An IROA Workflow for correction and normalization of ion suppression in mass spectrometry-based metabolomic profiling data

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044

The journal and authors wish to retract the article entitled 'Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images' cited above [...].

PCA-Plus: Enhanced principal component analysis with illustrative applications to batch effects and their quantitation

Background

Principal component analysis (PCA), a standard approach to analysis and visualization of large datasets, is commonly used in biomedical research for detecting similarities and differences among groups of samples. We initially used conventional PCA as a tool for critical quality control of batch and trend effects in multi-omic profiling data produced by The Cancer Genome Atlas (TCGA) project of the NCI. We found, however, that conventional PCA visualizations were often hard to interpret when inter-batch differences were moderate in comparison with intra-batch differences; it was also difficult to quantify batch effects objectively. We, therefore, sought enhancements to make the method more informative in those and analogous settings.

Results

We have developed algorithms and a toolbox of enhancements to conventional PCA that improve the detection, diagnosis, and quantitation of differences between or among groups, e.g., groups of molecularly profiled biological samples. The enhancements include (i) computed group centroids; (ii) sample-dispersion rays; (iii) differential coloring of centroids, rays, and sample data points; (iii) trend trajectories; and (iv) a novel separation index (DSC) for quantitation of differences among groups.

Conclusions

PCA-Plus has been our most useful single tool for analyzing, visualizing, and quantitating batch effects, trend effects, and class differences in molecular profiling data of many types: mRNA expression, microRNA expression, DNA methylation, and DNA copy number. An early version of PCA-Plus has been used as the central graphical visualization in our MBatch package for near-real-time surveillance of data for analysis working groups in more than 70 TCGA, PanCancer Atlas, PanCancer Analysis of Whole Genomes, and Genome Data Analysis Network projects of the NCI. The algorithms and software are generic, hence applicable more generally to other types of multivariate data as well. PCA-Plus is freely available in a down-loadable R package at our MBatch website.

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for deconvolving large cohorts of disease tissues, and potentially cancer.

Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images

BACKGROUND

Ovarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images.

METHODS

We have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response.

RESULTS

The study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results.

CONCLUSIONS

This type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.

Ether phospholipids are required for mitochondrial reactive oxygen species homeostasis

Mitochondria are hubs where bioenergetics, redox homeostasis, and anabolic metabolism pathways integrate through a tightly coordinated flux of metabolites. The contributions of mitochondrial metabolism to tumor growth and therapy resistance are evident, but drugs targeting mitochondrial metabolism have repeatedly failed in the clinic. Our study in pancreatic ductal adenocarcinoma (PDAC) finds that cellular and mitochondrial lipid composition influence cancer cell sensitivity to pharmacological inhibition of electron transport chain complex I. Profiling of patient-derived PDAC models revealed that monounsaturated fatty acids (MUFAs) and MUFA-linked ether phospholipids play a critical role in maintaining ROS homeostasis. We show that ether phospholipids support mitochondrial supercomplex assembly and ROS production; accordingly, blocking de novo ether phospholipid biosynthesis sensitized PDAC cells to complex I inhibition by inducing mitochondrial ROS and lipid peroxidation. These data identify ether phospholipids as a regulator of mitochondrial redox control that contributes to the sensitivity of PDAC cells to complex I inhibition.

RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data

SUMMARY

Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting.

AVAILABILITY AND IMPLEMENTATION

The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The origin of bladder cancer from mucosal field effects

Whole-organ mapping was used to study molecular changes in the evolution of bladder cancer from field effects. We identified more than 100 dysregulated pathways, involving immunity, differentiation, and transformation, as initiators of carcinogenesis. Dysregulation of interleukins signified the involvement of inflammation in the incipient phases of the process. An aberrant methylation/expression of multiple HOX genes signified dysregulation of the differentiation program. We identified three types of mutations based on their geographic distribution. The most common were mutations restricted to individual mucosal samples that targeted uroprogenitor cells. Two types of mutations were associated with clonal expansion and involved large areas of mucosa. The α mutations occurred at low frequencies while the β mutations increased in frequency with disease progression. Modeling revealed that bladder carcinogenesis spans 10-15 years and can be divided into dormant and progressive phases. The progressive phase lasted 1-2 years and was driven by β mutations.

