Posts Tagged ‘Translational Medicine’

LIVE 3:15PM – 5:00PM US-India BioPharma & Healthcare Summit, June 2, 2016, Marriott Cambridge, MA



Leaders in Pharmaceutical Business Intelligence (LPBI) Group

will cover in Real Time using Social Media the

10th US-India BioPharma & Healthcare Summit,

June 2, 2016

Aviva Lev-Ari, PhD, RN will be streaming LIVE from the 

Marriott Cambridge, MA







3-15 PM – 4-05 PM Panel Discussion: Cardiovascular and Metabolic Diseases- Matters of the heart and the body

Dr. Michael Rosenblatt, Executive Vice President & Chief Medical Officer, Merck & Co.

  • What are the trends
  • Hoe Genomics affects
  • Effect behavior by data from Devices like FitBit
  • Gene Editing
  • COllaboration potential with India


  • Neil McDonnell, PharmD, Chief Executive Officer, Metacrine
  • Dr. Anthony Muslin, Vice President, Head of the Cardiovascular & Fibrosis Unit, Sanofi
          1. Area of study: insulin sensitizers
          2. NASH — effects of insulin sensitizers on
          3. CVD — effects of insulin sensitizers on
          4. CNS — effects of insulin sensitizers on
          5. Genomic in Metabolic: Blocking hormone
          6. Two drugs that showed effect on CVD
          7. Antidiabetics inn NASH
          8. Diabetes and Renal
          9. Collaboration with India: VC, Pharma, Seizened Management Teams
  • Dr. Robert Plenge, VP and Head of Translational Medicine, Merck Research Labs
  1. Cholesterol – gene editing vs drug for the mutation
  2. Data analytics of big data — build Teams with al capabilities
  • Dr. Arthur Tzianabos, President & CEO, Homology Medicines
  1. Genomics and Genetics – few have been studied yet,
  2. genes to be targeted for editing
  3. In CVD – Gene Editing – cure disease at the origin: repair the gene transcription
  4. Ittalia, Editas
  5. In vivo genetics: Viruses and nanoparticles
  • Dr. Murali Vemula, Founder & President, Nivarta
  1. CVD
  2. Small molecule

Questions from the Floor

  • Burden of disease is high in India and CHina, Lovostatin, Metformin, Anti-Hypertensive — Decrease the burden og the disease
  • India has capabilities in IT — Harness that to Genomics?
4-05 PM – 4-55 PM Panel Discussion: Regulatory Policies to foster R&D Innovation

Dr. William Chin, Executive Vice President, PhRMA

  1. Regulatory – Roadblock and barriers
  2. Regulatory – Catalist
  3. This is a Bias: Partners with Academia, Industry Cell therapy, CRISPR
  4. Biomarkers – Approval by Regulator is not forthcoming


  • Dr. Ariz Ahammed IAS, Joint Secretary, Department of Pharmaceuticals, Govt. of India
  • Dr. Christopher Corsico, Chief Medical Officer, Boehringer Ingelheim GmbH
  1. If benefit is clear – Regulatory will act fast to accelerate
  2. Gov’t vs Regulators that are TOO much partnering with Companies applying for approval
  • Rajiv Kaul, Portfolio Manager, Fidelity Investments
  1. Intellectual Capital from all over the WOrld arrive to Cambridge
  2. Gov’t and industry to come together otherwise the Cost of Capital is too high
  3. Regulatory is necessary for Public Safety
  4. Regulatory is a barrier if a company stock depends 100% on Approval  – Colatico – approved in 3 Month
  5. Speed is important to Patients and to investors and to Patients
  6. New classification Noval Inventions: Fast approval – investors like this class of drugs
  • K.L. Sharma, IAS, Joint Secretary, Ministry of Health & Family Welfare, Govt. of India
  1. Public Health is of concern
  2. safety and efficacy is of great importance
  3. sociopolitical factors affect various States in India – Harmonization need be accomplished across States
  • Dr. Tsutomu Une, Corporate Advisor, Daiichi Sankyo Co., Ltd.
  1. Understanding he Patient and the family
  2. Priorities for patints vs Industry selfishness

Questions from the Floor

  1. Drug – device not fast approve – WHAT need to be done to accelerate this process
  2. Exploratory Development Submission —  New Scientific Concept  – concept and the Science behind and value – educate the Regulator – harmonize among regulatory agencies
  3. Concept presenented to Investors EARLY before we have the data
4-55 PM – 5-25 PM Participants comments
5-25 PM – 5-30 PM Closing Remarks

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The Need for an Informatics Solution in Translational Medicine

Curator: Larry H. Bernstein, MD, FCAP




White paper



Informatics Designed for the Translational Scientist Developing treatments that take individual variability into account (“personalized medicine”) has given rise to a new discipline in science: translational research or translational medicine. Scientists in this field work to translate biological phenomena into targeted, evidence-based medicines that improve health and treat disease by more optimally matching drugs and individuals. Currently, at least 95 percent of pharmaceutical companies are performing translational research and the translational efforts are driving many of the new therapies entering the clinic today. But those advances don’t come for free. According to the National Center for Advancing Translational Science, translational medicine has “increased research costs and complexity,” and is on par with more traditional clinical challenges of recruiting, study design, and regulatory burdens in driving clinical study costs.


1 It enables translational researchers to easily search, access, and integrate complex, multivariate data, leading to proof or refutation of hypotheses and new questions and discoveries.

2 It’s designed and built from the ground up to serve translational scientists; an out-of-the-box solution, not a generic solution topped off for translational purposes.

3 The universe of supported data types is flexible and ever-expanding as new data types are identified as useful for translational research.

4 It leverages the cloud to improve productivity and collaboration while lowering total costs.


Current tools do not enable the translational researchers to engage directly and intuitively with the available data to affirm or refute a hypothesis. There is no easy means for scientists to search for and access integrated data so they can better identify and characterize biomarkers and develop the most efficient drug to treat a specific disease. Even the types of questions they can ask of the data are limited.

To gain the computational and bioinformatics power to analyze all the data, translational scientists most often call on IT counterparts or biostatisticians and data scientists to create custom applications. This creates its own problems. First, it can restrict the type of inquiry researchers can pose, inadequately focusing on the aftermath of an instrument run, for example. Secondly, it can take several iterations (not to mention days or weeks) before IT is able to serve up what the researcher needs – even if they deliver exactly what the researcher asked for.

New science needs new information solutions – self-service solutions that enable any scientist to engage directly with data more quickly and at a lower cost. These new solutions must address a different type of workflow, one that starts with a scientific question rather than the outcome of an experiment.

“Unless you can start harnessing data and making sense of it, in an automated way, with systems that are engineered to solve big data problems, you’ll be overwhelmed by the data very quickly,” says Nicolas Encina, vice president of the Innovation Lab at PerkinElmer. “You can no longer effectively manage this data manually and you certainly can’t analyze or process it manually either.”


“Too often, people think about data oriented from the informaticist’s or technologist’s point of view,” says Daniel Weaver, senior product manager for translational medicine informatics. “PerkinElmer Signals™ for Translational presents the data in a way a regular scientist will be able to understand. It’s organized around concepts a scientist gets, around the subjects of clinical trials, patient visits, samples collected, etc.”

Before PerkinElmer Signals™ for Translational, most scientists would query data, for example, based on results from a certain day or sample run. To glean more knowledge required manual analysis of multiple data sets layered in Excel spreadsheets. With the growth of data from R&D and clinical research, this task became even more challenging. The new self-service PerkinElmer Signals™ for Translational platform, however, automatically gathers disparate data to answer more open-ended questions, such as, “Do elderly female patients with KRAS mutant breast cancer have increased localization of protein ‘X’ to the nucleus?”

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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.


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.”



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




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



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
 Medical comorbidities
 Prior history of thrombosis
Cancer-associated risk factors
 Primary site
 Cancer histology (higher for adenocarcinoma than squamous cell)
 Time after initial diagnosis (highest in first 3-6 months)
Treatment-associated risk factors
 Anti-angiogenic agents
 Hormonal therapy
 Erythropoiesis-stimulating agents
 Indwelling venous access devices
Currently widely available
 Platelet count (≥350,000/mm3)23
 Leukocyte count (> 11,000/mm3)23
 Hemoglobin (< 10 g/dL)23
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

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.


·         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

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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

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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

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

Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

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.



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.

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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.

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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.

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

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

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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.

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1:15PM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

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

1:15 p.m. – Keynote Speaker – International Genetics Health and Disease

International Genetics Health and Disease

The principles of personalized medicine and how they affect the lives of people acknowledge no national boundaries. Although there are some differences among the diverse populations around the world in terms of their genetic variation, the general principles of personalized medicine apply uniformly across many populations. Dr. Periz will discuss how personalized medicine is viewed across the many European countries with particular emphasis on how Spain is implementing it into its medical care.

Keynote Speaker

Antonio L. Andreu Periz, M.D.
Director, Instituto de Salud Carlos III, Madrid

@insalud_es  @CIBER-BBN

Governmental & Public Health National Organization like a combination of CDC and “Hybrid NIH in the US”

Personalized Medicine (PM) in Europe

Europe and Spain — PM is changing Medical Practice, regulations standard of care.


In Europe 28 National systems in Spain alone 17 systems

Implementation of PM in Europe: Hospitals, Regulation,

  • develop proof of concept
  • identify mechanisms
  • bring basic research to clinical
  • incorporation into a Portfolio of policies on PM

Horizon 2020 in EU – 2016 launch action on PM in various countries in EU

  • Translational level for all EC members
  • Coalition of 28 Research Centers in Europe to promote PM
  • Sharing Databases, Data on HC, infrastructure for Translational research
  • Biomarkers
  • clinical trials

CSA – Coordination Support Action

  • PerMed 500,000 Euro for 5 years, 9 operating partners, representatives of Ministry of Health, Israel and Canada Ministry of Health are included
  • Research Agenda for PM in Europe – SWOT Analysis
  • Recommendations for UC to start PM in 2016
  • – basic research
  • – translation
  • – ICTs
  • – Regulatory

SPAIN – Initiatives on PM: Aggregation of Knowledge

  • One single organization collaborates with 22 Institutions on Biomedical research – Concentration in Barcelona and in Madrid
  • Projects of Excellence: PhD level Projects – Clinical Practice: Imaging, Endocrinology, genomics, cardiology
  • 2014 — 35 Applicants – not all are on Cancer 25% are in Cancer 75% are in other clinical Fields
  • 12Million Euros will fund 1/4 of the applicants
  • PhD Thesis on PM – common project 2 yr governmental institute and 2 years in biotech industry

EAPM – Europe Alliance for PM

  • raise awareness on HOW PM CAN SHAPE Healthcare in Europe: Diagnosis, Treatment,
  • Specialized Treatment for Europe’s Patient (STEPs) – Five Steps

Global alliances to shape Medical Practice based on PM – Collaboration Industry and Academia

  • PMC in the US (Personalized Medical Coalition)
  • PerMEd in Europe (coalition  in Europe  supporting innovation in personalized medicine)
  • EAPM (European Alliance for Personalized Medicine)



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







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

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


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

The Future of Personalized Medicine

Special Guest Speaker

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

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

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

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

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

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

WHAT FDA is doing on Genetics Information as PARTNERS in Medicine

25% of drugs approved are Targeted therapies

LABELING drugs on genetic information

diagnostics test — identify good respondents

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

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

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

Translational Scientist are added.

