Prospective development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with recurrent/metastatic cancer to immune checkpoint inhibitors. Issue 2 (16th February 2021)

• Record Type: Journal Article
• Authors: Zhou, Jian-Guo; Donaubauer, Anna-Jasmina; Frey, Benjamin; Becker, Ina; Rutzner, Sandra; Eckstein, Markus; Sun, Roger; Ma, Hu; Schubert, Philipp; Schweizer, Claudia; Fietkau, Rainer; Deutsch, Eric; Gaipl, Udo; Hecht, Markus
• Is Part Of: Journal for immunotherapy of cancer. Volume 9:Issue 2(2021)
• Publication Date: 2021-02-16
• Subjects: biomarkers -- tumor -- immunotherapy -- programmed cell death 1 receptor -- tumor microenvironment -- tumor biomarkers; Cancer -- Immunotherapy -- Periodicals; Cancer -- Immunological aspects -- Periodicals; Tumors -- Immunological aspects -- Periodicals; Immunotherapy -- Periodicals; 616.99406105
• DOI: 10.1136/jitc-2020-001845
• Languages: English
• ISSNs: 2051-1426

Abstract

Abstract : Background: The predictive power of novel biological markers for treatment response to immune checkpoint inhibitors (ICI) is still not satisfactory for the majority of patients with cancer. One should identify valid predictive markers in the peripheral blood, as this is easily available before and during treatment. The current interim analysis of patients of the ST-ICI cohort therefore focuses on the development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with metastatic cancer to ICI targeting the programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) axis. Methods: A total of 104 patients were prospectively enrolled. 54 immune cell subsets were prospectively analyzed in patients' peripheral blood by multicolor flow cytometry before treatment with ICI (pre-ICI; n=89), and after the first application of ICI (n=65). Pre-ICI, patients were randomly allocated to a training (n=56) and a validation cohort (n=33). Univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator Cox model were used to create a predictive immune signature, which was also checked after the first ICI, to consider the dynamics of changes in the immune status. Results: Whole blood samples were provided by 89 patients pre-ICI and by 65 patients after the first ICI. We identified a LIPS which is based on five immune cell subtypes: CD14 high monocytes, CD8+/PD-1 + T cells, plasmacytoid dendritic cells, neutrophils, and CD3 + /CD56 + /CD16 + natural killer (NK)T cells. The signature achieved a high accuracy (C-index 0.74 vs 0.71) for predicting overall survival (OS) benefit in both the training and the validation cohort. In both cohorts, the low-risk group had significantly longer OS than the high-risk group (HR 0.26, 95% CI 0.12 to 0.56, p=0.00025; HR 0.30, 95% CI 0.10 to 0.91, p=0.024, respectively). Regarding the whole cohort, LIPS also predicted progression-free survival (PFS). The identified LIPS was not affected by clinicopathological features with the exception of brain metastases. NKT cells and neutrophils of the LIPS can be used as dynamic predictive biomarkers for OS and PFS after first administration of the ICI. Conclusion: Our study identified a predictive LIPS for survival of patients with cancer treated with PD-1/PD-L1 ICI, which is based on immune cell subsets in the peripheral whole blood. Trial registration number: NCT03453892 .