Genomic and clinical predictors for improving estimator precision in randomized trials of breast cancer treatments

The hope that genomic biomarkers would dramatically and immediately improve care for common, complex diseases has been tempered by slow progress in their translation beyond bioinformatics. We propose a novel use of genomic information where the goal is to improve estimator precision in a randomized trial. We analyze the potential precision gains from the popular MammaPrint genomic signature and clinical variables in simulations of randomized trials analyzed using covariate adjustment.

We apply an estimator for the average treatment effect in the trial that adjusts for prognostic baseline variables to improve precision [1]. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct simulation studies based on resampling genomic and clinical data of breast cancer patients from four publicly available observational studies.

Separate simulation studies were conducted based on each of the four data sets, with sample sizes ranging from 115 to 307. Adjusting only for clinical variables provided gains of -1%, 5%, 6%, 17%, compared to the unadjusted estimator. Adjusting only for the MammaPrint genomic signature provided gains of 2%, 4%, 4%, 5%. Simultaneously adjusting for clinical variables and the genomic signature provided gains of 2%, 6%, 7%, 16%. The differences between precision gains from adjusting for genomic plus clinical variables, versus only clinical variables, were -1%, 0%, 1%, 3%.

Adjusting only for clinical variables led to substantial precision gains (at least 5%) in three of the four data sets, with a 1% precision loss in the remaining data set. These gains were unchanged or increased when sample sizes were doubled in our simulations. The precision gains due to incorporating genomic information, beyond the gains from adjusting for clinical variables, were not substantial.