An expanded universe of cancer targets.

The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.

A High-Throughput Platform to Identify Small-Molecule Inhibitors of CRISPR-Cas9.

The precise control of CRISPR-Cas9 activity is required for a number of genome engineering technologies. Here, we report a generalizable platform that provided the first synthetic small-molecule inhibitors of Streptococcus pyogenes Cas9 (SpCas9) that weigh <500 Da and are cell permeable, reversible, and stable under physiological conditions. We developed a suite of high-throughput assays for SpCas9 functions, including a primary screening assay for SpCas9 binding to the protospacer adjacent motif, and used these assays to screen a structurally diverse collection of natural-product-like small molecules to ultimately identify compounds that disrupt the SpCas9-DNA interaction. Using these synthetic anti-CRISPR small molecules, we demonstrated dose and temporal control of SpCas9 and catalytically impaired SpCas9 technologies, including transcription activation, and identified a pharmacophore for SpCas9 inhibition using structure-activity relationships. These studies establish a platform for rapidly identifying synthetic, miniature, cell-permeable, and reversible inhibitors against both SpCas9 and next-generation CRISPR-associated nucleases.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Inhibition of Dihydroorotate Dehydrogenase Overcomes Differentiation Blockade in Acute Myeloid Leukemia.

While acute myeloid leukemia (AML) comprises many disparate genetic subtypes, one shared hallmark is the arrest of leukemic myeloblasts at an immature and self-renewing stage of development. Therapies that overcome differentiation arrest represent a powerful treatment strategy. We leveraged the observation that the majority of AML, despite their genetically heterogeneity, share in the expression of HoxA9, a gene normally downregulated during myeloid differentiation. Using a conditional HoxA9 model system, we performed a high-throughput phenotypic screen and defined compounds that overcame differentiation blockade. Target identification led to the unanticipated discovery that inhibition of the enzyme dihydroorotate dehydrogenase (DHODH) enables myeloid differentiation in human and mouse AML models. In vivo, DHODH inhibitors reduced leukemic cell burden, decreased levels of leukemia-initiating cells, and improved survival. These data demonstrate the role of DHODH as a metabolic regulator of differentiation and point to its inhibition as a strategy for overcoming differentiation blockade in AML.

Advancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes.

Small-molecule probes can illuminate biological processes and aid in the assessment of emerging therapeutic targets by perturbing biological systems in a manner distinct from other experimental approaches. Despite the tremendous promise of chemical tools for investigating biology and disease, small-molecule probes were unavailable for most targets and pathways as recently as a decade ago. In 2005, the NIH launched the decade-long Molecular Libraries Program with the intent of innovating in and broadening access to small-molecule science. This Perspective describes how novel small-molecule probes identified through the program are enabling the exploration of biological pathways and therapeutic hypotheses not otherwise testable. These experiences illustrate how small-molecule probes can help bridge the chasm between biological research and the development of medicines but also highlight the need to innovate the science of therapeutic discovery.

Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.

Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.

Regulation of ferroptotic cancer cell death by GPX4.

Ferroptosis is a form of nonapoptotic cell death for which key regulators remain unknown. We sought a common mediator for the lethality of 12 ferroptosis-inducing small molecules. We used targeted metabolomic profiling to discover that depletion of glutathione causes inactivation of glutathione peroxidases (GPXs) in response to one class of compounds and a chemoproteomics strategy to discover that GPX4 is directly inhibited by a second class of compounds. GPX4 overexpression and knockdown modulated the lethality of 12 ferroptosis inducers, but not of 11 compounds with other lethal mechanisms. In addition, two representative ferroptosis inducers prevented tumor growth in xenograft mouse tumor models. Sensitivity profiling in 177 cancer cell lines revealed that diffuse large B cell lymphomas and renal cell carcinomas are particularly susceptible to GPX4-regulated ferroptosis. Thus, GPX4 is an essential regulator of ferroptotic cancer cell death.

An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.

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 (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 ß-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.

Identification of regulators of polyploidization presents therapeutic targets for treatment of AMKL.

The mechanism by which cells decide to skip mitosis to become polyploid is largely undefined. Here we used a high-content image-based screen to identify small-molecule probes that induce polyploidization of megakaryocytic leukemia cells and serve as perturbagens to help understand this process. Our study implicates five networks of kinases that regulate the switch to polyploidy. Moreover, we find that dimethylfasudil (diMF, H-1152P) selectively increased polyploidization, mature cell-surface marker expression, and apoptosis of malignant megakaryocytes. An integrated target identification approach employing proteomic and shRNA screening revealed that a major target of diMF is Aurora kinase A (AURKA). We further find that MLN8237 (Alisertib), a selective inhibitor of AURKA, induced polyploidization and expression of mature megakaryocyte markers in acute megakaryocytic leukemia (AMKL) blasts and displayed potent anti-AMKL activity in vivo. Our findings provide a rationale to support clinical trials of MLN8237 and other inducers of polyploidization and differentiation in AMKL.

DNA-encoded library-enabled discovery of proximity-inducing small molecules.