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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





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Pharmacogenomics needs new materials that are inert against the host and specifically  active to modulate molecular metabolism towards wanted homeostasis of the physiological system.  These can come from natural resources or men-made.  That is why we must know the origin  to  improve.     Recently, Synthetic Biology, even though it is a developing upcoming field, it is generating mile stones for applications in the clinic, the biotechnology industry and in basic molecular research. As  a result, it created a multidisciplinary expertise from scientists to engineers.  Among other things extending the search to first life on Earth is one of the many alternatives.  Here I like to present how synthetic biology can be initiated onto Translational Medicine from adiscovery of molecules from the sea.

Microorganisms played a role in evolution to start a life.  99 % of our genome is related to microbial organisms. initially there was a classical  Microbiology, then evolved to Industrial Microbiology and Biotechnology then Microbial Genomics and now Microbiome and Health became the focus.  Finally,  the circle is getting tide into how microbiome involved with healthy and disease state of human? How they can be used that is what it really means to include microorganisms into human health for diagnostics and targeted therapies?

Or should we start from  scarcity?

Microbiology is my first formal education and  building block.  Simple but help to understand system biology and  the mechanism of life in a nut shell.   The closest field is embryonic stem cell biology for building “synthesizing” a whole new organism.  Then  system biology and developmental biology also gain interest.

The real  remember the month of October in 2001 when DOE reported that they sequenced 23 organisms in Walnut Creek.  Having seen presentation to identify microorganisms through complex crystal structure assays through chemical pathway  at the Microbial Genomics Meeting organized by ASM in Monterey, CA in 2001.

Discovery of microorganisms in marine life like in Mediterranean Sea, containing 38% salt,is very similar with finding circulating disease making cells.   Yet, they are similar since both search for a specific needle in the pile.  Furthermore, the unique behavior of enzymes from microbial organisms such as Taq polymerase or restriction enzymes made it possible for us to develop new technologies for copying and propagating significant sequences.  When these early molecular biology methods are combined with the power of genomics and knowledge of unique structures in molecular physiology, it is possible to design better and sensitive sensors or build an organism to rptect or fix the need of the body.  neither sensors nor synthesized organism model are complete since one is missing the basic element of life “transformation of information” the other is missing the integrity that once nature provided in a single simple cell.

Having sensory smart chip/band/nanomolecule to redesign the cells may also possible if only we know the combination.  Thus, we have options to deliver if we know what to be carried.

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(Figure: The combined strategy of gene-based screening and bioactivity-based screening for marine microbial natural products (MMNPs) discovery, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3705366/figure/marinedrugs-11-00700-f002/)

As we come across, novel pathways or primary pathways of physiology gain significant interest to determine marine microbial compound for therapeutics since they are further away from the evolution three that gives an advantage for biomedical/translational scientist to avoid most part of th eimmune responses such as inflammation, toxicity. Yes, indeed these are not scientific tails but true since currently, 16 of 20 marine antitumor compounds under clinical trial are derived from microbial sources because marine microorganisms are a major source for MMNP discovery.  However, isolation of these organisms.  For example, pretreatment methods, enrichment, physical, and chemical techniques (e.g., dry heat, exposure to 1%–1.5% phenol, sucrose-gradient centrifugation, and filtration through cellulose membrane filters) can be applied to increase especially the less abundant specific groups of marine microorganisms, . A variety of pretreatment methods including recovery of these microorganisms.  This reminds me ecosystem of the soil, since in soil the trouble is identifying the specific culture among millions of others.

Regardless of the case,  nutrients are the key for selecting and isolating any organisms but specifically, as a result any marine microbes have specific nutrient requirements for growth (e.g., sponge extract ) or chemical (e.g., siderophores, signal molecules, non-traditional electron donors, and electron acceptors.  This also should remind us subject of Biology 101 Essential Vitamins and Minerals.  What we eat who we are.

For example, Bruns et al. employed technique where they employed different carbon substrates (agarose, starch, laminarin, xylan, chitin, and glucose) at low concentrations (200 μM each) so that they can  improve the cultivation efficiency of bacteria from the Gotland Deep in the central Baltic Sea. As a result of this growth medium they were able to elevate yield, which is created higher cultivation efficiencies (up to 11% in fluid media) compared to other studies.

Yet, another component must be addressed that is culture medium such as ionic strength for a microbila growth. For example, Tsueng et al. study on marine actinomycete genus Salinispora that can produce bioactive secondary metabolites such as desferrioxamine, saliniketals, arenamides, arenimycin and salinosporamide.  However, they observed that  three species of SalinisporaS. arenicolaS. tropica, and S. pacifica require a high ionic strength but  S. arenicolahas a lower growth requirement for ionic strength than S. tropica and S. pacificaUsing after assaying them against sodium chloride-based and lithium chloride-based media. As  aresult, there is a specificity for growth. 

In addition, energy must be supported imagine that in marine organisms the metabolism is very unique, may be slow and possibly.  However, the main criteria is  most of them grow under low oxygen conditions like tumors.  Warburg effect posed a  problem for human but helped microorganisms to survive and evolve.  One’s weakness the other’s strength make a great teamwork for solving diseases of human kind es especially for cancer. 

This reminds us to utilize minerals, electrons specifically after all the simplest form of carbon metabolism based on biochemical pathways like Crebs cycle, one carbon metabolism and amino acid metabolism etc. Even though 90% of human body made up off microbial origin there are microorganisms that are not cultured yet.

The irony is less than 1% of microorganisms can be cultured.  Furthermore, they are not included for representing the total phylogenetic diversity. Therefore, majority of work concentrated on finding and cultivating the uncultured majority of the microbial world for MMNPs’.  For example,  an uncultivated bacterial symbiont of the marine sponge Theonella swinhoei  producing many antitumor compounds such as pederin, mycalamide A, and onnamide A.

In any conditions if any living needs to be recognized and remembered, their place would be either on top or the bottom of the stack. Microbiome searches for specificity among tone of other organisms to recognize the disease, changes in cell differentiation and pathways or marine microbiologist search for uncommon scarce organisms. Yet, both of them are beneficial with their unique way.

Then what is the catch or fuss?  The catch is screening to identify what makes this organism unique that can be use for human health. Translational medicine may start from the beginning of life from microorganisms created.  This can be called with its newly coined named”synthetic biology” but if we go further than the conventional screening methods which include bioactivity-guided screening and gene-guided screening  and increase the power with genomics we may call it “synthetic genomics”.

As  a result these signature sequences establishes the “unique” biomarkers  or therpaeutics to be used for drug discovery, making vaccines, and remodulating the targeted cells. How?

These microorganisms secrete these metabolites or proteins to their growth medium just like a soluble protein, if you will like a inflammation factor or any other secreted protein of our human body cells. Collecting substrate or extract the pellet could be the choice.   in a nut shell this require at least three steps: First, finding the bioactivity, apply bioactivity-guided screening for direct detection of  the activity such as antimicrobial, antitumor, antiviral, and antiparasitic activities.  Second, a bioinformatic assessment of the secondary metabolite biosynthetic potential in the absence of fully assembled pathways or genome sequences. Third, application on cell lines and possible onto model organisms can improve the process of MMNP discovery so that allow us to prioritize strains for fermentation studies and chemical analysis. 

In summary, establish the culture growth, analyze bioactivity and discover the new gene product to be used.  Here is an example, first they  isolated Marinispora sp from the saline culture.  Next step,  identify new sources of bioactive secondary metabolites, gene-guided screening has been deployed to search target genes associated with NPs biosynthetic pathways, e.g., the fragments between ketosynthase and methylmalonyl-CoA transferase of polyketides (PKS) type I, enediyne PKS ketosynthase gene, O-methyltransferase gene, P450 monooxygenase gene, polyether epoxidase gene, 3-hydroxyl-3-methylglutaryl coenzyme A reductase gene, dTDP-glucose-4,6-dehydratase (dTGD) gene, and halogenase gene. The, apply bioinformatics that includes synthesizing the knowledge with  homology-based searches and phylogenetic analyses, gene-based screening  to predict new secondary metabolites discovered by isolates or environments.  Finally, identify the sequnce for PCR and use against a cell line or model organisms. In this case,  CNQ-140 based on significant antibacterial activities  against drug-resistant pathogens (e.g., MRSA) and impressive and selective cancer cell cytotoxicities (0.2–2.7 μM of MIC50 values) against six melanoma cell lines provided significant outcome. They are recognized as antitumor antibiotics with a new structural class, marinomycins A–D

This is a great method but there are two botle necks: 1. 99% of microbial organisms are not cultured in the labs. 2. Finding the optimum microbial growth and screening takes time. Thus, assesments can me done through metagenomics.  However, metagenomics has its shortcomings since on face of living unless applications applied in vivo in vitro results may not be valid.  The disadvantage of  metagenomics can be listed as:  1. inability of efficient acquisition of intact gene fragment,  2. incompatibility of expression elements such as promoter in a heterologous host.  On the pther hand, there can be possible resolution to avoid these factors  so metagenomics-based MMNP discovery can be plausable such as development  in  synthetic biology by large DNA fragment assembly techniques for artificial genome synthesis and synthetic microbial chassis suitable for different classes of MMNP biosynthesis.

However, many gene clusters have been identified by combined power of genomics and biioinformatics for MNP discovery.  This is  mainly necessary since  secondary metabolites usually biosynthesized by large multifunctional synthases that acts in a sequential assembly lines like adding carboxylic acid and amino acid building blocks into their products.  


Simmons TL, Coates RC, Clark BR, Engene N, Gonzalez D, Esquenazi E, Dorrestein PC, Gerwick W

Proc Natl Acad Sci U S A. 2008 Mar 25; 105(12):4587-94.

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Risk of Bias in Translational Science

Author: Larry H. Bernstein, MD, FCAP


Curator: Aviva Lev-Ari, PhD, RN


Assessment of risk of bias in translational science

Andre Barkhordarian1, Peter Pellionisz2, Mona Dousti1, Vivian Lam1,Lauren Gleason1, Mahsa Dousti1, Josemar Moura3 and Francesco Chiappelli14*  

1Oral Biology & Medicine, School of Dentistry, UCLA, Evidence-Based Decisions Practice-Based Research Network, Los Angeles, USA

2Pre-medical program, UCLA, Los Angeles, CA

3School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

4Evidence-Based Decisions Practice-Based Research Network, UCLA School of Dentistry, Los Angeles, CA

Journal of Translational Medicine 2013, 11:184   http://dx.doi.org/10.1186/1479-5876-11-184

This is an Open Access article distributed under the terms of the Creative Commons Attribution License 


Risk of bias in translational medicine may take one of three forms:

  1. a systematic error of methodology as it pertains to measurement or sampling (e.g., selection bias),
  2. a systematic defect of design that leads to estimates of experimental and control groups, and of effect sizes that substantially deviate from true values (e.g., information bias), and
  3. a systematic distortion of the analytical process, which results in a misrepresentation of the data with consequential errors of inference (e.g., inferential bias).

Risk of bias can seriously adulterate the internal and the external validity of a clinical study, and, unless it is identified and systematically evaluated, can seriously hamper the process of comparative effectiveness and efficacy research and analysis for practice. The Cochrane Group and the Agency for Healthcare Research and Quality have independently developed instruments for assessing the meta-construct of risk of bias. The present article begins to discuss this dialectic.


As recently discussed in this journal [1], translational medicine is a rapidly evolving field. In its most recent conceptualization, it consists of two primary domains:

  • translational research proper and
  • translational effectiveness.

This distinction arises from a cogent articulation of the fundamental construct of translational medicine in particular, and of translational health care in general.

The Institute of Medicine’s Clinical Research Roundtable conceptualized the field as being composed by two fundamental “blocks”:

  • one translational “block” (T1) was defined as “…the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention and their first testing in humans…”, and
  • the second translational “block” (T2) was described as “…the translation of results from clinical studies into everyday clinical practice and health decision making…” [2].

