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Supportive Treatments: Hold the Mind Strong During Cancer

Demet Sag, PhD

 

Psychiatric treatments

Cancer is described under a general terminology of uncontrolled cell proliferation and changes that results in out of control development. Thus, correcting the cell division and immune control are the two focus areas. Yet, on the other side of the coin like any given terminal diseases there is another big factor that needs to be resolved that is mental health. This is usually not well discussed among many. After all fighting with a disease is a game of strength. I think that is one of the reason we say congrats to many cancer survivors since they won not only with their treatment but also with their psychological strength. However, it is like a balloon after the disease the battle is still on.

mind games

Here are the few articles discussing mainly advanced cancer patient’s psychiatric conditions, their clinical treatments, and training of the healthcare givers including oncologists, nurses, social workers, and other ancillary staff.

 

Last fifty years there is an improvement to cure mental illnesses yet there are many unresolved issues like passing blood brain barrier or specificity etc.  Many of these drugs also used for the adjuvant treatment of cancer-related symptoms. Some of these are pain, hot flashes, pruritus, nausea and vomiting, fatigue, and cognitive impairment.  However, the condition of cancer patient requires making psychopharmacology to improve quality life of cancer patients.

 

There new drugs with less side-effects and safer pharmacological profiles, has been a major advance in clinical psycho-oncology.

Since at least 25-30% of patients with cancer and an even higher percentage of patients in an advanced phase of illness meet the criteria for a psychiatric diagnosis, including depression, anxiety, stress-related syndromes, adjustment disorders, sleep disorders and delirium.

 

About 50% of patients with advanced cancer meet criteria for a psychiatric disorder, the most common being adjustment disorders (11%-35%) and major depression (5%-26%).

 

At least 30-40% of patients with cancer and even a higher percentage of patients in an advanced phase of illness.

 

 

In addition, age is a big issue since the outcomes and treatments changes based on expectations and challenges in their life. It is now possible to diagnose early and treat more means tolerance level to aggressive treatments also increases.  In older patients aging and cancer and in younger patient’s career and relationships broken. This is not just a longevity but improving the quality of life of a patient after cancer’s transition from likely death to survival.  Therefore, it is equally important to give their life back fully so there is an increased awareness on psychosocial issues and quality of life.

 

For example, there is a Psycho-oncology group in National Cancer Center. They are now conducting several clinical studies such as biological studies (neuro-imaging studies), studies to establish novel treatment strategy (n-3 poly unsaturated fatty acid), and multi-faceted intervention study (screening and individually tailored psychotherapy and pharmacotherapy). Hope to see more studies combining not only treat the physiological symptoms but psychological factors.

 

 

 

 

Table 1. Prevalence of Psychiatric Disorders in Advanced Cancer
  Advanced disease Terminal illness Caregivers
Adjustment disorder 14%–34.7% 10.6%–16.3%
Anxiety disorders
 Generalized anxiety 3.2%–5.3% 5.80% 3.50%
 Panic disorder 4.20% 5.50% 8.00%
 Post-traumatic stress 2.40% 0% 4.00%
 Unspecified 4.70%
 Any 6%–8.2% 13.90%

 

 

table 2 Psychiatric medication

 

 

References:

PMID: 26012508

Mehta RD1Roth AJ2. Psychiatric considerations in the oncology setting. CA Cancer J Clin. 2015 Jul-Aug;65(4):300-14. doi: 10.3322/caac.21285. Epub 2015 May 26.

 

PMID:23949568

Caruso R1Grassi LNanni MGRiba M. Psychopharmacology in psycho-oncology. Curr Psychiatry Rep. 2013 Sep;15(9):393. doi: 10.1007/s11920-013-0393-0.

 

PMID: 24716500

Grassi L1Caruso RHammelef KNanni MGRiba M.  Efficacy and safety of pharmacotherapy in cancer-related psychiatric disorders across the trajectory of cancer care: a review.  Int Rev Psychiatry. 2014 Feb;26(1):44-62. doi: 10.3109/09540261.2013.842542.

 

PMID: 17847017

Miovic M1Block S. Psychiatric disorders in advanced cancer. Cancer. 2007 Oct 15;110(8):1665-76.

 

PMID: 15387268

Akechi T1Nakano TUchitomi Y. [Scientific background of psycho-oncology].  Seishin Shinkeigaku Zasshi. 2004;106(6):764-71. [Article in Japanese]

 

PMID: 25417593

Thekdi SM1Trinidad ARoth A. Psychopharmacology in cancer.

Curr Psychiatry Rep. 2015 Jan;17(1):529. doi: 10.1007/s11920-014-0529-x.

 

References for the table treatment:

Holland JC,Morrow GR,Schmale A, et al. A randomized clinical trial of alprazolam versus progressive muscle relaxation in cancer patients with anxiety and depressive symptoms. J Clin Oncol. 1991; 9: 1004–1011.

 

Razavi D,Kormoss N,Collard A,Farvacques C,Delvaux N. Comparative study of the efficacy and safety of trazodone versus clorazepate in the treatment of adjustment disorders in cancer patients: a pilot studyJ Int Med Research. 1999; 27: 264–272.

 

Pugliese P,Perrone M,Nisi E, et al. An integrated psychological strategy for advanced colorectal cancer patients. Health Qual Life Outcomes. 2006; 4: 9.

 

Kornblith AB,Dowell JM,Herndon JE2nd, et al. Telephone monitoring of distress in patients aged 65 years or older with advanced stage cancer: a cancer and leukemia group B study. Cancer. 2006; 107: 2706–2714.

 

Goodwin PJ,Leszcz M,Ennis M, et al. The effect of group psychosocial support on survival in metastatic breast cancer. N Engl J Med. 2001; 345: 1719–1726. Web of Science® Times Cited: 249

 

Classen C,Butler LD,Koopman C, et al. Supportive-expressive group therapy and distress in patients with metastatic breast cancer: a randomized clinical intervention trial. Arch Gen Psychiatry. 2001; 58: 494–501. Web of Science® Times Cited: 156

 

Pirl WF,Siegel GI,Goode MJ,Smith MR. Depression in men receiving androgen deprivation therapy for prostate cancer: a pilot study. Psychooncology. 2002; 11: 518–523. Web of Science® Times Cited: 43

 

Chochinov HM,Wilson KG,Enns M, et al. Desire for death in the terminally ill. Am J Psychiatry. 1995; 152: 1185–1191.  Web of Science® Times Cited: 309

 

CA Cancer J Clin. 2015 Jul-Aug;65(4):300-14. doi: 10.3322/caac.21285. Epub 2015 May 26.

Hanna A,Sledge G,Mayer ML, et al. A phase II study of methylphenidate for the treatment of fatigue. Support Care Cancer. 2006;14: 210–215. Web of Science® Times Cited: 18

 

Dalton SO,Johansen C,Mellemkjaer L, et al. Antidepressant medications and risk for cancer. Epidemiology. 2000; 11: 171–176.PubMed,Web of Science® Times Cited: 31

 

Pitman RK,Lanes DM,Williston SK, et al. Psychophysiologic assessment of posttraumatic stress disorder in breast cancer patients.Psychosomatics. 2001; 42: 133–140.  Web of Science® Times Cited: 21

 

Derogatis LR,Morrow GR,Fetting J, et al. The prevalence of psychiatric disorders among cancer patients. JAMA. 1983; 249:751–757.PubMed, Web of Science® Times Cited: 837

 

Travado L,Grassi L,Gil F,Ventura C,Martins C;Southern European Psycho-Oncology Study Group. Physician-patient communication among Southern European cancer physicians: the influence of psychosocial orientation and burnout.Psychooncology. 2005; 14: 661–670.  Web of Science® Times Cited: 16

 

“CREATE Trial Providing Valuable Information on Epoetin Treatment for Anemia.” Hematology Week August 25, 2003: 10.

 

“Doubts Over Epoetin in Cancer.” SCRIP World Pharmaceutical News October 24, 2003: 24.

 

“Researcher Working on Medical Patch to Deliver Marijuanalike Drug.” Cancer Weekly September 9, 2003: 126.

 

 

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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)3133

 

 

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 disordersGenome 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 TFBreast 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 PeptideMolecular 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 cancerBreast Cancer Management, 2015, 4, 4, 187

Ami Schattner, Meital Adi, Mobile menace- floating aortic arch thrombusThe 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 developmentThrombosis 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 DiseasesBulletin 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 cancerClinical 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 disordersGenome 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 coagulopathiesThrombosis Research, 2014, 133, S70

Axel C. Matzdorff, David Green, Management of venous thromboembolism in cancer patientsReviews 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 techniquesJournal 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 thrombosisJournal 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 cancerCurrent Opinion in Hematology, 2014, 21, 5, 423

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

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In the name of Translation from a food born pathogen to  a friendly vaccine: Listeria monocytogenes

Curator: Demet Sag, PhD, CRA, GCP

Is it a far fetch? Friend or Foe?

 Listeriosis

Listeria monocytogenes is a Gram-positive, facultative intracellular pathogen bacterium.  It is used as a prototypes for an experimental model to understand the fundamental processes of adaptive immunity and virulence.  10 species of L. monocytogenes is identified in both humans and animals, L. ivanovii mainly infects ungulates (eg. sheep and cattle), while other species (L. innocua, L. seeligeri, L. welshimeri, L. grayi, L. marthii, L. rocourtiae, L. fleischmannii and L. weihenstephanensis) are essentially saprophytes. Within the species of L. monocytogenes, several serovars (e.g., 4b, 1/2a, 1/2b and 1/2c) are highly pathogenic and account for a majority of clinical isolations.

Gram-negative bacteria has inner and outer membranes and they are most studied; yet mechanics of protein secretion across the single cell membrane of Gram-positive is not. The protein secretion in gram positive bacteria is complex not only it requires translocation of polypeptides across the bacterial membrane into the highly charged environment of the membrane-cell wall interface but also folding specifically. As a result, protein folding mechanism and stability investigated for the role of PrsA2 and PrsA-like so that optimizing the virulence and protein secretion become possible.

Pathogen: Listeriosis

Listeria monocytogenes is a food-borne pathogen determined in 1980s causing an opportunistic disease called listeriosis which is widespread in nature being part of the faecal flora of many mammals. In addition to contaminated food resources (1-10%), may occur sporadically or in outbreaks.   It can be difficult to control and may cause severe clinical outcomes, especially in pregnant women, children and the elderly. The mechanism of pathogenity based on simply altering the actin cytoskeleton structure. Infection causes a spectrum of illness, ranging from febrile gastroenteritis to invasive disease, including bacteraemia, sepsis, and meningoencephalitis.

 

This organisms copes well with bile acids and acidic environment such as glutamate decarboxylase and arginine deiminase systems to survive in competitive microbiome of GI.

This information may benefit developing effective vaccines, designing pharmabiotics; even including probiotics, prebiotics, or phages.

