Prediction of pathological response to neoadjuvant treatment in rectal cancer with a two-protein immunohistochemical score derived from stromal gene-profiling
Prediction of pathological response to neoadjuvant treatment in rectal cancer with a two-protein immunohistochemical score derived from stromal gene-profiling
复制标题
DOI：
10.1093/annonc/mdx293
复制
发表时间：
2017-09-01
影响因子：
50.5
通讯作者：
Mollevi, D. G.
中科院分区：
文献类型：
--
作者：
Goncalves-Ribeiro, S.;Sanz-Pamplona, R.;Mollevi, D. G.
背景：术前放化疗后手术直肠系膜切除术是局部晚期直肠癌的标准治疗方法。然而，预测患者对治疗的反应仍然是一个未实现的临床挑战。实验设计：使用激光捕获显微解剖，我们从预期预处理样本（n = 15）的间质和肿瘤腺体中分离RNA。通过杂交PrimeView Affymetrix阵列获得转录组谱。我们使用GSE39396模拟了癌相关成纤维细胞特异性基因过滤数据。结果：对有应答和无应答患者间质/肿瘤腺体差异表达基因的分析显示，大多数变化与间质室有关；主要对细胞外基质和核糖体成分进行编码。我们构建了一个癌相关成纤维细胞（CAF）特异性分类器，根据肿瘤消退等级（FN1、COL3A1、COL1A1、MMP2和IGFBP5）表达变化的基因。我们在患者队列（n = 38）中通过免疫组织化学染色在蛋白水平上评估了这五个基因。为了预测目的，我们使用了留一交叉验证模型，阳性预测值（PPV）为83.3%。随机森林发现FN1和COL3A1是最好的预测因子。重建留一交叉验证回归模型后，分类性能得到了提高，PPV为93.3%。使用独立队列进行分类器验证（n = 36）， PPV为88.2%。在多变量分析中，双蛋白分类器被证明是唯一独立的预测因子。结论：我们开发了一种双蛋白免疫组织化学分类器，可以很好地预测直肠癌对新辅助治疗的无反应。
Background: Preoperative chemoradiotherapy followed by surgical mesorectal resection is the standard of care for locally advanced rectal carcinomas. Yet, predicting that patients will respond to treatment remains an unmet clinical challenge.Experimental design: Using laser-capture microdissection we isolated RNA from stroma and tumour glands from prospective pre-treatment samples (n = 15). Transcriptomic profiles were obtained hybridising PrimeView Affymetrix arrays. We modelled a carcinoma-associated fibroblast-specific genes filtering data using GSE39396.Results: The analysis of differentially expressed genes of stroma/tumour glands from responder and non-responder patients shows that most changes were associated with the stromal compartment; codifying mainly for extracellular matrix and ribosomal components. We built a carcinoma-associated fibroblast (CAF) specific classifier with genes showing changes in expression according to the tumour regression grade (FN1, COL3A1, COL1A1, MMP2 and IGFBP5). We assessed these five genes at the protein level by means of immunohistochemical staining in a patient's cohort (n = 38). For predictive purposes we used a leave-one-out cross-validated model with a positive predictive value (PPV) of 83.3%. Random Forest identified FN1 and COL3A1 as the best predictors. Rebuilding the leave-one-out cross-validated regression model improved the classification performance with a PPV of 93.3%. An independent cohort was used for classifier validation (n = 36), achieving a PPV of 88.2%. In a multivariate analysis, the two-protein classifier proved to be the only independent predictor of response.Conclusion: We developed a two-protein immunohistochemical classifier that performs well at predicting the non-response to neoadjuvant treatment in rectal cancer.