Small molecules that induce protein-protein associations represent powerful tools to modulate cell circuitry. We sought to develop a platform for the direct discovery of compounds able to induce association of any two preselected proteins, using the E3 ligase von Hippel-Lindau (VHL) and bromodomains as test systems. Leveraging the screening power of DNA-encoded libraries (DELs), we synthesized ~1 million DNA-encoded compounds that possess a VHL-targeting ligand, a variety of connectors and a diversity element generated by split-and-pool combinatorial chemistry. By screening our DEL against bromodomains in the presence and absence of VHL, we could identify VHL-bound molecules that simultaneously bind bromodomains. For highly barcode-enriched library members, ternary complex formation leading to bromodomain degradation was confirmed in cells. Furthermore, a ternary complex crystal structure was obtained for our most enriched library member with BRD4BD1 and a VHL complex. Our work provides a foundation for adapting DEL screening to the discovery of proximity-inducing small molecules.

Plasticity of ether lipids promotes ferroptosis susceptibility and evasion.

Ferroptosis-an iron-dependent, non-apoptotic cell death process-is involved in various degenerative diseases and represents a targetable susceptibility in certain cancers1. The ferroptosis-susceptible cell state can either pre-exist in cells that arise from certain lineages or be acquired during cell-state transitions2-5. However, precisely how susceptibility to ferroptosis is dynamically regulated remains poorly understood. Here we use genome-wide CRISPR-Cas9 suppressor screens to identify the oxidative organelles peroxisomes as critical contributors to ferroptosis sensitivity in human renal and ovarian carcinoma cells. Using lipidomic profiling we show that peroxisomes contribute to ferroptosis by synthesizing polyunsaturated ether phospholipids (PUFA-ePLs), which act as substrates for lipid peroxidation that, in turn, results in the induction of ferroptosis. Carcinoma cells that are initially sensitive to ferroptosis can switch to a ferroptosis-resistant state in vivo in mice, which is associated with extensive downregulation of PUFA-ePLs. We further find that the pro-ferroptotic role of PUFA-ePLs can be extended beyond neoplastic cells to other cell types, including neurons and cardiomyocytes. Together, our work reveals roles for the peroxisome-ether-phospholipid axis in driving susceptibility to and evasion from ferroptosis, highlights PUFA-ePL as a distinct functional lipid class that is dynamically regulated during cell-state transitions, and suggests multiple regulatory nodes for therapeutic interventions in diseases that involve ferroptosis.

Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway.

Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness. A high-mesenchymal cell state observed in human tumours and cancer cell lines has been associated with resistance to multiple treatment modalities across diverse cancer lineages, but the mechanistic underpinning for this state has remained incompletely understood. Here we molecularly characterize this therapy-resistant high-mesenchymal cell state in human cancer cell lines and organoids and show that it depends on a druggable lipid-peroxidase pathway that protects against ferroptosis, a non-apoptotic form of cell death induced by the build-up of toxic lipid peroxides. We show that this cell state is characterized by activity of enzymes that promote the synthesis of polyunsaturated lipids. These lipids are the substrates for lipid peroxidation by lipoxygenase enzymes. This lipid metabolism creates a dependency on pathways converging on the phospholipid glutathione peroxidase (GPX4), a selenocysteine-containing enzyme that dissipates lipid peroxides and thereby prevents the iron-mediated reactions of peroxides that induce ferroptotic cell death. Dependency on GPX4 was found to exist across diverse therapy-resistant states characterized by high expression of ZEB1, including epithelial-mesenchymal transition in epithelial-derived carcinomas, TGFß-mediated therapy-resistance in melanoma, treatment-induced neuroendocrine transdifferentiation in prostate cancer, and sarcomas, which are fixed in a mesenchymal state owing to their cells of origin. We identify vulnerability to ferroptic cell death induced by inhibition of a lipid peroxidase pathway as a feature of therapy-resistant cancer cells across diverse mesenchymal cell-state contexts.

Selective covalent targeting of GPX4 using masked nitrile-oxide electrophiles.

We recently described glutathione peroxidase 4 (GPX4) as a promising target for killing therapy-resistant cancer cells via ferroptosis. The onset of therapy resistance by multiple types of treatment results in a stable cell state marked by high levels of polyunsaturated lipids and an acquired dependency on GPX4. Unfortunately, all existing inhibitors of GPX4 act covalently via a reactive alkyl chloride moiety that confers poor selectivity and pharmacokinetic properties. Here, we report our discovery that masked nitrile-oxide electrophiles, which have not been explored previously as covalent cellular probes, undergo remarkable chemical transformations in cells and provide an effective strategy for selective targeting of GPX4. The new GPX4-inhibiting compounds we describe exhibit unexpected proteome-wide selectivity and, in some instances, vastly improved physiochemical and pharmacokinetic properties compared to existing chloroacetamide-based GPX4 inhibitors. These features make them superior tool compounds for biological interrogation of ferroptosis and constitute starting points for development of improved inhibitors of GPX4.

Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function.

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules, using a custom negative-log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships. Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

Diversity-oriented synthesis yields novel multistage antimalarial inhibitors.

