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The emergence of resistant subpopulations often underlies the development of resistance to cancer therapy. Here, using a DNA barcoding approach, the authors demonstrate EGFR TKI treatment in non-small cell lung cancer enriches for resistant subpopulation which can be prevented by treatment with the multikinase inhibitor sorafenib via inhibition of MKNK, STAT3 and MCL1.

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.

Federated learning (FL) algorithms have emerged as a promising solution to train models for healthcare imaging across institutions while preserving privacy. Here, the authors describe the Federated Tumor Segmentation (FeTS) challenge for the decentralised benchmarking of FL algorithms and evaluation of Healthcare AI algorithm generalizability in real-world cancer imaging datasets.

MultiSTAAR provides a general and flexible statistical framework for functionally informed multi-trait rare variant analysis of biobank-scale sequencing studies by jointly analyzing multiple traits and incorporating annotation information.

Monoclonal antibodies and ligands targeting CD40 exhibit diverse agonistic and antitumor activities that are influenced by their design. Here, the authors identify mechanistic differences between clinically relevant anti-CD40 subclasses and CD40L, focusing on the dynamics and strengths of multi-bond formation at the single-molecule level.

Whether multimorbidity accelerates brain Aβ deposition in humans remains largely unknown. Here, the authors demonstrate that higher self-reported multimorbidity burden predicts increased brain Aβ accumulation rates in the ADNI cohort.

An analysis of 18 metagenomic datasets of individuals with colorectal cancer, adenomas and healthy controls yields improved cancer prediction accuracy based solely on gut metagenomics, as well as the identification of new species associated with the development of cancer.

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.

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 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.

In an updated report on 70 laboratory-confirmed highly pathogenic avian influenza A H5N1 cases in the USA between March 2024 and May 2025, the absence of human-to-human transmission to date was confirmed, with no known mutations of resistance to neuroaminidase inhibitors.

One carbon compounds such as CO₂, methanol and formate are cost-effective and environmentally friendly microbial feedstocks for biomanufacturing. Here, the authors report the oxygen tolerant reductive glycine pathway in Komagataella phaffii can co-assimilate CO₂, methanol and formate.

Genome-wide analyses identify 30 independent loci associated with obsessive–compulsive disorder, highlighting genetic overlap with other psychiatric disorders and implicating putative effector genes and cell types contributing to its etiology.

Multisystem inflammatory syndrome following SARS-CoV-2 infection results from increased serum levels of TGFβ, which impairs the reactivation of virus-specific T cells.

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.

Wastewater treatment plants are important reservoirs of antibiotic resistance genes (ARGs). Here, the authors analyze ARGs in a global collection of samples from wastewater treatment plants across six continents, providing insights into biotic and abiotic mechanisms that appear to control ARG diversity and distribution.

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

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.

Analyses of genome and exome sequencing data identify rare coding and structural variants associated with atrial fibrillation and highlight genetic links between atrial fibrillation and cardiomyopathies.

STAARpipeline is a comprehensive framework for flexible and scalable rare-variant association analysis using whole-genome sequencing data and annotation information.

Stereotactic needle biopsies are currently performed for diagnosis only in glioblastoma; this study highlights the depth of information available through multi-omics analysis.

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.

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.

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.

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.

A multi-ancestry genome-wide gene–smoking interaction study identifies 13 new loci associated with serum lipids.

Whole genome sequences enable discovery of rare variants which may help to explain the heritability of common diseases. Here the authors find that ultra-rare variants explain ~50% of coronary artery disease (CAD) heritability and highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Although the common genetic variants contributing to blood lipid levels have been studied, the contribution of rare variants is less understood. Here, the authors perform a rare coding and noncoding variant association study of blood lipid levels using whole genome sequencing data.

Phylogenomic analysis of 7,923 angiosperm species using a standardized set of 353 nuclear genes produced an angiosperm tree of life dated with 200 fossil calibrations, providing key insights into evolutionary relationships and diversification.

Using data from a single time point, passenger-approximated clonal expansion rate (PACER) estimates the fitness of common driver mutations that lead to clonal haematopoiesis and identifies TCL1A activation as a mediator of clonal expansion.

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.

This report from the 1000 Genomes Project describes the genomes of 1,092 individuals from 14 human populations, providing a resource for common and low-frequency variant analysis in individuals from diverse populations; hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites, can be found in each individual.

Analysis of whole-genome sequences of 25,465 individuals of European ancestry shows that rare variants contribute substantially to the heritability of height and body mass index.

Stig Bojesen, Georgia Chenevix-Trench, Alison Dunning and colleagues report common variants at the TERT-CLPTM1L locus associated with mean telomere length measured in whole blood. They also identify associations at this locus to breast or ovarian cancer susceptibility and report functional studies in breast and ovarian cancer tissue and cell lines.

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.

Sleep duration is associated with an adverse lipid profile. Here, the authors perform genome-wide gene-by-sleep interaction analysis and find 49 previously unreported lipid loci when considering short or long total sleep time.