Abstract

Abstract Profiling of new drug candidates on cancer cell line panels is an important tool to identify candidate drug response biomarkers for clinical studies. We have set up a platform, called Oncolines™, that comprises 102 genetically well-characterized cell lines from diverse tumor tissues. The cell lines are screened in parallel in a high-throughput proliferation assay based on ATP-lite read-out with 9 point dose-response curves in duplicate and manual inspection of curve fitting. We have recently shown that this workflow leads to highly reproducible IC50s which are necessary for genomic biomarker discovery [1]. Here we apply this platform to investigate BTK and CDK4/6 inhibitors. In previous work [1,2] we correlated the IC50 profiles of small molecule inhibitors through Anova to curated databases of somatic mutations and copy numbers. To obtain a more comprehensive view of oncogenic signaling inside a cell, we calculated in this study correlations between the log IC50s and basal gene expression levels for more than 18,000 genes. To reduce the number of false positives, we first considered only genes with a known role in cancer biology or drug resistance. Secondly, correlations were corrected for the average correlations seen in an Oncolines™ database of more than 150 anti-cancer agents [1]. Thirdly, we looked at co-correlation of genes that interact on a protein level (StringDB) or at a pathway level (Gene Set Analysis). Our method, called GeneNominator™, was validated by profiling the microtubule binder vincristine, the EGFR inhibitor gefitinib, and the MDM2 antagonist nutlin, and confirmed known pharmacogenomic relations.In a first study we tested three CDK4/6 inhibitors, ribociclib, palbociclib and abemaciclib. Although their response patterns cluster together, they show significant differences. For instance, palbociclib is more active on cell lines that express the transcription factor GATA3, which is linked to cyclin signalling in neuroblastoma.In a second study we tested the BTK inhibitors ibrutinib and acalabrutinib. Both show very different inhibition profiles across the 102 cell lines. Ibrutinib shows considerable activity in cell lines that overexpress ERBB4, FGFR2 and ERBB2, reflecting its biochemical inhibition of these kinases. Acalabrutinib, which is more selective, is most active in cell lines that highly express IRF4, a known genetic driver in multiple myeloma. and the B-cell receptor subunit CD79B, of which mutations are often found in diffuse large B-cell lymphoma. When mutations and copy number changes are studied, acalabrutinib is particularly active in cell lines with CREBBP or EZH2 mutations, which occur in diffuse large B-cell lymphomas. Our data demonstrate that our in vitro cell panel screening method can uncover new mechanistic information on clinically used anti-cancer agents. [1] Uitdehaag et al. (2016) Mol. Cancer Ther. 15, 3097-3109.[2] Uitdehaag et al. (2014) PLOS ONE 9: e92146 Citation Format: Joost C. Uitdehaag, Jeffrey J. Kooijman, Jeroen A.D.M. de Roos, Martine B.W. Prinsen, Jelle Dylus, Judith de Vetter, Nicole Willemsen-Seegers, Jos de Man, Suzanne J.C. van Gerwen, Rogier C. Buijsman, Guido J.R. Zaman. Cell line panel profiling reveals novel drug response biomarkers for BTK and CDK4/6 inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4907.

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