Viva La Evidence

We came across this brilliant music video  “Viva La Evidence” by Dr. James McCormack. It’s a parody of Coldplay's Viva La Vida and the lyrics cover many important topics of evidence based medicine, its history, principles and aims. What a fun way to explain the hierarchy of evidence, systematic review and questions to ask in critically appraising a study!

In the past, medicine relied heavily on physicians' experiences and prescribing contained an element of “trial and error” due to the varied individual response within a population. In the current post genomic era, this approach would not be optimal and a systematic, evidence-based approach has emerged as a solution to integrate the best research evidence with clinical expertise, and, patient values and expectations. Preferably, medication prescribing decisions are based not only on population data, but also on the individual patient’s genetic profile and drug metabolizing characteristics. Pharmacogenomics is the study of how the genetic makeup of an individual affects the efficacy and toxicity of drugs and can help move us closer towards the goal of “personalized medicine”.

For fans of evidence based medicine, check it out at :

http://www.youtube.com/watch?v=QUW0Q8tXVUc

Venlafaxine Pathway published in PG&G

Venlafaxine is a serotonin-norepinephrine reuptake inhibitor (SNRI) marketed for the treatment of depression disorders.

We recently have published the Venlafaxine Pathway summary in the Pharmacogenetics and Genomics Journal.

The article describes the metabolism of venlafaxine, with cytochrome P450 2D6 (CYP2D6) being the major enzyme involved in the formation of its metabolites. The pharmacokinetics of venlafaxine is affected by the CYP2D6 metabolizer phenotype.

Read the article for a discussion about the influence of genetic variations on venlafaxine metabolism and treatment outcome.

Find out more...

View the venlafaxine pathway on PharmGKB.

Read the publication:

PharmGKB summary: venlafaxine pathway.

Sangkuhl K, Stingl JC, Turpeinen M, Altman RB, Klein TE.

Pharmacogenet Genomics. 2013 Oct 14

PMID: 24128936

View all pathways on PharmGKB.

NIH funds ClinGen: a resource to establish which genetic variants are clinically relevant

The NIH (NHGRI and NICHD) has awarded more than $25 million over the course of 4 years for the development of the Clinical Genome Resource (ClinGen). The aim of ClinGen is to provide a framework for the evaluation of genetic variants that are clinically relevant, working with the NCBI which will distribute this information through the ClinVar database.

PharmGKB is very excited to be part of ClinGen through the program led at Stanford by co-investigators Carlos Bustamante, Mike Cherry and Teri Klein (for the PGx aspects). PharmGKB will be responsible for curating clinically relevant variants associated with drug response.

Further details:

ClinGen will be developed by a consortium made up of 3 groups, each with different but complementary goals:

1.     Development of standards for data collection and depositing into ClinVar, development of standards to analyze variants and determine whether they cause disease or are medically useful.

Investigators from: Brigham & Women’s Hospital, Boston; Geisinger Health System, Danville, PA; University of Utah, Salt Lake City; University of California, San Francisco.

2.     Develop and apply computing methods to process and analyze variants more effectively, determine which genomic variants have strong evidence for disease risk/ clinically important and prioritize variants for further study utilizing informatics tools and databases, and improve predictions for which variants are associated with disease-risk in non-white populations.      

Investigators from: Stanford University, Palo Alto, CA; Baylor College of Medicine, Houston.

3.     Defining categories of clinical relevance that can be assigned to genetic variants and study ways to integrate this into electronic medical records.

Investigators from: University of North Carolina, Chapel Hill; American College of Medical Genetics and Genomics, Bethesda, MD.

Read more:

•  NIH news
• Genomeweb

The Curation Economy

In an October 7th article in The Huffington Post blog section, Steve Rosenbaum discusses the development of a so-called "Curation Economy". Rosenbaum writes that the creation of content on the web is increasing at an exponential rate, leading to a massive amount of raw and unfiltered data. This includes information ranging from "Likes" on Facebook and YouTube videos, to content such as online news articles, and blog posts like this one. So how do internet users take advantage of this avalanche of information without being overwhelmed? Rosenbaum's answer is through the use of curators.

Though this post discusses general curation of web content, many of his points apply directly to PharmGKB, and the way that the knowledgebase manages its own deluge of information. The era of personalized medicine has brought with it a large increase in studies analyzing pharmacogenetic (PGx) associations. Though this is great for the field, the usefulness of this information is dependent on whether it can be organized and presented in a way that is helpful for all types of interested parties, such as researchers, students or clinicians. PharmGKB addresses this need through the work of curators, who are responsible for annotating, aggregating and integrating PGx study results. Through this process, curators are able to make vast quantities of PGx information useful, accessible and understandable to all types of users. Rosenbaum also makes an important note about how technology is necessary to assist in the finding, filtering and validating of content, and how curation cannot be an exclusively human enterprise. This is certainly true for PharmGKB, since manually keeping pace with the large volume of literature being published is a significant challenge. Current work in natural language processing (NLP) by the knowledgebase aims to assist curators in the identification and extraction of PGx relationships.

