A Single-cell Approach to Identify Biomarkers of Efficacy and Toxicity for ICI in NSCLC

Summary

Participation Requirements

Description

The investigators will collect tumor biopsies from 70 st.IV NSCLC patients before start of treatment with immune checkpoint inhibitors. These biopsies are taken during a medically required routine procedure for diagnostic purposes, and will be subjected to the following experimental procedures: Firs...

The investigators will collect tumor biopsies from 70 st.IV NSCLC patients before start of treatment with immune checkpoint inhibitors. These biopsies are taken during a medically required routine procedure for diagnostic purposes, and will be subjected to the following experimental procedures: First, scRNA-seq and TCR-seq will be applied on up to 5,000 randomly dissociated cells. Additionally, cell surface protein expression can be integrated with the transcriptional information. Various bioinformatics pipelines, including Seurat, will be used to identify different cell clusters, which through marker gene expression will be assigned to known cell types, cellular subtypes or phenotypes. For instance, this will enable the researchers to monitor the abundance of PD-1/PD-L1 expressing T cells, cytotoxic T-cells, immune-suppressive myeloid cells, etc. The following parameters at single-cell level will be relevant (non-exhaustive): The composition and relative abundancies of established immune cell types (e.g. T cells (CD4+, CD8+ and regulatory subsets), NK cells, B cells, MDSCs, macrophages, neutrophils, dendritic cells). Transcriptomic data for each of these immune cell subtypes will be analyzed, allowing characterization of specific gene expression programs that define specific phenotypic states. Composition of all stromal cellular subtypes identified by single-cell transcriptomics, including fibroblasts and endothelial cells. A gene regulatory network for each cell type and cellular subtype (or cell state) will be established and master transcriptional regulators will be identified. Individual T cells and T cell sub-clusters will be classified based on interferon activation, high rates of proliferation and transcription and increased granzyme expression, which are all indicative of T cell activation. Since high CD8+ T cell activity correlates with high immune checkpoint expression, T cell activity (based on granzyme expression) will be correlated with expression of other genes in these cells to identify co-regulated receptors, which possibly represent novel checkpoint molecules. Blood samples will be subjected to similar experimental procedures. First, PBMC are isolated using Ficoll density gradient centrifugation. Single-cell transcriptome analysis in combination with CITE- seq will be performed on 5000 PBMC. Cellular composition will be determined using the same bioinformatic pipelines as used for processing the tumor biopsies. As a second objective, immune profiling of the cellular composition of ICI-pneumonitis BAL fluid and PBMC will be performed using scRNA-seq, scTCR-seq and CITE-seq as previously outlined.

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