These are clearly two distinct facets of one meta-construct, as outlined in Figure 1. As signaled by others, “…Referring to T1 and T2 by the same name—translational research—has become a source of some confusion. The 2 spheres are alike in name only. Their goals, settings, study designs, and investigators differ…” [3].

1479-5876-11-184-1  Fig 1. TM construct

Figure 1. Schematic representation of the meta-construct of translational health carein general, and translational medicine in particular, which consists of two fundamental constructs: the T1 “block” (as per Institute of Medicine’s Clinical Research Roundtable nomenclature), which represents the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention as well as their first testing in humans, and the T2 “block”, which pertains to translation of results from clinical studies into everyday clinical practice and health decision making [[3]]. The two “blocks” are inextricably intertwined because they jointly strive toward patient-centered research outcomes (PCOR) through the process of comparative effectiveness and efficacy research/review and analysis for clinical practice (CEERAP). The domain of each construct is distinct, since the “block” T1 is set in the context of a laboratory infrastructure within a nurturing academic institution, whereas the setting of “block” T2 is typically community-based (e.g., patient-centered medical/dental home/neighborhoods [4]; “communities of practice” [5]).

For the last five years at least, the Federal responsibilities for “block” T1 and T2 have been clearly delineated. The National Institutes of Health (NIH) predominantly concerns itself with translational research proper – the bench-to-bedside enterprise (T1); the Agency for Healthcare Research Quality (AHRQ) focuses on the result-translation enterprise (T2). Specifically: “…the ultimate goal [of AHRQ] is research translation—that is, making sure that findings from AHRQ research are widely disseminated and ready to be used in everyday health care decision-making…” [6]. The terminology of translational effectiveness has emerged as a means of distinguishing the T2 block from T1.

Therefore, the bench-to-bedside enterprise pertains to translational research, and the result-translation enterprise describes translational effectiveness. The meta-construct of translational health care (viz., translational medicine) thus consists of these two fundamental constructs:

  • translational research and
  • translational effectiveness,

which have distinct purposes, protocols and products, while both converging on the same goal of new and improved means of

  • individualized patient-centered diagnostic and prognostic care.

It is important to note that the U.S. Patient Protection and Affordable Care Act (PPACA, 23 March 2010) has created an environment that facilitates the pursuit of translational health care because it emphasizes patient-centered outcomes research (PCOR). That is to say, it fosters the transaction between translational research (i.e., “block” T1)(TR) and translational effectiveness (i.e., “block” T2)(TE), and favors the establishment of communities of practice-research interaction. The latter, now recognized as practice-based research networks, incorporate three or more clinical practices in the community into

  • a community of practices network coordinated by an academic center of research.

Practice-based research networks may be a third “block” (T3)(PBTN) in translational health care and they could be conceptualized as a stepping-stone, a go-between bench-to-bedside translational research and result-translation translational effectiveness [7]. Alternatively, practice-based research networks represent the practical entities where the transaction between

  • translational research and translational effectiveness can most optimally be undertaken.

It is within the context of the practice-based research network that the process of bench-to-bedside can best seamlessly proceed, and it is within the framework of the practice-based research network that

  • the best evidence of results can be most efficiently translated into practice and
  • be utilized in evidence-based clinical decision-making, viz. translational effectiveness.

Translational effectiveness

As noted, translational effectiveness represents the translation of the best available evidence in the clinical practice to ensure its utilization in clinical decisions. Translational effectiveness fosters evidence-based revisions of clinical practice guidelines. It also encourages

  • effectiveness-focused,
  • patient-centered and
  • evidence-based clinical decision-making.

Translational effectiveness rests not only on the expertise of the clinical staff and the empowerment of patients, caregivers and stakeholders, but also, and

  • most importantly on the best available evidence [8].

The pursuit of the best available evidence is the foundation of

  • translational effectiveness and more generally of
  • translational medicine in evidence-based health care.

The best available evidence is obtained through a systematic process driven by

  • a research question/hypothesis that is articulated about clearly stated criteria that pertain to the
  • patient (P), the interventions (I) under consideration (C), for the sought clinical outcome (O), within a given timeline (T) and clinical setting (S).

PICOTS is tested on the appropriate bibliometric sample, with tools of measurements designed to establish the level (e.g., CONSORT) and the quality of the evidence. Statistical and meta-analytical inferences, often enhanced by analyses of clinical relevance [9], converge into the formulation of the consensus of the best available evidence. Its dissemination to all stakeholders is key to increase their health literacy in order to ensure their full participation

  • in the utilization of the best available evidence in clinical decisions, viz., translational effectiveness.

To be clear, translational effectiveness – and, in the perspective discussed above, translational health care – is anchored on obtaining the best available evidence,

  • which emerges from highest quality research.
  • which is obtained when errors are minimized.

In an early conceptualization [10], errors in research were presented as

  • those situations that threaten the internal and the external validity of a research study –

that is, conditions that impede either the study’s reproducibility, or its generalization. In point of fact, threats to internal and external validity [10] represent specific aspects of systematic errors (i.e., bias) in the

  • research design,
  • methodology and
  • data analysis.

Thence emerged a branch of science that seeks to

  • understand,
  • control and
  • reduce risk of bias in research.

Risk of bias and the best available evidence

It follows that the best available evidence comes from research with the fewest threats to internal and to external validity – that is to say, the fewest systematic errors: the lowest risk of bias. Quality of research, as defined in the field of research synthesis [11], has become synonymous with

  • low bias and contained risk of bias [1215].

Several years ago, the Cochrane group embarked on a new strategy for assessing the quality of research studies by examining potential sources of bias. Certain original areas of potential bias in research were identified, which pertain to

(a) the sampling and the sample allocation process, to measurement, and to other related sources of errors (reliability of testing),

(b) design issues, including blinding, selection and drop-out, and design-specific caveats, and

(c) analysis-related biases.

A Risk of Bias tool was created (Cochrane Risk of Bias), which covered six specific domains:

1. selection bias,

2. performance bias,

3. detection bias,

4. attrition bias,

5. reporting bias, and

6. other research protocol-related biases.

Assessments were made within each domain by one or more items specific for certain aspects of the domain. Each items was scored in two distinct steps:

1. the support for judgment was intended to provide a succinct free-text description of the domain being queried;

2. each item was scored high, low, or unclear risk of material bias (defined here as “…bias of sufficient magnitude to have a notable effect on the results or conclusions…” [16]).

It was advocated that assessments across items in the tool should be critically summarized for each outcome within each report. These critical summaries were to inform the investigator so that the primary meta-analysis could be performed either

  • only on studies at low risk of bias, or for
  • the studies stratified according to risk of bias [16].

This is a form of acceptable sampling analysis designed to yield increased homogeneity of meta-analytical outcomes [17]. Alternatively, the homogeneity of the meta-analysis can be further enhanced by means of the more direct quality-effects meta-analysis inferential model [18].

Clearly, one among the major drawbacks of the Cochrane Risk of Bias tool is

  • the subjective nature of its assessment protocol.

In an effort to correct for this inherent weakness of the instrument, the Cochrane group produced

  • detailed criteria for making judgments about the risk of bias from each individual item[16], and
  • that judgments be made independently by at least two people, with any discrepancies resolved by discussion [16].

This approach to increase the reliability of measurement in research synthesis protocols

  • is akin to that described by us [19,20] and by AHRQ [21].

In an effort to aid clinicians and patients in making effective health care related decisions, AHRQ developed an alternative Risk of Bias instrument for enabling systematical evaluation of evidence reporting [22]. The AHRQ Risk of Bias instrument was created to monitor four primary domains:

1. risk of bias: design, methodology, analysis scoring – low, medium, high

2. consistency: extent of similarity in effect sizes across studies within a bibliome scoring – consistent, inconsistent, unknown

3. directness: unidirectional link between the interventions of interest and the sought outcome, as opposed to multiple links in a casual chain scoring – direct, indirect

4. precision: extent of certainty for estimate of effect with respect to the outcome scoring – precise, imprecise In addition, four secondary domains were identified:

a. Dose response association: pattern of a larger effect with greater exposure (Present/Not Present/Not Applicable or Not Tested)

a. Confounders: consideration of confounding variables (Present/Absent)

a. Strength of association: likelihood that the observed effect is large enough that it cannot have occurred solely as a result of bias from potential confounding factors (Strong/Weak)

a. Publication bias

The AHRQ Risk of Bias instrument is also designed to yield an overall grade of the estimated risk of bias in quality reporting:

•Strength of Evidence Grades (scored as high – moderate – low – insufficient)

This global assessment, in addition to incorporating the assessments above, also rates:

–major benefit

–major harm

–jointly benefits and harms

–outcomes most relevant to patients, clinicians, and stakeholders

The AHRQ Risk of Bias instrument suffers from the same two major limitations as the Cochrane tool:

1. lack of formal psychometric validation as most other tools in the field [21], and

2. providing a subjective and not quantifiable assessment.

To begin the process of engaging in a systematic dialectic of the two instruments in terms of their respective construct and content validity, it is necessary

  • to validate each for reliability and validity either by means of the classic psychometric theory or generalizability (G) theory, which allows
  • the simultaneous estimation of multiple sources of measurement error variance (i.e., facets)
  • while generalizing the main findings across the different study facets.

G theory is particularly useful in clinical care analysis of this type, because it permits the assessment of the reliability of clinical assessment protocols.

  • the reliability and minimal detectable changes across varied combinations of these facets are then simply calculated [23], but
  • it is recommended that G theory determination follow classic theory psychometric assessment.

Therefore, we have commenced a process of revision the AHRQ Risk of Bias instrument by rendering questions in primary domains quantifiable (scaled 1–4),

  • which established the intra-rater reliability (r = 0.94, p < 0.05), and
  • the criterion validity (r = 0.96, p < 0.05) for this instrument (Figure 2).



Figure 2. Proportion of shared variance in criterion validity (A) and inter-rater reliability (B) in the AHRQ Risk of Bias instrument revised as described.
Two raters were trained and standardized 
[20] with the revised AHRQ Risk of Bias and with the R-Wong instrument, which has been previously validated[24]. Each rater independently produced ratings on a sample of research reports with both instruments on two separate occasions, 1–2 months apart. Pearson correlation coefficient was used to compute the respective associations. The figure shows Venn diagrams to illustrate the intersection between each two sets data used in the correlations. The overlap between the sets in each panel represents the proportion of shared variance for that correlation. The percent of unexplained variance is given in the insert of each panel.

A similar revision of the Cochrane Risk of Bias tool may also yield promising validation data. G theory validation of both tools will follow. Together, these results will enable a critical and systematic dialectical comparison of the Cochrane and the AHRQ Risk of Bias measures.


The critical evaluation of the best available evidence is critical to patient-centered care, because biased research findings are fundamentally invalid and potentially harmful to the patient. Depending upon the tool of measurement, the validity of an instrument in a study is obtained by means of criterion validity through correlation coefficients. Criterion validity refers to the extent to which one measures or predicts the value of another measure or quality based on a previously well-established criterion. There are other domains of validity such as: construct validity and content validity that are rather more descriptive than quantitative. Reliability however is used to describe the consistency of a measure, the extent to which a measurement is repeatable. It is commonly assessed quantitatively by correlation coefficients. Inter-rater reliability is rendered as a Pearson correlation coefficient between two independent readers, and establishes equivalence of ratings produced by independent observers or readers. Intra-rater reliability is determined by repeated measurement performed by the same subject (rater/reader) at two different points in time to assess the correlation or strength of association of the two sets of scores.

