A Single-cell Approach to Identify Biomarkers of Pulmonary Toxicity for Immune Checkpoint Blockade

Recruiting

• Conditions: Immune-related Adverse Events; Pneumonitis, Interstitial; Immunotherapy
• Interventions: Radiation: Radiotherapy; Drug: Targeted therapy; Drug: Immune checkpoint blockade

• Registration Number: NCT04807127
• Lead Sponsor: Universitaire Ziekenhuizen KU Leuven

Brief Summary

The main goal of this prospective non-interventional exploratory monocentric study is to characterize the immune cell composition of bronchoalveolar lavage (BAL) fluid from cancer patients experiencing cancer therapy-induced pneumonitis on a single-cell scale. These mechanistic insights can directly lead to putative diagnostic biomarkers and therapeutic targets.

A second highly clinically relevant hypothesis is that single-cell profiling of blood samples will reveal circulating biomarkers of ICB toxicity, making non-invasive diagnosis feasible.

Detailed Description

The investigators will apply single cell RNA- and TCR-sequencing on up to 5,000 single cells per sample. 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 make it possible 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, amongst others:

* 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.

* 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 subclusters 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.

Blood samples will be subjected to similar single-cell experimental procedures. First, peripheral blood mononuclear cells (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 BAL fluid cells.

Study Timeline

• Study Start: 2020-02-01
• Study First Submitted: 2021-03-10
• Study First Submitted QC: 2021-03-18
• Study First Posted: 2021-03-19
• Status Verified: 2024-06
• Last Update Submitted: 2024-06-28
• Last Update Posted: 2024-07-01
• Primary Completion: 2025-01-31
• Study Completion: 2025-01-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 60

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Radiotherapy induced pneumonitis	Radiotherapy	Cancer patients experiencing RT-pneumonitis
TKI-induced pneumonitis	Targeted therapy	Cancer patients experiencing TKI-induced pneumonitis
ICI-pneumonitis	Immune checkpoint blockade	Cancer patients experiencing ICI-pneumonitis

Primary Outcome Measures

Name	Time	Method
Immune cell proportions, as determined by scRNA-seq, present in ICI-/RT-/TKI-induced pneumonitis BAL fluid	From date of inclusion until study completion, on average 2 years	By identifying and statistically comparing the percentages of immune cell subtypes present in ICI-/RT-/TKI-induced pneumonitis BAL fluid, we aim to i) understand which immune processes drive these adverse events ii) identify putative molecular biomarkers iii) identify putative therapeutic targets
Differentially expressed genes in BAL fluid, as determined by scRNA-seq, discriminating ICI-/RT-/TKI-induced pneumonitis	From date of inclusion until study completion, on average 2 years	By identifying differentially expressed genes in ICI- vs. RT- vs. TKI-induced pneumonitis BAL fluid, we aim to i) understand which immune processes drive these adverse events ii) identify putative molecular biomarkers iii) identify putative therapeutic targets

Secondary Outcome Measures

Name	Time	Method
Immune cell proportions, as determined by scRNA-seq, present in ICI-/RT-/TKI-induced pneumonitis peripheral blood mononuclear cells	From date of inclusion until study completion, on average 2 years	By identifying and statistically comparing the percentages of immune cell subtypes present in ICI-/RT-/TKI-induced pneumonitis peripheral blood mononuclear cells, we aim to i) understand which immune processes drive these adverse events ii) identify putative molecular biomarkers iii) identify putative therapeutic targets
Differentially expressed genes in PBMC, as determined by scRNA-seq, discriminating ICI-/RT-/TKI-induced pneumonitis	From date of inclusion until study completion, on average 2 years	By identifying differentially expressed genes in ICI- vs. RT- vs. TKI-induced pneumonitis PBMC, we aim to i) understand which immune processes drive these adverse events ii) identify putative molecular biomarkers iii) identify putative therapeutic targets

Trial Locations

Locations (1)

Universitaire Ziekenhuizen Leuven; 🇧🇪 Leuven, Flemish Brabant, Belgium