anticoagulants and Cancer Coagulopathies

There are many studies on coagulation and use of anti-coagulants yet the same patient may also thrombose at any given time so the coagulant therapies should be under close surveillance.  The study (PMID:111278600) by Palereti et all in 2000 to many  compared this issue.

fig1_10.1002_cncr.23062

Palereti et al. showed that:

“The outcome of anticoagulation courses in 95 patients with malignancy with those of 733 patients without malignancy. All patients were participants in a large, nation-wide population study and were prospectively followed from the initiation of their oral anticoagulant therapy.

Based on 744 patient-years of treatment and follow-up, the rates of major (5.4% vs 0.9%), minor (16.2% vs 3.6%) and total (21.6% vs 4.5%) bleeding were statistically significantly higher in cancer patients compared with patients without cancer.

Bleeding was also a more frequent cause of early anticoagulation withdrawal in patients with malignancy (4.2% vs. 0.7%; p <0.01; RR 6.2 (95% CI 1.95-19.4). There was a trend towards a higher rate of thrombotic complications in cancer patients (6.8% vs. 2.5%; p = 0.058; RR 2.5 [CI 0.96-6.5]) but this did not achieve statistical significance”.

They concluded that “patients with malignancy treated with oral anticoagulants have a higher rate of bleeding and possibly an increased risk of recurrent thrombosis compared with patients without malignancy.”

http://www.cancernetwork.com/sites/default/files/figures_diagrams/1502FeinsteinFigure.png

1502FeinsteinFigure

Cancer and Coagulation in more detail at Molecular Level:

Cancer is a complex disease from its initiation to its treatment. In the body the response to drugs generates side effects for being foreign (immune responses and inflammation), toxic, or disturbing the hemostasis of the coagulation system. In addition, activation of oncogenic pathways cab also be activated that may not only effect the development of the cancer but also may induce oncogenes to activate dormant cancer cells. In the coagulation system the balance is important to keep anti-coagulant state, with oversimplification, such as having certain number of tissue factor (TF) that is a receptor determines the anticoagulant state. However, certain pro-oncogenic genes like RAS, EGFR, HER2, MET, SHH and loss of tumor suppressors (PTEN, TP53) change the gene regulation so they alter the expression, activity and vesicular release of coagulation effectors, as exemplified by tissue factor (TF). As a result, there is a bridge between the coagulation-related genes (coagulome) and specific cancer coagulapathies, such as in glioblastoma multiforme (GBM), medulloblastoma (MB), etc. Therefore, these coagulome can be a great target not only to inhibit angiogenesis and tumor growth but also prevent any coagulopathies, use in single genomics/circulating cancer cells as well as grading the level of cancer specifically.

Here in this figures Tumor-hemostatic system interactions http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f1.gif?v=1&t=ifxvwlxk&s=62da078fc1c8d85d58c256e83954181a16f7463b

and Microparticle (MP) production and activities in cancer are well summarized http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f2.gif?v=1&t=ifxvwlzv&s=13f9b775d7417f12e3ae5f879c09ac8825918d61

coagulation and cancer

 

 

http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f1.gif?v=1&t=ifxvwlxk&s=62da078fc1c8d85d58c256e83954181a16f7463b

Tumor-hemostatic system interactions. Tumor cells activate the hemostatic system in multiple ways. Tumor cells may release procoagulant tissue factor, cancer procoagulant and microparticles (MP) that can directly activate the coagulation cascade. Tumor cells may also activate the host’s hemostatic cells (endothelial cells and platelets), by either release of soluble factors or by direct adhesive contact, thus further enhancing clotting activation.

 

 tumor and coagulation cascade

 

http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f2.gif?v=1&t=ifxvwlzv&s=13f9b775d7417f12e3ae5f879c09ac8825918d61

Microparticle (MP) production and activities in cancer. Tumor cells actively release MP but also promote MP formation by platelets. Tissue factor (TF) and phosphatidylserine (PS) expression on the surfaces of both platelet- and tumor-derived MP are involved in blood clotting activation and thrombus formation. On the other hand, the elevated content of proangiogenic factors in platelet-derived MP (VEGF, vascular endothelial growth factor, FGF, fibroblast growth factor, PDGF, platelet-derived growth factor), render these elements also important mediators of the neangiogenesis process. Finally, intracellular transfer of MP may occur between cancer cells, leading to a horizontal propagation of oncogenes and amplification of their angiogenic phenotype.

 

Immune Response and Cancer with Coagulopathies:

  1. I. Goufman et al also suggested that plasma level of IgG autoantibodies to plasminogen changes during cancer coagulopathies.

Their data based on ELISA measurements of their patients:

  • with benign prostatic hyperplasia (n=25),
  • prostatic cancer (n=17),
  • lung cancer (n=15), and
  • healthy volunteers (n=44).

High levels of IgG to plasminogen were found

  • in 2 (12%) of 17 healthy women, in 1 (3.6%) of 27 specimens in a healthy man,
  • in 17 (68%) of 25 specimens in prostatic cancer,
  • in 10 (59%) of 17 specimens in lung cancer,
  • in 5 (30%) of 15 specimens in benign prostatic hyperplasia.

Comparison of plasma levels of anti-plasminogen IgG by affinity chromatography showed 3-fold higher levels in patients with prostatic cancer vs. healthy men.

Structure and function of platelet receptors initiating blood clotting.

There is a missed or overlooked concept about coagulation and cancer. In their article they mainly focused on the structure and function of key platelet receptors taking role in the thorombus formation and coagulation.

At the clinical level, recent studies reveal the link between coagulation and other pathophysiological processes, including platelet activation, inflammation, cancer, the immune response, and/or infectious diseases. These links are likely to underpin the coagulopathy associated with risk factors for venous thromboembolic (VTE) and deep vein thrombosis (DVT). At the molecular level, the interactions between platelet-specific receptors and coagulation factors could help explain coagulopathy associated with aberrant platelet function, as well as revealing new approaches targeting platelet receptors in diagnosis or treatment of VTE or DVT. Glycoprotein (GP)Ibα, the major ligand-binding subunit of the platelet GPIb-IX-V complex, that binds the adhesive ligand, von Willebrand factor (VWF), is co-associated with the platelet-specific collagen receptor, GPVI. The GPIb-IX-V/GPVI adheso-signaling complex not only initiates platelet activation and aggregation (thrombus formation) in response to vascular injury or disease but GPIbα also regulates coagulation through a specific interaction with thrombin and other coagulation factors.

Clinical Data and Some Samples of Biomarkers:

Development of biomarkers and management of cancer coagulapathies are underway since there are times this coagulapathies may be as deadly as the cancer itself.

The sample study and data from Reference: Alok A. Khorana, M.D. Cancer and Coagulation. Am J Hematol. 2012 May; 87(Suppl 1): S82–S87. Published online 2012 Mar 3. doi:  10.1002/ajh.23143 PMCID: PMC3495606. NIHMSID: NIHMS386379

Resource: PMC full text: Am J Hematol. Author manuscript; available in PMC 2013 May 1.

Published in final edited form as:

Am J Hematol. 2012 May; 87(Suppl 1): S82–S87.

Published online 2012 Mar 3. doi:  10.1002/ajh.23143

Copyright/License ►Request permission to reuse

Table 1

Selected Clinical Risk Factors and Biomarkers for Cancer-associated Thrombosis

Patient-associated risk factors
 Older age
 Race
 Gender
 Medical comorbidities
 Obesity
 Prior history of thrombosis
Cancer-associated risk factors
 Primary site
 Stage
 Cancer histology (higher for adenocarcinoma than squamous cell)
 Time after initial diagnosis (highest in first 3-6 months)
Treatment-associated risk factors
 Chemotherapy
 Anti-angiogenic agents
 Hormonal therapy
 Erythropoiesis-stimulating agents
 Transfusions
 Indwelling venous access devices
 Radiation
 Surgery
Biomarkers
 Currently widely available
 Platelet count (≥350,000/mm3)23
 Leukocyte count (> 11,000/mm3)23
 Hemoglobin (< 10 g/dL)23
 D-dimer25,26
 Investigational and/or not widely available
 Tissue factor (antigen expression, circulating microparticles, antigen or activity)31–33

 

 

Table 2

Predictive Model for chemotherapy-associated VTE23

Patient Characteristics Risk Score
Site of cancer
 Very high risk (stomach, pancreas) 2
 High risk (lung, lymphoma, gynecologic, bladder, testicular) 1
Prechemotherapy platelet count 350000/mm3 or more 1
Hemoglobin level less than 10g/dl or use of red cell growth factors 1
Prechemotherapy leukocyte count more than 11000/mm3 1
Body mass index 35kg/m2 or more 1

High-risk score ≥ 3

Intermediate risk score =1-2

Low-risk score =0

 

 
Rates of VTE According to Risk Score

Study Type, f/u N Low-risk (score=0) Intermediate–risk (score =1-2) High-risk (score≥3)
Khorana et al23, 2008 Development cohort, 2.5 mos 2701 0.8% 1.8% 7.1%
Khorana et al23, 2008 Validation cohort, 2.5 mos 1365 0.3% 2% 6.7%
Kearney et al67, 2009 Retrospective, 2 yrs 112 5% 15.9% 41.4%
Price et al68, 2010 Retrospective, pancreatic, NA 108 – * 14% 27%
Ay et al36, 2010 Prospective, 643 days 819 1.5% 9.6% (score= 2) 17.7%
3.8% (score=1)
Khorana et al69, 2010 Prospective**, 3 mos 30 – *** – 27%
Moore et al2, 2011 Retrospective, cisplatin-based chemotherapy only 932 13% 17.1% 28.2%
Mandala et al37, 2011 Retrospective, phase I patients only, 2 months 1,415 1.5% 4.8% 12.9%

NA=not available

*Pancreatic cancer patients are assigned a score of 2 based on site of cancer and therefore there were no patients in the low-risk category

**included 4-weekly screening ultrasonography

***enrolled only high-risk patients

Table 4

ASCO and NCCN Recommendations for Treatment of VTE in Cancer

ASCO NCCN
Initial treatment
LMWH is the preferred approach for the initial 5-10 days LMWH, UFH or factor Xa antagonists according to patient’s characteristics and clinical situation
Long term treatment
LMWH for at least 6 months is preferred. LMWH is preferred
VKA are acceptable when LMWH is not available. Indefinite anticoagulation in patients with active cancer or persistent risk factors
Indefinite anticoagulation in patients with active cancer.
Thrombolytic therapy in initial treatment
Restricted to patients with life- or limb-threatening thrombotic events Restricted to massive or submassive PE with moderate or severe right ventricular enlargement or dysfunction
Inferior vena cava filters
Restricted to patients with contraindications to anticoagulation or recurrent VTE despite adequate long-term LMWH Restricted to patients with contraindications to or failure of anticoagulation, cardiac or pulmonary dysfunction severe enough to make any new PE life-threatening or multiple PE with chronic pulmonary hypertension
Treatment of catheter-related thrombosis
NA LMWH or VKA for as long as catheter is in place or for 1 to 3 months after catheter removal
 Soluble P-selectin (> 53.1 ng/mL)65
 Factor VIII66
 Prothrombin fragment F 1+2 (>358 pmol/L) 26

 

 

 

Genome Analysis at the crossroads of Coagulation and Cancer

, Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disorders, Genome Medicine, 2015, 7,1

Phenotype similarity clustering of cases according to HPO terms. Heat map showing pairwise phenotypic similarity among affected members of pedigrees, cases with classical syndromes and cases with variants in ACTN1. The groups are ordered through complete-linkage hierarchical clustering within each class and P values of phenotypic similarity are shown in a scatterplot superimposed over a histogram showing the distribution of P values.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5
Download authors’ original image

Phenotype clusters 18 and 29. Illustrative subgraphs of the HPO showing terms for the phenotype clusters 18 (15 cases) and 29 (16 cases). Arrows indicate direct (solid) or indirect (dashed) is a relations between terms in the ontology. DMPV: decreased mean platelet volume; PA: phenotypic abnormality; Plt-agg: platelet aggregation abnormality.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5
Download authors’ original image

s13073-015-0151-5-5 s13073-015-0151-5-6

Rare variants identified inACTN1
Case Transcript variant ENST00000394419 Protein variant ENSP00000377941.4 HGMD variant Classification PLT, ×109/L MPV, fL, and/or presence of macrothrombocytes Bleeding phenotype
B200726 14:69392385 A/C F37C No LPV 57 18.1, macrothrombocytes None
B200207 14:69392358 C/T R46Q Yes PV 53 >13, macrothrombocytes None
B200209 PV 76 >13, macrothrombocytes Mild
B200212 PV 98 >13, macrothrombocytes None
B200254 PV 34 >13, macrothrombocytes None
B200735 PV 52 12.0, macrothrombocytes None
B200746 14:69392359 G/A R46W No LPV 96 15.2, macrothrombocytes None
B200197 14:69392344 G/C Q51E No LPV 113 >13, macrothrombocytes Mild
B200836 14:69387750 C/T V105I Yes PV 53 NA, macrothrombocytes None
B200837a PV 75 NA, macrothrombocytes None
B200671 14:69371375 C/T E225K Yes PV 97 13.7, macrothrombocytes Mild
B200716 PV 82 15.0, macrothrombocytes None
B200398 14:69369274 C/T V228I No LPV 31 15.4, macrothrombocytes Mild
B200280 14:69358897 C/T R320Q No LPV 108 15.1, macrothrombocytes Mild
B200281a LPV 111 13.9, macrothrombocytes None
B200835 14:69352254 C/T A425T No VUS 50 10.0, no macrothrombocytes Mild
B200283 14:69349768 A/G L547P No LPV 91 13.3, macrothrombocytes Mild
B200048 14:69349648 G/A A587V No VUS 390 NA, no macrothrombocytes Mild
B200284 14:69346749 G/T T737N No LPV 60 16.1, macrothrombocytes Mild
B200285a LPV 48 16.8, macrothrombocytes Mild
B200741 14:69346747 G/A R738W Yes PV 94 12.9, macrothrombocytes None
B200745 PV 70 14.5, macrothrombocytes None
B200750 14:69346746 C/T R738Q No LPV 106 14.0, macrothrombocytes None
B200414 14:69346704 C/G R752P No LPV 121 11.4, macrothrombocytes Mild

aAffected family member.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Rare variants identified inMYH9and validated by Sanger sequencing
Case Transcript variant ENST00000216181 Protein variant ENSP00000216181 HGMD variant Classification PLT, ×109/L MPV, fL and/or presence of macrothrombocytes OtherMYH9-RD characteristics
B200760 22:36744995 G/A S96L Yes PV 180 Macrothrombocytes None
B200771 22:36705438 C/A D578Y No VUS 184 10.1 None
B200423 22:36696237 G/A A971V No VUS 262 10.2 None
B200024 22:36691696 A/G S1114P Yes VUS 164 NA None
B200245 VUS 53 11.1, Macrothrombocytes None
B200243 22:36691115 G/A R1165C Yes PV 22 Macrothrombocytes None
B200594 PV 46 Macrothrombocytes None
B200595a PV 61 Macrothrombocytes None
B200614 22:36688151 C/T D1409N No VUS 319 9.8 None
B200752 VUS 149 10.1, Macrothrombocytes None
B200855 VUS 95 16.8, Macrothrombocytes None
B200208 22:36688106 C/T D1424N Yes PV 99 13.6 None
B200010 22:36685249 G/C S1480W No VUS 244 NA None
B200244 22:36678800 G/A R1933X Yes PV 26 Macrothrombocytes Döhle inclusions

Other MYH9-RD characteristics sought were the presence of Döhle inclusions, cataract, deafness or renal pathology.

aFather of B200594.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Pathogenic and likely pathogenic variants identified in genes associated with autosomal recessive and X-linked recessive bleeding and platelet disorders
Case Position Gene Ref Alt Genotype HGMD Effecta Haematological HPO terms Other HPO terms Classification:
Variant Phenotype
B200286 3:148881737 HPS3 G C C|C Yes Abnormal splicing Bleeding with minor or no trauma, subcutaneous haemorrhage, menorrhagia, postpartum haemorrhage, impaired ADP-induced platelet aggregation, impaired epinephrine-induced platelet aggregation, epistaxis, prolonged bleeding after surgery, prolonged bleeding after dental extraction, increased mean platelet volume. Hypothyroidism, visual impairment, nystagmus, albinism. PV Explained
B200412 3:148858819 HPS3 T TA T|TA No Frameshift Impaired epinephrine-induced platelet aggregation, bleeding with minor or no trauma, subcutaneous haemorrhage, epistaxis, menorrhagia, prolonged bleeding after surgery, abnormal dense granules. Ocular albinism. LPV Possibly explained
3:148876539 HPS3 G A G|A No W593a LPV
B200068 10:103827041 HPS6 C G C|G No L604V Increased mean platelet volume. Congenital cataract, strabismus, maternal diabetes. LPV Possibly explained
10:103827554 HPS6 C G C|G No L775V LPV
B200196 X:48542673 WAS C T T Yes T45M Thrombocytopenia, abnormal bleeding, decreased mean platelet volume, abnormal platelet shape. Recurrent infections. PV Explained
B200725 X:48544145 WAS T C C Yes F128S Monocytosis, neutrophilia, thrombocytopenia, leukocytosis, subcutaneous haemorrhage, gastrointestinal haemorrhage. PV Explained
B200443 X:138633272 F9 G A A Yes R191H Reduced factor IX activity, impaired ADP-induced platelet aggregation, bleeding with minor or no trauma, spontaneous haematomas, abnormal number of dense granules. PV Partially explained
B200452 X:154124407 F8 C G G Yes S2125T Reduced factor VIII activity, persistent bleeding after trauma, prolonged bleeding after surgery, prolonged bleeding after dental extraction, bleeding requiring red cell transfusion, impaired collagen-induced platelet aggregation, bleeding with minor or no trauma, joint haemorrhage, abnormal platelet shape, abnormal number of dense granules. PV Partially explained
B200772 X:154176011 F8 A G G No F692S Reduced factor VIII activity, bruising susceptibility, impaired ADP-induced platelet aggregation, impaired collagen-induced platelet aggregation, impaired thromboxane A2 agonist-induced platelet aggregation, impaired ristocetin-induced platelet aggregation, impaired arachidonic acid-induced platelet aggregation, impaired thrombin-induced platelet aggregation, abnormal platelet granules, bleeding with minor or no trauma. LPV Possibly partially explained

Alt: alternative; Ref: reference.

aEffect considered relative to the Consensus Coding Sequence (CCDS) for each gene.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Table 2

TFPI and TF tumor mRNA expression across clinicopathological breast cancer subtypes

  mRNA expression (tumor) Protein levels (plasma)
Characteristic Groups Total TFPI (α + β) P TFPIα P TFPIβ P TF P Total TFPI P Free TFPI P TF P
T-status T1 −0.146 0.054 −0.135 0.257 −0.084 0.201 −0.023 0.652 72.01 0.013 10.82 0.997 4.14 0.125
T2-T3 0.085 0.018 0.060 0.054 65.02 10.82 4.66
Grade G1-G2 −0.022 0.850 −0.005 0.424 −0.033 0.743 0.271 0.003 71.04 0.082 10.66 0.682 4.63 0.557
G3 −0.045 −0.113 0.004 −0.229 66.12 10.97 4.14
N-status Negative −0.109 0.091 −0.136 0.127 −0.082 0.104 0.005 0.881 69.93 0.183 10.77 0.869 4.95 0.282
Positive 0.104 0.078 0.110 0.032 66.00 10.90 4.14
ER status Positive −0.067 0.317 −0.082 0.557 −0.056 0.183 0.001 0.784 69.42 0.240 10.91 0.671 4.42 0.409
PR status Negative 0.076 0.011 0.123 0.057 65.44 10.52 5.28
Positive −0.131 0.021 −0.145 0.075 −0.112 0.014 0.085 0.244 69.81 0.195 11.19 0.175 4.32 0.246
HER2-status Negative 0.161 0.108 0.182 −0.127 65.92 10.08 5.04
Negative −0.072 0.054 −0.101 0.073 −0.041 0.154 0.004 0.731 68.45 0.893 10.68 0.287 4.47 0.428
Positive 0.313 0.301 0.228 0.103 69.09 12.05 4.78
HR status Yes 0.076 0.326 0.007 0.587 0.114 0.221 0.016 0.991 64.78 0.161 10.41 0.568 5.26 0.470
No −0.066 −0.080 −0.052 0.014 69.57 10.94 4.47
Triple-negative status Yes −0.051 0.886 −0.110 0.718 0.041 0.635 −0.158 0.326 63.21 0.072 10.06 0.345 5.23 0.969
No −0.029 −0.048 −0.027 0.055 69.73 10.99 4.57

Median values for TFPI and TF mRNA expression in tumors and protein levels in plasma according to clinically defined groups. Corresponding P-values (unadjusted) are shown. Significant P-values in bold. TFPI, tissue factor pathway inhibitor; TF, tissue factor; HER2, human epidermal growth factor receptor 2.Abbreviations: T, tumor; G, grade; N, node; ER, estrogen receptor; PR, progesterone receptor; HR, hormone receptor.

Table 3

Significant association between TFPI single nucleotide polymorphisms (SNPs) and clinicopathological characteristics and molecular subtypes

Characteristic SNP Risk allele Odds ratio 95% CI P False discovery rate
T status
T1 Reference Reference Reference Reference
T2 to T3 rs10153820 A 3.14 1.44, 6.86 0.004 0.056
TN status (ER-/PR-/HER2-negative)
No Reference Reference Reference Reference
Yes rs8176541a G 2.62 1.11, 5.35 0.026 0.092
rs3213739a G 2.58 1.34, 4.99 0.005 0.033
rs8176479a C 3.10 1.24, 7.72 0.015 0.071
rs2192824a T 2.44 1.39, 4.93 0.002 0.033
N status
Positive Reference Reference Reference Reference
Negative rs10179730 G 3.34 1.42, 7.89 0.006 0.083
Basal tumor subtype
Non-basal Reference Reference Reference Reference
Basal rs3213739a G 2.23 1.15, 4.34 0.018 0.107
rs8176479a C 2.79 1.12, 6.96 0.028 0.107
rs2192824a T 2.41 1.24, 4.65 0.009 0.107
rs10187622a C 5.20 1.17, 23.20 0.031 0.107
Luminal B tumor subtype
Non-luminal B Reference Reference Reference Reference
Luminal B rs16829086a T 2.09 1.03, 4.25 0.041 0.191
rs10179730a G 3.53 1.47, 8.46 0.005 0.066
rs10187622a T 2.73 1.24, 6.03 0.013 0.091
Normal-like tumor subtype
Non-normal-like Reference Reference Reference Reference
Normal-like rs5940 T 22.17 4.43, 110.8 0.0002 0.003

aSNPs representing a haplotype effect. SNPs are listed by ascending chromosome positions. TFPI, tissue factor pathway inhibitor; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor 2.

Table 4

Significant correlations between TFPI single nucleotide polymorphisms (SNPs) and TFPI mRNA expression in breast tumors

Probe SNP Region Alleles a Minor allele frequency Beta r P False discovery rate
TFPIα rs2192824b Intronic C:T 0.490 −0.209 −0.180 0.029 0.200
TFPIα rs7594359b Intronic C:T 0.483 −0.219 −0.184 0.025 0.200
TFPIβ rs3213739b Intronic G:T 0.417 0.187 0.213 0.010 0.032
TFPIβ rs8176479b Intronic C:A 0.238 0.184 0.192 0.021 0.049
TFPIβ rs2192824b Intronic C:T 0.490 −0.267 −0.273 0.001 0.011
TFPIβ rs12613071b Intronic T:C 0.158 0.284 0.208 0.011 0.032
TFPIβ rs2192825b Intronic T:C 0.466 −0.251 −0.249 0.002 0.012
TFPIβ rs7594359b Intronic C:T 0.483 −0.248 −0.247 0.002 0.012
TFPIα + β rs2192824b Intronic C:T 0.490 −0.168 −0.161 0.050 0.187
TFPIα + β rs12613071b Intronic T:C 0.158 0.238 0.164 0.048 0.187
TFPIα + β rs7594359b Intronic C:T 0.483 −0.190 −0.178 0.030 0.187

aMajor:minor. bSNPs representing a haplotype effect. mRNA expression was assayed by the Agilent Human V2 Gene Expression 8x60k array, and probes for tissue factor pathway inhibitor (TFPI)α, TFPIβ and total TFPI (TFPIα + β) mRNA were analyzed. Alleles for the positive DNA strand (UCSC annotated) are shown, and SNPs are listed by ascending chromosome positions.

“Eight TFPI SNPs were found to be correlated to total TFPI protein levels in patient plasma (Table 5). The A-T-A-C-T-A-C-G haplotype composed of these eight SNPs (rs8176541-rs3213739-rs8176479-rs2192824-rs2192825-rs16829088-rs7594359-rs10153820) represented a common haplotype (frequency 0.19) with quite strong correlation to total TFPI protein; r = 0.481 (B = 14.62, P = 6.35 × 10−10). No correlation between TFPI SNPs and free TFPI protein, or between TF SNPs and TF protein in plasma was observed (P >0.05, data not shown). Adjusting for age had no effect on the correlation (data not shown).”

Table 5

Significant correlations between TFPI single nucleotide polymorphisms (SNPs) and total TFPI protein levels in plasma

Protein SNP Region Alleles a Minor allele frequency Beta r P False discovery rate
Total TFPI rs8176541b Intronic G:A 0.283 15.64 0.571 7.69 × 10−14 1.08 × 10−12
Total TFPI rs3213739b Intronic G:T 0.417 11.35 0.488 5.38 × 10−10 3.77 × 10−9
Total TFPI rs8176479b Intronic C:A 0.238 12.22 0.480 1.20 × 10−9 5.62 × 10−9
Total TFPI rs2192824b Intronic C:T 0.490 −9.88 −0.404 3.81 × 10−7 1.07 × 106
Total TFPI rs2192825b Intronic T:C 0.466 −7.55 −0.301 2.40 × 10−4 5.30 × 10−4
Total TFPI rs16829088b Intronic G:A 0.250 11.23 0.424 1.00 × 10−7 3.51 × 10−7
Total TFPI rs7594359b Intronic C:T 0.483 −6.90 −0.275 6.90 × 10−4 0.001
Total TFPI rs10153820b Near 5UTR G:A 0.125 −7.79 −0.215 0.009 0.016

aMajor:minor. bSNPs representing a haplotype effect for total tissue factor pathway inhibitor (TFPI). Alleles for the positive DNA strand (UCSC annotated) are shown.

In sum, combination of molecular physiology and genomics will improve the conditions of the patients not only to diagnose early or to monitor the disease but also to streamline the current drugs to be more efficient and therapeutic.

References:

·         PMID: 25480646, Gardiner EE1, Andrews RK. Structure and function of platelet receptors initiating blood clotting. Adv Exp Med Biol. 2014;844:263-75. doi: 10.1007/978-1-4939-2095-2_13.

