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Gelareh Zadeh, Kenneth Aldape and colleagues present an integrative genomic analysis of schwannomas. In addition to finding recurrent mutations in ARID1A, ARID1B and DDR1, they identify a recurrent SH3PXD2A-HTRA1 fusion that confers increased proliferation, invasion and in vivo transformation, and is associated with sensitivity to MEK inhibition.

A deep learning model is used to classify central nervous system tumors based on their DNA methylation profile directly from histopathology, and showed high accuracy in a large set of external validation cohorts, potentially informing downstream treatment.

Radiation-induced meningiomas are often more aggressive than sporadic ones. In this study, the authors perform an exome, methylation and RNA-seq analysis of 31 cases of radiation-induced meningioma and show NF2 rearrangement, an observation previously unreported in the sporadic tumors.

The GLASS Consortium studies the evolutionary trajectories of 222 patients with a diffuse glioma to aid in our understanding of tumour progression and treatment failure

Hoang et al. developed a deep-learning framework called ENLIGHT–DeepPT that predicts therapy response based on imputed transcriptomics and shows predictive power across patient cohorts and cancer types.

Multi-omics datasets are integrated to generate a unified and clinically informed molecular classification of meningiomas.

Capicua (CIC) is a tumour suppressor in oligodendroglioma. Here, the authors show that ERK activation mediates CIC regulation via ubiquitination and degradation by PJA1 and a degradation resistant form of CIC enhances efficacy of ERK inhibition in glioblastoma.

Using data from a cohort of patients (n = 346) with lung adenocarcinoma and from multiple independent cohorts, DNA methylation signatures were evaluated to build and validate an accurate model predicting the development of brain metastasis.

In a large, partially prospective cohort of patients with molecularly profiled and clinically annotated meningioma, the extent of surgical resection and radiotherapy (RT) response correlate with molecular classification, which can be used in a molecular model to predict clinical outcomes in response to RT.

A cell-free DNA-methylation sequencing assay accurately identifies different brain tumor types using plasma samples.

Gene signatures associated with T cell resilience to tumor-derived immunosuppressive signals predict responses to immune-checkpoint inhibitors and adoptive cell therapy and identify FIBP as a negative regulator of T cell activity

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

Cell type deconvolution in tumor spatial transcriptomics (ST) data remains challenging. Here, the authors develop Spatial Cellular Estimator for Tumors (SpaCET) to infer cell types and intercellular interactions from ST data in cancer across different platforms, with improved performance over similar methods.

Malignant peripheral nerve sheath tumours are an aggressive form of sarcoma, with limited treatment options. Here, the authors utilise DNA methylation and transcriptomic data to identify two subtypes of tumours with potential therapeutic vulnerabilities.

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.

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.

Astroblastoma (AB) is an uncommon brain tumour and its origin remains unknown. Here, the authors perform integrative molecular analysis of 35 AB-like tumours and provide evidence that these arise in the context of epigenetic and genetic changes in neural progenitors occurring during brain development.

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.

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.

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.

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.

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.

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.

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.

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.

A safety and efficacy trial of a single intratumoral dose of the oncolytic adenovirus DNX-2401 followed by intravenous anti-PD-1 pembrolizumab in patients with recurrent glioblastoma shows an encouraging clinical benefit rate and 12 months overall survival.

An online approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups has been developed to help to improve current diagnostic standards.

Lu and colleagues delineate a pathway through which the PKM2 enzyme promotes aerobic glycolysis, known as the Warburg effect, in cancer cells. They show that EGFR-activated ERK phosphorylates PKM2, leading to its accumulation in the nucleus. Nuclear PKM2 subsequently promotes the c-Myc-dependent upregulation of genes involved in the Warburg effect, resulting in tumour growth.

Sinha and colleagues present PERCEPTION, a precision oncology computational pipeline that can predict the response and resistance of patients by analyzing single-cell transcriptomic data from their tumor samples.

Aberrant activation of β-catenin in the nucleus has been implicated in several cancers, but the mechanisms regulating nuclear β-catenin are not well understood. Here the authors identify Trim33 as new E3 ligase targeting nuclear β-catenin independently of Wnt signal.

Zhou et al. show that GSK3β phosphorylates KDM1A and induces its deubiquitylation by USP22, leading to demethylation of histone H3K4 and glioblastoma progression.

Expression of the tumour suppressor PTEN in disseminated primary tumour cells is lost after tumour cells metastasize to the brain, with downregulation instigated by microRNAs from astrocytes, which are transferred from cell to cell by exosomes; these findings reveal the dynamic nature of metastatic cancer cells when adapting to a new tissue environment.

With a comprehensive analysis of sequencing data, DNA copy number, gene expression and DNA methylation in a large number of human glioblastomas, The Cancer Genome Atlas project initiative provides a broad overview of the genes and pathways that are altered in this cancer type.

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

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.