Dataset Information

Development of a Flow Cytometry Assay to Predict Immune Checkpoint Blockade-Related Complications.

ABSTRACT: Treatment of advanced melanoma with combined immune checkpoint inhibitor (ICI) therapy is complicated in up to 50% of cases by immune-related adverse events (irAE) that commonly include hepatitis, colitis and skin reactions. We previously reported that pre-therapy expansion of cytomegalovirus (CMV)-reactive CD4⁺ effector memory T cells (T_EM) predicts ICI-related hepatitis in a subset of patients with Stage IV melanoma given αPD-1 and αCTLA-4. Here, we develop and validate a 10-color flow cytometry panel for reliably quantifying CD4⁺ T_EM cells and other biomarkers of irAE risk in peripheral blood samples. Compared to previous methods, our new panel performs equally well in measuring CD4⁺ T_EM cells (agreement = 98%) and is superior in resolving CD4⁺ CD197⁺ CD45RA^- central memory T cells (T_CM) from CD4⁺ CD197⁺ CD45RA⁺ naive T cells (T_naive). It also enables us to precisely quantify CD14⁺ monocytes (CV = 6.6%). Our new "monocyte and T cell" (MoT) assay predicts immune-related hepatitis with a positive predictive value (PPV) of 83% and negative predictive value (NPV) of 80%. Our essential improvements open the possibility of sharing our predictive methods with other clinical centers. Furthermore, condensing measurements of monocyte and memory T cell subsets into a single assay simplifies our workflows and facilitates computational analyses.

SUBMITTER: Schilling HL

PROVIDER: S-EPMC8637156 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Development of a Flow Cytometry Assay to Predict Immune Checkpoint Blockade-Related Complications.

Schilling Hannah-Lou HL Glehr Gunther G Kapinsky Michael M Ahrens Norbert N Riquelme Paloma P Cordero Laura L Bitterer Florian F Schlitt Hans J HJ Geissler Edward K EK Haferkamp Sebastian S Hutchinson James A JA Kronenberg Katharina K

Frontiers in immunology 20211116

Treatment of advanced melanoma with combined immune checkpoint inhibitor (ICI) therapy is complicated in up to 50% of cases by immune-related adverse events (irAE) that commonly include hepatitis, colitis and skin reactions. We previously reported that pre-therapy expansion of cytomegalovirus (CMV)-reactive CD4<sup>+</sup> effector memory T cells (T<sub>EM</sub>) predicts ICI-related hepatitis in a subset of patients with Stage IV melanoma given αPD-1 and αCTLA-4. Here, we develop and validate a ...[more]

PMID: 34868015

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Project description:BackgroundImmune checkpoint blockade (ICB) shows lasting benefits in advanced melanoma; however, not all patients respond to this treatment and many develop potentially life-threatening immune-related adverse events (irAEs). Identifying individuals who will develop irAEs is critical in order to improve the quality of care. Here, we prospectively demonstrate that the gut microbiome predicts irAEs in melanoma patients undergoing ICB.MethodsPre-, during, and post-treatment stool samples were collected from 27 patients with advanced stage melanoma treated with IPI (anti-CTLA-4) and NIVO (anti-PD1) ICB inhibitors at NYU Langone Health. We completed 16S rRNA gene amplicon sequencing, DNA deep shotgun metagenomic, and RNA-seq metatranscriptomic sequencing. The divisive amplicon denoising algorithm (DADA2) was used to process 16S data. Taxonomy for shotgun sequencing data was assigned using MetaPhlAn2, and gene pathways were assigned using HUMAnN 2.0. Compositionally aware differential expression analysis was performed using ANCOM. The Cox-proportional hazard model was used to assess the prospective role of the gut microbiome (GMB) in irAES, with adjustment for age, sex, BMI, immune ICB treatment type, and sequencing batch.ResultsTwo natural GMB clusters with distinct community compositions were identified from the analysis of 16S rRNA data (R2 = 0.16, p < 0.001). In Cox-proportional hazard modeling, these two clusters showed a near 7-fold differential risk for developing irAEs within 1 year of initiating treatment (HR = 6.89 [95% CI: 1.33-35.58]). Using shotgun metagenomics, we further identified 37 bacterial strains differentially expressed between the risk groups, with specific dominance of Bacteroides dorei within the high-risk GMB cluster and Bacteroides vulgatus in the low-risk cluster. The high-risk cluster also appeared to have elevated expression of several functional pathways, including those associated with adenosine metabolism (all FDR < 0.05). A sub-analysis of samples (n = 10 participants) at baseline and 6 and 12 weeks after the start of treatment revealed that the microbiome remained stable over the course of treatment (R2 = 0.88, p < 0.001).ConclusionsWe identified two distinct fecal bacterial community clusters which are associated differentially with irAEs in ICB-treated advanced melanoma patients.

Project description:Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The current approach for predicting the likelihood of response to ICB is a single proportional biomarker (PD-L1) expressed in immune and tumor cells (Combined Positive Score, CPS) without differentiation by cell type, potentially explaining its limited predictive value. Tertiary Lymphoid Structures (TLS) have shown a stronger association with ICB response than PD-L1. However, their exact composition, size, and spatial biology in HNSCC remain understudied. A detailed understanding of TLS is required for future use as a clinically applicable predictive biomarker.MethodsPre-ICB tumor tissue sections were obtained from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was designed, optimized, and applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (α Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). Spatial metrics were compared among groups. Serial tissue sections were scored for TLS in both H&E and mIF slides. A machine learning model was employed to measure the effect of these metrics on achieving a response to ICB (SD, PR, or CR).ResultsA higher density of B lymphocytes (CD20+) was found in responders compared to non-responders to ICB (p=0.022). A positive correlation was observed between mIF and pathologist identification of TLS (R 2 = 0.66, p-value= <0.0001). TLS trended toward being more prevalent in responders to ICB (p=0.0906). The presence of TLS within 100 μm of the tumor was associated with improved overall (p=0.04) and progression-free survival (p=0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy.ConclusionImmune cell densities and TLS spatial location within the tumor microenvironment play a critical role in the immune response to HNSCC and may potentially outperform CPS as a predictor of ICB response.

Dataset Information

Development of a Flow Cytometry Assay to Predict Immune Checkpoint Blockade-Related Complications.

Publications

Development of a Flow Cytometry Assay to Predict Immune Checkpoint Blockade-Related Complications.

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