Thanks to all who contributed and showed interest in my talk today at the AMIA Joint Summits on Translational Science. Thanks also to the amazing tweet army that did real time updates during the talk. I am pleased to post this year’s slides. As usual, I take responsibility for any mistakes, mischaracterizations, or misjudgments. The goal here is just to show the excitement of translational bioinformatics and some of the cool stuff that is happening.
The slides from the 2013 annual review of TBI are available here.
XML files (compatible with ENDNOTE and others) and a PDF bibliography are also available for the final list of papers (40 papers, xml, pdf), finalists (97, xml, pdf), semifinalists (240, xml, pdf), and quarterfinalists (348, xml, pdf).
Post suggestions for articles here any time, I have a place to keep them for when I turn my attention to this early each year.
I find it sometimes (always) easier to write 140 characters vs. an entire blog entry. But I will use this for expanding points, etc…I keep tweets to professional issues, as a rule.
I am posting my translational bioinformatics annual review 2012 slides and also bibliographies for the final 100 and then final 25 papers I presented yesterday at the AMIA TBI Summit. Thanks to all who helped out with this, but as always I take full responsibility for missing important work. I don’t think I highlighted anything undeserving, but who knows!
I gave my annual “Year in Review” talk to the AMIA Translational Bioinformatics Summit yesterday. It covers papers from approximately Feb 2010 to Feb 2011. So it is really a review for 2010, but I deliver it in 2011, so thus the naming. We have made great progress and so it was hard to choose papers to highlight. The slides are here. I have also posted the slides from previous years.
As you may know, I review the field of translational bioinformatics each year in an attempt to highlight key papers and trends from the year. I present this review at the AMIA Summit on Translational Bioinformatics. I review papers published in the period from January 2010 through February 2011. I am now accepting nominations for excellent papers that deserve highlighting, you can send your own work, but nominations of other people’s work is even more compelling.
I define “translational bioinformatics” fairly broadly as any informatics work developing or applying methods that link basic molecular, genetic, and cellular data to clinical concepts such as drugs, diseases, symptoms and patients.
If you would like to get a sense for the papers I have highlighted in the past, you can see them on a previous post to this blog.
I would like your nominations (add them as comment here, or email me) by February 15, so that I can narrow things down to the final list. As always, I will make the list available as the slides from my talk at the AMIA TB summit, as well as an endnote library.
Thanks and Happy New Year!
I have asked one of my students who is acting as a Teaching Assistant for a new course on personalized genomics at Stanford to comment on the recent Government Accounting Office testimony on Direct-to-Consumer Testing.
On July 22, 2010, the Government Accountability Office (GAO) released a testimony on Direct To Consumer (DTC) Genetic Testing companies. In the testimony, the GAO sent DNA from 5 anonymous donors to 4 anonymous DTC companies. At times, the results were astonishing, such as claims made by some of the companies taking advantage of ill-informed customers to sell custom supplements “based” on genetic test results. However, the testimony also revealed a fundamental disconnect in communication between science, medicine, and the public: a disconnect that has always existed, but is now being brought to the public eye, as recent technologies have begun to bridge the gap between scientists and consumers.
To preface, it is of course outrageous that anyone interpret a DTC genetic test as a diagnostic test (at least in their current form). Analysis of a personal genome is not a medical test. For the bulk of genetic markers, having a “high risk” allele for a disease is not even close to a diagnosis of the disease. It is simply an indicator that on average, in the particular population chosen by a research study (which are often small populations or populations selected to be enriched for a particular disease), individuals with that particular allele had a higher incidence of the disease in question than individuals without the allele (i.e. the “high risk” allele has a higher odds for the disease than the other). The companies then translate into a overall disease risk, which adjusts the prior probability of getting the disease by this odds ratio. Depending on which studies and genes/alleles a company takes into consideration, this risk may be vary considerably. In any case, the report provides a final probability of getting a disease, which may or may not actually be the same as the actual outcome. Just as an individual can get lung cancer without smoking, one can get diabetes even with a below average risk.
This is not to say all the calculations of the disease risk interpretations of all these companies are flawless (we haven’t verified the math and studies in all these companies), but the fact remains that there are legitimate scientific differences on how to interpret the data. While no particular method is outright “wrong,” there are better and worse ways to analyze results of genetic tests and competition among DTC companies for the highest quality interpretations should become increasingly important. Of course, it is objectively difficult to measure which interpretation is “best,” but this will change as more data become available both in predictive claims and possibilities for validation.
According to the testimony, the Department of Health and Human Services’ Secretary’s Advisory Committee on Genetics, Health, and Society notes that “[practitioners] cannot keep up with the pace of genetic tests and are not adequately prepared to use test information to treat patients appropriately.” While this may be true at present, this need not stop information from genetic tests from entering the clinic. A general practitioner may not be able to keep up with the latest advances in neurosurgery, but that’s where the specialist system thrives. In any case, just as clinicians are expected to demonstrate a basic level of competence in immunology in medical school, genetics must be treated the same way. Here at Stanford, a pilot project was launched to teach medical students about the field of genetic testing in an interactive classroom setting with state-of-the-art methods for analysis of personal genotypes.
Deceptive marketing, including “personalized supplements” (allegedly with celebrity endorsements) and drugs that may “repair damaged DNA” (allegedly called “epigenetics”), to say nothing of surreptitious testing and scientifically nonsensical claims, are inexcusable and irresponsible practices for any company, not limited to this particular market. However, the delicate matter of genetic testing and its use as a clinical guidance tool is a concept that must be explored further. The GAO uses the phrase “misleading test results”: it should be noted that while the current implementation of the reporting of test results may be in certain ways misleading, the framework of genetic testing is not in itself misleading. Proper interpretation is based in the same mathematical and biological context as much of today’s medicine. There is great potential for the use of genetic tests in the clinic, so long as results are carefully interpreted. While this was often limited to geneticists in the past, we hope that this can be soon accomplished by physicians and the public.