Duong Cuong, Greenawalt Danielle M, Kowalczyk Adam, Ciavarella Marianne L, Raskutti Garvesh, Murray William K, Phillips Wayne A, Thomas Robert J S
Division of Surgical Oncology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, Victoria, 3002, Australia.
Ann Surg Oncol. 2007 Dec;14(12):3602-9. doi: 10.1245/s10434-007-9550-1. Epub 2007 Sep 26.
The use of neoadjuvant therapy, in particular chemoradiotherapy (CRT), in the treatment of esophageal cancer (EC) remains controversial. The ability to predict treatment response in an individual EC patient would greatly aid therapeutic planning. Gene expression profiles of EC were measured and relationship to therapeutic response assessed.
Tumor biopsy samples taken from 46 EC patients before neoadjuvant CRT were analyzed on 10.5K cDNA microarrays. Response to treatment was assessed and correlated to gene expression patterns by using a support vector machine learning algorithm.
Complete clinical response at conclusion of CRT was achieved in 6 of 21 squamous cell carcinoma (SCC) and 11 of 25 adenocarcinoma (AC) patients. CRT response was an independent prognostic factor for survival (P < .001). A range of support vector machine models incorporating 10 to 1000 genes produced a predictive performance of tumor response to CRT peaking at 87% in SCC, but a distinct positive prediction profile was unobtainable for AC. A 32-gene classifier was produced, and by means of this classifier, 10 of 21 SCC patients could be accurately identified as having disease with an incomplete response to therapy, and thus unlikely to benefit from neoadjuvant CRT.
Our study identifies a 32-gene classifier that can be used to predict response to neoadjuvant CRT in SCC. However, because of the molecular diversity between the two histological subtypes of EC, when considering the AC and SCC samples as a single cohort, a predictive profile could not be resolved, and a negative predictive profile was observed for AC.
新辅助治疗，尤其是放化疗（CRT），在食管癌（EC）治疗中的应用仍存在争议。预测个体EC患者的治疗反应能力将极大地有助于治疗计划的制定。对EC的基因表达谱进行了测量，并评估了其与治疗反应的关系。
对46例接受新辅助CRT治疗前的EC患者的肿瘤活检样本进行10.5K cDNA微阵列分析。通过支持向量机学习算法评估治疗反应并将其与基因表达模式相关联。
21例鳞状细胞癌（SCC）患者中有6例、25例腺癌（AC）患者中有11例在CRT结束时实现了完全临床缓解。CRT反应是生存的独立预后因素（P <.001）。一系列包含10至1000个基因的支持向量机模型对CRT的肿瘤反应预测性能在SCC中达到峰值87%，但AC无法获得明显的阳性预测特征。生成了一个32基因分类器，通过该分类器，21例SCC患者中的10例可被准确识别为对治疗反应不完全的疾病，因此不太可能从新辅助CRT中获益。
我们的研究确定了一个32基因分类器，可用于预测SCC中新辅助CRT的反应。然而，由于EC的两种组织学亚型之间存在分子多样性，当将AC和SCC样本视为一个队列时，无法解析预测特征，并且观察到AC的阴性预测特征。
