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 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.

MED12 controls the response to multiple cancer drugs through regulation of TGF-ß receptor signaling.

Inhibitors of the ALK and EGF receptor tyrosine kinases provoke dramatic but short-lived responses in lung cancers harboring EML4-ALK translocations or activating mutations of EGFR, respectively. We used a large-scale RNAi screen to identify MED12, a component of the transcriptional MEDIATOR complex that is mutated in cancers, as a determinant of response to ALK and EGFR inhibitors. MED12 is in part cytoplasmic where it negatively regulates TGF-ßR2 through physical interaction. MED12 suppression therefore results in activation of TGF-ßR signaling, which is both necessary and sufficient for drug resistance. TGF-ß signaling causes MEK/ERK activation, and consequently MED12 suppression also confers resistance to MEK and BRAF inhibitors in other cancers. MED12 loss induces an EMT-like phenotype, which is associated with chemotherapy resistance in colon cancer patients and to gefitinib in lung cancer. Inhibition of TGF-ßR signaling restores drug responsiveness in MED12(KD) cells, suggesting a strategy to treat drug-resistant tumors that have lost MED12.

Author Correction: The evolutionary history of lethal metastatic prostate cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations.

Accumulating evidence implicates heterogeneity within cancer cell populations in the response to stressful exposures, including drug treatments. While modeling the acute response to various anticancer agents in drug-sensitive human tumor cell lines, we consistently detected a small subpopulation of reversibly "drug-tolerant" cells. These cells demonstrate >100-fold reduced drug sensitivity and maintain viability via engagement of IGF-1 receptor signaling and an altered chromatin state that requires the histone demethylase RBP2/KDM5A/Jarid1A. This drug-tolerant phenotype is transiently acquired and relinquished at low frequency by individual cells within the population, implicating the dynamic regulation of phenotypic heterogeneity in drug tolerance. The drug-tolerant subpopulation can be selectively ablated by treatment with IGF-1 receptor inhibitors or chromatin-modifying agents, potentially yielding a therapeutic opportunity. Together, these findings suggest that cancer cell populations employ a dynamic survival strategy in which individual cells transiently assume a reversibly drug-tolerant state to protect the population from eradication by potentially lethal exposures.

Mutually exclusive genetic interactions and gene essentiality shape the genomic landscape of primary melanoma.

Melanoma is a heterogenous malignancy with an unpredictable clinical course. Most patients who present in the clinic are diagnosed with primary melanoma, yet large-scale sequencing efforts have focused primarily on metastatic disease. In this study we sequence-profiled 524 American Joint Committee on Cancer Stage I-III primary tumours. Our analysis of these data reveals recurrent driver mutations, mutually exclusive genetic interactions, where two genes were never or rarely co-mutated, and an absence of co-occurring genetic events. Further, we intersected copy number calls from our primary melanoma data with whole-genome CRISPR screening data to identify the transcription factor interferon regulatory factor 4 (IRF4) as a melanoma-associated dependency. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.

OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN: Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS: Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION: This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

The evolutionary history of lethal metastatic prostate cancer.

Cancers emerge from an ongoing Darwinian evolutionary process, often leading to multiple competing subclones within a single primary tumour. This evolutionary process culminates in the formation of metastases, which is the cause of 90% of cancer-related deaths. However, despite its clinical importance, little is known about the principles governing the dissemination of cancer cells to distant organs. Although the hypothesis that each metastasis originates from a single tumour cell is generally supported, recent studies using mouse models of cancer demonstrated the existence of polyclonal seeding from and interclonal cooperation between multiple subclones. Here we sought definitive evidence for the existence of polyclonal seeding in human malignancy and to establish the clonal relationship among different metastases in the context of androgen-deprived metastatic prostate cancer. Using whole-genome sequencing, we characterized multiple metastases arising from prostate tumours in ten patients. Integrated analyses of subclonal architecture revealed the patterns of metastatic spread in unprecedented detail. Metastasis-to-metastasis spread was found to be common, either through de novo monoclonal seeding of daughter metastases or, in five cases, through the transfer of multiple tumour clones between metastatic sites. Lesions affecting tumour suppressor genes usually occur as single events, whereas mutations in genes involved in androgen receptor signalling commonly involve multiple, convergent events in different metastases. Our results elucidate in detail the complex patterns of metastatic spread and further our understanding of the development of resistance to androgen-deprivation therapy in prostate cancer.