Compound NSC84167 selectively targets NRF2-activated pancreatic cancer by inhibiting asparagine synthesis pathway

Nuclear factor erythroid 2-related factor 2 (NRF2) is aberrantly activated in about 93% of pancreatic cancers. Activated NRF2 regulates multiple downstream molecules involved in cancer cell metabolic reprogramming, translational control, and treatment resistance; however, targeting NRF2 for pancreatic cancer therapy remains largely unexplored. In this study, we used the online computational tool CellMinerTM to explore the NCI-60 drug databases for compounds with anticancer activities correlating most closely with the mRNA expression of NQO1, a marker for NRF2 pathway activity. Among the >100,000 compounds analyzed, NSC84167, termed herein as NRF2 synthetic lethality compound-01 (NSLC01), was one of the top hits (r = 0.71, P < 0.001) and selected for functional characterization. NSLC01 selectively inhibited the viabilities of four out of seven conventional pancreatic cancer cell lines and induced dramatic apoptosis in the cells with high NRF2 activation. The selective anticancer activity of NSLC01 was further validated with a panel of nine low-passage pancreatic patient-derived cell lines, and a significant reverse correlation between log(IC50) of NSLC01 and NQO1 expression was confirmed (r = -0.5563, P = 0.024). Notably, screening of a panel of nine patient-derived xenografts (PDXs) revealed six PDXs with high NQO1/NRF2 activation, and NSLC01 dramatically inhibited the viabilities and induced apoptosis in ex vivo cultures of PDX tumors. Consistent with the ex vivo results, NSLC01 inhibited the tumor growth of two NRF2-activated PDX models in vivo (P < 0.01, n = 7-8) but had no effects on the NRF2-low counterpart. To characterize the mechanism of action, we employed a metabolomic isotope tracer assay that demonstrated that NSLC01-mediated inhibition of de novo synthesis of multiple amino acids, including asparagine and methionine. Importantly, we further found that NSLC01 suppresses the eEF2K/eEF2 translation elongation cascade and protein translation of asparagine synthetase. In summary, this study identified a novel compound that selectively targets protein translation and induces synthetic lethal effects in NRF2-activated pancreatic cancers.

Abstract 87: Asparagine synthetase (ASNS) expression predicts response to the GLS1 inhibitor IPN60090 in ovarian cancer through selective modulation of redox homeostasis

Inhibitors of tumor metabolism have shown promise in the pre-clinical and clinical settings, however success is likely dependent upon identification of responsive patient populations to drive maximum benefit. We have previously disclosed the development of the GLS1 inhibitor, IPN60090, which is currently progressing through Phase 1 studies (NCT03894540). Current efforts are focused on developing additional patient stratification biomarkers to define those patients who will most benefit from IPN60090 single-agent treatment or combination strategies. Here we demonstrate that IPN60090 elicited a specific set of metabolic alterations and selectively inhibited the growth of high grade serous ovarian cancer (HGSOC) models in vitro and in vivo. In IPN60090-sensitive OvCa cell lines, GLS1 inhibition induced glutathione (GSH) depletion, inhibited glutamine anapleurosis (GLN), and altered cell cycle kinetics resulting from depletion of intracellular nucleotide pools and accumulation of DNA damage. Untargeted metabolic profiling of IPN60090-sensitive and -insensitive cell lines revealed that the differential response was driven by the ability of insensitive cell lines to maintain intracellular pools of glutamate (GLU), and consequently GSH, through consumption of aspartate and alanine. We examined two transaminases whose activity may result in aspartate or alanine depletion in cells, asparagine synthetase (ASNS) and glutamate pyruvate transaminase 2 (GPT2), and found that ASNS expression predicted response to IPN60090. In vivo, growth of both subcutaneous and orthotopic ASNSlow OvCa tumors was inhibited by IPN60090, while ASNShigh tumors were resistant to IPN60090. Leveraging tissue microarrays from tumor biopsies collected at MD Anderson Cancer Center, we developed an IHC assay for ASNS to determine the percentage of ASNS null or low patients that would benefit from IPN60090 treatment. Upon validation and CLIA certification, this assay was deployed across archival patient biopsies collected in the Department of Investigational Cancer Therapeutics at MD Anderson Cancer Center, and identified patients who showed no ASNS staining (ASNSnull) in their tumors, suggesting that they may benefit from treatment with IPN60090. Taken together, through a comprehensive translational effort we have identified ASNS as a predictive biomarker of response to GLS1 inhibitor-based therapeutic regimens.

