Spatial multi-omics and Quantitative Modeling Integration to Elucidate Biophysical Principles of Tumor Evolution and the Microenvironment

Recent advances in spatial multi-omics technologies and computational methods have transformed our ability to map the molecular, cellular, and structural landscape of tumors in situ, providing unprecedented insights into the complex ecosystem of the tumor microenvironment (TME). However, as spatial data continue to grow in both depth and dimensionality, the underlying biophysical principles that govern the phenotypic states of the TME remain poorly defined. This complexity—characterized by high dimensionality and large data volumes—calls for methodological innovation to bridge the translational gap, linking multi-omics data to biophysical function, and ultimately, to clinical outcomes.

This Research Topic aims to address the critical challenge of linking high-dimensional multi-omics data to functional understanding and clinical relevance, such as the identification of prognostic biomarkers. Despite the wealth of information encoded in these datasets, translating them into insights about the biophysical behaviors of the tumor microenvironment remains a major bottleneck. To address this, we seek innovative methodologies such as spatial quantification, artificial intelligence, biophysical modeling, statistical frameworks, and quantitative systems pharmacology (QSP). By implementing data-rich measurements, this effort aims to advance a mechanistic and clinically actionable understanding of disease dynamics in the context of immuno-oncology.

Scope and Information for Authors
We welcome contributions that explore the intersection of quantitative analysis and biophysics. Topics of interest include, but are not limited to:

Digital pathology and image analysis for characterizing TME heterogeneity, focusing on biophysical properties such as spatial constraints, tissue organization, and cellular and molecular functions.

Innovative holistic AI approaches applied to the analysis of high-dimensional, large-volume spatial and multi-omics data, enabling biophysical inference.

Interpretable integration and quantifications of multi-omics data, aiming at the identification of novel predictive and prognostic biomarkers, as well as uncovering biophysical principles governing disease progression.

Quantitative systems pharmacology (QSP) and other mechanistic modeling approaches linking molecular-, cellular-, and tissue-scale dynamics to clinical outcomes.

We invite original research, methods, reviews, and perspective articles that advance this emerging interface between spatial biology, biophysics, and cancer evolution.

Article types and fees

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

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

Keywords: Computational biology, artificial intelligence, Spatial multi-omics, Biophysics, Tumor microenvironment, Immuno-Oncology, Image analysis, Mechanisic models, Systems pharmacology

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.