Integrating multi-omics data to optimize immunotherapy in endometrial cancer: a comprehensive study

Area of Science:

• Oncology
• Immunology
• Genomics

Background:

• Immunotherapy is crucial for endometrial cancer (EC) but benefits only a subset of patients.
• Understanding individual immune response is key to optimizing EC immunotherapy.

Purpose of the Study:

• To integrate multi-omics data to identify determinants of immunotherapy response in EC.
• To differentiate EC patients into responders (R) and non-responders (NR) and analyze underlying mechanisms.

Main Methods:

• Utilized multi-omics data with TIDE, EaSIeR, and ImmunoPhenoScore for patient stratification.
• Performed functional enrichment, gene set variant analysis, and tumor microenvironment (TME) deconvolution (ESTIMATE, TIMER, EPIC).
• Conducted somatic mutation, copy number variant (CNV), and single-cell RNA sequencing (scRNA-seq) analyses.

Main Results:

• Elderly EC patients (≥65 years) showed higher immunotherapy response rates.
• NR patients exhibited enrichment in cellular differentiation, angiogenesis, and proliferation, with higher tumor purity and lower immune checkpoint expression.
• Responders (R) showed stronger immune correlation, enriched pathways, and distinct TME composition, with higher mutation frequencies in genes like RPRD1B and CTNNB1.
• M1-like tumor-associated macrophages (TAMs) correlated with response, inhibiting tumor growth, while M2-like TAMs were enriched in non-responders, promoting angiogenesis.

Conclusions:

• Multi-omics data reveal genomic variants and TAMs significantly influence EC immunotherapy response.
• M1-like TAMs are crucial for effective immunotherapy, suggesting their potential as a therapeutic target.
• This research enhances understanding of EC immunotherapy mechanisms for better treatment optimization.