Published online Jul 24, 2016. doi: 10.5410/wjcu.v5.i2.80
Peer-review started: April 27, 2016
First decision: June 16, 2016
Revised: June 27, 2016
Accepted: July 11, 2016
Article in press: July 13, 2016
Published online: July 24, 2016
Processing time: 89 Days and 18 Hours
There is significant variation in clinical outcome between patients diagnosed with prostate cancer (CaP). Although useful, statistical nomograms and risk stratification tools alone do not always accurately predict an individual’s need for and response to treatment. The factors that determine this variation are not fully elucidated. In particular, cellular response to androgen ablation and subsequent paracrine/autocrine adaptation is poorly understood and despite best therapies, median survival in castrate resistant patients is only approximately 35 mo. We propose that one way of understanding this is to look for correlates in other comparable malignancies, such as breast cancer, where markers of at least 4 distinct gene clusters coding for 4 different phenotypic subtypes have been identified. These subtypes have been shown to demonstrate prognostic significance and successfully guide appropriate treatment regimens. In this paper we assess and review the evidence demonstrating parallels in the biology and treatment approach between breast and CaP, and consider the feasibility of patients with CaP being stratified into different molecular classes that could be used to complement prostate specific antigen and histological grading for clinical decision making. We show that there are significant correlations between the molecular classification of breast and CaP and explain how techniques used successfully to predict response to treatment in breast cancer can be applied to the prostate. Molecular phenotyping is possible in CaP and identification of distinct subtypes may allow personalised risk stratification way beyond that currently available.
Core tip: This paper demonstrates that prostate cancer (CaP) has defined molecular subtypes in a similar manner to breast cancer. The molecular classification and subsequent personalised treatment of breast cancer has revolutionised its management. It is becoming increasingly apparent that the same principles may be applied to CaP, allowing more individualised treatment and informing clinical decision making.