Abstract：
Ovarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classification system despite extensive research efforts. This study integrated five distinct multi-omics datasets from eight multicentric cohorts, applying a combination of ten clustering algorithms and ninety-nine machine learning models. This methodology has enabled us to refine the molecular subtyping of OC, leading to the development of a novel Consensus Machine Learning-driven Signature (CMLS). Our analysis delineated two prognostically significant cancer subtypes (CS), each marked by unique genetic and immunological signatures. Notably, CS1 is associated with an adverse prognosis. Leveraging a subtype classifier, we identified five key genes (CTHRC1, SPEF1, SCGB3A1, FOXJ1, and C1orf194) instrumental in constructing the CMLS. Patients classified within the high CMLS group exhibited a poorer prognosis and were characterized by a \"cold tumor\" phenotype, indicative of an immunosuppressive microenvironment rich in MDSCs, CAFs, and Tregs. Intriguingly, this group also presented higher levels of tumor mutation burden (TMB) and tumor neoantigen burden (TNB), factors that correlated with a more favorable response to immunotherapy compared to their low CMLS counterparts. In contrast, the low CMLS group, despite also displaying a \"cold tumor\" phenotype, showed a favorable prognosis and a heightened responsiveness to chemotherapy. This study\'s findings underscore the potential of targeting immune-suppressive cells, particularly in patients with high CMLS, as a strategic approach to enhance OC prognosis. Furthermore, the redefined molecular subtypes and risk stratification, achieved through sophisticated multi-omics analysis, provide a framework for the selection of therapeutic agents.
摘要：
卵巢癌(OC),以其明显的异质性而闻名，尽管进行了广泛的研究，但长期以来一直回避统一的分类系统。这项研究整合了来自八个多中心队列的五个不同的多组数据集，应用十种聚类算法和九十九种机器学习模型的组合。这种方法使我们能够完善OC的分子亚型，导致了一种新颖的共识机器学习驱动签名(CMLS)的开发。我们的分析描绘了两种具有预后意义的癌症亚型(CS)，每个都有独特的遗传和免疫特征。值得注意的是,CS1与不良预后相关。利用子类型分类器，我们确定了五个关键基因（CTHRC1，SPEF1，SCGB3A1，FOXJ1和C1orf194）有助于构建CMLS。高CMLS组患者预后较差，表现为“冷肿瘤”表型，指示富含MDSCs的免疫抑制微环境，CAF,和Tregs.有趣的是，该组还呈现较高水平的肿瘤突变负荷（TMB）和肿瘤新抗原负荷（TNB），与低CMLS对应物相比，与免疫疗法更有利的反应相关的因素。相比之下,低CMLS组，尽管还显示出“冷肿瘤”表型，显示良好的预后和对化疗的反应性增强。这项研究的发现强调了靶向免疫抑制细胞的潜力，特别是在高CMLS患者中，作为提高OC预后的战略方法。此外，重新定义的分子亚型和风险分层，通过复杂的多组学分析，提供了选择治疗剂的框架。
