State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; Guangzhou Darui Biotechnology Co, Ltd High-Tech Development Zone, Guangzhou, Guangdong, China; Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, Guangdong, China.
Int J Radiat Oncol Biol Phys. 2021 Jun 1;110(2):482-491. doi: 10.1016/j.ijrobp.2021.01.005. Epub 2021 Jan 9.
To construct and validate a predicting genotype signature for pathologic complete response (pCR) in locally advanced rectal cancer (PGS-LARC) after neoadjuvant chemoradiation.
Whole exome sequencing was performed in 15 LARC tissues. Mutation sites were selected according to the whole exome sequencing data and literature. Target sequencing was performed in a training cohort (n = 202) to build the PGS-LARC model using regression analysis, and internal (n = 76) and external validation cohorts (n = 69) were used for validating the results. Predictive performance of the PGS-LARC model was compared with clinical factors and between subgroups. The PGS-LARC model comprised 15 genes.
The area under the curve (AUC) of the PGS model in the training, internal, and external validation cohorts was 0.776 (0.697-0.849), 0.760 (0.644-0.867), and 0.812 (0.690-0.915), respectively, and demonstrated higher AUC, accuracy, sensitivity, and specificity than cT stage, cN stage, carcinoembryonic antigen level, and CA19-9 level for pCR prediction. The predictive performance of the model was superior to clinical factors in all subgroups. For patients with clinical complete response (cCR), the positive prediction value was 94.7%.
The PGS-LARC is a reliable predictive tool for pCR in patients with LARC and might be helpful to enable nonoperative management strategy in those patients who refuse surgery. It has the potential to guide treatment decisions for patients with different probability of tumor regression after neoadjuvant therapy, especially when combining cCR criteria and PGS-LARC.
构建并验证新辅助放化疗后局部晚期直肠癌(PGS-LARC)病理完全缓解(pCR)的预测基因型特征(PGS)。
对 15 例 LARC 组织进行全外显子测序。根据全外显子测序数据和文献选择突变位点。在训练队列(n=202)中进行靶向测序,使用回归分析构建 PGS-LARC 模型,并使用内部(n=76)和外部验证队列(n=69)验证结果。比较 PGS-LARC 模型与临床因素和亚组之间的预测性能。PGS-LARC 模型由 15 个基因组成。
训练、内部和外部验证队列中 PGS 模型的曲线下面积(AUC)分别为 0.776(0.697-0.849)、0.760(0.644-0.867)和 0.812(0.690-0.915),其预测 pCR 的 AUC、准确性、灵敏度和特异性均高于 cT 分期、cN 分期、癌胚抗原水平和 CA19-9 水平。该模型在所有亚组中的预测性能均优于临床因素。对于临床完全缓解(cCR)患者,阳性预测值为 94.7%。
PGS-LARC 是预测 LARC 患者 pCR 的可靠工具,可能有助于使拒绝手术的患者能够采用非手术治疗策略。它有可能指导新辅助治疗后肿瘤消退率不同的患者的治疗决策,尤其是结合 cCR 标准和 PGS-LARC 时。