Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.
Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.
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DOI：
10.1158/1078-0432.ccr-18-0784
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发表时间：
2018-10-15
期刊：
影响因子：
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通讯作者：
Waldron L
中科院分区：
文献类型：
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作者：
Chen GM;Kannan L;Geistlinger L;Kofia V;Safikhani Z;Gendoo DMA;Parmigiani G;Birrer M;Haibe-Kains B;Waldron L
大多数卵巢癌是高级别浆液性组织学，这与预后不良有关。手术和化疗是治疗的主要手段，而分子表征对于引导靶向治疗选择是必要的。为此，已经提出了各种基于基因表达的高级别浆液性卵巢癌（HGSOC）亚型的计算方法，但它们的重叠和鲁棒性仍然未知。我们通过对公开表达数据的荟萃分析评估了三种主要的亚型分类器，并评估了亚型稳健性和分类器一致性的统计标准。我们开发了一个共识分类器，该分类器基于多种方法的共识来表示肿瘤的亚型分类，并输出置信度得分。使用我们的表达数据纲要，我们研究了基于目前提出的亚型的肿瘤子集不可分类的可能性。HGSOC亚型分类器在我们的数据汇编中表现出中度成对一致性（58.9%-70.9%，p < 10−5），并与数据集荟萃分析中的总生存率相关（p < 10−5）。当前的子类型不符合跨多个数据集重新聚类的鲁棒性的统计标准（预测强度< 0.6）。在一致分类的样本上训练新的亚型分类器，以产生与患者年龄、存活率、肿瘤纯度和淋巴细胞浸润相关的患者肿瘤的共识分类。一种新的共识卵巢亚型分类器代表了方法的共识，并证明了癌症分类方法的重要性，这些方法不需要将所有肿瘤分配到不同的亚型。
The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown. We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors are unclassifiable based on currently proposed subtypes. HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%, p < 10−5) and are associated with overall survival in a meta-analysis across datasets (p < 10−5). Current subtypes do not meet statistical criteria for robustness to re-clustering across multiple datasets (Prediction Strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration. A new consensus ovarian subtype classifier represents the consensus of methods, and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype.