Thursday, January 28, 2016

January SNPits Summary

The January issue of SNPits, the University of Florida’s Personalized Medicine Program e-newsletter, summarizes pharmacogenetics studies.

The first is a 2009 article from the journal Gastroenterology. The authors conducted a multi center, randomized controlled trial whereby patients were given high or low-dose celecoxib, or placebo, and were followed up with colonoscopies at one and three years. The authors reported that risk of cardiovascular adverse events may be influenced by CYP2C9 genotype, and that risk of developing adenoma may not differ by high vs. low dose celecoxib for all genotypes. The authors conclude that further research into the implications of CYP2C9 genotype for the routine use of celecoxib is needed.

Read the summary here:

The second is from 2015 and comes from the journal Thrombosis Research. The authors measured residual platelet reactivity (RPR) in coronary heart disease patients who were on duel anti platelet therapy (aspirin and clopidogrel). Some patients were also taking esomeprazole, a proton pump inhibitor. Clopidogrel is activated by CYP2C19, and esomeprazole is a major substrate of CYP2C19. The authors investigated whether concomitant use of esomeprazole and clopidogrel, as well as whether polymorphisms in CYP2C19 in patients taking esomeprazole and clopidogrel had any influence on RPR or adverse events. The authors reported that concomitant use of esomeprazole and clopidogrel had no significant effect on RPR, or outcomes regardless of CYP2C19 phenotype as compared to patients not taking esomeprazole. However, RPR did vary according to the number of CYP2C19 loss of function alleles in a patient, regardless of esomeprazole use.

Read the summary here:

Tuesday, January 19, 2016

The Pharmacogenetic Phenome Compendium, A Chemical–Genetic Interaction Map

Development of new drugs is a complicated and difficult process, with many initially promising compounds falling by the wayside due to unforeseen off-target or genotype-specific effects. In an effort to address some of these problems a new paper by Breinig et. al. describes a high-throughput process for screening small molecules against a library of cancer cells with known gene knockouts in key signaling pathways. Using 384-well plates for testing the drug-cell combinations and an automated image analysis pipeline, the authors tracked changes in twenty different key drug-specific phenotypic features such as the cell count, shape, and DNA appearance.  Based on the drug-specific phenotypic differences the authors produced a chemical-genomic interaction map of the cancer cells.  These data were used to examine how drugs perturb genetic networks and to investigate cross-talk between pathways, as well as to explain some of the observed off-target activity and drug synergism. The group has released this data online as the Pharmacogenetic Phenome Compendium (PGPC) for other researchers to use.

Thursday, January 14, 2016

Constellation: tool for automated CYP2D6 phenotype assignment from WGS

A team from Children’s Mercy and the University of Missouri in Kanas City including Andrea Gaedigk and Greyson Twist published an article about a CYP2D6 phenotype assignment tool in Genomic Medicine ( Constellation, a probabilistic scoring system, enables automated ascertainment of CYP2D6 activity scores based on CYP2D6 diplotypes from whole-genome sequences (WGS).

CYP2D6 is involved in the metabolism of about 25% of drugs in clinical use and genetic variations leading to functional consequences affecting drug efficacy and risk of adverse events.  
The gene is highly polymorphic with over 100 allelic variants (star alleles) assigned including CYP2D6 copy number variations and rearrangements with the neighboring CYP2D7. This high degree of variation, high sequence similarity to CYP2D7 and CYP2D8, GC content, repetitive and low-complexity sequences are challenges in analyzing this locus.

The performance of the developed algorithm is evaluated by comparing the CYP2D6 diplotype assigned by the probabilistic WGS analysis using Constellation with the diplotype determined by manual integration (consensus reference) of results obtained by quantitative copy-number assessment, a panel of TaqMan genotype assays, and Sanger sequencing of long-range genomic PCR in 61 samples. Phenotype prediction is consistent between the consensus reference and Constellation calls with the exception of three cases. Constellation was able to accurately identify all poor and ultrarapid metabolizers in WGS data. 

The authors anticipate Constellation to be extensible to identify variations in other pharmacogenomic-relevant genes, enabling future uses of WGS data.

