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Analysis of genomic data from 981 colorectal cancers from participants in 11 countries reveals variations in mutational signatures of microsatellite-stable cancers that are dependent on geographical origin and age at which the cancer was diagnosed.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.

The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.

In somatic cells the mechanisms maintaining the chromosome ends are normally inactivated; however, cancer cells can re-activate these pathways to support continuous growth. Here, the authors characterize the telomeric landscapes across tumour types and identify genomic alterations associated with different telomere maintenance mechanisms.

Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes.

An analysis of 2,954 genomes from 38 cancer subtypes identified 19,166 retrotransposition events in 35% of samples. Aberrant LINE-1 retrotranspositions can lead to the deletion of tumor-suppressor genes as well as the amplification of oncogenes.

Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors across 38 cancer types is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.

The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

In this study the authors consider the structural variants (SVs) present within cancer cases of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. They report hundreds of genes, including known cancer-associated genes for which the nearby presence of a SV breakpoint is associated with altered expression.

Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

Some cancer patients first present with metastases where the ___location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

Many tumours exhibit hypoxia (low oxygen) and hypoxic tumours often respond poorly to therapy. Here, the authors quantify hypoxia in 1188 tumours from 27 cancer types, showing elevated hypoxia links to increased mutational load, directing evolutionary trajectories.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

Major histocompatibility complex (MHC) loss of heterozygosity, allele-specific mutation and measurement of expression and repression (MHC Hammer) detects disruption to human leukocyte antigens due to mutations, loss of heterogeneity, altered gene expression or alternative splicing. Applied to lung and breast cancer datasets, the tool shows that these aberrations are common across cancer and can have clinical implications.

Immune lymphocyte estimation from nucleotide sequencing (ImmuneLENS) infers B cell and T cell fractions from whole-genome sequencing data. Applied to the 100,000 Genomes Project datasets, circulating T cell fraction provides sex-dependent and prognostic insights in patients.

Swanton and colleagues present a clonal expression signature, ORACLE, which, in combination with clinicopathological and molecular risk factors, can predict survival of patients with lung adenocarcinoma, and they prospectively validate its prognostic value.

RNA sequencing data and tumour pathology observations of non-small-cell lung cancers indicate that the immune cell microenvironment exerts strong evolutionary selection pressures that shape the immune-evasion capacity of tumours.

Analysis of whole-genome sequencing data across 2,658 tumors spanning 38 cancer types shows that chromothripsis is pervasive, with a frequency of more than 50% in several cancer types, contributing to oncogene amplification, gene inactivation and cancer genome evolution.

A meta-analysis of genome-wide association studies of type 2 diabetes (T2D) identifies more than 600 T2D-associated loci; integrating physiological trait and single-cell chromatin accessibility data at these loci sheds light on heterogeneity within the T2D phenotype.

Sears and colleagues found that ongoing replication stress pathways and T cell clonal responses differentiate liver and lung recurrence and are associated with different clinical outcomes in the context of pancreatic ductal adenocarcinoma.

A study examines the diversity of extrachromosomal DNA elements in cancer, and provides details on the frequency and origin of extrachromosomal DNA and its role in the development of different types of cancer.

Analysis of mitochondrial genomes (mtDNA) by using whole-genome sequencing data from 2,658 cancer samples across 38 cancer types identifies hypermutated mtDNA cases, frequent somatic nuclear transfer of mtDNA and high variability of mtDNA copy number in many cancers.

Efficient, large-scale genomic analysis is facilitated on the cloud by a computational tool with error-diagnosing and self-healing capabilities.

Analyses of the TRACERx study unveil the relationship between tissue morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk of lung adenocarcinomas.

Aromatase inhibitors are an effective treatment for ER+ breast cancer, but response is variable. Here, the authors undertake molecular analyse of response from the POETIC trial and identify several factors associated with response.

The clinical implications of molecular heterogeneity in HER2-positive breast cancer remain to be explored. Here, the authors investigate the transcriptomic landscape of HER2-positive breast cancer patients receiving adjuvant trastuzumab in the ALTTO clinical trial and identify five distinct molecular subtypes.

Combination of epidemiology, preclinical models and ultradeep DNA profiling of clinical cohorts unpicks the inflammatory mechanism by which air pollution promotes lung cancer

Results of the TRACERx study shed new light into the association between body composition and body weight with survival in individuals with non-small cell lung cancer, and delineate potential biological processes and mediators contributing to the development of cancer-associated cachexia.

Fusion of myoblasts is essential for muscle development and repair, but the molecular mechanism underlying this process remains unclear. Here, the authors show, using chicken embryos as a model, that TGFβ signalling inhibits fusion via a receptor complementation mechanism, and indicate the involvement of endocytic degradation of activated receptors in modulation of this process.

A dataset of 16 plant traits sampled from 2,461 individual trees from 74 tropical forest sites around the world is used to show a strong link between climate and plant functional diversity and redundancy, with drier tropical forests likely being less able to respond to declines in water availability.

Andrew Morris, Mark McCarthy, Michael Boehnke and colleagues report a meta-analysis of genome-wide association studies for type 2 diabetes, including 26,488 cases and 83,964 controls from populations of European, east Asian, south Asian and Mexican and Mexican American ancestry. They identify seven loci newly associated with type 2 diabetes and examine the genetic architecture of disease across populations.

Genome-wide association and fine-mapping analyses in ancestrally diverse populations implicate candidate causal genes and mechanisms underlying type 2 diabetes. Trans-ancestry genetic risk scores enhance transferability across populations.