Wei-Wei Xiao1, Min Li2, Zhi-Wei Guo3, Rong Zhang4, Shao-Yan Xi5, Xiang-Guo Zhang6, Yong Li7, De-Qing Wu7, Yu-Feng Ren8, Xiao-Lin Pang9, Xiang-Bo Wan9, Kun Li3, Chun-Lian Zhou3, Xiang-Ming Zhai3, Zhi-Kun Liang10, Qiao-Xuan Wang1, Zhi-Fan Zeng1, Hui-Zhong Zhang5, Xue-Xi Yang11, Ying-Song Wu11, Ming Li10, Yuan-Hong Gao12. 1. 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. 2. 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. 3. Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, Guangdong, China. 4. 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 Endoscopy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China. 5. 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 Pathology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China. 6. Department of Radiation Oncology, Affiliated Yuebei People Hospital of Shantou University Medical College, ShaoGuan, Guangdong, China. 7. Department of General Surgery, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China. 8. Department of Radiation Oncology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China. 9. Department of Radiation Oncology, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China. 10. Guangzhou Darui Biotechnology Co, Ltd High-Tech Development Zone, Guangzhou, Guangdong, China. 11. 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. 12. 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. Electronic address: gaoyh@sysucc.org.cn.
Abstract
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.
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.
Authors: Isabella Kuniko T M Takenaka; Thais F Bartelli; Alexandre Defelicibus; Juan M Sendoya; Mariano Golubicki; Juan Robbio; Marianna S Serpa; Gabriela P Branco; Luana B C Santos; Laura C L Claro; Gabriel Oliveira Dos Santos; Bruna E C Kupper; Israel T da Silva; Andrea S Llera; Celso A L de Mello; Rachel P Riechelmann; Emmanuel Dias-Neto; Soledad Iseas; Samuel Aguiar; Diana Noronha Nunes Journal: Front Oncol Date: 2022-03-22 Impact factor: 6.244