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Analyses of genome and transcriptome data are unable to accurately predict protein levels and function in tumour samples. Here, the authors carry out a comprehensive protein analysis in 3,467 samples from the cancer genome atlas, providing a resource to study the prognostic and therapeutic potential of tumour proteins.

Current clinical practice is organized according to tissue or organ of origin of tumors. Now, The Cancer Genome Atlas (TCGA) Research Network has started to identify genomic and other molecular commonalities among a dozen different types of cancer. Emerging similarities and contrasts will form the basis for targeted therapies of the future and for repurposing existing therapies by molecular rather than histological similarities of the diseases.

Larsson Omberg and colleagues write a Commentary describing the collaborative model used by the Pan-Cancer Working Group of The Cancer Genome Atlas. Pan-Cancer members used the Synapse software platform to share and evolve data, results and methods to perform integrative analyses of genome-wide molecular data for 12 cancer types.

The Mouse Cancer Cell line Atlas (MCCA) provides major advances towards a mechanistic understanding of cancer genomes.

Together with an accompanying paper presenting a transcriptomic atlas of the mouse lemur, interrogation of the atlas provides a rich body of data to support the use of the organism as a model for primate biology and health.

Genome-wide association analyses using data from 19 biobanks identify variants influencing risk of thyroid diseases and yield polygenic risk scores associated with features of aggressive thyroid cancer.

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.

Genomic, transcriptomic and DNA methylation data from tissue samples from 208 Chinese patients with prostate cancer define the landscape of alterations in this population, and comparison with data from Western cohorts suggests that the disease may stratify into different molecular subtypes.

A large, publicly available dataset integrating RNA, whole-exome, T cell receptor and 16S rRNA sequencing from patients with colon cancer enables the discovery of a prognostic score consisting of tumor, immune and microbial features.

This study utilizes Stereo-seq to comprehensively map the geospatial architecture of breast cancer, revealing venular niches that promote immune infiltration and better clinical outcomes, offering new insights into tumor microenvironment regulation.

Endocrinologists have traditionally focused on studying one hormone or organ system at a time. Here the authors use transcriptomic data from the mouse lemur to globally characterize primate hormonal signaling, describing hormone sources and targets, identifying conserved and primate specific regulation, and elucidating principles of the network.

Multiomic single-cell analyses of 15 Down syndrome fetal cortical samples identify widespread disruption of neurodevelopmental transcriptional programs, driven by three dosage-sensitive chromosome 21 transcription factors.

A cross-sectional study from four African countries shows the importance of investigating the gut microbiome in previously under-represented populations and provides a framework for equitable microbiome research.

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.

Disease heterogeneity complicates precision medicine, which focuses on single conditions and ignores shared mechanisms. Here the authors introduce ‘pan-disease’ analysis using a deep learning model on multi-organ data, identifying 11 AI-derived biomarkers that reveal new therapeutic targets and pathways, enhancing patient stratification for disease risk monitoring and drug discovery.

Nicole Soranzo, Tim Spector, Gabi Kastenmüller and colleagues report a large-scale analysis of genetic variants influencing human blood metabolite levels. They identify genome-wide significant associations at 145 loci, providing a framework for exploring relationships between genetic variation, metabolism and complex disease.

Single-cell profiling studies of the human gastrointestinal tract are increasing, offering an excellent opportunity to generate the first Human Gut Cell Atlas. This Roadmap presents a structured direction towards this goal and provides a detailed overview of the major challenges.

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.

Liang and colleagues establish a high-quality protein expression resource for 8,000 The Cancer Genome Atlas patient samples and 900 Cancer Cell Line Encyclopedia cell lines for approximately 450 proteins, which they use to identify synthetic lethality pairs and metastasis markers.

The human transcriptome from 300 tissues and cell lines reveals novel non-coding RNAs.

A study from the FANTOM consortium using single-molecule cDNA sequencing of transcription start sites and their usage in human and mouse primary cells, cell lines and tissues reveals insights into the specificity and diversity of transcription patterns across different mammalian cell types.

A single-cell atlas of the human lungs, integrating data from 2.4 million cells from 486 individuals and including samples from healthy and diseased lungs, provides a roadmap for the generation of organ-scale cell atlases.

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.

