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Anderson localization is a linear phenomenon, with ongoing debate about the impact of nonlinearity. This study shows that nonlinearity strengthens the localization of surface-gravity waves in a canal with a random bottom through experimental measurements and numerical simulations.

Humanized glucagon-like peptide 1 receptor (GLP1R) mouse models are used to investigate the neural circuitry through which small-molecule GLP1R agonists modulate feeding, with implications for how these orally delivered weight-loss drugs engage brain reward circuits.

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.

RNA velocity is a widely used method to predict the fate of single cells. Here the authors show that the concept can be adapted to predict the fate of individual human subjects, using RNA velocity of whole blood at a single point in time to predict future clinical outcomes and treatment responses.

DNA-sequencing data from primary tumours and paired metastases from participants in the TRACERx lung study and PEACE autopsy programme are used to analyse the metastatic diversity of advanced non-small cell lung cancer and the seeding patterns that underpin it.

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.

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.

As presented at the 2026 AACR Annual Meeting, in a phase 2 trial, treatment of patients with high-risk smoldering multiple myeloma with BCMA-targeting CAR T cell therapy ciltacabtagene autoleucel was safe and led to encouraging rates of clinical responses.

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 combination of high-resolution spatial imaging, spatial proteomics and transcriptional data reveals sparse and heterogeneous bacterial signals in gliomas and brain metastases.

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

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

mRNA vaccines targeting SARS-CoV-2 also sensitize tumours to immune checkpoint inhibitors.

Mass spectrometry (MS)-based proteomics is increasingly central to systems biology. Here, the authors present a high-throughput, multi-organ workflow that profiles 11,472 proteins in 507 mouse samples, enabling rapid, system-level evaluation of drug efficacy and toxicity.

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 APOE-ε4 allele is the strongest genetic risk factor for late-onset Alzheimer’s disease, but it is not deterministic. Here, the authors show that common genetic variation changes how APOE-ε4 influences cognition.

Integration of mosquito and non-human primate surveillance and viral metagenomics reveals yellow fever virus outbreak dynamics at an urban–forest interface in Brazil.

cellSTAAR advances noncoding rare variant association analysis by integrating single-cell genomics data.

Robustness checks and reproduction of analyses with existing and updated data based on 110 articles in economics and political science journals with data and code-sharing requirements found high levels of robustness and reproducibility and determined that robustness was not dependent on author characteristics or data availability.

A highly potent and selective small-molecule catalytic inhibitor of the protein lysine methyltransferase NSD2 shows therapeutic efficacy in preclinical models of KRAS-driven pancreatic cancer and lung cancer.

Experimental realizations of discrete time crystals have mainly involved 1D models with Ising-like couplings. Here, the authors realize a 2D discrete time crystal with anisotropic Heisenberg coupling on a quantum simulator based on superconducting qubits, uncovering a rich phase diagram.

Notch1 is frequently activated promoting T-cell acute lymphoblastic leukaemia (T-ALL). Here, the authors show that Notch1 induces oxidative phosphorylation dependency in T-ALL and synergism when inhibiting both mitochondrial complex I and glutaminolysis in preclinical murine and human xenograft models.

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.

Immune checkpoint inhibitors (ICIs) have shown limited efficacy in recurrent high-grade astrocytoma (rHGA). Here the authors report the results of a Phase 1/randomized Phase 2b trial of laser interstitial thermal therapy followed by anti-PD1 pembrolizumab in patients with rHGA.

Lung adenocarcinomas bearing the ID2 mutational signature display increased LINE-1 retrotransposon activity, which contributes to their fast evolutionary dynamics and aggressive phenotype.

Humanity’s Last Exam, a multi-modal benchmark at the frontier of human knowledge, is designed to be an expert-level closed-ended academic benchmark with broad subject coverage.

Treatment with neoadjuvant BRAF/MEK-targeted therapy results in higher rates of major pathological response in female compared with male patients with melanoma, and pharmacological inhibition of androgen receptor signalling improved the responses of male and female mice to BRAF/MEK-targeted therapy.

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.