Pan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets
Huiwen Che, Tatjana Jatsenko, Liesbeth Lenaerts, Luc Dehaspe, Leen Vancoillie, Nathalie Brison, Ilse Parijs, Kris Van Den Bogaert, D. Fischerová, Ruben Heremans, C. Landolfo, A. C. Testa, Adriaan Vanderstichele, Lore Liekens, Valentina Pomella, Agnieszka Woźniak, Christophe Dooms, Els Wauters, Sigrid Hatse, Kevin Punie, Patrick Neven, Hans Wildiers, Sabine Tejpar, Diether Lambrechts, An Coosemans, D. Timmerman, Peter Vandenberghe, Frédéric Amant, Joris Vermeesch
Abstract
BACKGROUND: Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis, and monitoring. We hypothesized that mining the patterns of cfDNA shallow whole-genome sequencing datasets from patients with cancer could improve cancer detection. METHODS: By applying unsupervised clustering and supervised machine learning on large cfDNA shallow whole-genome sequencing datasets from healthy individuals (n = 367) and patients with different hematological (n = 238) and solid malignancies (n = 320), we identified cfDNA signatures that enabled cancer detection and typing. RESULTS: Unsupervised clustering revealed cancer type-specific sub-grouping. Classification using a supervised machine learning model yielded accuracies of 96% and 65% in discriminating hematological and solid malignancies from healthy controls, respectively. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. The potential utility of managing a specific cancer was demonstrated by classifying benign from invasive and borderline adnexal masses with an area under the curve of 0.87 and 0.74, respectively. CONCLUSIONS: This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.