Three Big Steps Toward Pharmacogenomics in Rheumatology


Biomarkers and genetic subtypes are coming to light in rheumatology, but substantial barriers remain to their use in regular clinical medicine. The author suggests 3 solutions.

Biomarkers and genetic subtypes are regularly coming to light in rheumatology, but substantial barriers remain before many of them will become useful in regular clinical medicine. Some of these are more logistic than scientific.

A few weeks ago, an item in Nature News about ClinVar, a new database under development at the National Institutes of Health in which clinical testing laboratories can deposit their data, piqued my interest about what other databases we need for more powerful pharmacogenomic studies.

Big Step 1:  Tear down the silos

Too often, data from one interesting pharmacogenomic study (e.g., data on treatment response from a genome-wide association study or GWAS) are completely separate from another dataset that can be used to interpret the data (e.g., RNA sequencing). Yes, specialized labs that generated the data can integrate the data for their own analysis. And yes, they can release individual datasets into the public for others to stitch together. But is this really what we need?

Somehow, we need to make data available in a manner that is fully integrated and interoperable. One simple example of this is GWAS for autoimmune diseases. Since 2006, a large number of genetic data have been published. Still, there is no single place to go see results for all autoimmune diseases, despite the fact that there is tremendous shared overlap among the genetic basis for these diseases.

Now, a series of manuscripts are being published on the Immunochip (a chip designed for GWAS studies of major autoimmune and inflammatory disorders) for diseases like inflammatory bowel disease and rheumatoid arthritis. There is no one place to visualize these results, nor to integrate them with other genomic datasets, thereby limiting the value of these rich genomic datasets.  Immunobase comes close, but it has limitations.

Big Step 2: Unlock clinical trial data

Academics are not the only ones who lock their data in a vault. The same is true of clinical trial data generated in industry. There is a trend to release clinical trial data to the public, as was recently announced by GlaxoSmithKline.

This is a great first step, but if the clinical trial data remains siloed from the genomic data, there will be limited value for discovery research.

Big Step 3: Empower the community

Just because data are open access does not mean they are actually "accessible" to a community of scientists to analyze. Specialized software is required to bring the data together and the right community together.

Towards this end, Sage Bionetworks ( has developed Synapse - "a collaborative compute space that allows scientists to share and analyze data together." To demonstrate its power, Sage has hosted a competition pertaining to breast cancer survival. As posted on the Synapse website: "The goal of the breast cancer prognosis challenge is to assess the accuracy of computational models designed to predict breast cancer survival based on clinical information about the patient's tumor as well as genome-wide molecular profiling data including gene expression and copy number profiles."

Just imagine an integrated database that includes genome sequencing, transcriptional profiling, and clinical outcomes, all made available to the general community for data analysis. Who will make this happen?

Until someone does, the time between now and rheumatologists’ ability to use the information continues to stretch toward some undefined point in the future.

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