Li Bin, Eschrich Steven A, Berglund Anders, Mitchell Melissa, Fenstermacher David, Danaee Hadi, Dai Hongyue, Sullivan Daniel, Trepicchio William L, Dalton William S
Takeda Pharmaceuticals International Company, Takeda Data Science Institute, Cambridge, MA, United States.
H Lee Moffitt Cancer Center and Research Institute, Biostatistics and Bioinformatics, Tampa, FL, United States.
JMIR Res Protoc. 2017 Mar 20;6(3):e45. doi: 10.2196/resprot.7289.
One approach to identify patients who meet specific eligibility criteria for target-based clinical trials is to use patient and tumor registries to prescreen patient populations.
Here we demonstrate that the Total Cancer Care (TCC) Protocol, an ongoing, observational study, may provide a solution for rapidly identifying patients with CD30-positive tumors eligible for CD30-targeted therapies such as brentuximab vedotin.
The TCC patient gene expression profiling database was retrospectively screened for CD30 gene expression determined using HuRSTA-2a520709 Affymetrix arrays (GPL15048). Banked tumor tissue samples were used to determine CD30 protein expression by semiquantitative immunohistochemistry. Statistical comparisons of Z- and H-scores were performed using R statistical software (The R Foundation), and the predictive value, accuracy, sensitivity, and specificity of CD30 gene expression versus protein expression was estimated.
As of March 2015, 120,887 patients have consented to the institutional review board-approved TCC Protocol. A total of 39,157 fresh frozen tumor specimens have been collected, from which over 14,000 samples have gene expression data available. CD30 RNA was expressed in a number of solid tumors; the highest median CD30 RNA expression was observed in primary tumors from lymph node, soft tissue (many sarcomas), lung, skin, and esophagus (median Z-scores 1.011, 0.399, 0.202, 0.152, and 1.011, respectively). High level CD30 gene expression significantly enriches for CD30-positive protein expression in breast, lung, skin, and ovarian cancer; accuracy ranged from 72% to 79%, sensitivity from 75% to 100%, specificity from 70% to 76%, positive predictive value from 20% to 40%, and negative predictive value from 95% to 100%.
The TCC gene expression profiling database guided tissue selection that enriched for CD30 protein expression in a number of solid tumor types. Such an approach may improve screening efficiency for enrolling patients into biomarker-based clinical trials.
识别符合基于靶点的临床试验特定入选标准的患者的一种方法是利用患者和肿瘤登记处对患者群体进行预筛选。
在此我们证明，正在进行的观察性研究“全面癌症护理（TCC）方案”可能为快速识别有资格接受诸如贝林妥欧单抗等CD30靶向治疗的CD30阳性肿瘤患者提供一种解决方案。
对TCC患者基因表达谱数据库进行回顾性筛选，以确定使用HuRSTA - 2a520709 Affymetrix阵列（GPL15048）测定的CD30基因表达。利用储存的肿瘤组织样本通过半定量免疫组织化学来确定CD30蛋白表达。使用R统计软件（R基金会）进行Z分数和H分数的统计比较，并估计CD30基因表达与蛋白表达的预测值、准确性、敏感性和特异性。
截至2015年3月，120887名患者已同意参与经机构审查委员会批准的TCC方案。总共收集了39157份新鲜冷冻肿瘤标本，其中超过14000份样本有基因表达数据。CD30 RNA在多种实体瘤中表达；在淋巴结、软组织（许多肉瘤）、肺、皮肤和食管的原发性肿瘤中观察到最高的CD30 RNA表达中位数（中位数Z分数分别为1.011、0.399、0.202、0.152和1.011）。在乳腺癌、肺癌、皮肤癌和卵巢癌中，高水平的CD30基因表达显著富集CD30阳性蛋白表达；准确性范围为72%至79%，敏感性为75%至100%，特异性为70%至76%，阳性预测值为20%至40%，阴性预测值为95%至100%。
TCC基因表达谱数据库指导了组织选择，在多种实体瘤类型中富集了CD30蛋白表达。这种方法可能提高将患者纳入基于生物标志物的临床试验的筛选效率。
