Eunhae Cho1, In Ja Park2, Seung-Seop Yeom3, Seung Mo Hong4, Jung Bok Lee5, Yeon Wook Kim6, Mi-Ju Kim6, Hye Min Lim6, Seok-Byung Lim3, Chang Sik Yu3, Jin Cheon Kim3. 1. Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 2. Department of Colon and Rectal Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: ipark@amc.seoul.kr. 3. Department of Colon and Rectal Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 4. Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 5. Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. 6. Asan Institute for Life Science, Asan Medical Center, Seoul, Korea.
Abstract
PURPOSE: Although preoperative chemoradiotherapy (PCRT) is regarded as a standard treatment for locally advanced rectal cancer, there is no reliable biomarker for predicting responsiveness to PCRT. We aimed to develop a biomarker model for predicting response to PCRT. METHODS AND MATERIALS: We included 184 patients who received PCRT followed by surgical resection and categorized them as good responders (complete or near-complete regression) or poor responders (all other patients). Candidate gene mRNAs were isolated from formalin-fixed paraffin-embedded tumor specimens and analyzed using the NanoString nCounter gene expression assay. Stepwise logistic regression analysis was used to select genes in discovery and training phases. A quantitative radio-responsiveness prediction model was developed and validated using internal cross-validation groups, and the model's predictive value was assessed based on the area under the receiver operating characteristic curve (AUC). RESULTS: By comparing the gene expressions between good and poor responders, we created a multigene mRNA model using FZD9, HRAS, ITGA7, MECOM, MMP3, NKD1, PIK3CD, and PRKCB. This panel showed good ability to predict treatment response (AUC: 0.846 for the whole data set). Internal cross-validation was performed to evaluate the model's predictive stability among 3 cohorts, which provided AUC values of 0.808-0.909. The satisfactory diagnostic performance of the radio-response prediction index persisted regardless of other clinicopathologic features such as clinical T or N stage, interval between radiation and surgery, and pretreatment carcinoembryonic antigen levels (P = .001, 95% CI, 0.686-0.905). CONCLUSIONS: We developed a multigene mRNA-based biomarker model that allows prediction of rectal cancer response to PCRT, which may help identify patients who will benefit most from PCRT.
PURPOSE: Although preoperative chemoradiotherapy (PCRT) is regarded as a standard treatment for locally advanced rectal cancer, there is no reliable biomarker for predicting responsiveness to PCRT. We aimed to develop a biomarker model for predicting response to PCRT. METHODS AND MATERIALS: We included 184 patients who received PCRT followed by surgical resection and categorized them as good responders (complete or near-complete regression) or poor responders (all other patients). Candidate gene mRNAs were isolated from formalin-fixed paraffin-embedded tumor specimens and analyzed using the NanoString nCounter gene expression assay. Stepwise logistic regression analysis was used to select genes in discovery and training phases. A quantitative radio-responsiveness prediction model was developed and validated using internal cross-validation groups, and the model's predictive value was assessed based on the area under the receiver operating characteristic curve (AUC). RESULTS: By comparing the gene expressions between good and poor responders, we created a multigene mRNA model using FZD9, HRAS, ITGA7, MECOM, MMP3, NKD1, PIK3CD, and PRKCB. This panel showed good ability to predict treatment response (AUC: 0.846 for the whole data set). Internal cross-validation was performed to evaluate the model's predictive stability among 3 cohorts, which provided AUC values of 0.808-0.909. The satisfactory diagnostic performance of the radio-response prediction index persisted regardless of other clinicopathologic features such as clinical T or N stage, interval between radiation and surgery, and pretreatment carcinoembryonic antigen levels (P = .001, 95% CI, 0.686-0.905). CONCLUSIONS: We developed a multigene mRNA-based biomarker model that allows prediction of rectal cancer response to PCRT, which may help identify patients who will benefit most from PCRT.
Authors: Daniel Rodrigues de Bastos; Mércia Patrícia Ferreira Conceição; Ana Paula Picaro Michelli; Jean Michel Rocha Sampaio Leite; Rafael André da Silva; Ricardo Cesar Cintra; Jeniffer Johana Duarte Sanchez; Cesar Augusto Sam Tiago Vilanova-Costa; Antonio Márcio Teodoro Cordeiro Silva Journal: Eur J Breast Health Date: 2020-12-24
Authors: Waleed S Al Amri; Diana E Baxter; Andrew M Hanby; Lucy F Stead; Eldo T Verghese; James L Thorne; Thomas A Hughes Journal: Breast Cancer Res Treat Date: 2020-07-30 Impact factor: 4.872