To establish the reliability of research quality assessment tools it is necessary, as we previously noted [20]:

•a) to train multiple readers in sharing a common view for the cognitive interpretation of each item. Readers must possess declarative knowledge a factual form of information known to be static in nature a certain depth of knowledge and understanding of the facts about which they are reviewing the literature. They must also have procedural knowledge known as imperative knowledge that can be directly applied to a task in this case a clear understanding of the fundamental concepts of research methodology, design, analysis and inference.

•b) to train the readers to read and evaluate the quality of a set of papers independently and blindly. They must also be trained to self-monitor and self-assess their skills for the purpose of insuring quality control.

•c) to refine the process until the inter-rater correlation coefficient and Cohen coefficient of agreement are about 0.9 (over 81% shared variance). This will establishes that the degree of attained agreement among well-trained readers is beyond chance.

•d) to obtain independent and blind reading assessments from readers on reports under study.

•e) to compute means and standard deviation of scores for each question across the reports, repeat process if the coefficient of variations are greater than 5% (i.e., less than 5% error among the readers across each questions).

The quantification provided by instruments validated in such a manner to assess the quality and the relative lack of bias in the research evidence allows for the analysis of the scores by means of the acceptable sampling protocol. Acceptance sampling is a statistical procedure that uses statistical sampling to determine whether a given lot, in this case evidence gathered from an identified set of published reports, should be accepted or rejected [12,25]. Acceptable sampling of the best available evidence can be obtained by:

•convention: accept the top 10 percentile of papers based on the score of the quality of the evidence (e.g., low Risk of Bias);

•confidence interval (CI95): accept the papers whose scores fall at of beyond the upper confidence limit at 95%, obtained with mean and variance of the scores of the entire bibliome;

•statistical analysis: accept the papers that sustain sequential repeated Friedman analysis.

To be clear, the Friedman test is a non-parametric equivalent of the analysis of variance for factorial designs. The process requires the 4-E process outlined below:

•establishing a significant Friedman outcome, which indicates significant differences in scores among the individual reports being tested for quality;

•examining marginal means and standard deviations to identify inconsistencies, and to identify the uniformly strong reports across all the domains tested by the quality instrument

•excluding those reports that show quality weakness or bias

•executing the Friedman analysis again, and repeating the 4-E process as many times as necessary, in a statistical process akin to hierarchical regression, to eliminate the evidence reports that exhibit egregious weakness, based on the analysis of the marginal values, and to retain only the group of report that harbor homogeneously strong evidence.

Taken together, and considering the domain and the structure of both tools, expectations are that these analyses will confirm that these instruments are two related entities, each measuring distinct aspects of bias. We anticipate that future research will establish that both tools assess complementary sub-constructs of one and the same archetype meta-construct of research quality.


  1. Jiang F, Zhang J, Wang X, Shen X: Important steps to improve translation from medical research to health policy.

    J Trans Med 2013, 11:33. BioMed Central Full Text OpenURL

  2. Sung NS, Crowley WF Jr, Genel M, Salber P, Sandy L, Sherwood LM, Johnson SB, Catanese V, Tilson H, Getz K, Larson EL, Scheinberg D, Reece EA, Slavkin H, Dobs A, Grebb J, Martinez RA, Korn A, Rimoin D:Central challenges facing the national clinical research enterprise.

    JAMA 2003, 289:1278-1287. PubMed Abstract | Publisher Full Text OpenURL

  3. Woolf SH: The meaning of translational research and why it matters.

    JAMA 2008, 299(2):211-213. PubMed Abstract | Publisher Full Text OpenURL

  4. Chiappelli F: From translational research to translational effectiveness: the “patient-centered dental home” model.

    Dental Hypotheses 2011, 2:105-112. Publisher Full Text OpenURL

  5. Maida C: Building communities of practice in comparative effectiveness research. In Comparative effectiveness and efficacy research and analysis for practice (CEERAP): applications for treatment options in health care. Edited by Chiappelli F, Brant X, Cajulis C. Heidelberg: Springer–Verlag; 2012.

    Chapter 1


  6. Agency for Healthcare Research and Quality: Budget estimates for appropriations committees, fiscal year (FY) 2008: performance budget submission for congressional justification.

    Performance budget overview 2008.

    http://www.ahrq.gov/about/cj2008/cjweb08a.htm#Statement webcite. Accessed 11 May 2013


  7. Westfall JM, Mold J, Fagnan L: Practice-based research—“blue highways” on the NIH roadmap.

    JAMA 2007, 297:403-406. PubMed Abstract | Publisher Full Text OpenURL

  8. Chiappelli F, Brant X, Cajulis C: Comparative effectiveness and efficacy research and analysis for practice (CEERAP) applications for treatment options in health care. Heidelberg: Springer–Verlag; 2012. OpenURL

  9. Dousti M, Ramchandani MH, Chiappelli F: Evidence-based clinical significance in health care: toward an inferential analysis of clinical relevance.

    Dental Hypotheses 2011, 2:165-177. Publisher Full Text OpenURL

  10. Campbell D, Stanley J: Experimental and quasi-experimental designs for research. Chicago, IL: Rand-McNally; 1963. OpenURL

  11. Littell JH, Corcoran J, Pillai V: Research synthesis reports and meta-analysis. New York, NY: Oxford Univeristy Press; 2008. OpenURL

  12. Chiappelli F: The science of research synthesis: a manual of evidence-based research for the health sciences. Hauppauge NY: NovaScience Publisher, Inc; 2008. OpenURL

  13. Higgins JPT, Green S: Cochrane handbook for systematic reviews of interventions version 5.0.1. Chichester, West Sussex, UK: John Wiley & Sons. The Cochrane collaboration; 2008. OpenURL

  14. CRD: Systematic Reviews: CRD’s guidance for undertaking reviews in health care. National Institute for Health Research (NIHR). University of York, UK: Center for reviews and dissemination; 2009. PubMed Abstract| Publisher Full Text OpenURL

  15. McDonald KM, Chang C, Schultz E: Closing the quality Gap: revisiting the state of the science. Summary report. U.S. Department of Health & Human Services. AHRQ, Rockville, MD: Summary report. AHRQ publication No. 12(13)-E017; 2013. OpenURL

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State of Cardiology on Wall Stress, Ventricular Workload and Myocardial Contractile Reserve: Aspects of Translational Medicine (TM)

Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC


Article Curator, Aviva Lev-Ari, PhD, RN

This article is based on and all citations are from the following two articles that have appeared in Journal of Translational Medicine in 2013


Identifying translational science within the triangle of biomedicine


Griffin M Weber

Journal of Translational Medicine 2013, 11:126 (24 May 2013)


Integrated wall stress: a new methodological approach to assess ventricular

workload and myocardial contractile reserve


Dong H, Mosca H, Gao E, Akins RE, Gidding SS and Tsuda T

Journal of Translational Medicine 2013, 11:183 (7 August 2013)

In this article we expose the e-Reader to

A. The State of Cardiology on

  • wall stress
  • ventricular workload and
  • myocardial contractile reserve

B. Innovations in a Case Study in Cardiology Physiological Research on above subjects

C. Prevailing Models in Translational Medicine

D. Mapping of One Case Study in Cardiology Physiological Research onto Weber’s Triangle of Biomedicine.

The mapping facilitate e-Reader’s effort to capture the complexity of aspects of Translational Medicine and visualization of the distance on this Triangle between where the results of this case study are and the Human Corner — the Roadmap of the “bench-to-bedside” research, or the “translation” of physiological and basic science research into practical clinical applications.

This article has the following sections:


Author:  Justin Pearlman, MD, PhD, FACC

Translational medicine aims to fast track the pathway from scientific discovery to clinical applications and assessment of benefits. Cardiovascular examples include novel biomarkers of disease, new heart assist devices, new technologies for catheter intervention, and new medications. The Institute of Medicine’s Clinical Research Roundtable describes translation medicine in two fundamental blocks:  “…the transfer of new understandings of disease mechanisms gained in the laboratory into the development of new methods for diagnosis, therapy, and prevention [with] first testing in humans…”, and  “…the translation of results from clinical studies into everyday clinical practice and health decision making…” [2].

Identifying where contributions are achieving translation has been addressed by the biometric tool called the triangle of biomedine [3].


  1. Jiang F, Zhang J, Wang X, Shen X: Important steps to improve translation from medical research to health policy.J Trans Med 2013, 11:33. BioMed Central Full Text OpenURL
  2. Sung NS, Crowley WF Jr, Genel M, Salber P, Sandy L, Sherwood LM, Johnson SB, Catanese V, Tilson H, Getz K, Larson EL, Scheinberg D, Reece EA, Slavkin H, Dobs A, Grebb J, Martinez RA, Korn A, Rimoin D: Central challenges facing the national clinical research enterprise.JAMA 2003, 289:1278-1287. PubMed Abstract | Publisher Full Text
  3. Identifying translational science within the triangle of biomedicineGriffin M WeberJournal of Translational Medicine 2013, 11:126 (24 May 2013)
  4. Woolf SH: The meaning of translational research and why it matters.JAMA 2008, 299(2):211-213. PubMed Abstract | Publisher Full Text OpenURL
  5. Chiappelli F: From translational research to translational effectiveness: the “patient-centered dental home” model.Dental Hypotheses 2011, 2:105-112. Publisher Full Text OpenURL
  6. Maida C: Building communities of practice in comparative effectiveness research.In Comparative effectiveness and efficacy research and analysis for practice (CEERAP): applications for treatment options in health care. Edited by Chiappelli F, Brant X, Cajulis C. Heidelberg: Springer–Verlag; 2012.
  7. Agency for Healthcare Research and QualityBudget estimates for appropriations committees, fiscal year (FY) 2008: performance budget submission for congressional justification. 
    http://www.ahrq.gov/about/cj2008/cjweb08a.htm#Statement webcite. Accessed 11 May 2013OpenURL
  8. Westfall JM, Mold J, Fagnan L: Practice-based research—“blue highways” on the NIH roadmap.JAMA 2007, 297:403-406. PubMed Abstract | Publisher Full Text OpenURL
  9. Chiappelli F, Brant X, Cajulis C: Comparative effectiveness and efficacy research and analysis for practice (CEERAP) applications for treatment options in health care. Heidelberg: Springer–Verlag; 2012. OpenURL
  10. Dousti M, Ramchandani MH, Chiappelli F: Evidence-based clinical significance in health care: toward an inferential analysis of clinical relevance.Dental Hypotheses 2011, 2:165-177. Publisher Full Text
  11. CRD: Systematic Reviews: CRD’s guidance for undertaking reviews in health care. National Institute for Health Research (NIHR). University of York, UK: Center for reviews and dissemination; 2009. PubMed Abstract | Publisher Full Text OpenURL
  12. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L, Sterne JA, Cochrane Bias Methods Group; Cochrane Statistical Methods Group:The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials.British Med J 2011, 343:d5928. Publisher Full Text OpenURL
  13. Bartolucci AA, Hillegas WB: Overview, strengths, and limitations of systematic reviews and meta-analyses. In Understanding evidence-based practice: toward optimizing clinical outcomes. Edited by Chiappelli F, Brant XMC, Oluwadara OO, Neagos N, Ramchandani MH. Heidelberg: Springer–Verlag; 2010.
  14. Jüni P, Altman DG, Egger M: Systematic reviews in health care: assessing the quality of controlled clinical trials.British Med J 2001, 323(7303):42-46. Publisher Full Text OpenURL
  15. Chiappelli F, Arora R, Barkhordarian B, Ramchandani M: Evidence-based clinical research: toward a New conceptualization of the level and the quality of the evidence.Annals Ayurvedic Med 2012, 1:60-64. OpenURL
  16. Chiappelli F, Barkhordarian A, Arora R, Phi L, Giroux A, Uyeda M, Kung K, Ramchandani M:Reliability of quality assessments in research synthesis: securing the highest quality bioinformation for HIT.Bioinformation 2012, 8:691-694. PubMed Abstract | Publisher Full Text |PubMed Central Full Text OpenURL
  17. Shavelson RJ, Webb NM: Generalizability theory: 1973–1980.Br J Math Stat Psychol 1981, 34:133-166. Publisher Full Text OpenURL
  18. Chiappelli F, Navarro AM, Moradi DR, Manfrini E, Prolo P: Evidence-based research in complementary and alternative medicine III: treatment of patients with Alzheimer’s disease.Evidence-Based Comp Alter Med 2006, 3:411-424. Publisher Full Text OpenURL
  19. Montgomery C: Statistical quality control: a modern introduction. Chichester, West Sussex, UK: Johm Wiley & sons; 2009. OpenURL

