Machine learning to detect the SINEs of cancer.

Douville, C; Lahouel, K; Kuo, A; Grant, H; Avigdor, BE; Curtis, SD; Summers, M; Cohen, JD; Wang, Y; Mattox, A; Dudley, J; Dobbyn, L; Popoli, M; Ptak, J; Nehme, N; Silliman, N; Blair, C; Romans, K; Thoburn, C; Gizzi, J; Schoen, RE; Tie, J; Gibbs, P; Ho-Pham, LT; Tran, BNH; Tran, TS; Nguyen, TV; Goggins, M; Wolfgang, CL; Wang, T-L; Shih, I-M; Lennon, AM; Hruban, RH; Bettegowda, C; Kinzler, KW; Papadopoulos, N; Vogelstein, B; Tomasetti, C

Machine learning to detect the SINEs of cancer.

Douville, C Lahouel, K Kuo, A Grant, H Avigdor, BE Curtis, SD Summers, M Cohen, JD Wang, Y Mattox, A Dudley, J Dobbyn, L Popoli, M Ptak, J Nehme, N Silliman, N Blair, C Romans, K Thoburn, C Gizzi, J Schoen, RE Tie, J Gibbs, P Ho-Pham, LT Tran, BNH Tran, TS Nguyen, TV Goggins, M Wolfgang, CL Wang, T-L Shih, I-M Lennon, AM Hruban, RH Bettegowda, C Kinzler, KW Papadopoulos, N Vogelstein, B Tomasetti, C

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Publisher:: American Association for the Advancement of Science (AAAS)
Publication Type:: Journal Article
Citation:: Sci Transl Med, 2024, 16, (731), pp. eadi3883
Issue Date:: 2024-01-24

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Field	Value	Language
dc.contributor.author	Douville, C
dc.contributor.author	Lahouel, K
dc.contributor.author	Kuo, A
dc.contributor.author	Grant, H
dc.contributor.author	Avigdor, BE
dc.contributor.author	Curtis, SD
dc.contributor.author	Summers, M
dc.contributor.author	Cohen, JD
dc.contributor.author	Wang, Y
dc.contributor.author	Mattox, A
dc.contributor.author	Dudley, J
dc.contributor.author	Dobbyn, L
dc.contributor.author	Popoli, M
dc.contributor.author	Ptak, J
dc.contributor.author	Nehme, N
dc.contributor.author	Silliman, N
dc.contributor.author	Blair, C
dc.contributor.author	Romans, K
dc.contributor.author	Thoburn, C
dc.contributor.author	Gizzi, J
dc.contributor.author	Schoen, RE
dc.contributor.author	Tie, J
dc.contributor.author	Gibbs, P
dc.contributor.author	Ho-Pham, LT
dc.contributor.author	Tran, BNH
dc.contributor.author	Tran, TS
dc.contributor.author	Nguyen, TV
dc.contributor.author	Goggins, M
dc.contributor.author	Wolfgang, CL
dc.contributor.author	Wang, T-L
dc.contributor.author	Shih, I-M
dc.contributor.author	Lennon, AM
dc.contributor.author	Hruban, RH
dc.contributor.author	Bettegowda, C
dc.contributor.author	Kinzler, KW
dc.contributor.author	Papadopoulos, N
dc.contributor.author	Vogelstein, B
dc.contributor.author	Tomasetti, C
dc.date.accessioned	2024-02-27T06:40:25Z
dc.date.available	2024-02-27T06:40:25Z
dc.date.issued	2024-01-24
dc.identifier.citation	Sci Transl Med, 2024, 16, (731), pp. eadi3883
dc.identifier.issn	1946-6234
dc.identifier.issn	1946-6242
dc.identifier.uri	http://hdl.handle.net/10453/175902
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	American Association for the Advancement of Science (AAAS)
dc.relation.ispartof	Sci Transl Med
dc.relation.isbasedon	10.1126/scitranslmed.adi3883
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	06 Biological Sciences, 11 Medical and Health Sciences
dc.subject.classification	3206 Medical biotechnology
dc.subject.classification	4003 Biomedical engineering
dc.subject.mesh	Humans
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Neoplasms
dc.subject.mesh	Short Interspersed Nucleotide Elements
dc.subject.mesh	Machine Learning
dc.subject.mesh	Aneuploidy
dc.subject.mesh	Humans
dc.subject.mesh	Neoplasms
dc.subject.mesh	Aneuploidy
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Short Interspersed Nucleotide Elements
dc.subject.mesh	Machine Learning
dc.subject.mesh	Humans
dc.subject.mesh	Reproducibility of Results
dc.subject.mesh	Neoplasms
dc.subject.mesh	Short Interspersed Nucleotide Elements
dc.subject.mesh	Machine Learning
dc.subject.mesh	Aneuploidy
dc.title	Machine learning to detect the SINEs of cancer.
dc.type	Journal Article
utslib.citation.volume	16
utslib.location.activity	United States
utslib.for	06 Biological Sciences
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2024-02-27T06:40:24Z
pubs.issue	731
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	731

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/175902