Jan 132013
 

In medicine, a biomarker is a term often used to refer to measurable characteristics that reflects the severity or presence of some disease state. Pharmaceutical companies devote tremendous money and effort in identifying biomarkers; a drug is dead without biomarkers. However, in academia, biomarker research seems to hit a wall. There has been many 50-gene, 70-gene, 100-gene, etc biomarker panels that were derived from some cohorts which totally fail to work in other cohorts. As a results, computational biomarker studies are now often published in BMC type of journals. What can be done?

Cancer is a heterogeneous disease, it is not surprising that biomarkers derived from genomics studies in medium sized cohorts fail in other cohorts. Most of the time, scientists don’t know the mechanisms underlying these biomarkers and why they work or fail. Theoretically, better biological knowledge (from pathways, signal transduction, protein-protein interaction, transcriptional and epigenetic regulation, microRNA regulation, etc) could help understand the mechanism of the biomarkers. As a results, the biomarkers identified with mechanistic understanding might not be the strongest (e.g. most differentially expressed) but the most robust (reproducible). Besides, biomarker with a few features is a concept much older than genomics techniques such as expression microarrays and RNA-seq. As the cost of RNA-seq continues to fall, why not use whole transcriptome, as it should be the most informative and robust biomarker? In addition, some biomarker tests have been patented and off-limits to many companies. Transcriptome biomarker would overcome this barrier, since every gene could be considered.

  2 Responses to “Transcriptome as Biomarkers”

  1. Dr. Liu,

    Good to read through your blog!

    Agree with investing into network based biomarkers given a low yield from GEP based biomarkes and importantly focus more on regulatory interactions rather than lone gene expression domain. However, challenge remain in network based approach because of ‘dynamic’ transcriptomic regulation and overarching tumor heterogeneity. On top of these, I see need of more time-stamped or longitudinal data as compare to wider availability of ‘cross-sectional’ TCGA and other available public cancer datasets.

    Samir

  2. […] traditionally oncologists use qPCR, small microarrays, and recently nano string. I wrote this blog about transcriptome as a biomarker in 2013, and I still believe in it 4 years later. Nowadays […]

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