The methylation status of circulating cell-free DNA (cfDNA) can be informative about recent cell death events. Here the authors present an approach to determine the tissue origins of cfDNA, using a reference methylation atlas of 25 human tissues and cell types, and find that cfDNA from patients reveals tissue contributions that agree with clinical findings.

ctDNA is a useful tool for the diagnosis of cancer, however, it is usually focused on targeted deep sequencing. Here, the authors develop a methodology to assess TET-assisted pyridine borane whole-genome sequencing data for cancer detection without a matched biopsy.

In breast cancer the contribution of different genetic variants to disease heritability is complex and not fully understood. Here, the authors present a network-based analysis in 84,567 patients studying ~7.3 million variants, identifying gene modules associated with breast cancer survival.

Analysis of pre- and post-chemotherapy samples collected from 22 patients with high-risk neuroblastoma using single-nucleus RNA and ATAC sequencing and spatial omics characterizes therapy-related changes in cell composition and the tumor microenvironment.

ATF6α activation in human and preclinical models of hepatocellular carcinoma is significantly associated with an aggressive tumour phenotype characterized by reduced survival, glycolytic reprogramming and local immunosuppression.

Effective early detection and intervention strategies for lung cancer are urgently needed. The authors of this Review summarize the current state of lung cancer screening and provide future directions for optimal implementation, including biomarker development. They also discuss precancer interception strategies that could transform both lung cancer prevention and early intervention.

Together with a companion paper, the generation of a transcriptomic atlas for the mouse lemur and analyses of example cell types establish this animal as a molecularly tractable primate model organism.

The global impact of somatic structural variants (SSVs) on gene expression in childhood cancers is unclear. Here, the authors analyse cancer genome and RNA sequencing data of 854 pediatric brain tumours and report a landscape of genes deregulated by SSVs.

Belderbos and colleagues generate a multimodal single-cell atlas of healthy human bone marrow spanning infancy, adolescence and adulthood.

We describe a human DNA methylome atlas based on deep whole-genome bisulfite sequencing, allowing fragment-level analysis of cell-type-specific markers and providing an essential resource for studies of gene regulation and for deconvolution of cell mixtures and liquid biopsies.

Analyses of 2,083 globally distributed group A Streptococcus (GAS) genomes enable the development of a compendium of all GAS vaccine antigen sequences, providing a platform for population-genomics-informed vaccine design.

Genetic variants associated with susceptibility to pancreatic cancer have been identified using genome wide association studies (GWAS). Here, the authors combine data from over 9000 patients and perform a meta-analysis to identify five novel loci linked to pancreatic cancer.

Integrating incomplete multi-omics data remains a key challenge in precision oncology. IntegrAO, an unsupervised framework that integrates diverse omics, enables accurate patient classification even with incomplete datasets.

Allele-preferential transcription factor binding can influence pancreatic ductal adenocarcinoma risk loci function. Here, the authors show allele-specific JunB and JunD binding at chr1p36.33 and propose a role for KLHL17 in protein homeostasis by mitigating inflammation.

There is high prevalence of whole genome duplication (WGD) in high grade serous ovarian cancer. Here, the authors compare tumours with and without WGD and find that those that acquired WGD early during tumour evolution are associated with worse survival and have the lowest expression of MHC-II.

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.

Investigating genetically correlated cancers can enhance our understanding of carcinogenesis. Here, the authors perform Pan-cancer and cross-population GWAS meta-analysis to identify novel cancer risk loci and highlight shared heritability between breast and prostate cancer.

scAtlasVAE is a deep learning-based model for cross-atlas integration. Here it enables the development of a large-scale human CD8⁺ T cell atlas with integrated T cell receptor data.

Mammographic density is a strong risk factor for breast cancer. Here, the authors identify several new loci associated dense area, non-dense area and percent density, and highlight a shared genetic basis for mammographic density and breast cancer.

A meta-analysis of genome-wide association studies across different populations highlights new risk loci and provides a genetic risk score that can stratify prostate cancer risk across ancestries.

Lazarevic et al. reveal a shared loss of a TBX5-dependent atrial gene network in cardiomyocytes and a disease-specific network gain in activated fibroblasts across atrial fibrillation and heart failure models.

A multi-ancestry genome-wide association meta-analysis of kidney cancer identifies 63 regions associated with disease susceptibility including one locus that was associated with increased risk in individuals with African ancestry.

Somatic mutations are identified from circulating cell-free DNA using a single-molecule-based lowpass whole-genome sequencing method. The regional distribution of mutations can identify a tumor-specific mutational profile in patients with cancer and can be used to monitor patients through treatment.