Defining a Cancer Dependency Map.

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.

ß-Catenin-driven cancers require a YAP1 transcriptional complex for survival and tumorigenesis.

Wnt/ß-catenin signaling plays a key role in the pathogenesis of colon and other cancers; emerging evidence indicates that oncogenic ß-catenin regulates several biological processes essential for cancer initiation and progression. To decipher the role of ß-catenin in transformation, we classified ß-catenin activity in 85 cancer cell lines in which we performed genome-scale loss-of-function screens and found that ß-catenin active cancers are dependent on a signaling pathway involving the transcriptional regulator YAP1. Specifically, we found that YAP1 and the transcription factor TBX5 form a complex with ß-catenin. Phosphorylation of YAP1 by the tyrosine kinase YES1 leads to localization of this complex to the promoters of antiapoptotic genes, including BCL2L1 and BIRC5. A small-molecule inhibitor of YES1 impeded the proliferation of ß-catenin-dependent cancers in both cell lines and animal models. These observations define a ß-catenin-YAP1-TBX5 complex essential to the transformation and survival of ß-catenin-driven cancers.

WRN helicase is a synthetic lethal target in microsatellite unstable cancers.

Synthetic lethality-an interaction between two genetic events through which the co-occurrence of these two genetic events leads to cell death, but each event alone does not-can be exploited for cancer therapeutics1. DNA repair processes represent attractive synthetic lethal targets, because many cancers exhibit an impairment of a DNA repair pathway, which can lead to dependence on specific repair proteins2. The success of poly(ADP-ribose) polymerase 1 (PARP-1) inhibitors in cancers with deficiencies in homologous recombination highlights the potential of this approach3. Hypothesizing that other DNA repair defects would give rise to synthetic lethal relationships, we queried dependencies in cancers with microsatellite instability (MSI), which results from deficient DNA mismatch repair. Here we analysed data from large-scale silencing screens using CRISPR-Cas9-mediated knockout and RNA interference, and found that the RecQ DNA helicase WRN was selectively essential in MSI models in vitro and in vivo, yet dispensable in models of cancers that are microsatellite stable. Depletion of WRN induced double-stranded DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models required the helicase activity of WRN, but not its exonuclease activity. These findings show that WRN is a synthetic lethal vulnerability and promising drug target for MSI cancers.

Genetic and transcriptional evolution alters cancer cell line drug response.

Human cancer cell lines are the workhorse of cancer research. Although cell lines are known to evolve in culture, the extent of the resultant genetic and transcriptional heterogeneity and its functional consequences remain understudied. Here we use genomic analyses of 106 human cell lines grown in two laboratories to show extensive clonal diversity. Further comprehensive genomic characterization of 27 strains of the common breast cancer cell line MCF7 uncovered rapid genetic diversification. Similar results were obtained with multiple strains of 13 additional cell lines. Notably, genetic changes were associated with differential activation of gene expression programs and marked differences in cell morphology and proliferation. Barcoding experiments showed that cell line evolution occurs as a result of positive clonal selection that is highly sensitive to culture conditions. Analyses of single-cell-derived clones demonstrated that continuous instability quickly translates into heterogeneity of the cell line. When the 27 MCF7 strains were tested against 321 anti-cancer compounds, we uncovered considerably different drug responses: at least 75% of compounds that strongly inhibited some strains were completely inactive in others. This study documents the extent, origins and consequences of genetic variation within cell lines, and provides a framework for researchers to measure such variation in efforts to support maximally reproducible cancer research.

Computational estimation of quality and clinical relevance of cancer cell lines.

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.

A PGC1α-mediated transcriptional axis suppresses melanoma metastasis.

Melanoma is the deadliest form of commonly encountered skin cancer because of its rapid progression towards metastasis. Although metabolic reprogramming is tightly associated with tumour progression, the effect of metabolic regulatory circuits on metastatic processes is poorly understood. PGC1α is a transcriptional coactivator that promotes mitochondrial biogenesis, protects against oxidative stress and reprograms melanoma metabolism to influence drug sensitivity and survival. Here, we provide data indicating that PGC1α suppresses melanoma metastasis, acting through a pathway distinct from that of its bioenergetic functions. Elevated PGC1α expression inversely correlates with vertical growth in human melanoma specimens. PGC1α silencing makes poorly metastatic melanoma cells highly invasive and, conversely, PGC1α reconstitution suppresses metastasis. Within populations of melanoma cells, there is a marked heterogeneity in PGC1α levels, which predicts their inherent high or low metastatic capacity. Mechanistically, PGC1α directly increases transcription of ID2, which in turn binds to and inactivates the transcription factor TCF4. Inactive TCF4 causes downregulation of metastasis-related genes, including integrins that are known to influence invasion and metastasis. Inhibition of BRAFV600E using vemurafenib, independently of its cytostatic effects, suppresses metastasis by acting on the PGC1α-ID2-TCF4-integrin axis. Together, our findings reveal that PGC1α maintains mitochondrial energetic metabolism and suppresses metastasis through direct regulation of parallel acting transcriptional programs. Consequently, components of these circuits define new therapeutic opportunities that may help to curb melanoma metastasis.

