Development of a new clinical nomogram including velocity rate of disease progression to predict outcome in metastatic colorectal cancer patients treated with bevacizumab beyond progression: A subanalysis from tribe trial
Authors: E. Dell'Aquila, F. Pantano, D. Rossini, M. Stellato, S. Lonardi, G. Masi, M. Schirripa, F. Marmorino, C. Antoniotti, S. Murgioni, G. Tomasello, M. Ronzoni, P. Racca, B. Vincenzi, G. Allegrini, F. Urbano, A. Buonadonna, M. Banzi, G. Tonini, C. Cremolini, A. Falcone, D. Santini
Journal: Annals of Oncology (2018)

Introduction: In metastatic colorectal cancer (mCRC) there is the unmeet clinical need to predict outcome to second line therapy. Our aim was to test if a new nomogram including the velocity rate of disease progression (VRPD), a new dynamic marker, can predict outcome in terms of second line progression free survival (PFS) in mCRC patients (pts) enrolled in TRIBE trial. Methods: Continuous variables are presented as mean and median and 95% CI of the mean and median respectively and dichotomous parameters as frequencies and percentages. The normal distribution of continuous variables was explored with the Shapiro-Wilk normality test. The prognostic value in term of second line Progression Free Survival was explored using Cox Regression in continuous variables and Log-Rank Test in dichotomous variables. Variables demonstrated to be statistically significant at univariate analysis were evaluated in multivariate fashion using Cox proportional hazard regression model. Variables demonstrated to be statistically significant at univariate analysis were also selected to be included in nomogram construction. To avoid loss of statistical power, missing data in baseline characteristics were handled using a multiple imputation technique based on Predictive Mean Matching Algorithm. We applied a Penalized Cox Regression Model based on least absolute shrinkage and selection operator (lasso) penalized estimation method with the penalty parameter selected by 10-fold cross-validation with λ selection rule set at minimal. The model with minimum cross-validated error included 8 variables: primary cancer located in Right Colon; PS at baseline; presence of metastasesother than liver, lung, peritoneum, bone or CNS at baseline; LDH levels at the time of first disease progression; VRPD; BRAF Mutation; Alkaline Phosphatase at baseline; WBC count at baseline. The nomogram was evaluated in terms of discrimination and calibration. Discrimination was assessed with the area under the curve (AUC) at receiver operating characteristics analysis (with values ranging from 0.5 for no discrimination to 1.0 for perfect discrimination). To assess potential changes with time of the model discrimination performance, time-dependent AUC values were analyzed every 3 months from the month 3 to month 12 of follow-up using inverse-probability- of-censoring weights method. Calibration was investigated by plotting the predicted and observed probabilities of events at 9 months. All discrimination and calibration measures were internally validated using Repeated Cross Validation (Repeated cross-validation fold number:10; Repeated Times: 20). All analyses were conducted with R statistical software (version 3.3.2) equipped with the "glmnet," "hdnom," and "mice" packages. Alltests were 2-tailed and a p value <0.05 was considered statistically significant. Results: The nomogram included 8 variables: right colon; baseline Performance Status (PS); presence of metastases other than liver, lung, peritoneum, bone or CNS at baseline; LDH levels at first disease progression; VRPD; BRAF status; baseline Alkaline Phosphatase; baseline WBC count. The nomogram was internally validated using Repeated Cross Validation showing a good performance in terms of discrimination and calibration. Conclusion: This nomogram, including VRPD and other consolidated parameters, can be used to predict second line PFS in mCRC pts. Further studies are needed to test VRPD in a larger validation set.