 

Further Reading:

Mari Tinholt, Hans Kristian Moen Vollan, Kristine Kleivi Sahlberg, Sandra Jernström, Fatemeh Kaveh, Ole Christian Lingjærde,Rolf Kåresen, Torill Sauer, Vessela Kristensen, Anne-Lise Børresen-Dale, Per Morten Sandset, Nina Iversen, Tumor expression, plasma levels and genetic polymorphisms of the coagulation inhibitor TFPI are associated with clinicopathological parameters and survival in breast cancer, in contrast to the coagulation initiator TF, Breast Cancer Research, 2015, 17, 1

 Chaabane, L. Tei, L. Miragoli, L. Lattuada, M. von Wronski, F. Uggeri, V. Lorusso, S. Aime, In Vivo MR Imaging of Fibrin in a Neuroblastoma Tumor Model by Means of a Targeting Gd-Containing Peptide, Molecular Imaging and Biology, 2015,

Daniela Bianconi, Alexandra Schuler, Clemens Pausz, Angelika Geroldinger, Alexandra Kaider, Heinz-Josef Lenz, Gabriela Kornek, Werner Scheithauer, Christoph C. Zielinski, Ingrid Pabinger, Cihan Ay, Gerald W. Prager, Integrin beta-3 genetic variants and risk of venous thromboembolism in colorectal cancer patients, Thrombosis Research, 2015,

Olumide B Gbolahan, Trista J Stankowski-Drengler, Abiola Ibraheem, Jessica M Engel, Adedayo A Onitilo, Management of chemotherapy-induced thromboembolism in breast cancer, Breast Cancer Management, 2015, 4, 4, 187

Ami Schattner, Meital Adi, Mobile menace- floating aortic arch thrombus, The American Journal of Medicine, 2015,

Chuang-Chi Liaw, Hung Chang, Tsai-Sheng Yang, Ming-Sheng Wen, Pulmonary Venous Obstruction in Cancer Patients,Journal of Oncology, 2015, 2015, 1

Esther Rabizadeh, Izhack Cherny, Doron Lederfein, Shany Sherman, Natalia Binkovsky, Yevgenia Rosenblat, Aida Inbal, The cell-membrane prothrombinase, fibrinogen-like protein 2, promotes angiogenesis and tumor development, Thrombosis Research, 2015, 136, 1, 118

Anna Falanga, Marina Marchetti, Laura Russo, The mechanisms of cancer-associated thrombosis, Thrombosis Research,2015, 135, S8

I. Goufman, V. N. Yakovlev, N. B. Tikhonova, R. B. Aisina, K. N. Yarygin, L. I. Mukhametova, K. B. Gershkovich, D. A. Gulin,Autoantibodies to Plasminogen and Their Role in Tumor Diseases, Bulletin of Experimental Biology and Medicine, 2015, 158,4, 493

Trisha A. Rettig, Julie N. Harbin, Adelaide Harrington, Leonie Dohmen, Sherry D. Fleming, Evasion and interactions of the humoral innate immune response in pathogen invasion, autoimmune disease, and cancer, Clinical Immunology, 2015, 160, 2,244

Sarah K Westbury, Ernest Turro, Daniel Greene, Claire Lentaigne, Anne M Kelly, Tadbir K Bariana, Ilenia Simeoni, Xavier Pillois, Antony Attwood, Steve Austin, Sjoert BG Jansen, Tamam Bakchoul, Abi Crisp-Hihn, Wendy N Erber, Rémi Favier,Nicola Foad, Michael Gattens, Jennifer D Jolley, Ri Liesner, Stuart Meacham, Carolyn M Millar, Alan T Nurden, Kathelijne Peerlinck, David J Perry, Pawan Poudel, Sol Schulman, Harald Schulze, Jonathan C Stephens, Bruce Furie, Peter N Robinson, Chris van Geet, Augusto Rendon, Keith Gomez, Michael A Laffan, Michele P Lambert, Paquita Nurden, Willem H Ouwehand, Sylvia Richardson, Andrew D Mumford, Kathleen Freson, Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disorders, Genome Medicine, 2015, 7,1

Ades, S. Kumar, M. Alam, A. Goodwin, D. Weckstein, M. Dugan, T. Ashikaga, M. Evans, C. Verschraegen, C. E. Holmes,Tumor oncogene (KRAS) status and risk of venous thrombosis in patients with metastatic colorectal cancer,Journal of Thrombosis and Haemostasis, 2015, 13, 6

Marcel Levi, Cancer-related coagulopathies, Thrombosis Research, 2014, 133, S70

Axel C. Matzdorff, David Green, Management of venous thromboembolism in cancer patients, Reviews in Vascular Medicine,2014, 2, 1, 24

Claude Bachmeyer, Milène Buffo, Bérénice Soyez, No Evidence Not to Prescribe Thromboprophylaxis in Hospitalized Medical Patients with Cancer, The American Journal of Medicine, 2014, 127, 7, e33

Nathalie Magnus, Esterina D’Asti, Brian Meehan, Delphine Garnier, Janusz Rak, Oncogenes and the coagulation system – forces that modulate dormant and aggressive states in cancer, Thrombosis Research, 2014, 133, S1

Maria Sofra, Anna Antenucci, Michele Gallucci, Chiara Mandoj, Rocco Papalia, Claudia Claroni, Ilaria Monteferrante, Giulia Torregiani, Valeria Gianaroli, Isabella Sperduti, Luigi Tomao, Ester Forastiere, Perioperative changes in pro and anticoagulant factors in prostate cancer patients undergoing laparoscopic and robotic radical prostatectomy with different anaesthetic techniques, Journal of Experimental & Clinical Cancer Research, 2014, 33, 1, 63

Taslim A. Al-Hilal, Farzana Alam, Jin Woo Park, Kwangmeyung Kim, Ick Chan Kwon, Gyu Ha Ryu, Youngro Byun, Prevention effect of orally active heparin conjugate on cancer-associated thrombosis, Journal of Controlled Release, 2014, 195, 155

Samridhi Sharma, Sandipan Ray, Aliasgar Moiyadi, Epari Sridhar, Sanjeeva Srivastava, Quantitative Proteomic Analysis of Meningiomas for the Identification of Surrogate Protein Markers, Scientific Reports, 2014, 4, 7140

W. Yau, P. Liao, J. C. Fredenburgh, A. R. Stafford, A. S. Revenko, B. P. Monia, J. I. Weitz, Selective depletion of factor XI or factor XII with antisense oligonucleotides attenuates catheter thrombosis in rabbits,Blood, 2014, 123, 13, 2102

Anna Falanga, Laura Russo, Viola Milesi, The coagulopathy of cancer, Current Opinion in Hematology, 2014, 21, 5, 423

Sarah J. Barsam, Raj Patel, Roopen Arya, Anticoagulation for prevention and treatment of cancer-related venous thromboembolism, British Journal of Haematology, 2013, 161, 6

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Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle and Histone Modulation

Posted in Biomarkers & Medical Diagnostics, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Human Immune System in Health and in Disease, Imaging-based Cancer Patient Management, tagged , , , , , , , , , on October 14, 2015| Leave a Comment »

Renal (Kidney) Cancer: Connections in Metabolism at Krebs cycle  and Histone Modulation

Curator: Demet Sag, PhD, CRA, GCP

Through Histone Modulation

Renal cell carcinoma accounts for only 3% of total human malignancies but it is still the most common type of urological cancer with a high prevalence in elderly men (>60 years of age).

ICD–10 C64
ICD–9-CM 189.0
ICD-O M8312/3
OMIM 144700 605074
DiseasesDB 11245
MedlinePlus 000516
eMedicine med/2002

Most kidney cancers are renal cell carcinomas (RCC). RCC lacks early warning signs and 70 % of patients with RCC develop metastases. Among them, 50 % of patients having skeletal metastases developed a dismal survival of less than 10 % at 5 years.

There are three main histopathological entities:

  1. Clear cell RCC (ccRCC), dominant in histology (65%)
  2. Papillary (15-20%) and
  3. Chromophobe RCC (5%).

There are very rare forms of RCC shown in collecting duct, mucinous tubular, spindle cell, renal medullary, and MiTF-TFE translocation carcinomas.

Subtypes of clear cell and papillary RCC, and a new subtype, clear cell papillary http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f6.jpg

Different subtypes of clear cell RCC can be defined by HIF patterns as well as by transcriptomic expression as defined by ccA and ccB subtypes. Papillary RCC also demonstrates distinct histological subtypes. A recently described variant denoted as clear cell papillary RCC is VHL wildtype (VHL WT), while other clear cell tumors are characterized by VHL mutation, loss, or inactivation (VHL MT).

KEY POINTS

  • Renal cell cancer is a disease in which malignant (cancer) cells form in tubules of the kidney.
  • Smoking and misuse of certain pain medicines can affect the risk of renal cell cancer.
  • Signs of renal cell cancer include
  • Blood in your urine, which may appear pink, red or cola colored
  • A lump in the abdomen.
  • Back pain just below the ribs that doesn’t go away
  • Weight loss
  • Fatigue
  • Intermittent fever

 

Factors that can increase the risk of kidney cancer include:

  • Older age.
  • High blood pressure (hypertension).
  • Treatment for kidney failure.(long-term dialysis to treat chronic kidney failure)
  • Certain inherited syndromes.
  • von Hippel-Lindau disease

Tests that examine the abdomen and kidneys are used to detect (find) and diagnose renal cell cancer.

The following tests and procedures may be used:

There are 3 treatment approaches for Renal Cancer:

Stages of Renal Cancer:

Stage I Tumour of a diameter of 7 cm (approx. 23⁄4 inches) or smaller, and limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage II Tumour larger than 7.0 cm but still limited to the kidney. No lymph node involvement or metastases to distant organs.
Stage III
any of the following		 Tumor of any size with involvement of a nearby lymph node but no metastases to distant organs. Tumour of this stage may be with or without spread to fatty tissue around the kidney, with or without spread into the large veins leading from the kidney to the heart.
Tumour with spread to fatty tissue around the kidney and/or spread into the large veins leading from the kidney to the heart, but without spread to any lymph nodes or other organs.
Stage IV
any of the following		 Tumour that has spread directly through the fatty tissue and the fascia ligament-like tissue that surrounds the kidney.
Involvement of more than one lymph node near the kidney
Involvement of any lymph node not near the kidney
Distant metastases, such as in the lungs, bone, or brain.
Grade Level Nuclear Characteristics
Grade I Nuclei appear round and uniform, 10 μm; nucleoli are inconspicuous or absent.
Grade II Nuclei have an irregular appearance with signs of lobe formation, 15 μm; nucleoli are evident.
Grade III Nuclei appear very irregular, 20 μm; nucleoli are large and prominent.
Grade IV Nuclei appear bizarre and multilobated, 20 μm or more; nucleoli are prominent

 

GENETICS:

90% or more of kidney cancers are believed to be of epithelial cell origin, and are referred to as renal cell carcinoma (RCC), which are further subdivided based on histology into clear-cell RCC (75%), papillary RCC (15%),

chromophobe tumor (5%), and oncocytoma (5%).

Nephrectomy continues to be the cornerstone of treatment for localized renal cell carcinoma (RCC). Research is still underway to developed targeted agents against the vascular endothelial growth factor (VEGF) molecule and related pathways as well as inhibitors of the mammalian target of rapamycin (mTOR),

clear cell RCC (ccRCC) doesn’t respond well to radiation chemotherapy due to high radiation resistancy.  The hallmark genetic features of solid tumors such as KRAS or TP53 mutations are also absent. However, there is a well-designed association presented between ccRCC and mutations in the VHL gene

Hereditary RCC, accounts for around 4% of cases, has been a relatively dominant area of RCC genetics.

Causative genes have been identified in several familial cancer syndromes that predispose to RCC including

  • VHLmutations in von Hippel-Lindau disease that predispose to ccRCC and VHL is somatically mutated in up to 80% of ccRCC
  • METmutations in familial papillary renal cancer,
  • dominantly activating kinase domainMET mutation reported in 4–10% of sporadic papillary RCC[2].
  • FH (fumarate hydratase) mutations in hereditary leiomyomatosis and renal cell cancer that predispose to papillary RCC
  • FLCN(folliculin) mutations in Birt-Hogg-Dubé syndrome that predispose to primarily chromophobe RCC.

In addition, there are germline mutations:

  • in theTSC1/2 genes predispose to tuberous sclerosis complex where approximately 3% of cases develop ccRCC,
  • in the SDHB(succinate dehydrogenase type B) in patients with paraganglioma syndrome shows elevated risk to develop multiple types of RCC.

GWAS in almost 6000 RCC cases demonstrated that loci on 2p21 and 11q13.3 play a role in RCC. Although EPAS1 gene encoding a transcription factor operative in hypoxia-regulated responses in  2p21 , 11q13.3 has no known coding genes.

There has been, however, comparatively less progress in the elaboration of the somatic genetics of sporadic RCC.

Absent mutations in sporadic RCC:

  • somaticFH mutations
  • somatic mutations ofTSC12 and SDHB

Present mutations in sporadic ccRCC (chromophobe RCC) are

  • TSC1mutations occur in 5% of ccRCCs and
  • somatic mutations inFLCN  rare
  • may predict for extraordinary sensitivity to mTORC1 inhibitors clinically.

The COSMIC database reports somatic point mutations in TP53 in 10% of cases, KRAS/HRAS/NRAS combined ≤1%, CDKN2A 10%, PTEN 3%, RB1 3%, STK11/LKB1 ≤1%, PIK3Ca ≤1%, EGFR1% and BRAF ≤1% in all histological samples. Further information can be found at (http://www.sanger.ac.uk/ genetics/CGP/cosmic/) for the  RCC somatic genetics.

HIF- and hypoxia-mediated epigenetic regulation work together due to histone modification because HIF activate several chromatin demethylases, including JMJD1A (KDM3A), JMJD2B (KDM4B), JMJD2C (KDM4C) and JARID1B (KDM5B), all of which are directly targeted by HIF.

Overview of Histone 3 modifications implicated in RCC genetics http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f1.jpg

A number of histone modifying genes are mutated in renal cell carcinoma. These include the H3K36 trimethylase SETD2, the H3K27 demethylase UTX/KDM6A, the H3K4 demethylase JARID1C/KDM5C and the SWI/SNF complex compenent PBRM1, shown in this cartoon to represent their relative activities on Histone H3.

Hyper-methylation is observed on RASSF1 highly (50% f RCC) yet less on VHL and CDKN2A, yet there is a methylation and silencing observed on TIMP3 and secreted frizzled-related protein 2.

RCC is ONE OF THE “CILIOPATHIES” among Polycystic Kidney Disease (PKD), Tuberous Sclerosis Complex (TSC) and VHL Syndrome. The main display of cysts is dysfunctional primary cilia.

Mol Cancer Res. Author manuscript; available in PMC 2013 Jan 1.

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117

pVHL mutants are categorized as Class A, B and C depending on the affected step in pVHL protein quality control http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f2.jpg

VHL proteostasis involves the chaperone mediated translocation of nascent VHL peptide from the ribosome to the TRiC/CCT chaperonin, where folding occurs in an ATP dependent process. The VBC complex is formed while VHL is bound to TRiC, and the mature complex is then released. Three different classes of mutation exist: Class A mutations prevent binding of VHL to TRiC, and abrogate folding into a mature complex. Class B mutations prevent association of Elongins C and B to VHL. Class C mutations inhibit interaction between VHL and HIF1 a.

# 193300. VON HIPPEL-LINDAU SYNDROME; VHL ICD+, Links
VON HIPPEL-LINDAU SYNDROME, MODIFIERS OF, INCLUDED
Cytogenetic locations: 3p25.3 , 11q13.3
Matching terms: lindau, disease, von, hippellindau, hippel
  • Birt-Hogg-Dube syndrome,
# 135150. BIRT-HOGG-DUBE SYNDROME; BHD ICD+, Links
Cytogenetic location: 17p11.2 
Matching terms: birthoggdube, syndrome, birt, hogg, dube
  • tuberous sclerosis
# 191100. TUBEROUS SCLEROSIS 1; TSC1 ICD+, Links
Cytogenetic location: 9q34.13 
Matching terms: tuber, sclerosi, tuberous
  • familial papillary renal cell carcinoma.
# 144700. RENAL CELL CARCINOMA, NONPAPILLARY; RCC ICD+, Links
NONPAPILLARY RENAL CARCINOMA 1 LOCUS, INCLUDED
Cytogenetic locations: 3p25.3 , 3p25.3 , 3q21.1 , 8q24.13 , 12q24.31 , 17p11.2 , 17q12 
Matching terms: renal, familial, papillary, carcinoma, cell

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4358399/bin/467fig3.jpg

Model for the control of the fate of nephron progenitor cells. Eya1 lies genetically upstream of Six2. Six2 labels the nephron progenitor cells, which can either maintain a progenitor state and self-renew or differentiate via the Wnt4-mediated MET. Wnt4 expression is under the direct control of Wt1. β-Catenin is involved in both progenitor cell fates through activation of different transcriptional programs. Active nuclear phosphorylated Yap/Taz shifts the progenitor balance toward the self-renewal fate. Eya1 and Six2 interact directly with Mycn, leading to dephosphorylation of Mycn pT58, stabilization of the protein, increased proliferation, and potentially a shift of the nephron progenitor toward self-renewal. Genes activated in Wilms’ tumors are depicted in green, and inactivated genes are in blue. Deregulation of Yap/Taz in Wilms’ tumors results in phosphorylated Yap not being retained in the cytoplasm as it should, but it translocates to the nucleus and thus shifts the progenitor cell balance toward self-renewal. This model is likely a simplification, as it presumes that all Wilms’ tumors, regardless of causative mutation, are caused by the same mechanism.

Epigenetic aberrations associated with Wilms’ tumor

Chinese Case Study: PMCID: PMC4471788

They u8ndertook this study based on association of low circulating adiponectin concentrations with a higher risk of several cancers, including renal cell carcinoma. Thus they demonstrated that by case–control study that ADIPOQ rs182052 is significantly associated with ccRCC risk.

They investigated the frequency of three single nucleotide polymorphisms (SNPs), rs182052G>A, rs266729C>G, rs3774262G>A, in the adiponectin gene (ADIPOQ).  1004 registered patients with clear cell renal cell carcinoma (ccRCC) compared with 1108 healthy subjects (n = 1108).

The first table presents the characteristics of 1004 patients with clear cell renal cell carcinoma and 1108 cancer-free controls from a Chinese Han population. The Second and third table shows the SNP results.

Table 1: The characteristics of the examined population.

Variable Cases, n (%) Controls, n (%) P-value†
1004 (100) 1108 (100)
Age, years
 ≤44 195 (19.4) 230 (20.8) 0.559
 45–64 580 (57.8) 644 (58.1)
 ≥65 229 (22.8) 234 (21.1)
Sex
 Male 711 (70.8) 815 (73.6) 0.160
 Female 293 (29.2) 293 (26.4)
BMI, kg/m2
 <25 480 (47.8) 589 (53.2) 0.014
 ≥25 524 (52.2) 519 (46.8)
Smoking status
 Never 455 (45.3) 529 (47.7) 0.265
 Ever/current 549 (54.7) 579 (52.3)
Hypertension
 No 639 (63.6) 780 (70.4) 0.001
 Yes 365 (36.4) 328 (29.6)
Fuhrman grade
 I 40 (4.0)
 II 380 (37.8)
 III 347 (34.6)
 IV 175 (17.4)
 Missing 62 (6.2)
Stage at diagnosis
 I 738 (73.5)
 II 71 (7.1)
 III 19 (1.9)
 IV 176 (17.5)

†Pearson’s χ2-test.

Table 2:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value†
rs182052
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
rs266729
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
rs3774262
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

†Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Table 3:

Association between ADIPOQ single nucleotide polymorphisms (SNP) and clear cell renal cell carcinoma risk

SNP HWE Cases, n(%) Controls, n(%) Crude OR (95% CI) P-value Adjusted OR (95% CI) P-value†
rs182052
 GG 0.636 249 (24.8) 315 (28.4) 1.00 1.00
 AG 485 (48.3) 544 (49.1) 1.13 (0.92–1.39) 0.253 1.11 (0.90–1.37) 0.331
 AA 270 (26.9) 249 (22.5) 1.37 (1.08–1.75) 0.010 1.36 (1.07–1.74) 0.013
 AG/AA versusGG 1.20 (0.99–1.46) 0.060 1.19 (0.98–1.45) 0.086
 AA versusGG/AG 1.28 (1.04–1.57) 0.019 1.27 (1.04–1.56) 0.019
rs266729
 CC 0.143 502 (50.0) 572 (51.6) 1.00 1.00
 CG 398 (39.6) 434 (39.2) 1.05 (0.88–1.25) 0.635 1.05 (0.87–1.26) 0.633
 GG 104 (10.4) 102 (9.2) 1.16 (0.86–1.57) 0.324 1.17 (0.86–1.58) 0.307
 CG/GG versusCC 1.07 (0.91–1.29) 0.456 1.07 (0.90–1.27) 0.445
 GG versus CC/CG 1.19 (0.83–1.59) 0.377 1.15 (0.86–1.54) 0.353
rs3774262
 GG 0.106 482 (48.0) 523 (47.2) 1.00 1.00
 AG 420 (41.8) 459 (41.4) 0.99 (0.83–1.20) 0.938 0.99 (0.82–1.19) 0.905
 AA 102 (10.2) 126 (11.4) 0.88 (0.66–1.17) 0.381 0.90 (0.67–1.20) 0.463
 AG/AA versusGG 0.98 (0.80–1.16) 0.711 0.97 (0.82–1.15) 0.722
 AA versusGG/AG 0.88 (0.67–1.18) 0.372 0.90 (0.68–1.19) 0.465

Bold values indicate significance.

†Adjusted for age, sex, BMI, smoking status, and hypertension. CI, confidence interval; OR, odds ratio; HWE, Hardy–Weinberg equilibrium.

Molecular Genetics Level for Physiology (Function):

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503866/bin/10585_2015_9731_Fig6_HTML.jpg

a The protein–protein interaction for the identified 8 proteins in STRING (10 necessary proteins/genes were added into the network so as to find the potential strong connection among them. The red dotted lines circled three main pathways. b The ingenuity pathway analysis (IPA) for all these 18 genes showing that oxidative phosphorylation, mitochondria dysfunction and granzyme A are the significantly activated pathways (fold change over 1.5, P < 0.05). c The possible mechanism related mitochondria functions: unspecific condition like inflammation, carcinogens, radiation (ionizing or ultraviolet), intermittent hypoxia, viral infections which is carcinogenesis in our study that damages a cell’s oxidative phosphorylation. Any of these conditions can damage the structure and function of mitochondria thus activating a respiratory chain changes (Complex I, II, III, IV) and also cytochrome c release. When the mitochondrial dysfunction persists, it produces genome instability (mtDNA mutation), and further lead to malignant transformation (metastasis) via increased ROS and apoptotic resistance. (Color figure online)

RENAL CELL CARCINOMA AND METABOLISM goes hand to hand in genes encoding enzymes of the Krebs cycle suppress tumor formation in kidney cells. This includes Succinate dehydrogenase (SDH), Fumarate hydratase (FH).  As a result of accumulation of succinate or fumarate causes the inhibition of a family of 2-oxoglutarate-dependent dioxygeneases.

The FH and SDH genes function as two-hit tumor suppressor genes.

SDH has a complex of 4 different polypeptides (SDHA-D) function in electron transfer, catalyzes the conversion of succinate to fumarate. Furthermore, heterozygous germline mutations in SDHsubunits predispose to pheochromocytoma/paraganglioma. FH function to convert fumarate to malate.  When its mutations presented as heterozygous germline, it predisposes hereditary leiomyomatosis and renal cell cancer (HLRCC). Among them about 20–50% of HLRCC families are typically papillary-type 2 (pRCC-2) and overwhelmingly aggressive.RCC is increasingly being recognized as a metabolic disease, and key lesions in nutrient sensing and processing have been detected.

Regulation of Prolyl Hydroxylases and Keap1 by Krebs cycle http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f4.jpg

Regulation of Prolyl Hydroxylases by Tricarboxylic Acid (TCA) Cycle Intermediates. Prolyl hydroxylases use TCA cycle intermediates to help catalyze the oxygen, iron and ascorbate dependent- addition of a hydroxyl side chain to a Pro402 and Pro564 of HIF alpha subunits, leading to VHL binding and degradation. Defects in either fumarate hydratase or succinate dehydrogenase will drive up levels of fumarate and succinate, which competitively bind prolyl hydroxylases, and prevent HIF prolyl hydroxylation. This results in higher intracellular HIF levels.

Regulation of mTORC1 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f5.jpg

HIF regulation and mTOR pathway connections. Hypoxia blocks HIF expression in a TSC1/2 and REDD dependent pathway [155]. HIF1α appears to be both TORC1 and TORC2 dependent, whereas HIF2α is only TORC2 dependent [275]. Signaling via TORC2 appears to upregulate HIF2α in an AKT dependent manner [69].

TREATMENT:

Based on the types of renal cancers the treatment method may vary but the general scheme is:

 

Drugs Approved for Kidney (Renal Cell) Cancer

Food and Drug Administration (FDA) approved drugs for kidney (renal cell) cancer. Some of the drug names link to NCI’s Cancer Drug Information summaries.

T cell regulation in RCC http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399969/bin/nihms380694f7.jpg

Immune regulation of renal tumor cells. A: When an antigen presenting cell (APC) engages a T-cell via a cognate T-cell receptor (TCR) and CD28, T-cell cell activation occurs. B: Early and late T-cell inhibitory signals are mediated via CTLA-4 and PD-1 receptors, and this occurs via engagement of the APC via B7 and PD-L1, respectively. C: Inhibitory antibodies against CTLA-4 and PD-1 can overcome T-cell downregulation and once again allow cytokine production.

Phase III Trials of Targeted Therapy in Metastatic Renal Cell Carcinoma

Trial Number
of
patients		 Clinical setting RR (%) PFS (months) OS (months)
VEGF-Targeted Therapy
*AVOREN

Bevacizumab +
IFNa
vs.IFNa[270]

 649 First-line 31 vs. 12 10.2 vs. 5.5
(p<0.001)		 23.3 vs. 21.3
(p=0.129)
*CALBG 90206

Bevacizumab +
IFNa
vs.IFNa[271]

 732 First-line 25.5 vs. 13 8.4 vs. 4.9
(p<0.001)		 18.3 vs. 17.4
(p=0.069)
Sunitinib vs.
IFNa[248]		 750 First-line 47 vs. 12 11 vs. 5
(p=0.0001)		 26.4 vs. 21.8
(p=0.051)
*TARGET

Sorafenib vs.
Placebo[272]

 903 Second-line

(post-cytokine)

 10 vs. 2 5.5 vs. 2.8
(p<0.01)		 17.8vs.15.2
(p=0.88)
Pazopanib vs.
placebo[273]		 435 First line/second line

(post-cytokine)

 30 vs. 3 9.2 vs. 4.2
(p<0.0001)		 22.9 vs. 20.5
(p=0.224)
*AXIS

Axitinib vs.
sorafenib [269]

 723 Second line

(post-sunitinib, cytokine,
bevacizumab or
temsirolimus)

 19 vs. 9
(p=0.0001)		 6.7 vs. 4.7
(p<0.0001)		 Not reported
mTOR-Targeted Therapy
*ARCC
Temsirolimus
vs. Tem + IFNa
vs. IFNa[249]		 624 First line, ≥ 3 poor risk
featuresa		 9 vs. 5 3.8 vs. 1.9 for
IFNa
monotherapy
(p=0.0001)		 10.9 vs. 7.3 for
IFNa(p=0.008)
*RECORD-1
Everolimus vs.
placebo [274]		 410 Second line
(post sunitinib and/or
sorafenib)		 2 vs. 0 4.9 vs. 1.9

(p<0.0001)

 14.8 vs. 14.5

RCC renal cell carcinoma, RR response rate, OS overall survival, PFS progression free survival, VEGFvascular endothelial growth factor, IFNa interferon alpha, mTOR mammalian target of rapamycin. AVORENAVastin fOr RENal cell cancer, CALBG Cancer and Leukemia Group B. TARGET Treatment Approaches in Renal Cancer Global Evaluation Trial. AXIS Axitinib in Second Line. ARCC Advanced Renal-Cell Carcinoma. RECORD-1 REnal Cell cancer treatment withOral RAD001 given Daily.

aIncluding serum lactate dehydrogenase level of more than 1.5 times the upper limit of the normal range, a hemoglobin level below the lower limit of the normal range; a corrected serum calcium level of more than 10 mg per deciliter (2.5 mmol per liter), a time from initial diagnosis of renal-cell carcinoma to randomization of less than 1 year, a Karnofsky performance score of 60 or 70, or metastases in multiple organs.

PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242

Table: RCC-Associated Antigens (RCCAA) Recognized by T Cells.

Antigen Antigen
Category		 Frequency of
Expression
Among RCC
Tumors (%)		 CD8+ T cell
recognition:
Patients with
HLA Class I
Allele(s)		 CD4+ T cell
recognition:
Patients with
HLA Class II
Allele(s)		 References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Survivina ML 100 Multiple Multiple 114
OFA-iLR OF 100 A2 NR 115, 116
IGFBP3a, b ML 97 NR Multiple 117, 118
EphA2a ML > 90 A2 DR4 17, 44, 119
RU2AS Antisense
transcript		 > 90 B7 NR 120
G250
(CA-IX) a, b		 RCC 90 A2, A24 Multiple 47, 51
EGFRa, b ML 85 A2 NR 121, 122
HIFPH3a ML 85 A24 NR 123
c-Meta ML > 80 A2 NR 124
WT-1a ML 80 A2, A24 NR 125–128
MUC1a, b ML 76 A2 DR3 46, 129, 130
5T4 ML 75 A2, Cw7 DR4 54, 131–133
iCE aORF 75 B7 NR 134
MMP7a ML 75 A3 Multiple 117, 135, 136
Cyclin D1a ML 75 A2 Multiple 117, 137, 138
HAGE b CT 75 A2 DR4 139
hTERT a, b ML > 70 Mutliple Multiple 140–142
FGF-5 Protein splice variant > 60 A3 NR 143
mutVHLa, b ML > 60 NR NR 144
MAGE-A3 b CT 60 Multiple Multiple 145
SART-3 ML 57 Mulitple NR 146–149
SART-2 ML 56 A24 NR 150
PRAME b CT 40 Multiple NR 151–154
p53a, b Mutant/WT
ML		 32 Mutliple Multiple 155, 156
MAGE-A9b CT >30 A2 NR 157
MAGE-A6b CT 30 Mutliple DR4 18, 158
MAGE-D4b CT 30 A25 NR 159
Her2/neua ML 10–30 Multiple Multiple 45, 160–164
SART-1a ML 25 Multiple NR 165–167
RAGE-1 CT (ORF2/5) 21 Mutliple Multiple 151, 157, 168, 169
TRP-1/ gp75 ML 11 A31 DR4 151, 170–172

A summary is provided for RCCAA that have been defined at the molecular level. RCCAA are characterized with regard to their antigen category, their prevalence of (over)expression among total RCC specimens evaluated, whether RCCAA expression is modulated by hypoxia or tumor DNA methylation status, and which HLA class I and class II alleles have been reported to serve as presenting molecules for T cell recognition of peptides derived from a given RCCAA.

Abbreviations: CT = Cancer-Testis Antigens; ML = Multi-lineage Antigens; NR = Not Reported; OF = Oncofetal Antigen; aORF = altered open reading frame; ORF = open reading frame; RCC = Renal cell carcinoma; WT = Wild-Type;

aHypoxia-Induced;

bHypomethylation-Induced.

PMC full text: Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.

Open Access J Urol. 2010 Aug; 2010(2): 125–141. doi:  10.2147/RRU.S7242
Expected Impact on Teff versus Suppressor Cells
Co-Therapeutic Agent Teff
priming		 Teff
function		 Teff
survival		 Teff
(TME)		 Treg/
MDSC		 References found in Open Access J Urol. Author manuscript; available in PMC 2013 Jul 8.
Cytokines
IL-2 ↑ ↑ +/− ↑ ↑ (Treg) 173–175
IL-7 ↑ ↑ ↑ ↑ ↑ (Treg) 176–178
IL-12 ↑ ↑ ↑ ↑ – (Treg), ↓ (MDSC) 179–181
IL-15 ↑ ↑ ↑ ↑ ↑ (Treg)* 182, 183
IL-18 ↑ ↑ ↑ ↑ ↓ (Treg) 184–186
IL-21 ↑ ↑ ↑ ? +/− (Treg) 187–190
IFN-α ↑ ↑ ↑ ↑ +/− (Treg) 175, 191–194
IFN-γ ↑ ↑ -? ? ↑ ↑ (Treg); ↑ ?(MDSC) 195–197
GM-CSF ↑ ↑ ? ↑ ↑ (Treg); ↑(MDSC) 198–202
Coinhibitory Antagonist
CTLA-4 ↑ ↑ ? ↑ ↓ (Treg) 203, 204
PD1/PD1L ↑ ↑ ↑ ↑ ↓ (Treg) 205–207
Costimulatory Agonist
CD40/CD40L ↑ ↑ ↑ ↑ ↑ (Treg); ↑(MDSC) 208–211
GITR/GITRL ↑ ↑ ↑ ↑ ↓ (Treg); ↓ (MDSC) 212, 213
OX40/OX86 ↑ ↑ ↑ ↑ ↑↓ (Treg); ↓ (MDSC) 214–219
4-1BB/4-1BBL ↑ ↑ ↑ ↑ ↑ (Treg) 220–224
TLR Agonists
Imiquimod (TLR7) ↑ ↑ ↑ ↑ ? 225–227
Resiquimod (TLR8) ↑ ↑ ↑ ? ? 228, 229
CpG (TLR9) ↑ ↑ ↑ ↑ ↓ (Treg) 230–232
Anti-Angiogenic
VEGF-Trap – – ? ? – 233
Sunitinib ↑ ↑ ? ↑ ↓ (Treg/MDSC) 98, 100, 234
Sorafenib ↓ ↓ ↓ ? ↓ (MDSC) 235
Bevacizumab ↑ ↑ ? ? ↓ (MDSC) 236, 237
Gefitinib (IRESSA) ? ? ? ? ? 238, 239
Cetuximab ? ↑ ? ? ? 240
mTOR Inhibitors
Temsirolimus/Everolimus ↓ ↓ ↓ ? ↓ (Treg) 241
Treg/MDSC Inhibitors
Iplimumab (CTLA-4) ↑ ↑ ? ↑ ↓ (Treg) 242, 243
ONTAK (CD25) +/− +/− ? ? ↓ (Treg) 244
Anti-TGFβ/TGFβR ↑ ↑ ↑ ↑ ↓ (Treg) 245–247
Anti-IL10/IL10R ↑ ↑ ↑ +/− ↓ (Treg) 248, 249
Anti-IL35/IL35R ↑? ↑? ↑? ↑? ↓ (Treg) 250
1-methyl trytophan ↑ ↑ ? ? ↓ (MDSC) 251
ATRA ↑ ↑ ? ? ↑ (Treg), ↓ (MDSC) 90–93

Agents that are currently or soon-to-be in clinical trials are summarized with regard to their anticipated impact(s) on Type-1 anti-tumor T cell (Te) activation, function, survival and recruitment into the TME. Additional anticipated effects of drugs on suppressor cells (Treg and MDSC) are also summarized. Key: ↑, agent is expected to increase parameter; ↓, agent is expected to inhibit parameter; +/−, minimal increase or decrease is expected in parameter as a consequence of treatment with agent; ?, unknown effect of agent on parameter.

