Categorisation of patients based on immune profiles: a new approach to identifying candidates for response to checkpoint inhibitors. Issue 4 (29th April 2021)

• Record Type: Journal Article
• Authors: Bornschlegl, Svetlana; Gustafson, Michael P; Delivanis, Danae A; Ryder, Mabel; Liu, Minetta C; Vasmatzis, George; Hallemeier, Chris L; Park, Sean S; Roberts, Lewis R; Parney, Ian F; Jelinek, Diane F; Dietz, Allan B
• Is Part Of: Clinical & translational immunology. Volume 10:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-04-29
• Subjects: checkpoint inhibitors -- CTLA‐4 -- immune monitoring -- immune profile -- PD‐1 -- programmed death 1; Immunologic diseases -- Periodicals; Immunology -- Periodicals; Clinical medicine -- Periodicals; Immune System Diseases -- therapy; Immunotherapy; Immunologic Factors -- therapeutic use; Translational Medical Research; Molecular Targeted Therapy; Clinical medicine; Immunologic diseases; Immunology; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 616.079
• DOI: 10.1002/cti2.1267
• Languages: English
• ISSNs: 2050-0068

Abstract

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. Abstract : Here, we have developed a multiparameter flow cytometry assay that allowed us to measure checkpoint proteins CTLA‐4 and PD‐1 on 19 leucocyte populations in the peripheral blood. By looking at the data independent of disease, we used unsupervised hierarchical clustering of PD‐1 expression on patients with different types of cancers and identified five patient profiles. These profiles, or groups of immunologically similar patients, can be used for predicting responders vs non‐responders and applied towards decisions in choosing appropriate inhibitors.