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Gains and losses of DNA are prevalent in cancer and emerge as a consequence of inter-related processes of replication stress, mitotic errors, spindle multipolarity and breakage–fusion–bridge cycles, among others, which may lead to chromosomal instability and aneuploidy1,2. These copy number alterations contribute to cancer initiation, progression and therapeutic resistance3–5. Here we present a conceptual framework to examine the patterns of copy number alterations in human cancer that is widely applicable to diverse data types, including whole-genome sequencing, whole-exome sequencing, reduced representation bisulfite sequencing, single-cell DNA sequencing and SNP6 microarray data. Deploying this framework to 9,873 cancers representing 33 human cancer types from The Cancer Genome Atlas6 revealed a set of 21 copy number signatures that explain the copy number patterns of 97% of samples. Seventeen copy number signatures were attributed to biological phenomena of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, chromothripsis and haploidization. The aetiologies of four copy number signatures remain unexplained. Some cancer types harbour amplicon signatures associated with extrachromosomal DNA, disease-specific survival and proto-oncogene gains such as MDM2. In contrast to base-scale mutational signatures, no copy number signature was associated with many known exogenous cancer risk factors. Our results synthesize the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes that give rise to these alterations.

Clustered somatic mutations are common in cancer genomes and previous analyses reveal several types of clustered single-base substitutions, which include doublet- and multi-base substitutions1–5, diffuse hypermutation termed omikli6, and longer strand-coordinated events termed kataegis3,7–9. Here we provide a comprehensive characterization of clustered substitutions and clustered small insertions and deletions (indels) across 2,583 whole-genome-sequenced cancers from 30 types of cancer10. Clustered mutations were highly enriched in driver genes and associated with differential gene expression and changes in overall survival. Several distinct mutational processes gave rise to clustered indels, including signatures that were enriched in tobacco smokers and homologous-recombination-deficient cancers. Doublet-base substitutions were caused by at least 12 mutational processes, whereas most multi-base substitutions were generated by either tobacco smoking or exposure to ultraviolet light. Omikli events, which have previously been attributed to APOBEC3 activity6, accounted for a large proportion of clustered substitutions; however, only 16.2% of omikli matched APOBEC3 patterns. Kataegis was generated by multiple mutational processes, and 76.1% of all kataegic events exhibited mutational patterns that are associated with the activation-induced deaminase (AID) and APOBEC3 family of deaminases. Co-occurrence of APOBEC3 kataegis and extrachromosomal DNA (ecDNA), termed kyklonas (Greek for cyclone), was found in 31% of samples with ecDNA. Multiple distinct kyklonic events were observed on most mutated ecDNA. ecDNA containing known cancer genes exhibited both positive selection and kyklonic hypermutation. Our results reveal the diversity of clustered mutational processes in human cancer and the role of APOBEC3 in recurrently mutating and fuelling the evolution of ecDNA.

Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that—in the case of the interdependent tumor cell morphology and adapted immune response—reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Esophageal squamous cell carcinoma (ESCC) shows remarkable variation in incidence that is not fully explained by known lifestyle and environmental risk factors. It has been speculated that an unknown exogenous exposure(s) could be responsible. Here we combine the fields of mutational signature analysis with cancer epidemiology to study 552 ESCC genomes from eight countries with varying incidence rates. Mutational profiles were similar across all countries studied. Associations between specific mutational signatures and ESCC risk factors were identified for tobacco, alcohol, opium and germline variants, with modest impacts on mutation burden. We find no evidence of a mutational signature indicative of an exogenous exposure capable of explaining differences in ESCC incidence. Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC)-associated mutational signatures single-base substitution (SBS)2 and SBS13 were present in 88% and 91% of cases, respectively, and accounted for 25% of the mutation burden on average, indicating that APOBEC activation is a crucial step in ESCC tumor development.

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues. published

Lung cancer in never smokers (LCINS) is a common cause of cancer mortality but its genomic landscape is poorly characterized. Here high-coverage whole-genome sequencing of 232 LCINS showed 3 subtypes defined by copy number aberrations. The dominant subtype (piano), which is rare in lung cancer in smokers, features somatic UBA1 mutations, germline AR variants and stem cell-like properties, including low mutational burden, high intratumor heterogeneity, long telomeres, frequent KRAS mutations and slow growth, as suggested by the occurrence of cancer drivers' progenitor cells many years before tumor diagnosis. The other subtypes are characterized by specific amplifications and EGFR mutations (mezzo-forte) and whole-genome doubling (forte). No strong tobacco smoking signatures were detected, even in cases with exposure to secondhand tobacco smoke. Genes within the receptor tyrosine kinase-Ras pathway had distinct impacts on survival; five genomic alterations independently doubled mortality. These findings create avenues for personalized treatment in LCINS.