Abbreviations: ATRA, all-trans retinoic acid; CTLA-4, cytotoxic T Lymphocyte antigen 4; GITR(L), glucocorticoid-induced TNF receptor (ligand); GM-CSF, granulocyte-macrophage colony stimulating factor; IFN, interferon; IL, interleukin; MDSC, myeloid-derived suppressor cell; PD1/PD1L, programmed cell death 1 (ligand); TGF-β(R), tumor necrosis factor-β(receptor); TLR, Toll-like receptor; TME, tumor microenvironment; Treg, regulatory T cell; VEGF, vascular endothelial growth factor.

Alternative and Complementary Therapies for Cancer:

  • Art therapy
  • Dance or movement therapy
  • Exercise
  • Meditation
  • Music therapy
  • Relaxation exercises

Mol Cancer Res. 2012 Jul; 10(7): 859–880. Published online 2012 May 25. doi:  10.1158/1541-7786.MCR-12-0117 PMCID: PMC3399969 NIHMSID: NIHMS380694

State-of-the-science: An update on renal cell carcinoma

Eric Jonasch,1 Andrew Futreal,1 Ian Davis,2 Sean Bailey,2 William Y. Kim,2 James Brugarolas,3 Amato Giaccia,4 Ghada Kurban,5 Armin Pause,6 Judith Frydman,4 Amado Zurita,1 Brian I. Rini,7 Pam Sharma,8Michael Atkins,9 Cheryl Walker,8,* and W. Kimryn Rathmell2,*

Go to:

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[Discovery Medicine; ISSN: 1539-6509; Discov Med 18(101):341-350, December 2014.Copyright © Discovery Medicine. All rights reserved.]

 

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Observations on Human Papilloma Virus and Cancer

Posted in Biological Networks, Biomarkers & Medical Diagnostics, Cancer and Current Therapeutics, Pharmacogenomics, tagged , , , , on October 13, 2015| Leave a Comment »

Observations on Human Papilloma Virus and Cancer

Curator: Demet Sag, PhD, CRA, GCP 

 

What is Human Papilloma Virus?

 HPV 220px-HPV-16_genome_organization

Human papillomavirus

Taxonomy ID: 10566
Inherited blast name: viruses
Rank: species
Genetic code: Translation table 1 (Standard)
Host: vertebrates| human
Other names:

synonym: human papillomavirus HPV
synonym: Human Papilloma Virus

Lineage( full )

Viruses; dsDNA viruses, no RNA stage; Papillomaviridae; unclassified Papillomaviridae; Human papillomavirus types
   Entrez records   
Database name Subtree links Direct links
Nucleotide 7,782 7,775
Protein 2,611 2,604
Structure 3 3
Genome 1 1
Popset 34 34
PubMed Central 4,742 4,742
Gene 21 21
SRA Experiments 43 43
Probe 12 12
Assembly 1 1
Bio Project 6 6
Bio Sample 53 53
PubChem BioAssay 5 5
Taxonomy 8 1
Human papillomavirus
Specialty Infectious disease, gynecologyHPV_

WHO_RHR_08.14_eng-Cervical cancer, human papillomavirus (HPV), and HPV vaccinesWHO= papilloma virus info
ICD–10 B97.7
ICD–9-CM 078.1 079.4
DiseasesDB 6032
eMedicine med/1037
MeSH D030361

ICTV homepage

WHO= papilloma virus info

WHO_RHR_08.14_eng-Cervical cancer, human papillomavirus (HPV), and HPV vaccines

Why is it related to Human Cancer?

 Since its first presumed diagnosis in women by an Italian Physician back in 1800s many developments took place to identify the real causative agents (PMID:19135222). Especially in 1970s the full discovery and relation between HPV and cancer established. Human papilloma virus (HPV)  is the second common cancer death in women, although HPV vaccines helped to decrease the morbidity rate there are complications due to vaccines.  Still there is an increase with cervical cancer estimated to be  490,000.

CDC also provided simple information for public on HPV since there is a misunderstanding that some people think it is like herpes or HIV viruses.  Yet, pathology is much different and changes based on age since younger women or girls can fight off but after age 30 predisposition of HPV as a cancer increases. (http://www.cdc.gov/cancer/hpv/pdf/HPV_Testing_2012_English.pdf)

Cervical cancer is responsible for 10–15% of cancer-related deaths in women worldwide1,2. The etiological role of infection with high-risk human papilloma viruses (HPV) in cervical carcinomas is well established.

 

  Relationship of mutational spectrum and rates with clinicopathological characteristics in cervical carcinoma presented 

 

 

Relationship of mutational spectrum and rates with clinicopathological characteristics in cervical carcinoma presented at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161954/bin/nihms610939f1.jpg

All panels are aligned with vertical tracks representing 115 individuals. The data is sorted in order by histology (middle panel) and total mutational rate (top panel). The relative frequencies of nucleotide mutations occurring at cytosines preceeded by thymines (Tp*C) or at cytosines followed by guanines (*CpG) sites are depicted in red and orange respectively, on the second panel. The bottom heatmap shows the distribution of mutations in significantly mutated genes (q<0.1) in squamous cell carcinomas and adenocarcinomas in the order listed in the following Table, TP53, ERBB2 and KRAS were significant recurrence (q<0.1) among cancer driver genes reported in COSMIC.

Nature. Author manuscript; available in PMC 2014 Sep 12.  Published in final edited form as: Nature. 2014 Feb 20; 506(7488): 371–375.

Genes with Significantly Recurrent Somatic Mutations in Cervical Carcinomas

Gene Description Nonsilent mutations Relative frequency Patients Unique sites Silent mutations Indel + null q
SQUAMOUS CELL CARCINOMA (N=79)
FBXW7** F-box and WD repeat domain containing 7 12 15% 12 8 0 2 4.03E-12
PIK3CA phosphoinositide-3-kinase, catalytic, alpha polypeptide 11 14% 10 5 0 1 <9.08e-12
MAPK1** mitogen-activated protein kinase 1 6 8% 6 3 0 0 0.000671
HLA-B+ major histocompatibility complex, class I, B 7 9% 6 7 1 3 0.00169
STK11 serine/threonine kinase 11 3 4% 2 2 0 1 0.012
EP300+ E1A binding protein p300 13 16% 12 13 1 4 0.0354
NFE2L2+ nuclear factor (erythroid-derived 2)-like 2 3 4% 3 2 0 0 0.0597
PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 5 6% 5 5 0 3 0.0693
ADENOCARCINOMA (N=24)
ELF3* E74-like factor 3 (ets domain transcription factor, epithelial-specific) 3 13% 3 3 0 3 0.03
CBFB* core-binding factor, beta subunit 2 8% 2 2 0 1 0.0342

Indel: insertions or deletions;

Null: nonsense, frameshft or splice-site mutations;

q: q value, false discovery rate (Benjamini-Hochberg procedure).

**Genes with mutations observed in only squamous cell carcinomas

*Genes with mutations observed in only adenocarcinomas

+Genes with a majority of mutations occurring in squamous cell carcinomas.

Following figure (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161954/bin/nihms610939f2.jpg)

  Novel recurrent somatic mutations in cervical carcinoma

Novel recurrent somatic mutations in cervical carcinoma

The locations of somatic mutations in novel significantly mutated genes in 115 cervical carcinoma, FBXW7, MAPK1, HLA-B, EP300, NFE2L2 and ELF3 are shown in the context of protein domain models derived from UniProt and Pfam annotations. Numbers refer to amino acid residues. Each filled circle represents an individual mutated tumor sample: missense and silent mutations are represented by filled black and grey circles, respectively while nonsense, frameshift, and splice site mutations are represented by filled red circles and red text. Domains are depicted with various colors with an appropriate key located on the right hand of each domain model.

 Relationships between HPV integration, copy number amplifications and gene expression in cervical carcinoma

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161954/bin/nihms610939f3.jpg

Relationships between HPV integration, copy number amplifications and gene expression in cervical carcinoma

Panel (a) shows comparative histograms of true and simulated genomic distances between HPV integration sites and the nearest copy number amplification (log segmean difference >0.5). Panel (b) shows boxplots of gene expression levels across 79 cervical tumors for 41 genes with chimeric human-HPV read pairs. The expression levels for tumors with HPV integration in the respective genes are highlighted in red circles. Panel (c) shows scatter plots comparing copy number alterations and gene expression levels across 79 tumors in selected integration site genes. The red circles represent data for the tumors with HPV integration events involving the respective genes.

 

Table. Diseases Associated With Specific HPV Types (e-Medicine)

Nongenital Cutaneous Disease HPV Type
Common warts (verrucae vulgaris) 1, 2, 4, 26, 27, 29, 41, 57, 65, 75-78
Plantar warts (myrmecias) 1, 2, 4, 60, 63
Flat warts (verrucae planae) 3, 10, 27, 28, 38, 41, 49
Butcher’s warts (common warts of people who handle meat, poultry, and fish) 1-4, 7, 10, 28
Mosaic warts 2, 27, 57
Ungual squamous cell carcinoma 16
Epidermodysplasia verruciformis (benign) 2, 3, 10, 12, 15, 19, 36, 46, 47, 50
Epidermodysplasia verruciformis (malignant or benign) 5, 8-10, 14, 17, 20-25, 37, 38
Nonwarty skin lesions 37, 38
Nongenital Mucosal Disease HPV Type
Respiratory papillomatosis 6, 11
Squamous cell carcinoma of the lung 6, 11, 16, 18
Laryngeal papilloma (recurrent respiratory papillomatosis)[17] 2, 6, 11, 16, 30, 40, 57
Laryngeal carcinoma 6, 11
Maxillary sinus papilloma 57
Squamous cell carcinoma of the sinuses 16, 18
Conjunctival papillomas 6, 11
Conjunctival carcinoma 16
Oral focal epithelial hyperplasia (Heck disease) 13, 32
Oral carcinoma 16, 18
Oral leukoplakia 16, 18
Squamous cell carcinoma of the esophagus 16, 18
Anogenital Disease HPV Type
Condylomata acuminata 1-6, 10, 11, 16, 18, 30, 31, 33, 35, 39-45, 51-59, 70, 83
Bowenoid papulosis 16, 18, 34, 39, 40, 42, 45
Bowen disease 16, 18, 31, 34
Giant condylomata (Buschke-Löwenstein tumors) 6, 11, 57, 72, 73
Unspecified intraepithelial neoplasia 30, 34, 39, 40, 53, 57, 59, 61, 62, 64, 66-69
Low-grade squamous intraepithelial lesions (LGSIL) 6, 11, 16, 18, 26, 27, 30, 31, 33-35, 40, 42-45, 51-58, 61, 62, 67-69, 71-74, 79, 81-84
High-grade squamous intraepithelial lesions (HGSIL) 6, 11, 16, 18, 31, 33, 35, 39, 42, 44, 45, 51, 52, 56, 58, 59, 61, 64, 66, 68, 82
Carcinoma of vulva 6, 11, 16, 18
Carcinoma of vagina 16
Carcinoma of cervix[18, 19] 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68, 70, 73, 82
Carcinoma of anus 16, 31, 32, 33
Carcinoma in situ of penis (erythroplasia of Queyrat) 16
Carcinoma of penis 16, 18

Epidemiology

 epidemiology of HPV in the world

“Human papillomavirus (HPV) has become synonymous with cervical cancer, but its actual footprint is much bigger” said James Mitchell Crow. (PMID: 229324377  James Mitchell Crow. “HPV: The global burden”. Nature 488 S2–S3 (30 August 2012) doi:10.1038/488S2a Published online  29 August 2012).

Every year, over 27,000 women and men are affected by a cancer caused by HPV— that’s a new case every 20 minutes.

Persistent HPV infection can cause cervical and other cancers including:

Pathology:

Virus Diseases [C02]
   DNA Virus Infections [C02.256]

Papillomavirus Infections [C02.256.650]
Warts [C02.256.650.810]  +
Virus Diseases [C02]
   Tumor Virus Infections [C02.928]

Papillomavirus Infections [C02.928.725]

 

 

(PMID: 229324377)

 

 

Diagnostics:

 

In the lab few places propagating HPV. There are measures that need to be taken by the laboratory personnel. CDC as well as WHO published various articles to inform public.

Sensitivity and testing for Pap smear and HPV DNA testing in the detection of CIN2+

Test Sensitivity Specificity
Pap smear 53-55.4% 96.3-96.8%
High-risk HPV DNA testing 94.6-96.1% 90.7-94.1%
Pap smear + high-risk HPV testing 100% 92.5%

Cuzick J, Clavel C, Petry KU, Meijer CJ, Hoyer H, Ratnam S, Szarewski A, Birembaut P, Kulasingam S, Sasieni P, Iftner T. Overview of the European and North American studies on HPV testing in primary cervical cancer screening. Int J Cancer. 2006; 119(5):1095.

Mayrand MH, Duarte-Franco E, Rodrigues I, Walter SD, Hanley J, Ferenczy A, Ratnam S, Coutlée F, Franco EL, Canadian Cervical Cancer Screening Trial Study Group.

Human papillomavirus DNA versus Papanicolaou screening tests for cervical cancer N Engl J Med. 2007;357(16):1579.

Best Pract Res Clin Obstet Gynaecol. Author manuscript; available in PMC 2013 Apr 22. (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632360/)

HPV Genotyping tests1

HPV genotyping test HPV types detected
Cervista® HPV 16/18 (Hologic, Inc;
Marlborough, MA)a		 HR HPV types 16 and 18
Digene HPV Genotyping PS Test (Qiagen;
Hilden, Germany)		 HR HPV types 16, 18, and 45
Roche LINEAR ARRAY HPV Genotyping
Test (Roche; Basel, Switzerland)		 37 LR and HR HPV types
Innogenetics INNO-LiPA HPV Genotyping
Extra (Innogenetics; Gent, Belgium)		 28 LR and HR HPV types
SPF10 Line Probe Assay HPV-typing System
(Roche; Basel,
Switzerland)		 Recognizes most genital
tract HPV types
Papillocheck1 (Greiner Bio-One;
Frickenhausen Germany)		 18 HR and 6 LR HPV types
RealTime High Risk HPV Assay (Abbott
Laboratories;Abbott Park, IL)		 HPV types 16 and 18
HPV Genotyping LQ Test (Qiagen Inc;
Valencia, CA)		 18 HR HPV types
Seeplex HPV4A ACE (Seegene; Rockville,
MD)		 HPV types 16 and 18
CLART HPV 2 (Genomica; Madrid, Spain) 35 LR and HR HPV types
GenoFlow HPV Array (DiagCor; North Point,
Hong Kong)		 33 LR and HR HPV types
fHPV Typing (molGENTIX; Barcelona, Spain) 15 LR and HR HPV types

HPV, human papillomavirus; HR, high-risk; LR, low-risk.

aFDA-approved test.

1Schutzbank TE, Ginocchio CC. Assessment of clinical and analytical performance characteristics of an HPV genotyping test. Diagn Cytopathol. 2011 Apr 6. doi:10.1002/dc.21661.

Most papillomas are sufficiently distinct to be clinically recognizable. Bowenoid papulosis may be mistaken for lichen planus, psoriasis, seborrheic keratoses, or condylomata acuminata.

In additions to the conditions listed in the differential diagnosis, other problems to be considered include the following:

  • Acanthosis nigricans
  • Acrochordon
  • Actinic keratoses
  • Anogenital malignancy
  • Anogenital warts in children
  • Bowenoid papulosis
  • Carbon dioxide laser surgery for intraepithelial cervical neoplasms
  • Cervical polyp
  • Condyloma latum
  • Corns and calluses
  • Dermatitis papillaris
  • Endoscopic gynecologic surgery
  • Epidermodysplasia verruciformis
  • Fordyce spots
  • Hymenal remnants
  • Hypopigmentation
  • Keloid and hypertrophic scar
  • Keratoacanthoma
  • Laryngeal papillomatosis of neonates and infants
  • Malignant tumors of the mobile tongue
  • Micropapillomatosis labialis
  • Nevi
  • Pap test
  • Pityriasis versicolor
  • Psoriasis (plaque)
  • Recurrent respiratory papillomatosis
  • Seborrheic keratosis
  • Sinonasal papillomas, treatment
  • Skin tags (fibroepithelial polyps)
  • Verrucous carcinoma
  • Vestibular papillomatosis

Differential Diagnoses

 

 

Treatment:

1.       Immunomodulators

Class Summary

Immune response modifiers have immunomodulatory effects and are used for treatment of external anogenital warts (EGWs) or condylomata acuminata. Interferon alfa, beta, and gamma may be administered topically, systemically, and intralesionally. They stimulate production of cytokines and demonstrate strong antiviral activity.

View full drug information

Imiquimod (Aldara, Zyclara)

Imiquimod is an imidazoquinolinamine derivative that has no in vitro antiviral activity but does induce macrophages to secrete cytokines such as interleukin (IL)-2 and interferon alfa and gamma. Its mechanisms of action are unknown. Imiquimod has been studied extensively and is a new therapy relative to other EGW treatments. It may be more effective in women than in men.

Imiquimod is dispensed as an individual dose. Patients are advised to wash the affected area with mild soap and water upon awakening and to remove residual drug.

View full drug information

Interferon alfa-n3 (Alferon N)

Interferon alfa is a protein product either manufactured from a single-species recombinant DNA process or obtained from pooled units of donated human leukocytes that have been induced by incomplete infection with a murine virus.

The mechanisms by which interferon alfa exerts antiviral activity are not understood clearly. However, modulation of the host immune response may play an important role. This agent is indicated for intralesional treatment of refractory or recurring external condyloma acuminatum and is particularly useful for patients who have not responded satisfactorily to other treatment modalities (eg, podophyllin, surgical excision, laser therapy, or cryotherapy).

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Interferon alfa-2b (Intron A)

This is a protein product manufactured by recombinant DNA technology. Its mechanism of antitumor activity is not clearly understood; however, direct antiproliferative effects against malignant cells and modulation of host immune response may play important roles. Its immunomodulatory effects include suppression of tumor cell proliferation, enhancement of macrophage phagocytic activity, and augmentation of lymphocyte cytotoxicity.

This agent is indicated for intralesional treatment of refractory or recurring external condyloma acuminatum and is particularly useful for patients who have not responded satisfactorily to other treatment modalities (eg, podophyllin, surgical excision, laser therapy, or cryotherapy).

2.       Keratolytic Agents

Class Summary

Antimitotic drugs arrest dividing cells in mitosis, resulting in the death of proliferating cells. They cause cornified epithelium to swell, soften, macerate, and then desquamate. Many of them are chemotherapeutic agents. The drugs listed below are used specifically for treatment of EGWs or condylomata acuminata.

Keratolytic agents are used to aid in removal of keratin in hyperkeratotic skin disorders, including corns, ichthyoses, common warts, flat warts, and other benign verrucae.

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Podofilox (Condylox)

Podofilox is a topical antimitotic that can be synthesized chemically or purified from the plant families Coniferae and Berberidaceae (eg, species of Juniperus and Podophyllum). It is the active agent of podophyllin resin and is available as a 0.5% solution. Treatment results in necrosis of visible wart tissue; the exact mechanism of action is unknown. Treatment should be limited to no more than 10 cm2 of wart tissue, and no more than 0.5 mL/day of solution should be given. This is a patient-applied therapy.

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Podophyllum resin (Podocon-25)

Podophyllin is derived from May apple (Podophyllum peltatum Linné) and contains the active agent podophyllotoxin, a cytotoxic substance that arrests mitosis in metaphase. American podophyllum contains one fourth the amount of podophyllotoxin that Indian podophyllum does. The potency of podophyllin varies considerably between batches. The exact mechanism of action is unknown.

Podophyllin is used as a topical treatment for benign growths, including external genital and perianal warts, papillomas, and fibroids. It results in necrosis when applied to anogenital warts. Only a trained medical professional can apply it, and it cannot be dispensed to a patient.

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Trichloroacetic acid 85% (Tri-Chlor)

Trichloroacetic acid (TCA) is a highly corrosive desiccating agent that cauterizes skin, keratin, and other tissues and is used to burn lesions. Although it is caustic, it causes less local irritation and systemic toxicity than other agents in the same class. However, response often is incomplete, and recurrence is common.

Most clinicians use 25-50% TCA, although some use concentrations as high as 85% and then neutralize with either water or bicarbonate. Tissue sloughs and subsequently heals in 7-10 days. TCA therapy is less destructive than laser surgery, electrocautery, or cryotherapy.

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Salicylic acid (Compound W, Dr. Scholl’s Clear Away Warts, Freezone)

By dissolving the intercellular cement substance, salicylic acid produces desquamation of the horny layer of skin without affecting the structure of viable epidermis. It is used for removal of nongenital cutaneous warts, particularly common or plantar warts. Before application, wash the affected area. The wart may be soaked in warm water for 5 minutes. Dry the area thoroughly.

3.       Antineoplastics, Antimetabolite

Class Summary

Antimetabolites interfere with nucleic acid synthesis and inhibit cell growth and proliferation. These are topical preparations that contain the fluorinated pyrimidine 5-fluorouracil (5-FU). Although these chemotherapeutic agents are not formally approved for use against warts, some studies have demonstrated a benefit against EGWs or condylomata acuminata.

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Fluorouracil topical (Efudex, Carac, Fluoroplex)

Topical 5-FU interferes with DNA synthesis by blocking the methylation of deoxyuridylic acid and inhibits thymidylate synthetase, which subsequently reduces cell proliferation. Its primary indication is for topical treatment of actinic keratoses. Although it is not approved by the US Food and Drug Administration (FDA) for the treatment of warts, it has been used in adults, particularly for warts resistant to other forms of treatment. It is used for management of superficial basal cell carcinomas.

The solution contains either 2% or 5% 5-FU in propylene glycol, tris (hydroxymethyl) aminomethane, hydroxypropyl cellulose, paraben, and disodium edetate. The cream contains 5% 5-FU in white petrolatum, stearyl alcohol, propylene glycol, polysorbate 60, and paraben. When topical 5-FU is applied to the lesion, the area undergoes a sequence of erythema, vesiculation, desquamation, erosion, and reepithelialization.

4.       Topical Skin Products

Class Summary

Sinecatechins is another topical product that has gained FDA approval for genital warts.

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Sinecatechins (Veregen)

Sinecatechins ointment is a botanical drug product for topical use that consists of extract from green tea leaves. It contains 15% sinecatechins and is available in 15- and 30-g tubes. Its mode of action is unknown, but it does elicit antioxidant activity in vitro. Sinecatechins ointment is indicated for topical treatment of external genital and perianal warts (condylomata acuminata) in immunocompetent patients.

5.       Vaccines, Inactivated, Viral

Class Summary

Three vaccines are available for the prevention of HPV-associated dysplasias and neoplasia, including cervical, vulvar, vaginal, and anal cancer; genital warts (condylomata acuminata); and precancerous genital lesions.

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Human papillomavirus vaccine, nonavalent (Gardasil 9)

Recombinant vaccine that targets 9 HPV types (6, 11, 16, 18, 31, 33, 45, 52, and 58). It is indicated for females aged 9-26 years to prevent cervical, vulvar, vaginal, and anal cancer. It is also indicated to prevent genital warts and dysplastic lesions (eg, cervical, vulvar, vaginal, anal).

It is also indicated for boys aged 9-15 years for prevention of anal cancer, genital warts, and anal intraepithelial neoplasia. In addition to the approved indications, the CDC recommends vaccinating males aged 16 through 21 years not previously vaccinated. CDC recommendations also include men through age 26 years not previously vaccinated. Vaccination is also recommended by the CDC among men who have sex with men and among immunocompromised persons (including those with HIV infection) if not vaccinated previously through age 26 years.

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Human papillomavirus vaccine, quadrivalent (Gardasil)

The quadrivalent HPV recombinant vaccine was the first vaccine indicated to prevent cervical cancer, genital warts (condylomata acuminata), and precancerous genital lesions (eg, cervical adenocarcinoma in situ; cervical intraepithelial neoplasia grades I-III; vulvar intraepithelial neoplasia grades II and III; and vaginal intraepithelial neoplasia grades II and III) due to HPV types 6, 11, 16, and 18. Its efficacy is mediated by humoral immune responses following immunization series.

The quadrivalent vaccine is FDA-approved for females aged 9-26 years and is under FDA priority review to evaluate efficacy in women aged 27-45 years. It is indicated for boys and men aged 11-26 years for prevention of condylomata acuminata caused by HPV types 6 and 11. It is also indicated in people aged 9-26 years for prevention of anal cancer and associated precancerous lesions.

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Human papillomavirus vaccine, bivalent (Cervarix)

The bivalent HPV vaccine is a recombinant vaccine prepared from the L1 protein of HPV types 16 and 18. It is indicated in girls and women aged 10-25 years for the prevention of diseases caused by oncogenic HPV types 16 and 18 (eg, cervical cancer, cervical intraepithelial neoplasia grade II or higher, adenocarcinoma in situ, and cervical intraepithelial neoplasia grade I).

 

HPV Vaccines: Indications Approved and HPV Types by Specific Vaccines

Indicated to Prevent HPV 9-valent* HPV 4-valent HPV 2-valent
Girls and Women
Approved ages 9-26 y 9-26 y 9-25 y
Cervical cancer HPV types 16, 18, 31, 33, 45, 52, and 58 HPV types 16 and 18 HPV types 16 and 18
Vulvar cancer HPV types 16, 18, 31, 33, 45, 52, and 58 HPV types 16 and 18 Not approved
Vaginal cancer HPV types 16, 18, 31, 33, 45, 52, and 58 HPV types 16 and 18 Not approved
Anal cancer HPV types 16, 18, 31, 33, 45, 52, and 58 HPV types 16 and 18 Not approved
Genital warts (condyloma acuminata) HPV types 6 and 11 HPV types 6 and 11 Not approved
Cervical intraepithelial neoplasia (CIN) grade 2/3 and cervical adenocarcinoma in situ (AIS) HPV types 6, 11, 16, 18, 31, 33, 45, 52, and 58 HPV types 6, 11, 16, and 18 HPV types 16 and 18
Cervical intraepithelial neoplasia (CIN) grade 1 HPV types 6, 11, 16, 18, 31, 33, 45, 52, and 58 HPV types 6, 11, 16, and 18 HPV types 16 and 18
Vulvar intraepithelial neoplasia (VIN) grades 2 and 3 HPV types 6, 11, 16, 18, 31, 33, 45, 52, and 58 HPV types 6, 11, 16, and 18 Not approved
Vaginal intraepithelial neoplasia (VaIN) grades 2 and 3 HPV types 6, 11, 16, 18, 31, 33, 45, 52, and 58 HPV types 6, 11, 16, and 18 Not approved
Anal intraepithelial neoplasia (AIN) grades 1, 2, and 3 HPV types 6, 11, 16, 18, 31, 33, 45, 52, and 58 HPV types 6, 11, 16, and 18 Not approved
Boys and Men
Approved ages 9-15 y* 9-26 y Not approved
Anal cancer HPV types 16, 18, 31, 33, 45, 52, and 58 HPV types 16 and 18 Not approved
Genital warts (condyloma acuminata) HPV types 6 and 11 HPV types 6 and 11 Not approved
Anal intraepithelial neoplasia (AIN) grades 1, 2, and 3 HPV types 6, 11, 16, 18, 31, 33, 45, 52, and 58 HPV types 6, 11, 16, and 18 Not approved
*The CDC recommends vaccinating males 16-21 y not previously vaccinated, and through age 26 y among men who have sex with men and among immunocompromised persons (including those with HIV infection) if not vaccinated previously

 

 

Clinical Trials:

 

Two trials of clinically approved human papillomavirus (HPV) vaccines, Females United to Unilaterally Reduce Endo/Ectocervical Disease (FUTURE I/II) and the Papilloma Trial Against Cancer in Young Adults (PATRICIA), reported a 22% difference in vaccine efficacy (VE) against cervical intraepithelial neoplasia grade 2 or worse in HPV-naïve subcohorts; however, serological testing methods and the HPV DNA criteria used to define HPV-unexposed women differed between the studies.

The risk of newly detected human papillomavirus (HPV) infection and cervical abnormalities in relation to HPV type 16/18 antibody levels at enrollment in PATRICIA (Papilloma Trial Against Cancer in Young Adults; NCT00122681).

The control arm of PATRICIA (PApilloma TRIal against Cancer In young Adults,NCT00122681) was used to investigate the risk of progression from cervical HPV infection to cervical intraepithelial neoplasia (CIN) or clearance of infection, and associated determinants.

