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Supplementary Tables S1-S15 from Biomarker Accessible and Chemically Addressable Mechanistic Subtypes of BRAF Melanoma

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posted on 2023-04-03, 21:22 authored by Banu Eskiocak, Elizabeth A. McMillan, Saurabh Mendiratta, Rahul K. Kollipara, Hailei Zhang, Caroline G. Humphries, Changguang Wang, Jose Garcia-Rodriguez, Ming Ding, Aubhishek Zaman, Tracy I. Rosales, Ugur Eskiocak, Michael P. Smith, Jessica Sudderth, Kakajan Komurov, Ralph J. Deberardinis, Claudia Wellbrock, Michael A. Davies, Jennifer A. Wargo, Yonghao Yu, Jef K. De Brabander, Noelle S. Williams, Lynda Chin, Helen Rizos, Georgina V. Long, Ralf Kittler, Michael A. White

Supplemental Table S1. Genome-wide RNAi toxicity screen z-scores, related to Supplemental Figure S1, Supplemental Table S2. Percent DNA copy number gain in melanoma tumor samples compared to normal skin, related to Supplemental Figure S1, Supplemental Table S3. Melanoma panel siRNA toxicity, related to Supplemental Figure S1, Supplemental Table S4. Whole genome transcript profiles, related to Supplemental Figure S1, Supplemental Table S5. GISTIC analysis and expression correlation of candidate melanoma hits, related to Supplemental Figure S1, Supplemental Table S6. SOX10 ChIPSeq, related to Supplemental Figure S3, Supplemental Table S7. SOX10 ChIPSeq motif targets, related to Supplemental Figure S3, Supplemental Table S8. SOX10 ChIPSeq motif targets hypergeometric distribution analysis, related to Supplemental Figure S3, Supplemental Table S9. Whole genome transcript arrays with siRNAs, related to Supplemental Figure S3, Supplemental Table S10. Candidate SOX10 targets, related to Supplemental Figure S3, Supplemental Table S11. Expression correlations in patient samples, related to Supplemental Figure S3, Supplemental Table S12. Elastic net prediction scores, related to Figures 1, 2 and 3, Supplemental Figures S4, S5 and S7, Supplemental Table S13. Elastic net prediction scores, related to Figure 2, Supplemental Table S14. S2N analysis of gene transcripts between TBK1i-sensitive and -resistant BRAF wild-type melanoma cell lines, related to Figure 3, Supplemental Table S15. TMT-phosphoproteomics peptide quantification, related to Figure S12.

Funding

Welch Foundation

NIH

American Cancer Society

CPRIT

History

ARTICLE ABSTRACT

Genomic diversity among melanoma tumors limits durable control with conventional and targeted therapies. Nevertheless, pathologic activation of the ERK1/2 pathway is a linchpin tumorigenic mechanism associated with the majority of primary and recurrent disease. Therefore, we sought to identify therapeutic targets that are selectively required for tumorigenicity in the presence of pathologic ERK1/2 signaling. By integration of multigenome chemical and genetic screens, recurrent architectural variants in melanoma tumor genomes, and patient outcome data, we identified two mechanistic subtypes of BRAFV600 melanoma that inform new cancer cell biology and offer new therapeutic opportunities. Subtype membership defines sensitivity to clinical MEK inhibitors versus TBK1/IKBKϵ inhibitors. Importantly, subtype membership can be predicted using a robust quantitative five-feature genetic biomarker. This biomarker, and the mechanistic relationships linked to it, can identify a cohort of best responders to clinical MEK inhibitors and identify a cohort of TBK1/IKBKϵ inhibitor–sensitive disease among nonresponders to current targeted therapy.Significance: This study identified two mechanistic subtypes of melanoma: (1) the best responders to clinical BRAF/MEK inhibitors (25%) and (2) nonresponders due to primary resistance mechanisms (9.9%). We identified robust biomarkers that can detect these subtypes in patient samples and predict clinical outcome. TBK1/IKBKϵ inhibitors were selectively toxic to drug-resistant melanoma. Cancer Discov; 7(8); 832–51. ©2017 AACR.See related commentary by Jenkins and Barbie, p. 799.This article is highlighted in the In This Issue feature, p. 783