Genetic Pathology Evaluation Centre, Vancouver, BC, Canada.
Mod Pathol. 2011 Apr;24(4):512-21. doi: 10.1038/modpathol.2010.215. Epub 2010 Dec 3.
With the emerging evidence that the five major ovarian carcinoma subtypes (high-grade serous, clear cell, endometrioid, mucinous, and low-grade serous) are distinct disease entities, management of ovarian carcinoma will become subtype specific in the future. In an effort to improve diagnostic accuracy, we set out to determine if an immunohistochemical panel of molecular markers could reproduce consensus subtype assignment. Immunohistochemical expression of 22 biomarkers were examined on tissue microarrays constructed from 322 archival ovarian carcinoma samples from the British Columbia Cancer Agency archives, for the period between 1984 and 2000, and an independent set of 242 cases of ovarian carcinoma from the Gynaecologic Tissue Bank at Vancouver General Hospital from 2001 to 2008. Nominal logistic regression was used to produce a subtype prediction model for each of these sets of cases. These models were then cross-validated against the other cohort, and then both models were further validated in an independent cohort of 81 ovarian carcinoma samples from five different centers. Starting with data for 22 markers, full model fit, backwards, nominal logistic regression identified the same nine markers (CDKN2A, DKK1, HNF1B, MDM2, PGR, TFF3, TP53, VIM, WT1) as being most predictive of ovarian carcinoma subtype in both the archival and tumor bank cohorts. These models were able to predict subtype in the respective cohort in which they were developed with a high degree of sensitivity and specificity (κ statistics of 0.88±0.02 and 0.86±0.04, respectively). When the models were cross-validated (ie using the model developed in one case series to predict subtype in the other series), the prediction equation's performances were reduced (κ statistics of 0.70±0.04 and 0.61±0.04, respectively) due to differences in frequency of expression of some biomarkers in the two case series. Both models were then validated on the independent series of 81 cases, with very good to excellent ability to predict subtype (κ=0.85±0.06 and 0.78±0.07, respectively). A nine-marker immunohistochemical maker panel can be used to objectively support classification into one of the five major subtypes of ovarian carcinoma.
随着越来越多的证据表明,五种主要卵巢癌亚型(高级别浆液性、透明细胞性、子宫内膜样、黏液性和低级别浆液性)是不同的疾病实体,卵巢癌的治疗将在未来成为特定亚型。为了提高诊断准确性,我们着手确定一组免疫组织化学标记物是否可以重现共识亚型分配。在从不列颠哥伦比亚癌症署档案中 1984 年至 2000 年期间收集的 322 例存档卵巢癌样本的组织微阵列上,以及从温哥华综合医院妇科组织库中 2001 年至 2008 年期间收集的 242 例卵巢癌独立样本中,检测了 22 种生物标志物的免疫组织化学表达。名义逻辑回归用于为每一组病例生成一个亚型预测模型。然后,将这些模型交叉验证到另一组,然后在来自五个不同中心的 81 例卵巢癌样本的独立队列中进一步验证这两个模型。从 22 个标记物的数据开始,向后全模型拟合的名义逻辑回归确定了同样的 9 个标记物(CDKN2A、DKK1、HNF1B、MDM2、PGR、TFF3、TP53、VIM、WT1)是存档和肿瘤库队列中最能预测卵巢癌亚型的标记物。这些模型能够以高灵敏度和特异性预测各自队列中的亚型(κ统计值分别为 0.88±0.02 和 0.86±0.04)。当模型交叉验证(即使用一个病例系列中的模型预测另一个系列中的亚型)时,由于两个病例系列中某些生物标志物表达频率的差异,预测方程的性能降低(κ 统计值分别为 0.70±0.04 和 0.61±0.04)。然后,这两个模型在 81 例独立系列中进行了验证,对亚型的预测能力非常好到极好(κ=0.85±0.06 和 0.78±0.07)。一个由 9 种免疫组织化学标记物组成的标记面板可以用于客观地支持分类为五种主要卵巢癌亚型之一。