Aureon Biosciences, Yonkers, NY 10701, USA.
BJU Int. 2012 Jan;109(2):207-13. doi: 10.1111/j.1464-410X.2011.10316.x. Epub 2011 Jul 6.
To develop a systems-based model for predicting prostate cancer-specific survival (PCSS) using a conservatively managed cohort with clinically localized prostate cancer and long-term follow-up.
Transurethral prostate (TURP) specimens in tissue microarray format and medical records from a 758 patient cohort were obtained. Slides were stained with haematoxylin and eosin (H&E), imaged and digitally outlined for invasive tumour. Additional sections were analysed with two multiplex quantitative immunofluorescence (IF) assays for cytokeratin-18 (epithelial cells), 4'-6-diamidino-2-phenylindole(nuclei), p63/high-molecular-weight keratin (basal cells), androgen receptor (AR) and α-methyl CoA-racemase, Ki67, phosphorylated AKT (pAKT)and CD34. Images were acquired with spectral imaging software. H&E and IF images were evaluated with image analysis algorithms; feature data were integrated with clinical variables to construct prognostic models for outcome.
Using a training set of 256 patients with 24% events, one clinical variable (Gleason score) and two tissue-specific characteristics (H&E morphometry and tumour-specific pAKT levels) were identified (concordance index [CoI] 0.79, sensitivity 76%, specificity 86%, hazard ratio [HR] 6.6) for predicting PCSS. Validation on an independent cohort of 269 patients with 29% events yielded a CoI of 0.76, sensitivity 59%, specificity 80% and HR of 3.6. Both H&E and IF features were selected in a multivariate setting and added incremental prognostic value to the Gleason score alone (CoI 0.77 to CoI 0.79). Furthermore, global Ki67 expression and AR levels in Gleason grade 3 tumours were both univariately associated with outcome; however, neither was selected in the final model.
A previously validated prostate needle-biopsy systems modelling approach that integrates clinical data with reproducible methods to assess H&E morphometry and biomarker expression, provided incremental benefit to the TURP Gleason score for predicting PCSS. Ki67 and AR, known to be associated with outcome in the prostate needle biopsy, were not associated with PCSS in multivariate models using TURP specimens.
利用保守治疗的局限性前列腺癌和长期随访的患者队列，建立一个基于系统的前列腺癌特异性生存（PCSS）预测模型。
获取了 758 例患者队列的经尿道前列腺切除术（TURP）标本和病历。载玻片用苏木精和伊红（H&E）染色，用数字方法对侵袭性肿瘤进行成像和轮廓描绘。用两个多指标定量免疫荧光（IF）检测试剂盒对另外的切片进行分析，检测角蛋白-18（上皮细胞）、4'-6-二脒基-2-苯基吲哚（细胞核）、p63/高分子量角蛋白（基底细胞）、雄激素受体（AR）和α-甲基 CoA-差向异构酶、Ki67、磷酸化 AKT（pAKT）和 CD34。使用光谱成像软件获取图像。用图像分析算法评估 H&E 和 IF 图像；将特征数据与临床变量相结合，构建用于预测结果的预后模型。
在一个包含 256 例患者（24%发生事件）的训练集里，发现一个临床变量（Gleason 评分）和两个组织特异性特征（H&E 形态计量学和肿瘤特异性 pAKT 水平）（一致性指数 [CoI] 0.79，灵敏度 76%，特异性 86%，风险比 [HR] 6.6）可用于预测 PCSS。在一个包含 269 例患者（29%发生事件）的独立队列中进行验证，得出的 CoI 为 0.76，灵敏度 59%，特异性 80%，HR 为 3.6。在多变量环境中，H&E 和 IF 特征均被选择，并单独增加了 Gleason 评分的预后价值（CoI 从 0.77 增加到 0.79）。此外，全球 Ki67 表达和 Gleason 3 级肿瘤中的 AR 水平均与结局单因素相关；然而，在最终模型中均未被选择。
一种基于系统的前列腺针活检建模方法，该方法整合了临床数据和可重复的方法来评估 H&E 形态计量学和生物标志物表达，为 TURP Gleason 评分预测 PCSS 提供了额外的益处。Ki67 和 AR 在前列腺针活检中与结局相关，但在使用 TURP 标本的多变量模型中与 PCSS 无关。