 

Nutrition:

Altering dietary habit assumed to control a disease. The effects of various fatty acids on bacterial clearance and disease outcome through suppression or activation of immune responses can’t be simplified down to one or two kinds of fatty acids in foodborne pathogens. Commonly they have a specialized carbohydrate metabolism so they can utilize fatty acids of host and the host may use the end products for an energy resource. The compared food-borne pathogens include Salmonella sp., Campylobacter sp.,Shiga toxin-producing Escherichia coli, Shigella sp., Listeria monocytogenes, and Staphylococcus aureus.

 

Genetics:

This bacterium has a complex transcriptional machinery to adept, invade several types of cells, and survive. It happens through RNA-based regulation in bacteria in cell biology at the chromatin level during bacterial infection.  This includes clathrin, atypical mitochondrial fragmentation, and several hundred non-coding RNAs (ncRNAs) in the Listeria genome.  Patho-epigenetics becoming an attractive field. Improved bioinformatics may help to classify these changes under specific regulatory mechanisms and networks to determine their function and use.

 

The Toxin, Vaccine and Immunotheraphy

The virulence of Listeria monocytogenes mainly depends on a listeriolysin O (LLO) which is a thiol-activated, cholesterol-dependent, pore-forming toxin, and highly immunogenic. In addition, biochemically, LLO, a toxin that belongs to the family of cholesterol-dependent cytolysins (CDCs), exhibits potent cell type-non-specific toxicity and is a source of dominant CD4(+) and CD8(+) T cell epitopes. Hence, it is the major target for innate and adaptive immune responses in different animal models and humans.

 

As a result, during infection bacteria escape from phagocytosis, allow bacteria to infest the cells and multiply.  Thus, due to it’s naturally immunomodulation role this mechanisms is under investigation so that it can be used for cancer immunotherapies for developing immune tolerance. Since it has effective cytotoxicity.   Thus, co-administration of this toxin or using as an adjuvant with vaccine vectors are also under research.  LLO has diverse biological activities such as cytotoxicity, apoptosis induction, endoplasmic reticulum stress response, modulation of gene expression,

 

Since FDA approved Sipuleucel-T (Provenge, Dendreon, Seattle, WA), which consists of antigen-loaded dendritic cells (DCs), there is a boom in immunotherapy applications. Yet, there is a shortcoming of this application because of its limited scope in immune response.  However, Listeria monocytogenes (Lm) naturally targets DCs in vivo and stimulates both innate and adaptive cellular immunity. Lm-based vaccines engineered to express cancer antigens have demonstrated striking efficacy applications.

 

Meningitis

On the other hand, there is a caution to be taken in clinics since L. monocytogenes most often presents as acute bacterial meningitis, particularly in weaken immune system of patients such as elderly, already sick patients as secondary infection/opportunistic, and those with already immune fragile state. L. monocytogenes CNS the infections may present as acute bacterial meningitis, meningoencephalitis, or acute encephalitis.

 

References and Further readings:

 

PMCID: PMC3574585 PMID: 22595054

Le DT(1), Dubenksy TW Jr, Brockstedt DG. “Clinical development of Listeria monocytogenes-based immunotherapies”. 20. Semin Oncol. 2012 Jun;39(3):311-22. doi: 10.1053/j.seminoncol.2012.02.008.

 

PMCID: PMC3987759 PMID: 24826075

Liu D(1).“Molecular approaches to the identification of pathogenic and nonpathogenic Listeriae”.  16. Microbiol Insights. 2013 Jul 22;6:59-69. doi: 10.4137/MBI.S10880. eCollection 2013.

 

PMCID: PMC4385656 PMID: 25874208

Hernández-Flores KG(1), Vivanco-Cid H(2).  Biological effects of listeriolysin O: implications for vaccination. Biomed Res Int. 2015;2015:360741. doi: 10.1155/2015/360741. Epub 2015 Mar 22.

 

PMCID: PMC4369580 PMID: 25241232

Maertens de Noordhout C(1), Devleesschauwer B(2), Angulo FJ(3), Verbeke G(4), Haagsma J(5), Kirk M(6), Havelaar A(7), Speybroeck N(8). “The global burden of listeriosis: a systematic review and meta-analysis”. 2. Lancet Infect Dis. 2014 Nov;14(11):1073-82. doi: 10.1016/S1473-3099(14)70870-9. Epub 2014 Sep 15.

 

PMID: 24911203

Cossart P(1), Lebreton A(2).  “A trip in the “New Microbiology” with the bacterial pathogen Listeria Monocytogenes”. 3. FEBS Lett. 2014 Aug 1;588(15):2437-45. doi: 10.1016/j.febslet.2014.05.051. Epub 2014 Jun 6.

 

PMCID: PMC4005144  PMID: 24822197

Hernandez-Milian A(1), Payeras-Cifre A(1). “What is new in listeriosis?”. Biomed Res Int. 2014;2014:358051. doi: 10.1155/2014/358051. Epub 2014 Apr 14.

 

PMCID: PMC4179725  PMID: 25325017

Schultze T(1), Izar B(2), Qing X(1), Mannala GK(1), Hain T(1). “Current status of antisense RNA-mediated gene regulation in Listeria  monocytogenes”. 5. Front Cell Infect Microbiol. 2014 Sep 30;4:135. doi: 10.3389/fcimb.2014.00135.

eCollection 2014.

 

PMCID: PMC3924034  PMID: 24592357

Guariglia-Oropeza V(1), Orsi RH(1), Yu H(2), Boor KJ(1), Wiedmann M(1), Guldimann C(1).   “Regulatory network features in Listeria monocytogenes-changing the way we talk”. 6. Front Cell Infect Microbiol. 2014 Feb 14;4:14. doi: 10.3389/fcimb.2014.00014.

eCollection 2014.

 

PMCID: PMC3920067  PMID: 24575393

D’Orazio SE(1). ”Animal models for oral transmission of Listeria monocytogenes”. 7. Front Cell Infect Microbiol. 2014 Feb 11;4:15. doi: 10.3389/fcimb.2014.00015. eCollection 2014.

 

PMCID: PMC3921577  PMID: 24575392

Cahoon LA(1), Freitag NE(1). “Listeria monocytogenes virulence factor secretion: don’t leave the cell without a Chaperone”.   8. Front Cell Infect Microbiol. 2014 Feb 12;4:13. doi: 10.3389/fcimb.2014.00013.eCollection 2014.

 

PMCID: PMC3913888  PMID: 24551601

Gahan CG(1), Hill C(2).“Listeria monocytogenes: survival and adaptation in the gastrointestinal tract”.  9. Front Cell Infect Microbiol. 2014 Feb 5;4:9. doi: 10.3389/fcimb.2014.00009. eCollection 2014.

 

PMCID: PMC4008456   PMID: 24800178 

Pol J(1), Bloy N(1), Obrist F(1), Eggermont A(2), Galon J(3), Hervé Fridman W(4), Cremer I(4), Zitvogel L(5), Kroemer G(6), Galluzzi L(7).

“Trial Watch: DNA vaccines for cancer therapy”. 10. Oncoimmunology. 2014 Jan 1;3(1):e28185. Epub 2014 Apr 1.

 

PMID: 24018504

Carrillo-Esper R(1), Carrillo-Cordova LD, Espinoza de los Monteros-Estrada I, Rosales-Gutiérrez AO, Uribe M, Méndez-Sánchez N.   “Rhombencephalitis by Listeria monocytogenes in a cirrhotic patient: a case report and literature review”.  11. Ann Hepatol. 2013 Sep-Oct;12(5):830-3.

 

PMCID: PMC3708349 PMID: 23698167

Harrison LM(1), Balan KV, Babu US. “Dietary fatty acids and immune response to food-borne bacterial infections”.  12. Nutrients. 2013 May 22;5(5):1801-22. doi: 10.3390/nu5051801.

 

PMCID: PMC3899140 PMID: 23399758

Sun R(1), Liu Y. “Listeriolysin O as a strong immunogenic molecule for the development of new anti-tumor vaccines”. 13. Hum Vaccin Immunother. 2013 May;9(5):1058-68. doi: 10.4161/hv.23871. Epub 2013 Feb 11.

 

 

PMCID: PMC3638699  PMID: 23653659

Sherrid AM(1), Kollmann TR. “Age-dependent differences in systemic and cell-autonomous immunity to L. Monocytogenes”. 14. Clin Dev Immunol. 2013;2013:917198. doi: 10.1155/2013/917198. Epub 2013 Apr 7.

 

PMCID: PMC3543101 PMID: 23125201

Pizarro-Cerdá J(1), Kühbacher A, Cossart P.” Entry of Listeria monocytogenes in mammalian epithelial cells: an updated view”. Cold Spring Harb Perspect Med. 2012 Nov 1;2(11). pii: a010009. doi: 10.1101/cshperspect.a010009.

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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).

ICD10 C64
ICD9-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 (= 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 alphamTOR 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 115116
IGFBP3ab ML 97 NR Multiple 117118
EphA2a ML > 90 A2 DR4 1744119
RU2AS Antisense
transcript
> 90 B7 NR 120
G250
(CA-IX) ab
RCC 90 A2, A24 Multiple 4751
EGFRab ML 85 A2 NR 121122
HIFPH3a ML 85 A24 NR 123
c-Meta ML > 80 A2 NR 124
WT-1a ML 80 A2, A24 NR 125128
MUC1ab ML 76 A2 DR3 46129130
5T4 ML 75 A2, Cw7 DR4 54131133
iCE aORF 75 B7 NR 134
MMP7a ML 75 A3 Multiple 117135136
Cyclin D1a ML 75 A2 Multiple 117137138
HAGE b CT 75 A2 DR4 139
hTERT ab ML > 70 Mutliple Multiple 140142
FGF-5 Protein splice variant > 60 A3 NR 143
mutVHLab ML > 60 NR NR 144
MAGE-A3 b CT 60 Multiple Multiple 145
SART-3 ML 57 Mulitple NR 146149
SART-2 ML 56 A24 NR 150
PRAME b CT 40 Multiple NR 151154
p53ab Mutant/WT
ML
32 Mutliple Multiple 155156
MAGE-A9b CT >30 A2 NR 157
MAGE-A6b CT 30 Mutliple DR4 18158
MAGE-D4b CT 30 A25 NR 159
Her2/neua ML 1030 Multiple Multiple 45160164
SART-1a ML 25 Multiple NR 165167
RAGE-1 CT (ORF2/5) 21 Mutliple Multiple 151157168169
TRP-1/ gp75 ML 11 A31 DR4 151170172

