Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations

药品 计算生物学 背景(考古学) 耐受性 医学 体内 精确肿瘤学 临床试验 癌症 肿瘤科 生物 生物信息学 内科学 药理学 不利影响 生物技术 古生物学
作者
Azadeh C. Bashi,Elizabeth A. Coker,Krishna C. Bulusu,Patricia Jaaks,Claire Crafter,Howard Lightfoot,Marta Milo,Katrina McCarten,David F. Jenkins,Dieudonne van der Meer,James T. Lynch,Syd Barthorpe,Courtney L. Andersen,Simon T. Barry,Alexandra Beck,Justin Cidado,Jacob Gordon,Caitlin Hall,James Hall,Iman Mali,Tatiana Mironenko,Kevin Mongeon,James Morris,Laura Richardson,Paul D. Smith,Omid Tavana,Charlotte Tolley,Frances Thomas,Brandon S. Willis,Wanjuan Yang,Mark J. O’Connor,Ultan McDermott,Susan E. Critchlow,Lisa Drew,Stephen E. Fawell,Jerome T. Mettetal,Mathew J. Garnett
出处
期刊:Cancer Discovery [American Association for Cancer Research]
卷期号:14 (5): 846-865 被引量:3
标识
DOI:10.1158/2159-8290.cd-23-0388
摘要

Abstract Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific “emergent” biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. Significance: We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of “emergent” combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695
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