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.

Macroautophagy (hereafter referred to as autophagy) is an evolutionarily conserved catabolic process necessary for normal recycling of cellular constituents and for appropriate response to cellular stress. Although several genes belonging to the core molecular machinery involved in autophagosome formation have been discovered, relatively little is known about the nature of signaling networks controlling autophagy upon intracellular or extracellular stimuli. We discovered ATG8-like proteins (MAP1LC3B, GABARAP and GABARAPL1) as novel interactors of MAPK15/ERK8, a MAP kinase involved in cell proliferation and transformation. Based on the role of these proteins in the autophagic process, we demonstrated that MAPK15 is indeed localized to autophagic compartments and increased, in a kinase-dependent fashion, ATG8-like proteins lipidation, autophagosome formation and SQSTM1 degradation, while decreasing LC3B inhibitory phosphorylation. Interestingly, we also identified a conserved LC3-interacting region (LIR) in MAPK15 responsible for its interaction with ATG8-like proteins, for its localization to autophagic structures and, consequently, for stimulation of the formation of these compartments. Furthermore, we reveal that MAPK15 activity was induced in response to serum and amino-acid starvation and that this stimulus, in turn, required endogenous MAPK15 expression to induce the autophagic process. Altogether, these results suggested a new function for MAPK15 as a regulator of autophagy, acting through interaction with ATG8 family proteins. Also, based on the key role of this process in several human diseases, these results supported the use of this MAP kinase as a potential novel therapeutic target.

Cancer cells that express oncogenic alleles of RAS typically require sustained expression of the mutant allele for survival, but the molecular basis of this oncogene dependency remains incompletely understood. To identify genes that can functionally substitute for oncogenic RAS, we systematically expressed 15,294 open reading frames in a human KRAS-dependent colon cancer cell line engineered to express an inducible KRAS-specific shRNA. We found 147 genes that promoted survival upon KRAS suppression. In particular, the transcriptional coactivator YAP1 rescued cell viability in KRAS-dependent cells upon suppression of KRAS and was required for KRAS-induced cell transformation. Acquired resistance to Kras suppression in a Kras-driven murine lung cancer model also involved increased YAP1 signaling. KRAS and YAP1 converge on the transcription factor FOS and activate a transcriptional program involved in regulating the epithelial-mesenchymal transition (EMT). Together, these findings implicate transcriptional regulation of EMT by YAP1 as a significant component of oncogenic RAS signaling.

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of preexisting minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Notably, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have major implications for PDX-based modeling of human cancer.

The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.

Dysregulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway occurs frequently in human cancer. PTEN tumor suppressor or PIK3CA oncogene mutations both direct PI3K-dependent tumorigenesis largely through activation of the AKT/PKB kinase. However, here we show through phosphoprotein profiling and functional genomic studies that many PIK3CA mutant cancer cell lines and human breast tumors exhibit only minimal AKT activation and a diminished reliance on AKT for anchorage-independent growth. Instead, these cells retain robust PDK1 activation and membrane localization and exhibit dependency on the PDK1 substrate SGK3. SGK3 undergoes PI3K- and PDK1-dependent activation in PIK3CA mutant cancer cells. Thus, PI3K may promote cancer through both AKT-dependent and AKT-independent mechanisms. Knowledge of differential PI3K/PDK1 signaling could inform rational therapeutics in cancers harboring PIK3CA mutations.

Whole-exome sequencing of cell-free DNA (cfDNA) could enable comprehensive profiling of tumors from blood but the genome-wide concordance between cfDNA and tumor biopsies is uncertain. Here we report ichorCNA, software that quantifies tumor content in cfDNA from 0.1× coverage whole-genome sequencing data without prior knowledge of tumor mutations. We apply ichorCNA to 1439 blood samples from 520 patients with metastatic prostate or breast cancers. In the earliest tested sample for each patient, 34% of patients have ≥10% tumor-derived cfDNA, sufficient for standard coverage whole-exome sequencing. Using whole-exome sequencing, we validate the concordance of clonal somatic mutations (88%), copy number alterations (80%), mutational signatures, and neoantigens between cfDNA and matched tumor biopsies from 41 patients with ≥10% cfDNA tumor content. In summary, we provide methods to identify patients eligible for comprehensive cfDNA profiling, revealing its applicability to many patients, and demonstrate high concordance of cfDNA and metastatic tumor whole-exome sequencing.

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.

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from short-term transcriptional responses to treatment.

Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts. We provide a web app to browse predictions: http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting datasets.

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.

Cancer evolution poses a central obstacle to cure, as resistant clones expand under therapeutic selection pressures. Genome sequencing of relapsed disease can nominate genomic alterations conferring resistance but sample collection lags behind, limiting therapeutic innovation. Genome-wide screens offer a complementary approach to chart the compendium of escape genotypes, anticipating clinical resistance. We report genome-wide open reading frame (ORF) resistance screens for first- and third-generation epidermal growth factor receptor (EGFR) inhibitors and a MEK inhibitor. Using serial sampling, dose gradients, and mathematical modeling, we generate genotype-fitness maps across therapeutic contexts and identify alterations that escape therapy. Our data expose varying dose-fitness relationship across genotypes, ranging from complete dose invariance to paradoxical dose dependency where fitness increases in higher doses. We predict fitness with combination therapy and compare these estimates to genome-wide fitness maps of drug combinations, identifying genotypes where combination therapy results in unexpected inferior effectiveness. These data are applied to nominate combination optimization strategies to forestall resistant disease.

