Cherylle Goebel1, Christopher L Louden2, Robert McKenna3, Osita Onugha3, Andrew Wachtel4, Thomas Long5. 1. Goebel Consulting Inc, Research and Development, Mountain View, CA, U.S.A. cherylleg@lcproteomics.com. 2. Louden Consulting, Statistics, San Antonio, TX, U.S.A. 3. Providence Saint John's Health Center/John Wayne Cancer Institute, Thoracic Surgery, Santa Monica, CA, U.S.A. 4. Cedar-Sinai Medical Center, Pulmonary Medicine, Los Angeles, CA, U.S.A. 5. Lung Cancer Proteomics LLC, Executive Board, Hebron, IN, U.S.A.
Abstract
BACKGROUND/AIM: In 2016 in the United States, 7 of 10 patients were estimated to die following lung cancer diagnosis. This is due to a lack of a reliable screening method that detects early-stage lung cancer. Our aim is to accurately detect early stage lung cancer using algorithms and protein biomarkers. PATIENTS AND METHODS: A total of 1,479 human plasma samples were processed using a multiplex immunoassay platform. 82 biomarkers and 6 algorithms were explored. There were 351 NSCLC samples (90.3% Stage I, 2.3% Stage II, and 7.4% Stage III/IV). RESULTS: We identified 33 protein biomarkers and developed a classifier using Random Forest. Our test detected early-stage Non-Small Cell Lung Cancer (NSCLC) with a 90% accuracy, 80% sensitivity, and 95% specificity in the validation set using the 33 markers. CONCLUSION: A specific, non-invasive, early-detection test, in combination with low-dose computed tomography, could increase survival rates and reduce false positives from screenings. Copyright
BACKGROUND/AIM: In 2016 in the United States, 7 of 10 patients were estimated to die following lung cancer diagnosis. This is due to a lack of a reliable screening method that detects early-stage lung cancer. Our aim is to accurately detect early stage lung cancer using algorithms and protein biomarkers. PATIENTS AND METHODS: A total of 1,479 human plasma samples were processed using a multiplex immunoassay platform. 82 biomarkers and 6 algorithms were explored. There were 351 NSCLC samples (90.3% Stage I, 2.3% Stage II, and 7.4% Stage III/IV). RESULTS: We identified 33 protein biomarkers and developed a classifier using Random Forest. Our test detected early-stage Non-Small Cell Lung Cancer (NSCLC) with a 90% accuracy, 80% sensitivity, and 95% specificity in the validation set using the 33 markers. CONCLUSION: A specific, non-invasive, early-detection test, in combination with low-dose computed tomography, could increase survival rates and reduce false positives from screenings. Copyright
Authors: Cherylle Goebel; Christopher L Louden; Robert Mckenna; Osita Onugha; Andrew Wachtel; Thomas Long Journal: BMC Cancer Date: 2020-02-21 Impact factor: 4.430