Maurer Jochen, Rübner Matthias, Kuo Chao-Chung, Klein Birgit, Franzen Julia, Wittenborn Julia, Kupec Tomas, Najjari Laila, Fasching Peter, Stickeler Elmar
Clinic for Gynecology and Obstetrics, University Hospital RWTH Aachen, Aachen, Germany.
Center for Integrated Oncology (CIO), Aachen, Bonn, Cologne, Düsseldorf (ABCD), Pauwelsstraße 30, D 52074 Aachen, Germany.
Ther Adv Med Oncol. 2024 Dec 13;16:17588359241299563. doi: 10.1177/17588359241299563. eCollection 2024.
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.
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.
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.
乳腺癌是女性中最常见的癌症，八分之一的女性在其一生中会患此病。对50至69岁女性实施集中组织的乳房X线筛查是朝着早期发现迈出的重要一步。然而，参与率仅达到约50%的符合条件女性，原因之一是乳房压迫带来的疼痛，这被认为是不参与这一非常重要项目的主要问题。因此，将当前研究聚焦于检测乳腺癌的疼痛较轻且侵入性较小的技术具有高度临床相关性。液体活检通过检测诸如微小RNA（miRNA）、循环肿瘤DNA（ctDNA）或播散肿瘤细胞等独特分子提供了这种选择。
在此，我们展示了首个用于对女性尿液中的miRNA进行测序以检测乳腺癌，随后检测内在亚型特异性miRNA模式并在这方面实施一种新型随机森林算法的概念验证方法。为此，我们对82份尿液样本进行了miRNA测序，其中32份来自乳腺癌患者（9份腔面A型、8份腔面B型、9份三阴性和6份HER2型），50份为健康对照样本。
使用随机森林算法，我们鉴定出一个由275个miRNA组成的特征，可用于检测尿液中的浸润性乳腺癌。此外，我们还鉴定出了乳腺癌主要内在亚型（特别是腔面A型、腔面B型、HER2富集型和三阴性乳腺癌）的独特miRNA表达模式。这种实验方法具体验证了miRNA测序作为尿液样本中乳腺癌检测技术的有效性，并为不同的乳腺癌相关医疗程序（如筛查以及治疗监测）开启了一种新的、简便且无痛的程序之门。
