Cancer therapies are typically effective in subsets of patients, reflecting the molecular diversity of cancers and motivating the need for predictive biomarkers of response. Biomarker-guided therapy is increasingly useful in oncology, yet biomarker discovery remains complicated by the large number of molecular features that make it difficult to distinguish causal determinants from spurious associations. To address this challenge, we combined functional genomic screening, proteomics, and drug sensitivity profiling to discover response biomarkers for a number of therapies used in the treatment of Peripheral T-Cell Lymphomas (PTCL). First, we used genome-wide CRISPR-dCas9 interference screens in PTCL cells under drug treatment to identify a shortlist of genes whose knockdown directly increases or decreases drug sensitivity. Next, we profiled drug responses across a diverse panel of 30 PTCL cell cultures and, from the shortlist, identified genes whose protein abundance correlated with drug sensitivity. Genes detected by both approaches are causal determinants of drug response and correlates of drug response across the panel of cultures, making them promising candidates for predictive biomarkers. Basal expression of the reduced folate carrier SLC19A1 was a strong predictor of pralatrexate sensitivity, consistent with its role as the primary transporter for pralatrexate uptake. Simulated clinical trials predicted that biomarker-guided patient selection could improve the power to detect significant benefit of adding pralatrexate to frontline chemotherapy in PTCL. These findings illustrate how functional genetic screens can augment correlative studies to identify candidate biomarkers of drug response, and suggest the potential for precise use of pralatrexate for PTCL.
Pantazis et al. (Sat,) studied this question.
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