Breathomics as Predictive Biomarker for Checkpoint Inhibitor Response

Summary

Participation Requirements

Description

Immune checkpoint blockade with anti-PD-1 and anti-PD-L1 antibodies has become a new standard of care in several cancer types as NSCLC and melanoma. However, in biomarker-unselected patient populations, overall response rate (ORR) depending on type of cancer and whether single or combination treatme...

Immune checkpoint blockade with anti-PD-1 and anti-PD-L1 antibodies has become a new standard of care in several cancer types as NSCLC and melanoma. However, in biomarker-unselected patient populations, overall response rate (ORR) depending on type of cancer and whether single or combination treatment is chosen remains still only 20%-60%. Though overall well tolerated approximately 5-10% of patients treated with PD1/PD-L1 targeting agents will experience grade 3 or 4 toxicities, including potentially life-threatening auto-immune toxicities such as colitis, hepatitis, and pneumonitis. Therefore, due to high costs of treatment and its possible complications, improved selection of patients is a crucial goal and an easily available non-invasive, point-of-care test for better patient selection is very much needed. A promising approach in this regard is the analysis of volatile organic compounds (VOCs) in breath. Breath analysis for the detection of VOCs is increasingly investigated for its utility in diagnosis and management of cancer. Electronic noses (eNoses) are promising as cheap and clinically-practical devices that are designed to detect patterns of VOCs. Recently published prospective observational data showed very promising discriminant function for breathprint analysis for non-response to immunotherapy in NSCLC patients. The principle goal of this study is to validate a prior study that found that breathomics-based classifiers predicted 12-week early progression vs non-progression in advanced NSCLC patients treated with nivolumab or pembrolizumab. Secondarily, we will expand assessment of breathomic-based classifiers to include other cohorts of advanced tumors treated with ICI, and also consider using response instead of non-progression as an endpoint. Exploratory goals include refinement of the breathomics classifier using alternative machine-learning techniques, and correlate with other biomarkers of immunotherapy outcomes.

Tracking Information