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.

Playing with the GWAS Catalog

When I was looking at an article in PLoS Biology, I noticed that the abstract listed a comprehensive government database for genome-wide association studies (the “GWAS Catalog”). This database provides a lot of interesting information. In order to get a feel for the data in the GWAS Catalog, I looked at the data for four specific diseases (autism, prostate cancer, type I diabetes, and type II diabetes).

[If you are a non-scientist looking at this database, stronger genetic associations (which should more accurately predict genetic predisposition to a disease) should have low p-values and should be reproducible between different studies.]

1) Autism - There were 3 studies included in the GWAS Catalog. The first two studies identified the same exact region (but with slightly different variants), and the third study identified a different but nearby region. Although I think that there is probably something interesting going on in this region of chromosome 5, I don’t think it is worth getting very excited about the specific variants identified in these studies. For example, the p-values for the autism studies are the lowest out of the four diseases that I analyzed (meaning autism has the weakest genetic component and/or the genetic component of autism is the most complex to model). Furthermore, the most recent study showed that the expression levels of SEMA5A (one of the genes listed in the GWAS Catalog for autism) are very similar for autistic and normal people (see Fig 2. if you have access to this article). The authors of this study claim that gene expression in autistic patients is significantly lower than in normal patients, but I think the statistical significance may be due to an over-fitting problem because they only look at 20 autism patients and 10 control patients (and I have a hard time believing this was enough data to adjust for “age at brain acquisition, post-mortem interval and sex”). The genes with the strongest genetic association in the first study (CDH10 and CDH9) also have similar expression patterns in both autistic and control patents, and the authors of this first study report that this difference is not statistically significant. Of course, the autism variants may be non-functional yet retain similar gene expression levels, but I would still seriously question the strength of any of the specific variants listed in these studies.

2) Prostate Cancer – I looked at the data for prostate cancer, type I diabetes, and type II diabetes because variants for these three diseases are included in at least two of the three major genomic tests listed in “The Language of Life.”  More specifically, the three major genomic testing companies gave completely different predictions regarding Dr. Collins’ risk of getting prostate cancer. The GWAS Catalog lists 11 studies (10 of which have significant associations), and the genetic associations for prostate cancer were much stronger than for autism (p-values equal 3 x 10^-33 vs. 2 x 10^-10, respectively). Highly significant genetic associations were found within the 8q24.21 and 17q12 regions in several independent studies, but many associations are only found in individual studies. According to “The Language of Life”, deCODE has 13 variants for prostate cancer, Navigenics has 9 variants, and 23andMe has 5 variants. Based upon what I’ve seen in the GWAS Catalog, I think that there probably are at least 5 strong, reproducible variants that could be used to calculate genetic predisposition to prostate cancer, but I am not certain if there 13 variants with well-established genetic associations. However, calculating genetic association for several variants at the same time can be tricky, and the difference in test results may be a problem with the underlying models for calculating genetic association more so than the individual variants considered for the analysis.

3) Type I Diabetes – Type I diabetes has a very strong genetic component, and the molecular basis for this disease is well understood. In these respects, the data in the GWAS Catalog are a good reflection of what is known about this disease. The strongest associations had the lowest p-value out of all the diseases considered (5 x 10^-134 for a variant within the Major Histocompatibility Complex, or MHC), and either MHC or HLA (which is part of the MHC) had the strongest genetic association for 4 out of the 8 studies in the GWAS Catalog. This makes a lot of sense because the MHC displays antigens to immune system (thereby telling the body which cells to attack) and type I diabetes is due to due to an autoimmune response where the immune system attacks and destroys the insulin-producing beta cells in the pancreas. It bothered me that some studies reported pretty different results, but that is why I think that it is necessary to only use reproducible associations for genetic testing.

4) Type II Diabetes – The GWAS Catalog contained 15 studies on type II diabetes (12 of which had significant results), which is the highest number of studies listed for the four diseases that I looked at. The strongest associations for type II diabetes had p-values similar to prostate cancer, but higher than type I diabetes. This makes sense because type I diabetes has a stronger genetic component than type II diabetes, so type II diabetes should have weaker associations than type I diabetes. The 8 genes listed as predictors of type II diabetes in “The Language of Life” (TCF7L2, IGF2BP2, CDKN2A, CDKAL1, KCNJ11, HHEX, SLC20A8, and PPARG) were pretty well represented among the different studies listed in the GWAS Catalog, so I bet the predictors of genetic predisposition to type II diabetes are pretty good.