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Clinical Trials Publications:

Kreimer AR, Rodriguez AC, Hildesheim A, Herrero R, Porras C, Schiffman M, González P, Solomon D, Jiménez S, Schiller JT, Lowy DR, Quint W, Sherman ME, Schussler J, Wacholder S; CVT Vaccine Group. Proof-of-principle evaluation of the efficacy of fewer than three doses of a bivalent HPV16/18 vaccine. J Natl Cancer Inst. 2011 Oct 5;103(19):1444-51. doi: 10.1093/jnci/djr319. Epub 2011 Sep 9.

Kemp TJ, Hildesheim A, Safaeian M, Dauner JG, Pan Y, Porras C, Schiller JT, Lowy DR, Herrero R, Pinto LA. HPV16/18 L1 VLP vaccine induces cross-neutralizing antibodies that may mediate cross-protection. Vaccine. 2011 Mar 3;29(11):2011-4. doi: 10.1016/j.vaccine.2011.01.001. Epub 2011 Jan 15.
Additional publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):

Kreimer AR, Struyf F, Del Rosario-Raymundo MR, Hildesheim A, Skinner SR, Wacholder S, Garland SM, Herrero R, David MP, Wheeler CM; Costa Rica Vaccine Trial and PATRICIA study groups. Efficacy of fewer than three doses of an HPV-16/18 AS04-adjuvanted vaccine: combined analysis of data from the Costa Rica Vaccine and PATRICIA trials. Lancet Oncol. 2015 Jul;16(7):775-86. doi: 10.1016/S1470-2045(15)00047-9. Epub 2015 Jun 9.

Gonzalez P, Hildesheim A, Herrero R, Katki H, Wacholder S, Porras C, Safaeian M, Jimenez S, Darragh TM, Cortes B, Befano B, Schiffman M, Carvajal L, Palefsky J, Schiller J, Ocampo R, Schussler J, Lowy D, Guillen D, Stoler MH, Quint W, Morales J, Avila C, Rodriguez AC, Kreimer AR; Costa Rica HPV Vaccine Trial (CVT) Group. Rationale and design of a long term follow-up study of women who did and did not receive HPV 16/18 vaccination in Guanacaste, Costa Rica. Vaccine. 2015 Apr 27;33(18):2141-51. doi: 10.1016/j.vaccine.2015.03.015. Epub 2015 Mar 18.

Lang Kuhs KA, Porras C, Schiller JT, Rodriguez AC, Schiffman M, Gonzalez P, Wacholder S, Ghosh A, Li Y, Lowy DR, Kreimer AR, Poncelet S, Schussler J, Quint W, van Doorn LJ, Sherman ME, Sidawy M, Herrero R, Hildesheim A, Safaeian M; Costa Rica Vaccine Trial Group. Effect of different human papillomavirus serological and DNA criteria on vaccine efficacy estimates. Am J Epidemiol. 2014 Sep 15;180(6):599-607. doi: 10.1093/aje/kwu168. Epub 2014 Aug 19.

Hildesheim A, Wacholder S, Catteau G, Struyf F, Dubin G, Herrero R; CVT Group. Efficacy of the HPV-16/18 vaccine: final according to protocol results from the blinded phase of the randomized Costa Rica HPV-16/18 vaccine trial. Vaccine. 2014 Sep 3;32(39):5087-97. doi: 10.1016/j.vaccine.2014.06.038. Epub 2014 Jul 10.

Lang Kuhs KA, Gonzalez P, Rodriguez AC, van Doorn LJ, Schiffman M, Struijk L, Chen S, Quint W, Lowy DR, Porras C, DelVecchio C, Jimenez S, Safaeian M, Schiller JT, Wacholder S, Herrero R, Hildesheim A, Kreimer AR; Costa Rica Vaccine Trial Group. Reduced prevalence of vulvar HPV16/18 infection among women who received the HPV16/18 bivalent vaccine: a nested analysis within the Costa Rica Vaccine Trial. J Infect Dis. 2014 Dec 15;210(12):1890-9. doi: 10.1093/infdis/jiu357. Epub 2014 Jun 23.

Lang Kuhs KA, Gonzalez P, Struijk L, Castro F, Hildesheim A, van Doorn LJ, Rodriguez AC, Schiffman M, Quint W, Lowy DR, Porras C, Delvecchio C, Katki HA, Jimenez S, Safaeian M, Schiller J, Solomon D, Wacholder S, Herrero R, Kreimer AR; Costa Rica Vaccine Trial Group. Prevalence of and risk factors for oral human papillomavirus among young women in Costa Rica. J Infect Dis. 2013 Nov 15;208(10):1643-52. doi: 10.1093/infdis/jit369. Epub 2013 Sep 6.

Herrero R, Quint W, Hildesheim A, Gonzalez P, Struijk L, Katki HA, Porras C, Schiffman M, Rodriguez AC, Solomon D, Jimenez S, Schiller JT, Lowy DR, van Doorn LJ, Wacholder S, Kreimer AR; CVT Vaccine Group. Reduced prevalence of oral human papillomavirus (HPV) 4 years after bivalent HPV vaccination in a randomized clinical trial in Costa Rica. PLoS One. 2013 Jul 17;8(7):e68329. doi: 10.1371/journal.pone.0068329. Print 2013.

Clarke M, Schiffman M, Wacholder S, Rodriguez AC, Hildesheim A, Quint W; Costa Rican Vaccine Trial Group. A prospective study of absolute risk and determinants of human papillomavirus incidence among young women in Costa Rica. BMC Infect Dis. 2013 Jul 8;13:308. doi: 10.1186/1471-2334-13-308.

Castro FA, Quint W, Gonzalez P, Katki HA, Herrero R, van Doorn LJ, Schiffman M, Struijk L, Rodriguez AC, DelVecchio C, Lowy DR, Porras C, Jimenez S, Schiller J, Solomon D, Wacholder S, Hildesheim A, Kreimer AR; Costa Rica Vaccine Trial Group. Prevalence of and risk factors for anal human papillomavirus infection among young healthy women in Costa Rica. J Infect Dis. 2012 Oct 1;206(7):1103-10. Epub 2012 Jul 30.

Kreimer AR, González P, Katki HA, Porras C, Schiffman M, Rodriguez AC, Solomon D, Jiménez S, Schiller JT, Lowy DR, van Doorn LJ, Struijk L, Quint W, Chen S, Wacholder S, Hildesheim A, Herrero R; CVT Vaccine Group. Efficacy of a bivalent HPV 16/18 vaccine against anal HPV 16/18 infection among young women: a nested analysis within the Costa Rica Vaccine Trial. Lancet Oncol. 2011 Sep;12(9):862-70. doi: 10.1016/S1470-2045(11)70213-3. Epub 2011 Aug 22. Erratum in: Lancet Oncol. 2011 Nov;12(12):1096.

Wacholder S, Chen BE, Wilcox A, Macones G, Gonzalez P, Befano B, Hildesheim A, Rodríguez AC, Solomon D, Herrero R, Schiffman M; CVT group. Risk of miscarriage with bivalent vaccine against human papillomavirus (HPV) types 16 and 18: pooled analysis of two randomised controlled trials. BMJ. 2010 Mar 2;340:c712. doi: 10.1136/bmj.c712.

Dessy FJ, Giannini SL, Bougelet CA, Kemp TJ, David MP, Poncelet SM, Pinto LA, Wettendorff MA. Correlation between direct ELISA, single epitope-based inhibition ELISA and pseudovirion-based neutralization assay for measuring anti-HPV-16 and anti-HPV-18 antibody response after vaccination with the AS04-adjuvanted HPV-16/18 cervical cancer vaccine. Hum Vaccin. 2008 Nov-Dec;4(6):425-34. Epub 2008 Nov 11.

Hildesheim A, Herrero R, Wacholder S, Rodriguez AC, Solomon D, Bratti MC, Schiller JT, Gonzalez P, Dubin G, Porras C, Jimenez SE, Lowy DR; Costa Rican HPV Vaccine Trial Group. Effect of human papillomavirus 16/18 L1 viruslike particle vaccine among young women with preexisting infection: a randomized trial. JAMA. 2007 Aug 15;298(7):743-53.

Related Articles at Leaders in Pharmaceutical Intelligence on HPV:

Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, CANCER BIOLOGY & Innovations in Cancer Therapy, Disease Biology, Small Molecules in Development of Therapeutic Drugs, FDA Regulatory Affairs, Genomic Testing: Methodology for Diagnosis,Infectious Disease & New Antibiotic Targets, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , , ,, , , on September 18, 2012 | 3 Comments »

Reporter: Aviva Lev-Ari, PhD, RN Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing September 18, 2012 By a GenomeWeb staff reporter NEW YORK (GenomeWeb News) – The presence or absence of human papillomavirus DNA on its own in an individual’s head or neck cancer does not provide enough information […]

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Human Papilloma Virus and Cancer

Posted in Uncategorized on October 13, 2015 | Edit | Leave a Comment »

What is Human Papilloma Virus?   Human papillomavirus Taxonomy ID: 10566 Inherited blast name: viruses Rank: species Genetic code: Translation table 1 (Standard) Host: vertebrates| human Other names: synonym: human papillomavirus HPV synonym: Human Papilloma Virus Lineage( full ) Viruses; dsDNA viruses, no RNA stage; Papillomaviridae; unclassified Papillomaviridae; Human papillomavirus types    Entrez records    Database name Subtree links Direct links Nucleotide 7,782 7,775 Protein 2,611 2,604 Structure 3 […]

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Viruses and Cancer: A Walk on the Memory Lane

Posted in Uncategorized, tagged , , , on October 12, 2015 | Edit | 4 Comments »

Viruses and Cancer: A Walk on the Memory Lane Curator: Demet Sag, PhD, CRA, GCP   One of the other mechanism where cancer and microorganisms establish a close relationship is viruses. They are vicious sometimes as they adept fast even we don’t call them a real organism since they require a living cell to survive. […]

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Bacillus Calmette–Guérin (BCG) for Superficial Bladder Cancer

Posted in Uncategorized on October 12, 2015 | Edit | Leave a Comment »

Bacillus Calmette–Guérin (BCG) for Superficial Bladder Cancer Curator: Demet Sag, PhD, CRA, GCP Bladder cancer is arising from the epithelial lining, specifically the urothelium of the urinary bladder with a five year survival rate. The common one is transitional cell carcinoma (90%) and the remaining 10% of bladder cancer are squamous cell carcinoma, adenocarcinoma, sarcoma, small cell carcinoma.  This is a […]

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Helicobacter pylorum

Posted in Cancer and Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, tagged , , , , on October 11, 2015 |Leave a Comment »

Helicobacter pylorum Larry H. Bernstein, MD, FCAP, Curator LPBI The Nobel Prize in Physiology or Medicine 2005 Photo: C. Northcott Barry J. Marshall Photo: U. Montan J. Robin Warren  Affiliation at the time of the award: NHMRC Helicobacter pylori Research Laboratory, QEII Medical Centre, Nedlands, Australia, University of Western Australia, Perth, Australia The Nobel Prize […]

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Papilloma viruses for cervical cancer

Posted in Cancer and Therapeutics, Clinical & Translational, Clinical Diagnostic, Curation,Diagnostics and Lab Tests, tagged , , on October 10, 2015 | Leave a Comment »

 Papilloma viruses for cervical cancer Larry H. Bernstein, MD, FCAP, Curator LPBI Practice Bulletin No. 131: Screening for Cervical Cancer Obstetrics & Gynecology: November 2012 doi: http://10.1097/AOG.0b013e318277c92a The incidence of cervical cancer in the United States has decreased more than 50% in the past 30 years because of widespread screening with cervical cytology. In 1975, […]

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Curation of Recently Halted Oncology Trials Due to Serious Adverse Events – 2015

Posted in Cancer and Therapeutics, Clinical & Translational, Drug Toxicity, FDA, FDA Regulatory Affairs, Health Law & Patient Safety, Pharmaceutical Drug Discovery,Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment,pharmacologic toxicities, tagged , , , , , , , , , , , , , , , on October 7, 2015 | Leave a Comment »

Curation of Recently Halted Oncology Trials Due to Serious Adverse Events – 2015 Curator: Stephen J. Williams, Ph.D. The following is reports of oncology clinical trials in 2015 which have been halted for Serious Adverse Events (SAE), in most instances of an idiopathic nature. For comparison I have listed (as of this writing) the oncology […]

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Papilloma viruses for cervical cancer

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Practice Bulletin No. 131: Screening for Cervical Cancer

Obstetrics & Gynecology:

The incidence of cervical cancer in the United States has decreased more than 50% in the past 30 years because of widespread screening with cervical cytology. In 1975, the rate was 14.8 per 100,000 women. By 2008, it had been reduced to 6.6 per 100,000 women. Mortality from the disease has undergone a similar decrease from 5.55 per 100,000 women in 1975 to 2.38 per 100,000 women in 2008 (1). The American Cancer Society (ACS) estimates that there will be 12,170 new cases of cervical cancer in the United States in 2012, with 4,220 deaths from the disease (2). Cervical cancer is much more common worldwide, particularly in countries without screening programs, with an estimated 530,000 new cases of the disease and 275,000 resultant deaths each year (3, 4). When cervical cancer screening programs have been introduced into communities, marked reductions in cervical cancer incidence have followed (5, 6).

New technologies for cervical cancer screening continue to evolve as do recommendations for managing the results. In addition, there are different risk-benefit considerations for women at different ages, as reflected in age-specific screening recommendations. The ACS, the American Society for Colposcopy and Cervical Pathology (ASCCP), and the American Society for Clinical Pathology (ASCP) have recently updated their joint guidelines for cervical cancer screening (7), and an update to the U.S. Preventive Services Task Force recommendations also has been issued (8). The purpose of this document is to provide a review of the best available evidence regarding screening for cervical cancer.

Study Backs Co-Testing for Cervical Cancer

A positive co-test result was more sensitive than either a positive HPV-only test or a positive Pap-only test.

http://www.medpagetoday.com/HematologyOncology/CervicalCancer/51016

Charles Bankhead

Cervical cancer screening with a test for human papillomavirus (HPV) resulted in a 50% higher rate of false-negative results versus Pap testing and three times greater versus co-testing, a large retrospective study showed.

Data encompassing more than 250,000 women showed a false-negative rate of 18.6% compared with 12.2% for Pap testing. With a false-negative rate of 5.5%, screening women with the HPV test and Pap test missed the fewest cancers.

The results support clinical guidelines that recommend co-testing, according to authors of a report in Cancer Cytopathology. The results differ dramatically, however, from those of previous studies that have consistently shown greater diagnostic accuracy for the HPV test compared with the Pap test.

“The reason that women are screened is that they want to be protected from cervical cancer,” study author R. Marshall Austin, MD, PhD, of Magee-Women’s Hospital and the University of Pittsburgh, told MedPage Today. “The previous trials have generally focused on cervical intraepithelial neoplasia 2 or 3, so-called precancer. The difference is that most of what we call precancer will actually never develop into cancer.

“The unique thing about this study, and what makes it so important, is that we looked at over 500 invasive cervical cancers, which are what women want to be protected against, and looked at the effectiveness of the methods of testing.”

A year ago, the FDA approved Roche’s cobas assay for HPV DNA as a first-line test for cervical cancer screening, following a unanimous vote for approval by an FDA advisory committee.

The approval was based primarily on a pivotal trial involving 47,200 women at high risk for cervical cancer. The primary endpoint was the proportion of patients who developed grade ≥3 cervical intraepithelial neoplasia (≥CIN3). The results showed a greater than 50% reduction in the incidence of ≥CIN3 with the DNA test versus Pap testing.

Austin and colleagues retrospectively analyzed clinical records for 256,648 average-risk women, ages 30 to 65, all of whom underwent co-testing as a screen for cervical cancer and subsequently had a cervical biopsy within a year of co-testing. The primary objective was to determine the sensitivity of the three screening methods for detection of biopsy-proven ≥CIN3 and invasive cancer.

The results showed that 74.7% of the women had a positive HPV test, 73.8% had an abnormal Pap test (atypical squamous cells of undetermined significance or worse), 89.2% had a positive co-test, and 1.6% had ≥CIN3.

Biopsy results showed that co-testing had the highest sensitivity for ≥CIN3 (98.8% versus 94% for HPV test only and 91.3% for Pap testing alone, P<0.0001). The Pap test had greater specificity versus HPV testing alone or co-testing (26.3% versus 25.6% versus 10.9%, P<0.0001).

Investigators identified 526 patients who developed biopsy-proven invasive cervical cancer. Of those patients, 98 tested negative by HPV assay only, 64 by Pap test only, and 29 by co-testing.

Given the average risk of the patient population included in the study, the results are broadly applicable to women in the age range studied, regardless of baseline risk for cervical cancer, Austin said.

The results are clearly at odds with previously reported comparative data showing superiority for the HPV assay versus Pap testing as a standalone screening test, but the reasons for the inconsistency aren’t clear, said Debbie Saslow, PhD, of the American Cancer Society (ACS) in Atlanta.

The data also show that co-testing is better than either test alone, which supports current ACS recommendations for cervical cancer screening.

“The current approach, according to the American Cancer Society and 25 other organizations that worked with us on our last guideline, co-testing is the preferred strategy,” Saslow told MedPage Today. “This paper completely backs that up. Even though a Pap alone is acceptable, clearly, co-testing is the best way to go.”

Noting that only half of women in the U.S. do not under go co-testing despite clinical guidelines recommending it for more than a decade, Saslow asked, “What’s taking so long?”

Earlier this year, several organizations released joint “interim guidance” regarding cervical cancer screening. Described as an aid to clinical decision-making until existing guidelines are updated, the interim guidance characterized the HPV-DNA test as an acceptable alternative to Pap testing as a primary screening test.

Acknowledging that the guidance focused on use of the HPV assay as a single test, interim guidance lead author Warner Huh, MD, of the University of Alabama at Birmingham, noted that “Every single study worldwide that has looked at this issue shows the same result: HPV testing outperforms Pap testing.”

In their article, Austin and colleagues argued that the HPV assay should be evaluated in comparison with the Pap test but as an alternative to co-testing.

“HPV-only primary screening for cervical cancer presents many challenges for clinicians,” the authors said. “Questions arise regarding its effectiveness, its long-term risk, and when it is the best option for a particular patient.

“Clinicians had similar questions when co-testing was first recommended for women 30 and older in 2006,” they added. “Since then the adoption of co-testing has steadily increased, with approximately 50% of physicians co-testing women 30 and older, but it is still not done at the recommended level.”

The study had some limitations. The authors could not confirm that the cervical biopsy results were from women who did not have an intervening screening test or treatment with a different provider during the study period.

Also, the authors were unable to draw conclusions based on the overall population of women who were screened for cervical cancer because the dataset consisted of screening results of women who underwent biopsies.

 

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Novel biomarkers for targeting cancer immunotherapy

Posted in Advanced Drug Manufacturing Technology, Bio Instrumentation in Experimental Life Sciences Research, Biomarkers & Medical Diagnostics, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Chemical Genetics, Clinical Diagnostics, Immunology, Immunotherapy, tagged , , , on October 10, 2015| Leave a Comment »

Novel biomarkers for targeting cancer immunotherapy

Curator: Larry H. Bernstein, MD, FCAP

 

EFFICACY AND POTENCY TESTING: CELLULAR IMMUNITY
http://www.ablinc.com/efficacy_and_potency_testing-cellular_Immunity.php?gclid=CIGI953juMgCFcuQHwodtyUJ0w

ABL has decades of experience working with human and animal samples to determine the efficacy, activity, and potency of vaccines and therapeutics. Our animal facility is located in close proximity to our laboratories allowing for fresh samples to be delivered in a timely manner for testing in ABL’s laboratories. ABL has a wealth of experience processing many different types of samples (blood, fluids, tissues, washes, etc) and viably freezing cells for shipment or testing at a later date.

In our continuing effort to ensure we are providing our clients with reliable and consistent data, ABL has worked with some of the top academic labs and experts in the country to cross validate our assays and sample collection techniques. This helps give our clients the assurance that the information they receive from ABL is accurate and can be used to make the significant decisions about their product candidates.

Our goal in providing a wide range of testing capabilities is to ensure the data accuracy to help our clients remove the risk associated with product development.

Capabilities

  • Determining absolute values and percentages of CD4 T-cells, CD8 T-cells, B cells, and NK cells from whole blood samples
  • Examine memory T-cell responses by FACS
  • NK functionality
  • Quantify secreted cytokines
  • ELISPOT: human, NHP, and murine samples
  • Intracellular cytokine staining
  • Luminex
  • FACS analysis to quantitate or determine production of cytokines, including IFN-gamma, TNF-alpha, IL-2, IL-4, IL-5, IL-6, and IL-10
  • Flex array system to target other cytokines/chemokines
  • Cytometric bead array
  • Lymphoproliferation assay

The state-of-the-art, non-toxic Immunotherapy protocols of the Issels® Immuno-Oncology Centers are designed to restore the body’s own complex immune and defense mechanisms to recognize and eliminate cancer cells.

They are always highly personalized and can be combined with gene-targeted or special standard cancer therapies according to individual needs.

The integrative Issels® Immuno-Oncology system is the result of extensive clinical and scientific research and has become internationally known for its remarkable rate of complete long-term remissions of advanced and standard therapy-resistant cancers.

Issels® Immuno-Oncology is based on and an expansion of the comprehensive strategy developed at the world’s first hospital specializing in the treatment of advanced and standard-therapy resistant cancers with 120 beds solely dedicated to immunotherapy based cancer treatment. Immunotherapy is now considered the most advanced of all cancer treatments.

➤ Cytokines, NK Cells, LAK Cells, Stem Cells

➤ Advanced Gene-Targeted Therapies

Cancer immunotherapy research is evolving to more targeted strategies

Discoveries in immune pathway research have helped refine cancer immunotherapy strategies to become more targeted.1,2

THE HISTORY OF CANCER IMMUNE RESEARCH1-7

history-of-immunotherapy

history-of-immunotherapy

EXPLORING A MORE PERSONALIZED APPROACH TO CANCER IMMUNOTHERAPY RESEARCH

With the evolution to more targeted strategies, research is focusing on identifying predictors of individual immune response through specific tumor characteristics and factors in the tumor microenvironment, such as

  • The presence of tumor-infiltrating immune cells8
    • The ability of immune cells to infiltrate the tumor microenvironment may be a key criterion for a variety of immune-directed strategies, and could indicate which tumors are more likely to respond
  • Gene expression patterns in tumors, particularly the genes involved in immune response9
  • Cell surface protein expression
    • PD-L1 expression on tumor cells and tumor-infiltrating immune cells10,11
    • MUC1 expression on tumor cells12

REFERENCES

  1. Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity. 2013;39:1-10. PMID: 23890059
  2. Mellman I, Coukos G, Dranoff G. Cancer immunotherapy comes of age. Nature. 2011;480:480-489. PMID: 22193102
  3. Lesterhuis WJ, Haanen JB, Punt CJ. Cancer immunotherapy—revisited. Nat Rev Drug Discov. 2011;10:591-600. PMID: 21804596
  4. National Institutes of Health ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT01494688. Accessed March 4, 2015.
  5. National Institutes of Health ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT00739609. Accessed March 4, 2015.
  6. Glienke W, Esser R, Priesner C, et al. Advantages and applications of CAR-expressing natural killer cells. Front Pharmacol.2015;6:21. doi: 10.3389/fphar.2015.00021. PMID: 25729364
  7. National Institutes of Health ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT01303705. Accessed March 4, 2015.
  8. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol.2013;14:1014-1022. PMID: 24048123
  9. Ji RR, Chasalow SD, Wang L, et al. An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol Immunother. 2012;61:1019-1031. PMID: 22146893
  10. Taube JM, Anders RA, Young GD, et al. Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci Transl Med. 2012;4:127ra37. PMID: 22461641  

 Cancer immunotherapy research: exploring the immune response against cancer

Cancer immunotherapy research seeks to understand how to utilize the body’s adaptive immune defense against cancer’s ability to evolve and evade destruction.1,2

The cancer immunity cycle characterizes the complex interactions between the immune system and cancer

The cancer immunity cycle describes a process of how one’s own immune system can protect the body against cancer. When performing optimally, the cycle is self-sustaining. With subsequent revolutions of the cycle, the breadth and depth of the immune response can be increased.1

Image of the cancer immunity cycle,featuring dendritic cells and active T cells, and how the immune system attacks cancer cells, leading to tumor apoptosis]

STEPS 1-3: INITIATING AND PROPAGATING ANTICANCER IMMUNITY1

  • Oncogenesis leads to the expression of neoantigens that can be captured by dendritic cells
  • Dendritic cells can present antigens to T cells, priming and activating cytotoxic T cells to attack the cancer cells

STEPS 4-5: ACCESSING THE TUMOR1

  • Activated T cells travel to the tumor and infiltrate the tumor microenvironment

STEPS 6-7: CANCER-CELL RECOGNITION AND INITIATION OF CYTOTOXICITY1

  • Activated T cells can recognize and kill target cancer cells
  • Dying cancer cells release additional cancer antigens, propagating the cancer immunity cycle

Tumors can evade immune destruction

By disrupting the processes of the cancer immunity cycle throughout the body, tumors can avoid detection by the immune system and limit the extent of immune destruction.1-3

http://www.researchcancerimmunotherapy.com/images/overview/evading-immune-destruction/tumor-microenv.png

Tumor microenvironment  –  Disrupting antigen detection

 

Lymph node – Inhibiting T-cell activation by dendritic cells

 Image of dendritic cell activating T cell, step 3 of cancer immunity cycle

Blood vessel   –    Blocking T-cell infiltration into tumor

 Image of T cell infiltrating tumor, step 5 of cancer immunity cycle

Tumor microenvironment –  Suppressing cytotoxic T-cell activity

Engaging the immune response: a unique approach to cancer management

Cancer immunotherapy strategies are designed to engage the immune system against tumors. This approach is unique in the oncology setting and introduces new considerations for cancer management.1,2

Tumors can evade immune destruction

By disrupting the processes of the cancer immunity cycle throughout the body, tumors can avoid detection by the immune system and limit the extent of immune destruction.1-3

tumor-microenv-sm Disrupting antigen detection

tumor-microenv-sm Disrupting antigen detection

http://www.researchcancerimmunotherapy.com/images/overview/evading-immune-destruction/tumor-microenv-sm.png

CONSIDERATIONS FOR CANCER IMMUNOLOGY

Duration of response

The immune response has the ability to adapt with cancer as it evolves, and can become self-propagating once the cancer immunity cycle is initiated. Immune-directed strategies aim to leverage these attributes, with the goal of inducing a durable antitumor effect.3-5

Pseudo-progression

Image showing T-cell infiltration into the tumor site can cause pseudoprogression]T-cell infiltration to the tumor site may cause an apparent increase in tumor size or the appearance of new lesions. This inflammatory effect can be misinterpreted as progressive disease, as it can be difficult to differentiate the different cell types in radiographic imaging. New criteria have been developed to better capture immune-related response patterns, and may guide evaluation of immunotherapies in clinical trials, and potentially in clinical care.1,2,6

Immune-related adverse events

While the goal of cancer immunotherapy research is to understand how to activate specific components of the immune response, the potential for off-target effects exists. Adverse event profiles may vary among different immune-directed strategies. As strategies grow more targeted, the recognition and management of immune-related adverse events will evolve.1,3

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Biomarkers for personalized oncology: recent advances and future challenges

Posted in Biomarkers & Medical Diagnostics, tagged , , on October 8, 2015| Leave a Comment »

Biomarkers for personalized oncology: recent advances and future challenges 

Writer and Curator:  Larry H Bernstein, MD, FCAP

LPBI

3.5 Biomarkers for personalized oncology: recent advances and future challenges 

Kalia M
Metabolism. 2015 Mar;64(3 Suppl 1):S16-21
http://dx.doi.org:/10.1016/j.metabol.2014.10.027

Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells and oncology is a branch of medicine that deals with tumors. The last decade has seen significant advances in the development of biomarkers in oncology that play a critical role in understanding molecular and cellular mechanisms which drive tumor initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical-pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and gastrointestinal tumors; anaplastic lymphoma kinase (ALK) inhibitors in lung cancer with EML4-ALk fusion; HER2/neu blockage in HER2/neu-positive breast cancer; and epidermal growth factor receptors (EGFR) inhibition in EGFR-mutated lung cancer. This review presents the current state of our knowledge of biomarkers in five selected cancers: chronic myeloid leukemia, colorectal cancer, breast cancer, non-small cell lung cancer and melanoma.

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Accumulation of 2-hydroxyglutarate is not a biomarker for malignant progression of IDH-mutated low grade gliomas

Posted in Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, tagged , , , on October 8, 2015| Leave a Comment »

Accumulation of 2-hydroxyglutarate is not a biomarker for malignant progression of IDH-mutated low grade gliomas 

Larry H Bernstein, MD, FCAP, Curator

LPBI

UPDATED 9/26/2021

Blockade of Glutathione Metabolism in IDH1-Mutated Glioma

Xiaoying Tang, Xiao Fu, Yang Liu, Di Yu, Sabrina J. Cai and Chunzhang Yang

source: https://mct.aacrjournals.org/content/19/1/221

Abstract

Mutations in genes encoding isocitrate dehydrogenases (IDH) 1 and 2 are common cancer-related genetic abnormalities. Malignancies with mutated IDHs exhibit similar pathogenesis, metabolic pattern, and resistance signature. However, an effective therapy against IDH1-mutated solid tumor remains unavailable. In this study, we showed that acquisition of IDH1 mutation results in the disruption of NADP+/NADPH balance and an increased demand for glutathione (GSH) metabolism. Moreover, the nuclear factor erythroid 2–related factor 2 (Nrf2) plays a key protective role in IDH1-mutated cells by prompting GSH synthesis and reactive oxygen species scavenging. Pharmacologic inhibition of the Nrf2/GSH pathway via brusatol administration exhibited a potent tumor suppressive effect on IDH1-mutated cancer in vitro and in vivo. Our findings highlight a possible therapeutic strategy that could be valuable for IDH1-mutated cancer treatment.

