Development and validation of a circulating microRNA panel for the early detection of breast cancer

• Published in: British journal of cancer Vol. 126; no. 3; pp. 472 - 481
• Main Authors: Zou, Ruiyang, Loke, Sau Yeen, Tang, Yew Chung, Too, Heng-Phon, Zhou, Lihan, Lee, Ann S. G., Hartman, Mikael
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.02.2022; Nature Publishing Group
• Subjects: 631/67/1347; 631/67/1857; 631/67/2322; 692/53/2421; Adult; Aged; Aged, 80 and over; Biomarkers, Tumor - genetics; Biomedical and Life Sciences; Biomedicine; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Cancer Research; Cancer screening; Case-Control Studies; Circulating MicroRNA - genetics; Drug Resistance; Early Detection of Cancer - methods; Epidemiology; Female; Gene Expression Profiling; Humans; Mammography; Medical screening; MicroRNAs; Middle Aged; Minority & ethnic groups; miRNA; Molecular Medicine; Neoplasm Staging; Oncology; Prediction models; ROC Curve; White people
• ISSN: 0007-0920; 1532-1827; 1532-1827
• DOI: 10.1038/s41416-021-01593-6

Background Mammography is widely used for breast cancer screening but suffers from a high false-positive rate. Here, we perform the largest comprehensive, multi-center study to date involving diverse ethnic groups, for the identification of circulating miRNAs for breast cancer screening. Methods This study had a discovery phase ( n  = 289) and two validation phases ( n  = 374 and n  = 379). Quantitative PCR profiling of 324 miRNAs was performed on serum samples from breast cancer (all stages) and healthy subjects to identify miRNA biomarkers. Two-fold cross-validation was used for building and optimising breast cancer-associated miRNA panels. An optimal panel was validated in cohorts with Caucasian and Asian samples. Diagnostic ability was evaluated using area under the curve (AUC) analysis. Results The study identified and validated 30 miRNAs dysregulated in breast cancer. An optimised eight-miRNA panel showed consistent performance in all cohorts and was successfully validated with AUC, accuracy, sensitivity, and specificity of 0.915, 82.3%, 72.2% and 91.5%, respectively. The prediction model detected breast cancer in both Caucasian and Asian populations with AUCs ranging from 0.880 to 0.973, including pre-malignant lesions (stage 0; AUC of 0.831) and early-stage (stages I–II) cancers (AUC of 0.916). Conclusions Our panel can potentially be used for breast cancer screening, in conjunction with mammography.