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) 173175
IL-7 ↑ (Treg) 176178
IL-12 – (Treg), ↓ (MDSC) 179181
IL-15 ↑ (Treg)* 182183
IL-18 ↓ (Treg) 184186
IL-21 ? +/− (Treg) 187190
IFN-α +/− (Treg) 175191194
IFN-γ -? ? ↑ ↑ (Treg); ↑ ?(MDSC) 195197
GM-CSF ? ↑ (Treg); ↑(MDSC) 198202
Coinhibitory Antagonist
CTLA-4 ? ↓ (Treg) 203204
PD1/PD1L ↓ (Treg) 205207
Costimulatory Agonist
CD40/CD40L ↑ (Treg); ↑(MDSC) 208211
GITR/GITRL ↓ (Treg); ↓ (MDSC) 212213
OX40/OX86 ↑↓ (Treg); ↓ (MDSC) 214219
4-1BB/4-1BBL ↑ (Treg) 220224
TLR Agonists
Imiquimod (TLR7) ? 225227
Resiquimod (TLR8) ? ? 228229
CpG (TLR9) ↓ (Treg) 230232
Anti-Angiogenic
VEGF-Trap ? ? 233
Sunitinib ? ↓ (Treg/MDSC) 98100234
Sorafenib ? ↓ (MDSC) 235
Bevacizumab ? ? ↓ (MDSC) 236237
Gefitinib (IRESSA) ? ? ? ? ? 238239
Cetuximab ? ? ? ? 240
mTOR Inhibitors
Temsirolimus/Everolimus ? ↓ (Treg) 241
Treg/MDSC Inhibitors
Iplimumab (CTLA-4) ? ↓ (Treg) 242243
ONTAK (CD25) +/− +/− ? ? ↓ (Treg) 244
Anti-TGFβ/TGFβR ↓ (Treg) 245247
Anti-IL10/IL10R +/− ↓ (Treg) 248249
Anti-IL35/IL35R ↑? ↑? ↑? ↑? ↓ (Treg) 250
1-methyl trytophan ? ? ↓ (MDSC) 251
ATRA ? ? ↑ (Treg), ↓ (MDSC) 9093

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,*

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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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Monoclonal Antibody Therapy and Market

Demet Sag, PhD, CRA, GCP 

 

Monoclonal Antibody treatment means a biological therapy where monoclonal antibodies is used to initiate development of specific antibodies (protein molecules produced by the B cells as a primary immune defense), so that they can fight against antigens (substances that are capable of inducing a specific immune response) specifically to kill extracellular/ cell surface target.  Thus, the application of this types of therapies are not limited to cancer but also rheumatoid arthritis, multiple sclerosis, Alzheimer’s disease, and some infectious diseases such as Ebola.

To eliminate or reduce the effects of chemotherapeutic agents. Thus chemotherapeutics agents attached to monoclonal antibodies.



Diagnostic process:

Monoclonal antibodies again used as a vehicle to locate the tumorigenic cancer cells in the body. There can be several methods but one of them is carrying radioactive substances to cancer cells so that they can be labelled in vivo.  However, there are less invasive ways to do as well. As a result, there are new combination of methods such as:

  • nuclear imaging,
  • surgical mapping, and
  • direct therapy in multiple settings either alone, or in conjunction with chemotherapeutic agents, adjuvant.


How do monoclonal antibody drugs work?

 525px-Monoclonal_antibodies.svg



  1. Naked monoclonal antibodies:

  • Make the cancer cell more visible to the immune system.

Action is to boost immune system.

Example: Alemtuzumab (Campath®), chronic lymphocytic leukemia (CLL) by binding to the CD52 antigen on lymphocytes.

 


T cell targets for immunoregulatory antibody therapy

  • Block immune checkpoint inhibitor proteins

        

 Treatments that target PD-1 or PD-L1.

 PD-1 is a checkpoint protein on T cells, called “off switch” of T cells since PD-1 prevents from attacking other cells in the body. Yet, when it is overexpressed on the cancer cells, tumors escape from immune system, because when PD-1 binds to PD-L1, T cells thinks these cells are body’s own normal cells.

http://www.nature.com/nature/journal/v515/n7528/images/515496a-f1.jpg

Checkpoint blockade activates antitumour immunity.

a, Tumour cells express both cancer-driving mutations and ‘passenger’ mutations that cause the expression of neoantigens — ‘new’ molecular structures that, when presented by MHC proteins on the cell surface, are recognized by T cells of the immune system as being foreign, leading to an immune response against the tumour. However, interactions between the receptor PD-1 and its ligand PD-L1, which are expressed on tumour cells, T cells and other immune cells such as macrophages, activate signalling pathways that inhibit T-cell activity and thus inhibit the antitumour immune response. b, Antibodies that block the PD-1 pathway by binding to PD-1 or PD-L1 can reactivate T-cell activity and proliferation, leading to enhanced antitumour immunity.

Examples are:

  • Pembrolizumab (Keytruda®)
  • Nivolumab (Opdivo®)

There is a possibility of developing an autoimmune reaction. The most common side effects include fatigue, cough, nausea, skin rash, and itching. Rarely more serious problems in the lungs, intestines, liver, kidneys, hormone-making glands, or other organs may occur.

 Treatments that target CTLA-4

 Another protein is CTLA-4 to control T cells, “off switch”.

Generation and regulation of anti-tumor immunity Biologic activities of CTLA-4 antibody blockade

Example: Ipilimumab (Yervoy®) is a monoclonal antibody that attaches to CTLA-4 and stops it from working. This can boost the body’s immune response against cancer cells.


  • Block antigens on cancer cells (or other nearby cells).

Example: Trastuzumab, when HER2 is activated, binds to these proteins and stops antigens from becoming active in breast and stomach cancer cells.

Example: Rituxan specifically attaches to CD20 that is found only on B cells so when these labelled B cells can be visible to immune system. There are certain types of lymphomas predisposed due to malfunctioning B cells.


  • Block growth signals. Prevent signal amplification for cell growth.

The cells like to amplify their message in danger or during certain metabolisms so they secrete or produce a type of chemicals called growth factors.  These factors then attaches to specific receptors on the surface of normal cells and cancer cells. Thus, signaling the cells to grow faster than the normal cells. The action is preventing the signals to be received by monoclonal.

 Example:

Cetuximab (Erbitux), targets epidermal growth factor. Thus its function utilized to cure colon cancer, head and neck cancers.


  • Stop new blood vessels from forming.

Tumors needs to grow so in the body they need blood vessel formation to feed the cell growth (angiogenesis)

Example; Bevacizumab (Avastin) targets vascular endothelial growth factor (VEGF) and blocks the angiogenesis.



  1. Conjugated monoclonal antibodies (tagged, labeled, or loaded antibodies).

 Deliver chemotherapy to cancer cells.

They are monoclonal antibodies (mAbs) joined to a chemotherapy drug or to a radioactive particle to locate cancer cells directly through targeting specific antigen after circulating in the bloodstream. They are used as a homing device.

Chemo-labeled antibodies: Also called as antibody-drug conjugates (ADCs) and provide powerful chemotherapy (or other) drugs attached to them.

  • Brentuximab vedotin (Adcetris®), an antibody that targets the CD30 antigen on lymphocytes, attached to MMAE (a chemo drug) against Hodgkin lymphoma and anaplastic large cell lymphoma.
  • Ado-trastuzumab emtansine (Kadcyla®, also called TDM-1), an antibody that targets the HER2 protein, attached to DM1 (a chemo drug) against cells overexpressing HER2 in breast cancer

 Toxin attached protein: Denileukin diftitox (Ontak®) is not an antibody but it is a protein, cytokine known as interleukin-2 (IL-2) and attached to diphtheria toxin that recognizes CD25 antigen to treat lymphoma of the skin (cutaneous T-cell lymphoma).


 Radiolabeled antibodies: Deliver radiation to cancer cells.

The other method, less preferred, is radiation-linked monoclonal antibodies.  This time low radiation in long term used to target the cancer cells but it is suggested that this method has elevated outcome to kill the cancer cells than conventional high-dose external beam radiation.

Example; Ibritumomab (Zevalin), is an approved treatment.  The targeted disease is for non-Hodgkin’s lymphoma.

Treatment with this type of antibody also referred as radioimmunotherapy (RIT).



  1. Bispecific monoclonal antibodies

 If the drug contains two parts of 2 different mAbs, meaning they can attach to 2 different proteins at the same time, they are called Bispecific monoclonal antibodies since they attack two proteins at the same time.

 

Example:  Blinatumomab (Blincyto), can attach CD 19 which is found on some leukemia and lymphoma cells and CD3 on T cells.  Thus, brings opponents, immune and malignant cancer cells, to defeat cancer.

  nature_graphic_immune-system_08.01.15

THE OTHER SIDE OF THE COIN: SAFETY

 Possible side effects of monoclonal antibodies

 Delivery is intravenously and since Mabs are themselves are proteins sometimes presents side effects like an allergic reaction yet compared to chemotherapy drugs these effects are much less. .

  • Fever
  • Chills
  • Weakness
  • Headache
  • Nausea
  • Vomiting
  • Diarrhea
  • Low blood pressure
  • Rashes

Examples:

  • Bevacizumab (Avastin®), high blood pressure, bleeding, poor wound healing, blood clots, and kidney damage.
  • Cetuximab (Erbitux®), serious rashes in some people.

Manufacturing of Monoclonal Antibodies and Market

“Since 2000, the therapeutic market for monoclonal antibodies has grown exponentially. The current “big 5” therapeutic antibodies on the market are bevacizumab, trastuzumab (both oncology), adalimumab, infliximab (both autoimmune and inflammatory disorders, ‘AIID’) and rituximab (oncology and AIID) accounted for 80% of revenues in 2006. In 2007, eight of the 20 best-selling biotechnology drugs in the U.S. are therapeutic monoclonal antibodies. Scolnik, Pablo A. (2009). “mAbs: A business perspective”. MAbs 1 (2): 179–184. doi:10.4161/mabs.1.2.7736. PMC 2725420. PMID 20061824.

This rapid growth in demand for monoclonal antibody production has been well accommodated by the industrialization of mAb manufacturing”. Kelley, Brian (2009). “Industrialization of mAb production technology”. MAbs 1 (5): 443–452. doi:10.4161/mabs.1.5.9448. PMC 2759494. PMID 20065641.

mabs0105_0443_fig001http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759494/bin/mabs0105_0443_fig001.jpg

Model mAb production plant design and capabilities. A model large scale mAb production plant employs multiple bioreactors configured to supply a single purification train. A plant having six individual 15 kL bioreactors is potentially capable of supplying 10 tons of purified mAb per year using conventional technologies, or 4–5 products with 1 ton demands. This enormous capacity per plant would result in a marked decrease in drug substance production costs, and results in significant excess capacity throughout the biopharmaceutical industry.

