Circulating microRNA-based screening tool for breast cancer

Abstract

Circulating microRNAs (miRNAs) are increasingly recognized as powerful biomarkers in several pathologies, including breast cancer. Here, their plasmatic levels were measured to be used as an alternative screening procedure to mammography for breast cancer diagnosis.A plasma miRNA profile was determined by RT-qPCR in a cohort of 378 women. A diagnostic model was designed based on the expression of 8 miRNAs measured first in a profiling cohort composed of 41 primary breast cancers and 45 controls, and further validated in diverse cohorts composed of 108 primary breast cancers, 88 controls, 35 breast cancers in remission, 31 metastatic breast cancers and 30 gynecologic tumors.A receiver operating characteristic curve derived from the 8-miRNA random forest based diagnostic tool exhibited an area under the curve of 0.81. The accuracy of the diagnostic tool remained unchanged considering age and tumor stage. The miRNA signature correctly identified patients with metastatic breast cancer. The use of the classification model on cohorts of patients with breast cancers in remission and with gynecologic cancers yielded prediction distributions similar to that of the control group.Using a multivariate supervised learning method and a set of 8 circulating miRNAs, we designed an accurate, minimally invasive screening tool for breast cancer.

Keywords: biomarkers; breast cancer; circulating microRNAs; minimally invasive screening.

Figure 1. Study design

A diagram describing the random forest-based methodology. The profiling cohort (n = 86) contains 41 patients with primary breast cancer and 45 controls. The validation cohort (n = 196) contains 108 patients with primary breast cancer and 88 controls. The other cancer cohort (n = 96) contains 35 patients with breast cancer in remission, 31 patients with metastatic breast cancer and 30 patients with gynecologic cancer.

Figure 2. The 8 miRNAs present in the diagnostic signature

(A) The results of statistical analyses comparing the expression of the 8 miRNAs present in the diagnostic signature between different groups. The 8 diagnostic miRNAs were compared between primary breast cancer patients, breast cancer patients in remission, metastatic breast cancer patients, gynecologic cancer patients and the controls. P-values and Benjamini-Hochberg adjusted P-values were obtained using the Mann-Whitney U test. (B) The relative expression (mean fold change) of the 8 diagnostic miRNAs in patients with primary breast cancer, patients with breast cancer in remission, patients with metastatic breast cancer and patients with gynecologic cancer compared to controls.

Figure 3. Circulating miRNA-based diagnostic tool performance in the validating cohort

(A) The ROC curve of the diagnostic miRNA model applied to the validating cohort. The AUC obtained is 0.81. (B) Model outcome distributions for the primary breast cancers, controls, metastatic breast cancers, breast cancers in remission, and gynecologic cancers. The x-axis corresponds to the model predictions. The dashed line represents the chosen threshold used to compute the sensitivity and specificity values for each cohort. The table reports the AUC, sensitivity and specificity in the validation cohort and the sensitivity and specificity in the other cancer cohort. The true positive count for the metastatic breast cancers is 25. The true negative count for breast cancers in remission and gynecologic cancers is 14.

Figure 4. Comparison of the accuracy between the diagnostic miRNA signature, mammographic screenings and CA15.3 assays

(A) While the diagnostic performance of mammographic screenings is weaker in women under 50 yr (32), the AUC of the 8 miRNA-based diagnostic model was stable for women both under and over 50 yr. (B) The CA15.3 assay is not useful for the early diagnosis of breast cancer. While the CA15.3 AUC increases proportionally to the tumor stage (33), our model performance was stable regardless of the tumor stage.