Dataset Information

DNA Methylation Markers for Breast Cancer Detection in the Developing World.

ABSTRACT: PURPOSE:An unmet need in low-resource countries is an automated breast cancer detection assay to prioritize women who should undergo core breast biopsy and pathologic review. Therefore, we sought to identify and validate a panel of methylated DNA markers to discriminate between cancer and benign breast lesions using cells obtained by fine-needle aspiration (FNA).Experimental Design: Two case-control studies were conducted comparing cancer and benign breast tissue identified from clinical repositories in the United States, China, and South Africa for marker selection/training (N = 226) and testing (N = 246). Twenty-five methylated markers were assayed by Quantitative Multiplex-Methylation-Specific PCR (QM-MSP) to select and test a cancer-specific panel. Next, a pilot study was conducted on archival FNAs (49 benign, 24 invasive) from women with mammographically suspicious lesions using a newly developed, 5-hour, quantitative, automated cartridge system. We calculated sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) compared with histopathology for the marker panel. RESULTS:In the discovery cohort, 10 of 25 markers were selected that were highly methylated in breast cancer compared with benign tissues by QM-MSP. In the independent test cohort, this panel yielded an AUC of 0.937 (95% CI = 0.900-0.970). In the FNA pilot, we achieved an AUC of 0.960 (95% CI = 0.883-1.0) using the automated cartridge system. CONCLUSIONS:We developed and piloted a fast and accurate methylation marker-based automated cartridge system to detect breast cancer in FNA samples. This quick ancillary test has the potential to prioritize cancer over benign tissues for expedited pathologic evaluation in poorly resourced countries.

SUBMITTER: Downs BM

PROVIDER: S-EPMC6825533 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

DNA Methylation Markers for Breast Cancer Detection in the Developing World.

Downs Bradley M BM Mercado-Rodriguez Claudia C Cimino-Mathews Ashley A Chen Chuang C Yuan Jing-Ping JP Van Den Berg Eunice E Cope Leslie M LM Schmitt Fernando F Tse Gary M GM Ali Syed Z SZ Meir-Levi Danielle D Sood Rupali R Li Juanjuan J Richardson Andrea L AL Mosunjac Marina B MB Rizzo Monica M Tulac Suzana S Kocmond Kriszten J KJ de Guzman Timothy T Lai Edwin W EW Rhees Brian B Bates Michael M Wolff Antonio C AC Gabrielson Edward E Harvey Susan C SC Umbricht Christopher B CB Visvanathan Kala K Fackler Mary Jo MJ Sukumar Saraswati S

Clinical cancer research : an official journal of the American Association for Cancer Research 20190712 21

<h4>Purpose</h4>An unmet need in low-resource countries is an automated breast cancer detection assay to prioritize women who should undergo core breast biopsy and pathologic review. Therefore, we sought to identify and validate a panel of methylated DNA markers to discriminate between cancer and benign breast lesions using cells obtained by fine-needle aspiration (FNA).<b>Experimental Design:</b> Two case-control studies were conducted comparing cancer and benign breast tissue identified from c ...[more]

PMID: 31300453

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Project description:BackgroundDetection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers.MethodsWe performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach.ResultsA total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort.ConclusionsTOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.

Dataset Information

DNA Methylation Markers for Breast Cancer Detection in the Developing World.

Publications

DNA Methylation Markers for Breast Cancer Detection in the Developing World.

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