Systemic immune characteristics predicting toxicity to immune checkpoint inhibitors in patients with advanced breast cancer

Background

Immune checkpoint inhibitors (ICIs) are among the most promising treatment options for cancer. However, frequent and sometimes life-threatening immune-related adverse events (irAEs) are associated with ICI treatment. Therefore, it is imperative to establish a model for predicting the risk of irAEs to identify high-risk groups, enable more accurate clinical risk‒benefit analysis for ICI treatment and decrease the incidence of irAEs. However, no ideal model for predicting irAEs has been applied in clinical practice. The aim of this study was to analyze the systemic immune characteristics of patients with irAEs and establish a model for predicting the risk of irAEs.

Methods

We conducted a study to monitor irAEs in patients with advanced breast cancer undergoing immunotherapy during and following the treatment course. Peripheral blood mononuclear cells (PBMCs) were collected before and after two cycles of therapy. Mass cytometry time-of-flight (CyTOF) was employed to identify baseline and posttreatment immune cell subpopulations, and the relationships between the proportions of cells in these subpopulations and the occurrence of irAEs were explored. Additionally, we conducted subgroup analyses stratified by the anatomic location and time of onset of irAEs. Furthermore, we developed a logistic regression model to predict the risk of irAEs and validated this model using two independent validation cohorts from the Gene Expression Omnibus (GEO) database (accession numbers GSE189125 and GSE186143).

Results

By analyzing 106 blood samples and samples from two independent validation cohorts (n = 16 and 60 patients), we found that high proportions of CXCR3⁺CCR6⁺CD4⁺ T cells and CD38⁺CD86⁺CXCR3⁺CCR6⁺CD8⁺ T cells and a low proportion of CXCR3^lowCD56^dim natural killer (NK) cells at baseline were significantly correlated with the incidence of irAEs (P = 0.0029, P < 0.001, and P = 0.0017, respectively). In the subgroup analysis, we observed consistent results in patients with immune-related pneumonitis (ir-pneumonitis) and immune-related thyroiditis (ir-thyroiditis). In the early irAE group, the baseline proportion of CXCR3⁺CCR6⁺CD4⁺ T cells was greater than that in the late irAE group (P = 0.011). An analysis of PBMCs before and after ICI treatment revealed thatthe dynamic changes in the proportions of naïve CD4⁺ T cells and CXCR3^lowCD56^dim NK cells were closely related to irAE occurrence. Finally, we ultimately developed a model for predicting the risk of irAEs, which yielded an area under the receiver operating characteristic curve (AUROC) of 0.79 in the training cohort and an AUROC of 0.75 in the single-cell validation cohort (GSE189125).

Conclusions

These findings indicate that different populations of immune cells are associated with different irAEs and that characterization of these cells may be used as biomarkers to predict the risk of specific toxicities. This will facilitate the management of irAEs and may lead to a reduction in the incidence of irAEs.