Identification of an immune classification for cervical cancer and integrative analysis of multiomics data


Background: To understand the molecular mechanisms of the antitumour response, we analysed the immune landscape of cervical cancer to identify novel immune molecular classes.

Methods: We established a stable immune molecular classification using a nonnegative matrix factorization algorithm and validated the correlation in two validation sets of 249 samples.

Results: Approximately 78% of cervical cancers (CCs) (228/293) were identified to show significant enrichment in immune cells (e.g., CD8 T cells and macrophages), a type I IFN response, enhanced cytolytic activity and high PDCD1, and these CCs were referred to as the “immune class”. We further identified two subtypes of the immune class: active immune and exhausted subtypes. Although the active immune subtype was characterized by enrichment of IFN signatures and better survival, the exhausted subtype expressed activated stroma, a wound healing signature, enhanced M2 macrophages and absence of CD8 T cells and the TGF-β response signature. Integrative analysis of multiomics data identified EGFR, JUN, MYC, FN1 and SERPINE1 as key modulators of the tumour immune microenvironment and potential targets for combination therapies which was validated in two validation sets.

Conclusions: Our study introduces a novel immune classification that might predict ideal candidates to receive immunotherapy or specific combination therapies.