Journal article
Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study.
- Dienstmann R Computational Oncology, Sage Bionetworks, Seattle, USA.
- Mason MJ Computational Oncology, Sage Bionetworks, Seattle, USA.
- Sinicrope FA Division of Medical Oncology, Mayo Clinic and Mayo Comprehensive Cancer Center, Rochester.
- Phipps AI Epidemiology Department, University of Washington and Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
- Tejpar S Molecular Digestive Oncology Unit, University Hospital Gasthuisberg, Leuven, Belgium.
- Nesbakken A Department of Gastrointestinal Surgery, Institute of Clinical Medicine, and K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, Oslo, Norway.
- Danielsen SA Department of Molecular Oncology, Institute for Cancer Research, and K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, Oslo, Norway.
- Sveen A Department of Molecular Oncology, Institute for Cancer Research, and K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, Oslo, Norway.
- Buchanan DD Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Clendenning M Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Rosty C Colorectal Oncogenomics Group, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Bot B Computational Oncology, Sage Bionetworks, Seattle, USA.
- Alberts SR Division of Medical Oncology, Mayo Clinic and Mayo Comprehensive Cancer Center, Rochester.
- Milburn Jessup J Diagnostics Evaluation Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institute of Health, Rockville, USA.
- Lothe RA Department of Molecular Oncology, Institute for Cancer Research, and K.G. Jebsen Colorectal Cancer Research Centre, Oslo University Hospital, Oslo, Norway.
- Delorenzi M SIB Swiss Institute Bioinformatics, Lausanne, Switzerland.
- Newcomb PA Epidemiology Department, University of Washington and Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
- Sargent D Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, USA.
- Guinney J Computational Oncology, Sage Bionetworks, Seattle, USA.
- 2017-04-29
Published in:
- Annals of oncology : official journal of the European Society for Medical Oncology. - 2017
BRAF mutation
KRAS mutation
colon cancer
microsatellite instability
prognosis
Adult
Aged
Aged, 80 and over
Biomarkers, Tumor
Colonic Neoplasms
Female
Humans
Kaplan-Meier Estimate
Male
Middle Aged
Neoplasm Staging
Proportional Hazards Models
Retrospective Studies
Treatment Outcome
Young Adult
English
Background
TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation.
Patients and methods
After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n = 3016)-N0147 (NCT00079274) and PETACC3 (NCT00026273)-was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n = 1499), and also externally in different population cohorts of chemotherapy-treated (n = 949) or -untreated (n = 1080) CC patients, and an additional series without treatment annotation (n = 782).
Results
TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61-0.68 in the TNM alone model to 0.63-0.71 in models with added molecular markers, 0.65-0.73 with clinicopathological features and 0.66-0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively.
Conclusions
Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients.
TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation.
Patients and methods
After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n = 3016)-N0147 (NCT00079274) and PETACC3 (NCT00026273)-was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n = 1499), and also externally in different population cohorts of chemotherapy-treated (n = 949) or -untreated (n = 1080) CC patients, and an additional series without treatment annotation (n = 782).
Results
TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61-0.68 in the TNM alone model to 0.63-0.71 in models with added molecular markers, 0.65-0.73 with clinicopathological features and 0.66-0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively.
Conclusions
Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients.
- Language
-
- English
- Open access status
- hybrid
- Identifiers
-
- DOI 10.1093/annonc/mdx052
- PMID 28453697
- Persistent URL
- https://sonar.ch/global/documents/292124
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