Machine learning to detect the SINEs of cancer

Author:

Douville Christopher12345ORCID,Lahouel Kamel6789ORCID,Kuo Albert2459ORCID,Grant Haley2359,Avigdor Bracha Erlanger2345ORCID,Curtis Samuel D.2345ORCID,Summers Mahmoud2345ORCID,Cohen Joshua D.2345ORCID,Wang Yuxuan2345ORCID,Mattox Austin2345ORCID,Dudley Jonathan234510,Dobbyn Lisa2345ORCID,Popoli Maria2345ORCID,Ptak Janine234511ORCID,Nehme Nadine2345,Silliman Natalie234511ORCID,Blair Cherie234511,Romans Katharine2345,Thoburn Christopher4ORCID,Gizzi Jennifer4ORCID,Schoen Robert E.1213ORCID,Tie Jeanne141516ORCID,Gibbs Peter141517ORCID,Ho-Pham Lan T.1819ORCID,Tran Bich N. H.20ORCID,Tran Thach S.2021ORCID,Nguyen Tuan V.2021222324ORCID,Goggins Michael2341025ORCID,Wolfgang Christopher L.26,Wang Tian-Li1027ORCID,Shih Ie-Ming1027,Lennon Anne Marie2452528ORCID,Hruban Ralph H.210ORCID,Bettegowda Chetan234529ORCID,Kinzler Kenneth W.2345ORCID,Papadopoulos Nickolas2345ORCID,Vogelstein Bert234511ORCID,Tomasetti Cristian6789ORCID

Affiliation:

1. Division of Quantitative Sciences, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

2. Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

3. Ludwig Center, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

4. Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

5. Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.

6. Center for Cancer Prevention and Early Detection, City of Hope, Duarte, CA 91010, USA.

7. Center for Cancer Prevention and Early Detection, City of Hope, Division of Mathematics for Cancer Evolution and Early Detection, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA.

8. Division of Integrated Cancer Genomics, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.

9. Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, MD 21205, USA.

10. Department of Pathology, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

11. Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

12. Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.

13. Department of Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA.

14. Division of Personalized Oncology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia.

15. Department of Medical Oncology, Melbourne, VIC 3000, Australia.

16. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3011, Australia.

17. Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia.

18. BioMedical Research Center, Pham Ngoc Thach University of Medicine, Ho Chi Minh City 72510, Vietnam.

19. Clinical Genetics Research Group, Saigon Precision Medicine Research Center, Ho Chi Minh City 72512, Vietnam.

20. Saigon Precision Medicine Research Center, Ho Chi Minh City 72512, Vietnam.

21. School of Biomedical Engineering, University of Technology Sydney, NSW 2007, Australia.

22. Tâm Anh Research Institute, Ho Chi Minh City, Vietnam.

23. Centre for Health Technologies, University of Technology, NSW 2007, Australia.

24. School of Population Health, University of New South Wales, NSW 2003, Australia.

25. Department of Medicine, Johns Hopkins Medical Institutes, 733 N. Broadway, Baltimore, MD 21205, USA.

26. Department of Surgery, NYU Langone, New York City, NY 11209, USA.

27. Department of Gynecology and Obstetrics, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA.

28. Department of Surgery, Johns Hopkins Medical Institutes, 733 N. Broadway, Baltimore, MD 21205, USA.

29. Department of Neurosurgery, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD 21205, USA.

Abstract

We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature—the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.

Publisher

American Association for the Advancement of Science (AAAS)

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