Bogdan Luca, Postdoctoral Scholar,
Gentles Lab, Stanford

Abstract:
A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis leading to substantial overtreatment. It remains an enigma that, in contrast to breast cancer, unsupervised analyses of global expression profiles have not currently defined robust categories of prostate cancer with distinct clinical outcomes.

Here we present the derivation of a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. Our analyses were based on the hypothesis that previous attempts to classify prostate cancer have been unsuccessful because individual samples of prostate cancer frequently have heterogeneous compositions. We applied an unsupervised Bayesian procedure called Latent Process Decomposition to deconvolve the intra-tumoural heterogeneity of prostate cancer. This approach led to the identification of 8 cancer subtypes with distinct clinical outcomes and biological properties.