Open AccessPan-Cancer Detection and Typing by Mining Patterns in Large Genome-Wide Cell-Free DNA Sequencing Datasets
Author(s) -
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
Publication year - 2022
Publication title -
clinical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.705
H-Index - 218
eISSN - 1530-8561
pISSN - 0009-9147
DOI - 10.1093/clinchem/hvac095
Subject(s) - cancer , typing , dna sequencing , computational biology , genome , cluster analysis , cancer detection , oncology , artificial intelligence , medicine , biology , computer science , dna , gene , genetics
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.
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