Characterizing Mutational Signatures in Human Cancer Cell Lines Reveals Episodic APOBEC Mutagenesis.

Multiple signatures of somatic mutations have been identified in cancer genomes. Exome sequences of 1,001 human cancer cell lines and 577 xenografts revealed most common mutational signatures, indicating past activity of the underlying processes, usually in appropriate cancer types. To investigate ongoing patterns of mutational-signature generation, cell lines were cultured for extended periods and subsequently DNA sequenced. Signatures of discontinued exposures, including tobacco smoke and ultraviolet light, were not generated in vitro. Signatures of normal and defective DNA repair and replication continued to be generated at roughly stable mutation rates. Signatures of APOBEC cytidine deaminase DNA-editing exhibited substantial fluctuations in mutation rate over time with episodic bursts of mutations. The initiating factors for the bursts are unclear, although retrotransposon mobilization may contribute. The examined cell lines constitute a resource of live experimental models of mutational processes, which potentially retain patterns of activity and regulation operative in primary human cancers.

A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds.

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

A Landscape of Pharmacogenomic Interactions in Cancer.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Prospective derivation of a living organoid biobank of colorectal cancer patients.

In Rspondin-based 3D cultures, Lgr5 stem cells from multiple organs form ever-expanding epithelial organoids that retain their tissue identity. We report the establishment of tumor organoid cultures from 20 consecutive colorectal carcinoma (CRC) patients. For most, organoids were also generated from adjacent normal tissue. Organoids closely recapitulate several properties of the original tumor. The spectrum of genetic changes within the "living biobank" agrees well with previous large-scale mutational analyses of CRC. Gene expression analysis indicates that the major CRC molecular subtypes are represented. Tumor organoids are amenable to high-throughput drug screens allowing detection of gene-drug associations. As an example, a single organoid culture was exquisitely sensitive to Wnt secretion (porcupine) inhibitors and carried a mutation in the negative Wnt feedback regulator RNF43, rather than in APC. Organoid technology may fill the gap between cancer genetics and patient trials, complement cell-line- and xenograft-based drug studies, and allow personalized therapy design. PAPERCLIP.

Effective drug combinations in breast, colon and pancreatic cancer cells.

Combinations of anti-cancer drugs can overcome resistance and provide new treatments1,2. The number of possible drug combinations vastly exceeds what could be tested clinically. Efforts to systematically identify active combinations and the tissues and molecular contexts in which they are most effective could accelerate the development of combination treatments. Here we evaluate the potency and efficacy of 2,025 clinically relevant two-drug combinations, generating a dataset encompassing 125 molecularly characterized breast, colorectal and pancreatic cancer cell lines. We show that synergy between drugs is rare and highly context-dependent, and that combinations of targeted agents are most likely to be synergistic. We incorporate multi-omic molecular features to identify combination biomarkers and specify synergistic drug combinations and their active contexts, including in basal-like breast cancer, and microsatellite-stable or KRAS-mutant colon cancer. Our results show that irinotecan and CHEK1 inhibition have synergistic effects in microsatellite-stable or KRAS-TP53 double-mutant colon cancer cells, leading to apoptosis and suppression of tumour xenograft growth. This study identifies clinically relevant effective drug combinations in distinct molecular subpopulations and is a resource to guide rational efforts to develop combinatorial drug treatments.

Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens.

Functional genomics approaches can overcome limitations-such as the lack of identification of robust targets and poor clinical efficacy-that hamper cancer drug development. Here we performed genome-scale CRISPR-Cas9 screens in 324 human cancer cell lines from 30 cancer types and developed a data-driven framework to prioritize candidates for cancer therapeutics. We integrated cell fitness effects with genomic biomarkers and target tractability for drug development to systematically prioritize new targets in defined tissues and genotypes. We verified one of our most promising dependencies, the Werner syndrome ATP-dependent helicase, as a synthetic lethal target in tumours from multiple cancer types with microsatellite instability. Our analysis provides a resource of cancer dependencies, generates a framework to prioritize cancer drug targets and suggests specific new targets. The principles described in this study can inform the initial stages of drug development by contributing to a new, diverse and more effective portfolio of cancer drug targets.

Single-cell transcriptomic analysis of human pleura reveals stromal heterogeneity and informs in vitro models of mesothelioma.

