Machine Learning‐Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening. Issue 18 (29th July 2020) - Tanos, Rita; Tosato, Guillaume; Otandault, Amaelle; Al Amir Dache, Zahra; Pique Lasorsa, Laurence; Tousch, Geoffroy; El Messaoudi, Safia; Meddeb, Romain; Diab Assaf, Mona; Ychou, Marc; Du Manoir, Stanislas; Pezet, Denis; et al.
Abstract: While the utility of circulating cell‐free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 289 healthy individuals and 983 patients with various cancer types; after age resampling, this evaluation is done independently and the variables are combined using a machine learning approach. Implementation of a decision tree prediction model for the detection and classification of healthy and cancer patients shows unprecedented performance for 0, I, and II colorectal cancer stages (specificity, 0.89 and sensitivity, 0.72). Consequently, the methodological proof of concept of using both quantitative and structural biomarkers, and classification with a machine learning method are highlighted, as an efficient strategy for cancer screening. It is foreseen that the classification rate may even be improved by the addition of such biomarkers to fragmentomics, methylation, or the detection of genetic alterations. The optimization of such a multianalyte strategy with this machine learning method is therefore warranted. Abstract : Certain circulating DNA structural characteristics enable the distinction between plasma from healthy and cancer individuals. Using this observation, a machine learning decision tree prediction model is implemented for noninvasive early detection. The high sensitivity and specificity of the test on more than 1000 individuals supports the belief in the need for machine learning assistance when combining quantitative and/or multiple biomarkers.