Supplementary Data from Leveraging Systematic Functional Analysis to Benchmark an In Silico Framework Distinguishes Driver from Passenger MEK Mutants in Cancer

Hanrahan, Aphrothiti J.; Sylvester, Brooke E.; Chang, Matthew T.; Elzein, Arijh; Gao, Jianjiong; Han, Weiwei; Liu, Ye; Xu, Dong; Gao, Sizhi P.; Gorelick, Alexander N.; Jones, Alexis M.; Kiliti, Amber J.; Nissan, Moriah H.; Nimura, Clare A.; Poteshman, Abigail N.; Yao, Zhan; Gao, Yijun; Hu, Wenhuo; Wise, Hannah C.; Gavrila, Elena I.; Shoushtari, Alexander N.; Tiwari, Shakuntala; Viale, Agnes; Abdel-Wahab, Omar; Merghoub, Taha; Berger, Michael F.; Rosen, Neal; Taylor, Barry S.; Solit, David B.

doi:10.1158/0008-5472.22426042.v1

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Supplementary Data from Leveraging Systematic Functional Analysis to Benchmark an In Silico Framework Distinguishes Driver from Passenger MEK Mutants in Cancer

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posted on 2023-03-31, 03:44 authored by Aphrothiti J. Hanrahan, Brooke E. Sylvester, Matthew T. Chang, Arijh Elzein, Jianjiong Gao, Weiwei Han, Ye Liu, Dong Xu, Sizhi P. Gao, Alexander N. Gorelick, Alexis M. Jones, Amber J. Kiliti, Moriah H. Nissan, Clare A. Nimura, Abigail N. Poteshman, Zhan Yao, Yijun Gao, Wenhuo Hu, Hannah C. Wise, Elena I. Gavrila, Alexander N. Shoushtari, Shakuntala Tiwari, Agnes Viale, Omar Abdel-Wahab, Taha Merghoub, Michael F. Berger, Neal Rosen, Barry S. Taylor, David B. Solit

This supplementary data file contains 5 figures pertaining to the tumor distribution of MEK1 hotspot mutations, functional characterization of further MEK1/2 mutants with and without MEK or ERK inhibitor treatment, and the sequence paralogy alignment of MEK1 and MEK2. This file also contains 4 tables detailing hotspot analysis q values for MEK1/2 mutants, tumor incidence of MEK1 in-frame deletions, and a summary of MEK1/2 paralogous residues and their concordant activation status.

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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. 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.See related commentary by Whitehead and Sebolt-Leopold, p. 4042

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Carcinogenesis Cell Signaling Protein serine-threonine kinases Computational Methods Molecular modeling Gene Technologies Cytogenetics Genome Biology Functional genomics Structural genomics Oncogenes & Tumor Suppressors Small Molecule Agents Kinase inhibitors

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Supplementary Data from Leveraging Systematic Functional Analysis to Benchmark an In Silico Framework Distinguishes Driver from Passenger MEK Mutants in Cancer

Funding

NIH

NCI

American Cancer Society

History

ARTICLE ABSTRACT

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