Benefits to a 3-tier system for DTC genetic testing

The popular genetic testing company 23andMe has two rankings for genetic associations present in the scientific literature: Established Research Reports and Preliminary Research Reports.  The relatively recent GAO report on genetic testing claimed that 23andMe provided "reports that showed conflicting predictions for the same DNA and profile, but did not explain how to interpret these different results" (and the response from 23andMe can be viewed here).  However, I think this system provides a useful basis to improve education about genomics research and genetic testing.  In fact, I think it would be even better to provide 3-tier system to describe genetic associations.

In particular, I think genetic associations can be classified as "Based upon Preliminary Evidence," "Based Upon Reproducible Evidence," or "Therapeutically Useful."  In this case, I would consider "Reproducible" associations to be equivalent to 23andMe's "Established Research Reports."  I would consider "Therapeutically Useful" associations to be those that have been proven useful in terms of significantly reducing patient mortality or morbidity.

The "Therapeutically Useful" classification is important because there could be many reasons why individuals with a particular mutation may have a increased risk of dying from cancer that is statistically significant, but information about that particular mutation may not be important for informative for making medical decisions.  For example, models for genetic predisposition may be complicated for certain diseases, and it may be necessary to incorporate currently unknown information about other mutations in order to provide an accurate estimate of risk to develop a given disease.   Also, there are cases where environmental factors may be more important than genetic factors.  And the list goes on.

In practice, I think the FDA could play a role in helping define the third category of genetic tests, and I can think of at least two ways to implement this.  First, genetic testing companies could post some sort of "FDA-approved" icon for tests that have shown to produce positive results when applied in a clinical setting.  Second, the FDA could post a listing of genetic tests that have been proven useful through clinical trails and provide a list of appropriate treatments that correspond to a given test result.

There are benefits to having access to genetic information that doesn't necessarily meet the criteria for a "Therapeutically Useful" test.  For example, there may be no "FDA-approved" diagnostic for a particular problem and an experimental prediction may be the best possible resource.  Furthermore, the FDA has indicated that it does not see a need to regulate the release of "raw genetic information," and it is acceptable to communicate information about genetic associations though other means.  For example, the FDA would never censor an article in the New York Time about a new discovery or preliminary result.  Genetic tests that are"Based upon Preliminary Evidence" or "Based Upon Reproducible Evidence" are basically indicating that "Hey, you have this mutation that has been described in the scientific literature."  If it is too confusing to provide separate predictions for each tier of genetic associations, then genetic testing companies should at least be able to provide a list of publications describing a mutation of interest (and possibly provide a brief summary of the findings).

Information from DTC genetic testing can also fundamentally increase understanding about human genetics and contribute to scientific research, as indicated by 23andMe's publication in PLoS Genetics.  In fact, 23andMe could actually help establish "Therapeutically Useful" tests if customers could upload clinical information that is directly incorporated into their models.  Likewise, companies like PatientsLikeMe could help test the therapeutic value of genetic tests if patients could upload their genetic information

In general, I think individuals should take a much time as they reasonably can to research a topic prior to making a life-altering decision.  Even if a diagnostic is 98% accurate, what if you happen to be in the minority that gets a false-positive?  Even well-established tests can have false positives.  Important information can be gained from independent tests, consulting with a physician or genetic counselor (or getting second opinions from multiple professionals), or even talking to friends who might have went though similar situations.

Although I understand that a "3-tier" system for genetic testing may be confusing for people at first,  I think this system could be a useful tool to educate the public about genetic testing and encourage individuals to take a more active role in making medical decisions.  In fact,  a recent post by John Timmer concluded with the suggestion that heavy regulation of the DTC testing industry will probably not be necessary if a sufficiently large proportion of the general public took the initiative to better educate themselves.

Paper on Microarray Analysis of "Watchful Waiting" Prostate Cancer Cohort

Last week, I noticed an interesting paper that was published in BMC Medical Genomics this March.

The authors of this paper wanted to use microarrays to develop an effective prostate cancer diagnostic defined by gene expression patterns.  More specifically, the authors were studying tissue samples taken from the Swedish "Watchful Waiting" cohort.  This large collection of patients developed prostate cancer between 1977 and 1999.  The length of follow-up time for clinical data recorded in this study is significantly longer than has been used in any other attempt to develop a microarray-based prostate cancer diagnostic.  In some cases, clinical information about individuals in this cohort was recorded over 2 decades before the microarray was even invented.

There were two aspects of this study I found particularly interesting.  First, it is pretty rare to find a cohort studied as carefully as the Watchful Waiting cohort.  Second, the authors concluded that "none of the predictive models using molecular profiles significantly improved over models using clinical variables only."

