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Machine learning to detect the SINEs of cancer

癌症 计算生物学 分析物 非整倍体 肿瘤科 DNA测序 生物 内科学 医学 计算机科学 生物信息学 DNA 遗传学 染色体 基因 化学 色谱法
作者
Christopher Douville,Kamel Lahouel,Albert Kuo,Haley Grant,Bracha Erlanger Avigdor,Samuel D. Curtis,Mahmoud Summers,Joshua D. Cohen,Yuxuan Wang,Austin K. Mattox,Jonathan C. Dudley,Lisa Dobbyn,Maria Popoli,Janine Ptak,Nadine T. Nehme,Natalie Silliman,Cheríe Blair,Katharine Romans,Christopher Thoburn,Jennifer Gizzi,Robert E. Schoen,Jeanne Tie,Peter Gibbs,Lan T. Ho‐Pham,Bich Tran,Thach Tran,Tuan V. Nguyen,Michael Goggins,Christopher L. Wolfgang,Tian‐Li Wang,Ie‐Ming Shih,Anne Marie Lennon,Ralph H. Hruban,Chetan Bettegowda,Kenneth W. Kinzler,Nickolas Papadopoulos,Bert Vogelstein,Cristian Tomasetti
出处
期刊:Science Translational Medicine [American Association for the Advancement of Science]
卷期号:16 (731) 被引量:4
标识
DOI:10.1126/scitranslmed.adi3883
摘要

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.
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