This past week, I attended the 2012 Pacific Symposium on Biocomputing, a week of science in the sun. For me, the week started with the “Systems Pharmacogenomics: Bridging the Gap” workshop. The session started with the systems theme from Trey Ideker, discussing epistatic interactions in yeast and how to apply the results to GWAS and eQTLs, bringing up challenges in the complexity of the analysis. Fortunately, model organisms are a bit easier to work with, and generating all pairwise combinations of, say, gene knockouts in yeast, is a difficult, but not unreasonable task. Pankaj Agrawal brought a perspective from industry and how systems are being used at GSK. He described the use of high-throughput analysis, like the Connectivity Map and GWAS data, for pharmaceutical purposes like drug repurposing: when a GWAS hit lies in a pharmacogene (for a different phenotype than the drug’s indication), seems like a good time to think about the drug for other purposes.
While the first talks were highly optimistic, the reality became slightly more apparent in Stephane Bourgeois’s talk on the International Warfarin Pharmacogenomic Consortium (IWPC)’s meta-analysis of warfarin data. Warfarin is a poster-child of genomic personalized medicine, as ~55% of variance in stable warfarin dose can be explained by clinical and genetic factors. While this would be marked as a success by most people studying complex traits, it suffers the same problems when trying to explain the remainder of the variance. Bourgeois presented a large meta-analysis that was only able to explain incrementally more variance, despite increases in power. Here, there is a need to be more clever and integrate systems approaches (like the epistasis ones Trey Ideker talked about) to generate results of clinical significance (not to mention the burden is greater: getting a drug label approved by the FDA is slightly more difficult than getting a paper in NEJM). Russ Altman talked about using very different sources of data, including molecular data, text mining, expression data, and adverse event reports, for pharmacogenomic inference. Afterwards, the discussion brought up some new challenges. In single celled organisms, the consequence of a gene-gene interaction may be apparent, but when it comes to systems pharmacogenomics, the presence of multiple different cell types in multiple different organs complicates matters further. This will require new methods, such as rapid mutagenesis to simulate the effect of rare variants.
The evening got interesting when a debate on the ethics of informed consent and return of genetic results to patients was discussed. The premise involved a resolution where patients would be sequenced and their genetic results linked to their EMR and shared with researchers. Greg Hampikian and Eric Meslin debated the merits and downsides of this resolution, asking the audience to move to two sides of the room depending on their stance at the time. The concepts of patient “dignity” were discussed, as well as data privacy and openness and who should be allowed access to the data. The notion of “genetic exceptionalism” was challenged and scientists’ motives were questioned. The concept of an “opt-out” system was discussed, where proponents did not share the fears that the opponents did, while the opponents lamented the lack of proper informed consent. The debate was heated, to say the least.
The next day, Elaine Mardis’s keynote talk brought a number of applications of computational methods to clinical outcomes. Methods to detect variation in heterogeneous samples, as well as sequencing followed by re-sequencing, were used for characterizing relapse. The rest of the day involved text-mining approaches to pharmacogenomics, extracting drug-gene and drug-drug relationships from mining pharmacogenomic literature, as well as a followup to the Systems Pharmacogenomics workshop, which was quite practical, discussing issues of applying results from a model organism to humans (how to get from a pathway in one species to the homologous pathway in another) and using electronic medical records for biomedical validation.
At the discussion following the Personalized Medicine session (at which I presented the Interpretome platform), the privacy discussion resurfaced. We discussed issues of using genetic information in courses such as Stanford’s course in Personalized Medicine, as well as individual’s reactions to obtaining genetic information and whether individuals and patients undergo anxiety or change behaviors in response to the information.
All in all, the week was an interesting look into the interplay of complex mathematical modeling, biological discovery, and the practical and ethical issues involved (not to mention a great week in Hawaii with a great group of scientists). It is a conference I would highly recommend and am looking forward to going back soon.