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

Area of Science:

• Oncology
• Genetics
• Biomarker Discovery

Background:

• Early lung cancer detection significantly reduces mortality.
• Clonal hematopoietic mutations in immune cells may serve as predictive biomarkers.
• Existing screening methods have limitations.

Purpose of the Study:

• To investigate the potential of clonal hematopoietic mutations in blood for predicting lung cancer.
• To develop and validate a blood-based screening panel for lung cancer.

Main Methods:

• Identification of 98 clonal hematopoietic mutations from lung cancer samples.
• Development of a logistic regression machine learning model using these mutations.
• Validation of the model on independent lung cancer and non-cancer blood samples.

Main Results:

• The developed model achieved 94.12% sensitivity and 85.96% specificity in classifying lung cancer.
• The model demonstrated high accuracy across 18 diverse cohorts.
• The approach utilizes standard sequencing and a curated set of mutations.

Conclusions:

• Clonal hematopoietic mutations in blood can accurately predict lung cancer occurrence.
• This approach offers a viable strategy for developing an accurate blood-based lung cancer screening panel.
• The method is based on accessible sequencing technology and a focused set of biomarkers.