eTumorMetastasis: A Network-based Algorithm Predicts Clinical Outcomes Using Whole-exome Sequencing Data of Cancer Patients

• Published in: Genomics, proteomics & bioinformatics Vol. 19; no. 6; pp. 973 - 985
• Main Authors: Milanese, Jean-Sébastien, Tibiche, Chabane, Zaman, Naif, Zou, Jinfeng, Han, Pengyong, Meng, Zhigang, Nantel, Andre, Droit, Arnaud, Wang, Edwin
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01.12.2021; Elsevier; Department of Medicine,McGill University,Montreal H3G 2M1,Canada; Genomics Center,Centre Hospitalier Universitaire de Québec-UniversitéLaval Research Center,Quebec G1V 4G2,Canada%Human Health Therapeutics,National Research Council Canada,Montreal H4P 2R2,Canada%Department of Biochemistry&Molecular Biology,Medical Genetics,and Oncology,University of Calgary,Calgary T2N 4N1,Canada%Department of Biochemistry&Molecular Biology,Medical Genetics,and Oncology,University of Calgary,Calgary T2N 4N1,Canada; Human Health Therapeutics,National Research Council Canada,Montreal H4P 2R2,Canada; Department of Biochemistry&Molecular Biology,Medical Genetics,and Oncology,University of Calgary,Calgary T2N 4N1,Canada; Institute of Biotechnology,Chinese Academy of Agricultural Sciences,Beijing 100086,China%Genomics Center,Centre Hospitalier Universitaire de Québec-UniversitéLaval Research Center,Quebec G1V 4G2,Canada%Human Health Therapeutics,National Research Council Canada,Montreal H4P 2R2,Canada; Alberta Children's Hospital Research Institute and Arnie Charbonneau Cancer Research Institute,University of Calgary,Calgary T2N 4N1,Canada
• Subjects: algorithms; ancestry; bioinformatics; Breast cancer; breast neoplasms; breasts; cancer recurrence; data collection; genes; genomics; Machine learning; neoplasm cells; Original Research; Predictive model; proteomics; Sequencing data; Systems biology; Breast cancer; Sequencing data; Systems biology; Predictive model; Machine learning; Predictive models; System biology
• ISSN: 1672-0229; 2210-3244
• DOI: 10.1016/j.gpb.2020.06.009

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn’t favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the ‘most recent common ancestor’ of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.