Categorisation of patients based on immune profiles: a new approach to identifying candidates for response to checkpoint inhibitors

• Main Authors: Svetlana Bornschlegl, Michael P Gustafson, Danae A Delivanis, Mabel Ryder, Minetta C Liu, George Vasmatzis, Chris L Hallemeier, Sean S Park, Lewis R Roberts, Ian F Parney, Diane F Jelinek, Allan B Dietz
• Format: Article
• Language: English
• Published: Wiley 2021-01-01
• Series: Clinical & Translational Immunology
• Subjects: checkpoint inhibitors; CTLA‐4; immune monitoring; immune profile; PD‐1; programmed death 1
• Online Access: https://doi.org/10.1002/cti2.1267

Abstract Objectives Inhibitors to the checkpoint proteins cytotoxic T‐lymphocyte‐associated protein 4 (CTLA‐4) and programmed cell death protein 1 (PD‐1) are becoming widely used in cancer treatment. However, a lack of understanding of the patient response to treatment limits accurate identification of potential responders to immunotherapy. Methods In this study, we assessed the expression of PD‐1 and CTLA‐4 on 19 leucocyte populations in the peripheral blood of 74 cancer patients. A reference data set for PD‐1 and CTLA‐4 was established for 40 healthy volunteers to determine the normal expression patterns for these checkpoint proteins. Results Unsupervised hierarchical clustering found four immune profiles shared across the solid tumor types, while chronic lymphocytic leukaemia patients had an immune profile largely unique to them. Furthermore, we measured these leucocyte populations on an additional cohort of 16 cancer patients receiving the PD‐1 inhibitor pembrolizumab in order to identify differences between responders and non‐responders, as well as compared to healthy volunteers (n = 20). We observed that cancer patients had pre‐treatment PD‐1 and CTLA‐4 expression on their leucocyte populations at different levels compared to healthy volunteers and identified two leucocyte populations positive for CTLA‐4 that had not been previously described. We found higher levels of PD‐1+ CD3+ CD4− CD8− cells in patients with progressive disease and have identified it as a potential biomarker of response, as well as identifying other significant differences in phenotypes between responders and non‐responders. Conclusion These results are suggestive that categorisation of patients based on immune profiles may differentiate responders from non‐responders to immunotherapy for solid tumors.