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Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.This Perspective proposes that data from multiple modalities, including molecular diagnostics, radiological and histological imaging and codified clinical data, should be integrated by multimodal machine learning models to advance the prognosis and treatment management of patients with cancer.

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.Mutations in cancer cells can generate tumour-specific neoepitopes, which are attractive targets for anticancer vaccines. This Review discusses the mechanisms of neoantigen T cell recognition and computational approaches to predict which neoantigens might confer proficient antitumour immunity in a given clinical context.

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods’ predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods. Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.

Cancer-associated fibroblasts (CAFs) are a diverse cell population within the tumour microenvironment, where they have critical effects on tumour evolution and patient prognosis. To define CAF phenotypes, we analyse a single-cell RNA sequencing (scRNA-seq) dataset of over 16,000 stromal cells from tumours of 14 breast cancer patients, based on which we define and functionally annotate nine CAF phenotypes and one class of pericytes. We validate this classification system in four additional cancer types and use highly multiplexed imaging mass cytometry on matched breast cancer samples to confirm our defined CAF phenotypes at the protein level and to analyse their spatial distribution within tumours. This general CAF classification scheme will allow comparison of CAF phenotypes across studies, facilitate analysis of their functional roles, and potentially guide development of new treatment strategies in the future. Cancer-associated fibroblasts (CAFs) have different subtypes and play diverse roles in the tumour microenvironment. Here, the authors use single-cell RNA-seq and multiplex imaging mass cytometry data to propose a CAF classification scheme of nine subtypes across different cancer types.

Despite being the most frequently altered oncogenic protein in solid tumours, KRAS has historically been considered ‘undruggable’ owing to a lack of pharmacologically targetable pockets within the mutant isoforms. However, improvements in drug design have culminated in the development of inhibitors that are selective for mutant KRAS in its active or inactive state. Some of these inhibitors have proven efficacy in patients with KRASG12C-mutant cancers and have become practice changing. The excitement associated with these advances has been tempered by drug resistance, which limits the depth and/or duration of responses to these agents. Improvements in our understanding of RAS signalling in cancer cells and in the tumour microenvironment suggest the potential for several novel combination therapies, which are now being explored in clinical trials. Herein, we provide an overview of the RAS pathway and review the development and current status of therapeutic strategies for targeting oncogenic RAS, as well as their potential to improve outcomes in patients with RAS-mutant malignancies. We then discuss challenges presented by resistance mechanisms and strategies by which they could potentially be overcome.The RAS oncogenes are among the most common drivers of tumour development and progression but have historically been considered undruggable. The development of direct KRAS inhibitors has changed this paradigm, although currently clinical use of these novel therapeutics is limited to a select subset of patients, and intrinsic or acquired resistance presents an inevitable challenge to cure. Herein, the authors provide an overview of the RAS pathway in cancer and review the ongoing efforts to develop effective therapeutic strategies for RAS-mutant cancers. They also discuss the current understanding of mechanisms of resistance to direct KRAS inhibitors and strategies by which they might be overcome.

Over the past decade, CRISPR has become as much a verb as it is an acronym, transforming biomedical research and providing entirely new approaches for dissecting all facets of cell biology. In cancer research, CRISPR and related tools have offered a window into previously intractable problems in our understanding of cancer genetics, the noncoding genome and tumour heterogeneity, and provided new insights into therapeutic vulnerabilities. Here, we review the progress made in the development of CRISPR systems as a tool to study cancer, and the emerging adaptation of these technologies to improve diagnosis and treatment.The advent of CRISPR technologies has enabled programmable nucleic acid editing in mammalian cells. In this Review, Katti et al. outline the enormous progress that has been made in the application of CRISPR tools to the study of cancer and also describe the potential use of CRISPR systems in clinical cancer management including diagnosis and treatment.

The Catalogue of Somatic Mutations in Cancer (COSMIC) Cancer Gene Census (CGC) is an expert-curated description of the genes driving human cancer that is used as a standard in cancer genetics across basic research, medical reporting and pharmaceutical development. After a major expansion and complete re-evaluation, the 2018 CGC describes in detail the effect of 719 cancer-driving genes. The recent expansion includes functional and mechanistic descriptions of how each gene contributes to disease generation in terms of the key cancer hallmarks and the impact of mutations on gene and protein function. These functional characteristics depict the extraordinary complexity of cancer biology and suggest multiple cancer-related functions for many genes, which are often highly tissue-dependent or tumour stage-dependent. The 2018 CGC encompasses a second tier, describing an expanding list of genes (currently 145) from more recent cancer studies that show supportive but less detailed indications of a role in cancer.

Over the past two decades, elucidation of the genetic defects that underlie cancer has resulted in a plethora of novel targeted cancer drugs. Although these agents can initially be highly effective, resistance to single-agent therapies remains a major challenge. Combining drugs can help avoid resistance, but the number of possible drug combinations vastly exceeds what can be tested clinically, both financially and in terms of patient availability. Rational drug combinations based on a deep understanding of the underlying molecular mechanisms associated with therapy resistance are potentially powerful in the treatment of cancer. Here, we discuss the mechanisms of resistance to targeted therapies and how effective drug combinations can be identified to combat resistance. The challenges in clinically developing these combinations and future perspectives are considered.Single-agent therapies targeting specific dysregulated pathways in cancer can be highly effective, but drug resistance frequently develops. Here, Bernards and colleagues discuss the mechanisms underlying resistance to targeted therapies, and assess how these can be suppressed by using tailored combination therapies.

Cancer-associated fibroblasts (CAFs) are the predominant components of the tumor microenvironment (TME) and influence cancer hallmarks, but without systematic investigation on their ubiquitous characteristics across different cancer types. Here, we perform pan-cancer analysis on 226 samples across 10 solid cancer types to profile the TME at single-cell resolution, illustrating the commonalities/plasticity of heterogenous CAFs. Activation trajectory of the major CAF types is divided into three states, exhibiting distinct interactions with other cell components, and relating to prognosis of immunotherapy. Moreover, minor CAF components represent the alternative origin from other TME components (e.g., endothelia and macrophages). Particularly, the ubiquitous presentation of endothelial-to-mesenchymal transition CAF, which may interact with proximal SPP 1 + tumor-associated macrophages, is implicated in endothelial-to-mesenchymal transition and survival stratifications. Our study comprehensively profiles the shared characteristics and dynamics of CAFs, and highlight their heterogeneity and plasticity across different cancer types. Browser of integrated pan-cancer single-cell information is available at https://gist-fgl.github.io/sc-caf-atlas/ . Cancer-associated fibroblasts (CAFs) are a predominant and critical component of the tumour microenvironment. Here, the authors integrate and analyse single-cell RNA-seq data of CAFs across 10 common solid cancer types, identifying their plasticity and interactions with other cell types.