Personalized Treatment Selection via Product Partition Models with Covariates

TL;DR

This paper introduces a novel, fully Bayesian clustering model for personalized treatment selection that accounts for patient heterogeneity, improving treatment response predictions in cancer genomics.

Contribution

It extends product partition models with covariates using the Normalized Generalized Gamma process, providing a flexible, model-based approach for personalized treatment assignment.

Findings

Performs well with heterogeneous covariates in simulations

Identifies patient clusters with specific response probabilities

Enhances treatment response prediction in cancer genomics case study

Abstract

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model-based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the Normalized Generalized Gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model-based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.