  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.
