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Supplementary figure legends. Supplementary methods, including additional filtering and heuristics for sensitivity data processing and ACME analysis and additional methods for western blotting and immunostaining. Supplementary Table S8. Small molecules used in validation studies. Supplementary Table S9. CCLs used in the validation studies. Supplementary Table S10. Clarification of growth media. Supplementary Table S11. Significant synergistic and antagonistic combinations in LS513 cells.

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.

Supplemental Figure S1. Further characterization of the small-molecule Informer Set and comparisons between CTRP v1 and v2. Supplemental Figure S2. Cellular features of the CCL panel. Supplemental Figure S3. Details on ACME analysis described in this paper. Supplemental Figure S4. ACME identifies clinically relevant associations between small-molecule sensitivity and cancer cell features. Supplemental Figure S5. Using ACME to investigate small-molecule mechanism of action. Supplemental Figure S6. Microtubule regrowth assay in NCIH661 cells. Supplemental Figure S7. Combined IGF1R and ALK inhibition in ALK overexpressed neuroblastoma. Supplemental Figure S8. Using ACME to inform combination screening.

Diversity-oriented synthesis (DOS)is a powerful strategy to prepare molecules with underrepresented features in commercial screening collections, resulting in the elucidation of novel biological mechanisms. In parallel to the development of DOS, DNA-encoded libraries (DELs) have emerged as an effective, efficient screening strategy to identify protein binders. Despite recent advancements in this field, most DEL syntheses are limited by the presence of sensitive DNA-based constructs. Here, we describe the design, synthesis, and validation experiments performed for a 3.7 million-member DEL, generated using diverse skeleton architectures with varying exit vectors, derived from DOS, to achieve structural diversity beyond what is possible by varying appendages alone. We will make this DEL available to the academic scientific community to increase access to novel structural features and accelerate early-phase drug discovery.

Summary: Cancer is often seen as a disease of mutations and chromosomal abnormalities. However, some cancers, including pediatric rhabdoid tumors (RTs), lack recurrent alterations targetable by current drugs and need alternative, informed therapeutic options. To nominate potential targets, we performed a high-throughput small-molecule screen complemented by a genome-scale CRISPR-Cas9 gene-knockout screen in a large number of RT and control cell lines. These approaches converged to reveal several receptor tyrosine kinases (RTKs) as therapeutic targets, with RTK inhibition effective in suppressing RT cell growth in vitro and against a xenograft model in vivo. RT cell lines highly express and activate (phosphorylate) different RTKs, creating dependency without mutation or amplification. Downstream of RTK signaling, we identified PTPN11, encoding the pro-growth signaling protein SHP2, as a shared dependency across all RT cell lines. This study demonstrates that large-scale perturbational screening can uncover vulnerabilities in cancers with “quiet” genomes. : Using a diverse set of rhabdoid tumor cell lines and both small-molecule and CRISPR-Cas9 gene-knockout screening, Oberlick et al. find high expression and dependency upon a wide range of receptor tyrosine kinases (RTKs) and SHP2 downstream. These RTK inhibitors are also effective against a rhabdoid tumor mouse model. Keywords: rhabdoid tumors, SMARCB1, high-throughput drug screening, genome-wide CRISPR screening, receptor tyrosine kinase, PTPN11

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.

SummaryThe high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene β-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ~19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters, and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.

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