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The advent of massively parallel sequencing technologies has driven the analysis of cancer genomes at an unprecedented resolution. Sequence data from thousands of patients highlight the distinct sets of driver mutations among patients with the same cancer tissue type, and single-cell sequencing technologies have revealed heterogeneity within the subclones of single tumours as they evolve. Identifying and characterizing these mutations and their diversity is essential for the development of personalized therapies. Next-generation sequencing technologies have also been applied to study the epigenomes and transcriptomes of cancer, thus paving the way for an integrated understanding of cancer pathology.
This collectionshowcases how cancer genomics has informed our understanding of cancer pathogenesis, unravelled potential future therapeutic targets and driven advances that are starting to translate into the clinic. This resource provides a comprehensive bench-to-bedside overview of cancer genomics, which will be useful to researchers and clinicians alike.
In this Perspective, Lim et al. discuss the potential benefits of, and the challenges associated with, translating single-cell genomic approaches from research to clinical settings.
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.
In this Review the authors provide an overview of key algorithmic developments, popular tools and emerging technologies used in the bioinformatic analysis of genomes. They also describe how such analysis can identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes.
Profiling tumours by next-generation sequencing can improve diagnostic accuracy, assess for heritable cancer risk and guide treatment selection. The authors review efforts to enhance the clinical utility of cancer genomic profiling through integrative analyses of tumour and germline variants, as well as by characterizing allelic context and mutational signatures that influence therapy response.
Genome-scale sequencing data have revealed statistical properties of mutagenesis in humans. Statistical analyses that interpret these patterns and incorporate knowledge on DNA replication and repair pathways can provide mechanistic models that shed light on the origin of spontaneous human mutation in the germ line.
Both genetic and non-genetic factors underlie the intratumoural heterogeneity that fuels cancer evolution. This Review discusses the application of single-cell multi-omics technologies to the study of cancer evolution, which capture and integrate the different layers of heritable information and reveal their complex interplay.
Aneuploidy contributes to tumorigenesis, but the underlying processes are not well understood. This Review explains the context dependency of aneuploidy in cancer and discusses its clinical potential as a prognostic marker and a therapeutic target.
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.
CRISPR–Cas genome editing and next-generation sequencing are driving advances in cancer modelling and functional cancer genomics. Their application to autochthonous mouse models of human cancer to generate and analyse multiplexed and/or combinatorial alterations in vivo is reviewed here.
This article discusses how integrating different omics data types — such as DNA sequencing, transcriptomics and metabolomics — can provide a rich view of healthy and disease states, including novel clinical diagnoses. The authors discuss the value of the different data types, as well as strategies, considerations and challenges for multi-omic integration in various disease contexts.
Although cancer genome sequencing is becoming routine in cancer research, cancer transcriptome profiling through methods such as RNA sequencing (RNA-seq) provides information not only on mutations but also on their functional cellular consequences. This Review discusses how technical and analytical advances in cancer transcriptomics have provided various clinically valuable insights into gene expression signatures, driver gene prioritization, cancer microenvironments, immuno-oncology and prognostic biomarkers.
The abundance and heterogeneity of mutations in cancer create challenges for understanding their effects, but such functional characterization will be crucial for optimizing clinical care. In this Review, the authors discuss diverse computational tools and systems biology experimental strategies for elucidating the functional effects of cancer mutations, including consequences on gene regulation, protein structure and local and global perturbations of molecular interaction networks.
The use of phylogenetics in cancer genomics is increasing owing to a growing appreciation of the importance of evolutionary theory to cancer progression. The authors provide guidance on the design and analysis of tumour phylogeny studies by surveying the range of phylogenetic methods and tools available to the cancer researcher and discussing their key applications and the unsolved problems in the field.