Abstract

BackgroundWhile immune checkpoint blockade has greatly improved clinical outcomes in diseases such as melanoma, there remains a need for predictive biomarkers to determine who will likely benefit most from which therapy. To date, most biomarkers of response have been identified in the tumors themselves. Biomarkers that could be assessed from peripheral blood would be even more desirable, because of ease of access and reproducibility of sampling.MethodsWe used mass cytometry (CyTOF) to comprehensively profile peripheral blood of melanoma patients, in order to find predictive biomarkers of response to anti-CTLA-4 or anti-PD-1 therapy. Using a panel of ~ 40 surface and intracellular markers, we performed in-depth phenotypic and functional immune profiling to identify potential predictive biomarker candidates.ResultsImmune profiling of baseline peripheral blood samples using CyTOF revealed that anti-CTLA-4 and anti-PD-1 therapies have distinct sets of candidate biomarkers. The distribution of CD4+ and CD8+ memory/non-memory cells and other memory subsets was different between responders and non-responders to anti-CTLA-4 therapy. In anti-PD-1 (but not anti-CTLA-4) treated patients, we discovered differences in CD69 and MIP-1β expressing NK cells between responders and non-responders. Finally, multivariate analysis was used to develop a model for the prediction of response.ConclusionsOur results indicate that anti-CTLA-4 and anti-PD-1 have distinct predictive biomarker candidates. CD4+ and CD8+ memory T cell subsets play an important role in response to anti-CTLA-4, and are potential biomarker candidates. For anti-PD-1 therapy, NK cell subsets (but not memory T cell subsets) correlated with clinical response to therapy. These functionally active NK cell subsets likely play a critical role in the anti-tumor response triggered by anti-PD-1.

Highlights

  • While immune checkpoint blockade has greatly improved clinical outcomes in diseases such as melanoma, there remains a need for predictive biomarkers to determine who will likely benefit most from which therapy

  • Live intact single cells gated from the unstimulated peripheral blood mononuclear cell (PBMC) of a healthy donor (Fig. 1a) could be clearly grouped into distinct subsets (Fig. 1b), including B cells (CD19 +CD20+), CD4+ T cells (CD3+CD4+), CD8+ T cells (CD3 +CD8+), Natural Killer (NK) cells (CD3−CD56+CD16+/lo), and monocytes (CD33+CD14+)

  • We discovered that a higher frequency of memory T cells in baseline PBMC is a potential biomarker candidate to predict clinical response to antiCTLA-4 treatment in advanced melanoma patients

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Summary

Introduction

While immune checkpoint blockade has greatly improved clinical outcomes in diseases such as melanoma, there remains a need for predictive biomarkers to determine who will likely benefit most from which therapy. There persists a need for predictive biomarkers that facilitate patient selection and treatment decisions, since not all Immune monitoring of peripheral blood is attractive for generating predictive biomarkers for cancer immunotherapy, due to the ease of accessing blood versus tumor tissue. Most studies to date have used conventional flow cytometry, which limits the number of markers that can be simultaneously detected. To overcome this limitation, we used mass cytometry (CyTOF®) to extensively detect different cell lineages, activation markers, cytokines and Subrahmanyam et al Journal for ImmunoTherapy of Cancer (2018) 6:18 cytotoxicity markers (Table 3). CyTOF is based on the principle of mass spectrometry for the detection of metal tags, avoiding the spectral overlap seen with flow cytometry [7]

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