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Longitudinal plasma proteomic profiling of patients with non-small cell lung cancer undergoing immune checkpoint
Michal Harel1, Coren Lahav1, Eyal Jacob1
1OncoHost Ltd, Binyamina, Israel.
Background:
Immune checkpoint inhibitors (ICIs) have revolutionized the cancer therapy landscape due to long-term benefits in patients with advanced metastatic disease. However, robust predictive biomarkers for response are still lacking and treatment resistance is not fully understood.
Methods:
We profiled approximately 800 pre-treatment and on-treatment plasma proteins from 143 ICI-treated patients with non-small cell lung cancer (NSCLC) using ELISA-based arrays. Different clinical parameters were collected from the patients including specific mutations, smoking habits, and body mass index, among others. Machine learning algorithms were used to identify a predictive signature for response. Bioinformatics tools were used for the identification of patient subtypes and analysis of differentially expressed proteins and pathways in each response group.
Results:
We identified a predictive signature for response to treatment comprizing two proteins (CXCL8 and CXCL10) and two clinical parameters (age and sex). Bioinformatic analysis of the proteomic profiles identified three distinct patient clusters that correlated with multiple parameters such as response, sex and TNM (tumors, nodes, and metastasis) staging. Patients who did not benefit from ICI therapy exhibited significantly higher plasma levels of several proteins on-treatment, and enrichment in neutrophil-related proteins.
Conclusions:
Our study reveals potential biomarkers in blood plasma for predicting response to ICI therapy in patients with NSCLC and sheds light on mechanisms underlying therapy resistance.
Insights
Researchers identified a predictive signature for immune checkpoint inhibitor (ICI) therapy response in non-small cell lung cancer (NSCLC) using plasma proteins and clinical data. This finding may improve patient selection and understanding of ICI resistance mechanisms.
Area of Science:
- Oncology
- Immunology
- Proteomics
Background:
- Immune checkpoint inhibitors (ICIs) offer long-term benefits for advanced metastatic cancers.
- Predictive biomarkers for ICI response and understanding treatment resistance remain significant challenges in non-small cell lung cancer (NSCLC).
Purpose of the Study:
- To identify predictive biomarkers for ICI therapy response in NSCLC patients.
- To explore mechanisms underlying ICI treatment resistance.
Main Methods:
- Plasma proteins (approx. 800) were profiled from 143 ICI-treated NSCLC patients using ELISA-based arrays.
- Machine learning algorithms identified a predictive signature for treatment response.
- Bioinformatics tools analyzed patient subtypes and differentially expressed proteins/pathways.
Main Results:
- A predictive signature comprising two proteins (CXCL8, CXCL10) and clinical parameters (age, sex) was identified.
- Three distinct patient clusters correlated with response, sex, and TNM staging.
- Non-responders showed higher on-treatment plasma protein levels, including neutrophil-related proteins.
Conclusions:
- Blood plasma biomarkers can potentially predict ICI therapy response in NSCLC.
- The study provides insights into the mechanisms of ICI therapy resistance.
