Literature DB >> 23650411

Evaluation of tumor-size response metrics to predict overall survival in Western and Chinese patients with first-line metastatic colorectal cancer.

Laurent Claret1, Manish Gupta, Kelong Han, Amita Joshi, Nenad Sarapa, Jing He, Bob Powell, René Bruno.   

Abstract

PURPOSE: To assess new metrics of tumor-size response to predict overall survival (OS) in colorectal cancer (CRC) in Western and Chinese patients. PATIENTS AND METHODS: Various metrics of tumor-size response were estimated using longitudinal tumor size models and data from two phase III studies that compared bevacizumab plus chemotherapy versus chemotherapy as first-line therapy in Western (n = 923) and Chinese (n = 203) patients with CRC. Baseline prognostic factors and tumor-size metrics estimates were assessed in multivariate models to predict OS. Predictive performances of the models were assessed by simulating multiple replicas of the phase III studies.
RESULTS: Time to tumor growth (TTG) was the best metric to predict OS. TTG fully captured bevacizumab effect. Chinese ethnicity had no impact on OS or on the TTG-OS relationships. The model correctly predicted OS distributions in each arm as well as bevacizumab hazard ratio (model prediction, 0.75 v 0.68 observed in Western patients; 95% prediction interval, 0.62 to 0.91).
CONCLUSION: TTG captured therapeutic benefit with bevacizumab in first-line CRC patients. Chinese ethnicity had no impact. Longitudinal tumor size data coupled with model-based approaches may offer a powerful alternative in the design and analysis of early clinical studies.

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Year:  2013        PMID: 23650411     DOI: 10.1200/JCO.2012.45.0973

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  54 in total

Review 1.  Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications.

Authors:  Núria Buil-Bruna; José-María López-Picazo; Salvador Martín-Algarra; Iñaki F Trocóniz
Journal:  Oncologist       Date:  2015-12-14

2.  Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.

Authors:  Lili Zhao; Susan Murray; Laura H Mariani; Wenjun Ju
Journal:  Stat Med       Date:  2020-07-27       Impact factor: 2.373

3.  Prognostic value of treatment-related factors in metastatic colorectal cancer using a stop-and-go strategy.

Authors:  C J S Kronborg; A R Jensen
Journal:  Int J Colorectal Dis       Date:  2014-07-27       Impact factor: 2.571

4.  Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers.

Authors:  Lili Zhao; Dai Feng; Brian Neelon; Marc Buyse
Journal:  Stat Med       Date:  2015-01-29       Impact factor: 2.373

5.  Evaluating Continuous Tumor Measurement-Based Metrics as Phase II Endpoints for Predicting Overall Survival.

Authors:  Ming-Wen An; Xinxin Dong; Jeffrey Meyers; Yu Han; Axel Grothey; Jan Bogaerts; Daniel J Sargent; Sumithra J Mandrekar
Journal:  J Natl Cancer Inst       Date:  2015-08-21       Impact factor: 13.506

6.  Modeling the Relationship Between Exposure to Abiraterone and Prostate-Specific Antigen Dynamics in Patients with Metastatic Castration-Resistant Prostate Cancer.

Authors:  Xu Steven Xu; Charles J Ryan; Kim Stuyckens; Matthew R Smith; Fred Saad; Thomas W Griffin; Youn C Park; Margaret K Yu; Peter De Porre; An Vermeulen; Italo Poggesi; Partha Nandy
Journal:  Clin Pharmacokinet       Date:  2017-01       Impact factor: 6.447

7.  Resampling the N9741 trial to compare tumor dynamic versus conventional end points in randomized phase II trials.

Authors:  Manish R Sharma; Elizabeth Gray; Richard M Goldberg; Daniel J Sargent; Theodore G Karrison
Journal:  J Clin Oncol       Date:  2014-10-27       Impact factor: 44.544

8.  Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer.

Authors:  Chiara Zecchin; Ivelina Gueorguieva; Nathan H Enas; Lena E Friberg
Journal:  Br J Clin Pharmacol       Date:  2016-06-08       Impact factor: 4.335

Review 9.  Computational oncology--mathematical modelling of drug regimens for precision medicine.

Authors:  Dominique Barbolosi; Joseph Ciccolini; Bruno Lacarelle; Fabrice Barlési; Nicolas André
Journal:  Nat Rev Clin Oncol       Date:  2015-11-24       Impact factor: 66.675

Review 10.  Optimal design of trials to demonstrate the utility of genomically-guided therapy: Putting Precision Cancer Medicine to the test.

Authors:  Rodrigo Dienstmann; Jordi Rodon; Josep Tabernero
Journal:  Mol Oncol       Date:  2014-07-15       Impact factor: 6.603

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