Antonio F Di Narzo1, Sabine Tejpar1, Simona Rossi1, Pu Yan1, Vlad Popovici1, Pratyaksha Wirapati1, Eva Budinska1, Tao Xie1, Heather Estrella1, Adam Pavlicek1, Mao Mao1, Eric Martin1, Weinrich Scott1, Fred T Bosman1, Arnaud Roth1, Mauro Delorenzi2. 1. SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland (AFDN, SR, VP, PW, EB, MD); Digestive Oncology Unit and Center for Human Genetics, University Hospital Gasthuisberg, Leuven, Belgium (ST); Department of Pathology, Lausanne University, Lausanne, Switzerland (PY, FB); Oncology Research Unit, Worldwide Research and Development, Pfizer Inc., La Jolla, CA (TX, HE, AP, MM, EM, WS); Oncosurgery, Geneva University Hospital Geneva, Switzerland (AR); SAKK Coordination Center, Bern, Switzerland (AR); Ludwig Center for Cancer Research (MD) and Oncology Department, University of Lausanne, Lausanne, Switzerland (MD). 2. SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland (AFDN, SR, VP, PW, EB, MD); Digestive Oncology Unit and Center for Human Genetics, University Hospital Gasthuisberg, Leuven, Belgium (ST); Department of Pathology, Lausanne University, Lausanne, Switzerland (PY, FB); Oncology Research Unit, Worldwide Research and Development, Pfizer Inc., La Jolla, CA (TX, HE, AP, MM, EM, WS); Oncosurgery, Geneva University Hospital Geneva, Switzerland (AR); SAKK Coordination Center, Bern, Switzerland (AR); Ludwig Center for Cancer Research (MD) and Oncology Department, University of Lausanne, Lausanne, Switzerland (MD). mauro.delorenzi@unil.ch.
Abstract
BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.
BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.
Authors: Luigi Laghi; Francesca Negri; Federica Gaiani; Tommaso Cavalleri; Fabio Grizzi; Gian Luigi De' Angelis; Alberto Malesci Journal: Int J Mol Sci Date: 2020-12-18 Impact factor: 5.923
Authors: Jing Zhu; Natasha G Deane; Keeli B Lewis; Chandrasekhar Padmanabhan; M Kay Washington; Kristen K Ciombor; Cynthia Timmers; Richard M Goldberg; R Daniel Beauchamp; Xi Chen Journal: Sci Rep Date: 2016-09-14 Impact factor: 4.379