Abstract
Cellular senescence (CS) plays an important role in cancer development and treatment. However, the heterogeneity of CS among different types of cancer and its impacts on patient prognosis and therapy response remain to be fully elucidated. This study performed a comprehensive pan-cancer analysis on the CS landscape of 33 cancer types and 29 normal tissues. The molecular subgroups of CS were identified based on the expression of CS-related genes. Multi-platform data including prognosis, microbiota, immune microenvironment, multi-omics, and drug sensitivity were used to investigate the associations with the CS subgroups. Additionally, 12 single-cell datasets and 19 immunotherapy cohorts were collected to evaluate the CS subgroups. We characterized five pan-cancer CS subgroups with distinct biological features, named Inflamm-aging, DNA Damage Response, Autophagy, Immunologically Quiet, and Metabolic Disorder. The CS subgroups showed cancer-type and tissue-type specific distribution, and revealed significant associations with cancer prognosis, intratumoral microbiota, immunophenotypic features, and multi-omic alterations. The immunophenotypic features of the CS subgroups were verified by immunohistochemistry staining in tissue microarrays. Single-cell analyses indicated the CS heterogeneity among cancer types at the single-cell level. Furthermore, we developed a machine-learning model integrating CS-related cancer driver genes to infer the CS subgroups, and verified its prediction capability for immunotherapy response and prognosis in independent cohorts. Finally, potential therapeutic agents and targets were identified in the CS subgroups, which might have therapeutic implications for patients. Overall, this study provided a CS-derived classification scheme to expand the existing understanding of CS heterogeneity. The CS subgroups can reveal distinct CS states among cancer types and may contribute to improving personalized therapeutic strategies.