SummaryMalignant Pleural Mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Using the largest series of whole-genome sequencing data integrated with transcriptomic and epigenomic data using multi-omic factor analysis, we demonstrate that MPM heterogeneity arises from four sources of variation: tumor cell morphology, ploidy, adaptive immune response, and CpG island methylator phenotype. Previous genomic studies focused on describing only the tumor cell morphology factor, although we robustly find the three other sources in all publicly available cohorts. We prove how these sources of variation explain the biological functions performed by the cancer cells, and how genomic events shape MPM molecular profiles. We show how these new sources of variation help understand the heterogeneity of the clinical behavior of MPM and drug responses measured in cell lines. These findings unearth the interplay between MPM functional biology and its genomic history, and ultimately, inform classification, prognostication and treatment.Graphical abstract

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for

Esophageal squamous cell carcinoma (ESCC) shows a remarkable variation in incidence which is not fully explained by known lifestyle and environmental risk factors. It has been speculated that an unknown exogenous exposure(s) could be responsible. Here we combine the fields of mutational signature analysis with cancer epidemiology to study 552 ESCC genomes from eight countries with varying incidence rates. The mutational profiles of ESCC were similar across all countries studied. Associations between specific mutational signatures and ESCC risk factors were identified for tobacco, alcohol, opium and germline variants, with modest impacts on mutation burden. We find no evidence of a mutational signature indicative of an exogenous exposure capable of explaining the differences in ESCC incidence. APOBEC associated mutational signatures SBS2 and SBS13 were present in 88% and 91% of cases respectively and accounted for a quarter of the mutation burden on average, indicating that activation of APOBEC is a crucial step in ESCC tumor development.

The gains and losses of DNA that emerge as a consequence of mitotic errors and chromosomal instability are prevalent in cancer. These copy number alterations contribute to cancer initiaition, progression and therapeutic resistance. Here, we present a conceptual framework for examining the patterns of copy number alterations in human cancer using whole-genome sequencing, whole-exome sequencing, and SNP6 microarray data making it widely applicable to diverse datasets. Deploying this framework to 9,873 cancers representing 33 human cancer types from the TCGA project revealed a set of 19 copy number signatures that explain the copy number patterns of 93% of TCGA samples. 15 copy number signatures were attributed to biological processes of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, and chromothripsis. The aetiology of four copy number signatures are unexplained and some cancer types have unique patterns of amplicon signatures associated with extrachromosomal DNA, disease-specific survival, and gains of proto-oncogenes such as MDM2. In contrast to base-scale mutational signatures, no copy number signature associated with known cancer risk factors. The results provide a foundation for exploring patterns of copy number changes in cancer genomes and synthesise the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes giving rise to copy number changes.

SUMMARYMutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

Unresolved conflicts among healthcare professionals can lead to difficult patient care consequences. This scoping review examines the current healthcare literature that reported sources and consequences of conflict associated with individual, interpersonal, and organisational factors. We identified 99 articles published between 2001 and 2015 from PubMed, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medical Database. Most reviewed studies relied on healthcare professionals' perceptions and beliefs associated with conflict sources and consequences, with few studies reporting behavioural or organisational change outcomes. Individual conflict sources included personal traits, such as self-focus, self-esteem, or worldview, as well as individuals' conflict management styles. These conflicts posed threats to one's physical, mental, and emotional health and to one's ability to perform at work. Interpersonal dynamics were hampered by colleagues' uncivil behaviours, such as low degree of support, to more destructive behaviours including bullying or humiliation. Perceptions of disrespectful working environment and weakened team collaboration were the main interpersonal conflict consequences. Organisational conflict sources included ambiguity in professional roles, scope of practice, reporting structure, or workflows, negatively affecting healthcare professionals' job satisfactions and intent to stay. Future inquiries into healthcare conflict research may target the following: shifting from research involving single professions to multiple professions; dissemination of studies via journals that promote interprofessional research; inquiries into the roles of unconscious or implicit bias, or psychological capital (i.e., resilience) in healthcare conflict; and diversification of data sources to include hospital or clinic data with implications for conflict sources.