Personalized in-silico drug response prediction based on the genetic landscape of muscle-invasive bladder cancer

Krentel, F.; Singer, F.; Rosano Gonzalez, L.; Gibb, E. A.; Liu, Y.; Davicioni, E.; Keller, N.; Stekhoven, D.; Kruithof-De Julio, M.; Seiler, R.

doi:10.1101/2020.05.22.101428

bioRxiv · 10.1101/2020.05.22.101428

Personalized in-silico drug response prediction based on the genetic landscape of muscle-invasive bladder cancer

Krentel, F.·Singer, F.·Rosano Gonzalez, L.·Gibb, E. A.·Liu, Y.·Davicioni, E.·Keller, N.·Stekhoven, D.·Kruithof-De Julio, M.·Seiler, R.

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Abstract

In bladder cancer (BLCA) there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. The extraordinarily high molecular heterogeneity of BLCA might explain its wide range of therapy responses to empiric treatments. To better stratify patients for treatment response, we present a highly automated workflow for in-silico drug response prediction based on a tumors individual multi-omic profile. Within the TCGA-BLCA cohort, the algorithm identified a panel of 21 genes and 72 drugs, that suggested personalized treatment for 94,7% of patients - including five genes not yet reported as biomarkers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. Manual curation revealed pitfalls of current, and potential of future drug-gene interaction databases. Functional testing in patient derived models and/or clinical trials are next steps to validate our in-silico drug predictions.

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Krentel, F., Singer, F., Rosano Gonzalez, L., Gibb, E. A., Liu, Y., Davicioni, E., Keller, N., Stekhoven, D., Kruithof-De Julio, M., Seiler, R.. 2020-05-26. Personalized in-silico drug response prediction based on the genetic landscape of muscle-invasive bladder cancer. https://doi.org/10.1101/2020.05.22.101428

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