Innovative estimation of survival using log-normal survival modelling on ACCENT database

Background:

The ACCENT database, with individual patient data for 20 898 patients from 18 colon cancer clinical trials, was used to support Food and Drug Administration (FDA) approval of 3-year disease-free survival as a surrogate for 5-year overall survival. We hypothesised substantive differences in survival estimation with log-normal modelling rather than standard Kaplan–Meier or Cox approaches.

Methods:

Time to relapse, disease-free survival, and overall survival were estimated using Kaplan–Meier, Cox, and log-normal approaches for male subjects aged 60–65 years, with stage III colon cancer, treated with 5-fluorouracil-based chemotherapy regimens (with 5FU), or with surgery alone (without 5FU).

Results:

Absolute differences between Cox and log-normal estimates with (without) 5FU varied by end point. The log-normal model had 5.8 (6.3)% higher estimated 3-year time to relapse than the Cox model; 4.8 (5.1)% higher 3-year disease-free survival; and 3.2 (2.2)% higher 5-year overall survival. Model checking indicated greater data support for the log-normal than the Cox model, with Cox and Kaplan–Meier estimates being more similar. All three model types indicate consistent evidence of treatment benefit on both 3-year disease-free survival and 5-year overall survival; patients allocated to 5FU had 5.0–6.7% higher 3-year disease-free survival and 5.3–6.8% higher 5-year overall survival.

Conclusion:

Substantive absolute differences between estimates of 3-year disease-free survival and 5-year overall survival with log-normal and Cox models were large enough to be clinically relevant, and warrant further consideration.

Detecting deficient DNA mismatch repair in stage II and III colon cancers

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Background: Deficient DNA mismatch repair (MMR) results in microsatellite instability (MSI) that is detected in ∼15% of sporadic colon cancers. MMR status has been shown to provide prognostic and predictive information. We developed a model to predict MMR deficiency using clinically available data, and thereby facilitate the selection of patient tumors for MMR testing.

Methods: Data were utilized from stage II and III colon carcinoma patients (n = 2016) who participated in 5-fluorouracil-based adjuvant studies (NCCTG, FFCD, NCIC, GIVIO, NSABP) and an Italian cohort. MMR status in tumors had been determined by MSI testing or by immunohistochemistry for hMLH1 and hMSH2 proteins. Logistic regression and a recursive partitioning and amalgamation analysis was used to identify factors (histologic grade, gender, tumor site, stage, age, lymph node status, T-stage) predictive of MMR status.

Results: Of the cancers, 357 (17.7%) showed deficient MMR. Tumor site was the most important predictor of MMR status followed by histologic grade, then stage (II vs. III) and then gender. Distal tumors had a low likelihood of deficient MMR (5% rate overall), whereas proximal tumors had a greater likelihood of deficient MMR (30%). For patients with proximal tumors, the addition of histologic grade and stage increased the prediction of deficient MMR (Table). Using tumor site, histologic grade, and stage, the logistic regression model showed excellent discrimination (c-statistic = 0.80).

Conclusions: Routine clinicopathological data can facilitate the identification of MMR deficient cases. Tumor site and histologic grade were the strongest predictors of MMR deficiency. Within proximal, poorly differentiated tumors, stage was highly predictive. These findings suggest that our model can assist in selecting sporadic colon cancers for MMR testing for use in clinical decision-making, especially for stage II patients.

[Table: see text]

[Table: see text]