Moving into Multi-Dimensions: The Future of Biomarker Development

Moving into Multi-Dimensions: The Future of Biomarker Development

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By Jane Cook
August 6, 2021

Are individual biomarkers a thing of the past? Biomarker identification has historically been a challenging task across diseases and drugs. The accepted workflow is to determine a single biomarker gene whose expression can be used for patient stratification. But this workflow may in fact be what makes finding significant biomarkers more difficult than it needs to be.

Biomarker Development

Just as only a small percentage of genetic traits and diseases are Mendelian, or caused by a single mutation in a single gene, the expression of one biomarker gene is not the only factor that will indicate how a patient will respond to a certain drug or their likelihood of developing a given disease.

Multidimensional Biomarkers

This is where multidimensional biomarkers come in, facilitated by advances in next-gen sequencing techniques and bioinformatics. Multidimensional biomarkers take advantage of the quantity of data we can generate now by combining all the biologically significant signals into one extremely powerful predictive biomarker – something that would have been difficult for bench biologists in the past and made simple by bioinformatics service providers.

Multidimensional Biomarkers for Cancer Treatment

One company, Cofactor Genomics, is betting big on this approach. Using RNA-seq and machine learning, Cofactor Genomics is developing a streamlined assay to discover multidimensional biomarkers for cancer treatments.

There are, of course, examples of the strong predictive power of a single biomarker- take the revolutionary discovery of the BRCA1 and BRCA2 mutations that convincingly indicate a high likelihood of developing the associated hereditary breast cancer. PD-L1 is another good example- this is the biomarker that indicates a high likelihood of successful treatment of certain cancers with the drug Keytruda.

Bioinformatics Analysis

What multidimensional biomarkers can accomplish is combining known biomarkers with RNA signatures of other biomarkers to increase predictive power and ultimately, improve patient outcomes. The bioinformatics analysis challenge of biomarker development is not going away, but this new, multidimensional approach can move that needle forward.

Jane Cook, Journalist & Content Writer, Bridge Informatics

Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].


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