Antimalarial drugs have thus far been chiefly derived from two sources-natural products and synthetic drug-like compounds. Here we investigate whether antimalarial agents with novel mechanisms of action could be discovered using a diverse collection of synthetic compounds that have three-dimensional features reminiscent of natural products and are underrepresented in typical screening collections. We report the identification of such compounds with both previously reported and undescribed mechanisms of action, including a series of bicyclic azetidines that inhibit a new antimalarial target, phenylalanyl-tRNA synthetase. These molecules are curative in mice at a single, low dose and show activity against all parasite life stages in multiple in vivo efficacy models. Our findings identify bicyclic azetidines with the potential to both cure and prevent transmission of the disease as well as protect at-risk populations with a single oral dose, highlighting the strength of diversity-oriented synthesis in revealing promising therapeutic targets.

Data-analysis strategies for image-based cell profiling.

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

DNA Barcoding a Complete Matrix of Stereoisomeric Small Molecules.

It is challenging to incorporate stereochemical diversity and topographic complexity into DNA-encoded libraries (DELs) because DEL syntheses cannot fully exploit the capabilities of modern synthetic organic chemistry. Here, we describe the design, construction, and validation of DOS-DEL-1, a library of 107 616 DNA-barcoded chiral 2,3-disubsituted azetidines and pyrrolidines. We used stereospecific C-H arylation chemistry to furnish complex scaffolds primed for DEL synthesis, and we developed an improved on-DNA Suzuki reaction to maximize library quality. We then studied both the structural diversity of the library and the physicochemical properties of individual compounds using Tanimoto multifusion similarity analysis, among other techniques. These analyses revealed not only that most DOS-DEL-1 members have "drug-like" properties, but also that the library more closely resembles compound collections derived from diversity synthesis than those from other sources (e.g., commercial vendors). Finally, we performed validation screens against horseradish peroxidase and carbonic anhydrase IX, and we developed a novel, Poisson-based statistical framework to analyze the results. A set of assay positives were successfully translated into potent carbonic anhydrase inhibitors (IC50 = 20.1-68.7 nM), which confirmed the success of the synthesis and screening procedures. These results establish a strategy to synthesize DELs with scaffold-based stereochemical diversity and complexity that does not require the development of novel DNA-compatible chemistry.

RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines.

Motivation: In recent years there have been several efforts to generate sensitivity profiles of collections of genomically characterized cell lines to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based on cellular, genetic, or expression biomarkers of cancer cells. However, a remaining challenge is an efficient way to identify accurate sets of biomarkers to validate. To address this challenge, we developed methodology using gene-expression profiles of human cancer cell lines to predict the responses of these cell lines to a panel of compounds. Results: We developed an iterative weighting scheme which, when applied to elastic net, a regularized regression method, significantly improves the overall accuracy of predictions, particularly in the highly sensitive response region. In addition to application of these methods to actual chemical sensitivity data, we investigated the effects of sample size, number of features, model sparsity, signal-to-noise ratio, and feature correlation on predictive performance using a simulation framework, particularly for situations where the number of covariates is much larger than sample size. While our method aims to be useful in therapeutic discovery and understanding of the basic mechanisms of action of drugs and their targets, it is generally applicable in any domain where predictions of extreme responses are of highest importance. Availability and implementation: The iterative and other weighting algorithms were implemented in R. The code is available at https://github.com/kiwtir/RWEN. The CTRP data are available at ftp://caftpd.nci.nih.gov/pub/OCG-DCC/CTD2/Broad/CTRPv2.1_2016_pub_NatChemBiol_12_109/ and the Sanger data at ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/release-6.0/. Supplementary information: Supplementary data are available at Bioinformatics online.

Synergistic Effects of Stereochemistry and Appendages on the Performance Diversity of a Collection of Synthetic Compounds.

Target- and phenotype-agnostic assessments of biological activity have emerged as viable strategies for prioritizing scaffolds, structural features, and synthetic pathways in screening sets, with the goal of increasing performance diversity. Here, we describe the synthesis of a small library of functionalized stereoisomeric azetidines and its biological annotation by "cell painting," a multiplexed, high-content imaging assay capable of measuring many hundreds of compound-induced changes in cell morphology in a quantitative and unbiased fashion. Using this approach, we systematically compare the degrees to which a core scaffold's biological activity, inferred from its effects on cell morphology, is affected by variations in stereochemistry and appendages. We show that stereoisomerism and appendage diversification can produce effects of similar magnitude, and that the concurrent use of these strategies results in a broader sampling of biological activity.

Integrating phenotypic small-molecule profiling and human genetics: the next phase in drug discovery.

Over the past decade, tremendous progress in high-throughput small molecule-screening methods has facilitated the rapid expansion of phenotype-based data. Parallel advances in genomic characterization methods have complemented these efforts by providing a growing list of annotated cell line features. Together, these developments have paved the way for feature-based identification of novel, exploitable cellular dependencies, subsequently expanding our therapeutic toolkit in cancer and other diseases. Here, we provide an overview of the evolution of phenotypic small-molecule profiling and discuss the most significant and recent profiling and analytical efforts, their impact on the field, and their clinical ramifications. We additionally provide a perspective for future developments in phenotypic profiling efforts guided by genomic science.