Another look at predicting molecular breast cancer subtypes from the METABRIC data

Abstract

Classifying patients into different clusters based on genomic data can offer valuable insights into their projected disease-specific survival trajectories over time. Here we apply two supervised learning methods---Nearest Shrunken Centroids and LASSO--- to the METABRIC Breast Cancer data {metabric} of 1980 patients and 754 genes to perform this task. The {pamr} R package implements the Nearest Shrunken Centroids classifier and the { glmnet} R package is used to fit an ungrouped multinomial model, a grouped multinomial model, and a One-Versus-Rest model. Splitting our data into discovery and validation sets, we evaluate all four models' classification performance and the survival implications of their class predictions using cross validation and Kaplan-Meier curves. We find that the One-Versus-Rest model produces the lowest misclassification error of 0.0572 and the lowest median log-rank test of 0.380 statistic measuring the similarity between its Kaplan-Meier curves and the discovery set's true Kaplan-Meier curves. We show that a multinomial classification task split into several LASSO binomial classifiers offers promising results for patient clustering.