Blood Test for Breast Cancer Screening through the Detection of Tumor-Associated Circulating Transcripts

Liquid biopsy has been emerging for early screening and treatment monitoring at each cancer stage. However, the current blood-based diagnostic tools in breast cancer have not been sufficient to understand patient-derived molecular features of aggressive tumors individually. Herein, we aimed to develop a blood test for the early detection of breast cancer with cost-effective and high-throughput considerations in order to combat the challenges associated with precision oncology using mRNA-based tests. We prospectively evaluated 719 blood samples from 404 breast cancer patients and 315 healthy controls, and identified 10 mRNA transcripts whose expression is increased in the blood of breast cancer patients relative to healthy controls. Modeling of the tumor-associated circulating transcripts (TACTs) is performed by means of four different machine learning techniques (artificial neural network (ANN), decision tree (DT), logistic regression (LR), and support vector machine (SVM)). The ANN model had superior sensitivity (90.2%), specificity (80.0%), and accuracy (85.7%) compared with the other three models. Relative to the value of 90.2% achieved using the TACT assay on our test set, the sensitivity values of other conventional assays (mammogram, CEA, and CA 15-3) were comparable or much lower, at 89%, 7%, and 5%, respectively. The sensitivity, specificity, and accuracy of TACTs were appreciably consistent across the different breast cancer stages, suggesting the potential of the TACTs assay as an early diagnosis and prediction of poor outcomes. Our study potentially paves the way for a simple and accurate diagnostic and prognostic tool for liquid biopsy.

Automated summary

This study aimed to develop a cost-effective blood test for the early detection of breast cancer using mRNA-based tests. A total of 719 blood samples from breast cancer patients and healthy controls were evaluated, and 10 mRNA transcripts with increased expression in breast cancer patients were identified. Machine learning techniques were employed to model tumor-associated circulating transcripts (TACTs), and the artificial neural network (ANN) model showed superior sensitivity, specificity, and accuracy compared to other models. The TACTs assay had higher sensitivity than conventional assays and consistent performance across different cancer stages, suggesting its potential for early diagnosis and prediction of poor outcomes.