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).

Summary The 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 (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 Bcl2-family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and accelerate discovery of drugs matched to patients by their cancer genotype and lineage.

Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.

Clear-cell carcinomas (CCCs) are a histological group of highly aggressive malignancies commonly originating in the kidney and ovary. CCCs are distinguished by aberrant lipid and glycogen accumulation and are refractory to a broad range of anti-cancer therapies. Here we identify an intrinsic vulnerability to ferroptosis associated with the unique metabolic state in CCCs. This vulnerability transcends lineage and genetic landscape, and can be exploited by inhibiting glutathione peroxidase 4 (GPX4) with small-molecules. Using CRISPR screening and lipidomic profiling, we identify the hypoxia-inducible factor (HIF) pathway as a driver of this vulnerability. In renal CCCs, HIF-2α selectively enriches polyunsaturated lipids, the rate-limiting substrates for lipid peroxidation, by activating the expression of hypoxia-inducible, lipid droplet-associated protein ( HILPDA ). Our study suggests targeting GPX4 as a therapeutic opportunity in CCCs, and highlights that therapeutic approaches can be identified on the basis of cell states manifested by morphological and metabolic features in hard-to-treat cancers.

Ferroptosis, an iron-dependent, non-apoptotic cell death program, is involved in various degenerative diseases and represents a targetable vulnerability in certain cancers 1 . The ferroptosissusceptible cell state can either preexist in cells arising from certain lineages or be acquired during cell-state transitions 2-5 . Precisely how ferroptosis susceptibility is dynamically regulated remains poorly understood. Using genome-wide CRISPR/Cas9 suppressor screens, we identify the peroxisome organelle as a critical contributor to ferroptosis sensitivity in human renal and ovarian carcinoma cells. By lipidomic profiling, we show that peroxisomes contribute to ferroptosis through the synthesis of polyunsaturated ether phospholipids (PUFA-ePLs), an understudied lipid *

Restoring functional β-cell mass is an important therapeutic goal for both type 1 and type 2 diabetes (1). While proliferation of existing β-cells is the primary means of β-cell replacement in rodents (2), it is unclear whether a similar principle applies to humans, as human β-cells are remarkably resistant to stimulation of division (3,4). Here, we show that 5-iodotubercidin (5-IT), an annotated adenosine kinase inhibitor previously reported to increase proliferation in rodent and porcine islets (5), strongly and selectively increases human β-cell proliferation in vitro and in vivo. Remarkably, 5-IT also increased glucose-dependent insulin secretion after prolonged treatment. Kinome profiling revealed 5-IT to be a potent and selective inhibitor of the dual-specificity tyrosine phosphorylation–regulated kinase (DYRK) and cell division cycle–like kinase families. Induction of β-cell proliferation by either 5-IT or harmine, another natural product DYRK1A inhibitor, was suppressed by coincubation with the calcineurin inhibitor FK506, suggesting involvement of DYRK1A and nuclear factor of activated T cells signaling. Gene expression profiling in whole islets treated with 5-IT revealed induction of proliferation- and cell cycle–related genes, suggesting that true proliferation is induced by 5-IT. Furthermore, 5-IT promotes β-cell proliferation in human islets grafted under the kidney capsule of NOD-scid IL2Rgnull mice. These results point to inhibition of DYRK1A as a therapeutic strategy to increase human β-cell proliferation.

BackgroundLarge-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications.FindingsThis microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at “The Cell Image Library” (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied.ConclusionsBecause computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.