This article reminds us that though the exponential creation of data is exciting prospect, is it essential to be able to manage this information, and extract meaningful and relevant data. This is true for normal web users, pharmacogenetic researchers, and people in various fields worldwide.

You can read Steve Rosenbaum's article here or here, and visit PharmGKB here.

CPIC publishes guidelines for IFNL3 (IL28B) and peginterferon alpha based regimens

Guidelines regarding the use of IFNL3 (formerly known as IL28B) genotypes in peginterferon alpha based therapies have been accepted for publication in the Clinical Pharmacology and Therapeutics by the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Hepatitis C virus (HCV) infection affects more than 150 million people worldwide and is one of the leading causes of cirrhosis and hepatocellular carcinoma. Treatment for chronic HCV infection includes combination pegylated-interferon alpha 2a or 2b (PEG-IFN α) and ribavirin (RBV) therapy as well as protease inhibitors. The response rate varies greatly among patients and had been especially low for patients with HCV genotype 1 and 4.  Currently IFNL3 (IL28B) variations (rs12979860 and rs8099917) are the strongest baseline predictor of response to PEG-interferon-α and ribavirin therapy in HCV genotype 1 patients.  Patients with the favorable response genotype (rs12979860 CC) have increased likelihood of response (higher SVR rate) to PEG-IFN α and RBV therapy as compared to patients with unfavorable response genotypes (rs12979860 CT or TT). With protease inhibitor regimens, the IFNL3 genotype predicts response and also predicts eligibility for the shorter durations of therapy. The IFNL3 genotypes along with other clinical and genetic factors may guide patients and clinicians in their treatment decisions. 


For details, see the CPIC guideline, accepted article preview and supplement for IFNL3 (IL28B) and peginterferon alpha based regimens.

For other CPIC guidelines see the list of CPIC publications and guidelines in progress.

 

CYP2D6 genotype and tamoxifen response: meta-analysis of heterogeneous population

Tamoxifen treatment results in substantial inter-individual variability of outcome, possibly due to genetic variability in CYP2D6.  The clinical usefulness of CYP2D6 genotyping for prediction of tamoxifen outcome has not been established because of conflicting reports.   This controversy is well illustrated by secondary analyses from 3 large adjuvant tamoxifen trials (ATAC, BIG 1-98, and ABCSG 8), which came to different conclusions regarding the association between CYP2D6 genotype variants.  The International Tamoxifen Pharmacogenomics Consortium (ITPC) was established to aggregate the worldwide experience regarding CYP2D6 and the clinical outcomes of tamoxifen as adjuvant therapy for breast cancer. It comprises twelve research projects from nine countries and three continents that contributed clinical and genetic data for a total of 4,973 breast cancer patients treated with tamoxifen. Investigators from ITPC performed a meta-analysis on data from 4,973 tamoxifen treated patients and the result was just released in Journal Clinical Pharmacology and Therapeutics. The meta-analysis showed that “CYP2D6 poor metabolizer status was associated with poorer Invasive Disease-Free Survival (IDFS: HR=1.25; 95% CI 1.06, 1.47; P=0.009)” in patients meeting strict criterion. “However, CYP2D6 was not statistically significant when tamoxifen duration, menopausal status, and annual follow-up were not specified (Criterion 2, n=2443; 49%; P=0.25) nor when no exclusions were applied (Criterion 3, n=4935; 99%; P=0.38)”. This study demonstrates the complexity of performing a retrospective biomarker study. The lack of effect in the entire heterogeneous population highlights the need for prospective studies to fully establish the value of CYP2D6 genotyping in tamoxifen therapy.

The data used in the meta-analysis are available for download on the PharmGKB.

 --Blog post written by Li Gong

New EGFR VIP Summary Published in PG&G

The epidermal growth factor receptor (EGFR) is involved in the growth of many epithelial malignancies, such as non-small-cell lung cancer (NSCLC), pancreatic and colorectal cancer. It has therefore been a key target for the development of cancer therapeutics. These include tyrosine kinase inhibitors (gefinitib and erolinitib) and monoclonal antibodies (cetuximab and panitumumab).
   
The PharmGKB Very Important Pharmacogene (VIP) Summary for EGFR provides a concise overview of the role of EGFR as a drug target in the treatment of cancer. Key somatic and germline EGFR polymorphisms associated with drug responsiveness and acquired drug resistance are discussed.

• View the interactive EGFR VIP Summary on PharmGKB
• See FDA and EMA drug labels linked to EGFR
• View EGFR variants associated with drug response and resistance

Read the publication:
PharmGKB summary: very important pharmacogene information for the epidermal growth factor receptor. Hodoglugil U, Carrillo MW, Hebert JM, Karachaliou N, Rosell RC, Altman RB, Klein TE.
Pharmacogenet Genomics. 2013 Aug 18. (Epub ahead of print)