Cell-cell interactions as predictive and prognostic markers for drug responses in cancer

The tumor microenvironment (TME) is composed of a diverse and dynamic spectrum of cell types, cellular activities, and cell-cell interactions (CCI). Understanding the complex CCI within the TME is critical for advancing cancer treatment strategies, including modulating or predicting drug responses. Recent advances in omics technologies, including spatial transcriptomics and proteomics, have allowed improved mapping of CCI within the TME. The integration of omics insights from different platforms may facilitate the identification of novel biomarkers and therapeutic targets. This review discusses the latest computational methods for inferring CCIs from different omics data and various CCI and drug databases, emphasizing their applications in predicting drug responses. We also comprehensively summarize recent patents, clinical trials, and publications that leverage these cellular interactions to refine cancer treatment approaches. We believe that the integration of these CCI-focused technologies can improve personalized therapy for cancer patients, thereby optimizing treatment outcomes and paving the way for next-generation precision oncology.