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Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer

BACKGROUND: Colorectal cancer (CRC) patients with different molecular phenotypes, including microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and somatic mutations in BRAF and KRAS gene, vary in treatment response and prognosis. However, molecular phenotyping under adequate q...

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Autores principales: Yu, Zhuojun, Yu, Huichuan, Zou, Qi, Huang, Zenghong, Wang, Xiaolin, Tang, Guannan, Bai, Liangliang, Zhou, Chuanhai, Zhuang, Zhuokai, Xie, Yumo, Wang, Heng, Xu, Gaopo, Chen, Zijian, Fu, Xinhui, Huang, Meijin, Luo, Yanxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6968822/
https://www.ncbi.nlm.nih.gov/pubmed/32021277
http://dx.doi.org/10.2147/OTT.S234495
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author Yu, Zhuojun
Yu, Huichuan
Zou, Qi
Huang, Zenghong
Wang, Xiaolin
Tang, Guannan
Bai, Liangliang
Zhou, Chuanhai
Zhuang, Zhuokai
Xie, Yumo
Wang, Heng
Xu, Gaopo
Chen, Zijian
Fu, Xinhui
Huang, Meijin
Luo, Yanxin
author_facet Yu, Zhuojun
Yu, Huichuan
Zou, Qi
Huang, Zenghong
Wang, Xiaolin
Tang, Guannan
Bai, Liangliang
Zhou, Chuanhai
Zhuang, Zhuokai
Xie, Yumo
Wang, Heng
Xu, Gaopo
Chen, Zijian
Fu, Xinhui
Huang, Meijin
Luo, Yanxin
author_sort Yu, Zhuojun
collection PubMed
description BACKGROUND: Colorectal cancer (CRC) patients with different molecular phenotypes, including microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and somatic mutations in BRAF and KRAS gene, vary in treatment response and prognosis. However, molecular phenotyping under adequate quality control in a community-based setting may be difficult. We aimed to build the nomograms based on easily accessible clinicopathological characteristics to predict molecular phenotypes. METHODS: Three hundred and six patients with pathologically confirmed stage I-IV CRC were included in the cohort. The assays for MSI, CIMP, and mutations in BRAF and KRAS gene were performed using resected tumor samples. The candidate predictors were identified from clinicopathological variables using multivariate Logistic regression analyses to construct the nomograms that could predict each molecular phenotype. RESULTS: The incidences of MSI, CIMP, BRAF mutation and KRAS mutation were 25.3% (72/285), 2.5% (7/270), 3.4% (10/293), and 34.8% (96/276) respectively. In the multivariate Logistic analysis, poor differentiation and high neutrophil/lymphocyte ratio (NLR) were independently associated with MSI; poor differentiation, high NLR and high carcinoembryonic antigen/tumor size ratio (CSR) were independently associated with CIMP; poor differentiation, lymphovascular invasion and high CSR were independently associated with BRAF mutation; poor differentiation, proximal tumor, mucinous tumor and high NLR were independently associated with KRAS mutation. Four nomograms for MSI, CIMP, BRAF mutation and KRAS mutation were developed based on these independent predictors, the C-indexes of which were 61.22% (95% CI: 60.28–62.16%), 95.57% (95% CI: 95.20–95.94%), 83.56% (95% CI: 81.54–85.58%), and 69.12% (95% CI: 68.30–69.94%) respectively. CONCLUSION: We established four nomograms using easily accessible variables that could well predict the presence of MSI, CIMP, BRAF mutation and KRAS mutation in CRC patients.
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spelling pubmed-69688222020-02-04 Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer Yu, Zhuojun Yu, Huichuan Zou, Qi Huang, Zenghong Wang, Xiaolin Tang, Guannan Bai, Liangliang Zhou, Chuanhai Zhuang, Zhuokai Xie, Yumo Wang, Heng Xu, Gaopo Chen, Zijian Fu, Xinhui Huang, Meijin Luo, Yanxin Onco Targets Ther Original Research BACKGROUND: Colorectal cancer (CRC) patients with different molecular phenotypes, including microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and somatic mutations in BRAF and KRAS gene, vary in treatment response and prognosis. However, molecular phenotyping under adequate quality control in a community-based setting may be difficult. We aimed to build the nomograms based on easily accessible clinicopathological characteristics to predict molecular phenotypes. METHODS: Three hundred and six patients with pathologically confirmed stage I-IV CRC were included in the cohort. The assays for MSI, CIMP, and mutations in BRAF and KRAS gene were performed using resected tumor samples. The candidate predictors were identified from clinicopathological variables using multivariate Logistic regression analyses to construct the nomograms that could predict each molecular phenotype. RESULTS: The incidences of MSI, CIMP, BRAF mutation and KRAS mutation were 25.3% (72/285), 2.5% (7/270), 3.4% (10/293), and 34.8% (96/276) respectively. In the multivariate Logistic analysis, poor differentiation and high neutrophil/lymphocyte ratio (NLR) were independently associated with MSI; poor differentiation, high NLR and high carcinoembryonic antigen/tumor size ratio (CSR) were independently associated with CIMP; poor differentiation, lymphovascular invasion and high CSR were independently associated with BRAF mutation; poor differentiation, proximal tumor, mucinous tumor and high NLR were independently associated with KRAS mutation. Four nomograms for MSI, CIMP, BRAF mutation and KRAS mutation were developed based on these independent predictors, the C-indexes of which were 61.22% (95% CI: 60.28–62.16%), 95.57% (95% CI: 95.20–95.94%), 83.56% (95% CI: 81.54–85.58%), and 69.12% (95% CI: 68.30–69.94%) respectively. CONCLUSION: We established four nomograms using easily accessible variables that could well predict the presence of MSI, CIMP, BRAF mutation and KRAS mutation in CRC patients. Dove 2020-01-13 /pmc/articles/PMC6968822/ /pubmed/32021277 http://dx.doi.org/10.2147/OTT.S234495 Text en © 2020 Yu et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Yu, Zhuojun
Yu, Huichuan
Zou, Qi
Huang, Zenghong
Wang, Xiaolin
Tang, Guannan
Bai, Liangliang
Zhou, Chuanhai
Zhuang, Zhuokai
Xie, Yumo
Wang, Heng
Xu, Gaopo
Chen, Zijian
Fu, Xinhui
Huang, Meijin
Luo, Yanxin
Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer
title Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer
title_full Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer
title_fullStr Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer
title_full_unstemmed Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer
title_short Nomograms for Prediction of Molecular Phenotypes in Colorectal Cancer
title_sort nomograms for prediction of molecular phenotypes in colorectal cancer
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6968822/
https://www.ncbi.nlm.nih.gov/pubmed/32021277
http://dx.doi.org/10.2147/OTT.S234495
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