Production:

Production capacity estimates for mammalian cell-derived mAbsa

Year CMO Product company Total Capacity at 2 g/L Capacity at 5 g/L
2007 500 kL 1,800 kL 2,300 kL 70 tons/yr 170 tons/yr
2010 700 kL 2,700 kL 3,400 kL 100 tons/yr 255 tons/yr
2013 1,000 kL 3,000 kL 4,000 kL 120 tons/yr 300 tons/yr

aCapacity estimates from ref. Ransohoff TC, Ecker DM, Levine HL, Miller J. Cell culture manufacturing capacity: trends and outlook through 2013. PharmSource. 2008

mabs0105_0443_fig002

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759494/bin/mabs0105_0443_fig003.jpg

Estimated demand for therapeutic mAbs and Fc-fusion products in 2009. The total demand for the top 15 mAbs and Fc-fusions in 2009 is estimated to be approximately 7 tons, with the four largest volume products requiring approximately one ton per year. More than half of the products were estimated to require less than 200 kg per year.

mabs0105_0443_fig004 mabs0105_0443_fig003

Distribution of average wholesale prices for mAb and Fc-fusions in 2008. The average U.S. wholesale prices per gram for 15 commercial mAbs and Fc-fusions are shown. The minimum is approximately $2,000 per gram, and the median is approximately $8,000 per gram. Note that a significant price erosion (50% of the minimum shown here) for a product with modest demand (100 kg/yr) could result in an unprofitable market, as revenues for the therapeutic product ($100 million/yr) may never provide a positive return on investment.

Sensitivity analysis of mAb drug substance COGs for the model plant (six 15kL bioreactors)

Titer (g/L) Plant capacity (tons/yr) Raw materials ($/gm) Depreciation & labor ($/gm)b Fill/Finish costs per vial ($) Total Drug Product Cost ($/vial)
Cell culturea Purification 100 mg 1 gm
0.5 1 20 100 22 134
2 4 4 4 25 10 13 43
5 10 2 10 12 26

aAssumes medium cost of $8/L.

bBased on the model plant ($500 M capital investment + 250 staff = $100 M per year).

Estimated cost breakdown for three production scenarios

Model large-scale plant Small-scale plant using disposables CMO
Basis: 5 g/L 6 × 15 kL n × 2 kL 15 kL
Capital Investmenta $500 M $125 M Difference in annual cost for two best alternatives ($M/yr)
Depreciationb($/yr) $50 M $12.5 M
Raw Materialsc $10/gm $20/gm $10/gm
Labor ($/yr)d $50 M $20 M
CMO $3 M/batche
COGs $/gm 10 ton/yr 20 23 60 $30 M
1 ton/yr 110 53 60 $7 M
0.1 ton/yr 1,010 345 60 $29 M

aThe new facility based on disposables is assumed to cost just one-quarter of model plant to build, and uses only the number of bioreactors (‘n’) needed to satisfy the demand.

bA 10-year straight line depreciation is used to estimate the depreciation costs.

cRaw material costs per gram are assumed to be slightly higher for the disposable facility.

dLabor costs for the new facility are assumed to be just 40% of the model plant (100 vs 250 staff, respectively).

eA constant cost per batch is assumed for the CMO, all-inclusive of production, testing and release.

Sales and Marketing

PMC full text: MAbs. 2009 Mar-Apr; 1(2): 179–184.

FDA-approved marketed mAbs

Name Structure Target Indication Path Approval (Y) Sales % Top 20
Generic Trade Landing Expansion
First Tier (U.S. $B)
infliximab Remicade® Ch TNF CD RA O, A, P, F 4.6 $5.0 9.84
AS
PA
UC
PP
rituximab Rituxan®, Ch CD20 NHL RA O, P 5.1 $4.9 9.62
MabThera® DLBC
1-NHL
trastuzumab Herceptin® Hm HER2 mBC BC F, P 7.5 $4.3 8.45
bevacizumab Avastin® Hm VEGF mCRC mCRC F, P 7.1 $3.6 7.15
NSCLC
HER2- BCa
adalimumab Humira® Hu TNF RA RA O 3.7 $3.1 6.04
JIA
PA
AS
CD
PP
cetuximab Erbitux® Ch EGFR mCRC SCCHN A, P 9.7 $1.4 2.73
ranibizumab Lucentis® Hm VEGF AMD P 6.8 $1.2 2.39
palivizumab Synagis® Hm RSV RSV P 3.6 $1.1 2.25
Second Tier (U.S. $M)
tositumomab Bexxar® Mu CD20 NHLb NHLc 13.7 $10.3 0.02
alemtuzumab Campath® Hm CD52 B-CLL B-CLLd A, P, F 10.4e $108.0 0.21
certolizumab pegol Cimzia® Hm TNF CD P n/a n/a n/a
gemtuzumab ozogamicin Mylotarg® Hm CD33 AML P, A, O 6.5 $60.0 0.12
muromonab-CD3 Orthoclone Okt3® Mu CD3 OR OR n/a $150.0 0.30
efalizumab Raptçiva® Hm CD11a PS 10e $163.0 0.32
abciximab ReoPro® Ch GP IIb/IIIa AC CI O n/a $380.0 0.75
basiliximab Simulect® Ch CD25 OR O, P n/a $300.0 0.59
eculizumab Soliris® Hm C5 PNH O, P n/a $230.0 0.45
natalizumab Tysabri® Hm a-4 integrin MS CD A 10.6e $100.0 0.20
panitumumab Vectibix® Hu EGFR mCRC A, P, F 7.4 $365.0 0.72
omalizumab Xolair® Hm IgE AA 9.7 $472.0 0.93
daclizumab Zenapax® Hm CD25 OR ORp O, P n/a $60.0 0.12
ibritumomab tiuxetan Zevalin® Mu CD20 NHL P, A, O, F 10.2 $17.0 0.03

Abbreviations: Structure: Ch, chimeric; Hm, humanized; Hu, human; Mu, murine. Regulatory Path: A, accelerated approval; F, fast-track; P, priority review; O, orphan indication. 1-, first-line therapy; a, conditional approval; b, rituximab refractory; c, refractory to chemotherapy; d, single-agent; e, estimate; m, metastatic; n/a, information not available; p, prophylaxis. Sources: 20 Compounds that defined biotech, Signals online magazine at www.signalsmag.com; ReCap database; Biopharmaceutical Products in the U.S. and European markets 6th edition, Ronald A. Rader, ed; Pharma Sales and BioPharmInsights databases; Reichert JM, Ph. D.; personal communications. Development times and sales estimates for some Second Tier mAbs are based on limited information.

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Dolgin E. FDA narrows drug label usage. Nature. 2009 Aug 27;460(7259):1069. doi: 10.1038/4601069a. PubMed PMID: 19713906.

Ellis LM, Reardon DA. Cancer: The nuances of therapy. Nature. 2009 Mar 19;458(7236):290-2. doi: 10.1038/458290a. PubMed PMID: 19295595.

Izumi Y, Xu L, di Tomaso E, Fukumura D, Jain RK. Tumour biology: herceptin acts as an anti-angiogenic cocktail.  Nature. 2002 Mar 21;416(6878):279-80. PubMed PMID: 11907566.

Kim KJ, Li B, Winer J, Armanini M, Gillett N, Phillips HS, Ferrara N. Inhibition of vascular endothelial growth factor-induced angiogenesis suppresses tumour growth in vivo. Nature. 1993 Apr 29;362(6423):841-4. PubMed PMID: 7683111.

Sredni B, Caspi RR, Klein A, Kalechman Y, Danziger Y, Ben Ya’akov M, Tamari T, Shalit F, Albeck M. A new immunomodulating compound (AS-101) with potential therapeutic application. A new immunomodulating compound (AS-101) with potential therapeutic application. Nature. 1987 Nov 12-18;330(6144):173-6. PubMed PMID: 3118216.

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Thorpe PE, Mason DW, Brown AN, Simmonds SJ, Ross WC, Cumber AJ, Forrester JA. Selective killing of malignant cells in a leukaemic rat bone marrow using an antibody-ricin conjugate. Nature. 1982 Jun 17;297(5867):594-6. PubMed PMID: 7088145.

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Waldmann, Thomas A. (2003). “Immunotherapy: past, present and future”. Nature Medicine 9 (3): 269–277. doi:10.1038/nm0303-269PMID 12612576.

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(John, Martin et al. 2005, Robert, Ann et al. 2006, Albert, Edvardas et al. 2012, Claro, Karen et al. 2012, Gideon, Nancy et al. 2013, Michael, Ke et al. 2013, Thomas, Albert et al. 2013, Hyon-Zu, Barry et al. 2014, Larkins, Scepura et al. 2015, Sandra, Ibilola et al. 2015, Sean, Gideon et al. 2015)Hudson PJ, Souriau C (January 2003). “Engineered antibodies”. Nat. Med. 9 (1): 129–34. doi:10.1038/nm0103-129PMID 12514726.

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Joyce1, Johanna A.; Fearon, Douglas T. (April 3, 2015). “T cell exclusion, immune privilege, and the tumor microenvironment”. Science 348 (6230 74-80).doi:10.1126/science.aaa6204.

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Goel, Niti; Stephens, Sue (2010). “Certolizumab Pegol”. MAbs 2 (2): 137–147.doi:10.4161/mabs.2.2.11271PMC 2840232PMID 20190560.

Chames, Patrick; Baty, Daniel (2009). “Bispecific antibodies for cancer therapy: The light at the end of the tunnel?”. MAbs 1 (6): 539–547. doi:10.4161/mabs.1.6.10015.PMC 2791310PMID 20073127.

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Selected FDA Approved Mab Drugs:

(John, Martin et al. 2005, Robert, Ann et al. 2006, Albert, Edvardas et al. 2012, Claro, Karen et al. 2012, Gideon, Nancy et al. 2013, Michael, Ke et al. 2013, Thomas, Albert et al. 2013, Hyon-Zu, Barry et al. 2014, Larkins, Scepura et al. 2015, Sandra, Ibilola et al. 2015, Sean, Gideon et al. 2015)

Albert, D., K. Edvardas, G. Joseph, C. Wei, S. Haleh, L. L. Hong, D. R. Mark, B. Satjit, W. Jian, G. Christine, B. Julie, B. B. Laurie, R. Atiqur, S. Rajeshwari, F. Ann and P. Richard (2012). “U.S. Food and Drug Administration Approval: Ruxolitinib for the Treatment of Patients with Intermediate and High-Risk Myelofibrosis.” Clinical Cancer Research: 3212-3217.

Claro, R. A. d., M. Karen, K. Virginia, B. Julie, K. Aakanksha, H. Bahru, O. Yanli, S. Haleh, L. Kyung, K. Kallappa, R. Mark, S. Marjorie, B. Francisco, C. Kathleen, C. Xiao Hong, B. Janice, A. Lara, K. Robert, K. Edvardas, F. Ann and P. Richard (2012). “U.S. Food and Drug Administration Approval Summary: Brentuximab Vedotin for the Treatment of Relapsed Hodgkin Lymphoma or Relapsed Systemic Anaplastic Large-Cell Lymphoma.” Clinical Cancer Research: 5845-5849.

Gideon, M. B., S. S. Nancy, C. Patricia, C. Somesh, T. Shenghui, S. Pengfei, L. Qi, R. Kimberly, M. P. Anne, T. Amy, E. K. Kathryn, G. Laurie, L. R. Barbara, C. W. Wendy, C. Bo, T. Colleen, H. Patricia, I. Amna, J. Robert and P. Richard (2013). “First FDA approval of dual anti-HER2 regimen: pertuzumab in combination with trastuzumab and docetaxel for HER2-positive metastatic breast cancer.” Clinical cancer research : an official journal of the American Association for Cancer Research: 4911-4916.