 This article has the following EIGHT Sections:

I. Key Explanation Models for the Translational Process in BioMedicine, aka Translational Medicine (TM)

II. TM Model selection in this article, for mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

III. Limitations of the TM Model to explain the Translational Process in BioMedicine

IV. Mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

V. Clinical Implications of the Case Study in Cardiology Physiological Research

VI. Limitations of the Case Study in Cardiology Physiological Research

VII. The State of Cardiology on

  • wall stress
  • ventricular workload and
  • myocardial contractile reserve

VIII. What are the Innovations of the Case Study in Cardiology Physiological Research

I. Key Explanation Models for the Translational Process in BioMedicine, aka Translational Medicine (TM)

The National Institutes of Health (NIH) Roadmap places special emphasis on “bench-to-bedside” research, or the “translation” of basic science research into practical clinical applications. The Clinical and Translational Science Awards (CTSA) Consortium is one example of the large investments being made to develop a national infrastructure to support translational science, which involves reducing regulatory burdens, launching new educational initiatives, and forming partnerships between academia and industry. However, while numerous definitions have been suggested for translational science, including the qualitative T1-T4 classification, a consensus has not yet been reached. This makes it challenging to measure the impact of these major policy changes.


Model A: QUALTITATIVE T1-T4 CLASSIFICATION [(7) & (8-10) in Weber’s list of Reference, below]

In biomedicine, translational science is research that has gone from “bench” to “bedside”, resulting in applications such as drug discovery that can benefit human health  [16]. However, this is an imprecise description. Numerous definitions have been suggested, including the qualitative T1-T4 classification [7].

Several bibliometric techniques have been developed to quantitatively place publications in the translational spectrum. Narin assigned journals to fields, and then grouped these fields into either “Basic Research” or “Clinical Medicine” [8-10]. Narin also developed another classification called research levels, in which journals are assigned to “Clinical Observation” (Level 1), “Clinical Mix” (Level 2), “Clinical Investigation” (Level 3), or “Basic Research” (Level 4) [8]. He combines Levels 1 and 2 into “Clinical Medicine” and Levels 3 and 4 to “Biomedical Research”.

Model B: Average research level of a collection of articles as the mean of the research levels of those articles

Lewison developed methods to score the translational research level of individual articles from keywords within the articles’ titles and addresses. He defines the average research level of a collection of articles as the mean of the research levels of those articles [1113] .  For validity, one must assume that the keywords reflect content fairly and without bias. If the government adapts such a scoring system to influence funding in order to promote translational research, that will create a bias.

Model C:  “Translatability” of drug development projects 

A multidimensional scoring system has been developed to assess the “translatability” of drug development projects [29,30]. This requires manual review of the literature which poses difficulties for scalability and consistency across reviewers and over time.

Model D: Fontelo’s  59 words and phrases suggesting that the article is Translational 

Fontelo identified 59 words and phrases, which when present in the titles or abstracts of articles, suggest that the article is translational [31]. It is an interesting sampling method, but it may present a bias to particular styles of presentation.

Model E:  The triangle of biomedicine by Griffin M Weber – This Model is the main focus of this article



The Triangle of Biomedicine uses a bibliometric approach to map PubMed articles onto a graph. The corners of the triangle represent research related to animals, to cells and molecules. The position of a publication on the graph is based on its topics, as determined by its Medical Subject Headings (MeSH). Translation is defined as movement of a collection of articles, or the articles that cite those articles, towards the human corner.


The Triangle of Biomedicine provides a quantitative way of determining if an individual scientist, research organization, funding agency, or scientific field is producing results that are relevant to clinical medicine. Validation of the method examined examples that have been previously described in the literature, comparing it to other methods of measuring translational science.


The Triangle of Biomedicine is a novel way to identify translational science and track changes over time. This is important to policy makers in evaluating the impact of the large investments being made to accelerate translation. The Triangle of Biomedicine also provides a simple visual way of depicting this impact, which can be far more powerful than numbers alone. As with any metric, its limitations and potential biases should always be kept in mind. As a result, it should be used to supplement rather than replace alternative methods of measuring or defining translational science. What is unique, though, to the Triangle of Biomedicine, is its simple visual way of depicting translation, which can be far more powerful to policy makers than numbers alone.


Translational science; Bibliometric analysis; Medical subject headings; Data visualization; Citation analysis

II. TM Model selection in this article, for mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

Model E:  The triangle of biomedicine by Griffin M Weber

In this study, we analyze the 20 million publications in the National Library of Medicine’s PubMed database by extending these bibliometric approaches in three ways: (1) We divide basic science into two subcategories, research done on animals or other complex organisms and research done on the cellular or molecular level. We believe it is important to make this distinction due to the rapid increase in “-omics” research and related fields in recent years. (2) We classify articles using their Medical Subject Headings (MeSH), which are assigned based on the content of the articles. Journal fields, title keywords, and addresses only approximate an article’s content. (3) We map the classification scheme onto a graphical diagram, which we call the Triangle of Biomedicine, which makes it possible to visualize patterns and identify trends over time.

Article classification technique

Using a simple algorithm based on an article’s MeSH descriptors, we determined whether each article in PubMed contained research related to three broad topic areas—animals and other complex organisms (A), cells and molecules (C), or humans (H). An article can have more than one topic area. Articles about both animals and cells are classified as AC, articles about both animals and humans are AH, articles about cells and humans are CH, and articles about all three are ACH. Articles that have none of these topic areas are unclassified by this method.

In order to identify translational research, we constructed a trilinear graph [21], where the three topic areas are placed at the corners of an equilateral triangle, with A on the lower-left, C on the top, and H on the lower-right. The midpoints of the edges correspond to AC, AH, and CH articles, and the center of the triangle corresponds to ACH articles.

An article can be plotted on the Triangle of Biomedicine according to the MeSH descriptors that have been assigned to it. For example, if only human descriptors, and no animal or cell descriptors have been assigned to an article, then it is classified as an H article and placed at the H corner. An article with both animal and cell descriptors, and no human descriptors, is classified as an AC article and placed at the AC point. A collection of articles is represented by the average position of its articles. Although an individual article can only be mapped to one of seven points, a collection of articles can be plotted anywhere in the triangle.

An imaginary line, the Translational Axis, can be drawn from the AC point to the H corner. The position of one or more articles when projected onto this axis is the Translational Index (TI). By distorting the Triangle of Biomedicine by bringing the A and C corners together at the AC point, the entire triangle can be collapsed down along the Translational Axis to the more traditional depiction of translational science being a linear path from basic to clinical research. In other words, the Triangle of Biomedicine does not replace the traditional linear view, but rather provides additional clarity into the path research takes towards translation.

Summary of categories

Mapping A-C-H categories to Narin’s basic-clinical classification scheme

The National Library of Medicine (NLM) classifies journals into different disciplines, such as microbiology, pharmacology, or neurology, with the use of Broad Journal Headings. We used Narin’s mappings to group these disciplines into basic research or clinical medicine. Individual articles were given a “basic research” score of 1 if they were in a basic research journal and 0 if they were in a “clinical medicine” journal. For each A-C-H category, a weighted average of its articles’ scores was calculated, with the weights being the inverse of the total number of basic research (4,316,495) and clinical medicine (11,689,341) articles in PubMed. That gives a numeric value for the fraction of articles within a category that are basic research, which is corrected for the fact that PubMed as a whole has a greater number of clinical medicine articles.

Mapping A-C-H categories to Narin’s four-level classification scheme

For each of his four research levels, Narin selected a prototype journal to conduct his analyses:The Journal of the American Medical Association (JAMA, Level 1), The New England Journal of Medicine (NEJM, Level 2), The Journal of Clinical Investigation (JCI, Level 3), and The Journal of Biological Chemistry (JBC, Level 4). Each is widely considered a leading journal and has over 25,000 articles spanning more than 50 years. For each A-C-H category, we determined the number of articles from each of these four journals and calculated a weighted average of their research levels, with the weights being the inverse of the total number of articles each journal has in PubMed.

III. Limitations of the TM Model to explain the Translational Process in BioMedicine:  The triangle of biomedicine by Griffin M Weber

This work is limited in several ways. It takes at least a year for most articles to be assigned MeSH descriptors. During that time the articles cannot be classified using the method described in this paper. Also, our classification method is based on a somewhat arbitrary set of MeSH descriptors—different descriptors could have been used to map articles to A-C-H categories. However, the ones we used seemed intuitive and they produced results that were consistent with Narin’s classification schemes. Finally, any metric based on citation analysis is dependent on the particular citation database used, and there are significant differences among the leading databases [22]. In this study, we used citations in PubMed that are derived from PubMed Central because they are freely available in their entirety, and therefore our method can be used without subscriptions to commercial citation databases, such as Scopus and Web of Science, which are cost-prohibitive to most people. However, because these commercial databases have a greater number of citations and index different journals than PubMed, they might show shorter or alternative paths towards translation (i.e., fewer citation generations or less time). Though, as described in our Methods, there is evidence that suggests these differences might be relatively small. Selecting the best citation database for identifying translational research is a topic for future research.

Another area of future research could attempt to identify a subset of H articles that truly reflect changes in health practice and create a separate category P for these articles. This might be possible, for example, by using Khoury’s approach of using PubMed’s “publication type” categorization of each article to select for those that are clinical trials or practice guidelines [7]. This could be visualized in the Triangle of Biomedicine by moving H articles to the center of the triangle and placing P articles in the lower-right corner, thereby highlighting research that has translated beyond H into health practice.

IV. Mapping the fit of a Case Study in Cardiology Physiological Research, within the TM Model selected

The triangle of biomedicine by Griffin M Weber


Figure 1. Disciplines mapped onto the Triangle of Biomedicine.The corners of the triangle correspond to animal (A), cellular or molecular (C), and human (H) research. The dashed blue line indicates the Translational Axis from basic research to clinical medicine. The position of each circle represents the average location of the articles in a discipline. The size of the circle is proportional to the number of articles in that discipline. The color of the circle indicates the Translational Distance (TD)—the average number of citation generations needed to reach an H article. The position of the light blue box connected to each discipline represents the average location of articles citing publications in that discipline. To provide clarity, not all disciplines are shown. Note however, that if authors knew this measurement would be applied and could affect their funding, then they might increase human study citation of basic research to game the “translational distance.”

