Correlation Of Circulating Tumor Enumeration And Immune Checkpoint Marker Expression On Cd103+, Cd4+T Cells In Non-Small Cell Lung Cancer Tissue.

109 Background: Non-small cell lung cancer (NSCLC) has a poor prognosis as most patients are at advanced stage when diagnosed. Targeted therapy and immunotherapy in recent years has significantly improved NSCLC patient outcome. In this study, we employed cell-by-cell immune and cancer marker profiling of the primary tumor cells to investigate possible signatures that might predict the presence or absence of circulating tumor cells (CTCs). Methods: We performed a comprehensive study on 10 NSCLC patient tissue samples with paired blood samples. The solid tissue biopsy samples were dissociated into single cells by non-enzymatic tissue homogenization. The single cell suspensions were stained with a total 25 immune, cancer markers and a DNA content dye and analyzed with advanced, high-parameter flow cytometry. CTCs were isolated and analyzed from the paired peripheral blood. Results: Out of the 26 unique cell markers stained, we investigated a total of 72 biomarkers for their correlation with CTC number. Strong correlations were observed between CTC number and the frequency of immune checkpoint marker expressing lymphocytes, especially with the immune checkpoint marker expressing CD103+CD4+ T lymphocytes. CTC number is also correlated with the frequency of PD-L1 expressing cancer cells and cancer cell DNA content. In contrast, CTC number inversely correlated to the frequency of CD44+E-cadherin- cancer cells. Unsupervised clustering analysis based on the biomarker analysis separated the CTC negative patients from the CTC positive patients. Conclusions: Profiling multiple immune and cancer markers on cancer samples with multi-parametric flow cytometry allowed us to obtain protein expression information at the single cell level. Clustering analysis of the proteomic data revealed a signature driven by checkpoint marker expression on CD103+CD4+ T cells that could potentially be predictive of CTCs.