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Matthew Meyerson, Ramaswamy Govindan and colleagues examine the exome sequences and copy number profiles of 660 lung adenocarcinoma and 484 lung squamous cell carcinoma tumors. They identify novel significantly mutated genes and amplification peaks and find that around half of the tumors have at least five predicted neoepitopes.

Chemotherapy options for patients with extensive-stage small-cell lung cancer (SCLC) are limited. A recent phase III trial assessed the combination of carboplatin and pemetrexed but this regimen produced inferior survival results compared with the standard carboplatin and etoposide regimen. The combination of carboplatin and etoposide remains the standard first-line chemotherapy option for the treatment of patients with extensive-stage SCLC.

Basal cells, rather than neuroendocrine cells, have been identified as the probable origin of small cell lung cancer and other neuroendocrine–tuft cancers, explaining neuroendocrine–tuft heterogeneity and offering new perspectives for targeting lineage plasticity.

An integrated transcriptome, genome, methylome and proteome analysis of over 200 lung adenocarcinomas reveals high rates of somatic mutations, 18 statistically significantly mutated genes including RIT1 and MGA, splicing changes, and alterations in MAPK and PI(3)K pathway activity.

The genomic and host factors that drive the progression of pre-invasive lesions in non-small cell lung cancer are poorly understood. Studying these factors can advance our knowledge of lung cancer biology, aid in the development of better screening strategies and improve patient outcomes.

Comprehensive analyses of 178 lung squamous cell carcinomas by The Cancer Genome Atlas project show that the tumour type is characterized by complex genomic alterations, with statistically recurrent mutations in 11 genes, including TP53 in nearly all samples; a potential therapeutic target is identified in most of the samples studied.

Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

The MAPK pathway is an important therapeutic target in pancreatic ductal adenocarcinoma (PDAC), but success is limited by pathway reactivation, which drives resistance. Here, the authors investigate the mechanism underlying HER2-reactivation post KRAS-MAPK inhibition, identifying combination of MAPK and HER2 inhibition as a therapeutic strategy.

Treatment of KRAS^G12C-mutant cancer cells with the KRAS(G12C) inhibitor AMG 510 leads to durable response in mice, and anti-tumour activity in patients suggests that AMG 510 could be effective in patients for whom treatments are currently lacking.

Analysis of copy number alterations in 1,451 patient-derived xenografts (PDXs) and matched patient tumor samples shows strong conservation from patient tumors through late-passage PDXs and a lack of systematic copy number evolution driven by the mouse host.

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.

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.

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.

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.

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.

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.

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.

Analysing the regulatory consequences of mutations and splice variants at large scale in cancer requires efficient computational tools. Here, the authors develop RegTools, a software package that can identify splice-associated variants from large-scale genomics and transcriptomics data with efficiency and flexibility.

Small cell lung cancer (SCLC) patients frequently relapse and become resistant to chemotherapy. Here, the authors analyse the genomic and transcriptomic landscape of primary and relapsed SCLC patients as well as in vitro models, and discover that activation of WNT signalling can drive chemotherapy resistance.

Published sequencing data sets of cancer samples could be used to identify genetic variants associated with the risk of developing cancer. Here, Luet al. analyse over 4,000 tumour-normal pairs to reveal variable frequencies of inherited susceptibilities across 12 cancer types and find enrichment of functionally validated missense variants of unknown significance.

Patients with oligometastases who develop a small number of metastatic lesions might achieve long-term survival with the use of ablative surgery or stereotactic radiotherapy. More patients are receiving aggressive treatment for oligometastatic disease, yet long-term survival might not be due to the treatments themselves, but rather to the selection of patients with slow-growing indolent disease. The authors examine the key evidence supporting or refuting the existence of an oligometastatic state and its appropriate treatment.

Including patient-specific information about nearby somatic and germline alterations improves the accuracy of neoantigen prediction, potentially impacting cancer vaccine design.

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.

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.

A machine learning approach for refinement of somatic variant calls automates this process and reduces bias stemming from inter-reviewer variability.

A database of known drug-gene interactions, with information derived from many public sources, allows the identification of genes that are currently targeted by a drug and the membership of genes in a category, such as kinase genes, that have a high potential for drug development.

Despite lower levels of most targetable alterations, a strong association with a history of smoking and generally higher levels of PD-L1 expression, patients with squamous non-small-cell lung cancer (LUSC) have inferior outcomes on immune-checkpoint inhibitors (ICIs) compared to those with non-squamous disease. In this Review, the authors describe the available evidence on the role of ICIs and of potential novel therapies in patients with LUSC, as well as highlighting important unresolved challenges and future research directions in this historically overlooked subtype.