For this article we selected A Case Study in Cardiology Physiological Research

Integrated wall stress: a new methodological approach to assess ventricular workload and myocardial contractile reserve  

Hailong Dong124Heather Mosca1Erhe Gao3Robert E Akins1Samuel S Gidding2and Takeshi Tsuda12*

This study appeared in 2013 in the Journal of Translational Medicine. It studied mice, creating heart attacks in order to evaluate the physiologic significance of “integrated wall stress” (IWS) as a marker of total ventricular workload. The measure IWS was obtained by integrating continuous wall stress curve by accumulating wall stress values at millisecond sampling intervals over one minute, in order to include in  wall stress effects of heart rate and contractility (inotropic status of the myocardium). As an example of translational medicine, it raises numerous issues. As a mouse study, it qualifies as basic science. It examines the impact of heart attack on changes inducible by the inotropic agent dobutamine. If the concept were to influence clinical care and outcomes, it would qualify as translational. All of the tools applied to the mice are applicable to patients: heart attacks (albeit not purposefully induced), the echocardiography measurements, and the dobutamine impact. That enables citation of human studies in the references, and ready application to human studies in the future. Mice however have much faster heart rates, so the choice of one minute for the integral may have different significance for humans. Gene expression was also measured. The authors conclude IWS represents  a balance between external ventricular workload and intrinsic myocardial contractile reserve. The fact that the Journal has the word “translational” may represent a bias. Many of the links between animal and human focused references occur electively in the discussion section. The authors propose the measurement might help identify pre-clinical borderline failing of contractility. If so, the full axis of translational value will require that IWS can improve outcomes. Currently, blood levels of brain naturetic peptide are used as a marker of myocardial strain that may help identify early failing contractility. Presumably, early recognition could identify a population that might benefit from early intervention to forestall progression. Evidence based medicine will have difficulties. First, it is biased by the “Will Roger’s Effect” whereby early recognition of a disease subdivides the lowest class, inherently shifting the apparent status of each half of the subdivision (Will Roger’s made a joke that when Oklahoma residents moved to California for the gold rush, they improved the average intelligence of both groups, an observation adapted to explain a redefinition bias). Second, the actual basis for a change in clinical application will be complex, with political as well as scientific influences. Third, it will be even more difficult to discern its impact on outcomes, even if targeted therapy for patients with distinctive IWS is associated with an apparent improvement in outcomes. Convincing documentation would require extensive comparisons and controlled studies, but once a method is clinically adapted, it is commonly considered unethical to perform a controlled study in which the “preferred method” is not applied to a group.

V. Clinical Implications of the Case Study in Cardiology Physiological Research


Wall stress is a useful concept to understand the progression of ventricular remodeling. We measured cumulative LV wall stress throughout the cardiac cycle over unit time and tested whether this “integrated wall stress (IWS)” would provide a reliable marker of total ventricular workload.

Methods and results

We applied IWS to mice after experimental myocardial infarction (MI) and sham-operated mice, both at rest and under dobutamine stimulation. Small infarcts were created so as not to cause subsequent overt hemodynamic decompensation. IWS was calculated over one minute through simultaneous measurement of LV internal diameter and wall thickness by echocardiography and LV pressure by LV catheterization. At rest, the MI group showed concentric LV hypertrophy pattern with preserved LV cavity size, LV systolic function, and IWS comparable with the sham group. Dobutamine stimulation induced a dose-dependent increase in IWS in MI mice, but not in sham mice; MI mice mainly increased heart rate, whereas sham mice increased LV systolic and diastolic function. IWS showed good correlation with a product of peak-systolic wall stress and heart rate. We postulate that this increase in IWS in postMI mice represents limited myocardial contractile reserve.


We hereby propose that IWS provides a useful estimate of total ventricular workload in the mouse model and that increased IWS indicates limited LV myocardial contractile reserve.


Wall stress; Ventricular workload; Myocardial contractile reserve; Ventricular remodeling

Clinical implications

IWS can be estimated by obtaining IWS index, which is calculated non-invasively by simultaneous M-mode echocardiogram and cuff blood pressure measurement, i.e., PS-WS instead of ES-WS and heart rate. This will provide a sensitive way to detect subclinical borderline failing myocardium in which the decline in LV myocardial contractile reserve precedes apparent LV dysfunction. This method may be clinically useful to address LV myocardial reserve in those patients who are not amenable to perform on exercise stress test, such as immediate post-operative patients under mechanical ventilation, critically ill patients with questionable LV dysfunction, and patients with primary muscular disorders and general muscular weakness (i.e., Duchenne muscular dystrophy).

VI. Limitations of the Case Study in Cardiology Physiological Research

There are certain limitations in this study.

  • First, wall stress measurement is reliable when there is an equal wall thickness with symmetrical structure. Obviously, with the creation of small MI, there is an asymmetry of LV myocardium in both structure and consistency (myocardium vs. scar tissue). However, the scar tissue is small and restricted to the LV apex (approximately 14% of entire LV myocardium [5]). In fact, most of LV wall was thickened after induction of this small experimental MI. Nevertheless, we acknowledge that this is our major limitation.
  • Secondly, there is an individual variability in response to dobutamine stimulation even in sham mice. Although the average sham mice (n = 5) showed only a modest increase in HR, PS-WS, and IWS during dobutamine stimulation, one mouse presented in Figure 1 showed a notable increase in HR and PS-WS in response to dobutamine. Nevertheless, even with increased HR and PS-WS, the calculated IWS remained relatively unchanged in the sham-operated mice.
  • Lastly, the reliability of IWS index is based upon the stipulation that ED-WS is significantly low compared with the systolic wall stress. Thus, IWS index may not be accurate in obvious volume overload cases and/or dilated hearts with LV dysfunction where ED-WS is significantly higher than that in normal condition. Of note, ED-WS in human is higher than that in mice in relation to PS-WS, probably around 15 to 20% of PS-WS [12].

VII. State of Cardiology on

  • wall stress
  • ventricular workload and
  • myocardial contractile reserve

Ventricular remodeling is a chronic progressive pathological process that results in heart failure after myocardial infarction (MI) or persistent unrelieved biomechanical overload [1,2]. Persistent and unrelieved biomechanical overload in combination with activation of inflammatory mediators and neurohormones is thought to be responsible for progressive ventricular remodeling after MI [3,4], but studies to investigate specific mechanisms in animals are hampered by the difficulty involved in quantifying biomechanical workload in vivo. The magnitude of ventricular remodeling advances in line with progressive ventricular geometric changes including myocardial hypertrophy and chamber dilatation with accompanying functional deterioration [1,2]. Previously, we proposed that post-ischemic ventricular remodeling is a pathological spectrum ranging from benign myocardial hypertrophy to progressive heart failure in the mouse model in which the prognosis is primarily determined by the magnitude of residual hemodynamic effects [5]. However, there has been no optimum quantitative measurement of ventricular workload as a contributory indicator of ventricular remodeling other than wall stress theory to explain how ventricular dilatation and hypertrophy develop after loss of viable working myocardium [6,7].

The concept of ventricular wall stress was introduced by Strauer et al. as a primary determinant of myocardial oxygen demand [8]. They indicated that overall myocardial energy demand depends upon intramyocardial wall tension, inotropic state of the myocardium, and heart rate. Wall stress theory is commonly introduced to explain development of concentric hypertrophy in chronic pressure overload and progressive ventricular dilatation in the failing heart. One study argued that peak-systolic wall stress increased as LV function worsened in a chronic volume overloaded status [9], and another suggested that peak-systolic wall stress closely reflected LV functional reserve during exercise [10]. However, the effect of heart rate or myocardial contractility was not considered in either study. Heart rate has been shown to be one of several important factors contributing to myocardial oxygen consumption [11].

Herein, we introduce a novel concept of “integrated wall stress (IWS)” to assess its significance as a marker of total ventricular workload and to validate its physiological relevance in the mouse model. The concept of continuous LV wall stress measurement was reported previously, but authors did not address the overall effects of changing wall stress during the cardiac cycle on the working myocardium [12]. We have defined IWS as cumulative wall stress over unit time: IWS was obtained by integrating continuous wall stress curve by accumulating wall stress values at millisecond sampling intervals over 1 min. By calculating IWS, we were able to incorporate the effects of not only systolic wall stress, but also of heart rate and inotropic status of the myocardium. These data were analyzed against conventional hemodynamic parameters in animals with and without MI in conjunction with incremental dobutamine stress. We hypothesize that unchanged IWS represents stable ventricular myocardial contractile reserve and that increase in IWS implies an early sign of mismatch between myocardial reserve and workload imposed on ventricular myocardium.

VIII. What are the Innovations of the Case Study in Cardiology Physiological Research

IWS measures total wall stress throughout the cardiac cycle over a unit time (= 1 min) including the effect of heart rate and inotropic state of the ventricular myocardium, whereas one-spot measurement of PS-WS and ED-WS only reflects maximum and minimum wall stress during a cardiac cycle, respectively. We hypothesized that increase in IWS indicates failure of myocardium to counteract increased ventricular workload. We have measured IWS in the mouse model in various physiological and pathological conditions to validate this hypothesis. Unchanged IWS observed in sham operated mice may imply that the contractile reserve of ventricular myocardium can absorb the increased cardiac output, whereas increased IWS after MI suggests that ventricular workloads exceeds intrinsic myocardial contractile reserve. Thus, we postulate that IWS is a reliable physiological marker in indicating a balance between external ventricular workload and intrinsic myocardial contractile reserve.


IWS and myocardial reserve

“Wall stress theory” is an important concept in understanding the process of cardiac hypertrophy in response to increased hemodynamic loading [16]. When the LV myocardium encounters biomechanical overload, either pressure overload or volume overload, cardiac hypertrophy is naturally induced to normalize the wall stress so that myocardium can minimize the increase in myocardial oxygen demand; myocardial oxygen consumption depends mainly on systolic wall stress, heart rate, and contractility [8,17]. A question arises whether this hypertrophic response is a compensatory physiological adaptation to stabilize the wall stress or a pathological process leading to ventricular remodeling and heart failure. Physiological hypertrophy as seen in trained athletes reveals increased contractile reserve, whereas pathological hypertrophy shows a decrease in contractile reserve in addition to molecular expression of ventricular remodeling [1820]. However, what regulates the transition from compensatory adaptation to maladaptive process is not well understood.

Systolic wall stress has been studied extensively as a clinical marker for myocardial reserve. Systolic wall stress reflects the major determinants of the degree of LV hypertrophy and plays a predominant role in LV function and myocardial energy balance [17]. It has been shown that increased systolic wall stress inversely correlates with systolic function and myocardial reserve in patients with chronic volume overload [9,10,21], chronic pressure overload [22,23], and dilated cardiomyopathy [24]. However, one-point measurement of systolic wall stress does not encompass the effect of heart rate and contractile status, the other critical factors that affect myocardial oxygen demand [11]. The idea of IWS has been proposed to incorporate wall stress throughout the cardiac cycle and reflects the effects of heart rate and contractile status.

Myocardial oxygen consumption is determined mainly by ventricular wall stress, heart rate and contractility [17], which are all incorporated in IWS measurement. Continuous measurement of LV wall stress was previously reported in humans [12,15] and dogs [11] with a similar method, but not in mice. By integrating the continuous WS over one minute, we estimated the balance between myocardial contractile reserve and total external ventricular workload and examined its trend in relation to inotropic stimulation in the mouse heart in vivo. In this study, we have proposed unchanged IWS as a marker of sufficient myocardial contractile reserve, since increased wall stress demands higher myocardial oxygen consumption. Indeed, systolic wall stress does not increase with strenuous isometric exercise in healthy young athletes [25]. Thus, we propose that increase in IWS indicates diminished myocardial contractile reserve.


Small MI model as a unique model to study early phase of progressive ventricular remodeling

A complex series of protective and damaging events takes place after MI, resulting in increased ventricular workload [26]. Initial ventricular geometric change is considered as a primary compensatory response to counteract an abrupt loss of contractile tissue. In classical theories of wall stress, which rely on the law of Laplace, the mechanisms of progressive ventricular dilatation and functional deterioration of the LV are attributed to the increased wall stress that is not compensated by the intrinsic compensatory mechanisms [2,16]. Although this theory is obvious in advanced stage of heart failure, the subclinical ventricular remodeling following borderline cases such as following small MI with initial full compensatory response is not well explained.