Citation Format: Nakia D. Spencer, Christopher A. Bristow, Virginia Giulani, Meredith A. Miller, Alessandro Carugo, Angela L. Harris, Rosalba Minelli, Ningpeng Feng, Qing Chang, Michael J. Soth, Kang Le, John N. Weinstein, Philip L. Lorenzi, Jinsong Liu, Wei-Lien Wang, Timothy A. Yap, Giulio Draetta, Philip Jones, Timothy P. Heffernan, Jeffrey J. Kovacs. Asparagine synthetase (ASNS) expression predicts response to the GLS1 inhibitor IPN60090 in ovarian cancer through selective modulation of redox homeostasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 87.

Whole-genome characterization of lung adenocarcinomas lacking the RTK/RAS/RAF pathway

RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(-) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(-) cases are required to understand this important LUAD subset.

Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas

The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.

Abstract 5704: Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases

Brain metastases (BM) occur in 10-30% of patients with cancer. Approximately 200,000 new cases of brain metastases are diagnosed in the United States annually, with median survival after diagnosis ranging from 3 to 27 months. Recently, studies have identified significant genetic differences between BM and their corresponding primary tumors. It has been shown that BM harbor clinically actionable mutations that are distinct from those in the primary tumor samples. Additional genomic profiling of BM will provide deeper understanding of the pathogenesis of BM and suggest new therapeutic approaches.

We performed whole-exome sequencing of BM and matched tumors from 41 patients collected from renal cell carcinoma (RCC), breast cancer, lung cancer, and melanoma, which are known to be more likely to develop BM. We profiled total 126 fresh-frozen tumor samples and performed subsequent analyses of BM in comparison to paired primary tumor and extracranial metastases (ECM). We found that lung cancer shared the largest number of mutations between BM and matched tumors (83%), followed by melanoma (74%), RCC (51%), and Breast (26%), indicating that cancer type with high tumor mutational burden share more mutations with BM. Mutational signatures displayed limited differences, suggesting a lack of mutagenic processes specific to BM. However, point-mutation heterogeneity revealed that BM evolve separately into different subclones from their paired tumors regardless of cancer type, and some cancer driver genes were found in BM-specific subclones. These models and findings suggest that these driver genes may drive prometastatic subclones that lead to BM. 32 curated cancer gene mutations were detected and 71% of them were shared between BM and primary tumors or ECM. 29% of mutations were specific to BM, implying that BM often accumulate additional cancer gene mutations that are not present in primary tumors or ECM. Co-mutation analysis revealed a high frequency of TP53 nonsense mutation in BM, mostly in the DNA binding domain, suggesting TP53 nonsense mutation as a possible prerequisite for the development of BM. Copy number alteration analysis showed statistically significant differences between BM and their paired tumor samples in each cancer type (Wilcoxon test, p &lt; 0.0385 for all). Both copy number gains and losses were consistently higher in BM for breast cancer (Wilcoxon test, p =1.307e-5) and lung cancer (Wilcoxon test, p =1.942e-5), implying greater genomic instability during the evolution of BM.

Our findings highlight that there are more unique mutations in BM, with significantly higher copy number alterations and tumor mutational burden. These genomic analyses could provide an opportunity for more reliable diagnostic decision-making, and these findings will be further tested with additional transcriptomic and epigenetic profiling for better characterization of BM-specific tumor microenvironments.

Citation Format: Olivia W. Lee, Akash Mitra, Won-Chul Lee, Kazutaka Fukumura, Hannah Beird, Miles Andrews, Grant Fischer, John N. Weinstein, Michael A. Davies, Jason Huse, P. Andrew Futreal. Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5704.

