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The mutational effects of chemotherapies on healthy cells are unclear. Here, the authors show that the mutational signature of platinum-based drugs -but not 5-fluorouracil- is detectable in secondary acute myeloid leukemia, implying that the clonal expansion begins after the start of therapy.

High-depth sequencing of non-cancerous tissue from patients with metastatic cancer reveals single-base mutational signatures of alcohol, smoking and cancer treatments, and reveals how exogenous factors, including cancer therapies, affect somatic cell evolution.

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.

Lu et al. perform systematic functional analyses using data from the TRACERx cohort of patients with non-small-cell lung cancer and delineate how FAT1 regulates homologous recombination repair, chromosomal instability and whole-genome doubling with distinct mechanisms.

It is critical to understand what drives the progression of oesophageal adenocarcinoma (OAC) from a pre-cancerous state. Here, the authors use whole-genome sequencing to characterise the mutational processes and drivers of OAC progression from Barrett’s Oesophagus, as well as their prognostic associations.

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.

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.

An analysis of data from the Sherlock-Lung study provides insight into the mutational processes that contribute to lung cancer in never smokers, and looks at the possible role of factors such as air pollution and passive smoking.

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.

A longitudinal evolutionary analysis of 126 lung cancer patients with metastatic disease reveals the timing of metastatic divergence, modes of dissemination and the genomic events subject to selection during the metastatic transition.

Mutagenic lesions such as those that give rise to cancer frequently segregate—unrepaired—during cell division, resulting in phasing of multiple alleles across generations of daughter cells and consequent tumour heterogeneity.

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

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.

Analyses of in vivo models, cell lines and patient-derived samples show that apolipoprotein B mRNA-editing catalytic subunit 3B (APOBEC3B) not only restrains lung tumor initiation but also that its upregulation is associated with resistance to targeted therapies. This study highlights the complex and context-dependent role of APOBEC3B in lung cancer.

The role of altered replication timing (ART) during malignant transformation requires further exploration. Here, analysis of replication-timing sequencing and whole genome sequencing reveals a significant association of ART with the genomic and transcriptomic landscape during cancer evolution in lung and breast tumours.

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.

Identifying the genetic drivers of clonal haematopoiesis (CH) has been challenging due to their low frequencies and a lack of adequate tools. Here, the authors use a reverse calling to detect blood somatic mutations and the IntOGen pipeline to identify CH drivers in large cancer genomics data sets based on signals of positive selection.

Patient-derived xenografts are important tools for cancer drug development. Here, the authors develop models from 22 non-small cell lung cancer patients. They show genomic differences between models created from different spatial regions of tumours and a bottleneck on model establishment.

How strand-asymmetric processes such as replication and transcription interact with DNA damage to drive mechanisms of repair and mutagenesis is explored.

Integrated multi-omic analyses using samples from the TRACERx study highlight cross-talk between DNA hypermethylation and genomic lesions in non-small cell lung cancer.

Analysis of exome sequencing data from 42 baseline and progression biopsies from cetuximab-treated colorectal cancers reveals limited adaptive mutagenesis but shows a chemotherapy-induced mutational signature that is the main contributor of specific driver mutations that are enriched at acquired resistance.

A new computational approach to in silico mutagenesis screening allow comprehensive mapping of cancer driver mutations.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

Computational and machine-learning approaches that integrate genomic and transcriptomic variation from paired primary and metastatic non-small cell lung cancer samples from the TRACERx cohort reveal the role of transcriptional events in tumour evolution.

Measurements of subclonal expansion of ctDNA in the plasma before surgery may enable the prediction of future metastatic subclones, offering the possibility for early intervention in patients with non-small-cell lung cancer.

Analyses of multiregional tumour samples from 421 patients with non-small cell lung cancer prospectively enrolled to the TRACERx study reveal determinants of tumour evolution and relationships between intratumour heterogeneity and clinical outcome.

In lung adenocarcinoma, antibodies against endogenous retroviruses promote anti-tumour activity, and expression of endogenous retroviruses can predict outcomes of immunotherapy.

Analysis of whole-genome sequences from more than 3,500 metastatic tumors identifies mutational signatures associated with different chemotherapies and provides estimates of the relative contribution of different treatments to tumor mutational burden.

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.