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Li X, Dowling EK, Yan G, Dereli Z, Bozorgui B, Imanirad P, Elnaggar JH, Luna A, Menter DG, Pilié PG, Yap TA, Kopetz S, Sander C, Korkut A. Precision combination therapies based on recurrent oncogenic co-alterations. Cancer Discov 2022; 12:1542-1559. [PMID: 35412613 PMCID: PMC9524464 DOI: 10.1158/2159-8290.cd-21-0832] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 12/28/2021] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies.
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Affiliation(s)
- Xubin Li
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | | | - Gonghong Yan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zeynep Dereli
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Behnaz Bozorgui
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Parisa Imanirad
- Department of Systems Biology, and The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jacob H. Elnaggar
- Department of Microbiology, Immunology, and Parasitology, Louisiana State University Health Sciences Center, New Orleans, LA 70112
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - David G. Menter
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Patrick G. Pilié
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Timothy A. Yap
- Department of Investigational Cancer Therapeutics (Phase I Program), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Chris Sander
- cBio Center, Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Corresponding Author: Anil Korkut, Bioinformatics & Comp Biology, Phone: 718-300-0666, , 1515 Holcombe Blvd., Houston, Texas 77030-4009
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