Wednesday, January 13, 2016

Cisplatin FDA label changes

In 2013 we blogged about the controversy with the FDA labeling for cisplatin and its warning for TPMT variants and increased risk of ototoxicity in children. The 2012 label stated:

Certain genetic variants in the thiopurine S-methyltransferase gene (e.g., TPMT*3B and TPMT*3C) are associated with an increased risk of ototoxicity in children administered conventional doses of cisplatin...Children who do not have one of these TPMT gene variants remain at risk for ototoxicity. All pediatric patients receiving cisplatin should have audiometric testing at baseline, prior to each subsequent dose, of drug and for several years post therapy.”
The label change appeared to be based on a single study from 2009.  Several years have passed and there is still very limited data in the public domain about TPMT and risk for cisplatin-induced ototoxicity: four papers comprising seven studies and a meta-analysis [PMID:19898482] [PMID:23820299] [PMID:23588304] [PMID:25551397]. The meta-analysis found no association with any of the TPMT variants and that the studies were significantly heterogeneous in terms of cancer types, ethnicities, age and co-treatments, all of which influence risk for ototoxicity.

The FDA has requested changes to the cisplatin label removing the whole Pharmacogenomics section, to now state :
“Genetic factors (e.g. variants in the thiopurine S-methyltransferase [TPMT] gene) may contribute to cisplatin-induced ototoxicity; although this association has not been consistent across populations and study designs.”
This better reflects the current uncertainty about this gene-drug relationship and is more in line with the level of evidence we have assessed in our clinical annotations for the TPMT variants and cisplatin. 

Sunday, December 20, 2015

ACMG Issues a Revised Position Statement Regarding DTC Genetic Testing

The American College of Medical Genetics (ACMG) recently revised its position statement regarding direct-to-consumer (DTC) genetic testing. Briefly, the ACMG recommends that:

  • Knowledgeable professionals order the genetic test, which should be undertaken by a Clinical Laboratory Improvement Amendments (CLIA) accredited laboratory.

  • Board certified genetics experts, such as clinical geneticists, or genetic counselors should be available to assist with test interpretation

  • DTC genetic tests should incorporate family history and patient-specific information into result reports, which should be presented in a manner that is comprehensible by the consumer.

  • Consumers should be informed of the possibility of incidental findings and of the implications of these incidental findings for relatives.

  • The scientific evidence underlying the validity and utility of a genetic test should be explicitly stated. 

  • Consumers should be made aware of where test results will be stored, as well as the identities of the parties that will have access to the test results. 

Read the revised ACMG position statement.

Thursday, December 17, 2015

DIGITizE Suggests Implementing 2 CPIC Guidelines in CDS

The DIGITizE Action Collaborative has suggested that Clinical Decision Support (CDS) be implemented based on CPIC's TPMT/azathioprine and HLA-B/abacavir guidelines.  DIGITizE (Displaying and Integrating Genetic Information Through the EHR) is a collaborative group of the Institute of Medicine consisting of key stakeholders interested in integrating genomic information into electronic health records (EHR).  As part of their efforts to create support for a few very specific use cases, they have chosen to focus on the impact of (1) HLA-B*57:01 on abacavir hypersensitivity and (2) TPMT alleles on azathioprine dosing, consistent with the CPIC guidelines.  They recommend having patient genotypes in the EHR before prescribing these medications.

CPIC guidelines can be found on PharmGKB for TPMT/azathioprine and HLA-B/abacavir.

Friday, December 11, 2015

Combining large PGx datasets from cancer cell lines

Testing cancer cell lines in vitro for drug sensitivity is a cornerstone of preclinical drug development. Large publically available datasets can be found at The Genomics of Drug Sensitivity in Cancer Project (GDSE) and The Cancer Cell Line Encyclopedia (CCLE).

Studies attempting to combine large public datasets and analyzing for correlation questioned the reliability of the data due to limited concordance, reported in [PMID: 24284626], discussed in [PMID:24284624] and a confirmation study here.

A new report in Nature describes different methods to analyze the data from CCLE and GDSE and concludes that “data from either study yields similar predictors of drug response” [PMID:26570998].

These papers demonstrate the continuing difficulty trying to compare across large datasets. Such problems include comparing different experimental protocols and measurements for drug sensitivity across studies, trouble matching the drug and cell line names to ensure like comparison, discordance in the genotyping data, and drugs that had few examples of cell lines that were drug sensitive.  As always, attention to detail in the documentation and description of the experiments can help mitigate some of these difficulties. While development of standard testing protocols and data curation and reporting frameworks may lead to better validation of drug response predictors going forward there will always be the need for methods to filter the noise that is inevitable in large datasets.