Spotlight on methods to study cancer

Recent technological advances for studying the biology of tumors have expanded our understanding of cancer.

With a focus on the evolutionary forces that shape cancer cell behavior, some researchers work out ways to outmaneuver cancer.

Organoids are an emerging way to model the fraught complexity of tumors.

The tumor microenvironment is a source of cancer’s strength. Multi-omics and new spatial techniques stand to find ways to weaken it.

This Perspective discusses recent progresses in development of in vitro tumor models and the current challenges in this field.

This Perspective explores the challenges and future directions in the world of human tumor microbiome research.

This Review discusses the current challenges and the available methods to study the four physical hallmarks of cancer.

This Perspective outlines the datasets, access methods, data standards, infrastructure, governance and community-engagement strategies of the Human Tumor Atlas Network.

Spatial proteomics has transformed cancer research by providing unparalleled insights into the microenvironmental landscape of tumors. Here we discuss how these technologies have significantly advanced our understanding of cell–cell interactions, tissue organization and spatially coordinated mechanisms underlying antitumor immune responses, and will pave the way for emerging breakthroughs in cancer research.

Breakthroughs in AI and multimodal genomics are unlocking the ability to study the tumor microenvironment. We explore promising machine learning techniques to integrate and interpret high-dimensional data, examine cellular dynamics and unravel gene regulatory mechanisms, ultimately enhancing our understanding of tumor progression and resistance.

The development of mass spectrometry-based single-cell proteomics technologies opens unique opportunities to understand the functional crosstalk between cells that drive tumor development.

This Review describes spatial omics and multiplexed imaging technologies and their current and future impact in studying tumor heterogeneity and cancer biology.

The Perspective reviews the computational methods for subclonal reconstruction and recommends best practices for analyses and quality assessments.

Clonalscope is a method for cancer subclone detection leveraging copy number profiles estimated using spatial and single-cell sequencing data.

SAVANA is a tool to detect somatic structural variants and copy number aberrations using long-read sequencing data, offering high sensitivity, specificity and compatibility with or without germline controls.

This work integrates duplex sequencing with cost-effective Ultima sequencing to enhance the accuracy of whole-genome circulating cell-free DNA profiling.

scAtlasVAE is a deep learning-based model for cross-atlas integration. Here it enables the development of a large-scale human CD8⁺ T cell atlas with integrated T cell receptor data.

This work presents CalicoST for inferring allele-specific copy numbers and reconstructing spatial tumor evolution by using spatial transcriptomics data.

A CasRx-based screening platform for genome-scale long noncoding RNA transcriptome perturbation is described.

A dynamic, hormone-responsive mammary gland organoid system that can mimic complex physiological processes in vitro.

Netie, a hierarchical Bayesian model to infer the neoantigen evolution and immune selection pressure during tumor progression.

CODA: a method for 3D reconstruction of large serially sectioned tissues.