Dysregulation of EMT Drives the Progression to Clinically Aggressive Sarcomatoid Bladder Cancer

Sarcomatoid urothelial bladder cancer (SARC) displays a high propensity for distant metastasis and is associated with short survival. We report a comprehensive genomic analysis of 28 cases of SARC and 84 cases of conventional urothelial carcinoma (UC), with the TCGA cohort of 408 muscle-invasive bladder cancers serving as the reference. SARCs show a distinct mutational landscape, with enrichment of TP53, RB1, and PIK3CA mutations. They are related to the basal molecular subtype of conventional UCs and could be divided into epithelial-basal and more clinically aggressive mesenchymal subsets on the basis of TP63 and its target gene expression levels. Other analyses reveal that SARCs are driven by downregulation of homotypic adherence genes and dysregulation of the EMT network, and nearly half exhibit a heavily infiltrated immune phenotype. Our observations have important implications for prognostication and the development of more effective therapies for this highly lethal variant of bladder cancer.

Urothelial-to-Neural Plasticity Drives Progression to Small Cell Bladder Cancer

We report a comprehensive molecular analysis of 34 cases of small cell carcinoma (SCC) and 84 cases of conventional urothelial carcinoma (UC), with The Cancer Genome Atlas cohort of 408 conventional UC bladder cancers used as the reference. SCCs showed mutational landscapes characterized by nearly uniform inactivation of TP53 and were dominated by Sanger mutation signature 3 associated with loss of BRCA1/2 function. SCCs were characterized by downregulation of luminal and basal markers and were referred to as double-negative. Transcriptome analyses indicated that SCCs displayed lineage plasticity driven by a urothelial-to-neural phenotypic switch with a dysregulated epithelial-to-mesenchymal transition network. SCCs were depleted of immune cells, and expressed high levels of the immune checkpoint receptor, adenosine receptor A2A (ADORA2A), which is a potent inhibitor of immune infiltration. Our observations have important implications for the prognostication and development of more effective therapies for this lethal bladder cancer variant.

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SCCs show TP53/RB1 loss with mutational signature of BRCA1/2 loss of function

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SCCs are driven by neural phenotypic switch with dysregulated EMT network

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SCCs show depleted immune phenotype with upregulation of ADORA2A

Mechanism of Catalysis by l-Asparaginase

Two bacterial type II l-asparaginases, from Escherichia coli and Dickeya chrysanthemi, have played a critical role for more than 40 years as therapeutic agents against juvenile leukemias and lymphomas. Despite a long history of successful pharmacological applications and the apparent simplicity of the catalytic reaction, controversies still exist regarding major steps of the mechanism. In this report, we provide a detailed description of the reaction catalyzed by E. coli type II l-asparaginase (EcAII). Our model was developed on the basis of new structural and biochemical experiments combined with previously published data. The proposed mechanism is supported by quantum chemistry calculations based on density functional theory. We provide strong evidence that EcAII catalyzes the reaction according to the double-displacement (ping-pong) mechanism, with formation of a covalent intermediate. Several steps of catalysis by EcAII are unique when compared to reactions catalyzed by other known hydrolytic enzymes. Here, the reaction is initiated by a weak nucleophile, threonine, without direct assistance of a general base, although a distant general base is identified. Furthermore, tetrahedral intermediates formed during the catalytic process are stabilized by a never previously described motif. Although the scheme of the catalytic mechanism was developed only on the basis of data obtained from EcAII and its variants, this novel mechanism of enzymatic hydrolysis could potentially apply to most (and possibly all) l-asparaginases.

Comprehensive molecular characterization of mitochondrial genomes in human cancers

Mitochondria are essential cellular organelles that play critical roles in cancer. Here, as part of the International Cancer Genome Consortium/The Cancer Genome Atlas Pan-Cancer Analysis of Whole Genomes Consortium, which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types, we performed a multidimensional, integrated characterization of mitochondrial genomes and related RNA sequencing data. Our analysis presents the most definitive mutational landscape of mitochondrial genomes and identifies several hypermutated cases. Truncating mutations are markedly enriched in kidney, colorectal and thyroid cancers, suggesting oncogenic effects with the activation of signaling pathways. We find frequent somatic nuclear transfers of mitochondrial DNA, some of which disrupt therapeutic target genes. Mitochondrial copy number varies greatly within and across cancers and correlates with clinical variables. Co-expression analysis highlights the function of mitochondrial genes in oxidative phosphorylation, DNA repair and the cell cycle, and shows their connections with clinically actionable genes. Our study lays a foundation for translating mitochondrial biology into clinical applications.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.