American Association for Cancer Research
21598290cd150235-sup-145780_2_supp_3058746_nrhtdz.xlsx (9.43 MB)

Supplemental Tables S1 - S7 from Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

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posted on 2023-04-03, 20:49 authored by Brinton Seashore-Ludlow, Matthew G. Rees, Jaime H. Cheah, Murat Cokol, Edmund V. Price, Matthew E. Coletti, Victor Jones, Nicole E. Bodycombe, Christian K. Soule, Joshua Gould, Benjamin Alexander, Ava Li, Philip Montgomery, Mathias J. Wawer, Nurdan Kuru, Joanne D. Kotz, C. Suk-Yee Hon, Benito Munoz, Ted Liefeld, Vlado Dančík, Joshua A. Bittker, Michelle Palmer, James E. Bradner, Alykhan F. Shamji, Paul A. Clemons, Stuart L. Schreiber

Supplemental Table S1. Small-molecule Informer Set Description of the small-molecule informer set used in the sensitivity profiling experiment, including protein target or activity. Supplemental Table S2.Cancer cell-line panel Description of the cancer cell lines profiled in this experiment; for a clarification of growth media compositions, see Supplemental Table S10. Supplemental Table S3. Area-under-sensitivity-curve values Area-under-sensitivity-curve (AUC) values for each compound-cell line pair using the indices provided in Supplemental Table S1 and Supplemental Table S2. Supplemental Table S4. Compound target annotations. Compound target annotations used for ACME analysis of the compound dendrogram. Supplemental Table S5. Cellular feature annotations Cellular feature annotations used for ACME analysis of the cell-line dendrogram. Supplemental Table S6. Cell-line mutation annotations Mutation annotations of the cancer cell lines used in ACME analysis of the cell-line dendrogram. Supplemental Table S7. ACME results table Results of ACME analysis of sensitivity profiling data.



Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2).Significance: We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses. Cancer Discov; 5(11); 1210–23. ©2015 AACR.See related commentary by Gray and Mills, p. 1130.This article is highlighted in the In This Issue feature, p. 1111

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