Tissue Microarray sampling strategy for prostate cancer biomarker analysis
Tissue Microarray sampling strategy for prostate cancer biomarker analysis
复制标题
DOI：
10.1097/00000478-200203000-00004
复制
发表时间：
2002-03-01
影响因子：
5.6
通讯作者：
Pienta, KJ
中科院分区：
文献类型：
--
作者：
Rubin, MA;Dunn, R;Pienta, KJ
高密度组织微阵列（TMA）可用于分析大量样本中的蛋白质表达，但其用于临床生物标志物研究的用途可能限于异质性肿瘤，如前列腺癌。在这项研究中，优化和验证的肿瘤采样策略的前列腺癌的结果TMA进行。检测前列腺癌细胞增殖情况，采用Ki-67免疫组化法。使用数字图像分析（CAS 200，Bacus Labs，Lombard，IL，USA）对来自标准载玻片的前列腺癌的10个区域进行10次重复的增殖测量。在平行研究中，从10个区域中的每一个中取样5个匹配的组织微阵列样品芯（0.6mm直径）。使用自助回归分析对每个地区或样本的TMA样本数量的所有可能排列进行统计学模拟。统计分析比较了TMA样品与标准病理学免疫组织化学载玻片中Ki-67表达。TMA核心的最佳采样在3时达到，因为较少的TMA样本显著增加Ki-67变异性，而较大的数量并未显著提高准确性。为了验证这些结果，构建了包含来自88个病例的10个重复肿瘤样品的前列腺癌结果组织微阵列。与初始研究相似，使用1至10个随机选择的核心来评估每个病例的Ki-67表达，计算每个模型中使用的所有样本的表达的第90百分位数。使用该值，进行考克斯比例风险分析，以确定临床局限性前列腺癌根治性前列腺切除术后前列腺特异性抗原（PSA）复发时间的预测因素。多个模型的检查表明，4个核心是最佳的。使用4个核心的模型，考克斯回归模型表明Ki-67表达、术前PSA和手术切缘状态预测PSA复发的时间，风险比为1.49（95%置信区间[CI] 1.01-2.20，p = 0.047）、2.36（95% CI 1.15-4.85，p = 0.020）和9.04（95% CI 2.42-33.81，p = 0.001）。用3个核心来确定Ki-67表达的模型也被发现可以预测结果。总之，相对于标准肿瘤载玻片，需要3个核心来最佳地表示Ki-67表达。3 - 4个核心在前列腺癌结果阵列中给出了最佳预测值。少于3个核心的采样策略可能无法准确代表肿瘤蛋白表达。相反，超过4个核心不会增加重要信息。这个前列腺癌结果阵列应该是有用的，在评估其他推定的前列腺癌生物标志物。
High-density tissue microarrays (TMA) are useful for profiling protein expression in a large number of samples but their use for clinical biomarker studies may be limited in heterogeneous tumors like prostate cancer. In this study, the optimization and validation of a tumor sampling strategy for a prostate cancer outcomes TMA is performed. Prostate cancer proliferation determined by Ki-67 immunohistochemistry was tested. Ten replicate measurements of proliferation using digital image analysis (CAS200, Bacus Labs, Lombard, IL, USA) were made on 10 regions of prostate cancer from a standard glass slide. Five matching tissue microarray sample cores (0.6 mm diameter) were sampled from each of the 10 regions in the parallel study. A bootstrap resampling analysis was used to statistically simulate all possible permutations of TMA sample number per region or sample. Statistical analysis compared TMA samples with Ki-67 expression in standard pathology immunohistochemistry slides. The optimal sampling for TMA cores was reached at 3 as fewer TMA samples significantly increased Ki-67 variability and a larger number did not significantly improve accuracy. To validate these results, a prostate cancer outcomes tissue microarray containing 10 replicate tumor samples from 88 cases was constructed. Similar to the initial study, I to 10 randomly selected cores were used to evaluate the Ki-67 expression for each case, computing the 90th percentile of the expression from all samples used in each model. Using this value, a Cox proportional hazards analysis was performed to determine predictors of time until prostate-specific antigen (PSA) recurrence after radical prostatectomy for clinically localized prostate cancer. Examination of multiple models demonstrated that 4 cores was optimal. Using a model with 4 cores, a Cox regression model demonstrated that Ki-67 expression, preoperative PSA, and surgical margin status predicted time to PSA recurrence with hazard ratios of 1.49 (95% confidence interval [CI] 1.01-2.20, p = 0.047), 2.36 (95% CI 1.15-4.85, p = 0.020), and 9.04 (95% CI 2.42-33.81, p = 0.001), respectively. Models with 3 cores to determine Ki-67 expression were also found to predict outcome. In summary, 3 cores were required to optimally represent Ki-67 expression with respect to the standard tumor slide. Three to 4 cores gave the optimal predictive value in a prostate cancer outcomes array. Sampling strategies with fewer than 3 cores may not accurately represent tumor protein expression. Conversely, more than 4 cores will not add significant information. This prostate cancer outcomes array should be useful in evaluating other putative prostate cancer biomarkers.