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.