2021 World Conference on Lung Cancer (Posters)-FP05. Robust Discrimination of Lung Cancer via Microbial DNA Detection and Machine Learning Classification

FP05. Robust Discrimination of Lung Cancer via Microbial DNA Detection and Machine Learning Classification

Video Transcription

Video Summary

This video discusses the use of circulating cell-free microbial nucleic acids for liquid biopsy-based detection of lung cancer. The researchers analyzed the bacterial, viral, and archaeal nucleic acid content in treatment-naive whole-genome and transcriptome studies from the Cancer Genome Atlas. They trained machine learning models to discriminate between different types and stages of cancer using microbial DNA and RNA. They successfully differentiated various cancer types including ovarian, lung adenocarcinoma, low-grade colon adenocarcinoma, and primary breast cancer using microbial DNA-based classification. They also compared cell-free microbial DNA from lung cancer patients to healthy individuals, achieving accurate discrimination with high sensitivity and specificity. The goal is to use microbial nucleic acids as biomarkers for early-stage diagnostics of lung cancer.

Asset Subtitle

Eddie Adams

Meta Tag

Speaker Eddie Adams

Topic Liquid Biopsy and Other Non-invasive Diagnostic Modalities

Keywords

circulating cell-free microbial nucleic acids

liquid biopsy

lung cancer

microbial DNA

machine learning models