The pleural lining of the thorax regulates local immunity, inflammation and repair. A variety of conditions, both benign and malignant, including pleural mesothelioma, can affect this tissue. A lack of knowledge concerning the mesothelial and stromal cells comprising the pleura has hampered the development of targeted therapies. Here, we present the first comprehensive single-cell transcriptomic atlas of the human parietal pleura and demonstrate its utility in elucidating pleural biology. We confirm the presence of known universal fibroblasts and describe novel, potentially pleural-specific, fibroblast subtypes. We also present transcriptomic characterisation of multiple in vitro models of benign and malignant mesothelial cells, and characterise these through comparison with in vivo transcriptomic data. While bulk pleural transcriptomes have been reported previously, this is the first study to provide resolution at the single-cell level. We expect our pleural cell atlas will prove invaluable to those studying pleural biology and disease. It has already enabled us to shed light on the transdifferentiation of mesothelial cells, allowing us to develop a simple method for prolonging mesothelial cell differentiation in vitro.

Tissue Organoid Cultures Metabolize Dietary Carcinogens Proficiently and Are Effective Models for DNA Adduct Formation.

Human tissue three-dimensional (3D) organoid cultures have the potential to reproduce in vitro the physiological properties and cellular architecture of the organs from which they are derived. The ability of organoid cultures derived from human stomach, liver, kidney, and colon to metabolically activate three dietary carcinogens, aflatoxin B1 (AFB1), aristolochic acid I (AAI), and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), was investigated. In each case, the response of a target tissue (liver for AFB1; kidney for AAI; colon for PhIP) was compared with that of a nontarget tissue (gastric). After treatment cell viabilities were measured, DNA damage response (DDR) was determined by Western blotting for p-p53, p21, p-CHK2, and γ-H2AX, and DNA adduct formation was quantified by mass spectrometry. Induction of the key xenobiotic-metabolizing enzymes (XMEs) CYP1A1, CYP1A2, CYP3A4, and NQO1 was assessed by qRT-PCR. We found that organoids from different tissues can activate AAI, AFB1, and PhIP. In some cases, this metabolic potential varied between tissues and between different cultures of the same tissue. Similarly, variations in the levels of expression of XMEs were observed. At comparable levels of cytotoxicity, organoids derived from tissues that are considered targets for these carcinogens had higher levels of adduct formation than a nontarget tissue.

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets.

CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.

Pancreatic cancer organoids recapitulate disease and allow personalized drug screening.

We report the derivation of 30 patient-derived organoid lines (PDOs) from tumors arising in the pancreas and distal bile duct. PDOs recapitulate tumor histology and contain genetic alterations typical of pancreatic cancer. In vitro testing of a panel of 76 therapeutic agents revealed sensitivities currently not exploited in the clinic, and underscores the importance of personalized approaches for effective cancer treatment. The PRMT5 inhibitor EZP015556, shown to target MTAP (a gene commonly lost in pancreatic cancer)-negative tumors, was validated as such, but also appeared to constitute an effective therapy for a subset of MTAP-positive tumors. Taken together, the work presented here provides a platform to identify novel therapeutics to target pancreatic tumor cells using PDOs.

CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens.

BACKGROUND: CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from context-specific essential genes. This is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases. RESULTS: We have developed CoRe: an R package implementing existing and novel methods for the identification of core-fitness genes (at two different level of stringency) from joint analyses of multiple CRISPR-Cas9 screens. We demonstrate, through a fully reproducible benchmarking pipeline, that CoRe outperforms state-of-the-art tools, yielding more reliable and biologically relevant sets of core-fitness genes. CONCLUSIONS: CoRe offers a flexible pipeline, compatible with many pre-processing methods for the analysis of CRISPR data, which can be tailored onto different use-cases. The CoRe package can be used for the identification of high-confidence novel core-fitness genes, as well as a means to filter out potentially cytotoxic hits while analysing cancer dependency datasets for identifying and prioritising novel selective therapeutic targets.

SLFN11 informs on standard of care and novel treatments in a wide range of cancer models.

