An approach for developing a blood-based screening panel for lung cancer based on clonal hematopoietic mutations

Abstract

Early detection can significantly reduce mortality due to lung cancer. Presented here is an approach for developing a blood-based screening panel based on clonal hematopoietic mutations. Animal model studies suggest that clonal hematopoietic mutations in tumor infiltrating immune cells can modulate cancer progression, representing potential predictive bio-markers. The goal of this study was to determine if the clonal expansion of these mutations in blood samples could predict the occurrence of lung cancer. A set of 98 potentially pathogenic clonal hematopoietic mutations in tumor infiltrating immune cells were identified using sequencing data from lung cancer samples. These mutations were used as predictors to develop a logistic regression machine learning model. The model was tested on sequencing data from a separate set of 578 lung cancer and 545 non-cancer samples from 18 different cohorts. The logistic regression model correctly classified lung cancer and non-cancer blood samples with 94.12% sensitivity (95% Confidence Interval: 92.20–96.04%) and 85.96% specificity (95% Confidence Interval: 82.98–88.95%). Our results suggest that it may be possible to develop an accurate blood-based lung cancer screening panel using this approach. Unlike most other “liquid biopsies” currently under development, the approach presented here is based on standard sequencing protocols and uses a relatively small number of rationally selected mutations as predictors.