Despite advances in precision medicine, the clinical prospects for patients with ovarian and uterine cancers have not substantially improved. Here, we analyzed genome-scale CRISPR-Cas9 loss-of-function screens across 851 human cancer cell lines and found that frequent overexpression of SLC34A2-encoding a phosphate importer-is correlated with sensitivity to loss of the phosphate exporter XPR1, both in vitro and in vivo. In patient-derived tumor samples, we observed frequent PAX8-dependent overexpression of SLC34A2, XPR1 copy number amplifications and XPR1 messenger RNA overexpression. Mechanistically, in SLC34A2-high cancer cell lines, genetic or pharmacologic inhibition of XPR1-dependent phosphate efflux leads to the toxic accumulation of intracellular phosphate. Finally, we show that XPR1 requires the novel partner protein KIDINS220 for proper cellular localization and activity, and that disruption of this protein complex results in acidic "vacuolar" structures preceding cell death. These data point to the XPR1-KIDINS220 complex and phosphate dysregulation as a therapeutic vulnerability in ovarian cancer.

CRISPR-Cas9-based genetic screens have successfully identified cell type-dependent liabilities in cancer, including acute myeloid leukemia (AML), a devastating hematologic malignancy with poor overall survival. Because most of these screens have been performed in vitro using established cell lines, evaluating the physiologic relevance of these targets is critical. We have established a CRISPR screening approach using orthotopic xenograft models to validate and prioritize AML-enriched dependencies in vivo, including in CRISPR-competent AML patient-derived xenograft (PDX) models tractable for genome editing. Our integrated pipeline has revealed several targets with translational value, including SLC5A3 as a metabolic vulnerability for AML addicted to exogenous myo-inositol and MARCH5 as a critical guardian to prevent apoptosis in AML. MARCH5 repression enhanced the efficacy of BCL2 inhibitors such as venetoclax, further highlighting the clinical potential of targeting MARCH5 in AML. Our study provides a valuable strategy for discovery and prioritization of new candidate AML therapeutic targets. SIGNIFICANCE: There is an unmet need to improve the clinical outcome of AML. We developed an integrated in vivo screening approach to prioritize and validate AML dependencies with high translational potential. We identified SLC5A3 as a metabolic vulnerability and MARCH5 as a critical apoptosis regulator in AML, both of which represent novel therapeutic opportunities.This article is highlighted in the In This Issue feature, p. 275.

Human genome variation contributes to diversity in neurodevelopmental outcomes and vulnerabilities; recognizing the underlying molecular and cellular mechanisms will require scalable approaches. Here, we describe a "cell village" experimental platform we used to analyze genetic, molecular, and phenotypic heterogeneity across neural progenitor cells from 44 human donors cultured in a shared in vitro environment using algorithms (Dropulation and Census-seq) to assign cells and phenotypes to individual donors. Through rapid induction of human stem cell-derived neural progenitor cells, measurements of natural genetic variation, and CRISPR-Cas9 genetic perturbations, we identified a common variant that regulates antiviral IFITM3 expression and explains most inter-individual variation in susceptibility to the Zika virus. We also detected expression QTLs corresponding to GWAS loci for brain traits and discovered novel disease-relevant regulators of progenitor proliferation and differentiation such as CACHD1. This approach provides scalable ways to elucidate the effects of genes and genetic variation on cellular phenotypes.

Comprehensive analyses of cancer genomes promise to inform prognoses and precise cancer treatments. A major barrier, however, is inaccessibility of metastatic tissue. A potential solution is to characterize circulating tumor cells (CTCs), but this requires overcoming the challenges of isolating rare cells and sequencing low-input material. Here we report an integrated process to isolate, qualify and sequence whole exomes of CTCs with high fidelity using a census-based sequencing strategy. Power calculations suggest that mapping of >99.995% of the standard exome is possible in CTCs. We validated our process in two patients with prostate cancer, including one for whom we sequenced CTCs, a lymph node metastasis and nine cores of the primary tumor. Fifty-one of 73 CTC mutations (70%) were present in matched tissue. Moreover, we identified 10 early trunk and 56 metastatic trunk mutations in the non-CTC tumor samples and found 90% and 73% of these mutations, respectively, in CTC exomes. This study establishes a foundation for CTC genomics in the clinic.

Unlike most tumor suppressor genes, the most common genetic alterations in tumor protein p53 (TP53) are missense mutations1,2. Mutant p53 protein is often abundantly expressed in cancers and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models3-8. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53 wild-type and null cell lines. We found that loss or dominant-negative inhibition of wild-type p53 function reliably enhanced cellular fitness. By integrating these data with the Catalog of Somatic Mutations in Cancer (COSMIC) mutational signatures database9,10, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.

In vitro cancer cultures, including three-dimensional organoids, typically contain exclusively neoplastic epithelium but require artificial reconstitution to recapitulate the tumor microenvironment (TME). The co-culture of primary tumor epithelia with endogenous, syngeneic tumor-infiltrating lymphocytes (TILs) as a cohesive unit has been particularly elusive. Here, an air-liquid interface (ALI) method propagated patient-derived organoids (PDOs) from >100 human biopsies or mouse tumors in syngeneic immunocompetent hosts as tumor epithelia with native embedded immune cells (T, B, NK, macrophages). Robust droplet-based, single-cell simultaneous determination of gene expression and immune repertoire indicated that PDO TILs accurately preserved the original tumor T cell receptor (TCR) spectrum. Crucially, human and murine PDOs successfully modeled immune checkpoint blockade (ICB) with anti-PD-1- and/or anti-PD-L1 expanding and activating tumor antigen-specific TILs and eliciting tumor cytotoxicity. Organoid-based propagation of primary tumor epithelium en bloc with endogenous immune stroma should enable immuno-oncology investigations within the TME and facilitate personalized immunotherapy testing.

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.