The findings of this study seem to agree with an earlier post where I mentioned two earlier studies to show that GWAS data did not significantly improve risk models for heart disease and type II diabetes.  Although those studies utilized a fundamentally different tools for analysis (the earlier studies looked at genomic sequence whereas this newer study examined gene expression patterns), it was interesting to see examples of cases where genomic technology has not been able to improve upon existing clinical diagnostics.

Of course, these studies leave the reader asking several important questions.  For example, why do these large studies result in negative results?  How long will it take for genomic research to make substantial impacts on clinical diagnostics and therapeutics?  What are the practical limits for developing applications based upon medical genomic research?

I'm not going to even pretend like I know the answers to all of these questions.  Although I'm certain that genomic research will ultimately result in disappointing results for some major studies, this paper did provide some hope that genomic research can still pave the way for future breakthroughs.

For example, the authors discuss how there is significant heterogeneity within and between prostate cancer samples - expression patterns in one region of a given tumor can be significantly different than other regions of that same tumor, and this makes it especially difficult to compare gene expression patterns between different tumors.  It is also important to determine the optimal time to take tissue samples for analysis; diagnostics taken too far in the advance will not yield clinically useful information, and feasible treatments may not even exist for results of a diagnostic applied during a late stage of cancer development.  The authors also point out that several other diagnostic microarray studies resulted in similar lists of prostate cancer biomarkers.  In other words, microarray analysis can probably yield reasonably accurate results - the problem is that the biomarkers aren't a significant improvement over current diagnostics.

I find it encouraging that the authors have a plausible explanation for their negative results and that independent microarray studies have come to similar conclusions, and I continue to be hopeful that genomics research can help achieve important medical breakthroughs in the future.

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FYI, Nakagawa et al. 2008 is also an excellent prostate cancer study utilizing microarray data.

Seth Berkley's TED Talk on HIV/Flu Vaccine Development

Seth Berkley's recent TED talk focuses on HIV and influenza vaccine research.  In general, I think the talk does a good job of reviewing why it is so hard to develop an AIDS vaccine or a universal flu vaccine.  However, there are some times when I thought that Dr. Berkley was overselling the research results.

For example, Dr. Berkley says "let's take a look at a video that we're debuting at TED, for the first time, on how an effective HIV vaccine might work."  Now, I think the video does do a very good job illustrating the general principle of how vaccines work, but it does not provide any specific details regarding how an effective HIV vaccine can be developed.

At another point in the video, Dr. Berkley suggests that a universal flu vaccine can be created by designing vaccines that target conserved regions on the surface of the influenza vaccine.  These proteins would be located in roughly the equivalent of the blue region of the following 3D rendition of a flu virus (from http://johnfenzel.typepad.com/john_fenzels_blog/images/flu_image.jpg):

As you might imagine, these proteins have not been used because the scientists believed that the immune system would not respond well to them because the H and N spikes (the green and yellow things in the picture above) would block most antibodies produced during the immune response.  Judging from a quick search of the internet, it seems like most images agree with the picture shown above (and in the TED talk).

To be fair, I found one example of a flu virus with less densely packed surface proteins, and the candidate proteins (M2e proteins) may be large enough to clear enough room to interact with the host antibodies.  However, I fear this new vaccine design may be based on data which shows encouraging results during pre-clinical research but is not very effective during clinical trails.  That said, I would obviously be pleasantly surprised if this design does lead to a successful universal flu vaccine, and I honestly do think Dr. Berkley does a good job of broadly describing of how new technology can aid in rational vaccine design.

I also thought that Dr. Berkley did an excellent job describing how changes in vaccine production could significantly increase the effectiveness of flu vaccines.  Namely, Dr. Berkley points out that flu vaccines have been produced from chicken eggs ever since the 1940s.  Different flu strains vary in their ability to grow in chicken eggs, and production of flu vaccines using chicken eggs takes "more than half a year."  Dr. Berkley proposes a method that would allow companies to produce flu vaccines in E. coli.  I think this is an excellent strategy that could significant improve the process of vaccine development.

I think it is also worth mentioning that Dr. Berkley does acknowledge how hard it is to predict the future of vaccine development.  When asked to give a time line to expect an effective HIV vaccine, Dr. Berkley responds "everybody says it's 10 years, but it's been 10 years every 10 years."  In general, it is always important for people to always interpret preliminary research findings with a grain of salt.

Overall, I think Dr. Berkley does a good job providing an interesting talk about a very important subject.