Machine learning approach identifies miRNA signatures for breast cancer detection and classification from patient urine samples

Abstract Introduction Breast cancer is the most common cancer in women, with one in eight women suffering from this disease in her lifetime. The implementation of centrally organized mammography screening for women between 50 and 69 years of age was a major step in the direction of early detection and lead to a significant improvement in cure rates. Within the screening program, women undergo a mammogram every two years with an implemented centralized quality-controlled review process. However, the participation rate reached only approximately 50% of the eligible women. In addition to several others, the technical aspects of mammography, including painful compression of the breast, are cited as a reason for not participating in this very important program. Therefore, focusing current research on less painful and less invasive techniques for the detection of breast cancer seems to be highly clinically useful. Liquid biopsies offer this option with distinct molecules or cells in line with the research. Blood-based tests of circulating tumor cells (CTCs), cell-free DNA (ctDNA) and cell-free miRNA have been performed in a variety of studies and tumor entities. Methods We performed miRNA sequencing on 82 urine samples, 32 samples from breast cancer patients (9× luminal A, 8× luminal B, 9× triple-negative and 6× HER2) and 50 healthy control samples. Data were analyzed and interpreted using Random Forest analysis. Results We identified a signature of 275 miRNAs that allows the detection of invasive breast cancer in urine from breast cancer patients. Furthermore, we identified distinct miRNA expression patterns for the major intrinsic subtypes of breast cancer, specifically luminal A, luminal B, HER2-enriched and triple-negative breast cancer. Conclusions Here, we present the first approach for sequencing miRNAs in female urine to detect breast cancer and, subsequently, intrinsic subtype-specific miRNA patterns. This experimental approach specifically validates miRNA sequencing as a technique for breast cancer detection in urine samples and opens the door to a new, easy and painless procedure for regular breast cancer screening.