Bridging the gap between cancer cell line models and tumours using gene expression data.

Cancer cell line models are a cornerstone of cancer research, yet our understanding of how well they represent the molecular features of patient tumours remains limited. Our recent work provides a computational approach to systematically compare large gene expression datasets to better understand which cell lines most closely resemble each tumour type, as well as identify potential gaps in our current cancer models.

The Cancer Dependency Map enables drug mechanism-of-action investigations.

How do small molecules exert their effects in mammalian cells? This seemingly simple question continues to represent one of the fundamental challenges of modern translational science and as such has long been the subject of intense scientific scrutiny. In their recent study, Garnett and colleagues (Gonçalves et al, 2020) demonstrate proof-of-concept for a new way to attack this problem systematically for Oncology drugs, by identifying correlated CRISPR- and drug-killing profiles in the Cancer Dependency Map dataset.

Cyclin D1-Cdk4 controls glucose metabolism independently of cell cycle progression.

Insulin constitutes a principal evolutionarily conserved hormonal axis for maintaining glucose homeostasis; dysregulation of this axis causes diabetes. PGC-1α (peroxisome-proliferator-activated receptor-γ coactivator-1α) links insulin signalling to the expression of glucose and lipid metabolic genes. The histone acetyltransferase GCN5 (general control non-repressed protein 5) acetylates PGC-1α and suppresses its transcriptional activity, whereas sirtuin 1 deacetylates and activates PGC-1α. Although insulin is a mitogenic signal in proliferative cells, whether components of the cell cycle machinery contribute to its metabolic action is poorly understood. Here we report that in mice insulin activates cyclin D1-cyclin-dependent kinase 4 (Cdk4), which, in turn, increases GCN5 acetyltransferase activity and suppresses hepatic glucose production independently of cell cycle progression. Through a cell-based high-throughput chemical screen, we identify a Cdk4 inhibitor that potently decreases PGC-1α acetylation. Insulin/GSK-3ß (glycogen synthase kinase 3-beta) signalling induces cyclin D1 protein stability by sequestering cyclin D1 in the nucleus. In parallel, dietary amino acids increase hepatic cyclin D1 messenger RNA transcripts. Activated cyclin D1-Cdk4 kinase phosphorylates and activates GCN5, which then acetylates and inhibits PGC-1α activity on gluconeogenic genes. Loss of hepatic cyclin D1 results in increased gluconeogenesis and hyperglycaemia. In diabetic models, cyclin D1-Cdk4 is chronically elevated and refractory to fasting/feeding transitions; nevertheless further activation of this kinase normalizes glycaemia. Our findings show that insulin uses components of the cell cycle machinery in post-mitotic cells to control glucose homeostasis independently of cell division.

PIK3CA mutant tumors depend on oxoglutarate dehydrogenase.

Oncogenic PIK3CA mutations are found in a significant fraction of human cancers, but therapeutic inhibition of PI3K has only shown limited success in clinical trials. To understand how mutant PIK3CA contributes to cancer cell proliferation, we used genome scale loss-of-function screening in a large number of genomically annotated cancer cell lines. As expected, we found that PIK3CA mutant cancer cells require PIK3CA but also require the expression of the TCA cycle enzyme 2-oxoglutarate dehydrogenase (OGDH). To understand the relationship between oncogenic PIK3CA and OGDH function, we interrogated metabolic requirements and found an increased reliance on glucose metabolism to sustain PIK3CA mutant cell proliferation. Functional metabolic studies revealed that OGDH suppression increased levels of the metabolite 2-oxoglutarate (2OG). We found that this increase in 2OG levels, either by OGDH suppression or exogenous 2OG treatment, resulted in aspartate depletion that was specifically manifested as auxotrophy within PIK3CA mutant cells. Reduced levels of aspartate deregulated the malate-aspartate shuttle, which is important for cytoplasmic NAD+ regeneration that sustains rapid glucose breakdown through glycolysis. Consequently, because PIK3CA mutant cells exhibit a profound reliance on glucose metabolism, malate-aspartate shuttle deregulation leads to a specific proliferative block due to the inability to maintain NAD+/NADH homeostasis. Together these observations define a precise metabolic vulnerability imposed by a recurrently mutated oncogene.

