Multi-omics and single cell characterization of cancer immunosenescence landscape

Background:

• Cellular senescence (CS) is implicated in tumor progression, but its genetic associations across cancers, particularly concerning telomere length and immune cell evolution, remain underexplored.
• Existing research has not fully elucidated the relationship between cancer senescence signatures and telomere length or the evolutionary dynamics of malignant and immune cells at the CS level.

Purpose of the Study:

• To define a cellular senescence (CS)-associated signature and investigate its relationship with clinicopathological features, immune infiltration, and therapeutic responses in human cancers.
• To explore the evolutionary trends of malignant and immune cells at the CS level using single-cell analysis.
• To develop and validate a predictive model for cancer survival based on the CS signature.

Main Methods:

• Development of a "senescence signature" based on CS-associated genes.
• Correlation analysis of the senescence signature with patient prognosis, age, genomic instability, telomere length, and immune cell infiltration (lymphocytes, Treg cells, MDSCs).
• Single-cell RNA sequencing analysis to examine evolutionary trends of cancer and immune cells.
• Prediction of response to immune checkpoint inhibitors and targeted therapies (MEK1/2, ERK1/2, BCL-2 inhibitors).
• Development of a CS prediction model for cancer survival and an online application portal.

Main Results:

• A higher senescence signature was associated with a worse prognosis, older patient age, increased genomic instability, and longer telomeres.
• Elevated senescence signature correlated with increased lymphocytic infiltration and higher levels of pro-tumor immune cells like Treg cells and MDSCs.
• The senescence signature effectively predicted patient responses to immune checkpoint inhibitor therapy and sensitivity to MEK1/2, ERK1/2, and BCL-2 family inhibitors.
• Single-cell analysis revealed a consistent evolutionary trend between malignant and immune cells at the CS level.
• The MAPK signaling pathway and apoptotic processes were identified as key players in CS.

Conclusions:

• The defined senescence signature serves as a robust prognostic biomarker in human cancers.
• The senescence signature offers predictive value for immunotherapy and targeted therapy responses.
• Understanding cellular senescence dynamics provides insights into cancer evolution and potential therapeutic strategies.
• A novel CS prediction model and online tool are available for clinical application.