Genome-wide chemical mutagenesis screens allow unbiased saturation of the cancer genome and identification of drug resistance mutations.

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases, this is the consequence of specific gene mutations that have the potential to be targeted to resensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here, we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We used an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug-resistant patients.

XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data.

BACKGROUND: Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary. RESULTS: We developed the R-package XenofilteR, which separates mouse from human sequence reads based on the edit-distance between a sequence read and reference genome. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human DNA sequence data. These analyses revealed that XenofilteR removes > 99.9% of sequence reads of mouse origin while retaining human sequences. This allowed for mutation analysis of xenograft samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic tumor mutations. CONCLUSIONS: XenofilteR accurately dissects RNA and DNA sequences from mouse and human origin, thereby outperforming currently available tools. XenofilteR is open source and available at https://github.com/PeeperLab/XenofilteR .

Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells.

Mitochondrial genomes are separated from the nuclear genome for most of the cell cycle by the nuclear double membrane, intervening cytoplasm, and the mitochondrial double membrane. Despite these physical barriers, we show that somatically acquired mitochondrial-nuclear genome fusion sequences are present in cancer cells. Most occur in conjunction with intranuclear genomic rearrangements, and the features of the fusion fragments indicate that nonhomologous end joining and/or replication-dependent DNA double-strand break repair are the dominant mechanisms involved. Remarkably, mitochondrial-nuclear genome fusions occur at a similar rate per base pair of DNA as interchromosomal nuclear rearrangements, indicating the presence of a high frequency of contact between mitochondrial and nuclear DNA in some somatic cells. Transmission of mitochondrial DNA to the nuclear genome occurs in neoplastically transformed cells, but we do not exclude the possibility that some mitochondrial-nuclear DNA fusions observed in cancer occurred years earlier in normal somatic cells.

Potent organo-osmium compound shifts metabolism in epithelial ovarian cancer cells.

The organometallic "half-sandwich" compound [Os(η(6)-p-cymene)(4-(2-pyridylazo)-N,N-dimethylaniline)I]PF6 is 49× more potent than the clinical drug cisplatin in the 809 cancer cell lines that we screened and is a candidate drug for cancer therapy. We investigate the mechanism of action of compound 1 in A2780 epithelial ovarian cancer cells. Whole-transcriptome sequencing identified three missense mutations in the mitochondrial genome of this cell line, coding for ND5, a subunit of complex I (NADH dehydrogenase) in the electron transport chain. ND5 is a proton pump, helping to maintain the coupling gradient in mitochondria. The identified mutations correspond to known protein variants (p.I257V, p.N447S, and p.L517P), not reported previously in epithelial ovarian cancer. Time-series RNA sequencing suggested that osmium-exposed A2780 cells undergo a metabolic shunt from glycolysis to oxidative phosphorylation, where defective machinery, associated with mutations in complex I, could enhance activity. Downstream events, measured by time-series reverse-phase protein microarrays, high-content imaging, and flow cytometry, showed a dramatic increase in mitochondrially produced reactive oxygen species (ROS) and subsequent DNA damage with up-regulation of ATM, p53, and p21 proteins. In contrast to platinum drugs, exposure to this organo-osmium compound does not cause significant apoptosis within a 72-h period, highlighting a different mechanism of action. Superoxide production in ovarian, lung, colon, breast, and prostate cancer cells exposed to three other structurally related organo-Os(II) compounds correlated with their antiproliferative activity. DNA damage caused indirectly, through selective ROS generation, may provide a more targeted approach to cancer therapy and a concept for next-generation metal-based anticancer drugs that combat platinum resistance.

COSMIC: exploring the world's knowledge of somatic mutations in human cancer.

