An exploratory study to predict the efficacy and prognosis of immunotherapy for extensive-stage small cell lung cancer based on peripheral blood dynamic immune profiles.

Abstract

2549 Background: The high-dimensional classification information of peripheral blood mononuclear cells can provide abundant efficacy and prognosis-related data. However, in the field of immunotherapy for extensive-stage small cell lung cancer (ES-SCLC), the biomarkers that can predict the efficacy and prognosis need to be explored and clarified. Methods: Cytometry by Time-Of-Flight (CyTOF) was applied to the dynamic monitoring of immunotherapy using clinical resources such as dynamic peripheral blood from ES-SCLC patients. By labeling the following proteins: CD45, CD3, CD4, CD8, CD25, CD127, CD45RA, CD45RO, CCR7, TCRγδ, CD19, CD66b, CD14, CD56, CD16, CD11c, CD123, HLA-DR, CD38, CD57, CXCR3, CCR6, CCR4, CXCR5, CD95/Fas, LAG-3, Tim-3, CTLA-4, PD-L1, PD-1, CD278/ICOS, and TIGIT, this study performed high-dimensional fine-phenotyping of peripheral blood immune cells from ES-SCLC patients. We further explored the dynamic immune profile of peripheral blood that could predict the efficacy and prognosis of immunotherapy in combination with efficacy assessment and survival indicators. Results: 81 dynamic peripheral blood samples (baseline, after two cycles of treatment[C2], and progressive disease) were collected from ES-SCLC patients who received first-line immunotherapy combined with chemotherapy (n = 20) and chemotherapy alone (n = 7) in this study. In the immunotherapy group, a high percentage of senescent CD4+TEM/CD4+TEM at baseline (P = 0.029) was significantly associated with longer PFS. High TIGIT expression at baseline (P = 0.016) was significantly associated with shorter PFS. In addition, PD-1 (CD4+TCM, P = 0.017; Naive CD4+T, P = 0.031; pDCs, P = 0.031; NK, P = 0.007; Early NK, P = 0.007; Late NK, P = 0.02) and TIGIT (CD8+TEM, P = 0.046; NK, P = 0.038) expression levels at baseline in multiple cell subpopulations were significantly negatively correlated with OS. In contrast, the above peripheral blood immune profile was not a predictor in the chemotherapy group. In the immunotherapy group, peripheral blood dynamic monitoring showed that increased γδT cell percentage after treatment was significantly associated with longer PFS and OS (PFS, P = 0.035; OS, P = 0.032). Increased CD4+ TEM and CD4+ TCM percentage after treatment was significantly associated with shorter PFS and OS (CD4+ TEM: PFS, P = 0.021, OS, P = 0.036; CD4+ TCM: PFS, P = 0.01; OS, P = 0.014). Meanwhile, CTLA-4 and ICOS expression in total cells at progressive disease was significantly higher than C2, suggesting that it might be related to immunotherapy resistance. In the chemotherapy group, the above peripheral blood dynamic immune profile did not predict the efficacy and prognosis of chemotherapy. Conclusions: Dynamic peripheral blood immune profile can predict the efficacy and prognosis of immunotherapy in ES-SCLC.