Hyon-Zu, L., W. M. Barry, E. K. Virginia, R. Stacey, D. Pedro, S. Haleh, G. Joseph, B. Julie, F. Jeffry, M. Nitin, K. Chia-Wen, N. Lei, S. Marjorie, T. Mate, C. K. Robert, K. Edvardas, J. Robert, T. F. Ann and P. Richard (2014). “U.S. Food and drug administration approval: obinutuzumab in combination with chlorambucil for the treatment of previously untreated chronic lymphocytic leukemia.” Clinical cancer research : an official journal of the American Association for Cancer Research: 3902-3907.

John, R. J., C. Martin, S. Rajeshwari, C. Yeh-Fong, M. W. Gene, D. John, G. Jogarao, B. Brian, B. Kimberly, L. John, H. Li Shan, C. Nallalerumal, Z. Paul and P. Richard (2005). “Approval Summary for Erlotinib for Treatment of Patients with Locally Advanced or Metastatic Non–Small Cell Lung Cancer after Failure of at Least One Prior Chemotherapy Regimen.” Clinical Cancer Research 11(18).

Larkins, E., B. Scepura, G. M. Blumenthal, E. Bloomquist, S. Tang, M. Biable, P. Kluetz, P. Keegan and R. Pazdur (2015). “U.S. Food and Drug Administration Approval Summary: Ramucirumab for the Treatment of Metastatic Non-Small Cell Lung Cancer Following Disease Progression On or After Platinum-Based Chemotherapy.” The oncologist.

Michael, A., L. Ke, J. Xiaoping, H. Kun, W. Jian, Z. Hong, K. Dubravka, P. Todd, D. Zedong, R. Anne Marie, M. Sarah, K. Patricia and P. Richard (2013). “U.S. Food and Drug Administration approval: vismodegib for recurrent, locally advanced, or metastatic basal cell carcinoma.” Clinical cancer research : an official journal of the American Association for Cancer Research: 2289-2293.

Robert, C. K., T. F. Ann, S. Rajeshwari and P. Richard (2006). “United States Food and Drug Administration approval summary: bortezomib for the treatment of progressive multiple myeloma after one prior therapy.” Clinical cancer research : an official journal of the American Association for Cancer Research: 2955-2960.

Sandra, J. C., F.-A. Ibilola, J. L. Steven, Z. Lillian, J. Runyan, L. Hongshan, Z. Liang, Z. Hong, Z. Hui, C. Huanyu, H. Kun, D. Michele, N. Rachel, K. Sarah, K. Sachia, H. Whitney, K. Patricia and P. Richard (2015). “FDA Approval Summary: Ramucirumab for Gastric Cancer.” Clinical cancer research : an official journal of the American Association for Cancer Research: 3372-3376.

Sean, K., M. B. Gideon, Z. Lijun, T. Shenghui, B. Margaret, F. Emily, H. Whitney, L. Ruby, S. Pengfei, P. Yuzhuo, L. Qi, Z. Ping, Z. Hong, L. Donghao, T. Zhe, H. Ali Al, B. Karen, K. Patricia, J. Robert and P. Richard (2015). “FDA approval: ceritinib for the treatment of metastatic anaplastic lymphoma kinase-positive non-small cell lung cancer.” Clinical cancer research : an official journal of the American Association for Cancer Research: 2436-2439.

Thomas, M. H., D. Albert, K. Edvardas, C. K. Robert, M. K. Kallappa, D. R. Mark, H. Bahru, B. Julie, D. B. Jeffrey, H. Jessica, R. P. Todd, J. Josephine, A. William, M. Houda, B. Janice, D. Angelica, S. Rajeshwari, T. F. Ann and P. Richard (2013). “U.S. Food and Drug Administration Approval: Carfilzomib for the Treatment of Multiple Myeloma.” Clinical Cancer Research: 4559-4563.

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Lymph2 Generation and regulation of anti-tumor immunity

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Monoclonal Antibody Therapy: What is in the name or clear description?

Curator: Demet Sag, PhD, CRA, GCP 

What is in the name?

Nomenclature is important part of the scientific community so we can stay on the same page in all kinds of communications for clarity. Therefore, a defined nomenclature scheme for assigning generic, or nonproprietary, names to monoclonal antibody drugs is used by the World Health Organization’s International Nonproprietary Names (INN) and the United States Adopted Names (USAN). In general, word stems are used to identify classes of drugs, in most cases placed at the end of the word.

Knowing what Antibody relies on understanding of immune response system so that one can modify the cells, choose correct biomarkers from the primary pathways (like Notch, WNT etc), know signaling from outside to inside (like GPCRs, MAPKs, nuclear transcription receptors), personalized gene make up (genomics) and key gene regulation mechanisms. Thus, immunomodulation can be done for immunotherapies. The admiration of these cells generate the new era called biosensors.

  • All monoclonal antibody names end with the stem -mab.
  • Unlike most other pharmaceuticals, monoclonal antibody nomenclature uses different preceding word parts (morphemes) depending on antibody structure and function. These are officially called sub-stemsand sometimes erroneously infixes.
  • This nomenclature is also used for fragments of monoclonal antibodies, such as antigen binding fragments and single-chain variable fragments.

The nomenclature has been updated. The main criteria is naming the origin, target, make up/type of antibody, ans of course suffix to show it is a monoclonal antibody.

Components

Substem for origin/source. 

The substem preceding the -mab suffix denotes the animal from which the antibody is obtained.

The first monoclonal antibodies were produced

  • in mice (substem -o-), yielding the ending -omab; usually Mus musculus, the house mouse),
  • primates (-i-), yielding -imab;
  • usually Macaca irus, the Crab-eating Macaque.

Need and RD:

There was a dis-advantage of using non-human Abs since they induce immune responses that are generating side effects, such as provoking allergy reactions, due to fast clearance from the body lost effectiveness etc.

As a result, new types of monoclonal antibodies were engineered developed to avoid negative impacts.

Mainly placing human origin sequences:

  • Chimeric, the constant region is replaced with the human form so the substem used is -xi-., in which case it is called
  • Humanized, Part of the variable regions, typically everything but the complementarity determining regions, may also be substituted, so substem used is -zu-.
  • Partly chimeric and partly humanized antibodies use -xizu-.

*These three substems do not indicate the foreign species used for production.

Thus,

  • the human/mouse chimeric antibody ba-s-il-i-ximab ends in -ximab
  • the human/macaque antibody go-m-il-i-ximab ends in -ximab.
  • Pure human antibodies use -u-.

Rat/mouse hybrid antibodies:

  • They can be engineered with binding sites for two different antigens.
  • These drugs, termed trifunctional antibodies, have the substem -axo.

Substem for target
The substem preceding the source of the antibody refers to the medicine’s target.

Examples of targets are:

  • tumors,
  • organ systems like the circulatory system, or
  • Infectious agents like bacteria or viruses.

However;

  • The term targetdoes not imply what sort of action the antibody exerts.
  • Therapeutic, prophylactic and diagnostic agents are not distinguished by this nomenclature.

In the naming scheme as originally developed, these substems mostly consist of a consonant, a vowel, then another consonant. For ease of pronunciation and to avoid awkwardness, the final consonant may be dropped if the following source substem begins with a consonant (such as -zu- or -xi-).

Examples of these include:

  • -ci(r)- for the circulatory system,
  • -li(m)-for the immune system (limstands for lymphocyte) and
  • -ne(r)-or -neu(r)- for the nervous system.

This results in endings like –li-mu-mab (immune system, human) or –ci-ximab (circulatory system, chimeric, consonant dropped).

In 2009, new and shorter target substems were introduced.

They mostly consist of a consonant, plus a vowel which is omitted if the source substem begins with a consonant.

For example, human antibodies targeting the immune system receive names ending in -lumab instead of the old -limumab. Some endings like -ciximab remain unchanged.

Prefix
The prefix carries no special meaning and should be unique for each medicine.

Additional words
A second word may be added if there is another substance attached or linked. If the drug contains a radioisotope, the name of the isotope precedes the name of the antibody.

 

Examples

New convention

  • Olara-t-u-mab
  • is an antineoplastic. Its name is composed of olara- + -t- + -u- + -mab.
  • shows that the drug is a human monoclonal antibody acting against tumors.
  • Benra-li-zu-mab
  • a drug designed for the treatment of asthma,
  • benra--li- + -zu- + -mab, marking it as a humanized antibody acting on the immune system.

http://www.nature.com/polopoly_fs/7.10768.1369754844!/image/ASCO-cancer-graph.jpg_gen/derivatives/fullsize/ASCO-cancer-graph.jpg

Example FDA approved therapeutic monoclonal antibodies[1]
Antibody Brand name Company Approval date Type Target Indication
(Targeted disease)
Abciximab ReoPro Eli Lilly 1994 chimeric inhibition of glycoprotein IIb/IIIa Cardiovascular disease
Adalimumab Humira Abbott Laboratories 2002 human inhibition of TNF-α signaling Several auto-immune disorders
Alemtuzumab Campath Genzyme 2001 humanized CD52 Chronic lymphocytic leukemia
Basiliximab Simulect Novartis 1998 chimeric IL-2Rα receptor (CD25) Transplant rejection
Belimumab Benlysta GlaxoSmithKline 2011 human inihibition of B- cell activating factor Systemic lupus erythematosus
Bevacizumab Avastin Genentech/Roche 2004 humanized Vascular endothelial growth factor (VEGF) Colorectal cancerAge related macular degeneration (off-label)
Brentuximab vedotin Adcetris 2011 Chimeric CD30 Anaplastic large cell lymphoma (ALCL) andHodgkin lymphoma
Canakinumab Ilaris Novartis 2009 Human IL-1β Cryopyrin-associated periodic syndrome(CAPS)
Cetuximab Erbitux Bristol-Myers Squibb/Eli Lilly/Merck KGaA 2004 chimeric epidermal growth factor receptor Colorectal cancerHead and neck cancer
Certolizumab pegol[23] Cimzia UCB (company) 2008 humanized inhibition of TNF-α signaling Crohn’s disease
Daclizumab Zenapax Genentech/Roche 1997 humanized IL-2Rα receptor (CD25) Transplant rejection
Denosumab Prolia, Xgeva Amgen 2010 Human RANK Ligand inhibitor Postmenopausal osteoporosis, Solid tumor`s bony metasteses
Eculizumab Soliris Alexion Pharmaceuticals 2007 humanized Complement system protein C5 Paroxysmal nocturnal hemoglobinuria
Efalizumab Raptiva Genentech/Merck Serono 2002 humanized CD11a Psoriasis
Golimumab Simponi Johnson & Johnson/Merck & Co, Inc. 2009 Human TNF-alpha inihibitor Rheumatoid arthritisPsoriatic arthritis, andAnkylosing spondylitis
Ibritumomab tiuxetan Zevalin Spectrum Pharmaceuticals, Inc. 2002 murine CD20 Non-Hodgkin lymphoma (with yttrium-90 orindium-111)
Infliximab Remicade Janssen Biotech, Inc./Merck & Co 1998 chimeric inhibition of TNF-α signaling Several autoimmune disorders
Ipilimumab ( MDX-101 ) Yervoy 2011 Human blocks CTLA-4 Melanoma
Muromonab-CD3 Orthoclone OKT3 Janssen-Cilag 1986 murine T cell CD3 Receptor Transplant rejection
Natalizumab Tysabri Biogen Idec/Élan 2006 humanized alpha-4 (α4) integrin, Multiple sclerosis and Crohn’s disease
Nivolumab Obdivo 2014 Human blocks PD-1 Melanoma and SCC
Ofatumumab Arzerra 2009 Human CD20 Chronic lymphocytic leukemia
Omalizumab Xolair Genentech/Novartis 2004 humanized immunoglobulin E (IgE) mainly allergy-related asthma
Palivizumab Synagis MedImmune 1998 humanized an epitope of the RSV F protein Respiratory Syncytial Virus
Panitumumab Vectibix Amgen 2006 human epidermal growth factor receptor Colorectal cancer
Ranibizumab Lucentis Genentech/Novartis 2006 humanized Vascular endothelial growth factor A (VEGF-A) Macular degeneration
Rituximab Rituxan, Mabthera Biogen Idec/Genentech 1997 chimeric CD20 Non-Hodgkin lymphoma
Tocilizumab ( or Atlizumab ) Actemra and RoActemra 2010 Humanised Anti- IL-6R Rheumatoid arthritis
Tositumomab Bexxar GlaxoSmithKline 2003 murine CD20 Non-Hodgkin lymphoma
Trastuzumab Herceptin Genentech 1998 humanized ErbB2 Breast cancer
Ustekinumab Stelara Centocor 2013 IL-12 , IL-23 Psoriatic Arthritis, Plaque Psoriasis
Vedolizumab Entyvio Takeda 2014 humanized integrin α4β7 Crohn’s diseaseulcerative colitis

