Discovery of Novel Molecular Mechanisms and Biomarkers in Cancer through Spatial Omics and Temporal Sampling

Advances in cancer genomics have produced detailed molecular portraits of tumors. These insights have clarified classification schemas, driver genes, oncogenic programs, and tumor evolution. However, challenges remain. Intratumoral heterogeneity, tumor-microenvironment dynamics, and therapy response mechanisms are still not fully understood. Spatial omics technologies integrate spatial context with multi-omics readouts. They map cellular states and interactions within native tissue. Deliberate temporal sampling allows direct observation of tumor evolution from early stages through metastasis and treatment. This reveals transient states, resistant clones, and time-dependent biomarkers.

The combination of spatial resolution and longitudinal sampling uniquely positions researchers to (1) identify spatially restricted cell populations and signaling niches that drive disease progression, (2) discover biomarkers predictive of early disease or imminent relapse, and (3) reveal dynamic mechanisms of immune evasion and therapy resistance. Temporal sampling—whether sampling across disease stages, serial biopsies during therapy, or carefully designed preclinical time courses—provides the necessary temporal axis to distinguish cause from consequence, capture early events that seed metastasis, and define the kinetics of treatment response and recovery. Together, spatial-temporal profiling deepens mechanistic insight and accelerates translation by prioritizing biomarkers and targets with both biological plausibility and clinical relevance.

This Research Topic seeks studies that define molecular landscapes with spatial precision and temporal depth, and that use these data to uncover mechanisms of carcinogenesis, metastatic progression, and treatment resistance. Work that leverages cutting-edge spatial-omics modalities and innovative sampling designs to clarify how spatial-temporal features shape tumor heterogeneity, microenvironmental interactions, immune dynamics, and clinical outcomes is especially encouraged. Submissions that provide predictive markers, mechanistic insights, or validated targets with translational promise will receive priority over descriptive atlases. By integrating spatial context with temporal dynamics, this Research Topic aims to catalyze discoveries that improve early detection, refine prognostic and predictive models, and identify novel therapeutic strategies—ultimately advancing patient care.

Areas of interest include, but are not limited to:

• Molecular characterization of spatial-temporal heterogeneity across diverse cancer types, with emphasis on single-cell resolution or high-plex in situ readouts.

• Studies that reveal dynamic processes and key transitional events in carcinogenesis and metastasis, and how tumor and stromal compartments respond and adapt to therapy.

• Mechanistic investigations of cancer initiation, progression, and the emergence of therapeutic resistance illuminated by longitudinal spatial profiling.

• Identification and validation of molecular markers predictive of metastasis, survival, or treatment response, and construction of robust prediction models grounded in spatial-temporal data.

• Development and benchmarking of analytical frameworks, bioinformatics pipelines, and visualization tools tailored for spatial-omics and longitudinal cancer studies.

We invite submissions of Brief Research Reports, Data Reports, General Commentaries, Hypotheses & Theories, Methods papers, Mini-Reviews, Opinions, Original Research Articles, Perspectives, Reviews, and Systematic Reviews.