The Inter-link of Ageing, Cancer and Immunity: Findings from Real-world Retrospective Study

Background: Although the concept of declined immune function associated with cancer has been accepted extensively, real-world clinical studies focusing on analysis of the peripheral blood immune changes underlying ageing, immunity and cancer are scarce.

Methods: In this case-control study, we retrospectively analysed 1375 cancer patients and enrolled 275 age and gender matched healthy individuals. Flow cytometry was conducted to assess the immune changes. Further analysis was examined by SPSS 17.0 and GraphPad Prism 9 software.

Results: Cancer patients showed obviously decreased CD3 T, CD3CD4 Th, CD3CD8 CTL, CD19 B, CD16CD56 NK cell counts and lower percentage of PD-1 (programmed cell death protein-1, PD-1) positive cells than healthy control (P < 0.0001). For cancer patients, the reference range of circulating percentage of PD-1CD45 cells, PD-1CD3 T cells, PD-1CD3CD4 Th cells and PD-1CD3CD8 CTL (Cytotoxic T Lymphocyte, CTL) were 11.2% (95% CI 10.8%-11.6%), 15.5% (95% CI 14.7%-16.0%), 15.4% (95% CI 14.9%-16.0%) and 14.5% (95% CI 14.0%-15.5%), respectively. Moreover, the reduction of CD3 T, CD3CD4 Th, CD3CD8 CTL, CD19 B cell counts accompanied with age and stage advancing (P < 0.05). CD16CD56 NK cells decreased with stage, but elevated in aged and male cancer patients (P < 0.05). Additionally, the percentage of PD-1 positive cells varied across cancer types, raised with age and stage. Head and neck, pancreatic, gynaecological and lung demonstrated a higher level of the percentage of PD-1 positive cells than melanoma, prostate, and breast cancer (P < 0.05).

Conclusions: This study provides the reference range of the percentage of PD-1 positive cells on peripheral blood, confirms the decreased immune cells and a series of immune changes accompanying with cancer, expands our real world evidence to better understand the interactions of ageing, cancer and immunity. Moreover, the circulating percentage of PD-1 positive cells shows similar tumor type distribution with tumor mutational burden (TMB), supports that it maybe a potential predictive biomarker for immune checkpoint inhibitor therapy.