Recently, the bispecific antibodies, a novel class of therapeutic antibodies, have yielded promising results in clinical trials. In April 2009, the bispecific antibody catumaxomab was approved in the European Union.

References:

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Izumi Y, Xu L, di Tomaso E, Fukumura D, Jain RK. Tumour biology: herceptin acts as an anti-angiogenic cocktail.  Nature. 2002 Mar 21;416(6878):279-80. PubMed PMID: 11907566.

 

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Sredni B, Caspi RR, Klein A, Kalechman Y, Danziger Y, Ben Ya’akov M, Tamari T, Shalit F, Albeck M. A new immunomodulating compound (AS-101) with potential therapeutic application.  Nature. 1987 Nov 12-18;330(6144):173-6. PubMed PMID: 3118216.

 

Cobbold SP, Waldmann H. Therapeutic potential of monovalent monoclonal antibodies.

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Shouval D, Shafritz DA, Zurawski VR Jr, Isselbacher KJ, Wands JR. Immunotherapy in nude mice of human hepatoma using monoclonal antibodies against hepatitis B virus. Nature. 1982 Aug 5;298(5874):567-9. PubMed PMID: 7099252.

 

Thorpe PE, Mason DW, Brown AN, Simmonds SJ, Ross WC, Cumber AJ, Forrester JA. Selective killing of malignant cells in a leukaemic rat bone marrow using an antibody-ricin conjugate. Nature. 1982 Jun 17;297(5867):594-6. PubMed PMID:7088145.

 

Beverley PC. Antibodies and cancer therapy. Nature. 1982 Jun 3;297(5865):358-9. PubMed PMID: 7078646.

 

Trowbridge IS. Cancer monoclonals.  Nature. 1981 Nov 19;294(5838):204. PubMed PMID: 7300906.

 

Blythman HE, Casellas P, Gros O, Gros P, Jansen FK, Paolucci F, Pau B, Vidal Immunotoxins: hybrid molecules ofmonoclonal antibodiesand a toxin subunit specifically kill tumour cells.  Nature. 1981 Mar 12;290(5802):145-6. PubMed PMID:  7207595.

 

Selected FDA Approved Mab Drugs:

(John, Martin et al. 2005, Robert, Ann et al. 2006, Albert, Edvardas et al. 2012, Claro, Karen et al. 2012, Gideon, Nancy et al. 2013, Michael, Ke et al. 2013, Thomas, Albert et al. 2013, Hyon-Zu, Barry et al. 2014, Larkins, Scepura et al. 2015, Sandra, Ibilola et al. 2015, Sean, Gideon et al. 2015)

Albert, D., K. Edvardas, G. Joseph, C. Wei, S. Haleh, L. L. Hong, D. R. Mark, B. Satjit, W. Jian, G. Christine, B. Julie, B. B. Laurie, R. Atiqur, S. Rajeshwari, F. Ann and P. Richard (2012). “U.S. Food and Drug Administration Approval: Ruxolitinib for the Treatment of Patients with Intermediate and High-Risk Myelofibrosis.” Clinical Cancer Research: 3212-3217.

Claro, R. A. d., M. Karen, K. Virginia, B. Julie, K. Aakanksha, H. Bahru, O. Yanli, S. Haleh, L. Kyung, K. Kallappa, R. Mark, S. Marjorie, B. Francisco, C. Kathleen, C. Xiao Hong, B. Janice, A. Lara, K. Robert, K. Edvardas, F. Ann and P. Richard (2012). “U.S. Food and Drug Administration Approval Summary: Brentuximab Vedotin for the Treatment of Relapsed Hodgkin Lymphoma or Relapsed Systemic Anaplastic Large-Cell Lymphoma.” Clinical Cancer Research: 5845-5849.

Gideon, M. B., S. S. Nancy, C. Patricia, C. Somesh, T. Shenghui, S. Pengfei, L. Qi, R. Kimberly, M. P. Anne, T. Amy, E. K. Kathryn, G. Laurie, L. R. Barbara, C. W. Wendy, C. Bo, T. Colleen, H. Patricia, I. Amna, J. Robert and P. Richard (2013). “First FDA approval of dual anti-HER2 regimen: pertuzumab in combination with trastuzumab and docetaxel for HER2-positive metastatic breast cancer.” Clinical cancer research : an official journal of the American Association for Cancer Research: 4911-4916.

Hyon-Zu, L., W. M. Barry, E. K. Virginia, R. Stacey, D. Pedro, S. Haleh, G. Joseph, B. Julie, F. Jeffry, M. Nitin, K. Chia-Wen, N. Lei, S. Marjorie, T. Mate, C. K. Robert, K. Edvardas, J. Robert, T. F. Ann and P. Richard (2014). “U.S. Food and drug administration approval: obinutuzumab in combination with chlorambucil for the treatment of previously untreated chronic lymphocytic leukemia.” Clinical cancer research : an official journal of the American Association for Cancer Research: 3902-3907.

John, R. J., C. Martin, S. Rajeshwari, C. Yeh-Fong, M. W. Gene, D. John, G. Jogarao, B. Brian, B. Kimberly, L. John, H. Li Shan, C. Nallalerumal, Z. Paul and P. Richard (2005). “Approval Summary for Erlotinib for Treatment of Patients with Locally Advanced or Metastatic Non–Small Cell Lung Cancer after Failure of at Least One Prior Chemotherapy Regimen.” Clinical Cancer Research 11(18).

Larkins, E., B. Scepura, G. M. Blumenthal, E. Bloomquist, S. Tang, M. Biable, P. Kluetz, P. Keegan and R. Pazdur (2015). “U.S. Food and Drug Administration Approval Summary: Ramucirumab for the Treatment of Metastatic Non-Small Cell Lung Cancer Following Disease Progression On or After Platinum-Based Chemotherapy.” The oncologist.

Michael, A., L. Ke, J. Xiaoping, H. Kun, W. Jian, Z. Hong, K. Dubravka, P. Todd, D. Zedong, R. Anne Marie, M. Sarah, K. Patricia and P. Richard (2013). “U.S. Food and Drug Administration approval: vismodegib for recurrent, locally advanced, or metastatic basal cell carcinoma.” Clinical cancer research : an official journal of the American Association for Cancer Research: 2289-2293.

Robert, C. K., T. F. Ann, S. Rajeshwari and P. Richard (2006). “United States Food and Drug Administration approval summary: bortezomib for the treatment of progressive multiple myeloma after one prior therapy.” Clinical cancer research : an official journal of the American Association for Cancer Research: 2955-2960.

Sandra, J. C., F.-A. Ibilola, J. L. Steven, Z. Lillian, J. Runyan, L. Hongshan, Z. Liang, Z. Hong, Z. Hui, C. Huanyu, H. Kun, D. Michele, N. Rachel, K. Sarah, K. Sachia, H. Whitney, K. Patricia and P. Richard (2015). “FDA Approval Summary: Ramucirumab for Gastric Cancer.” Clinical cancer research : an official journal of the American Association for Cancer Research: 3372-3376.

Sean, K., M. B. Gideon, Z. Lijun, T. Shenghui, B. Margaret, F. Emily, H. Whitney, L. Ruby, S. Pengfei, P. Yuzhuo, L. Qi, Z. Ping, Z. Hong, L. Donghao, T. Zhe, H. Ali Al, B. Karen, K. Patricia, J. Robert and P. Richard (2015). “FDA approval: ceritinib for the treatment of metastatic anaplastic lymphoma kinase-positive non-small cell lung cancer.” Clinical cancer research : an official journal of the American Association for Cancer Research: 2436-2439.

Thomas, M. H., D. Albert, K. Edvardas, C. K. Robert, M. K. Kallappa, D. R. Mark, H. Bahru, B. Julie, D. B. Jeffrey, H. Jessica, R. P. Todd, J. Josephine, A. William, M. Houda, B. Janice, D. Angelica, S. Rajeshwari, T. F. Ann and P. Richard (2013). “U.S. Food and Drug Administration Approval: Carfilzomib for the Treatment of Multiple Myeloma.” Clinical Cancer Research: 4559-4563.

Further Reading:

Waldmann, Thomas A. (2003). “Immunotherapy: past, present and future”. Nature Medicine 9 (3): 269–277. doi:10.1038/nm0303-269. PMID 12612576.

Sharma, Pamanee; Allison, James P. (April 3, 2015). “The future of immune checkpoint therapy”. Science. doi:10.1126/science.aaa8172. Retrieved June 2015.

Gene Garrard Olinger, Jr., James Pettitt, Do Kim, Cara Working, Ognian Bohorov, Barry Bratcher, Ernie Hiatt, Steven D. Hume, Ashley K. Johnson, Josh Morton, Michael Pauly, Kevin J. Whaley, Calli M. Lear, Julia E. Biggins, Corinne Scully, Lisa Hensley, and Larry Zeitlin (2012). “Delayed treatment of Ebola virus infection with plant-derived monoclonal antibodies provides protection in rhesus macaques”. PNAS 109 (44): 18030–5.doi:10.1073/pnas.1213709109. PMC 3497800. PMID 23071322.