Introduction

Isocitrate dehydrogenases (IDH) are a family of enzymes that mediate the oxidative decarboxylation of isocitrate to α-ketoglutarate. These enzymes depend on NAD+/NADP+, as they reduce NAD+/NADP+ for NADH/NADPH production (1). While IDH1 and IDH2 isozymes are homodimers that use NADP+ as a cofactor, IDH3 is a heterotetramer that uses NAD+ as a cofactor (2). Genetic abnormalities in IDH1/2 are common in multiple types of human tumors. For example, mutations in IDH1/2 have been found in over 80% of World Health Organization grade II/III gliomas, including astrocytoma and oligodendroglioma (3). These mutations are found in 73% of secondary glioblastomas, which are derived from lower grade gliomas, but are less frequent in primary glioblastoma multiforme (4). Furthermore, the IDH1/2 mutations are commonly identified in acute myeloid leukemia (AML), central chondrosarcoma, central/periosteal chondromas, and cholangiocarcinoma (5, 6). However, despite the widespread prevalence of these mutations, effective therapies for IDH-mutated solid tumors remain unavailable.

The majority of cancer-associated IDH mutations are amino acid substitutions of an arginine residue in its catalytic center. For IDH1, the 132 arginine (R) residue is frequently altered to histidine (H) or cysteine (C). The R132H (73.67%) and R132C (13.35%) variant comprise over 87% of all IDH1 mutations in human. A seminal study by Dang and colleagues (7) revealed that these amino acid substitutions in IDH1 lead to a neomorphic activity of the enzyme, which is the NADPH-dependent consumption of α-ketoglutarate for 2-hydroxyglutarate (2-HG) production. The accumulation of 2-HG has been reported to associate with glioma oncogenesis by inhibiting α-ketoglutarate dioxygenases (8–10). Several pioneer studies also suggest that IDH mutants are associated with depletion of NADPH and glutathione (GSH), accompanied with elevated reactive oxygen species (ROS) levels (11, 12). The neomorphic catalytic function, 2-HG accumulation and occurrence of oxidative stress, suggest a distinctive oncogenesis mechanism, which could be exploited as a therapeutic vulnerability in IDH1-mutated malignancies.

In this study, we investigated the association between mutant IDH1 enzyme and ROS levels. Furthermore, we analyzed the role of nuclear factor erythroid 2–related factor 2 (Nrf2) in the regulation of GSH metabolism that maintains cellular redox homeostasis and survival. Moreover, we investigated the efficacy of Nrf2/GSH metabolism blockade as a therapeutic approach in IDH1-mutated malignancies.

Results

Neomorphic activity in cancer-associated mutant IDH1 triggers oxidative stress

To better understand the effect of IDH1 mutants on redox homeostasis, we established a doxycycline-induced IDH1-mutant U251 cell line (Supplementary Fig. S1A). We noticed that upon the expression of mutant IDH1 enzymes, the overall quantity of NADP decreased, in both of its oxidized (NADP+) and reduced (NADPH) forms (Fig. 1A). Moreover, the NADP+/NADPH ratio significantly increased (Fig. 1B), suggesting that the neomorphic enzyme activity of mutant IDH1 exhausted the cellular pool of NADPH. The balance between NADP+ and NADPH is a critical factor to maintain cellular redox homeostasis, as NADPH is a general cofactor in reductive biosynthetic reactions, and to provide electrons for metabolic pathways, such as the reduction of GSSG back to GSH and ROS neutralization (23). Furthermore, by quantification of H2O2, we showed that mutant IDH1 enzymes led to severe oxidative stress in both U251 cells and BTIC TS603 (Fig. 1C; Supplementary Fig. S2A). Such an increase in ROS levels depended on the presence of mutant enzymes. The treatment with AGI-5198, a specific inhibitor of mutant IDH1 (13), reduced ROS accumulation in cells expressing the IDH1 R132H variant (Fig. 1D). Furthermore, the elevated ROS levels led to oxidative stress to macromolecules and subcellular organelles, evidenced by increased lipid peroxidation (Fig. 1E and F) and mitochondrial ROS level (Fig. 1G and H). The ROS scavenger catalase and MnTBAP, but not mannitol, abrogated the accumulation of ROS in IDH1-mutated cells, indicating the majority form of oxidative stress is derived from hydrogen peroxide and superoxide anion (Supplementary Fig. S1B).

Figure 1.
Figure 1.

Cancer-associated IDH1 mutants trigger oxidative stress. A, NADP+ level was measured in U251 cells with doxycycline (Dox)-induced expression of IDH1-mutant enzymes (R132C and R132H). **, P < 0.01. B, Measurement of NADP+/NADPH ratio in U251 cells with expression of IDH1-mutant enzymes. **, P < 0.01. C, ROS-Glo measurement in U251 cells with expression of IDH1-mutant enzymes. **, P < 0.01. D, ROS-Glo measurement in IDH1-R132H U251 cells with AGI-5198 treatment (1 μmol/L, 24 hours; **, P < 0.01). E, Lipid peroxidation staining measures membrane oxidative damage in U251 cells with expression of IDH1-mutant enzymes. Scale bar, 10 μm. F, Quantification of lipid peroxidation in E. **, P < 0.01. G, MitoSOX staining measures mitochondrial ROS in IDH1-mutated U251 cells. Scale bar, 10 μm. H, Quantification of MitoSOX signal in G. **, P < 0.01.

GSH metabolism supports redox balance and survival in IDH1-mutated cells

Considering the remarkable ROS accumulation in IDH1-mutated cells, we speculate that antioxidant pathways, such as GSH-dependent ROS scavenging systems, may be triggered to maintain redox homeostasis. To test this hypothesis, we first measured the protein levels of GSH synthesis enzymes by Western blotting. We found that upon introduction of pathogenic mutant IDH1 enzymes, the levels of key enzymes in GSH biosynthesis, such as glutamate-cysteine ligase (GCLC, catalytic subunit; GCLM, modifier subunit), and cystine/glutamate transporter (SLC7A11, xCT transporter), increased (Fig. 2A). We also noticed that the levels of Nrf2 protein (NFE2L2), the major transcriptional factor responsible for ROS sensing, increased in IDH1-mutated cells. The enhancement of Nrf2 and GSH-dependent ROS scavenging pathways in IDH1-mutated U251 cells, as well as IDH1-mutated BTIC TS603, was also confirmed by qPCR (Fig. 2B; Supplementary Fig. S2B). To further understand the role of GSH in IDH1-mutated cells, we quantified GSH/GSSG levels in U251 cells expressing R132C/H IDH1 mutants. We recorded a substantial decrease in the GSH/GSSG ratio compared with that in cells expressing wild-type IDH1, suggesting that there is an elevated demand from GSH-dependent ROS scavenging (Fig. 2C). We also measured GSH/GSSG levels in BTICs, the result consistently showed that IDH1-mutated BTIC TS603 has lower GSH/GSSG ratio compared with IDH1 wild-type BTIC GSC827 and GSC 923 (Supplementary Fig. S2C). Moreover, the addition of an exogenous antioxidant enzyme, catalase, partially restored the GSH/GSSG ratio, indicating that the GSH/GSSG imbalance could be a result of GSH oxidation by hydrogen peroxide decomposition pathways (e.g., GSH peroxidases). Importantly, GSH biosynthesis exhibited a critical protective role in cells with mutant IDH1 enzyme. A loss-of-function experiment showed that 72 hours after genetic silencing of GCLC/GCLM resulted in remarkable apoptotic changes. The annexin V/PI apoptosis assay showed that apoptotic cell population increased remarkably upon siRNA treatment (R132H, siCont = 0.45% vs. siGCLM.2 = 31.2%; Fig. 2D and E). Moreover, Western blot analysis confirmed that cleaved caspase-3 was increased in U251 IDH1 R132H cells after genetic silencing of GCLC and GCLM (Supplementary Fig. S1C). ROS scavenger catalase restored caspase-3 cleavage in the presence of GCLC and GCLM RNA interference (Supplementary Fig. S1D). The increase of apoptosis was not observed when mutant IDH1 enzyme is absent, suggesting that the blockade of GSH metabolism is selectively toxic to IDH1-mutated cells. Furthermore, we recorded a great increase in cytoplasmic ROS levels after 48 hours of siRNA treatment, suggesting that apoptosis could be caused by ROS-derived cellular damage (Fig. 2F).

Figure 2.
Figure 2.

GSH de novo synthesis support cellular physiology in IDH1-mutated cells. A, Western blot measures the expression of GSH synthesis enzymes in U251 cells with IDH1-mutant expression. β-Actin was used as internal control. B, qRT-PCR analysis measures mRNA level of GSH synthesis enzymes. *, P < 0.05; **, P < 0.01. C, GSH and GSSG level was measured in IDH1-mutated U251 cells. Catalase (Cata) was used as exogenous ROS scavenger (500 U/mL, 24 hours; **, P < 0.01). D, Annexin V/PI apoptotic analysis in IDH1-mutated U251 cells with genetic silencing of GCLC and GCLM. E, Quantification of apoptotic cells in D. F, ROS-Glo assay in IDH1-mutated U251 cells with genetic silencing of GCLC and GCLM. Dox, doxycycline.

Activation of Nrf2 antioxidant pathway in IDH1-mutated cells

The transcription factor Nrf2 is a basic leucine zipper (bZIP) protein that regulates the cellular responses to oxidative stress by activating the expression of antioxidant genes (24). Under physiologic conditions, Nrf2 is tightly controlled by interaction with Kelch-like ECH-associated protein 1 (Keap1), which is a protein adaptor for E3 ubiquitin ligases, and proteasomal degradation. When cells are challenged by oxidative stress, Keap1–Nrf2 interaction is disrupted and the dissociated Nrf2 translocate into the nucleus for transcriptional activation (25). In IDH1-mutated cells, the increased ROS levels may trigger Nrf2 stabilization and gene transcription, which could be relevant to prompt GSH synthesis. To test this hypothesis, we first evaluated Nrf2-associated gene transcription via ARE-luciferase reporter assay. We found that mutant IDH1 expression was associated with enhanced Nrf2-dependent transcriptional activation (Fig. 3A). Furthermore, through ChIP assay, we demonstrated that the affinity of Nrf2 to antioxidant gene promoters was strongly enhanced after mutant IDH1 introduction, indicating that Nrf2 transactivation plays a central role in ROS homeostasis in IDH1-mutated cells (Fig. 3B). Consistent with these findings, genetic silencing of Nrf2 resulted in downregulation of antioxidant genes, such as GCLC, GCLM, HMOX1, NQO1, and SLC7A11 in IDH1-mutated cells (Fig. 3C; Supplementary Fig. S1E). The induction of Nrf2 transcription activity was accompanied by its prolonged protein stability. Immunoprecipitation assay showed that Nrf2 ubiquitination is compromised in the presence of mutant IDH1 (Fig. 3D). Furthermore, the protein stability of Nrf2 was elevated when mutant IDH1 enzymes were expressed (Fig. 3E and F). The protein half-lives of Nrf2 were prolonged from 22.4 to 49.2 minutes (R132C) or 62.3 minutes (R132H).

Figure 3.
Figure 3.

Nrf2 regulates GSH metabolism in IDH1-mutated cells. A, ARE-Luciferase reporter assay was performed in U251 cells with IDH1-mutant expression. *, P < 0.05. B, ChIP PCR assay showed antioxidant genes promoter affinity of Nrf2 in IDH1-mutated U251 cells. **, P < 0.01. C, Real-time PCR assay showed mRNA level of antioxidant genes after 48 hours genetic silencing of Nrf2 in U251 cells with IDH1-mutant expression. **, P < 0.01. D, Immunoprecipitation (IP) assay measures Nrf2 ubiquitination in U251 cells with IDH1-mutant expression. E, Cycloheximide (CHX) pulse chase assay measures Nrf2 protein stability in IDH1-mutated U251 cells. F, Quantification of Nrf2 half-lives from results in E. Dox, doxycycline; IB, immunoblot; n.s., not significant.

Suppressing Nrf2/GSH axis results in oxidative damage in IDH1-mutated cells

Considering the central role of Nrf2 in the physiology of IDH1-mutated cells, blockade of Nrf2/GSH axis may be an effective therapeutic approach for tumors with IDH1 R132 variants. To investigate this, we tested a Nrf2 inhibitor, brusatol, in IDH1-mutated cells. Brusatol has been shown to strongly reduce Nrf2 transcriptional activity and enhance chemosensitivity in transformed cells (21, 26). Here, we confirmed that brusatol promoted Nrf2 degradation in IDH1-mutated cells, as evidenced by increased Nrf2 protein ubiquitination (Fig. 4A). Moreover, cycloheximide pulse chase assay confirmed that Nrf2 protein stability is compromised upon brusatol treatment (Fig. 4B). The protein half-lives decreased for both IDH1 R132C (67.1 vs. 11.1 minutes) and R132H (41.6 vs. 10.34 minutes) variants (Fig. 4C). Furthermore, Western blot analysis showed that Nrf2 protein levels drastically decreased after brusatol treatment (Fig. 4D). Accordingly, ChIP-PCR assay showed that the Nrf2 affinity for DNA sharply decreased in the presence of brusatol (Fig. 4E).

Figure 4.
Figure 4.

Suppressing Nrf2/GSH axis results in oxidative damage in IDH1-mutated cells. A, Immunoprecipitation (IP) assay measures Nrf2 ubiquitination in U251 cells with IDH1-mutant expression after brusatol (Bru) treatment (40 nmol/L, 12 hours). B, Cycloheximide (CHX) pulse chase assay measures Nrf2 protein stability in IDH1-mutated U251 cells after brusatol treatment. C, Quantification of Nrf2 half-lives from results in B. D, Western blot measures the expression of GSH synthesis enzymes with brusatol treatment (40 nmol/L, 24 hours) in IDH1-mutated U251 cells. E, ChIP PCR assay showed antioxidant genes promoter affinity of Nrf2 with brusatol (40 nmol/L, 24 hours) in IDH1-mutated U251 cells. *, P < 0.05; **, P < 0.01. F, Annexin V/PI apoptosis assay showed apoptotic changes in IDH1-mutated U251 cells with brusatol treatment (40 nmol/L, 72 hours). Exogenous antioxidant catalase (Cata) was used as exogenous ROS scavenger. G, Quantification of apoptotic cells in F. ***, P < 0.001. H, ARE-Luciferase reporter assay showed Nrf2-associated gene transcription with brusatol (40 nmol/L, 24 hours) in IDH1-mutated U251 cells. *, P < 0.05; **, P < 0.01. I, GSH/GSSG measurement in IDH1-mutated U251 cells with brusatol treatment (40 nmol/L, 24 hours). **, P < 0.01. J, ROS-Glo measurement in IDH1-mutated U251 cells with brusatol treatment (40 nmol/L, 24 hours; **, P < 0.01). IB, immunoblot.

Importantly, the suppression of Nrf2 activity resulted into exacerbated oxidative damage and cell death in IDH1-mutated cells. Annexin V/PI flow cytometry assay showed that brusatol increased apoptotic rates by 1.9-fold and 2.7-fold in IDH1 R132C and R132H U251 cells, respectively (Fig. 4F and G). Consistently, brusatol also resulted in cell apoptosis in IDH1-mutated BTIC TS603, but the trend was much less in IDH1 wild-type BTICs (Supplementary Fig. S2D and S2E). We noticed that brusatol treatment resulted in reduced ARE-luciferase activity, suggesting that Nrf2 activity is suppressed in these cells (Fig. 4H). Quantification of GSH revealed that brusatol further reduced GSH availability in IDH1-mutated cells. Brusatol decreased the GSH/GSSG ratio by 75.8% and 75.9% in IDH1 R132C and R132H cells, respectively (Fig. 4I). Accordingly, the cytoplasmic levels of ROS were significantly elevated by brusatol treatment (Fig. 4J

Targeting Nrf2/GSH axis suppresses IDH1-mutated xenografts

The aforementioned in vitro experiments strongly indicate that the blockade of GSH metabolism could be a valuable approach to suppress malignancies with mutant IDH1 enzymes. To better test this hypothesis, we established a xenograft mice model based on a patient-derived IDH1-mutated cell line TS603 (Fig. 5A). Patient-derived TS603 glioma cells with intrinsic mutant IDH1 enzyme were injected into NSG immunocompromised mice to establish xenograft tumor. When the tumor mass approaches 50 mm3, mice were treated with either brusatol and/or the exogenous antioxidant NAC. Tumor growth curve showed that brusatol significantly reduced the expansion of tumor mass (Fig. 5B and C). Notably, NAC abolished the suppressive effect of brusatol, suggesting ROS played a critical role in brusatol effects on tumor growth. No significant loss of body weight was observed during the treatment (Supplementary Fig. S1F). Histologic analysis revealed that brusatol treatment reduced the expression of antioxidant genes such as Nrf2, SLC7A11, GCLC, and GCLM (Fig. 5D). NAC slightly restored antioxidant gene expression in the xenografts. On the other hand, brusatol led to reduced expression of Ki67, but elevated levels of DNA damage markers, γH2A.X and TUNEL (Fig. 5E). Similarly, NAC treatment minimized cytotoxicity in IDH1-mutated xenografts.

Figure 5.
Figure 5.

Targeting Nrf2/GSH axis suppresses IDH1-mutated xenografts. A, Schematic illustration for the xenograft and treatment schedule. B, Tumor growth curve of TS603 xenografts. n = 10 for each group. **, P < 0.01. C, Gross anatomy of TS603 xenografts. D, IHC assay showed the expression of Ki67, γH2AX, and terminal deoxynucleotidyl transferase–mediated dUTP nick end labeling (TUNEL) assay in tumor sections. Scale bar, 50 μm. E, IHC assay showed the expression of Nrf2, SLC7A11, GCLC, and GCLM in tumor sections. Scale bar, 50 μm. Bru, brusatol.

Discussion

IDH1-mutated malignancies and therapeutic approaches

Mutations of IDH1/2 genes are widespread genetic abnormalities detected in several types of human malignancies, including lower grade glioma, leukemia, chondroma, chondrosarcoma, and cholangiocarcinoma. Cancer-associated IDH mutations cause amino acid substitution of an arginine residue in the IDH enzyme catalytic center. Biochemical studies showed that IDH1 R132 mutants have elevated affinity for both NADPH (Km = 0.44 μmol/L) and α-ketoglutarate (Km = 965 μmol/L), indicating that the mutant enzyme prefers NADPH and α-ketoglutarate, whereas wild-type IDH enzyme prefers NADP+ and isocitrate for its catalytic function (7, 27).

Several pioneered studies showed that direct targeting mutant IDH1 enzyme is an effective treatment for IDH1-mutated hematopoietic malignancies, such as relapsed or refractory AML (28). Regarding IDH1-mutated solid tumors, Rohle and colleagues (13) showed that the inhibition of mutant IDH1 delayed IDH1-mutated xenograft expansion in vivo. However, preliminary data from several early phase clinical trials showed modest impact on objective response rate and delay of progression of IDH1-mutated solid tumors. More effective therapies, such as developing refined inhibitors of mutant IDH1, or targeting IDH-related pathways, have been urged to improve disease outcome of IDH1-mutated malignancies. Besides direct targeting the mutant enzyme, several lines of evidence show that metabolic reprogramming in IDH1-mutated cells could be targeted to synergize with conventional chemo/radiotherapies. We and other colleagues demonstrated that NAD+ depletion, as well as 2-HG–mediated deficiency in homologous DNA recombination establish vulnerability to PARP inhibitors in IDH1-mutated cells (29–32). The glutaminase inhibitor, CB-839, has also been proposed to be useful for the treatment of IDH1-mutated cancers (33, 34). In this study, we extended the investigation of effective therapy for IDH1-mutated cancer and discovered that Nrf2/GSH metabolism could be another therapeutic vulnerability in malignancies that harbor IDH1 mutation.

IDH-mutated cells develop dependency on GSH ROS scavenging

The production of ROS is involved in several aspects of cancer biology, such as genomic instability, loss of growth control, cellular motility, and tumor invasiveness (35, 36). On the other hand, excessive ROS is harmful to biological molecules, resulting in oxidative damage to DNA, lipid, and proteins (37). Maintaining appropriate ROS levels is key to cancer cells during oncogenesis and therapeutic resistance. GSH is an endogenous antioxidant tripeptide that participates in the elimination of reactive molecules, such as free radicals, peroxides, lipid peroxides, and metals. The thiol group in reduced GSH is responsible for its reducing activity, which alleviates oxidative stress through direct reduction of disulfide bonds in the cysteine residues of cytoplasmic proteins and eliminates ROS through the GSH-ascorbate cycle (38). For IDH1-mutated malignancies, several pioneered studies suggested the correlation with GSH depletion and ROS accumulation (39). Although failed control of intracellular ROS has been indicated with tumorigenesis process (40), there is still lack of a direct evidence showing IDH1-mutant–derived ROS promote tumor development.

In this study, we found that IDH1-mutated malignancies exhibit a tendency to suffer oxidative stress, as the introduction of mutant IDH1 is closely associated with elevated ROS levels in cytoplasm and mitochondria (Fig. 1C–H). Similarly, recent research suggests that tumoral GSH levels negatively correlate with 2-HG, suggesting that IDH-mutated cells have elevated demands for GSH (41). Our findings showed that, in IDH1-mutated cells, the key regulatory enzymes of GSH biosynthesis were upregulated to meet the increased demands of endogenous antioxidant systems (Fig. 2A–C). Loss-of-function experiments demonstrated that blocking GSH synthesis led to remarkably elevated ROS levels, oxidative stress, and apoptotic changes (Fig. 2D and E). Overall, our findings suggest that upregulation of GSH-based ROS scavenging pathways play a central role to maintain cellular homeostasis in IDH1-mutated cancers.

Nrf2-regulated GSH metabolism

The multifunctional transcriptional factor, Nrf2, governs the cellular response to oxidative stress by triggering antioxidant gene transcription. It regulates a variety of genes for electrophile and oxidant metabolism, as well as genes that support cellular survival under stress conditions (42). In this study, we recorded enhanced Nrf2 transcriptional activity that was associated with the presence of mutant IDH1 enzyme (Fig. 3A and B), suggesting that Nrf2-driven antioxidant response is a compensatory response to IDH1-associated oxidative stress. As a further validation, it was shown that several known Nrf2 transcription targets, such as GCLC, GCLM, HMOX1, NQO1, and SLC7A11, were upregulated in IDH1-mutated cells (Fig. 3C). Importantly, these genes play central role in cysteine uptake and de novo GSH biosynthesis, indicating that the protective effect of Nrf2 is a result of increased intracellular GSH pool. Protein stability tests showed that Nrf2 was less ubiquitinated and degraded in IDH1-mutated cells, which would lead to the activation of antioxidant expression (Fig. 3D–F). Moreover, the activation of Nrf2/antioxidant pathway not only relieves the metabolic stress for IDH1-mutated cells but may also support cellular viability and promote growth advantage during oncogenesis. Our findings highlighted the role of Nrf2-dependent GSH metabolism in IDH1-mutated cells, indicating a selective vulnerability of IDH1-mutated malignancies.

Targeting Nrf2/GSH metabolism as a new strategy for IDH1-mutated malignancies

Considering the critical role of Nrf2/GSH metabolism in cancer biology and therapeutic resistance, several attempts have been made to achieve specific targeting of this pathway using small-molecular compounds. For example, by high-throughput screening assay, Singh and colleagues (43) reported that the small-molecule compound ML-385 exhibits inhibitory effect on Nrf2 transcriptional activity. However, the effective dosage of ML-385 is too high for further preclinical studies in animals. In another example, Ren and colleagues (21) reported that brusatol, a quassinoid compound, exhibits potent inhibitory effect on Nrf2 transcriptional activity. In our study, we confirmed that brusatol is able to block Nrf2 activity in IDH1-mutated cells, which strongly suppressed antioxidant pathways, such as de novo GSH biosynthesis (Fig. 4). Interestingly, brusatol has been used as a sensitizer for conventional chemotherapy (21, 26, 44). In contrast, our study showed that at a similar dosage, brusatol monotherapy is sufficient to cause apoptotic changes in IDH1-mutated cells (45). We speculate that the brusatol potent efficacy is due to the induction of ROS levels, which leads to cell death in concert with the inhibition of endogenous antioxidants expression. The xenograft experiments confirmed this hypothesis. The introduction of an exogenous ROS scavenger, NAC, compromised the tumor-suppressing effect of brusatol, suggesting that ROS play a key role in brusatol-induced cytotoxicity (Fig. 5).

Overall, our study showed that neomorphic activity of mutant IDH1 enzyme results in mitochondrial and cytoplasmic ROS accumulation by disrupting NADP+/NADPH balance. The major regulator of antioxidant responses, Nrf2, controls the GSH de novo synthesis and plays a central role in the cellular physiology of IDH1-mutated cells. Blockade of Nrf2/antioxidant pathway exhibited selective cytotoxicity in cells with IDH1 mutation (Fig. 6). Our findings highlight the importance of GSH metabolism in IDH1-mutated cells and indicate a novel therapeutic approach for malignancies with IDH1 mutation.

Figure 6.
Figure 6.

Targeting Nrf2/GSH axis for IDH1-mutated cancers. Cancer-associated IDH1 mutants cause metabolic deficiency and accumulation of oxidative stress. Nrf2 plays a key protective role in IDH1-mutated cells by transcribing GSH synthesis enzymes. The enhanced de novo GSH synthesis neutralizes excessive ROS, and therefore avoids oxidative damages to macromolecules. Targeting Nrf2/GSH could be a novel therapeutic strategy in these types of human malignancies.

Accumulation of 2-hydroxyglutarate is not a biomarker for malignant progression in IDH-mutated low-grade gliomas

Tareq A. Juratli,† Mirko Peitzsch,† Kathrin Geiger, Gabriele Schackert, Graeme Eisenhofer, and Dietmar Krex
Neuro Oncol. 2013 Jun; 15(6): 682–690.
Online 2013 Feb 14. doi: 10.1093/neuonc/not006

Objectives

To determine whether accumulation of 2-hydroxyglutarate in IDH-mutated low-grade gliomas (LGG; WHO grade II) correlates with their malignant transformation and to evaluate changes in metabolite levels during malignant progression.

Methods

Samples from 54 patients were screened for IDH mutations: 17 patients with LGG without malignant transformation, 18 patients with both LGG and their consecutive secondary glioblastomas (sGBM; n = 36), 2 additional patients with sGBM, 10 patients with primary glioblastomas (pGBM), and 7 patients without gliomas. The cellular tricarboxylic acid cycle metabolites, citrate, isocitrate, 2-hydroxyglutarate, α-ketoglutarate, fumarate, and succinate were profiled by liquid chromatography–tandem mass spectrometry. Ratios of 2-hydroxyglutarate/isocitrate were used to evaluate differences in 2-hydroxyglutarate accumulation in tumors from LGG and sGBM groups, compared with pGBM and nonglioma groups.

Results

IDH1 mutations were detected in 27 (77.1%) of 37 patients with LGG. In addition, in patients with LGG with malignant progression (n = 18), 17 patients were IDH1 mutated with a stable mutation status during their malignant progression. None of the patients with pGBM or nonglioma tumors had an IDH mutation. Increased 2-hydroxyglutarate/isocitrate ratios were seen in patients with IDH1-mutated LGG and sGBM, in comparison with those with IDH1-nonmutated LGG, pGBM, and nonglioma groups. However, no differences in intratumoral 2-hydroxyglutarate/isocitrate ratios were found between patients with LGG with and without malignant transformation. Furthermore, in patients with paired samples of LGG and their consecutive sGBM, the 2-hydroxyglutarate/isocitrate ratios did not differ between both tumor stages.

Conclusion

Although intratumoral 2-hydroxyglutarate accumulation provides a marker for the presence of IDHmutations, the metabolite is not a useful biomarker for identifying malignant transformation or evaluating malignant progression.

Keywords: α-ketoglutarate, IDH1 mutations, liquid chromatography–tandem mass spectrometry, low-grade gliomas, secondary glioblastomas, 2-hydroxyglutarate

Low-grade gliomas (LGG) occur in the cerebral hemispheres and represent 10%–15% of all astrocytic brain tumors.1 Despite long-term survival in many patients, 50%–75% of patients with LGG eventually die of either progression of a low-grade tumor or transformation to a malignant glioma.2 The time to progression can vary from a few months to several years,3–5 and the median survival among patients with LGG ranges from 5 to 10 years.6,7 Among several risk factors, only age, histology, tumor location, and Karnofsky performance index have generally been accepted as prognostic factors for patients with LGG.8,9 As a prognostic molecular marker, only 1p19q codeletion was identified as such in pure oligodendrogliomas. However, this association was not seen in either astrocytomas or oligoastrocytomas.10

Somatic mutations in human cytosolic isocitrate dehydrogenases 1 (IDH1) were first described in 2008 in ∼12% of glioblastomas11 and later in acute myeloid leukemia, in which the reported mutations were missense and specific for a single R132 residue.11,12 Some gliomas lacking cytosolic IDH1 mutations were later observed to have mutations in IDH2, the mitochondrial homolog of IDH1.12IDH mutations are the most commonly mutated genes in many types of gliomas, with incidences of up to 75% in grade II and grade III gliomas.13,14 Further frequent mutations in patients with LGG were recently identified, including inactivating alterations in alpha thalassemia/mental retardation syndrome X-linked (ATRX), inactivating mutations in 2 suppressor genes, homolog of Drosophila capicua (CIC) and far-upstream binding protein 1 (FUBP1), in about 70% of grade II gliomas and 57% of sGBM.15–17 The association between ATRX mutations with IDHmutations and the association between CIC/FUBP1 mutations and IDH mutations and 1p/19q loss are especially common among the grade II-III gliomas and remarkably homogeneous in terms of genetic alterations and clinical characteristics.16

It was thought that IDH mutations might be a prognostic factor in LGG, predicting a prolonged survival from the beginning of the disease.18–23 However, this assumption, as shown in our and other earlier studies, had to be corrected because survival among patients who have LGG with IDH mutations is only improved after transformation to secondary high-grade gliomas.18,19,24 Furthermore, it had already been demonstrated that an IDH mutation is not a biomarker for further malignant transformation in LGG.18 IDH1 and IDH2 catalyze the oxidative decarboxylation of isocitrate to α-ketoglutarate (α-KG) and reduce NADP to NADPH.25 The mutations inactivate the standard enzymatic activity of IDH112 and confer novel activity on IDH1 for conversion of α-KG and NADPH to 2-hydroxyglutarate (2HG) and NADP+, supporting the evidence thatIDH1 and 2 are proto-oncogenes. This gain of function causes an accumulation of 2HG in glioma and acute myeloid leukemia samples.26,27 The 2HG levels in cancers with IDH mutations are found to be consistently elevated by 10–100-fold, compared with levels in samples lacking mutations of IDH1 or IDH2.26,28Nevertheless, how exactly the production or accumulation of 2HG by mutant IDH might drive cancer development is not well understood.