The EMT regulator ZEB2 is a novel dependency of human and murine acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease with complex molecular pathophysiology. To systematically characterize AML's genetic dependencies, we conducted genome-scale short hairpin RNA screens in 17 AML cell lines and analyzed dependencies relative to parallel screens in 199 cell lines of other cancer types. We identified 353 genes specifically required for AML cell proliferation. To validate the in vivo relevance of genetic dependencies observed in human cell lines, we performed a secondary screen in a syngeneic murine AML model driven by the MLL-AF9 oncogenic fusion protein. Integrating the results of these interference RNA screens and additional gene expression data, we identified the transcription factor ZEB2 as a novel AML dependency. ZEB2 depletion impaired the proliferation of both human and mouse AML cells and resulted in aberrant differentiation of human AML cells. Mechanistically, we showed that ZEB2 transcriptionally represses genes that regulate myeloid differentiation, including genes involved in cell adhesion and migration. In addition, we found that epigenetic silencing of the miR-200 family microRNAs affects ZEB2 expression. Our results extend the role of ZEB2 beyond regulating epithelial-mesenchymal transition (EMT) and establish ZEB2 as a novel regulator of AML proliferation and differentiation.

In vivo multiplexed interrogation of amplified genes identifies GAB2 as an ovarian cancer oncogene.

High-grade serous ovarian cancers are characterized by widespread recurrent copy number alterations. Although some regions of copy number change harbor known oncogenes and tumor suppressor genes, the genes targeted by the majority of amplified or deleted regions in ovarian cancer remain undefined. Here we systematically tested amplified genes for their ability to promote tumor formation using an in vivo multiplexed transformation assay. We identified the GRB2-associated binding protein 2 (GAB2) as a recurrently amplified gene that potently transforms immortalized ovarian and fallopian tube secretory epithelial cells. Cancer cell lines overexpressing GAB2 require GAB2 for survival and show evidence of phosphatidylinositol 3-kinase (PI3K) pathway activation, which was required for GAB2-induced transformation. Cell lines overexpressing GAB2 were as sensitive to PI3K inhibition as cell lines harboring mutant PIK3CA. Together, these observations nominate GAB2 as an ovarian cancer oncogene, identify an alternative mechanism to activate PI3K signaling, and underscore the importance of PI3K signaling in this cancer.

The present and future of the Cancer Dependency Map.

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data.

BACKGROUND: CRISPR-Cas9 dropout screens are formidable tools for investigating biology with unprecedented precision and scale. However, biases in data lead to potential confounding effects on interpretation and compromise overall quality. The activity of Cas9 is influenced by structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend to generate similar gene-independent responses to CRISPR-Cas9 targeting (proximity bias), possibly due to Cas9-induced whole chromosome-arm truncations or other genomic structural features and different chromatin accessibility levels. RESULTS: We benchmarked eight computational methods, rigorously evaluating their ability to reduce both CN and proximity bias in the two largest publicly available cell-line-based CRISPR-Cas9 screens to date. We also evaluated the capability of each method to preserve data quality and heterogeneity by assessing the extent to which the processed data allows accurate detection of true positive essential genes, established oncogenetic addictions, and known/novel biomarkers of cancer dependency. Our analysis sheds light on the ability of each method to correct biases under different scenarios. AC-Chronos outperforms other methods in correcting both CN and proximity biases when jointly processing multiple screens of models with available CN information, whereas CRISPRcleanR is the top performing method for individual screens or when CN information is not available. In addition, Chronos and AC-Chronos yield a final dataset better able to recapitulate known sets of essential and non-essential genes. CONCLUSIONS: Overall, our investigation provides guidance for the selection of the most appropriate bias-correction method, based on its strengths, weaknesses and experimental settings.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution.

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

RPL22 is a tumor suppressor in MSI-high cancers and a splicing regulator of MDM4.

