Spatially resolving cancer: from cell states to therapy

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Spatial multi-omics technologies are revolutionizing cancer research by linking molecular data to tissue architecture.
• Understanding cancer cell and microenvironment interactions is crucial for addressing lineage plasticity, immune evasion, and therapeutic resistance.

Purpose of the Study:

• To review key breakthroughs in spatial profiling and computational methods for cancer research.
• To highlight the integration of spatial data with computational pathology, multimodal data, and machine learning.
• To discuss challenges and propose a roadmap for clinical translation of spatial multi-omics.

Main Methods:

• Review of recent advances in spatial multi-omics technologies.
• Analysis of computational approaches for spatial data integration.
• Integration of spatial profiling with computational pathology and machine learning.

Main Results:

• Spatial multi-omics provides deep biological insights by contextualizing cancer cell states and interactions.
• Identification of clinically relevant molecular programs and spatial biomarkers.
• Advancements in integrating multimodal spatial data and computational pathology.

Conclusions:

• Spatial multi-omics offers actionable, spatially resolved molecular insights for advancing precision oncology.
• Overcoming data integration challenges is key to accelerating clinical translation.
• Future research should focus on developing robust analytical frameworks and clinical applications.