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A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms.

ABSTRACT: Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training (n = 125) and test (n = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted p < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.

SUBMITTER: Loke SY

PROVIDER: S-EPMC6966622 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms.

Loke Sau Yeen SY Munusamy Prabhakaran P Koh Geok Ling GL Chan Claire Hian Tzer CHT Madhukumar Preetha P Thung Jee Liang JL Tan Kiat Tee Benita KTB Ong Kong Wee KW Yong Wei Sean WS Sim Yirong Y Oey Chung Lie CL Lim Sue Zann SZ Chan Mun Yew Patrick MYP Ho Teng Swan Juliana TSJ Khoo Boon Kheng James BKJ Wong Su Lin Jill SLJ Thng Choon Hua CH Chong Bee Kiang BK Tan Ern Yu EY Tan Veronique Kiak-Mien VK Lee Ann Siew Gek ASG

Cancers 20191126 12

Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 mic ...[more]

PMID: 31769433

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Project description:BackgroundSuccessful breast cancer screening relies on timely follow-up of abnormal mammograms. Delayed or failure to follow-up abnormal mammograms undermines the potential benefits of screening and is associated with poorer outcomes. However, a comprehensive review of inadequate follow-up of abnormal mammograms in primary care has not previously been reported in the literature. This review could identify modifiable factors that influence follow-up, which if addressed, may lead to improved follow-up and patient outcomes.MethodsA systematic literature review to determine the extent of inadequate follow-up of abnormal screening mammograms in primary care and identify factors impacting on follow-up was conducted. Relevant studies published between 1 January, 1990 and 29 October, 2020 were identified by searching MEDLINE®, Embase, CINAHL® and Cochrane Library, including reference and citation checking. Joanna Briggs Institute Critical Appraisal Checklists were used to assess the risk of bias of included studies according to study design.ResultsEighteen publications reporting on 17 studies met inclusion criteria; 16 quantitative and two qualitative studies. All studies were conducted in the United States, except one study from the Netherlands. Failure to follow-up abnormal screening mammograms within 3 and at 6 months ranged from 7.2-33% and 27.3-71.6%, respectively. Women of ethnic minority and lower education attainment were more likely to have inadequate follow-up. Factors influencing follow-up included physician-patient miscommunication, information overload created by automated alerts, the absence of adequate retrieval systems to access patient's results and a lack of coordination of patient records. Logistical barriers to follow-up included inconvenient clinic hours and inconsistent primary care providers. Patient navigation and case management with increased patient education and counselling by physicians was demonstrated to improve follow-up.ConclusionsFollow-up of abnormal mammograms in primary care is suboptimal. However, interventions addressing amendable factors that negatively impact on follow-up have the potential to improve follow-up, especially for populations of women at risk of inadequate follow-up.

Project description:IntroductionMammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.MethodsWe recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology's Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.ResultsIn the multivariate model, age (odds ratio (OR)=1.05; 95% confidence interval (CI), 1.03 to 1.08; P<0.001), SNP panel relative risk (OR=2.30; 95% CI, 1.06 to 4.99, P=0.035) and BMI (≥30 kg/m2 versus <25 kg/m(2); OR=2.20; 95% CI, 1.05 to 4.58; P=0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR=2.20; 95% CI, 1.05 to 4.58; P=0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI<25 kg/m2. The SNP panel appeared to have predictive ability among both white and black women.ConclusionsBreast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.

Dataset Information

A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms.

Publications

A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms.

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