CyTOF Measurement of Immunocompetence Across Major Immune Cell Types

Subrahmanyam, Priyanka B.; Maecker, Holden T.

doi:10.1002/cpcy.27

Cited by 17 publications

(18 citation statements)

References 25 publications

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“…Patient samples were processed for intracellular cytokine staining by CyTOF as previously described [ 9 , 10 ]. Briefly, frozen PBMC were thawed and washed twice in complete medium (RPMI supplemented with Pen-Strep and L-glutamine).…”

Section: Methodsmentioning

confidence: 99%

Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients

Subrahmanyam¹,

Dong²,

Gusenleitner³

et al. 2018

j. immunotherapy cancer

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160

142

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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.Electronic supplementary materialThe online version of this article (10.1186/s40425-018-0328-8) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients

Subrahmanyam¹,

Dong²,

Gusenleitner³

et al. 2018

j. immunotherapy cancer

Self Cite

160

142

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“…PBMC immune profiling using flow cytometry of patients with melanoma treated with ICIs showed that patients with higher number of CD4+ and CD8+ T cells had better response than those with lower numbers, and that perhaps the ratio of T effector cells to T regulatory cells may be a good predictor of response [ 66 , 67 ]. Additionally, results from peripheral blood analysis correlated with tissue levels of CD8+ T cells [ 67 ]. A newer technique known as mass cytometry can simultaneously detect up to 40 markers to identify different cell subsets and determine their function by expression of cytokines, cytotoxicity and activation markers [ 68 ].…”

Section: Blood-derived Biomarkers—“liquid Biopsies”mentioning

confidence: 99%

The Multiple Potential Biomarkers for Predicting Immunotherapy Response—Finding the Needle in the Haystack

Adam

Becker

Chua

et al. 2021

Cancers

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Immune checkpoint inhibitors (ICIs) are being increasingly utilised in a variety of advanced malignancies. Despite promising outcomes in certain patients, the majority will not derive benefit and are at risk of potentially serious immune-related adverse events (irAEs). The development of predictive biomarkers is therefore critical to personalise treatments and improve outcomes. A number of biomarkers have shown promising results, including from tumour (programmed cell death ligand 1 (PD-L1), tumour mutational burden (TMB), stimulator of interferon genes (STING) and apoptosis-associated speck-like protein containing a CARD (ASC)), from blood (peripheral blood mononuclear cells (PBMCs), circulating tumour DNA (ctDNA), exosomes, cytokines and metal chelators) and finally the microbiome.

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“…CyTOF intracellular cytokine staining assay Cells were stained and prepared for CyTOF analysis as previously described (28). Briefly, frozen PBMC samples from participants were thawed and resuspended in complete medium (RPMI 1640 supplemented with 10% FBS, penicillin, streptomycin, and L-glutamine) with benzonase.…”

Section: Pbmc Collection and Storagementioning

confidence: 99%

Mass Cytometry Defines Virus-Specific CD4+ T Cells in Influenza Vaccination

et al. 2020

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The antiviral response to influenza virus is complex and multifaceted, involving many immune cell subsets. There is an urgent need to understand the role of CD4 + T cells, which orchestrate an effective antiviral response, to improve vaccine design strategies. In this study, we analyzed PBMCs from human participants immunized with influenza vaccine, using high-dimensional single-cell proteomic immune profiling by mass cytometry. Data were analyzed using a novel clustering algorithm, denoised ragged pruning, to define possible influenza virus–specific clusters of CD4 + T cells. Denoised ragged pruning identified six clusters of cells. Among these, one cluster (Cluster 3) was found to increase in abundance following stimulation with influenza virus peptide ex vivo. A separate cluster (Cluster 4) was found to expand in abundance between days 0 and 7 postvaccination, indicating that it is vaccine responsive. We examined the expression profiles of all six clusters to characterize their lineage, functionality, and possible role in the response to influenza vaccine. Clusters 3 and 4 consisted of effector memory cells, with high CD154 expression. Cluster 3 expressed cytokines like IL-2, IFN-γ, and TNF-α, whereas Cluster 4 expressed IL-17. Interestingly, some participants had low abundance of Clusters 3 and 4, whereas others had higher abundance of one of these clusters compared with the other. Taken together, we present an approach for identifying novel influenza virus–reactive CD4 + T cell subsets, a method that could help advance understanding of the immune response to influenza, predict responsiveness to vaccines, and aid in better vaccine design.

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CyTOF Measurement of Immunocompetence Across Major Immune Cell Types

Cited by 17 publications

References 25 publications

Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients

Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients

The Multiple Potential Biomarkers for Predicting Immunotherapy Response—Finding the Needle in the Haystack

Mass Cytometry Defines Virus-Specific CD4+ T Cells in Influenza Vaccination

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