Abstract B155: Combining cell panel screening with analysis of gene expression levels reveals features of drug response and resistance

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Abstract Screening of new drugs on large cell panels is an important tool to study the biologic mechanism of drug response. From a combination of parallel in vitro tests and bio-informatics analysis, important conclusions can be drawn, for instance about drug selectivity in a cellular context, about resistance mechanisms, and about the patient population in which a drug is effective. This makes cell panel screening indispensable in modern drug discovery. We have set up a platform called Oncolines™ that comprises 102 cell lines from diverse tumor tissues. All cell lines are screened in parallel in high-throughput proliferation assays based on ATP-lite™. Compounds are tested with 9 point dose-response curves in duplicate. Curves are visually inspected. In the past, we have shown that this workflow leads to highly reproducible IC50s, which are necessary for genomic biomarker discovery [1]. For instance, the IC50s have been coupled to curated databases of somatic mutations and copy numbers (1). However, these changes only reflect a small percentage of oncogenic transformations. A more comprehensive view of oncogenic signaling inside a cell can be obtained from mRNA expression levels (2). Here, we describe a workflow to investigate drug response in the 102 cell line Oncolines™ panel based on basal gene expression levels. Correlations between gene expression levels and the log IC50s were calculated for more than 18,000 genes. To reduce the number of false-positive correlations, we considered only genes with a known biologic role in cancer or that were clinically actionable. Secondly, we filtered out genes that correlated indiscriminately with drug responses, by correcting for the average correlations seen in our profiling database of more than 150 inhibitors. This filtered gene list was structured graphically by combining it with information on protein-protein interactions (using the StringDB) or information on which genes are part of the same pathways (a method called Gene Set Analysis). Our method reveals that gene overexpression correlates with drug responses for a number of targets. For instance, EGFR, IGFR, or ALK kinase inhibitors have more potent effects in cell lines that overexpress EGFR, IGFR, or ALK, respectively, and the MDM2 antagonist nutlin is more active in MDM2-overexpressing cell lines. This is irrespective of the genomic alterations driving overexpression of these genes. The distinct response profiles of the spectrum-selective BTK/EGFR/ERBB2 inhibitor ibrutinib and the more selective BTK inhibitor alcalabrutinib could be distinguished as well. Finally, the analysis shows mechanisms of drug resistance. For instance, the response of vincristine strongly correlates to cellular ABCB1 expression, the P-glycoprotein/MDR1 drug transporter, but also to ABCC3, another ABC transporter. Combining basal gene expression levels with cell panel data therefore allows discovery of novel mechanisms of drug response and resistance.