Study shown that our small MI model induced concentric hypertrophy without LV dilatation as if initial myocardial damage was completely compensated (Figure 2[5]. Although LV hypertrophy is induced initially to normalize the wall stress and to prevent ventricular dilatation, this hypertrophy is not altogether a physiological one because of decreased inotrophic and lusitropic reserve when stimulated with dobutamine (Figure 4) and because of simultaneous molecular and histological evidence of remodeling in the remote nonischemic LV myocardium (Figure 3). IWS and PS-WS become normalized in small MI at rest under anesthesia as a result of reactive hypertrophy accompanied by increased ANP and BNP mRNA level. Borderline maladaptive LVH is characterized by maintained LV performance at the expense of limited myocardial contractile reserve, and this abnormality can be unmasked by inotropic stimulation [18]. The trend of IWS at rest and with dobutamine stimulation suggests that MI mice were likely exposed to higher IWS during usual awake and active condition than sham-operated mice. In contrast, systolic wall stress in the pressure overload-induced LV hypertrophy showed a level comparable to that of sham both at rest and under stimulation by β1 adrenergic agonist, prenalterol, with comparable heart rate changes [27]. For this reason, IWS assessment by measuring cumulative WS in a unit time with and without inotropic stimuation should serve as a sensitive marker to assess whether induced LV hypertrophy is a compensatory physiological adaptation process or a pathological maladaptation process. Increased IWS that indicates imposed workload surpassing myocardial contractile reserve is likely to become a major driving factor in inducing progressive ventricular remodeling or initiating deleterious maladaptive processes after MI.


IWS represents myocardial oxygen demand that can be estimated non-invasively

Study demonstrated a very good correlation between IWS and the product of PS-WS and HR (“IWS index”) in both MI and sham-operated hearts (Figure 6). This formula appears physiologically acceptable provided that ED-WS is sufficiently low compared with the PS-WS (approximately 10%, as is shown in Figures 4B and C). ES-WS was previously introduced as a useful tool for assessing myocardial loading status and myocardial oxygen consumption, but its measurement requires complicated preparation [28,29]. Because there is an excellent correlation between PS-WS and ES-WS, it has been demonstrated that ES-WS can be substituted by PS-WS [28], which can be easily obtained non-invasively [30]. ES-WS was previously determined as a useful marker to quantify LV afterload and contractility that can be simply and accurately measured non-invasively [15]. As myocardial oxygen consumption is mainly dependent upon systolic wall stress, contractility, and heart rate, it seems reasonable to propose that IWS and IWS index represent the status of myocardial contractile reserve.

Conclusions & Next Phases in Translational Medicine and Cardiology Physiological Research

Author: Justin Pearlman, MD, PhD, FACC 

Visual and numeric scores that assess the commitment to translation of basic discoveries to measured impact on human outcomes followed by increased prevalence of the benefits is of course desirable, but fraught with challenges.  Metrics of translational medicine may lead to rewards that can “game” the system by promoting choices of MeSH codes that augment the score for individual articles and/or clusters of work from a center of research without correlation to the actual impact of the body of work. The fairness of a metric also must account for division of labor whereby one group of researchers achieves major basic discoveries that ferment useful applications to improved outcomes in patient care, while others focus on applications or application assessments that may have widely disparate degrees of impact on the reduction to practice, validation and dissemination of improved care.

Thus in order to promote useful metrics of translational medicine progress, we propose a set of metrics on the metrics:

1. impact of reviewer skill/bias

2. impact of author coding/bias

3. ability to assess an impact factor independent of author word choices

4. ability to credit basic research for its downstream impact on other researchers culminating in clinical applications, validation, and dissemination of human benefits

5. ability to discern pioneering advances from “me too” duplications of effort and minor variations on work of the same group or others

6. ability to assess cost effectiveness, including the occurrences of subsequent re-investigations to clarify issues that could have been addressed in the instance study

7. ability to compute contribution to quality life year gain per dollar of added care


Identifying translational science within the triangle of biomedicine


Griffin M Weber

Journal of Translational Medicine 2013, 11:126 (24 May 2013)

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Integrated wall stress: a new methodological approach to assess ventricular

workload and myocardial contractile reserve


Dong H, Mosca H, Gao E, Akins RE, Gidding SS and Tsuda T

Journal of Translational Medicine 2013, 11:183 (7 August 2013)

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  11. Colin P, Ghaleh B, Monnet X, Su J, Hittinger L, Giudicelli JF, Berdeaux A: Contributions of heart rate and contractility to myocardial oxygen balance during exercise.Am J Physiol Heart Circ Physiol 2003, 284:H676-H682. PubMed Abstract |Publisher Full Text OpenURL
  12. Grossman W, Jones D, McLaurin LP: Wall stress and patterns of hypertrophy in the human left ventricle.J Clin Invest 1975, 56:56-64. PubMed Abstract | Publisher Full Text |PubMed Central Full Text OpenURL
  13. Gao E, Lei YH, Shang X, Huang ZM, Zuo L, Boucher M, Fan Q, Chuprun JK, Ma XL, Koch WJ: A novel and efficient model of coronary artery ligation and myocardial infarction in the mouse.Circ Res 2010, 107:1445-1453. PubMed Abstract | Publisher Full Text |PubMed Central Full Text OpenURL
  14. Kamphoven JH, Stubenitsky R, Reuser AJ, Van Der Ploeg AT, Verdouw PD, Duncker DJ:Cardiac remodeling and contractile function in acid alpha-glucosidase knockout mice.Physiol Genomics 2001, 5:171-179. PubMed Abstract OpenURL
  15. Reichek N, Wilson J, St John Sutton M, Plappert TA, Goldberg S, Hirshfeld JW:Noninvasive determination of left ventricular end-systolic stress: Validation of the method and initial application.Circulation 1982, 65:99-108. PubMed Abstract | Publisher Full Text OpenURL
  16. Grossman W: Cardiac hypertrophy: Useful adaptation or pathologic process?Am J Med 1980, 69:576-584. PubMed Abstract | Publisher Full Text OpenURL
  17. Strauer BE: Left ventricular dynamics, energetics and coronary hemodynamics in hypertrophic heart disease.Eur Heart J 1983, 4(Suppl A):137-142. PubMed Abstract | Publisher Full Text OpenURL
  18. Fontanet HL, Perez JE, Davila-Roman VG: Diminished contractile reserve in patients with left ventricular hypertrophy and increased end-systolic stress during dobutamine stress echocardiography.Am J Cardiol 1996, 78:1029-1035. PubMed Abstract | Publisher Full Text OpenURL
  19. Force T, Michael A, Kilter H, Haq S: Stretch-activated pathways and left ventricular remodeling.J Card Fail 2002, 8:S351-S358. PubMed Abstract | Publisher Full Text OpenURL
  20. Weber KT, Clark WA, Janicki JS, Shroff SG: Physiologic versus pathologic hypertrophy and the pressure-overloaded myocardium.J Cardiovasc Pharmacol 1987, 10(Suppl 6):S37-S50. PubMed Abstract OpenURL
  21. Borow KM, Green LH, Mann T, Sloss LJ, Braunwald E, Collins JJ, Cohn L, Grossman W:End-systolic volume as a predictor of postoperative left ventricular performance in volume overload from valvular regurgitation.Am J Med 1980, 68:655-663. PubMed Abstract | Publisher Full Text OpenURL
  22. Krayenbuehl HP, Hess OM, Ritter M, Monrad ES, Hoppeler H: Left ventricular systolic function in aortic stenosis.Eur Heart J 1988, 9(Suppl E):19-23. PubMed Abstract | Publisher Full Text OpenURL
  23. Yuda S, Khoury V, Marwick TH: Influence of wall stress and left ventricular geometry on the accuracy of dobutamine stress echocardiography.J Am Coll Cardiol 2002, 40:1311-1319. PubMed Abstract | Publisher Full Text OpenURL
  24. Paraskevaidis IA, Tsiapras DP, Adamopoulos S, Kremastinos DT: Assessment of the functional status of heart failure in non ischemic dilated cardiomyopathy: an echo-dobutamine study.Cardiovasc Res 1999, 43:58-66. PubMed Abstract | Publisher Full Text OpenURL
  25. Haykowsky M, Taylor D, Teo K, Quinney A, Humen D: Left ventricular wall stress during leg-press exercise performed with a brief valsalva maneuver.Chest 2001, 119:150-154. PubMed Abstract | Publisher Full Text OpenURL
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Personalized Medicine: Clinical Aspiration of Microarrays

Reporter, Writer: Stephen J. Williams, Ph.D.

 In this month’s Science, Mike May (at http://www.sciencemag.org/site/products/lst_20130215.xhtml) describes some of the challenges and successes in introducing microarray analysis to the clinical setting.  Traditionally used for investigational research, microarray is now being developed, customized and used for biomarker analysis, prognostic and predictive value, in a disease-specific manner.

Challenges in data interpretation

      In an interview with Seth Crosby, director of the Genome Technology Access Center at Washington University School of Medicine in St. Louis, “the biggest challenge” in moving microarray to the clinical setting is data interpretation.  The current technology makes it possible to evaluate expression of thousands of genes from a patient’s sample however as Crosby describes is assigning clinical relevance to the data.  For example Crosby explains that Washington University had validated a panel of 45 oncology genes by next generation sequencing and are using these genes to develop diagnostic tests to screen patient tumors for the purpose of determining a personalized therapeutic strategy. Seth Crosby noted it took “hundreds of Ph.D. and M.D. hours” to sift through the hundreds of papers to determine which genes were relevant to a specific cancer type. However, he notes, that once we better understand which changes in the patient’s genome are related to a specific disease we will be able to narrow down the list and be able to produce both economical and more disease-relevant microarrays.

Is this aberration pathogenic or not?

     Microarrays are becoming an invaluable tool in cytogenetics, as eluded by Andy Last, executive vice president of the genetic analysis business unit at Affymetrix.  Certain diseases like Down syndrome have well characterized chromosomal alterations like additions or deletions of parts or entire chromosomes.  According to Affymetrix, the most common use of microarrays is for determining copy number variation.  However according to James Clough, vice president of clinical and genomic services at Oxford Gene Technology, given the hundreds of syndromes associated with chromosomal rearrangements, the challenge will be to determine if a small chromosomal aberration has pathologic significance, given that microarray affords much higher diagnostic yield and speed of analysis than traditional microscopic techniques.  To address this challenge, Oxford Gene Technologies, PerkinElmer, Affymetrix, and Agilent all have custom designed microarrays to evaluate disease specific copy number and SNP (single nucleotide polymorphism) microarrays.  For example PerkinElmer designed OncoChip™ to evaluate copy number variation in more than 1.800 cancer genes.  Agilent makes microarrays that evaluates both copy number variation such as its CGH (comparative genomic hybridization) plus SNP microarrays.  Patricia Barco, product manager for cytogenetics at Agilent, notes these arrays can be used in prenatal and postnatal research and cancer, and “can be customized from more than 28 million probes in our library”.

Custom Tools and Software to Handle the Onslaught of Big Data

     There is a need for FDA approved diagnostic tools based on microarrays. Pathwork Diagnostic’s has one such tool (the Pathwork Tissue of Origin test), which uses 2,000 transcript markers and a proprietary computational algorithm to determine from expression analysis, the tissue of origin of a patient’s tumor.  Pathwork also provides a fast, custom turn-around analytical service for pathologists who encounter difficult to interpret samples.  Illumina provides the Infinium HumanCore BeadChip family of microarrays, which can determine genetic variations for purposes of biological tissue banking.  This system uses a set of over 300,000 SNP probes plus 240,000 exome-based markers.

