Prostate cancer exosomes as molecular predictors of response to therapy - Fonseca, Pedro Miguel Borges (2016)
Prostate cancer is the second most common cancer in men and acording to the Globocan report from 2012, there will be over 1.2 million new cases and over 300 000 deaths, worldwide. Organ-defined prostate cancer is a curable disease, but then it develops into metastatic castration resistant prostate cancer, and it is usually a matter of time until the patient develops resistance to chemotherapy and becomes an incurable disease. This evokes the need for biomarkers that can predict disease progression and response to therapy. This would allow for a better stratification of the patients to receive the most efficacious, therapeutic treatment. Cancer cell derived exosomes, with their molecular cargo that represents the tumor cells, have been demonstrated to be a good source of such prognostic and predictive biomarkers. In this study we isolated and characterized exosomes derived from prostate cancer cells that are resistant to docetaxel and abiraterone acetate. One interesting finding is that the cells that are resistant to these two drugs secrete more exosomes than their sensitive counterparts. We compared the proteomic profile of these exosomes with exosomes from the parental, drug-sensitive cell lines. We have identified a number of putative markers that may constitute a predictive signature and are currently under validation. From our pilot studies we have identified that p-gp positive exosomes may function as predictive biomarkers for response to docetaxel, and that exosomes containing the splice variant 7 of the androgen receptor may have the potential to predict response to abiraterone acetate and enzalutamide. However, both of these biomarkers have to be validated in larger patient cohorts along with the newly identified proteins. In addition to proteomics, the exosomes are currently being analysed by transcriptomics, mRNA and miRNA arrays. These combined molecular data will provide valuable information in finding predictive signatures for response to therapy, in order to provide the patients with a more personalized medical care and stratified course of treatment, leading to improved overall survival.