Background/Objectives: Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The prognosis for many breast cancer subtypes has improved due to treatments targeting the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In contrast, patients with triple-negative breast cancer (TNBC) tumors, which lack all three commonly targeted membrane markers, more frequently relapse and have lower survival rates due to a lack of tumor-selective TNBC treatments. We aim to investigate TNBC mechanistic markers that could be targeted for treatment.Methods:We performed a secondary TNBC analysis of 196 samples across 10 publicly available bulk RNA-sequencing studies to better understand the molecular mechanism(s) of disease and predict robust mechanistic markers that could be used to improve the mechanistic understanding of and diagnostic capabilities for TNBC.Results:Our analysis identified ~12,500 significant differentially expressed genes (FDR-adjustedp-value < 0.05), including KIF14 and ELMOD3, and two significantly modulated pathways. Additionally, our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone), CD300LG, ASPM, and RGS1 (98.9% combined accuracy), as well as TNBC subtype-differentiating mechanistic markers, including the targets PDE3B, CFD, IFNG, and ADM, which have associated therapeutics that can potentially be repurposed to improve treatment options. We then experimentally and computationally validated a subset of these findings.Conclusions:The results of our analyses can be used to better understand the mechanism(s) of disease and contribute to the development of improved diagnostics and/or treatments for TNBC.
背景/目的：全球每年有230万女性被诊断出乳腺癌，其中68.5万人死亡（约占患者总数的30%）。针对雌激素受体（ER）、孕激素受体（PR）和人表皮生长因子受体2（HER2）的治疗已改善了许多乳腺癌亚型的预后。相比之下，三阴性乳腺癌（TNBC）肿瘤缺乏这三种常见的靶向膜标志物，由于缺乏肿瘤选择性的TNBC治疗方法，患者更易复发且生存率较低。本研究旨在探索可作为治疗靶点的TNBC机制性标志物。 方法：我们对10项公开可获取的批量RNA测序研究中的196个样本进行了二次TNBC分析，以更好地理解疾病的分子机制，并预测可用于提升对TNBC机制理解和诊断能力的稳健机制性标志物。 结果：我们的分析鉴定出约12,500个显著差异表达基因（FDR校正p值<0.05），包括KIF14和ELMOD3，以及两条显著调控的通路。此外，我们的新发现包括通过机器学习方法识别出的高精度机制性标志物，如CIDEC（单独准确率达97.1%）、CD300LG、ASPM和RGS1（组合准确率达98.9%），以及可区分TNBC亚型的机制性标志物，包括靶点PDE3B、CFD、IFNG和ADM，这些靶点已有相关疗法，可能通过重新利用以改善治疗选择。随后，我们通过实验和计算验证了部分发现。 结论：我们的分析结果可用于更好地理解疾病机制，并有助于开发改进的TNBC诊断和/或治疗方法。
肿瘤(癌症)患者之家