Random forest algorithm identifies miRNA signatures for breast cancer detection and classification from patient urine samples.

Abstract

Background and objectives: 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. However, the participation rate reaches approximately 50% of the eligible women, one reason being the painful compression of the breast, cited as a major issue 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 is highly clinically relevant. Liquid biopsies offer this option by detection of distinct molecules such as microRNAs (miRNAs) or circulating tumor DNA (ctDNA) or disseminated tumor cells.

Design and methods: Here, we present the first proof-of-concept approach for sequencing miRNAs in female urine to detect breast cancer and, subsequently, intrinsic subtype-specific miRNA patterns and implement in this regard a novel random forest algorithm. To this end, 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.

Results and conclusion: Using a random forest algorithm, we identified a signature of 275 miRNAs that allows the detection of invasive breast cancer in urine. 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. 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 different breast cancer-related medical procedures such as screening but also treatment monitoring.