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://cancer.sanger.ac.uk) is the world's largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer. Our latest release (v70; Aug 2014) describes 2 002 811 coding point mutations in over one million tumor samples and across most human genes. To emphasize depth of knowledge on known cancer genes, mutation information is curated manually from the scientific literature, allowing very precise definitions of disease types and patient details. Combination of almost 20,000 published studies gives substantial resolution of how mutations and phenotypes relate in human cancer, providing insights into the stratification of mutations and biomarkers across cancer patient populations. Conversely, our curation of cancer genomes (over 12,000) emphasizes knowledge breadth, driving discovery of unrecognized cancer-driving hotspots and molecular targets. Our high-resolution curation approach is globally unique, giving substantial insight into molecular biomarkers in human oncology. In addition, COSMIC also details more than six million noncoding mutations, 10,534 gene fusions, 61,299 genome rearrangements, 695,504 abnormal copy number segments and 60,119,787 abnormal expression variants. All these types of somatic mutation are annotated to both the human genome and each affected coding gene, then correlated across disease and mutation types.

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.

Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.

Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFß as key predictors.

BACKGROUND: It is uncertain which biological features underpin the response of rectal cancer (RC) to radiotherapy. No biomarker is currently in clinical use to select patients for treatment modifications. METHODS: We identified two cohorts of patients (total N = 249) with RC treated with neoadjuvant radiotherapy (45Gy/25) plus fluoropyrimidine. This discovery set included 57 cases with pathological complete response (pCR) to chemoradiotherapy (23%). Pre-treatment cancer biopsies were assessed using transcriptome-wide mRNA expression and targeted DNA sequencing for copy number and driver mutations. Biological candidate and machine learning (ML) approaches were used to identify predictors of pCR to radiotherapy independent of tumour stage. Findings were assessed in 107 cases from an independent validation set (GSE87211). FINDINGS: Three gene expression sets showed significant independent associations with pCR: Fibroblast-TGFß Response Signature (F-TBRS) with radioresistance; and cytotoxic lymphocyte (CL) expression signature and consensus molecular subtype CMS1 with radiosensitivity. These associations were replicated in the validation cohort. In parallel, a gradient boosting machine model comprising the expression of 33 genes generated in the discovery cohort showed high performance in GSE87211 with 90% sensitivity, 86% specificity. Biological and ML signatures indicated similar mechanisms underlying radiation response, and showed better AUC and p-values than published transcriptomic signatures of radiation response in RC. INTERPRETATION: RCs responding completely to chemoradiotherapy (CRT) have biological characteristics of immune response and absence of immune inhibitory TGFß signalling. These tumours may be identified with a potential biomarker based on a 33 gene expression signature. This could help select patients likely to respond to treatment with a primary radiotherapy approach as for anal cancer. Conversely, those with predicted radioresistance may be candidates for clinical trials evaluating addition of immune-oncology agents and stromal TGFß signalling inhibition. FUNDING: The Stratification in Colorectal Cancer Consortium (S:CORT) was funded by the Medical Research Council and Cancer Research UK (MR/M016587/1).

Genome-wide CRISPR screens identify the YAP/TEAD axis as a driver of persister cells in EGFR mutant lung cancer.

Most lung cancer patients with metastatic cancer eventually relapse with drug-resistant disease following treatment and EGFR mutant lung cancer is no exception. Genome-wide CRISPR screens, to either knock out or overexpress all protein-coding genes in cancer cell lines, revealed the landscape of pathways that cause resistance to the EGFR inhibitors osimertinib or gefitinib in EGFR mutant lung cancer. Among the most recurrent resistance genes were those that regulate the Hippo pathway. Following osimertinib treatment a subpopulation of cancer cells are able to survive and over time develop stable resistance. These 'persister' cells can exploit non-genetic (transcriptional) programs that enable cancer cells to survive drug treatment. Using genetic and pharmacologic tools we identified Hippo signalling as an important non-genetic mechanism of cell survival following osimertinib treatment. Further, we show that combinatorial targeting of the Hippo pathway and EGFR is highly effective in EGFR mutant lung cancer cells and patient-derived organoids, suggesting a new therapeutic strategy for EGFR mutant lung cancer patients.

Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations.

Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. SIGNIFICANCE: We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.