BACKGROUND: Schlafen 11 (SLFN11) has been linked with response to DNA-damaging agents (DDA) and PARP inhibitors. An in-depth understanding of several aspects of its role as a biomarker in cancer is missing, as is a comprehensive analysis of the clinical significance of SLFN11 as a predictive biomarker to DDA and/or DNA damage-response inhibitor (DDRi) therapies. METHODS: We used a multidisciplinary effort combining specific immunohistochemistry, pharmacology tests, anticancer combination therapies and mechanistic studies to assess SLFN11 as a potential biomarker for stratification of patients treated with several DDA and/or DDRi in the preclinical and clinical setting. RESULTS: SLFN11 protein associated with both preclinical and patient treatment response to DDA, but not to non-DDA or DDRi therapies, such as WEE1 inhibitor or olaparib in breast cancer. SLFN11-low/absent cancers were identified across different tumour types tested. Combinations of DDA with DDRi targeting the replication-stress response (ATR, CHK1 and WEE1) could re-sensitise SLFN11-absent/low cancer models to the DDA treatment and were effective in upper gastrointestinal and genitourinary malignancies. CONCLUSION: SLFN11 informs on the standard of care chemotherapy based on DDA and the effect of selected combinations with ATR, WEE1 or CHK1 inhibitor in a wide range of cancer types and models.

Use of preclinical models for malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is an aggressive cancer most commonly caused by prior exposure to asbestos. Median survival is 12-18 months, since surgery is ineffective and chemotherapy offers minimal benefit. Preclinical models that faithfully recapitulate the genomic and histopathological features of cancer are critical for the development of new treatments. The most commonly used models of MPM are two-dimensional cell lines established from primary tumours or pleural fluid. While these have provided some important insights into MPM biology, these cell models have significant limitations. In order to address some of these limitations, spheroids and microfluidic chips have more recently been used to investigate the role of the three-dimensional environment in MPM. Efforts have also been made to develop animal models of MPM, including asbestos-induced murine tumour models, MPM-prone genetically modified mice and patient-derived xenografts. Here, we discuss the available in vitro and in vivo models of MPM and highlight their strengths and limitations. We discuss how newer technologies, such as the tumour-derived organoids, might allow us to address the limitations of existing models and aid in the identification of effective treatments for this challenging-to-treat disease.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies.

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models.

In vitro cancer cell cultures are facile experimental models used widely for research and drug development. Many cancer cell lines are available and efforts are ongoing to derive new models representing the histopathological and molecular diversity of tumours. Cell models have been generated by multiple laboratories over decades and consequently their annotation is incomplete and inconsistent. Furthermore, the relationships between many patient-matched and derivative cell lines have been lost, and accessing information and datasets is time-consuming and difficult. Here, we describe the Cell Model Passports database; cellmodelpassports.sanger.ac.uk, which provides details of cell model relationships, patient and clinical information, as well as access to associated genetic and functional datasets. The Passports database currently contains curated details and standardized annotation for >1200 cell models, including cancer organoid cultures. The Passports will be updated with newly derived cell models and datasets as they are generated. Users can navigate the database via tissue, cancer-type, genetic feature and data availability to select a model most suitable for specific applications. A flexible REST-API provides programmatic data access and exploration. The Cell Model Passports are a valuable tool enabling access to high-dimensional genomic and phenotypic cancer cell model datasets empowering diverse research applications.

Inferred Ancestral Origin of Cancer Cell Lines Associates with Differential Drug Response.

Disparities between risk, treatment outcomes and survival rates in cancer patients across the world may be attributed to socioeconomic factors. In addition, the role of ancestry is frequently discussed. In preclinical studies, high-throughput drug screens in cancer cell lines have empowered the identification of clinically relevant molecular biomarkers of drug sensitivity; however, the genetic ancestry from tissue donors has been largely neglected in this setting. In order to address this, here, we show that the inferred ancestry of cancer cell lines is conserved and may impact drug response in patients as a predictive covariate in high-throughput drug screens. We found that there are differential drug responses between European and East Asian ancestries, especially when treated with PI3K/mTOR inhibitors. Our finding emphasizes a new angle in precision medicine, as cancer intervention strategies should consider the germline landscape, thereby reducing the failure rate of clinical trials.

GDSCTools for mining pharmacogenomic interactions in cancer.

Motivation: Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. Results: We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic datasets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (analysis of variance) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC and to implement new analytical methods. In addition, non-GDSC data resources can also be analysed since drug responses and genomic features can be encoded as CSV files. Contact: thomas.cokelaer@pasteur.fr or saezrodriguez.rwth-aachen.de or mg12@sanger.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting.

BACKGROUND: Genome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes. RESULTS: Applying our method to existing and newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised read counts, is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries. CONCLUSIONS: CRISPRcleanR is a versatile open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.