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Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations
Azadeh C Bashi1, Elizabeth A Coker2, Krishna C Bulusu1
1Oncology R&D, AstraZeneca, Cambridge, United Kingdom.
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.
Insights
This study screened 109 oncology drug combinations across 755 cancer cell lines, identifying three actionable combinations for specific patient populations. This research advances precision oncology by finding novel drug synergies.
Area of Science:
- Oncology
- Pharmacology
- Genomics
Background:
- Cancer drug combinations offer improved therapeutic responses and expanded treatment options.
- The vast number of potential combinations and context-specific responses necessitate systematic screening.
- Identifying clinically relevant combinations in defined patient subtypes is crucial for effective cancer therapy.
Purpose of the Study:
- To systematically screen 109 anticancer drug combinations from AstraZeneca's portfolio in 755 pan-cancer cell lines.
- To implement a novel approach using combination effect (Emax) and highest single agent (HSA) to assess combination benefits.
- To design a clinical translatability workflow for identifying actionable combinations with defined patient populations and biomarkers.
Main Methods:
- Screening of 109 drug combinations in a 7x7 concentration matrix across 755 pan-cancer cell lines, generating over 4 million viability measurements.
- Utilizing combination Emax and highest single agent (HSA) for assessing drug synergy and therapeutic benefit.
- Developing a clinical translatability workflow incorporating patient stratification, tolerability rationale, emergent biomarkers, and clinical dose relevance.
Main Results:
- Generated a data-rich resource from over 4 million sensitivity measurements for 109 drug combinations.
- Identified three actionable drug combinations in defined cancer types, including hematologic cancers.
- Confirmed in vitro and in vivo efficacy of selected combinations, focusing on apoptotic targets and emergent biomarkers.
Conclusions:
- Systematic screening and a novel assessment approach can identify clinically relevant oncology drug combinations.
- The developed workflow prioritizes combinations for clinical translatability, enabling precision medicine strategies.
- Three novel, actionable drug combinations were validated, offering potential new therapeutic options for specific cancer types.
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