Experiment Overview

Repository ID: FR-FCM-ZY34 Experiment name: High dimensional single cell analysis predicts response to anti-PD-1 immunotherapy MIFlowCyt score: 31.50%
Primary researcher: Carsten Krieg PI/manager: Carsten Krieg Uploaded by: Carsten Krieg
Experiment dates: 2017-02-28 - 2018-02-28 Dataset uploaded: Feb 2017 Last updated: Apr 2018
Keywords: [flow cytometry] [] [mass cytometry] [PD-1] [biomarker prediction] [immunotherapy] [immune signature] Manuscripts:
Organizations: University Zurich, Institute of Experimental Immunology, 8057 Zurich, (Switzerland)
Purpose: Immune checkpoint blockade has revolutionized cancer therapy. In particular, inhibition of programmed cell death protein 1 (PD-1) has proven to be effective for the treatment of metastatic melanoma and is increasingly used in other cancers. Despite a dramatic increase in progression-free survival (PFS), only a minority (<40%) of patients show durable clinical benefit. Thus, predictive biomarkers of clinical response to anti-PD1 immunotherapy are urgently needed. Our goal here was the identification of an immune signature in patient blood to discriminate responders and non-responders prior to treatment initiation.
Conclusion: The frequency of CD14+CD16-HLA-DRhi monocytes provides a strong predictive value for the stratification of the therapy response in melanoma patients undergoing anti-PD1 immunotherapy and the development of a screening tool to optimize patient selection.
Comments: We employed high-dimensional single cell cytometry by time of flight (CyTOF) and unbiased, custom algorithm-assisted characterization of the immune compartment in liquid biopsies of 40 patient samples before and during anti-PD-1 immunotherapy. Conventional multiparametric fluorescence flow cytometry validated the predictive signature in 31 blinded, independent samples.
Funding: The University Research Priority Program (URPP) in Translational Cancer Research, the Swiss National Science Foundation (310030_146130 and 316030_150768) and the European Union FP7 project ATECT supported this work.
Quality control: Mass cytometry acquisition was performed on a CyTOF2.1 (Helios) instrument. Pre -acquisition instrument performance was checked using standard beads and during acquisition a constant flow rate and event count was used. Data acquired by mass cytometry was normalized using the standalone MATLAB normalizer (Version 2013b)16, marker expression was controlled in FlowJo (Version10.1r5) and patient samples were de-barcoded using Boolean gating. For flow cytometry over different experiments channel voltages were adjusted using standard beads and compensation was performed using single stained controls.
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