A Genotype Signature for Predicting Pathologic Complete Response in Locally Advanced Rectal Cancer
| Autor: | Ying-Song Wu, Zhifan Zeng, Zhi-Wei Guo, Yuanhong Gao, De-Qing Wu, Rong Zhang, Chun-Lian Zhou, Xiaolin Pang, Yong Li, Min Li, Xiang-Guo Zhang, Qiaoxuan Wang, Shaoyan Xi, Yu-Feng Ren, Ming Li, Huizhong Zhang, Xiang-Bo Wan, Xue-Xi Yang, Xiang-Ming Zhai, Weiwei Xiao, Liang Zhikun, Kun Li |
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| Rok vydání: | 2020 |
| Předmět: |
Oncology
Male Cancer Research medicine.medical_specialty Genotype Colorectal cancer medicine.medical_treatment Sensitivity and Specificity 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Carcinoembryonic antigen Predictive Value of Tests Internal medicine Exome Sequencing medicine Humans Radiology Nuclear Medicine and imaging Antigens Tumor-Associated Carbohydrate Stage (cooking) Exome sequencing Neoadjuvant therapy Neoplasm Staging Radiation biology business.industry Rectal Neoplasms Area under the curve Reproducibility of Results Regression analysis Chemoradiotherapy Adjuvant Middle Aged medicine.disease Neoadjuvant Therapy Carcinoembryonic Antigen Treatment Outcome 030220 oncology & carcinogenesis Area Under Curve biology.protein Regression Analysis Female business Transcriptome |
| Zdroj: | International journal of radiation oncology, biology, physics. 110(2) |
| ISSN: | 1879-355X |
| Popis: | Purpose To construct and validate a predicting genotype signature for pathologic complete response (pCR) in locally advanced rectal cancer (PGS-LARC) after neoadjuvant chemoradiation. Methods and Materials 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. Results 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%. Conclusions 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. |
| Databáze: | OpenAIRE |
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