Janeway, Charles; Paul Travers; Mark Walport; Mark Shlomchik (2001).Immunobiology; Fifth Edition. New York and London: Garland Science. ISBN 0-8153-4101-6.

Janeway CA, Jr.; et al. (2005). Immunobiology. (6th ed.). Garland Science. ISBN 0-443-07310-4.

Modified from Carter P (November 2001). “Improving the efficacy of antibody-based cancer therapies”. Nat. Rev. Cancer 1 (2): 118–29. doi:10.1038/35101072.PMID 11905803.

Prof FC Breedveld (2000). “Therapeutic monoclonal antibodies”. Lancet.doi:10.1016/S0140-6736(00)01034-5.

Köhler G, Milstein C (August 1975). “Continuous cultures of fused cells secreting antibody of predefined specificity”. Nature 256 (5517): 495–7.Bibcode:1975Natur.256..495K. doi:10.1038/256495a0. PMID 1172191.

Nadler LM, Stashenko P, Hardy R, et al. (September 1980). “Serotherapy of a patient with a monoclonal antibody directed against a human lymphoma-associated antigen”.Cancer Res. 40 (9): 3147–54. PMID 7427932.

Stern M, Herrmann R (April 2005). “Overview of monoclonal antibodies in cancer therapy: present and promise”. Crit. Rev. Oncol. Hematol. 54 (1): 11–29.doi:10.1016/j.critrevonc.2004.10.011. PMID 15780905.

Carter P, Presta L, Gorman CM, et al. (May 1992). “Humanization of an anti-p185HER2 antibody for human cancer therapy”. Proc. Natl. Acad. Sci. U.S.A. 89 (10): 4285–9.Bibcode:1992PNAS…89.4285C. doi:10.1073/pnas.89.10.4285. PMC 49066.PMID 1350088.

Presta LG, Lahr SJ, Shields RL, et al. (September 1993). “Humanization of an antibody directed against IgE”. J. Immunol. 151 (5): 2623–32. PMID 8360482.

Jefferis, Roy; Marie-Paule Lefranc (July–August 2009). “Human immunoglobulin allotypes”. MAbs 1 (4): 332–338. doi:10.4161/mabs.1.4.9122. PMC 2726606.PMID 20073133.

Chapman, Kathryn; Nick Pullen, Lee Coney, Maggie Dempster, Laura Andrews, Jeffrey Bajramovic, Paul Baldrick, Lorrene Buckley, Abby Jacobs, Geoff Hale, Colin Green, Ian Ragan and Vicky Robinson (2009). “Preclinical development of monoclonal antibodies”.MAbs 1 (5): 505–516. doi:10.4161/mabs.1.5.9676. PMC 2759500. PMID 20065651.

Rang, H. P. (2003). Pharmacology. Edinburgh: Churchill Livingstone. p. 241. ISBN 0-443-07145-4.

Hooks MA, Wade CS, Millikan WJ (1991). “Muromonab CD-3: a review of its pharmacology, pharmacokinetics, and clinical use in transplantation”. Pharmacotherapy 11(1): 26–37. PMID 1902291.

Goel, Niti; Stephens, Sue (2010). “Certolizumab Pegol”. MAbs 2 (2): 137–147.doi:10.4161/mabs.2.2.11271. PMC 2840232. PMID 20190560.

Chames, Patrick; Baty, Daniel (2009). “Bispecific antibodies for cancer therapy: The light at the end of the tunnel?”. MAbs 1 (6): 539–547. doi:10.4161/mabs.1.6.10015.PMC 2791310. PMID 20073127.

Linke, Rolf; Klein, Anke; Seimetz, Diane (2010). “Catumaxomab: Clinical development and future directions”. MAbs 2 (2): 129–136. doi:10.4161/mabs.2.2.11221.

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Personalized Medicine – The California Initiative

Curator: Demet Sag, PhD, CRA, GCP

Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

It was necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data. It was called Gene Ontology Consortium. Three independent ontologies can be reached at  (http://www.geneontology.org) developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.

We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more:

·         the budding yeast, Saccharomyces cerevisiae, completed in 1996

·         the nematode worm Caenorhabditis elegans, completed in 1998

·         the fruitfly Drosophila melanogaster,

·         the flowering plant Arabidopsis thaliana

·         fission yeast Schizosaccharomyces pombe

·         the mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of commondiseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with un-relevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies), an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component incomplex diseases and
  • to fully understand the genetic pathways contributing tocomplex disease

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.

With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.

This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.

There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics

Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.

As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a key:

  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Table 1.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6 (18), which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.

Published online 2009 May 27. doi:  10.1073/pnas.0903103106

.

Table 2

Allele-Frequency Data for Nine Reproducible Associations

frequency
gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.

Table 3

PMC full text:

Am J Hum Genet. 2006 Jan; 78(1): 130–136.

Published online 2005 Nov 16. doi:  10.1086/499287

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Allele-Frequency Data for 39 Reported Associations

frequency
gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1. Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”

In 2002The International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed. In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population. In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability; these are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center (http://www.hapmap.org) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP).

In the Phase II HapMap we identified 32,996 recombination hotspots3,6,36 (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence (Supplementary Fig. 6).

In addition to many previously identified regions in HapMap Phase I including LARGESYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.

 

FUNCTIONAL GENOMICS AND DATA FOR MEDICINE:  BIOINFORMATICS/COMPUTER BIOLOGY

HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily to recognize and annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.

PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database).

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured:

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases: A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.

Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985Rollins et al. 1991).

The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating position-specific evolutionary conservation” (PSEC) scores.

Schematic illustration of the process for building PANTHER families.

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).

 

 

Further Reading

Human Phenome Project: Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.

 

Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogasterScience.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

Chervitz SA, et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 1999;27:74–78.

The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res. 1999;27:85–88.

Blake JA, et al. The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res. 2000;28:108–111.

Ball CA, et al. Integrating functional genomic information into the Saccharomyces Genome Database. Nucleic Acids Res. 2000;28:77–80.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001. The sequence of the human genome. Science 291: 1304–1351.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860–921.

Mi, H., Vandergriff, J., Campbell, M., Narechania, A., Lewis, S., Thomas, P.D., and Ashburner, M. 2003. Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res.

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25: 25–29.

 

Computational Biology

Attwood TK, Beck ME, Bleasby AJ, Parry-Smith DJ. PRINTS–a database of protein motif fingerprints. Nucleic Acids Res. 1994 Sep;22(17):3590-6.

Obenauer JC, Yaffe MB. Computational prediction of protein-protein interactions.

Methods Mol Biol. 2004;261:445-68. Review.

Aitken A. Protein consensus sequence motifs. Mol Biotechnol. 1999 Oct;12(3):241-53. Review.

Bork P, Koonin EV. Protein sequence motifs. Curr Opin Struct Biol. 1996 Jun;6(3):366-76. Review.

Hodgman TC. The elucidation of protein function by sequence motif analysis.  Comput Appl Biosci. 1989 Feb;5(1):1-13. Review.

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

Spencer CC, et al. The influence of recombination on human genetic diversity.PLoS Genet. 2006;2:e148.

Petes TD. Meiotic recombination hot spots and cold spots. Nature Rev. Genet.2001;2:360–369.

Griffiths RC, Tavaré S. The age of a mutation in a general coalescent tree. Stoch Models. 1998;14:273–295. doi: 10.1080/15326349808807471.

Gauderman WJ. Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med. 2002;21(1):35–50. doi: 10.1002/sim.973.

Attwood, T.K., Beck, M.E., Bleasby, A.J., and Parry-Smith, D.J. 1994. PRINTS—A database of protein motif fingerprints. Nucleic Acids Res. 22: 3590–3596.

Bairoch, A. 1991. PROSITE: A dictionary of sites and patterns in proteins. Nucleic Acids Res. 19 Suppl: 2241–2245.

Bairoch, A. and Apweiler, R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28: 45–48.

Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. 2002. The Pfam protein families database. Nucleic Acids Res. 30: 276–280.

Sonnhammer, E.L., Eddy, S.R., and Durbin, R. 1997. Pfam: A comprehensive database of protein domain families based on seed alignments. Proteins 28:405–420.

Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285–1293. [PubMed]

Thomas, P.D., Kejariwal, A., Campbell, M.J., Mi, H., Diemer, K., Guo, N., Ladunga, I., Ulitsky-Lazareva, B., Muruganujan, A., Rabkin, S., et al. 2003. PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31: 334–341.

HUGO Gene Nomenclature Committee (2011). HGNC Database.http://www.genenames.org/.

 

Population Genomics, GWAS, Inheritance, Heritability, Migration, Selection  an Evolution:

Dayhoff, M.O., Barker, W.C., and McLaughlin, P.J. 1974. Inferences from protein and nucleic acid sequences: Early molecular evolution, divergence of kingdoms and rates of change. Orig. Life 5: 311–330.

Joseph Lachance Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations BMC Med Genomics. 2010; 3: 57.

Joseph Lachance, Sarah A. Tishkoff  Biased Gene Conversion Skews Allele Frequencies in Human Populations, Increasing the Disease Burden of Recessive Alleles  Am J Hum Genet. 2014 October 2; 95(4): 408-420.

Hemalatha Kuppusamy, Helga M. Ogmundsdottir, Eva Baigorri, Amanda Warkentin, Hlif Steingrimsdottir, Vilhelmina Haraldsdottir, Michael J. Mant, John Mackey, James B. Johnston, Sophia Adamia, Andrew R. Belch, Linda M. Pilarski Inherited Polymorphisms in Hyaluronan Synthase 1 Predict Risk of Systemic B-Cell Malignancies but Not of Breast Cancer  PLoS One. 2014; 9(6): e100691.

Joseph Lachance, Sarah A. Tishkoff  Population Genomics of Human Adaptation

Annu Rev Ecol Evol Syst. Author manuscript; available in PMC 2014 November 5.

Published in final edited form as: Annu Rev Ecol Evol Syst. 2013 November; 44: 123–143

Joseph Lachance, Sarah A. Tishkoff SNP ascertainment bias in population genetic analyses: Why it is important, and how to correct it  Bioessays.

Erik Corona, Rong Chen, Martin Sikora, Alexander A. Morgan, Chirag J. Patel, Aditya Ramesh, Carlos D. Bustamante, Atul J. Butte Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration PLoS Genet. 2013 May; 9(5): e1003447.

Olga Y. Gorlova, Jun Ying, Christopher I. Amos, Margaret R. Spitz, Bo Peng, Ivan P. Gorlov J Derived SNP Alleles Are Used More Frequently Than Ancestral Alleles As Risk-Associated Variants In Common Human Diseases Bioinform Comput Biol.

Ani Manichaikul, Wei-Min Chen, Kayleen Williams, Quenna Wong, Michèle M. Sale, James S. Pankow, Michael Y. Tsai, Jerome I. Rotter, Stephen S. Rich, Josyf C. Mychaleckyj  Analysis of Family- and Population-Based Samples in Cohort Genome-Wide Association Studies Hum Genet.

Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science. 2008; 322(5903):881–888. doi: 10.1126/science.1156409.

Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–678. doi: 10.1038/nature05911.

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Tomlinson I, Webb E, Carvajal-Carmona L, Broderick P, Kemp Z, Spain S, Penegar S, Chandler I, Gorman M, Wood W. et al. A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21. Nature Genetics. 2007;39(8):984–988. doi: 10.1038/ng2085.

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Previously published articles:

 

Personalized Medicine in Cancer [3] larryhbern
Advances in Gene Editing Technology: New Gene Therapy Options in Personalized Medicine 2012pharmaceutical
Big Data for Personalized Medicine and Biomarker Discovery, May 5-6, 2015 | Philadelphia, PA 2012pharmaceutical
Tweets by @pharma_BI and by @AVIVA1950 for @PMWCIntl, #PMWC15, #PMWC2015 LIVE @Silicon Valley 2015 Personalized Medicine World Conference 2012pharmaceutical
Presentations Content for Track One @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA, January 26 to January 28, 2015 2012pharmaceutical
Views of Content Presentations – Track One @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA, January 26 to January 28, 2015 2012pharmaceutical
Word Associations of Twitter Discussions for 10th Annual Personalized Medicine Conference at the Harvard Medical School, November 12-13, 2014 2012pharmaceutical
8:30AM–12:00PM, January 28, 2015 – Morality, Ethics & Public Law in PM, LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
2:00PM–5:00PM, January 27, 2015 – Personalizing Evidence in the Learning Healthcare System & Biomarker Discovery Technologies, LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
9:15AM–2:00PM, January 27, 2015 – Regulatory & Reimbursement Frameworks for Molecular Testing, LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
7:45AM–9:15AM, January 27, 2015 – Risk, Reward & Innovation, LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
3:30PM –5:15PM, January 26, 2015 – NGS Applications: Impact of Genomics on Cancer Care @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
2:15PM – 3:00PM, January 26, 2015 – Impact of Genomics on Cancer Care @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
1:00PM – 1:15PM, January 26, 2015 – Clinical Methodologies of NGS – LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
10:30AM-12PM, January 26, 2015 – NGS Applications: Impact of Genomics on Cancer Care – LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
9AM-10AM, January 26, 2015 – Newborn & Prenatal Diagnosis – LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
7:55AM – 9AM, January 26, 2015 – Introduction and Overview – LIVE @Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA 2012pharmaceutical
Hamburg, Snyderman to Address Timely Issues in Personalized Medicine at 2015 Personalized Medicine World Conference in Silicon Valley 2012pharmaceutical
The Personalized Medicine Coalition welcomes the Administration’s focus on Personalized Medicine 2012pharmaceutical
Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA, January 26, 2015, 8:00AM to January 28, 2015, 3:30PM PST 2012pharmaceutical
TOTAL Views of Presentation Content per Presentation: 10th Annual Personalized Medicine Conference at the Harvard Medical School, November 12-13, 2014 2012pharmaceutical
Silicon Valley 2015 Personalized Medicine World Conference, Mountain View, CA, January 26, 2015, 8:00AM to January 28, 2015, 3:30PM PST 2012pharmaceutical
FDA Commissioner, Dr. Margaret A. Hamburg on HealthCare for 310Million Americans and the Role of Personalized Medicine 2012pharmaceutical
Tweeting on the 10th Annual Personalized Medicine Conference at the Harvard Medical School, November 12-13, 2014 2012pharmaceutical
Content of the Presentations at the 10th Annual Personalized Medicine Conference at the Harvard Medical School, November 12-13, 2014 2012pharmaceutical
2:15PM 11/13/2014 – Panel Discussion Reimbursement/Regulation @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
1:00PM 11/13/2014 – Panel Discussion Genomics in Prenatal and Childhood Disorders @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
11:30AM 11/13/2014 – Role of Genetics and Genomics in Pharmaceutical Development @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
10:15AM 11/13/2014 – Panel Discussion — IT/Big Data @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:30AM 11/13/2014 – Harvard Business School Case Study: 23andMe @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:00AM 11/13/2014 – Welcome from Gary Gottlieb, M.D., Partners HealthCare @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
4:00PM 11/12/2014 – Panel Discussion Novel Approaches to Personalized Medicine @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
3:15PM 11/12/2014 – Discussion Complex Disorders @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
1:45PM 11/12/2014 – Panel Discussion – Oncology @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
1:15PM 11/12/2014 – Keynote Speaker – International Genetics Health and Disease @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
11:30AM 11/12/2014 – Personalized Medicine Coalition Award & Award Recipient Speech @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
11:00AM 11/12/2014 – Keynote Speaker – Past, Present and Future of Personalized Medicine @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
9:20AM 11/12/2014 – Panel Discussion – Genomic Technologies @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
8:50AM 11/12/2014 – Keynote Speaker – CEO, American Medical Association @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
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 2012pharmaceutical
8:00AM 11/12/2014 – Welcome & Opening Remarks @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston 2012pharmaceutical
Hashtags and Twitter Handles for 10th Annual Personalized Medicine at Harvard Medical School, 11/12 – 11/13/2014 2012pharmaceutical
Personalized Medicine Coalition (PMC) – Upcoming Events 2012pharmaceutical
10th Annual Personalized Medicine Conference at the Harvard Medical School, November 12-13, 2014, The Joseph B. Martin Conference Center at Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 2012pharmaceutical
Personalized Medicine Coalition Recognizes Mark Levin with Award for Leadership 2012pharmaceutical
Research and Markets: Global Personalized Medicine Report 2014 – Scientific … – Rock Hill Herald (press release) 2012pharmaceutical
The Role of Medical Imaging in Personalized Medicine Dror Nir
CardioPredict™ Personalized Medicine Molecular Diagnostic Test 2012pharmaceutical
Life Sciences Circle Event: Next omics – Personalized Medicine beyond Genomics, December 11, 2013 5:30-8:30PM, The Broad Institute, Cambridge 2012pharmaceutical
Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn sjwilliamspa
Personalized medicine-based diagnostic test for NSCLC ritusaxena
Personalized Medicine and Colon Cancer tildabarliya
Systems Diagnostics – Real Personalized Medicine: David de Graaf, PhD, CEO, Selventa Inc. 2012pharmaceutical
Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center 2012pharmaceutical
Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing sjwilliamspa
Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk sjwilliamspa
Personalized Medicine: Clinical Aspiration of Microarrays sjwilliamspa
The Promise of Personalized Medicine larryhbern
Personalized Medicine in NSCLC larryhbern
Attitudes of Patients about Personalized Medicine larryhbern
Understanding the Role of Personalized Medicine larryhbern
Directions for Genomics in Personalized Medicine larryhbern
Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3 2012pharmaceutical
Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 2012pharmaceutical
Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com 2012pharmaceutical
Nanotechnology, personalized medicine and DNA sequencing tildabarliya
Personalized medicine gearing up to tackle cancer ritusaxena
Personalized Medicine Company Genection launched ritusaxena
Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS) 2012pharmaceutical
The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference,11/28-29, 2012, Harvard Medical School, Boston, MA 2012pharmaceutical
Personalized Medicine Coalition: Upcoming Events 2012pharmaceutical
Highlights from 8th Annual Personalized Medicine Conference, November 28-29, 2012, Harvard Medical School, Boston, MA 2012pharmaceutical
Personalized medicine-based cure for cancer might not be far away ritusaxena
GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial” 2012pharmaceutical
Congestive Heart Failure & Personalized Medicine: Two-gene Test predicts response to Beta Blocker Bucindolol 2012pharmaceutical
Personalized Medicine as Key Area for Future Pharmaceutical Growth 2012pharmaceutical
Clinical Genetics, Personalized Medicine, Molecular Diagnostics, Consumer-targeted DNA – Consumer Genetics Conference (CGC) – October 3-5, 2012, Seaport Hotel, Boston, MA 2012pharmaceutical
AGENDA – Personalized Diagnostics, February 16-18, 2015 | Moscone North Convention Center | San Francisco, CA Part of the 22nd Annual Molecular Medicine Tri-Conference 2012pharmaceutical
Arrowhead’s 6th Annual Personalized & Precision Medicine Conference is coming to San Francisco, October 29-30, 2014 2012pharmaceutical
Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School 2012pharmaceutical
Precision Medicine for Future of Genomics Medicine is The New Era Demet Sag, Ph.D., CRA, GCP
Precision Medicine Initiative: Now is a State Initiative in California 2012pharmaceutical
1:30 pm – 2:20 pm 3/26/2015, LIVE Precision Medicine: Who’s Paying? @ MassBio Annual Meeting 2015, Cambridge, MA, Sonesta Hotel, 3/26 – 3/27, 2015 2012pharmaceutical
We Celebrate >600,000 Views for our 2,830 Scientific Articles in Life Sciences and Medicine 2012pharmaceutical
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
Foundation Medicine: Roche has Taken Over at $1.2B and 52.4 percent to 56.3 percent of Foundation Medicine on a fully diluted basis 2012pharmaceutical
Bridging the Gap in Precision Medicine @UCSF 2012pharmaceutical
Germline Genes and Drug Targets: Medicine more Proactive and Disease Prevention more Effective. 2012pharmaceutical
Proteomics – The Pathway to Understanding and Decision-making in Medicine larryhbern
Multi-drug, Multi-arm, Biomarker-driven Clinical Trial for patients with Squamous Cell Carcinoma called the Lung Cancer Master Protocol, or Lung-MAP launched by NCI, Foundation Medicine, and Five Pharma Firms 2012pharmaceutical
Preventive Care: Anticipated Changes caused by Genomics in the Clinic and Personalised Medicine 2012pharmaceutical
Cancer Labs at School of Medicine @ Technion: Janet and David Polak Cancer and Vascular Biology Research Center 2012pharmaceutical
Reprogramming Adult Patient Cells into Stem Cells: the Promise of Personalized Genetic Therapy 2012pharmaceutical
US Personalized Cancer Genome Sequencing Market Outlook 2018 – 2012pharmaceutical
Summary of Translational Medicine – e-Series A: Cardiovascular Diseases, Volume Four – Part 1 larryhbern
Introduction to Translational Medicine (TM) – Part 1: Translational Medicine larryhbern
Cancer Diagnosis at the Crossroads: Precision Medicine Driving Change, 9/14 – 9/17/2014, Sheraton Seattle Hotel, Seattle WA 2012pharmaceutical
Genomic Medicine and the Bioeconomy: Innovation for a Better World May 12–16, 2014 • Boston, MA 2012pharmaceutical
Institute of Medicine (IOM) Report on Genome-based Therapeutics and Companion Diagnostics 2012pharmaceutical
“Medicine Meets Virtual Reality” – NextMed-MMVR21 Conference 2/19 – 2/22/2014, Manhattan Beach Marriott, Manhattan Beach, CA

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