Department of Obstetrics and Gynecology, Medical College of Wisconsin, United States of America.
Department of Pediatrics, Medical College of Wisconsin, United States of America.
Gynecol Oncol. 2019 Nov;155(2):324-330. doi: 10.1016/j.ygyno.2019.08.021. Epub 2019 Aug 30.
To date, The Cancer Genome Atlas (TCGA) has provided the most extensive molecular characterization of invasive cervical cancer (ICC). Analysis of reverse phase protein array (RPPA) data from TCGA samples showed that cervical cancers could be stratified into 3 clusters exhibiting significant differences in survival outcome: hormone, EMT, and PI3K/AKT. The goals of the current study were to: 1) validate the TCGA RPPA results in an independent cohort of ICC patients and 2) to develop and validate an algorithm encompassing a small antibody set for clinical utility.
Subjects consisted of 2 ICC patient cohorts with accompanying RPPA and clinical-pathologic data: 155 samples from TCGA (TCGA-155) and 61 additional, unique samples (MCW-61). Using data from 173 common RPPA antibodies, we replicated Silhouette clustering analysis in both ICC cohorts. Further, an index score for each patient was calculated from the survival-associated antibodies (SAAs) identified using Random survival forests (RSF) and the Cox proportional hazard regression model. Kaplan-Meier survival analysis and the log-rank test were performed to assess and compare cluster or risk group survival outcome.
In addition to validating the prognostic ability of the proteomic clusters reported by TCGA, we developed an algorithm based on 22 unique antibodies (SAAs) that stratified women with ICC into low-, medium-, or high-risk survival groups.
We provide a signature of 22 antibodies which accurately predicted survival outcome in 2 separate groups of ICC patients. Future studies examining these candidate biomarkers in additional ICC cohorts is warranted to fully determine their clinical potential.
迄今为止，癌症基因组图谱（TCGA）提供了最广泛的浸润性宫颈癌（ICC）分子特征描述。对 TCGA 样本的反向蛋白质阵列（RPPA）数据分析显示，宫颈癌可以分为 3 个聚类，在生存结果方面存在显著差异：激素、EMT 和 PI3K/AKT。本研究的目的是：1）验证 TCGA RPPA 结果在独立的 ICC 患者队列中；2）开发和验证包含小抗体集的算法，以实现临床应用。
研究对象包括 2 个 ICC 患者队列，伴有 RPPA 和临床病理数据：TCGA 中的 155 个样本（TCGA-155）和另外 61 个独特的样本（MCW-61）。我们使用 173 种常见的 RPPA 抗体，在两个 ICC 队列中复制了轮廓聚类分析。此外，我们使用随机生存森林（RSF）和 Cox 比例风险回归模型确定与生存相关的抗体（SAAs），计算每个患者的指数评分。通过 Kaplan-Meier 生存分析和对数秩检验评估和比较聚类或风险组的生存结果。
除了验证 TCGA 报道的蛋白质组聚类的预后能力外，我们还开发了一种基于 22 种独特抗体（SAAs）的算法，将 ICC 患者分为低、中或高风险的生存组。
我们提供了 22 种抗体的特征，这些抗体在 2 个独立的 ICC 患者群体中准确预测了生存结果。未来需要在其他 ICC 队列中研究这些候选生物标志物，以充分确定它们的临床潜力。