In the present study, we postulate that intratumoral 2HG could be a useful biomarker that predicts the malignant transformation of WHO grade II LGG. We therefore screened for IDH mutations in patients with LGG and measured the accumulation of 2HG in 2 populations of patients, patients with LGG with and without malignant transformation, with use of liquid chromatography–tandem mass spectrometry (LC-MS/MS). Furthermore, we compared the concentrations of 2HG in LGG and their consecutive secondary glioblastomas (sGBM) to evaluate changes in metabolite levels during the malignant progression.

Go to:Methods and Materials

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According to aforementioned criteria, a total of 72 tumor samples from 54 patients were analyzed (Table 1). The samples were from 17 patients with LGG without malignant transformation, 18 patients with both LGG and consecutive sGBM (n = 36), 2 additional patients with sGBM, 10 patients with pGBM, and 7 patients with nonglioma tumors. The nonglioma samples comprised 3 meningiomas, 2 metastases of breast cancers, 1 cavernoma, and 1 reactive gliosis from a patient with epilepsy.

Table 1.

Patient characteristics

DNA Isolation and IDH Mutation Detection

Tumor tissue samples were taken intraoperatively and were snap frozen at −80°C. To ensure a tumor cell content of at least 80% for nucleic acid extraction, control slides stained with hematoxylin and eosin were examined by the local neuropathologist. IDH1 and IDH2 mutations were assessed using direct DNA sequencing, as reported previously.18

Progression and Survival

Progression-free survival (PFS) was defined as the time from first diagnosis of an LGG to tumor progression or end of follow-up. Time to malignant transformation was defined as the time from the day of first surgery for an LGG to the day of surgery for malignant progression to a secondary high-grade glioma. Overall survival (OS) was the time from the day of first surgery to death or end of follow-up. All patient data were updated on June 15, 2012.

LC-MS/MS Analysis of Tricarboxylic Acid Cycle (TCA) Metabolites

Instrumentation included an AB Sciex QTRAP 5500 triple quadruple mass spectrometer coupled to a high-performance liquid chromatography (HPLC) system from Shimadzu containing a binary pump system, an autosampler, and a column oven. Targeted analyses of citrate, isocitrate, α-ketoglutarate (α-KG), succinate, fumarate (Sigma-Aldrich), and 2-hydroxyglutarate (2HG; SiChem GmbH) were performed in multiple reaction monitoring (MRM) scan mode with use of negative electrospray ionization (-ESI). Expected mass/charge ratios (m/z), assumed as [M-H+], were m/z 190.9, m/z 191.0, m/z 145.0, m/z 116.9, m/z 114.8, and m/z 147.0 for citrate, isocitrate, α-KG, succinate, fumarate, and 2HG, respectively. For quantification, ratios of analytes and respective stable isotope-labeled internal standards (IS) (Table 2) were used. For quantification of isocitrate and 2HG, stable isotope-labeled succinate was used as IS because of unavailability of labeled analogs. MRM transitions are summarized in Table 2.

Table 2.

MRM transitions and respective fragmentation parameters
…

Results  

Patient Characteristics

According to the malignant progression of the LGG, patients were divided into two groups: group 1 patients with LGG (LGG1) without malignant transformation and group 2 patients with LGG (LGG2) with histologically confirmed malignant progression.

Among 35 patients with LGG who were included in this study, 19 (54%) were women, and 16 (46%) were men. Furthermore, histologically, 6 patients had oligoastrocytomas, and the remaining 29 patients had diffuse astrocytomas.

The median age of all patients with LGG was 37.4 years at the time of the first diagnosis. Patients in LGG2 had a median age of 37.1 years, which did not differ significantly from that of patients in LGG1, who had a median age of 41.4 years.

The median time to malignant transformation among patients in the LGG2 group was 3.35 years (range, 2.5–5.4 years). The median OS among all patients with LGG was 13.1 years (11.4 years in LGG1 and 13.1 years in LGG2; P = .97).

IDH1 Mutation and Outcome

An IDH1 mutation was detected in 27 of 35 patients with LGG (77.1%), in 10 of 17 patients in LGG1 (59%), and in 17 of 18 patients in LGG2 (95%). In all cases, IDH1 mutations were found on R132. IDH2mutations were not detected in any of the patients. The IDH1 mutation status was stable during progression from LGG to sGBM in all patients in LGG2. None of the patients with pGBM or nonglioma had an IDHmutation. Patients with LGG with an IDH1 mutation had a median PFS of 3.3 years, which was comparable to that among patients with wild-type LGG (2.8 years; P > .05). Furthermore, the OS among patients with LGG with an IDH1 mutation was not statistically different at 13.0 years compared with that among patients with LGG without an IDH1 mutation, who had an OS of 9.3 years (P = .66).

LC-MS/MS Profiling of TCA Metabolites

TCA metabolites, citrate, isocitrate, α-ketoglutarate, succinate, fumarate, and 2-hydroxyglutarate were measured in glioma samples with and without an IDH1 mutation, in samples identified as primary GBM, and in nonglioma brain tumor specimens (Fig. 1). No differences in citrate, isocitrate, α-KG, succinate, and fumarate concentrations were found when comparing all of the latter groups. Concentrations of 2HG, a side product in IDH1-mutated gliomas, were 20–34-fold higher in IDH1-mutated gliomas (0.64–0.81 µmol/g), compared with non–IDH1-mutated LGG1 (P ≤ .001). No differences were observed between IDH1-mutated gliomas and IDH1-nonmutated LGG2 and sGBM, caused by strongly elevated 2HG levels in either 1 or 2 samples in these groups, respectively. Furthermore, in IDH1-mutated gliomas, 2HG concentrations were a mean of 20 times higher than in pGBM and nongliomas (P ≤ .001) (Fig. 1). No differences were observed between the single groups of IDH1-mutated gliomas LGG1, LGG2, and sGBM in relation to 2HG concentration.

Fig. 1.

Dot-box and whisker plots show concentration ranges for TCA metabolites measured in IDH1-nonmutated (IDH1wt) and IDH1-mutated (IDH1mut) LGG and sGBM and in pGBM and nonglioma tumor specimens; boxes span the 25th and 75th percentiles with median, whiskers …

To detect possible differences among the IDH1-mutated LGG1, LGG2, and sGBM, the α-KG/isocitrate and 2HG/isocitrate ratios were used in additional tests. Therefore, the direct precursor-product relation would correct for all differences possibly expected during pre-analytical processing. To prove this, analyte ratios ofIDH1-mutated and nonmutated gliomas were compared. IDH1-mutated gliomas showed a 2HG/isocitrate ratio that was 13 times higher (P ≤ .001) (Fig. 2A), which corresponds to a lower accumulation of 2HG inIDH1-nonmutated gliomas. α-KG/isocitrate ratios were determined to be approximately 10-fold higher inIDH1-mutated gliomas than in IDH1-nonmutated gliomas (P = .005) (Fig. 2B), which also implies lower accumulation of α-KG in IDH1-nonmutated gliomas.

Fig. 2.

2-Hydroxyglutarate to isocitrate ratios (A) and α-ketoglutarate to isocitrate ratios (B) for IDH1-nonmutated (IDH1wt) and IDH1-mutated (IDH1mut) gliomas (LGG and sGBM); boxes span the 25th and 75th percentiles with median, and whiskers represent …

2HG/isocitrate and α-KG/isocitrate ratios, respectively, were calculated in all 8 specimen groups (Fig. 3). In addition to the differences in 2HG/isocitrate ratios of IDH1-mutated and nonmutated gliomas (Fig. 2A), the ratios in IDH1-mutated gliomas were 4–9 times higher, compared with those in pGBM (P ≤ .001), and 3–6 times higher, compared with those in non-glioma tumor specimens, which was not statistically significant (Fig. 3A). In detail, ratios of 2HG and isocitrate were established to be 13, 9.4, and 22 times higher in IDH1-mutated LGG1, LGG2, and their consecutive sGBM, respectively, than in IDH1-nonmutated LGG1 (Fig. 3A). No significant differences were observed between IDH1-mutated gliomas and IDH1-nonmutated LGG2 and sGBM. The comparison of 2HG/isocitrate ratios between IDH1-nonmutated gliomas and IDH1-mutated LGG2 and sGBM showed no statistically significant differences. However, a trend toward higher ratios inIDH1-mutated LGG1/2 was seen. Furthermore, no differences could be determined by comparing 2HG/isocitrate ratios measured in the groups of IDH1-mutated LGG1 and LGG2. Although 2HG/isocitrate ratios in IDH1-mutated secondary glioblastomas are 1.7 and 2.3 times higher than in the LGG1 and LGG2 groups, respectively, no statistically significant differences were observed.

Fig. 3.

2-Hydroxyglutarate to isocitrate (A, C) and α-ketoglutarate to isocitrate (B, D) ratios for groups of IDH1-nonmutated (IDH1wt) and mutated gliomas (IDH1mut), pGBM, and nongliomas; dot-box and whisker plots (A, B) span the 25th and 75th percentiles …

The absence of a straight trend to higher 2HG/isocitrate ratios during malignant progression is shown by paired analysis of IDH1-mutated LGG2 and their consecutive sGBM (Fig. 3C). Similar findings were observed using the α-KG/isocitrate ratios. Although significant differences were found, with ratios approximately 10 times higher in IDH1-mutated glioblastomas than in IDH1-nonmutated glioblastomas (Fig. 2B), it was not possible to differentiate among the 3 IDH1-mutated glioblastoma groups LGG1, LGG2, and their consecutive sGBM with use of this analyte ratio (Fig. 3B and D).

Go to:Discussion     

On the basis of a comprehensive analysis of cellular TCA metabolites from several cohorts of patients with glioma and nonglioma, our study provides evidence that the level of 2HG accumulation is not suitable as an early biomarker for distinguishing patients with LGG in relation to their course of malignancy. To our knowledge, this is the first report of a paired analysis of 2HG levels in LGG and their consecutive sGBM showing stable 2HG accumulation during malignant progression. This fact assumes that malignant transformation of IDH-mutated LGG appears to be independent of their intracellular 2HG accumulation. Considering these results, we could not stratify patients with LGG into subgroups with distinct survival.

To date, little is known about biomarkers that may predict malignant transformation and, consequently, predict survival in patients with LGG. The investigation of biomarkers in this patient group is relevant because treatment interventions can be tailored to prolong survival, minimize treatment-related adverse effects, and, accordingly, maximize quality of life. In many previous studies, IDH mutations, as the most commonly detected mutations in LGG, were observed to be a significant prognostic factor in patients with glioma, often relating to improved survival among patients with LGG.18–23 However, only patients withIDH-mutated LGG with malignant progression have a prolonged OS.18,19,24 In an earlier conducted study, the analysis of 2 groups of patients with LGG with and without malignant transformation failed to provide a significant influence from the IDH mutation, neither on the PFS nor on the OS.18 In agreement with these data, in a recent study, we showed again that PFS and OS among patients with LGG with and without anIDH mutation did not differ significantly, despite the malignant transformation in LGG2. This result is not unexpected because the same patient population was analyzed in both studies.

Accumulation of 2HG in IDH1-mutated gliomas was first described by Dang et al.26 2HG accumulation is an important marker of IDH1/2-mutated gliomas and other neoplasms.26,29,30 Furthermore, it was assumed that 2HG accumulation might be a potential systemic biomarker of gliomas, but it was not detectable in serum samples from patients with glioma.31 Nevertheless, it has become possible in the meantime to detect 2HG production using magnetic resonance spectroscopy in a noninvasive manner to identify patients withIDH1 mutant brain tumors.32

Because 2HG accumulation provides one of the few potential read-outs for mutant IDH enzymatic activity, we suspected that different intratumoral levels of 2HG accumulation in patients with LGG (especially in those with an IDH mutation) may affect their clinical course in relation to malignant transformation. Therefore, intratumoral concentrations of 2HG and other TCA metabolites were quantified by LC-MS/MS, showing concentrations in IDH1-mutated glioma samples that were comparable to the levels described by Dang et al.26 As previously shown,22 no differences in metabolite levels were observed, with the exception of 2HG with increased accumulation in IDH-mutated gliomas, compared with IDH-nonmutated specimens. However, the fold-difference in 2HG levels between IDH1 mutant and IDH1 wild-type LGG in our study was smaller than in some other studies.26,30 As a reason for this issue, we identified 3 samples of IDH1 wild-type LGG and sGBM with strongly elevated 2HG levels. The fact that some IDH1 wild-type tumors might accumulate 2HG was previously described by Wise et al,33 who reported that the increased IDH2-dependent carboxylation of glutamine-derived α-KG in hypoxia is associated with a concomitant increased synthesis of 2HG in cells with wild-type IDH1 and IDH2. Thus, they concluded that, in further support of the increased mitochondrial reductive glutamine metabolism that they observed in hypoxia, the incubation in hypoxia can lead to elevated 2HG levels in cells lacking IDH1/2 mutations.

The ratio of 2HG to isocitrate and the ratio of α-KG to isocitrate were provided. The use of these ratios can function as an internal control because isocitrate is the direct precursor of α-KG and 2HG. However, intracellular accumulation of both metabolites (2HG and of α-KG) did not differ between both IDH-mutated LGG groups with and without malignant progression. In the same way, detection of 2HG concentrations and the 2HG/isocitrate ratios in patients with LGG and their consecutive sGBM were comparable to values during malignant progression. Correspondingly, all other assessed TCA metabolites remained stable during the malignant progression of the LGG to sGBM. Therefore, 2HG accumulation appears, at least for now, merely to represent a highly correlative and stable marker for an emerging class of somatic mutations in theIDH enzymes from the early stage of glioma development and after malignant transformation to high-grade gliomas.

An expected lower level of α-KG in IDH-mutated LGG and sGBM, in comparison with IDH-nonmutated LGG, pGBM, and nonglioma tumors, was not detected in our study. This is in concordance with a similar finding by Dang et al,26 who showed unaffected α-KG levels in whole-tumor cell lysates. However, the latter finding was in contrast to reported results by Zhao et al,34 who showed that forced expression of mutant IDHin cultured cells led to a dose-dependent decrease in α-ketoglutarate levels. A possible explanation for this finding is the mono-allelic heterozygous mutations of the IDH1 gene leading to expression of both wild-type and mutant IDH1 in a single cell and, therefore, to production of 2HG and α-KG at the same time in every mutated cell.11,12 Another possible reason is the reported evidence that the biochemical effects of mutantIDH1 on α-KG-dependent enzymes are not principally attributable to depletion of α-KG but are a competitive antagonism with α-KG.35–37 Thus, Xu et al postulated that IDH1 mutations alone do not reduce cellular level of α-KG sufficiently to have a significant tumorigenic consequence, but nonetheless these mutations sensitize α-KG–dependent dioxygenases to the inhibitory effect by the large amounts of intratumoral accumulated 2HG.35

Whether 2HG acts as a mutagen or plays a distinct role in gliomagenesis remains to be determined. Dang et al predicted that patients with LGG may benefit from the therapeutic inhibition of 2HG production, resulting in the slowing or halting of conversion of LGG into a lethal secondary glioblastoma, thus changing the course of the disease.26 However, our data confirm similar values of 2HG accumulation in the different LGG groups (with and without malignant progression) and present comparable ranges of 2HG in the low-grade and high-grade tumor stages. In addition, our study used the ratio of 2HG to isocitrate, which might provide a more sensitive screening tool for IDH-mutated LGG than an increase in the absolute concentration of 2HG.

Finally, more work is needed to provide valuable clues about the precise role that 2HG might play in the initiation and progression of LGG. Moreover, the value of 2HG as a useful biomarker for diagnosis or monitoring of the treatment response of LGG has not yet been realized.

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Outstanding Achievement in Pathology

Posted in BioIT: BioInformatics, Biological Networks, Biomarkers & Medical Diagnostics, Bone Disease and Musculoskeletal Disease, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Screening, Cardiac and Cardiovascular Surgical Procedures, Cell Biology, Cerebrovascular and Neurodegenerative Diseases, Peripheral Arterial Disease & Peripheral Vascular Surgery, tagged , , , , , , , on September 26, 2015| Leave a Comment »

Outstanding Achievement in Pathology

Curator: Larry H Bernstein, MD, FCAP

 

Olympus America Honors Outstanding Pathologists During First Annual “Unsung Heroes” Awards

Melville, Ny—Tracey Corey Handy, M.D., Chief Medical Examiner of Kentucky, and Matthew Zarka, M.D., affiliated with the University of Vermont and the Fletcher Allen Health Center, were recognized as the 1999 winners of the “Unsung Heroes” Awards. The awards, sponsored by Olympus America Inc., a world leading manufacturer of microscopes, in cooperation with the College of American Pathologists (CAP), were presented at a ceremony during the Fall CAP Conference in New Orleans.

The awards are the first in the on-going “Unsung Heroes” program sponsored by Olympus for the purpose of increasing public awareness of the vital and often invisible role pathologists have in saving lives. In addition to their expertise with a microscope, pathologists are the doctors who ensure that clinical laboratory testing is reliable and that diseases are accurately diagnosed. They are on the front lines whenever the public is threatened with disease. Their role in forensic science is crucial in helping prevent people from falling prey to abuse or avoidable illness. As Dan Biondi, Olympus Senior Vice President, points out, “Olympus is committed to supporting the work of the world’s pathologists and to advocating an educated patient population.”

Dr. Tracey Corey Handy is recognized as an “Unsung Hero” for her role in upgrading the well-being of children as Kentucky’s Chief Medical Examiner. Along with several colleagues, Dr. Handy founded the state’s “Living Forensics” team in 1991. Since its inception, the team has consulted on more than 700 cases of suspected child abuse. This effort has led to an increased conviction rate of abuse perpetrators and helps to reduce further cases of child abuse. In addition, Dr. Handy has initiated a program of routine screening for metabolic defects apparent in victims of Sudden Infant Death Syndrome (SIDS), which has resulted in the correct diagnosis of conditions that would have otherwise been attributed to SIDS. Dr. Handy has also chaired the state’s first child mortality review group that has resulted in the initiation of prevention programs, particularly in the event of accidental child death. A frequent speaker and contributor of her expertise to organizations throughout the country, she also teaches forensic pathology and has been published in more than a dozen peer-reviewed journals and books.

Dr. Matthew Zarka is recognized as an “Unsung Hero” for his efforts in aiding the extremely poor Mexican-Indian population in the remote mountain regions of Oaxaca, Mexico. Over the last two years Dr. Zarka has volunteered his time and services to bring much needed medical care to these impoverished communities. He and his OB/GYN team have been setting up the very first clinics throughout the area, enjoining the coffee companies of Mexico to spread word of the clinics to the local population and to help transport patients to the clinics. After each female patient underwent a gynecological examination, Dr. Zarka stained and read her Pap test. When needed, more extensive evaluations, biopsies, treatment and counsel were provided. Overwhelmingly successful, Dr. Zarka’s outreaching medical mission has grown to include additional professional staff. By volunteering his time and expertise, Dr. Zarka provides the only real access most people of the region have to modern medical care. His contribution has undoubtedly saved lives that might otherwise have been lost.

Stanford University

Benjamin Pinsky, MD, PhD, Assistant Professor of Pathology and Medicine (Infectious Diseases) is the recipient of the 2014 Siemens Healhcare Diagnostics Young Investigator Award.  This award “honors outstanding laboratory research in clinical microbiology or antimicrobial agents and is intended to further the career development of a young clinical scientist and promote awareness of clinical microbiology as a career.”

Stephen J. Galli, MD, Chair of Pathology, Professor of Pathology and Microbiology and Immunology, and the Mary Hewitt Loveless, MD Professor, is the recipient of the 2014 ASIP (American Society of Investigative Pathology) Rouse Whipple Award.  This award is presented to a senior scientist with a distinguished career in research who has advanced the understanding of disease and has continued productivity at the time of this award.

Dr. Raffick Bowen, Clinical Associate Professor and Associate Medical Director of SHC’s Clinical Chemistry and Immunology Laboratory is the recipient of the American Association of Clinical Chemistry’s Outstanding Speaker Award for 2013. This award recognizes his achievement in earning a speaker evaluation rating of 4.5 or higher during a 2013 continuing education activity accredited by AACC. The title of Dr. Bowen’s presentation is “Implementation of Autoverification in a Clinical Chemistry Laboratory: Theory to Practice”

Richard Kempson, MD,

Emeritus Professor of Pathology, is the recipient of the 2014 United States and Canadian Academy of Pathology (USCAP) President’s Award. The USCAP President’s Award is given annually to recognize an individual for outstanding service to the field of pathology.

Dr. Kempson is richly deserving of this award. Dr. Kempson has not only contributed substantially to the surgical pathology literature, particularly in gynecologic and soft tissue pathology but also, with Dr. Ronald Dorfman, he trained a substantial percentage of this and the next generation’s academic and community leaders in surgical pathology.

Dr. Kempson’s affiliation with Stanford University began in 1968 when he and Dr. Ronald Dorfman were recruited to Stanford to develop a program in surgical pathology. In short order, they established an internationally recognized residency and clinical fellowship program which went on to train more than 275 pathologists in the art and science of diagnostic surgical pathology. Dr. Kempson developed a distinctive teaching style that emphasized precise diagnostic criteria, approaching diagnosis with a broad morphologic differential diagnosis, and most importantly, always highlighting the relevance to patient management of the morphologic distinctions being made.

Prior to his recruitment to Stanford, Dr. Kempson was an Assistant Professor of Pathology and Surgical Pathology at Washington University. Dr. Kempson served as an Associate Professor of Pathology at Stanford from 1968 to 1974 and a Professor of Pathology from 1974 to 2001. In addition to his academic duties, he served as Co-Director of Surgical Pathology from 1968 until 2001. He also has served as President of the Association of Directors of Surgical Pathology (1993-1995), the United States and Canadian Academy of Pathology (1996) and the Arthur Purdy Stout Society (1996) and the California Society of Pathologists. The Richard Kempson, MD, Professorship in Surgical Pathology was established by the Department of Pathology in 2002 to honor him and his remarkable contributions to surgical pathology.

University of California, San Diego

A new era in diagnostics has emerged within the concept of Personalized Medicine. Imagine selecting cancer chemotherapy drugs based on knowledge of the precise mutations in a cancer. Can we predict who may have an adverse response to a medication based on that individual’s genetic blueprint? At UCSD, we are dedicated to making these resources available to our patients in the very near future. This is why we recently established the Pathology Center for Personalized Medicine. The goal of the Center is to conduct leading research necessary to form the foundation for advanced personalized medicine diagnostic testing and then to make this testing available in the CALM. For more information on the Center for Personalized Medicine, click here.

The research enterprise in Pathology at UCSD has grown dramatically in the past five years, and we are now amongst the top 15 programs in the country. Basic and translational research laboratories in the UCSD Pathology Department tackle important problems concerning cancer development and progression, angiogenesis, stem cell biology, neurodegenerative diseases, peripheral neuropathy, inflammation, infectious diseases, and wound healing. Our laboratories provide excellent environments for learning cell biology, molecular genetics, biochemistry, and animal physiology. Our faculty includes many active participants in the Biomedical Sciences (BMS) Graduate Program. For more information on this program, click here. We also have excellent opportunities for postdoctoral researchers. Please click here to visit our web page on summarizing the Pathology Department research enterprise. Then visit individual web pages for each of our faculty member to view specific research interests.

The Department of Pathology is home to both an outstanding Comparative Pathology and Medicine Program (for more information, click here) and the UCSD Research Ethics Program. We provide major educational support to the School of Medicine and the Skaggs School of Pharmacy and Pharmaceutical Sciences. For further information on these training opportunities, click here.

The La Jolla/San Diego community is a fertile environment for research and the pharmaceutical industry. The Sanford Burnham Medical Research Institute, the Scripps Research Institute, the Sidney Kimmel Cancer Center, the Salk Institute for Biological Studies, and the La Jolla Institute for Allergy and Immunology house exciting scientific programs and provide for numerous scientific collaborations. We also boast a plethora of biotechnology companies, located nearby on the La Jolla mesa.

The overall theme and focus of the Department of Pathology is to elucidate the molecular basis and pathology of human disease.  The faculty is comprised of basic, translational and physician scientists that utilize the latest techniques in genomics, proteomics, cell biology, molecular biology and physiology to develop new diagnostic and therapeutic approaches for a wide range of diseases, including cancer, neurological disease, microbial infection, and inflammatory disease.

Steven L. Gonias, M.D., Ph.D.

Our laboratory is interested in identifying and characterizing novel pathways by which proteases and their cell-surface receptors regulate cell physiology. We are particularly interested in the function of proteases in cancer but also have active projects related to peripheral nerve injury, Alzheimer’s disease and cardiovascular biology. One focus involves urokinase-type plasminogen activator (uPA), a serine protease and plasminogen activator that binds with high affinity to a GPI-anchored receptor called uPAR. This event activates multiple cell-signaling pathways that affect cell migration, survival, and phenotype. We are actively working to elucidate mechanisms by which uPAR-initiated cell-signaling promotes cancer metastasis. We are particularly interested in breast cancer, but also work on prostate cancer and cancers of the central nervous system.

The complex of uPA with its inhibitor, PAI-1, is a ligand for a receptor called LRP-1. LRP-1 also is the receptor for other ligands, including extracellular matrix proteins, growth factors and foreign toxins. Our laboratory elucidated a pathway in which LRP-1 regulates cell-signaling indirectly, by regulating the cell-surface level of uPAR. However, recent studies suggest that LRP-1 also directly regulates cell-signaling by binding adaptor proteins, such as Shc and JIP. By this mechanism, LRP-1 regulates cell survival and gene transcription. Our current re­search is aimed at determining the role of LRP-1 in cancer and peripheral nerve injury, using in vitro and in vivomodel systems. Using proteomics approaches, we also are actively investigating the ability of LRP-1 to model the composition of the plasma membrane.

Our third area of focus concerns the plasma protease inhibitor, alpha2M. Our laboratory has demonstrated that this protein functions as a conformation-dependent carrier of growth factors. Alpha2M may also function in cell-signaling by binding to LRP-1. By site-directed mutagenesis, we have iso­lated and individually modified various functional sites in this multifunc­tional protein.

David Bailey, MD, PhD

David N. Bailey received his Bachelor of Science degree in Chemistry “with high distinction” from Indiana University and his Doctor of Medicine degree from Yale University.  He completed a National Institutes of Health postdoctoral fellowship in Laboratory Medicine and a residency in Clinical Pathology, both at Yale, serving as Chief Resident in his final year.  He is certified in Clinical Pathology and Chemical Pathology by the American Board of Pathology.

Dr. Bailey joined the University of California (UC) San Diego faculty in 1977 and served as Director of the Toxicology Laboratory of UC San Diego Medical Center (1977-2007), Head of the Division of Laboratory Medicine (1983-1989, 1994-1998), Acting Chair (1986-1988) and permanent Chair of the Department of Pathology (1988-2001),  Director of the Pathology Residency Program (1986-1999), Director of Clinical Laboratories of UCSD Medical Center (1982-1999), Interim Vice Chancellor for Health Sciences and Dean of the UC San Diego School of Medicine (1999-2000 and 2006-2007), Deputy Vice Chancellor for Health Sciences (2001-2007), and Dean for Faculty & Student Matters in UC San Diego School of Medicine (2003-2007).  From 2007 to 2009, he was Vice Chancellor for Health Affairs, Dean of the School of Medicine, and Professor of Pathology and Laboratory Medicine at the University of California, Irvine.

Dr. Bailey was recognized by the Institute of Scientific Information as one of the world’s ten most cited authors in forensic sciences (1981-93). He received the Gerald T. Evans Award from the Academy of Clinical Laboratory Physicians and Scientists in 1993 for his leadership and service to the Academy.  Dr. Bailey has served as President of the California Association of Toxicologists (1981-1982), President of the Academy of Clinical Laboratory Physicians and Scientists (1988-89), and Secretary-Treasurer of the Association of Pathology Chairs (1996-99).  He has also served on the Chemical Pathology Test Development and Advisory Committee of the American Board of Pathology; the Editorial Boards of Clinical Chemistry, the Journal of Analytical Toxicology, and the American Journal of Clinical Pathology; the Doris A. Howell Foundation for Women’s Health Research Board of Directors; the Board of Directors of the George G. Glenner Alzheimer’s Family Centers, Inc.; the Board of Directors of the Children’s Hospital of Orange County; the Board of Directors of Children’s Healthcare of California; the Board of Directors of the Rady Children’s Hospital of San Diego; the Board of Directors of the Veterans Medical Research Foundation (San Diego); and the Executive Committee and Governing Board of the California Institute of Telecommunications and Information Technology, among others.

David A. Herold, M.D., Ph.D.

My laboratory research interests are in the area of mass spectrometry application to clinical diagnostics. This includes prostaglandins, trace metal and steroids. Additionally, we has been involved in the development and validation of “classical” clinical chemistry diagnostic tests. The application of the mass spectrometry to determine the validity of endocrine tests, in particular testosterone, has been of particular interest. We have been using GC-MS, LC-MS, and MS-MS techniques for these investigations. At the present time, we are involved with the use of Accelerator Mass Spectrometry for the determination of calcium flux in serum and urine using 41Ca as a marker. The purpose of these studies is to better understand bone remodeling in normal and diseased patients. We have also investigated the use of microfluidics for the application to clinical diagnostics to measure selected proteins in a rapid and accurate manner.

 

David Cheresh, Ph.D.

Tumor growth, invasion, stem cells and drug resistance. Molecular regulation of tumor growth and angiogenesis. Drug development targeting molecular pathways involved in tumor growth metastasis and angiogenesis.

The Cheresh laboratory focuses on the discovery of molecular pathways involved in the progression of cancer. Cheresh’s earlier work identified integrin αυβ3 as a biomarker of tumor angiogenesis and tumor progression, and was involved in the discovery of a drug called cilentigide which targets integrins αυβ3 and αυβ5.

The Cheresh laboratory has identified a series of critical microRNAs that regulate the growth of blood vessels.  These microRNAs control the angiogenic switch that occurs during the earliest stages of tumor growth and neovascularization in the retina.  As such one of these microRNAs may have therapeutic application as it is capable of maintaining blood vessels in the quiescent state.

Cheresh and colleagues have identified integrin αυβ3 as a biomarker of tumor stem cells during intrinsic or acquired resistance of a wide range of tumors including: cancer of the lung, pancreas, breast, and colon.   Cheresh and his lab discovered that αυβ3 expression is both necessary and sufficient to account for tumor stemness and drug resistance based on its ability to drive a molecular pathway regulating these processes.  This has led to the development of new therapeutic strategies to resensitize patients to drugs such as erlotinib and lapatinib that target EGFR.

The Cheresh laboratory has identified RAF kinase as an important target involved in tumor growth and angiogenesis.  They have developed a new drug design strategy to target RAF and other relevant kinases by designing allosteric inhibitors of these targets.  This is based on the use of defined chemical scaffolds to dock into an allosteric pocket on these kinases to render them inactive.  The combined use of in silico and biological screening has yielded drugs with nM anti-tumor activity that produce strong anti-tumor growth in mouse models following once a day oral dosing.   This approach appears to yield drugs that target tumors that are resistant to ATP mimetic inhibitors of RAF, Kit or PDGFR

John Lowe

Senior Director, Pathology

I joined Genentech in 2008 as Senior Director of Pathology, after having spent more than 18 years as an HHMI Investigator at the University of Michigan and then 3 years as Chair of Pathology at Case Western Reserve University School of Medicine. The role of Senior Director of Pathology in Research at Genentech offered attractive opportunities to do research in an outstanding, disease-focused scientific environment, while also helping to lead the scientific and research support activities of the Pathology department. These latter efforts help Genentech continue to make a major positive difference to the health and well being of a large number of patients afflicted with cancer, autoimmune syndromes, neurodegenerative diseases and other illnesses for which therapies are unsatisfactory or nonexistent.

An exceptional team of pathologists, laboratory managers, scientific associates and administrative staff in the department collaborate with me in these efforts. Additional outstanding pathologists, scientists, and managers continue to be recruited to assist us in ensuring that the department performs at the highest level. Our task is made more straightforward by the environment at Genentech, which is characterized by exceptionally bright, motivated and collaborative colleagues at every level, spectacular facilities, and workplace philosophies that are conducive to the highest levels of achievement.

Postdoctoral Mentor

The opportunity to mentor postdoctoral fellows at Genentech has been a stimulating and gratifying experience for me. This derives in part from the freedom afforded by the program to pursue research directions that are deemed to be important and interesting, even if these have no immediate therapeutic relevance. The special mentoring experience also derives from extraordinary breadth and quality of the core laboratories at Genentech, and the spectacular intellectual environment. Together, these circumstances provide an unparalleled opportunity for postdoctoral fellows, and their mentors, to engage in biomedical discovery of the highest caliber.

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A NEW ERA OF GENETIC MANIPULATION  

Posted in Academic Publishing, Bacterial Resistance, Diagnostics and Lab Tests, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Gene Regulation, Genetics & Pharmaceutical, Genome Biology, tagged , , , , , , , , , , , , , , , , , , on April 14, 2015| Leave a Comment »

Demet Sag, PhD, CRA, GCP

 

Gene engineering and editing specifically are becoming more attractive. There are many applications derived from microbial origins to correct genomes in many organisms including human to find solutions in health.

There are four customizable DNA specific binding protein applications to edit the gene expression in translational genomics. The targeted DNA double-strand breaks (DSBs) could greatly stimulate genome editing through HR-mediated recombination events.  We can mainly name these site-specific DNA DSBs:

 

  1. meganucleases derived from microbial mobile genetic elements (Smith et al., 2006),
  2. zinc finger (ZF) nucleases based on eukaryotic transcription factors (Urnov et al., 2005;Miller et al., 2007),
  3. transcription activator-like effectors (TALEs) from Xanthomonasbacteria (Christian et al., 2010; Miller et al., 2011; Boch et al., 2009; Moscou and Bogdanove, 2009), and
  4. most recently the RNA-guided DNA endonuclease Cas9 from the type II bacterial adaptive immune system CRISPR (Cong et al., 2013;Mali et al., 2013a).

There is a new ground breaking study published in Science by Valentino Gantz and Ethan Bier of the University of California, San Diego, described an approach called mutagenic chain reaction (MCR).

This group developed a new technology for editing genes that can be transferable change to the next generation by combining microbial immune defense mechanism, CRISPR/Cas9 that is the latest ground breaking technology for translational genomics with gene therapy-like approach.

  • In short, this so-called “mutagenic chain reaction” (MCR) introduces a recessive mutation defined by CRISPR/Cas9 that lead into a high rate of transferable information to the next generation. They reported that when they crossed the female MCR offspring to wild type flies, the yellow phenotype observed more than 95 percent efficiency.

 

Development and Applications of CRISPR-Cas9 for Genome Engineeri

Structural and Metagenomic Diversity of Cas9 Orthologs

(A) Crystal structure of Streptococcus pyogenes Cas9 in complex with guide RNA and target DNA.

(B) Canonical CRISPR locus organization from type II CRISPR systems, which can be classified into IIA-IIC based on their cas gene clusters. Whereas type IIC CRISPR loci contain the minimal set of cas9, cas1, andcas2, IIA and IIB retain their signature csn2 and cas4 genes, respectively.

(C) Histogram displaying length distribution of known Cas9 orthologs as described in UniProt, HAMAP protein family profile MF_01480.

(D) Phylogenetic tree displaying the microbial origin of Cas9 nucleases from the type II CRISPR immune system. Taxonomic information was derived from greengenes 16S rRNA gene sequence alignment, and the tree was visualized using the Interactive Tree of Life tool (iTol).

(E) Four Cas9 orthologs from families IIA, IIB, and IIC were aligned by ClustalW (BLOSUM). Domain alignment is based on the Streptococcus pyogenes Cas9, whereas residues highlighted in red indicate highly conserved catalytic residues within the RuvC I and HNH nuclease domains.

(Cell. Author manuscript; available in PMC 2015 Feb 27.Published in final edited form as:

Cell. 2014 Jun 5; 157(6): 1262–1278.doi:  10.1016/j.cell.2014.05.010)

 

The uniqueness of this study comes from:

 

  • There is a big difference between the new type of mutation and traditional mutation is expressivity of the character since previously mutations were passive and non-transferable at 100% rate. However,  in classical Mendelian Genetics, only one fourth f the recessive traits can be presented in new generation. Yet, in this case this can be achieve about 97% plus transferred to new generation.

 

  • MCR alterations is active that is they convert matching sequences at the same target site so mutated sites took over the wild type character without degenerating by wild type alleles segregating independently during the breeding process

 

  • Therefore, the altered sequences routinely replace the wild type (original) sequences at that site. The data demonstrated that among 92 flies, only one female became wild type but remaining 41 females had yellow eyes yet all 50 males showed wild type eye coloring at the second generation.

 

  • The genetic engineering of the genome occurred in a single generation with high efficiency.

 

Their technique developed by Gantz and Bier had three basic parts:

 

  1. Both somatic and germline cells expressed a Cas9 gene,

 

  1. A guide RNA (gRNA) targeted to a genomic sequence of interest,

 

  1. The Cas9/gRNA cassettes have the flanking homolog arms that matches the two genomic sequences immediately adjacent to either side of the target cut site

 

There are many applications in translational genomics that requires multiple steps to make it perfect for complicated organisms, such as plants, mosquitoes and human diseases.

Short Walk from Past to the Future of CRISPR/Cas9

Development and Applications of CRISPR-Cas9 for Genome Engineeri

The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA.

CRISPR/Cas systems are part of the adaptive immune system of bacteria and archaea, protecting them against invading nucleic acids such as viruses by cleaving the foreign DNA in a sequence-dependent manner.

The latest ground-breaking technology for genome editing is based on RNA-guided engineered nucleases, which already hold great promise due to their:

  • simplicity,
  • efficiency and
  • versality

Although CRISPR arrays were first identified in the Escherichia coli genome in 1987 (Ishino et al., 1987),

their biological function was not understood until 2005, when it was shown that the spacers were homologous to viral and plasmid sequences suggesting a role in adaptive immunity (Bolotin et al., 2005; Mojica et al., 2005; Pourcel et al., 2005).

Two years later, CRISPR arrays were confirmed to provide protection against invading viruses when combined with Cas genes (Barrangou et al., 2007).

The mechanism of this immune system based on RNA-mediated DNA targeting was demonstrated shortly thereafter (Brouns et al., 2008; Deltcheva et al., 2011; Garneau et al., 2010; Marraffini and Sontheimer, 2008).

 

The most widely used system is the type II clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 (CRISPR-associated) system from Streptococcus pyogenes (Jinek et al., 2012).

Then, five independent groups demonstrated that the two-component system was functional in eukaryotes (human, mouse and zebrafish), indicating that the other functions of the CRISPR locus genes were supported by endogenous eukaryotic enzymes (Cho et al., 2013, Cong et al., 2013, Hwang et al., 2013, Jinek et al., 2013 and Mali et al., 2013).

Beginning with target design, gene modifications can be achieved within as little as 1-2 weeks, and modified colonial cell lines can be derived within 2-3 weeks

 

Development and Applications of CRISPR-Cas9 for Genome Engineeri

Genome editing with site-specific nucleases.

Double-strand breaks induced by a nuclease at a specific site can be repaired either by non-homologous end joining (NHEJ) or homologous recombination (HR).  In most cases, NHEJ causes random insertions or deletions (indels), which can result in frameshift mutations if they occur in the coding region of a gene, effectively creating a gene knockout.

Alternatively, when the DSB generates overhangs, NHEJ can mediate the targeted introduction of a double-stranded DNA template with compatible overhangs

Even though the generation of breaks in both DNA strands induces recombination at specific genomic loci, NHEJ is by far the most common DSB repair mechanism in most organisms, including higher plants, and the frequency of targeted integration by HR remains much lower than random integration.

  • Unlike its predecessors, the CRISPR/Cas9 system does not require any protein engineering steps, making it much more straightforward to test multiple gRNAs for each target gene

 

  • Unlike ZFNs and TALENs, the CRISPR/Cas9 system can cleave methylated DNA in human cells (Hsu et al., 2013), allowing genomic modifications that are beyond the reach of the other nucleases (Ding et al., 2013).

 

  • The main practical advantage of CRISPR/Cas9 compared to ZFNs and TALENs is the ease of multiplexing. The simultaneous introduction of DSBs at multiple sites can be used to edit several genes at the same time (Li et al., 2013; Mao et al., 2013) and can be particularly useful to knock out redundant genes or parallel pathways.

 

  • Finally, the open access policy of the CRISPR research community has promoted the widespread uptake and use of this technology in contrast, for example, to the proprietary nature of the ZFN platform.

The community provides access to plasmids (e.g., via the non-profit repository Addgene), web tools for selecting gRNA sequences and predicting specificity:

Downside:

One area that will likely need to be addressed when moving to more complex genomes, for instance, is off-target CRISPR/Cas9 activity since fruit fly has only four chromosomes and less likely to have off-target effects. However, this study provided proof of principle.

  • Yet, this critics is not new since one of the few criticisms of the CRISPR/Cas9 technology is the relatively high frequency of off-target mutations reported in some of the earlier studies (Cong et al., 2013; Fu et al., 2013; Hsu et al., 2013; Jiang et al., 2013a; Mali et al., 2013; Pattanayak et al., 2013).

 

Several strategies have been developed to reduce off-target genome editing, the most important of which is the considered design of the gRNA.

 

  • fusions of catalytically inactive Cas9 and FokI nuclease have been generated, and these show comparable efficiency to the nickases but substantially higher (N140-fold) specificity than the wild-type enzyme (Guilinger et al., 2014; Tsai et al., 2014)

 

  • Altering the length of the gRNA can also minimize non-target modifications. Guide RNAs with two additional guanidine residues at the 5′ end were able to avoid off-target sites more efficiently than normal gRNAs but were also slightly less active at on-target sites (Cho et al., 2014)

Development and Applications of CRISPR-Cas9 for Genome Engineeri

What more:

The CRISPR/Cas9 system can be used for several purposes in addition to genome editing:

  • The ectopic regulation of gene expression, which can provide useful information about gene functions and can also be used to engineer novel genetic regulatory circuits for synthetic biology applications.

 

  • The external control of gene expression typically relies on the use of inducible or repressible promoters, requiring the introduction of a new promoter and a particular treatment (physical or chemical) for promoter activation or repression.

 

  • Disabled nucleases can be used to regulate gene expression because they can still bind to their target DNA sequence. This is the case with the catalytically inactive version of Cas9 which is known as dead Cas9 (dCas9).

 

  • Preparing the host for an immunotherapy is possible if it is combined with TLR mechanism:

On the other hand, the host mechanism needs to be review carefully for the design of an effective outcome.

The mechanism of microbial response and infectious tolerance are complex.

 

During microbial responses, Toll-like receptors (TLRs) play a role to differentiate and determine the microbial structures as a ligand to initiate production of cytokines and pro-inflammatory agents to activate specific T helper cells.

 

Uniqueness of TLR comes from four major characteristics of each individual TLR :

 

  1. ligand specificity,
  2. signal transduction pathways,
  3. expression profiles and
  4. cellular localization.

 

Thus, TLRs are important part of the immune response signaling mechanism to initiate and design adoptive responses from innate (naïve) immune system to defend the host.

 

TLRs are expressed cell type specific patterns and present themselves on APCs (DCs, MQs, monocytes) with a rich expression  levels Specific TLR stimulat ion links innate and acquired responses through simple recognition of pathogen-associated molecular patterns (PAMPs) or co-stimulation of PAMPs with other TLR or non-TLR receptors, or even better with proinflammatory cytokines.

 

Some examples of ligand – TLR specificity shown in Table1, which are bacterial lipopeptides, Pam3Cys through TLR2, double stranded (ds) RNAs through TLR3, lipopolysaccharide (LPS) through TLR4, bacterial flagellin through TLR5, single stranded RNAs through TLR7/8, synthetic anti-viral compounds imiquinod through TLR 7 and resiquimod through TLR8, unmethylated CpG DNA motifs through TLR9.

 

The specificity is established by correct pairing of a TLR with its proinflammatory cytokine(s), so that these permutations influence creation and maintenance of cell differentiat ion.

Development and Applications of CRISPR-Cas9 for Genome Engineeri

 

  • Immunotherapy: The immune cells can be used as a sensor to scavenger the circulating malformed cells in vivo diagnostics or attack and remember them, for instance, relapse of cancer, re-infection with a same or similar agent (bacteria or virus) etc.

Not only using unique microbial and other model organism properties but also using the human host defense mechanism during innate immune responses may bring a new combat to create a new method of precision medicine. This can be a new type of immunotherapy, immune cell mediated gene therapy or vaccine even a step for an in vivo diagnostics.

 

Molecular Genetics took a long road from discovery of restriction enzymes, developing PCR assays, cloning were the beginning. Now, having technology to sequence and compare the sequences between organisms also help to design more sophisticated methods.

Generating mutant lines in Drosophila with the classical genetics methods relies on P elements, a type of transposon and balancers after crossing selected flies with specific markers. This fly pushing is a very tedious work but powerful to identify primary pathways, mechanisms and gene interactions in system and translational  genomics.

 Thus, Microbial Immunomodulation is an important factor not only using the microorganisms or their mechanisms but also modulating the immune cells based on the host interaction may generate new types of diagnostics and targeted therapy tools.

 

Microbial immunomodulation. Microbes from the environment, and from the various microbiota, modulate the immune system. Some of this is due to direct effects of defined microbial products on elements of the immune system. But modulation of the immune system also secondarily alters the host–microbiota relationship and leads to changes in the composition of the microbiota, and so to further changes in immunoregulation (shown as indirect pathways). At the end of the day balance is the key for survival.

microbial immunomodulationGrahamnihms199923f2 A. W. Rook,*,1 Christopher A. Lowry,2 and Charles L. Raison3  Microbial ‘Old Friends’, immunoregulation and stress resilience  Evol Med Public Health. 2013; 2013(1): 46–64. Published online 2013 Apr 9. doi:  10.1093/emph/eot004 PMCID: PMC3868387

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881665/bin/nihms199923f2.jpg

 

CRISPR-Cas9 mediated NHEJ in transient transfection experiments.

Table 1.
Species Transformation method Cas9 codon optimization Promoters (Cas9,  gRNA) Target Mutation frequency Detection method Off-target (no. of sites analyzed) Detection method Multiplex (deletion) Reference
Arabidopsis thaliana PEG-protoplast transfection Arabidopsis (with intron) CaMV35SPDK, AtU6 PDS3<comma> FLS2 1.1–5.6% PCR + sequencing Li et al. (2013)
A. thaliana Leaf agroinfiltration Arabidopsis (with intron) CaMV35SPDK, AtU6 PDS3 2.70% PCR + sequencing Yes (48 bp) Li et al. (2013)
A. thaliana PEG-protoplast transfection Arabidopsis (with intron) CaMV35SPDK,  AtU6 RACK1b<comma> RACK1c 2.5–2.7% PCR + sequencing No (1 site) PCR + sequencing Li et al. (2013)
A. thaliana Leaf agroinfiltration C. reinhardtii CaMV35S, AtU6 Co-transfected GFP n.a. Pre-digested PCR + RE Jiang et al. 2013a and Jiang et al. 2013b
Nicotiana benthamiana PEG-protoplast transfection Arabidopsis (with intron) CaMV35SPDK, AtU6 PDS3 37.7–38.5% PCR + sequencing Li et al. (2013)
N. benthamiana Leaf agroinfiltration Arabidopsis (with intron) CaMV35SPDK,  AtU6 PDS3 4.80% PCR + sequencing Li et al. (2013)
N. benthamiana Leaf agroinfiltration Human CaMV35S,  AtU6 PDS 1.8–2.4% PCR + RE No (18 sites) PCR + RE Nekrasov et al. (2013)
N. benthamiana Leaf agroinfiltration C. reinhardtii CaMV35S, AtU6 Co-transfected GFP n.a. pre-digested PCR + RE Jiang et al. 2013a and Jiang et al. 2013b
N. benthamiana Leaf agroinfiltration Human CaMV35S, CaMV35S PDS 12.7–13.8% Upadhyay et al. (2013)
Nicotiana tabacum PEG-protoplast transfection Tobacco 2xCaMV35S, AtU6 PDS<comma> PDR6 16.27–20.3% PCR + RE Yes (1.8 kb) Gao et al. (2014)
Oryza sativa PEG-protoplast transfection Rice 2xCaMV35S, OsU3 PDS<comma> BADH2<comma> MPK2<comma> Os02g23823 14.5–38.0% PCR + RE Noa (3 sites) PCR + RE Shan et al. (2013)
O. sativa PEG-protoplast transfection Human CaMV35S,  OsU3 or OsU6 MPK5 3–8% RE + qPCR and T7E1 assay No (2 sites) Yes (1 site with a mismatch at position 12) RE + PCR Xie and Yang (2013)
O. sativa PEG-protoplast transfection Rice CaMV35S,  OsU6 SWEET14 n.a. pre-digested PCR + RE Jiang et al. 2013a and Jiang et al. 2013b
O. sativa PEG-protoplast transfection Rice ZmUbi,  OsU6 KO1 KOL5; CPS4 CYP99A2; CYP76M5 CYP76M6 n.a. PCR + sequencing Yes (115<comma> 170<comma> 245 kb) Zhou et al. (2014)
Triticum aestivum PEG-protoplast transfection Rice 2xCaMV35S, TaU6 MLO 28.50% PCR + RE Shan et al. (2013)
T. aestivum PEG-protoplast transfection Plant ZmUbi, TaU6 MLO-A1 36% T7E1 Wang et al. 2014a and Wang et al. 2014b
T. aestivum Agrotransfection of cells from immature embryos Human CaMV35S,  CaMV35S PDS<comma> INOX 18–22% PCR + sequencing Upadhyay et al. (2013)
T. aestivum Agrotransfection of cells from immature embryos Human CaMV35S,  CaMV35S INOX PCR + sequencing No* PCR + RE Yes (53 bp) Upadhyay et al. (2013)
Zea mays PEG-protoplast transfection Rice 2xCaMV35S,  ZmU3 IPK 16.4–19.1% PCR + RE Liang et al. (2014)
Citrus sinensis Leaf agroinfiltration Human CaMv35S,  CaMV35S PDS 3.2–3.9% PCR + RE No (8 sites) PCR + RE Jia et al. (2014)

 

 

 

References:

A brief overview of CRISPR-mediated immunity and explain how the emerging new properties of this defense system are being repurposed for genome engineering in bacteria, yeast, human cells, insects, fish, worms, plants, frogs, pigs, and rodents.

Also look at F1000Prime Rep. 2014; 6: 3. For the list of microorganisms use in CRISPR applications.

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Feng Z, Zhang B, Ding W, Liu X, Yang D, Wei P, Cao F, Zhu S, Zhang F, Mao Y, Zhu J. Efficient genome editing in plants using a CRISPR/Cas system. Cell Res.2013;23:1229–32. doi: 10.1038/cr.2013.114.

Li J, Norville JE, Aach J, McCormack M, Zhang D, Bush J, Church GM, Sheen J. Multiplex and homologous recombination-mediated genome editing in Arabidopsis and Nicotiana benthamiana using guide RNA and Cas9. Nat Biotechnol. 2013;31:688–91. doi: 10.1038/nbt.2654.

Nekrasov V, Staskawicz B, Weigel D, Jones JDG, Kamoun S. Targeted mutagenesis in the model plant Nicotiana benthamiana using Cas9 RNA-guided endonuclease. Nat Biotechnol. 2013;31:691–3. doi: 10.1038/nbt.2655.

Shan Q, Wang Y, Li J, Zhang Y, Chen K, Liang Z, Zhang K, Liu J, Xi JJ, Qiu J, Gao C. Targeted genome modification of crop plants using a CRISPR-Cas system.Nat Biotechnol. 2013;31:686–8. doi: 10.1038/nbt.2650.

 Xie K, Yang Y. RNA-Guided Genome Editing in Plants Using a CRISPR-Cas System. Mol Plant. 2013;6:1975–83. doi: 10.1093/mp/sst119.

Miao J, Guo D, Zhang J, Huang Q, Qin G, Zhang X, Wan J, Gu H, Qu L. Targeted mutagenesis in rice using CRISPR-Cas system. Cell Res. 2013;23:1233–6. doi: 10.1038/cr.2013.123.

Jiang W, Zhou H, Bi H, Fromm M, Yang B, Weeks DP. Demonstration of CRISPR/Cas9/sgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Res. 2013;41:e188. doi: 10.1093/nar/gkt780.

Upadhyay SK, Kumar J, Alok A, Tuli R. RNA Guided Genome Editing for Target Gene Mutations in Wheat. G3 (Bethesda) 2013

Nakayama T, Fish MB, Fisher M, Oomen-Hajagos J, Thomsen GH, Grainger RM. Simple and efficient CRISPR/Cas9-mediated targeted mutagenesis in Xenopus tropicalis. Genesis. 2013 doi: 10.1002/dvg.22720.

Tan W, Carlson DF, Lancto CA, Garbe JR, Webster DA, Hackett PB, Fahrenkrug SC. Efficient nonmeiotic allele introgression in livestock using custom endonucleases. Proc Natl Acad Sci USA. 2013;110:16526–31. doi: 10.1073/pnas.1310478110.

Li D, Qiu Z, Shao Y, Chen Y, Guan Y, Liu M, Li Y, Gao N, Wang L, Lu X, Zhao Y, Liu M. Heritable gene targeting in the mouse and rat using a CRISPR-Cas system.Nat Biotechnol. 2013;31:681–3. doi: 10.1038/nbt.2661.

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Wang H, Yang H, Shivalila CS, Dawlaty MM, Cheng AW, Zhang F, Jaenisch R. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell. 2013;153:910–8. doi: 10.1016/j.cell.2013.04.025.

 

Previously Published at Leaders in Pharmaceutical Intelligence:

 

CRISPR/Cas9: Contributions on Endoribonuclease Structure and Function, Role in Immunity and Applications in Genome Engineering larryhbern 2015/03/27
Published
CRISPR-CAS editing brings cloning of woolly mammoth one step closer to reality 2012pharmaceutical 2015/03/26
Published
GUIDE-seq: First genome-wide method of detecting off-target DNA breaks induced by CRISPR-Cas nucleases 2012pharmaceutical 2014/12/22
Published
The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century 2012pharmaceutical 2014/12/05
Published
CRISPR: Applications for Autoimmune Diseases @UCSF 2012pharmaceutical 2014/11/04
Published
“Gene Editing at CRISPR Speed”: Services and Tools 2012pharmaceutical 2014/10/29
Published
Licensing CRISPR-Cas9 Technology from Broad Institute: Clontech, Horizon Discovery, Sage Labs 2012pharmaceutical 2014/10/28
Published
CRISPR-Cas9 Discovery and Development of Programmable Genome Engineering – Gabbay Award Lectures in Biotechnology and Medicine – Hosted by Rosenstiel Basic Medical Sciences Research Center, 10/27/14 3:30PM Brandeis University, Gerstenzang 121 2012pharmaceutical 2014/10/26
Published
Inactivation of the human papillomavirus E6 or E7 gene in cervical carcinoma cells using a bacterial CRISPR/Cas 2012pharmaceutical 2014/10/24
Published
CRISPR-Cas9 Foundational Technology originated at UC, Berkeley & UCSF, Broad Institute is developing Biotech Applications — Intellectual Property emerging as Legal Potential Dispute 2012pharmaceutical 2014/06/18
Published
2:15 – 2:45, 6/13/2014, Jennifer Doudna “The biology of CRISPRs: from genome defense to genetic engineering” 2012pharmaceutical 2014/06/13
Published
CRISPR @MIT – Genome Surgery 2012pharmaceutical 2014/04/21
Published
Gene Therapy and the Genetic Study of Disease: @Berkeley and @UCSF – New DNA-editing technology spawns bold UC initiative as Crispr Goes Global 2012pharmaceutical 2014/03/27
Published
Evaluate your Cas9 Gene Editing Vectors: CRISPR/Cas Mediated Genome Engineering – Is your CRISPR gRNA optimized for your cell lines? 2012pharmaceutical 2014/03/25
Published
CRISPR-Cas: A powerful new tool for precise genetic engineering 2012pharmaceutical 2013/11/29
Published
A NEW ERA OF GENETIC MANIPULATION   Demet Sag, Ph.D., CRA, GCP 31 mins ago
Published
Manipulate Signaling Pathways [7.6] larryhbern 2015/04/08
Published
RNAi – On Transcription and Metabolic Control larryhbern 2015/03/26
Published
Real Time Conference Coverage for Scientific and Business Media: Unique Twitter Hashtags and Handles per Conference Presentation/Session 2012pharmaceutical 2015/03/24
Published
Advances in Gene Editing Technology: New Gene Therapy Options in Personalized Medicine 2012pharmaceutical 2015/03/16
Published
Annual Margaret Pittman Lecture, honors the NIH’s first female lab chief, March 11, 2015, 3:00:00 PM by Jennifer Doudna, Ph.D., University of California, Berkeley 2012pharmaceutical 2015/03/11
Published
Protecting Your Biotech IP and Market Strategy: Notes from Life Sciences Collaborative 2015 Meeting sjwilliamspa 2015/03/11
Published
Genomics Diagnostics Companies attractive to Institutional Investors 2012pharmaceutical 2015/03/10
Published
attn #1: Investors in HealthCare — Platforms in the Ecosystem of Regulatory & Reimbursement – Integrated Informational Platforms in Medical Devices, Global Oncology Drugs Market and Peer-Reviewed Curations: Cancer, Genomics and Cardiovascular – Draft 2012pharmaceutical 2015/02/24
Last Modified
attn #2: Investors in HealthCare — Cardiovascular Medical Devices: Platforms in the Ecosystem of Regulatory & Reimbursement with Integrated Informational Platforms of Peer-Reviewed Global Scientific Curations on Medical Devices and Cardiac Surgery, Interventional Cardiology and Cardiovascular Imaging – Draft 2012pharmaceutical 2015/02/24
Last Modified
attn #3: Investors in HealthCare — Platforms in the Ecosystem of Regulatory & Reimbursement – Integrated Informational Platforms in Orthopedic Medical Devices, and Global Peer-Reviewed Scientific Curations: Bone Disease and Orthopedic Medicine – Draft 2012pharmaceutical 2015/02/23
Last Modified
attn #7: Investors in HealthCare — Platforms in the Ecosystem of Regulatory & Reimbursement – Integrated Informational Platforms in Medical Devices, Global Oncology Drugs Market and Peer-Reviewed Curations: Cancer, Genomics and Cardiovascular – Draft 2012pharmaceutical 2015/02/22
Last Modified
attn #6: Investors in HealthCare — Platforms in the Ecosystem of Regulatory & Reimbursement – Integrated Informational Platforms in Medical Devices, Global Oncology Drugs Market and Peer-Reviewed Curations: Cancer, Genomics and Cardiovascular – Draft 2012pharmaceutical 2015/02/22
Last Modified
attn #5: Investors in HealthCare — Platforms in the Ecosystem of Regulatory & Reimbursement – Integrated Informational Platforms in Medical Devices, Global Oncology Drugs Market and Peer-Reviewed Curations: Cancer, Genomics and Cardiovascular – Draft 2012pharmaceutical 2015/02/22
Last Modified
attn #4: Investors in HealthCare — Platforms in the Ecosystem of Regulatory & Reimbursement – Integrated Informational Platforms in Medical Devices, Global Oncology Drugs Market and Peer-Reviewed Curations: Cancer, Genomics and Cardiovascular – Draft 2012pharmaceutical 2015/02/22
Last Modified
7:55AM – 9AM, January 26, 2015 – Introduction and Overview – LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical 2015/01/26
Published
Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method 2012pharmaceutical 2014/12/05
Published
3:15PM 11/12/2014 – Discussion Complex Disorders @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical 2014/11/12
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Twitter is Becoming a Powerful Tool in Science and Medicine sjwilliamspa 2014/11/06
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New Frontiers in Gene Editing: Transitioning From the Lab to the Clinic, February 19-20, 2015 | The InterContinental San Francisco | San Francisco, CA 2012pharmaceutical 2014/10/29
Published
Geneticist George Church: A Future Without Limits 2012pharmaceutical 2014/10/24
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Metabolomics is about Metabolic Systems Integration larryhbern 2014/10/13
Published
Using RNA-seq and targeted nucleases to identify mechanisms of drug resistance in acute myeloid leukemia 2012pharmaceutical 2014/09/26
Published
New Frontiers in Gene Editing — Cambridge Healthtech Institute’s Inaugural, February 19-20, 2015 | The Inter Continental San Francisco | San Francisco, CA 2012pharmaceutical 2014/08/27
Published
The role and importance of transcription factors larryhbern 2014/08/06
Published
  Pathology Emergence in the 21st Century larryhbern 2014/08/03
Published
  Regulation of somatic stem cell Function larryhbern 2014/07/29
Published
  Prediction of the Winner RNA Technology, the FRONTIER of SCIENCE on RNA Biology, Cancer and Therapeutics & The Start Up Landscape in Boston 2012pharmaceutical 2014/06/16
Published
  Lecture Contents delivered at Koch Institute for Integrative Cancer Research, Summer Symposium 2014: RNA Biology, Cancer and Therapeutic Implications, June 13, 2014 @MIT 2012pharmaceutical 2014/06/16
Published
  3:45 – 4:15, 2014, Scott Lowe “Tumor suppressor and tumor maintenance genes” 2012pharmaceutical 2014/06/13
Published
  11:30 – 12:00, 6/13/2014, Daniel Anderson “Intracellular RNA delivery” 2012pharmaceutical 2014/06/13
Published
  9:10 – 9:30, 6/13/2014, Phillip Sharp “Why RNA Biology?” Phillip Sharp, PhD Institute Professor, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology 2012pharmaceutical 2014/06/13
Published
  The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research sjwilliamspa 2014/06/11
Published
  Koch Institute for Integrative Cancer Research @MIT – Summer Symposium 2014: RNA Biology, Cancer and Therapeutic Implications, June 13, 2014 8:30AM – 4:30PM, Kresge Auditorium @MIT 2012pharmaceutical 2014/06/04
Published
  An expanded-DNA Biology from Scripps Research Institute: Beyond A-T and C-G: Applications for new Medicines and Nanotechnology 2012pharmaceutical 2014/05/11
Published
  Foundation Medicine reported 4,702 Clinical Tests in Q1, 715 were the FoundationOne Heme Cancer Test, average Reimbursement of $3,400 per Test 2012pharmaceutical 2014/05/08
Published
  The Cancer Research Concentration @ Leaders in Pharmaceutical Business Intelligence sjwilliamspa 2014/05/06
Published
  Aviva’s Perspective on New Oncology Database Asset Positioning by Business Scenario – Password protected 2012pharmaceutical 2014/05/05
Published
  Cancer Research: Curations and Reporting: Aviva Lev-Ari, PhD, RN 2012pharmaceutical 2014/04/20
Published
  Predictions on Biotech Sector’s Two-year Boom 2012pharmaceutical 2014/03/27
Published
  DNA: One man’s trash is another man’s treasure, but there is no JUNK after all Demet Sag, Ph.D., CRA, GCP 2013/06/24
Published
  Ribozymes and RNA Machines – Work of Jennifer A. Doudna 2012pharmaceutical 2013/04/15
Published
  Zebrafish—Susceptible to Cancer larryhbern 2013/04/02
Published
  Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling 2012pharmaceutical 2013/03/28
Published
  Directions for Genomics in Personalized Medicine larryhbern 2013/01/27
Published

 

 

About the author:

Dr Sag has a Bachelor’s degree in Basic and Industrial Microbiology as a Sum cum Laude among 450 graduating class of Science faculty,  an MSc in Microbial Engineering and Biotechnology (Bioprocessing improvement) and PhD in Molecular and Developmental Genetics (Functional Genome and Stem Cell Biology).