     Tools have also been developed to validate microarray results.  A common validation strategy is the use of quantitative real-time PCR to verify the expression changes seen on the microarray.  Life Technologies developed the TaqMan OpenArray Real Time PCR plates, which have 3,072 wells and can be custom-formatted using their library of eight million validated TaqMan assays.

Making Sense of the Big Data: Bridging the Knowledge Gap using Bioinformatics

          The use of microarray has spurned industries devoted to developing the bioinformatics software to analyze the massive amounts of data and provide clinical significance.  For example companies such as Expression Analysis use their bioinformatics software to provide pathway analysis for microarray data in order to translate the data into the biology.  Using such strategies can also validate the design of microarrays for various diseases.

Foundation Medicine, Inc., a molecular information company, provides cancer genomics test solutions. It offers FoundationOne, an informative genomic profile to identify a patient’s individual molecular alterations and match them with relevant targeted therapies and clinical trials. The company’s product enables physicians to recommend treatment options for patients based on the molecular subtype of their cancer.

The Canadian Bioinformatics Workshops series recently offered a course on using bioinformatic approaches to analyze clinical data generated from microarray approaches (http://bioinformatics.ca/workshops/2012/bioinformatics-cancer-genomics-bicg).   The course objectives are described below:

Course Objectives

Cancer research has rapidly embraced high throughput technologies into its research, using various microarray, tissue array, and next generation sequencing platforms. The result has been a rapid increase in cancer data output and data types. Now more than ever, having the bioinformatic skills and knowledge of available bioinformatic resources specific to cancer is critical. The CBW will host a 5-day workshop covering the key bioinformatics concepts and tools required to analyze cancer genomic data sets. Participants will gain experience in genomic data visualization tools which will be applied throughout the development of the skills required to analyze cancer -omic data for gene expression, genome rearrangement, somatic mutations and copy number variation. The workshop will conclude with analyzing and conducting pathway analysis on the resultant cancer gene list and integration of clinical data.

Successful Examples of Clinical Ventures Integrating Bioinformatics in Cancer Treatment Decision –Making

The University of Pavia, Italy developed a fully integrated oncology bioinformatics workflow as described on their website and at the ESMO 2012 Congress meeting:







ESMO 2012




Translational research


A. Zambelli, D. Segagni, V. Tibollo, A. Dagliati, A. Malovini, V. Fotia, S. Manera, R. Bellazzi; Pavia/IT

  • Body

The ONCO-i2b2 project, supported by the University of Pavia and the Fondazione Salvatore Maugeri (FSM), aims at supporting translational research in oncology and exploits the software solutions implemented by the Informatics for Integrating Biology and the Bedside (i2b2) research centre, an initiative funded by the NIH Roadmap National Centres for Biomedical Computing. The ONCO-i2b2 software is designed to integrate the i2b2 infrastructure with the FSM hospital information system and the Bruno Boerci Biobank, in order to provide well-characterized cancer specimens along with an accurate patients clinical data-base. The i2b2 infrastructure provides a web-based access to all the electronic medical records of cancer patients, and allow researchers analyzing the vast amount of biological and clinical information, relying on a user-friendly interface. Data coming from multiple sources are integrated and jointly queried.

In 2011 at AIOM Meeting we reported the preliminary experience of the ONCO-i2b2 project, now we’re able to present the up and running platform and the extended data set. Currently, more than 4400 specimens are stored and more than 600 of breast cancer patients give the consent for the use of specimens in the context of clinical research, in addition, more than 5000 histological reports are stored in order to integrate clinical data.

Within the ONCO-i2b2 project is possible to query and merge data regarding:

• Anonymous patient personal data;

• Diagnosis and therapy ICD9-CM subset from the hospital information system;

• Histological data (tumour SNOMED and TNM codes) and receptor profile testing (Her2, Ki67) from anatomic pathology database;

• Specimen molecular characteristics (DNA, RNA, blood, plasma and cancer tissues) from the Bruno Boerci Biobank management system.

The research infrastructure will be completed by the development of new set of components designed to enhance the ability of an i2b2 hive to utilize data generated by NGS technology, providing a mechanism to apply custom genomic annotations. The translational tool created at FSM is a concrete example regarding how the integration of different information from heterogeneous sources could bring scientific research closer to understand the nature of disease itself and to create novel diagnostics through handy interfaces.


All authors have declared no conflicts of interest.

NCI has under-taken a similar effort under the Recovery Act (the full text of the latest report is taken from their website http://www.cancer.gov/aboutnci/recovery/recoveryfunding/investmentreports/bioinformatics:

Cancer Bioinformatics: Recovery Act Investment Report

November 2009

Public Health Burden of Cancer

Cancer is the second leading cause of death in the United States after heart disease. In 2009, it is estimated that nearly 1.5 million new cases of invasive cancer will be diagnosed in this country and more than 560,000 people will die of the disease.

To learn more, visit:

Cancer Bioinformatics Program Overview

Over the past five years, NCI’s Center for Biomedical Informatics and Information Technology (CBIIT) has led the effort to develop and deploy the cancer Biomedical Informatics Grid® (caBIG) in partnership with the broader cancer community.  The caBIG network is designed to enable the integration and exchange of data among researchers in the laboratory and the clinic, simplify collaboration, and realize the potential of information-based (personalized) medicine in improving patient outcomes. caBIG has connected major components of the cancer community, including NCI-designated Cancer Centers, participating institutions of the NCI Community Cancer Centers Program (NCCCP), and numerous large-scale scientific endeavors, as well as basic, translational, and clinical researchers at public and private institutions across the United States and around the world.  Beyond cancer research, caBIG capabilities—infrastructure, standards, and tools—provide a prototype for linking other disease communities and catalyzing a new 21st-century biomedical ecosystem that unifies research and care. ARRA funding will allow NCI to accelerate the ongoing development of the Cancer Knowledge Cloud and Oncology Electronic Health Records (EHRs) initiatives, thereby providing for continued job creation in the areas of biomedical informatics development and application as well as healthcare delivery.

The caBIG Cancer Knowledge Cloud: Extending the Research Infrastructure

The Cancer Knowledge Cloud is a virtual biomedical capability that utilizes caBIG tools, infrastructure, and security frameworks to integrate distributed individual and organizational data, software applications, and computational capacity throughout the broad cancer research and treatment community. The Cancer Knowledge Cloud connects, integrates, and facilitates sharing of the diverse primary data generated through basic and clinical research and care delivery to enable personalized medicine. The cloud includes information generated through large-scale research projects such as The Cancer Genome Atlas (TCGA), the cancer Human Biobank (caHUB) tissue acquisition network, the NCI Functional Biology Consortium, the NCI Patient Characterization Center, and the NCI Preclinical Development Pipeline, academic and industry counterparts to these projects, and clinical observations (from entities such as the NCCCP) captured in oncology-extended Electronic Health Records.  Through the use of the caBIG Data Sharing and Security Framework, the Cloud will support appropriate sharing of information, supporting in silico hypothesis generation and testing, and enabling a learning healthcare system.

A caBIG-Based Rapid-Learning Healthcare System: Incorporating Oncology-Extended Electronic Healthcare Records (EHRs)

The 21st-century Cancer Knowledge Cloud will connect individuals, organizations, institutions, and their associated information within an information technology-enabled cycle of discovery, development, and clinical care—the paradigm of a rapid-learning healthcare system. This will transform these disconnected sectors into a system that is personalized, preventive, pre-emptive, and patient-participatory.  To be realized, this model requires the adoption of standards-based EHRs. Presently, however, no certified oncology-based EHR exists, and fewer than 3 percent of oncologists with outpatient-based practices utilize EHRs. caBIG has recently established a collaboration with the American Society of Clinical Oncology (ASCO) to develop an oncology-specific EHR (caEHR) specification based on open standards already in use in the oncology community that will utilize caBIG standards for interoperability. NCI will implement an open-source version of this specification to validate the specification and to provide a free alternative to sites that choose not to purchase a commercial system. The launch customer for the caEHR will be NCCCP participating sites. NCI will work with appropriate entities to provide a mechanism for certifying that caEHR implementations are consistent with the NCI/ASCO specification.

Bards Cancer Institute has another clinical bioinformatics program to support their clinical efforts:

Clinical Bioinformatics Program in Oncology at Barts Cancer Institute at Barts and the London School of Medicine


BCI HomeCancer Bioinformatics


Why we focus on Cancer Bioinformatics

Bioinformatics is a new interdisciplinary area involving biological, statistical and computational sciences. Bioinformatics will enable cancer researchers not only to manage, analyze, mine and understand the currently accumulated, valuable, high-throughput data, but also to integrate these in their current research programs. The need for bioinformatics will become ever more important as new technologies increase the already exponential rate at which cancer data are generated.

What we do

  • We work alongside clinical and basic scientists to support the cancer projects within BCI.  This is an ideal partnership between scientific experts, who know the research questions that will be relevant from a cancer biologist or clinician’s perspective, and bioinformatics experts, who know how to develop the proposed methods to provide answers.
  • We also conduct independent bioinformatics research, focusing on the development of computational and integrative methods, algorithms, databases and tools to tackle the analysis of the high volumes of cancer data.
  • We also are actively involved in the development of bioinformatics educational courses at BCI. Our courses offer a unique opportunity for biologists to gain a basic understanding in the use of bioinformatics methods to access and harness large complicated high-throughput data and uncover meaningful information that could be used to understand molecular mechanisms and develop novel targeted therapeutics/diagnostic tools.

Developing Criteria for Genomic Profiling in Lung Cancer:

A Report from U.S. Cancer Centers

In a report by Pao et. al., a group of clinicians organized a meeting to standardize some protocols for the integration of microarray and genomic data from lung cancer patients into the clinical setting.[1]  There has been ample evidence that adenocarcinomas could be classified into “clinically relevant molecular subsets” based on distinct genomic changes.  For example EGFR (epidermal growth factor receptor) exon 19 deletions and exon 21 point mutations predict sensitivity to tyrosine kinase inhibitors (TKIs) like gefitinib, whereas exon 20 insertions predict primary resistance[2].

However, as the authors note, “mutational profiling has not been widely accepted or adopted into practice in thoracic oncology”.  

     Therefore, a multi-institutional workshop was held in 2009 among participants from Massachusetts General Hospital (MGH) Cancer Center, Memorial Sloan-Kettering Cancer Center (MSKCC), the Dana-Farber/Bingham & Women’s Cancer Center (DF/BWCC), the M.D. Anderson Cancer Center (VICC), and the Vanderbilt-Ingram Cancer Center (VICC) to discuss their institutes molecular profiling programs with emphasis on:

·         Organization/workflow

·         Mutation detection technologies

·         Clinical protocols and reporting

·         Patient consent

In addition to the aforementioned challenges, the panel discussed further issues for developing improved science-driven criteria for determining targeted therapies including:

1)      Including pathologists into criteria development as pathology departments are usually the main repositories for specimens

2)      Developing integrated informatics systems

3)      Standardizing new target validation methodology across cancer centers


1.            Pao W, Kris MG, Iafrate AJ, Ladanyi M, Janne PA, Wistuba, II, Miake-Lye R, Herbst RS, Carbone DP, Johnson BE et al: Integration of molecular profiling into the lung cancer clinic. Clinical cancer research : an official journal of the American Association for Cancer Research 2009, 15(17):5317-5322.

2.            Wu JY, Wu SG, Yang CH, Gow CH, Chang YL, Yu CJ, Shih JY, Yang PC: Lung cancer with epidermal growth factor receptor exon 20 mutations is associated with poor gefitinib treatment response. Clinical cancer research : an official journal of the American Association for Cancer Research 2008, 14(15):4877-4882.

Other posts on this website on Cancer and Genomics include:

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