Defining Cancer Ecosystem States with AI and Multi-Omics

This Research Topic aims to bring together studies that use artificial intelligence and multi-omics integration to define, quantify and functionally interpret cancer cell and tumor ecosystem states. We are particularly interested in work that moves beyond traditional, tumor-cell–centric classifications to capture how microenvironmental composition, architecture and cell–cell communication shape disease behavior and therapeutic vulnerability.

A key objective is to highlight approaches that integrate bulk, single-cell and/or spatial datasets to derive reproducible ecosystem states, link them to progression, treatment response or resistance, and propose testable, mechanism-based hypotheses in cancer cell biology. We also seek contributions that benchmark analytical pipelines, address issues of generalizability across cohorts, or provide open resources and tools for the community, where these are grounded in experimentally derived biological data. By assembling computational, experimental and clinical perspectives, this Topic aims to accelerate the translation of ecosystem-based modelling into practical decision support for precision oncology.

We welcome Original Research, Methods, Brief Research Reports, Reviews and Perspectives. Topics of interest include, but are not limited to: AI- or machine-learning–based definitions of cancer ecosystem states; network medicine frameworks integrating multi-omics data; ecosystem-informed prognostic or predictive biomarkers; microenvironment-guided risk stratification and patient subtyping; reconstruction of cell–cell interaction networks from single-cell or spatial data; cross-cohort and pan-cancer comparisons of ecosystem states; and studies linking ecosystem features to targeted therapy, chemotherapy or immunotherapy response.

Submissions must include experimentally derived biological data (e.g., bulk, single-cell, spatial, or other omics data) and a clear connection to cancer cell or microenvironmental biology. Submissions that combine computational modelling with experimental validation, functional assays, or analysis of biologically characterized clinical cohorts / real-world patient data are especially encouraged; purely simulated or theoretical work without biological or clinical data is not suitable for this Topic. Authors are invited to clearly describe data and code availability to enhance reproducibility and reuse.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: artificial intelligence, machine learning, network medicine, multi-omics integration, tumor microenvironment, single-cell RNA sequencing, spatial transcriptomics, prognostic biomarkers

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.