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A subset of pediatric gliomas harbour alterations in fibroblast growth factor receptor (FGFR)-family proteins. Here, the authors characterise the genomic landscape of 11,635 gliomas across ages and use isogenic model systems to explore the underlying biology of FGFR1-altered gliomas and 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.

Puan and San Luis et al. find that FUT6, encoding a fucosyltransferase, is required for the “rolling” behavior of certain white blood cells that enables them to move from blood vessels to tissues. They show that FUT6 deficiency leads to a loss of the tetrasaccharide sLex on the surface of basophils, resulting in cells that are less sticky and therefore less able to form the necessary adhesions for exiting the blood vessel to drive the allergic reaction.

Functionalization of C–H bonds through direct hydrogen atom transfer (HAT) photocatalysis is an attractive synthetic reaction; however, many methods suffer from low catalytic efficiency. Now, the efficiency of direct HAT photocatalysis using photocatalyst eosin Y combined with Brønsted acids is reported, enabling the functionalization of unactivated C(sp³)–H bonds.

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.

Computational methods are becoming an increasingly important part of biological research. Using the Rosetta framework as an example, the authors demonstrate how community-driven development of computational methods can be done in a reproducible and reliable fashion.

A porous metal–organic framework with ultrawide channels and excellent chemical stability is now shown to be highly efficacious for the catalytic decomposition of chemical warfare agents containing phosphate ester bonds.

Using well-calibrated statistical methods the authors jointly analyze Undiagnosed Diseases Network genomes, identifying known and novel disease genes. Software is publicly available to support future cross-cohort rare disease discovery efforts.

Phosphoproteomics data analysis is limited by poor phosphosite annotation. Here, the authors use machine learning with pan-cancer data to build a co-regulation network of phosphosites and a model for predicting kinase-substrate associations, providing a framework for systematic data interpretation.

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.

Kinases regulate cellular processes, making their study essential for understanding cellular function and disease. Here, the authors evaluate methods to infer kinase activity from phosphoproteomics data and provide a toolkit to evaluate future methods.

A global network of researchers was formed to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity; this paper reports 13 genome-wide significant loci and potentially actionable mechanisms in response to infection.

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.

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.

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.

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 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.

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.

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.

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 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.

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.

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.

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.

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.

Toettcher, McNamara and colleagues use synthetic ‘signal-recording’ gene circuits on mouse gastruloids and find that cell sorting rearranges patchy domains of Wnt activity into a single pole, which defines the gastruloid anterior–posterior axis.

An atlas of the substrate specificities for the human tyrosine kinome reveals diversity of motif specificities and enables identification of kinase–substrate relationships and kinase regulation in phosphoproteomics experiments.

Here the authors present a framework to assess the temperature outcomes of decarbonization scenarios from institutions such as the IEA, BP and Shell. Scenarios are evaluated for consistency with the long-term temperature goal of the Paris Agreement.

Glucagon is hormone that signals via a dedicated g-protein coupled receptor, but downstream signaling is poorly understood. Here, Wu et al. uncover liver glucagon signaling using phosphoproteomics and define a role for the vesicle trafficking protein SEC22B in distinct metabolic actions.

Analysis of the kinase activity of 300 protein Ser/Thr kinases reveals that the substrate specificity of the kinome is substantially more diverse than expected and is driven extensively by negative selectivity

A first-in-class covalent inhibitor of creatine phosphagen energetics was developed that induced toxicity in creatine kinase-dependent AML cell lines and regulated proinflammatory cytokine production in macrophages.

If all new and updated national climate change mitigation pledges stemming from the Paris Agreement are implemented in full and on time, then 21st-century warming could be limited to just below 2 degrees Celsius.

Samples of different body regions from hundreds of human donors are used to study how genetic variation influences gene expression levels in 44 disease-relevant tissues.

The use of lung and colonic organoid systems to assess the susceptibility of lung and gut cells to SARS-CoV-2 and to screen FDA-approved drugs that have antiviral activity against SARS-CoV-2 is demonstrated.

Dynamic control over protein function is a central challenge in synthetic biology. Here the authors present an integrated computational and experimental workflow for engineering reversible protein switches; metal-chelating unnatural amino acids genetically encoded into two conformationally dynamic enzymes to yield robust switches.

Catalytic enantioselective halocyclization of alkenes is an important bond forming tool and a key step in natural product biosynthesis, but so far no examples of the enzymatic counterpart of this reaction on simple achiral olefins have been reported. Here, the authors describe examples of engineered flavin-dependent halogenases that catalyze halolactonization of olefins with high enantioselectivity and near-native catalytic activity.