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Altunel E, Roghani RS, Chen KY, Kim SY, McCall S, Ware KE, Shen X, Somarelli JA, Hsu DS. Development of a precision medicine pipeline to identify personalized treatments for colorectal cancer. BMC Cancer 2020; 20:592. [PMID: 32580713 PMCID: PMC7313200 DOI: 10.1186/s12885-020-07090-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 06/18/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Metastatic colorectal cancer (CRC) continues to be a major health problem, and current treatments are primarily for disease control and palliation of symptoms. In this study, we developed a precision medicine strategy to discover novel therapeutics for patients with CRC. METHODS Six matched low-passage cell lines and patient-derived xenografts (PDX) were established from CRC patients undergoing resection of their cancer. High-throughput drug screens using a 119 FDA-approved oncology drug library were performed on these cell lines, which were then validated in vivo in matched PDXs. RNA-Seq analysis was then performed to identify predictors of response. RESULTS Our study revealed marked differences in response to standard-of-care agents across patients and pinpointed druggable pathways to treat CRC. Among these pathways co-targeting of fibroblast growth factor receptor (FGFR), SRC, platelet derived growth factor receptor (PDGFR), or vascular endothelial growth factor receptor (VEGFR) signaling was found to be an effective strategy. Molecular analyses revealed potential predictors of response to these druggable pathways. CONCLUSIONS Our data suggests that the use of matched low-passage cell lines and PDXs is a promising strategy to identify new therapies and pathways to treat metastatic CRC.
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Affiliation(s)
- Erdem Altunel
- Department of Medicine, Division of Medical Oncology, Duke University Medical Center, 3008 Snyderman Building, 905 S. LaSalle St., Durham, NC, 27710, USA
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, USA
| | - Roham S Roghani
- Department of Medicine, Division of Medical Oncology, Duke University Medical Center, 3008 Snyderman Building, 905 S. LaSalle St., Durham, NC, 27710, USA
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, USA
| | - Kai-Yuan Chen
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - So Young Kim
- Duke Functional Genomics Core, Duke University, Durham, North Carolina, USA
| | - Shannon McCall
- Department of Pathology, Duke University, Durham, North Carolina, USA
| | - Kathryn E Ware
- Department of Medicine, Division of Medical Oncology, Duke University Medical Center, 3008 Snyderman Building, 905 S. LaSalle St., Durham, NC, 27710, USA
| | - Xiling Shen
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Jason A Somarelli
- Department of Medicine, Division of Medical Oncology, Duke University Medical Center, 3008 Snyderman Building, 905 S. LaSalle St., Durham, NC, 27710, USA
| | - David S Hsu
- Department of Medicine, Division of Medical Oncology, Duke University Medical Center, 3008 Snyderman Building, 905 S. LaSalle St., Durham, NC, 27710, USA.
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, USA.
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