She is an translational functional genomic scientist to develop diagnostics and targeted therapies by non-invasive methods for personalized medicine from bench to bedside and engineering tools through clinical trials and regulatory affairs.

You may contact with her at 858-729-4942 or by demet.sag@gmail.com if you have questions.

 

 

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8:20AM 11/12/2014 – Special Guest Keynote Speaker – The Future of Personalized Medicine @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

Posted in Academic Publishing, Conference Coverage with Social Media, Personalized and Precision Medicine & Genomic Research, Scientific & Biotech Conferences: Press Coverage, tagged , , , , , , , , , , on November 12, 2014| Leave a Comment »

8:20AM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

Reporter: Aviva Lev-Ari, PhD, RN

 

REAL TIME Coverage of the Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

 

8:20 a.m. Special Guest Keynote Speaker – The Future of Personalized Medicine

The Future of Personalized Medicine

Special Guest Speaker

Margaret Hamburg, M.D.
Commissioner of Food and Drugs Administration

[Her Father was President of IOM said at the introduction to the Keynote]

How to ask the right question is what HMS taught me best 

Increasing the knowledge of Biology, response to disease, preventive strategies.

2004 — Monumental year — One year after completion of sequencing the Genome

2008/9 – Breast Cancer – pharmacotherapy approved, a protein involved in triggering the disease.Target therapy – risk of disease identified

WHAT FDA is doing on Genetics Information as PARTNERS in Medicine

25% of drugs approved are Targeted therapies

LABELING drugs on genetic information

diagnostics test — identify good respondents

Companion Diagnostics – should be used in Targeted therapies. IGF1, HER2 expression and amplification

PM more important in ONCOLOGY , HepB, Cystic Fibrosis, differential response, CVD – expansion, more to be done

In 2002 — a Program to discuss Genetic information VSDS – New Genomics Program, National Center for Toxicology Research a participants

Translational Scientist are added.

Completion Genome sequencing — push to PM 2011 – Genomics evaluation Team for Safety.

Challenge – Drug, Biologics – interaction need coordination by Agency to discuss challenges and collaboration with out side Group.

Developers of Targeted therapies: Orphan Drugs, Biomarkers – expedited review to promote innovations, fast track breakthrough therapies. Opportunities of Scientist to engage discussion with FDA

 – ALL hands on Deck Approach at FDA – making products available, i.e. SCLC (small cell lung cancer)

Since 2005 – 25 Guidance Reports, i.e., Orphan Drugs and on Companion Diagnostics to be developed in tandem with drug development.

Companion Diagnostics – 3 month review, enforcement and direction – in the framework

FDA — needs to keep up with development in the Diagnostics and in the disease ares.

Illumina – Assays using SNIPS – FDA assesses a shared curated DB on mutation, reduce the review time significantly

FDA – NGS – reference libraries, Genomics Reference and Storage of genomics data

Tools and Capabilities  – support regulatory and science, statistical methods of analysis — implemented for Breast Cancer — signaled the way of new Partnerships and New Clinical Trials formats and methods in its development.

New diagnostics – AMP Program Alzheimer’s Disease, rheumatoid arthritis (RA), inflammatory bowel syndrome (IBS)

What Science is needed for the Regulators to effectively HELP spar innovation.

Pharmacogenomics, Pharmacogenetics — MAPPING the Human Genome and all other areas of “OMICS” – moving from Lab to bedside — requires expertise in Disease prevention, Difference in patients life, Standard medical practice

  • Biology and Pathways
  • Biomarkers
  • New diagnostics
  • Increased communication Universities, new paradigms models and continual effort of SHARING and coordination of shared resources

 

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

 

@HarvardPMConf

#PMConf

@SachsAssociates

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Prefacing the e-Book Epilogue: Metabolic Genomics and Pharmaceutics

Posted in Academic Publishing, Atherogenic Processes & Pathology, Best evidence, Bio Instrumentation in Experimental Life Sciences Research, Biochemical pathways, Biological Networks, Gene Regulation and Evolution, tagged , , , , , , , , , , on November 10, 2014| Leave a Comment »

Prefacing the e-Book Epilogue: Metabolic Genomics and Pharmaceutics

Author and Curator: Larry H. Bernstein, MD, FCAP

 

Adieu, adieu, adieu …

Sound of Music

Snoopy - Charlie happiness

Snoopy – Charlie happiness

This work has been a coming to terms with my scientific and medical end of career balancing in a difficult time after retiring, but it has been rewarding.  In the clinical laboratories, radiology, anesthesiology, and in pharmacy, there has been some significant progress in support of surgical, gynecological, developmental, medical practices, and even neuroscience directed disciplines, as well as epidemiology over a period of half a century.  Even then, cancer and neurological diseases have been most difficult because the scientific basic research has either not yet uncovered a framework, or because that framework has proved to be multidimensional.  In the clinical laboratory sciences, there has been enormous progress in instrumental analysis, with the recent opening of molecular methods not yet prepared for routine clinical use, which will be a very great challenge to the profession, which has seen the development of large sample volume, multianalite, high-throughput, low-cost support emerging for decades.  The capabilities now underway will also enrrich the the capabilities of the anatomic pathology suite and the capabilities of 3-dimensional radiological examination.  In both pathology and radiology, we have seen the division of the fields into major subspecialties.  The development of the electronic health record had to take lessons from the first developments in the separate developments of laboratory, radiology, and pharmacy health record systems, to which were added, full cardiology monitoring systems.  These have been unintegrated.  This made it difficult to bring forth a suitable patient health record because the information needed to support decision-making by practitioners was in separate “silos”.  The mathematical methods that are being applied to the -OMICS sciences, can be brought to bear on the simplification and amplification of the clinicians’ ability to make decisions with near “errorless” discrimination, still allowing for an element of “art” in carrying out the history, physical examination, and knowledge unique to every patient.

We are at this time opening a very large, complex, study of biology in relationship to the human condition.  This will require sufficient resources to be invested in the development of these for a better society, which I suspect, will go on beyond the life of my grandchildren.  Hopefully, the long-term dangers of climate change will be controlled in that time.  As a society, or as a group of interdependent societies, we have no long term interest in continuing self-destructive behaviors that have predominated in the history of mankind.  I now top off these discussions with some further elucidation of what lies before us.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Douglas B. Kell and Royston Goodacre
School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
Drug Discovery Today Feb 2014;19(2)  http://dx.doi.org/10.1016/j.drudis.2013.07.014

Metabolism represents the ‘sharp end’ of systems biology,

  • because changes in metabolite concentrations
  • are necessarily amplified relative to
  • changes in the transcriptome, proteome and enzyme activities,
  • which can be modulated by drugs.

To understand such behaviour, we therefore need
(and increasingly have)

  • reliable consensus (community) models of the human metabolic network
  • that include the important transporters.

Small molecule ‘drug’ transporters are in fact metabolite transporters,

  • because drugs bear structural similarities to metabolites known
  • from the network reconstructions and from measurements of the metabolome.

Recon2 represents the present state-of-the-art human metabolic
network reconstruction; it can predict inter alia:

  1. the effects of inborn errors of metabolism;
  2. which metabolites are exometabolites, and
  3. how metabolism varies between tissues and cellular compartments.

Even these qualitative network models are not yet complete. As our
understanding improves so do we recognize more clearly the need for a systems (poly)pharmacology.

Modelling biochemical networks – why we do so
There are at least four types of reasons as to why one would wish to model a biochemical network:

  1. Assessing whether the model is accurate, in the sense that it
    reflects – or can be made to reflect – known experimental facts.
  2. Establishing what changes in the model would improve the
    consistency of its behaviour with experimental observations
    and improved predictability, such as with respect to metabolite
    concentrations or fluxes.
  3. Analyzing the model, typically by some form of sensitivity
    analysis, to understand which parts of the system contribute
    most to some desired functional properties of interest.
  4. Hypothesis generation and testing, enabling one to analyse
    rapidly the effects of manipulating experimental conditions in
    the model without having to perform complex and costly
    experiments (or to restrict the number that are performed).

In particular, it is normally considerably cheaper to perform
studies of metabolic networks in silico before trying a smaller
number of possibilities experimentally; indeed for combinatorial
reasons it is often the only approach possible. Although
our focus here is on drug discovery, similar principles apply to the
modification of biochemical networks for purposes of ‘industrial’
or ‘white’ biotechnology.
Why we choose to model metabolic networks more than

  • transcriptomic or proteomic networks

comes from the recognition – made particularly clear

  • by workers in the field of metabolic control analysis

– that, although changes in the activities of individual enzymes tend to have

  • rather small effects on metabolic fluxes,
  • they can and do have very large effects on metabolite concentrations (i.e. the metabolome).

Modelling biochemical networks – how we do so

Although one could seek to understand the

  1. time-dependent spatial distribution of signalling and metabolic substances within indivi
    dual cellular compartments and
  2. while spatially discriminating analytical methods such as Raman spectroscopy and
    mass spectrometry do exist for the analysis of drugs in situ,
  • the commonest type of modelling, as in the spread of substances in
    ecosystems,
  • assumes ‘fully mixed’ compartments and thus ‘pools’ of metabolites.

Although an approximation, this ‘bulk’ modelling will be necessary for complex ecosystems such as humans where, in addition to the need for tissue- and cell-specific models, microbial communities inhabit this superorganism and the
gut serves as a source for nutrients courtesy of these symbionts.

Topology and stoichiometry of metabolic networks as major constraints on fluxes
Given their topology, which admits a wide range of parameters for
delivering the same output effects and thereby reflects biological
robustness,

  • metabolic networks have two especially important constraints that assist their accurate modelling:

(i) the conservation of mass and charge, and
(ii) stoichiometric and thermodynamic constraints.

These are tighter constraints than apply to signalling networks.

New developments in modelling the human metabolic network
Since 2007, several groups have been developing improved but nonidentical models of the human metabolic network at a generalised level and in tissue-specific forms. Following a similar community-driven strategy in Saccharomyces cerevisiae, surprisingly similar to humans, and in Salmonella typhimurium,

we focus in particular on a recent consensus paper that provides a highly curated and semantically annotated model of the human metabolic network, termed

In this work, a substantial number of the major groups active in this area came together to provide a carefully and manually constructed/curated network, consisting of some 1789 enzyme-encoding genes, 7440 reactions and 2626 unique metabolites distributed over eight cellular compartments.  A variety of dead-end metabolites and blocked reactions remain (essentially orphans and widows). But Recon2 was able to

  • account for some 235 inborn errors of metabolism,
  • a variety of metabolic ‘tasks’ (defined as a non-zero flux through a reaction or through a pathway leading to the production of a metabolite Q from a metabolite P).
  • filtering based on expression profiling allowed the construction of 65 cell-type-specific models.
  • Excreted or exometabolites are an interesting set of metabolites,
  • and Recon2 could predict successfully a substantial fraction of those

Role of transporters in metabolic fluxes

The uptake and excretion of metabolites between cells and their macrocompartments

  • requires specific transporters and in the order of one third of ‘metabolic’ enzymes,
  • and indeed of membrane proteins, are in fact transporters or equivalent.

What is of particular interest (to drug discovery), based on their structural similarities, is the increasing recognition (Fig. 3) that pharmaceutical drugs also

  • get into and out of cells by ‘hitchhiking’ on such transporters, and not –

to any significant extent –

  • by passing through phospholipid bilayer portions
    of cellular membranes.

This makes drug discovery even more a problem of systems biology than of biophysics.

role of solute carriers and other transporters in cellular drug uptake

role of solute carriers and other transporters in cellular drug uptake

Two views of the role of solute carriers and other transporters in cellular drug uptake. (a) A more traditional view in which all so-called ‘passive’drug uptake occurs through any unperturbed bilayer portion of membrane that might be present.
(b) A view in which the overwhelming fraction of drug is taken up via solute transporters or other carriers that are normally used for the uptake of intermediary metabolites. Noting that the protein:lipid ratio of biomembranes is typically 3:1 to 1:1 and that proteins vary in mass and density (a typical density is 1.37 g/ml) as does their extension, for example, normal to the ca. 4.5 nm lipid bilayer region, the figure attempts to portray a section of a membrane with realistic or typical sizes and amounts of proteins and lipids. Typical protein areas when viewed normal to the membrane are 30%, membranes are rather more ‘mosaic’ than ‘fluid’ and there is some evidence that there might be no genuinely ‘free’ bulk lipids (typical phospholipid masses are 750 Da) in biomembranes that are uninfluenced by proteins. Also shown is a typical drug: atorvastatin (LipitorW) – with a molecular mass of 558.64 Da – for size comparison purposes. If proteins are modelled as
cylinders, a cylinder with a diameter of 3.6 nm and a length of 6 nm has a molecular mass of ca. 50 kDa. Note of course that in a ‘static’ picture we cannot show the dynamics of either phospholipid chains or lipid or protein diffusion.

‘Newly discovered’ metabolites and/or their roles

To illustrate the ‘unfinished’ nature even of Recon2, which concentrates on the metabolites created via enzymes encoded in the human genome, and leaving aside the more exotic metabolites of drugs and foodstuffs and the ‘secondary’ metabolites of microorganisms, there are several examples of interesting ‘new’ (i.e. more or less recently recognised) human metabolites or roles thereof that are worth highlighting, often from studies seeking biomarkers of various diseases – for caveats of biomarker discovery, which is not a topic that we are covering here, and the need for appropriate experimental design. In addition, classes of metabolites not well represented in Recon2 are oxidised molecules such as those caused by nonenzymatic reaction of metabolites with free radicals such as the hydroxyl radical generated by unliganded iron. There is also significant interest in using methods of determining small molecules such as those in the
metabolome (inter alia) for assessing the ‘exposome’, in other words all the potentially polluting agents to which an
individual has been exposed.

Recently discovered effects of metabolites on enzymes 

Another combinatorial problem reflects the fact that in molecular enzymology it is not normally realistic to assess every possible metabolite to determine whether it is an effector (i.e.activator or inhibitor) of the enzyme under study. Typical proteins are highly promiscuous and there is increasing evidence for the comparative promiscuity of metabolites
and pharmaceutical drugs. Certainly the contribution of individual small effects of multiple parameter changes can have substantial effects on the potential flux through an overall pathway, which makes ‘bottom up’ modelling an inexact science. Even merely mimicking the vivo (in Escherichia coli) concentrations of K+, Na+, Mg2+, phosphate, glutamate, sulphate and Cl significantly modulated the activities of several enzymes tested relative to the ‘usual’ assay conditions. Consequently, we need to be alive to the possibility of many (potentially major) interactions of which we are as yet ignorant. One class of example relates to the effects of the very widespread post-translational modification on metabolic
enzyme activities.

A recent and important discovery (Fig. 4) is that a single transcriptome experiment, serving as a surrogate for fluxes through individual steps, provides a huge constraint on possible models, and predicts in a numerically tractable way and
with much improved accuracy the fluxes to exometabolites without the need for such a variable ‘biomass’ term. Other recent and related strategies that exploit modern advances in ‘omics and network biology to limit the search space in constraint-based metabolic modelling.

Fig 4. Workflow for expression-profile-constrained metabolic flux estimation

  1. Genome-scale metabolic model with gene-protein-reaction relationships
  2. Map absolute gene expression levels to reactions
  3. Maximise correlation between absolute gene expression and metabolic flux
  4. Predict fluxes to exometabolites
  5. Compare predicted with experimental fluxes to exometabolites

Drug Discovery Today

The steps in a workflow that uses constraints based on (i) metabolic network stoichiometry and chemical reaction properties (both encoded in the model) plus, and (ii) absolute (RNA-Seq) transcript expression profiles to enable the
accurate modelling of pathway and exometabolite fluxes. .

Concluding remarks – the role of metabolomics in systems pharmacology

What is becoming increasingly clear, as we recognize that to understand living organisms in health and disease we must treat them as systems, is that we must bring together our knowledge of the topologies and kinetics of metabolic networks with our knowledge of the metabolite concentrations (i.e. metabolomes) and fluxes. Because of the huge constraints imposed on metabolism by reaction stoichiometries, mass conservation and thermodynamics, comparatively few well-chosen ‘omics measurements might be needed to do this reliably (Fig. 4). Indeed, a similar approach exploiting constraints has come to the fore in denovo protein folding and interaction studies.

What this leads us to in drug discovery is the need to develop and exploit a ‘systems pharmacology’ where multiple binding targets are chosen purposely and simultaneously. Along with other measures such as phenotypic screening, and the integrating of the full suite of e-science approaches, one can anticipate considerable improvements in the rate of discovery of safe and effective drugs.

Metabolomics: the apogee of the omics trilogy
Gary J.!Patti, Oscar Yanes and Gary Siuzdak

Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be
quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine.

Metabolites are small molecules that are chemically transformed during metabolism and, as such, they provide a functional readout of cellular state. Unlike genes and proteins, the functions of which are subject to epigenetic regulation and posttranslational modifications, respectively, metabolites serve as direct signatures of biochemical activity and are therefore easier to correlate with phenotype. In this context, metabolite profiling, or metabolomics, has become a powerful approach that has been widely adopted for clinical diagnostics.

The field of metabolomics has made remarkable progress within the past decade and has implemented new tools that have offered mechanistic insights by allowing for the correlation of biochemical changes with phenotype.

In this Innovation article, we first define and differentiate between the targeted and untargeted approaches to metabolomics. We then highlight the value of untargeted metabolomics in particular and outline a guide to performing such studies. Finally, we describe selected applications of un targeted metabolomics and discuss their potential in cell biology.

  • metabolites serve as direct signatures of biochemical activity
  1. In some instances, it may be of interest to examine a defined set of metabolites by using a targeted approach.
  2. In other cases, an untargeted or global approach may be taken in which as many metabolites as possible are measured and compared between samples without bias.
  3. Ultimately, the number and chemical composition of metabolites to be studied is a defining attribute of any metabolomic experiment and shapes experimental design with respect to sample preparation and choice of instrumentation.

The targeted and untargeted workflow for LC/MS-based metabolomics.

a | In the triple quadrupole (QqQ)-based targeted metabolomic workflow, standard compounds for the metabolites of interest are first used to set up selected reaction monitoring methods. Here, optimal instrument voltages are determined and response curves are generated for absolute quantification. After the targeted methods have been established
on the basis of standard metabolites, metabolites are extracted from tissues, biofluids or cell cultures and analysed. The data output provides quantification only of those metabolites for which standard methods have been built.

b | In the untargeted metabolomic workflow, metabolites are first isolated from biological samples and subsequently analysed by liquid chromatography followed by mass spectrometry (LC/MS). After data acquisition, the results are processed by using bioinformatic software such as XCMS to perform nonlinear retention time alignment and identify peaks that are changing between the groups of samples measured. The m/z value s for the peaks of interest are searched in metabolite databases to obtain putative identifications. Putative identifications are then confirmed
by comparing tandem mass spectrometry (MS/MS) data and retention time data to that of standard compounds. The untargeted workflow is global in scope and outputs data related to comprehensive cellular metabolism.

Metabolic Biomarker and Kinase Drug Target Discovery in Cancer Using Stable Isotope-Based Dynamic Metabolic Profiling (SIDMAP)

László G. Boros1*, Daniel J. Brackett2 and George G. Harrigan3
1UCLA School of Medicine, Harbor-UCLA Research and Education Institute, Torrance, CA. 2Department of Surgery, University of Oklahoma Health Sciences Center & VA Medical Center, Oklahoma City, OK, 3Global High Throughput
Screening (HTS), Pharmacia Corporation, Chesterfield, MO.
Current Cancer Drug Targets, 2003, 3, 447-455.

Tumor cells respond to growth signals by the activation of protein kinases, altered gene expression and significant modifications in substrate flow and redistribution among biosynthetic pathways. This results in a proliferating phenotype
with altered cellular function. These transformed cells exhibit unique anabolic characteristics, which includes increased and preferential utilization of glucose through the non-oxidative steps of the pentose cycle for nucleic acid synthesis but limited denovo fatty acid synthesis and TCA cycle glucose oxidation. This primarily nonoxidative anabolic profile reflects an undifferentiated highly proliferative aneuploid cell phenotype and serves as a reliable metabolic biomarker to determine cell proliferation rate and the level of cell transformation/differentiation in response to drug treatment. Novel drugs effective in particular cancers exert their anti-proliferative effects by inducing significant reversions of a few specific non-oxidative anabolic pathways. Here we present evidence that cell transformation of various mechanisms is sustained by a unique
disproportional substrate distribution between the two branches of the pentose cycle for nucleic acid synthesis, glycolysis and the TCA cycle for fatty acid synthesis and glucose oxidation. This can be demonstrated by the broad labeling and unique specificity of [1,2-13C2]glucose to trace a large number of metabolites in the metabolome. Stable isotope-based dynamic metabolic profiles (SIDMAP) serve the drug discovery process by providing a powerful new tool that integrates the metabolome into a functional genomics approach to developing new drugs. It can be used in screening kinases and their metabolic targets, which can therefore be more efficiently characterized, speeding up and improving drug testing, approval and labeling processes by saving trial and error type study costs in drug testing.

Navigating the HumanMetabolome for Biomarker Identification and Design of Pharmaceutical Molecules

Irene Kouskoumvekaki and Gianni Panagiotou
Department of Systems Biology, Center for Biological Sequence Analysis, Building 208, Technical University of Denmark, Lyngby, Denmark
Hindawi Publishing Corporation  Journal of Biomedicine and Biotechnology 2011, Article ID 525497, 19 pages
http://dx.doi.org:/10.1155/2011/525497

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics.

Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we
discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants.

Metabolites are the byproducts of metabolism, which is itself the process of converting food energy to mechanical energy
or heat. Experts believe there are at least 3,000 metabolites that are essential for normal growth and development (primary metabolites) and thousands more unidentified (around 20,000, compared to an estimated 30,000 genes and 100,000 proteins) that are not essential for growth and development (secondary metabolites) but could represent prognostic, diagnostic, and surrogate markers for a disease state and a deeper understanding of mechanisms of disease.

Metabolomics, the study of metabolism at the global level, has the potential to contribute significantly to biomedical
research, and ultimately to clinical medical practice. It is a close counterpart to the genome, the transcriptome and the proteome. Metabolomics, genomics, proteomics, and other “-omics” grew out of the Human Genome Project, a massive research effort that began in the mid-1990s and culminated in 2003 with a complete mapping of all the genes in the human body. When discussing the clinical advantages of metabolomics, scientists point to the “real-world” assessment
of patient physiology that the metabolome provides since it can be regarded as the end-point of the “-omics” cascade. Other functional genomics technologies do not necessarily predict drug effects, toxicological response, or disease states at the phenotype but merely indicate the potential cause for phenotypical response. Metabolomics can bridge this information gap. The identification and measurement of metabolite profile dynamics of host changes provides the closest link to the various phenotypic responses. Thus it is clear that the global mapping of metabolic signatures pre- and postdrug treatment is a promising approach to identify possible functional relationships between medication and medical phenotype.

Human Metabolome Database (HMDB). Focusing on quantitative, analytic, or molecular scale information about
metabolites, the enzymes and transporters associated with them, as well as disease related properties the HMDB represents the most complete bioinformatics and chemoinformatics medical information database. It contains records for
thousands of endogenous metabolites identified by literature surveys (PubMed, OMIM, OMMBID, text books), data
mining (KEGG, Metlin, BioCyc) or experimental analyses performed on urine, blood, and cerebrospinal fluid samples.
The annotation effort is aided by chemical parameter calculators and protein annotation tools originally developed for
DrugBank.

A key feature that distinguishes the HMDB from other metabolic resources is its extensive support for higher level database searching and selecting functions. More than 175 hand-drawn-zoomable, fully hyperlinked human
metabolic pathway maps can be found in HMDB and all these maps are quite specific to human metabolism and
explicitly show the subcellular compartments where specific reactions are known to take place. As an equivalent to
BLAST the HMDB contains a structure similarity search tool for chemical structures and users may sketch or
paste a SMILES string of a query compound into the Chem-Query window. Submitting the query launches a
structure similarity search tool that looks for common substructures from the query compound that match the
HMDB’s metabolite database. The wealth of information and especially the extensive linkage to metabolic diseases
to normal and abnormal metabolite concentration ranges, to mutation/SNP data and to the genes, enzymes, reactions
and pathways associated with many diseases of interest makes the HMDB one the most valuable tool in the hands
of clinical chemists, nutritionists, physicians and medical geneticists.

Metabolomics in Drug Discovery and Polypharmacology Studies

Drug molecules generally act on specific targets at the cellular level, and upon binding to the receptors, they exert
a desirable alteration of the cellular activities, regarded as the pharmaceutical effect. Current drug discovery depends
largely on ransom screening, either high-throughput screening (HTS) in vitro, or virtual screening (VS) in silico. Because
the number of available compounds is huge, several druglikeness filters are proposed to reduce the number of compounds that need to be evaluated. The ability to effectively predict if a chemical compound is “drug-like” or “nondruglike” is, thus, a valuable tool in the design, optimization, and selection of drug candidates for development. Druglikeness is a general descriptor of the potential of a small molecule to become a drug. It is not a unified descriptor
but a global property of a compound processing many specific characteristics such as good solubility, membrane
permeability, half-life, and having a pharmacophore pattern to interact specifically with a target protein. These
characteristics can be reflected as molecular descriptors such as molecular weight, log P, the number of hydrogen bond
donors, the number of hydrogen-bond acceptors, the number of rotatable bonds, the number of rigid bonds, the
number of rings in a molecule, and so forth.

Metabolomics for the Study of Polypharmacology of Natural Compounds

Internationally, there is a growing and sustained interest from both pharmaceutical companies and public in medicine
from natural sources. For the public, natural medicine represent a holistic approach to disease treatment, with
potentially less side effects than conventional medicine. For the pharmaceutical companies, bioactive natural products
constitute attractive drug leads, as they have been optimized in a long-term natural selection process for optimal interaction with biomolecules. To promote the ecological survival of plants, structures of secondary products have evolved to interact with molecular targets affecting the cells, tissues and physiological functions in competing microorganisms,
plants, and animals. In this, respect, some plant secondary products may exert their action by resembling endogenous
metabolites, ligands, hormones, signal transduction molecules, or neurotransmitters and thus have beneficial
effects on humans.

Future Perspectives

Metabolomics, the study of metabolism at the global level, is moving to exciting directions.With the development ofmore
sensitive and advanced instrumentation and computational tools for data interpretation in the physiological context,
metabolomics have the potential to impact our understanding of molecular mechanisms of diseases. A state-of-theart
metabolomics study requires knowledge in many areas and especially at the interface of chemistry, biology, and
computer science. High-quality samples, improvements in automated metabolite identification, complete coverage of
the human metabolome, establishment of spectral databases of metabolites and associated biochemical identities, innovative experimental designs to best address a hypothesis, as well as novel computational tools to handle metabolomics data are critical hurdles that must be overcome to drive the inclusion of metabolomics in all steps of drug discovery and drug development. The examples presented above demonstrated that metabolite profiles reflect both environmental and genetic influences in patients and reveal new links between metabolites and diseases providing needed prognostic,diagnostic, and surrogate biomarkers. The integration of these signatures with other omic technologies is of utmost importance to characterize the entire spectrum of malignant phenotype.

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