Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study

Abstract

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.

Keywords: BRAF mutation; KRAS mutation; colon cancer; microsatellite instability; prognosis.

Figure 1.

Study workflow, with schematic representation of population used for initial correlative analysis, followed by data splits in training and validation cohorts, data imputation, and survival models.

Figure 2.

Overall survival Kaplan–Meier estimates across clinical trial cohorts of chemotherapy-treated patients (A and B), and multiple validation cohorts of chemotherapy-treated (C), -untreated (D) or unknown adjuvant therapy status (E). Univariate Cox models are detailed in supplementary Table S1, available at Annals of Oncology online.

Figure 3.

Risk discrimination and performance of overall survival models, with (A) C-index (error bars represent 95% confidence intervals) across training and validation cohorts, and (B) boxplot of distributions of bootstrapped iAUCs for Bayes factor estimation across all validation cohorts combined (except val4 because of missing clinicopathological annotation). (C) Relative proportion of explained variance in overall survival of the full model (in patient cohorts treated with adjuvant chemotherapy) that is accounted for by different categories of predictor covariates.