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Two papers by Kather and colleagues and Gerstung and colleagues develop workflows to predict a wide range of molecular alterations from pan-cancer digital pathology slides.

Czernilofsky et al. identified factors that reprogram stromal cells into an inflammatory, dysfunctional state, leading to the structural disorganization of lymph nodes in B cell lymphoma at single-cell and spatial resolutions.

Individuals who are at high risk of developing acute myeloid leukaemia can be identified years before diagnosis using genetic information from blood samples.

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 myelodysplastic syndromes (MDS) are a heterogeneous group of chronic blood cancers. Here, the authors analyse genomic and gene expression data from MDS patients to investigate how driver mutations alter gene expression, diagnostic clinical variables and survival.

Delphi-2M forecasts a person’s future health, covering more than 1,000 diseases, provides insights into co-morbidity dynamics and generates synthetic data for the training of AI models that have never seen actual data.

A workflow centred around base-specific in situ sequencing generates detailed maps of, and can phenotypically characterize, the unique set of subclones of cancers.

A method integrates perturbation mapping with 10X Visium spatial transcriptomics to map tumour genetic complexity and heterogeneity.

This paper uses multiomics to profile a large cohort of meningioma samples to highlight the role of the tumor microenvironment in driving the epigenetic changes underpinning tumor classification and prediction of outcome.

A study of the evolution of the SARS-CoV-2 virus in England between September 2020 and June 2021 finds that interventions capable of containing previous variants were insufficient to stop the more transmissible Alpha and Delta variants.

Heterogeneity in leiomyosarcomas (LMS) makes treatment of the disease challenging. Here the authors analyze LMS heterogeneity and molecular LMS subtypes using genomics and transcriptomics, finding origins in distinct lineages and associations with survival, in addition to the early emergence of metastatic clones.

Sequencing mutants in both normal skin and tumors that arise from it in a mouse model of ultraviolet light driven carcinogenesis reveals mutant selection changes as cancers develop. Only p53 mutants are selected throughout squamous carcinogenesis.

The detection of subclonal variants in heterogeneous cancer specimens is a challenge due to errors that occur during sequencing. In this study, a statistical algorithm and a sequencing strategy are reported that circumvent this issue and can accurately detect variants at a frequency as low as 1/10,000.

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.

Ageing normal oesophagus epithelium contains p53 mutant clones. Here the authors use transgenic mice to show how these clones form and contribute to cancer development.

Somatic mutational loads in cancer genomes change with chromatin domain boundaries. Different mutational processes lead to distinct somatic mutation distributions in active versus inactive domains, including during tumor evolution.

This study explores the relationship between telomere length and clonal hematopoiesis. Splicing factor and PPM1D gene mutations are more frequent in people with genetically predicted shorter telomere lengths, suggesting that these mutations protect against the consequences of telomere attrition.

Peter Campbell, Hartmut Döhner and colleagues present an analysis of genetic mutations and clinical information from 1,540 patients with acute myeloid leukemia, demonstrating the utility of clinical knowledge banks for personalized medicine. They show that use of their approach could reduce the number of hematopoietic cell transplants in patients with AML by up to 25% while maintaining survival rates.

Longitudinal molecular profiling of copy number alterations in patients with Barrett’s esophagus can identify patients at higher risk of developing esophageal cancer.

Currently available tools for the analysis of mutational signatures do not make use of all possible genomic properties aside from mutation patterns. Here the authors present TensorSignatures, an efficient framework that jointly infers mutational signatures and their genomic determinants.

An analysis of UK Biobank participants shows that the risk of developing different types of myeloid neoplasms can be inferred years before diagnosis. The authors integrate somatic gene mutations with blood test parameters into a predictive model, which could guide future strategies for early detection and prevention of these diseases.

Recent research has shown that mutational signatures reflective of the history of a cancer can be detected in a cancer genome. Here, using whole genome sequencing of DNA repair deficient and proficient nematodes exposed to genotoxins, the authors show that these mutational signatures reflect both the initial DNA damage that was inflicted and the repair processes that ensue.

Somatic mutations obtained from laser microdissected biopsies of human tissues are used to reconstruct the developmental phylogenies of these tissues back to the zygote.

PDL1 expression is a common biomarker for immunotherapy response in cancer, and it is usually quantified using immunohistochemistry. Here, the authors develop a weakly supervised learning approach combining multiple instance learning and a teacher-student framework to predict PDL1 expression from histopathological imaging.

While experts analyze cytomorphology to diagnose myelodysplastic syndromes, definitive diagnosis requires complementary information such as karyotype and molecular genetics testing. Here, the authors present a computational method that automatically detects, characterizes and helps identify blood cell characteristics associated with this group of diseases.

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

A long-term study of 385 human donors reports that driver gene mutations and age determine the lifelong dynamics of clonal haematopoiesis

Ritter et al. present a spatial transcriptomics and deep learning-based approach named NePSTA (neuropathology spatial transcriptomic analysis) and leverage it to improve neuropathological diagnostics and enhance central nervous system tumor subtype classification.

Here the authors find a genetic alteration in the popular “Four Core Genotypes” mouse model that is used to distinguish sex-biased phenotypes caused by sex chromosomes and gonads. This alteration increases the expression of some X-linked genes, which might confound the interpretation of the model.

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.

In this Review, the authors discuss our latest understanding of the spatial aspects of cancer evolution, including the roles of cancer subclonal structure, tissue architecture, and interactions between cancer cells and diverse cell types of the microenvironment at local and distant sites.

Epithelioids are genetically stable, self-sustaining three-dimensional cultures. They may be used to investigate various aspects of epithelial biology over several months without need for passaging. In this paper, mouse epithelioids are used to identify drivers of clonal expansion in the esophagus.

Li-Fraumeni syndrome (LFS) is associated with pathogenic germline TP53 variants and predisposes patients to cancer; understanding the evolution and drivers of LFS-related tumours remains crucial. Here, the authors analyse 22 LFS tumours using whole-genome sequencing and reconstruct the evolution and timing of somatic driver alterations.

Myeloid cell populations play a critical role in lung cancer progression. Here, the authors use scRNA-seq and spatial transcriptomics to identify changes in the phenotype of macrophages within the tumour microenvironment.

Whole-genome sequencing is used to analyse the landscape of somatic mutation in intestinal crypts from 16 mammalian species, revealing that rates of somatic mutation inversely scale with the lifespan of the animal across species.

Notch1 mutations have opposing effects on clonal growth in normal and tumor cells of the mouse esophagus. In a mouse model of squamous esophageal tumorigenesis, Notch1 blockade reduced premalignant tumor growth, suggesting that it might be an effective prevention strategy for the disease.

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.

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 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.

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.