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Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assessed cell2location in three different tissues and show improved mapping of fine-grained cell types. In the mouse brain, we discovered fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially mapped a rare pre-germinal center B cell population. In the human gut, we resolved fine immune cell populations in lymphoid follicles. Collectively, our results present сell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner. A Bayesian model maps the location of cell types in tissues with higher sensitivity.

Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative information from digital histopathology images. AI is expected to reduce workload for human experts, improve the objectivity and consistency of pathology reports, and have a clinical impact by extracting hidden information from routinely available data. Here, we describe how AI can be used to predict cancer outcome, treatment response, genetic alterations and gene expression from digitized histopathology slides. We summarize the underlying technologies and emerging approaches, noting limitations, including the need for data sharing and standards. Finally, we discuss the broader implications of AI in cancer research and oncology.Schmatko et al. review the application of artificial intelligence to digitized histopathology for cancer diagnosis, prognosis and classification and discuss its potential utility in the clinic and broader implications for cancer research and care.

Cells possess an armamentarium of DNA repair pathways to counter DNA damage and prevent mutation. Here we use C. elegans whole genome sequencing to systematically quantify the contributions of these factors to mutational signatures. We analyse 2,717 genomes from wild-type and 53 DNA repair defective backgrounds, exposed to 11 genotoxins, including UV-B and ionizing radiation, alkylating compounds, aristolochic acid, aflatoxin B1, and cisplatin. Combined genotoxic exposure and DNA repair deficiency alters mutation rates or signatures in 41% of experiments, revealing how different DNA alterations induced by the same genotoxin are mended by separate repair pathways. Error-prone translesion synthesis causes the majority of genotoxin-induced base substitutions, but averts larger deletions. Nucleotide excision repair prevents up to 99% of point mutations, almost uniformly across the mutation spectrum. Our data show that mutational signatures are joint products of DNA damage and repair and suggest that multiple factors underlie signatures observed in cancer genomes. 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.

The authors identify 11 discrete genetic subsets of acute myeloid leukemia on the basis of the expression and coexpression of particular mutations. Prospective studies may elucidate distinct approaches to their management. Acute myeloid leukemia (AML) is characterized by clonal expansion of undifferentiated myeloid precursors, resulting in impaired hematopoiesis and bone marrow failure. Although many patients with AML have a response to induction chemotherapy, refractory disease is common, and relapse represents the major cause of treatment failure. 1 Cancer develops from somatically acquired driver mutations, which account for the myriad biologic and clinical complexities of the disease. A classification of cancers that is based on causality is likely to be durable, reproducible, and clinically relevant. This is already evident in the case of AML, for which there has been a progressive shift from . . .

We present TensorSignatures, an algorithm to learn mutational signatures jointly across different variant categories and their genomic localisation and properties. The analysis of 2778 primary and 3824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that all signatures operate dynamically in response to genomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis in 7 different cancer types. The algorithm also extracts distinct signatures of replication- and double strand break repair-driven mutagenesis by APOBEC3A and 3B with differential numbers and length of mutation clusters. Finally, TensorSignatures reproduces a signature of somatic hypermutation generating highly clustered variants at transcription start sites of active genes in lymphoid leukaemia, distinct from a general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types. In summary, TensorSignatures elucidates complex mutational footprints by characterising their underlying processes with respect to a multitude of genomic variables. 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.

Recent studies show that aneuploidy and driver gene mutations precede cancer diagnosis by many years 1 – 4 . We assess whether these genomic signals can be used for early detection and pre-emptive cancer treatment using the neoplastic precursor lesion Barrett’s esophagus as an exemplar 5 . Shallow whole-genome sequencing of 777 biopsies, sampled from 88 patients in Barrett’s esophagus surveillance over a period of up to 15 years, shows that genomic signals can distinguish progressive from stable disease even 10 years before histopathological transformation. These findings are validated on two independent cohorts of 76 and 248 patients. These methods are low-cost and applicable to standard clinical biopsy samples. Compared with current management guidelines based on histopathology and clinical presentation, genomic classification enables earlier treatment for high-risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer. Longitudinal molecular profiling of copy number alterations in patients with Barrett’s esophagus can identify patients at higher risk of developing esophageal cancer.

Lung cancer is the second most frequently diagnosed cancer and the leading cause of cancer-related mortality worldwide. Tumour ecosystems feature diverse immune cell types. Myeloid cells, in particular, are prevalent and have a well-established role in promoting the disease. In our study, we profile approximately 900,000 cells from 25 treatment-naive patients with adenocarcinoma and squamous-cell carcinoma by single-cell and spatial transcriptomics. We note an inverse relationship between anti-inflammatory macrophages and NK cells/T cells, and with reduced NK cell cytotoxicity within the tumour. While we observe a similar cell type composition in both adenocarcinoma and squamous-cell carcinoma, we detect significant differences in the co-expression of various immune checkpoint inhibitors. Moreover, we reveal evidence of a transcriptional “reprogramming” of macrophages in tumours, shifting them towards cholesterol export and adopting a foetal-like transcriptional signature which promotes iron efflux. Our multi-omic resource offers a high-resolution molecular map of tumour-associated macrophages, enhancing our understanding of their role within the tumour microenvironment. 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.

Abstract Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Genetic analysis involving 2035 patients with a myeloproliferative disorder identified eight genomic subgroups with distinct clinical phenotypes, risk of leukemic transformation, and event-free survival.

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study. SpatialData is a user-friendly computational framework for exploring, analyzing, annotating, aligning and storing spatial omics data that can seamlessly handle large multimodal datasets.