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

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 BRAF^V600E 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, BRAF^V600E 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.