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

NSCLC Clinical Trial

Official title:

A Single-cell Approach to Identify Biomarkers of Efficacy and Toxicity for Immune Checkpoint Blockade in Non-small Cell Lung Cancer

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04807114
• Other study ID #: S63531
• Status: Recruiting
• Start date: February 1, 2020
• Est. completion date: January 31, 2024

Study information

• Verified date: March 2023
• Source: Universitaire Ziekenhuizen KU Leuven
• Contact: Els Wauters
• Phone: +3216340942
• Email: els.wauters[@]uzleuven.be
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The main goal of this prospective non-interventional exploratory study is to characterize the tumor micro-environment of advanced NSCLC in single-cell resolution, prior to immune checkpoint blockade exposure, and correlate the findings to clinical outcome. This approach will allow to generate new hypotheses regarding mechanism of action of ICI and (primary) resistance mechanisms. The long-term goal is that these novel mechanistic insights will be translated to a clinical setting to develop better biomarkers of ICI efficacy. Importantly, since the investigators will also sequentially profile the immune composition of peripheral blood, this research offers an opportunity to develop circulating (non-invasive) biomarkers.

A second aim is to characterize the immune cell composition of bronchoalveolar lavage (BAL) fluid from these ICI-treated cancer patients if they would develop ICI-pneumonitis. These mechanistic insights can directly lead to putative diagnostic biomarkers and therpeutic targets. Since single-cell profiling of blood samples will also be performed, circulating biomarkers of ICI toxicity can also be identified, making non-invasive diagnosis feasible.

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:

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 70
• Est. completion date: January 31, 2024
• Est. primary completion date: January 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 120 Years

Eligibility

Inclusion criteria:

• Adult M/F/X (>= 18 years)

• Histologically and clinically confirmed diagnosis of non-small cell lung cancer (according to IASLC Staging Handbook in Thoracic Oncology v7)

• Patients receiving first-line treatment per guidelines

• Not included in other clinical trials

• Signed informed consent form

Exclusion criteria:

• Collected material not suitable for further processing in this study (e.g. bad quality). This decision will be made in consultation with a lab technician and/or bio-informatician, specialized in single-cell analysis.

Related Conditions & MeSH terms

• Immunotherapy
• Lung Diseases
• Lung Diseases, Interstitial
• NSCLC

Intervention

Drug:
• Immune checkpoint inhibitor
Standard-of-care treatment for st.IV NSCLC (no driver mutation, PD-L1 > 50%)
• Chemotherapy + Immune checkpoint inhibitor
Standard-of-care treatment for st.IV NSCLC (no driver mutation, PD-L1 < 50%)

Locations

Country	Name	City	State
Belgium	Universitaire Ziekenhuizen Leuven	Leuven

Sponsors (2)

Lead Sponsor	Collaborator
Universitaire Ziekenhuizen KU Leuven	KU Leuven

Country where clinical trial is conducted

Belgium, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Immune cell proportions, as determined by scRNA-seq, present in tumor samples of ICI-treated st.IV NSCLC patients attaining objective response or not attaining objective response	By identifying and statistically comparing the percentages of immune cell subtypes present in responding vs. non-responding patients' tumors before start of ICI therapy, we aim to i) understand which immune processes drive response or resistance to ICI ii) identify putative molecular biomarkers iii) identify putative therapeutic targets	From date of inclusion until study completion, on average 2 years.
Primary	Immune cell proportions, as determined by scRNA-seq, present in peripheral blood of ICI-treated st.IV NSCLC patients attaining objective response or not attaining objective response, before and after 1 cycle of ICI	By identifying and statistically comparing the percentages of immune cell subtypes present in responding vs. non-responding patients' peripheral blood, we aim to i) understand which immune processes drive response or resistance to ICI ii) identify putative molecular biomarkers iii) identify putative therapeutic targets	From date of inclusion until study completion, on average 2 years.
Secondary	Immune cell proportions, as determined by scRNA-seq, present in ICI-induced pneumonitis BAL fluid	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	From date of inclusion until study completion, on average 2 years.
Secondary	Immune cell proportions, as determined by scRNA-seq, present in ICI-induced pneumonitis peripheral blood mononuclear cells	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	From date of inclusion until study completion, on average 2 years.