期刊
CANCER RESEARCH
卷 80, 期 19, 页码 4233-4243出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-20-0865
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资金
- Marie-Josee and Henry R. Kravis Center for Molecular Oncology (CMO), Cycle for Survival
- NIH [R01 CA229624, R01 CA234361, U54 OD020355, R01 CA207244, R01 CA204749, R35GM126985, R21-LM012790]
- T32 TROT fellowship [1 R01 CA201247]
- NIH/NCI (Cancer Center support grant) [P30 CA008748R01, R01 CA056821]
- NCI (ITCR grant) [U24-CA220457-01]
- American Cancer Society [RSG-15-067-01-TBG]
- Prostate Cancer Foundation
- Anna Fuller Fund
- Josie Robertson Foundation
- Fund for Innovation in Cancer Informatics from the Brown Performance Group
- Histiocyte Society
- Erdheim-Chester Disease Global Alliance
- Functional Genomics Initiative of Memorial Sloan Kettering Cancer Center
- Histiocytosis Association
- Leukemia & Lymphoma Society
- Frame Fund
- Swim Across America
- Ludwig Institute for Cancer Research
- Parker Institute for Cancer Immunotherapy
- Virginia B. Squiers Foundation
Despite significant advances in cancer precision medicine, a significant hurdle to its broader adoption remains the multitude of variants of unknown significance identified by clinical tumor sequencing and the lack of biologically validated methods to distinguish between functional and benign variants. Here we used functional data on MAP2K1 and MAP2K2 mutations generated in real-time within a co-clinical trial framework to benchmark the predictive value of a three-part in silico methodology. Our computational approach to variant classification incorporated hotspot analysis, three-dimensional molecular dynamics simulation, and sequence paralogy. In silico prediction accurately distinguished functional from benign MAP2K1 and MAP2K2 mutants, yet drug sensitivity varied widely among activating mutant alleles. These results suggest that multifaceted in silico modeling can inform patient accrual to MEK/ERK inhibitor clinical trials, but computational methods need to be paired with laboratory- and clinic-based efforts designed to unravel variabilities in drug response. Significance: Leveraging prospective functional characterization of MEK1/2 mutants, it was found that hotspot analysis, molecular dynamics simulation, and sequence paralogy are complementary tools that can robustly prioritize variants for biologic, therapeutic, and clinical validation.
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