Microsatellite instability-high (MSI-H) tumors are malignant tumors that, despite harboring a high mutational burden, often have intact TP53. One of the most frequent mutations in MSI-H tumors is a frameshift mutation in RPL22, a ribosomal protein. Here, we identified RPL22 as a modulator of MDM4 splicing through an alternative splicing switch in exon 6. RPL22 loss increases MDM4 exon 6 inclusion and cell proliferation and augments resistance to the MDM inhibitor Nutlin-3a. RPL22 represses the expression of its paralog, RPL22L1, by mediating the splicing of a cryptic exon corresponding to a truncated transcript. Therefore, damaging mutations in RPL22 drive oncogenic MDM4 induction and reveal a common splicing circuit in MSI-H tumors that may inform therapeutic targeting of the MDM4-p53 axis and oncogenic RPL22L1 induction.

Human aneuploid cells depend on the RAF/MEK/ERK pathway for overcoming increased DNA damage.

Aneuploidy is a hallmark of human cancer, yet the molecular mechanisms to cope with aneuploidy-induced cellular stresses remain largely unknown. Here, we induce chromosome mis-segregation in non-transformed RPE1-hTERT cells and derive multiple stable clones with various degrees of aneuploidy. We perform a systematic genomic, transcriptomic and proteomic profiling of 6 isogenic clones, using whole-exome DNA, mRNA and miRNA sequencing, as well as proteomics. Concomitantly, we functionally interrogate their cellular vulnerabilities, using genome-wide CRISPR/Cas9 and large-scale drug screens. Aneuploid clones activate the DNA damage response and are more resistant to further DNA damage induction. Aneuploid cells also exhibit elevated RAF/MEK/ERK pathway activity and are more sensitive to clinically-relevant drugs targeting this pathway, and in particular to CRAF inhibition. Importantly, CRAF and MEK inhibition sensitize aneuploid cells to DNA damage-inducing chemotherapies and to PARP inhibitors. We validate these results in human cancer cell lines. Moreover, resistance of cancer patients to olaparib is associated with high levels of RAF/MEK/ERK signaling, specifically in highly-aneuploid tumors. Overall, our study provides a comprehensive resource for genetically-matched karyotypically-stable cells of various aneuploidy states, and reveals a therapeutically-relevant cellular dependency of aneuploid cells.

Increased RNA and protein degradation is required for counteracting transcriptional burden and proteotoxic stress in human aneuploid cells.

Aneuploidy results in a stoichiometric imbalance of protein complexes that jeopardizes cellular fitness. Aneuploid cells thus need to compensate for the imbalanced DNA levels by regulating their RNA and protein levels, but the underlying molecular mechanisms remain unknown. Here, we dissected multiple diploid vs. aneuploid cell models. We found that aneuploid cells cope with transcriptional burden by increasing several RNA degradation pathways, and are consequently more sensitive to the perturbation of RNA degradation. At the protein level, aneuploid cells mitigate proteotoxic stress by reducing protein translation and increasing protein degradation, rendering them more sensitive to proteasome inhibition. These findings were recapitulated across hundreds of human cancer cell lines and primary tumors, and aneuploidy levels were significantly associated with the response of multiple myeloma patients to proteasome inhibitors. Aneuploid cells are therefore preferentially dependent on several key nodes along the gene expression process, creating clinically-actionable vulnerabilities in aneuploid cells.

Generation of a biliary tract cancer cell line atlas reveals molecular subtypes and therapeutic targets.

Biliary tract cancers (BTCs) are a group of deadly malignancies encompassing intrahepatic and extrahepatic cholangiocarcinoma, gallbladder carcinoma, and ampullary carcinoma. Here, we present the integrative analysis of 63 BTC cell lines via multi-omics clustering and genome- scale CRISPR screens, providing a platform to illuminate BTC biology and inform therapeutic development. We identify dependencies broadly enriched in BTC compared to other cancers as well as dependencies selective to the anatomic subtypes. Notably, cholangiocarcinoma cell lines are stratified into distinct lineage subtypes based on biliary or dual biliary/hepatocyte marker signatures, associated with dependency on specific lineage survival factors. Transcriptional analysis of patient specimens demonstrates the prognostic significance of these lineage subtypes. Additionally, we delineate strategies to enhance targeted therapies or to overcome resistance in cell lines with key driver gene mutations. Furthermore, clustering based on dependencies and proteomics data elucidates unexpected functional relationships, including a BTC subgroup with partial squamous differentiation. Thus, this cell line atlas reveals potential therapeutic targets in molecularly defined BTCs, unveils biologically distinct disease subtypes, and offers a vital resource for BTC research.