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This study nominates immune escape as an early event in colorectal cancer and shows how this can be driven through both genetic and epigenetic changes.

Sottoriva and colleagues combine next-generation sequencing and AI-aided histopathology to assess tumor evolvability in patient samples with long-term follow-up and find that it can be a strong predictor of recurrence in high-risk prostate cancer.

Andrea Sottoriva, Trevor Graham and colleagues analyze tumor sequencing data and show that a substantial proportion of cancers of many different types are characterized by neutral evolution resulting in a characteristic power-law distribution of the mutant allele frequencies. This neutral framework provides a new way to interpret cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity.

Based on a consensus conference of experts in the evolution and ecology of cancer, this article proposes a framework for classifying tumours that includes four evolutionary and ecological processes: neoplastic cell diversity and changes over time in that diversity, hazards to cell survival and available resources.

Whole-genome sequencing of more than 2,000 colorectal carcinoma samples provides a highly detailed view of the genomic landscape of this cancer and identifies new driver mutations.

Integration of mathematical modeling, ecological analyses of patient biopsies, and neoantigen heterogeneity suggests recruitment of immunosuppressive cells is key to initializing transformation from adenoma to carcinoma in human colorectal cancer.

In a model of pancreatic ductal adenocarcinoma, extrachromosomal DNAs are shown to be a source of high-level focal amplification driving MYC heterogeneity and phenotypic adaptation.

The genomic landscape of diffuse gliomas remains to be characterised. Here, the authors perform whole genome sequencing of 403 tumours and identify recurrent coding and non-coding genetic mutations, their associations with clinical outcomes and potential therapeutic targets.

Common driver mutations in colorectal cancer (CRC) are not always consistent with frequent mutational signatures. Here, the authors analyse spatially annotated colon crypts in CRC patients and find mutational signatures of pks+ E. coli that are consistent with driver mutations, suggesting a potential role of pks+ E. coli in carcinogenesis.

Tracking tumour evolution in a patient via circulating tumour DNA (ctDNA) is complicated due to the unknown mix of fragmented alleles from different cancer lesions. Here, the authors make use of a rapid autopsy program to demonstrate how representative ctDNA profiling is of metastasis, as well as presenting methylation profiling method to track evolutionary change.

Immune dN/dS is the ratio of nonsynonymous to synonymous mutations in the MHC-bound immunopeptidome. Application of immune dN/dS to cancer datasets shows that it distinguishes immune evasion and escape and predicts response to immunotherapies.

A study maps genetic and epigenetic heterogeneity of primary colorectal adenomas and cancers at single-clone resolution through spatial multi-omic profiling of individual glands and adjacent normal tissue.

Intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer, with most genetic intratumour variation having no detected phenotypic consequence and transcriptional plasticity being widespread within a tumour.

Understanding the dynamics of how drug resistance originates in cancer remains crucial, but it is not possible to observe them directly. Here, the authors construct a mathematical framework to infer drug resistance dynamics in cancer using lineage tracing and population size data, which is confirmed with experimental evidence and single-cell sequencing.

REVOLVER uses transfer learning on multi-region tumor sequencing data to jointly infer tumor evolution models in multiple individuals and to detect repeated evolutionary trajectories. Repeated evolution can be used to stratify the cohort.

This analysis uses computational modeling of clonal selection to measure evolutionary dynamics in primary human cancers. The method employs high-throughput sequencing data and simultaneously measures the selective advantage and time of appearance of subclones.

Evolutionary steering uses therapies to control tumour evolution by exploiting trade-offs. Here, using a barcoding approach applied to large cell populations, the authors explore evolutionary steering in lung cancer cells treated with EGFR inhibitors.

Quantifying somatic evolutionary processes in cancer and healthy tissue is a challenge. Here, the authors use single time point multi-region sampling of cancer and normal tissue, combined with evolutionary theory, to quantify in vivo mutation and cell survival rates per cell division.

Mathematical modeling of evolutionary dynamics of neoantigens and immune escape in growing tumors shows that strong negative selection for neoantigens inhibits tumor growth but also provides a strong selective pressure for the evolution of immune escape.

MOBSTER is an approach for subclonal reconstruction of tumors from cancer genomics data on the basis of models that combine machine learning with evolutionary theory, thus leading to more accurate evolutionary histories of tumors.

Analysis of live-cell imaging and single-cell genome sequencing data of colorectal cancer organoids identifies temporal dynamics of sub-chromosomal copy-number amplifications.

MicroRNA profiling of 1,302 human breast tumour samples provides an overview of the miRNA landscape and its regulation, revealing context-dependent interactions, broad prognostic value of miRNA signatures and an important modulatory role for miRNAs in the biology of breast tumours devoid of copy-number aberrations.

Mismatch repair-deficient colorectal cancer clones adapt their mutation landscape by toggling homopolymer sequences in MutS homolog 3 (MSH3) and MutS homolog 6 (MSH6). This increases the subclonal mutation rate and clonal diversity, favoring immune escape and tumor growth.

Genome sequence data from colorectal tumours show how adenomas progress to carcinomas on the fitness landscape.

Methods that analyze intra-tumor genetic heterogeneity often do not preserve the spatial context of tumor subclones. Here, the authors present BaseScope, a mutation-specific RNA in situ hybridization assay and spatially map colorectal cancer and adenoma KRAS, BRAF and PIK3CA driver gene mutant subclones.

Cancer cells can actively engage in overcoming microenvironmental constraints such as tissue stiffness through adapting their shapes; however it is unclear how microenvironmental cells shape cancer nuclear morphology in human tumors in situ. Here the authors merge machine learning, digital pathology and spatial statistics to study this issue; furthermore the authors identify decreased immune infiltration in the surrounding of diversified cancer cells in a subset of ovarian tumors.

Christina Curtis and colleagues simulate spatial tumor growth under different evolutionary models and compare their results to multiregion sequencing data. They find that it is possible to distinguish tumors driven by strong positive selection from those evolving neutrally or under weak selection and infer different evolutionary modes within and between tumor types.

Christina Curtis, Darryl Shibata and colleagues report genomic profiling of 349 individual glands sampled from 15 human colorectal tumors. They observe high within-tumor heterogeneity and mixing of subclones in distant tumor regions, supporting a model whereby tumor growth occurs predominantly as a single expansion, with most detectable subclonal mutations arising during the earliest phases of tumor growth.

Recent next-generation sequencing studies have captured the spatial and temporal evolutionary patterns that shape cancer. This Review provides an overview of the theoretical models of tumour evolution and discusses what to consider when inferring evolutionary dynamics from genomic data.

Defective ribonucleotide excision repair causes ID4, an indel cancer signature characterized by deletions of 2–5 base pairs.