Prostate cancer is a leading cause of cancer related morbidity and mortality in US males. Despite initial treatments with curative intent for localized stage (surgery or radiation) approximately one third patients will progress to advanced stages. This has translated into a significant and growing public health burden. Current clinical practice for advanced stage disease is chronic (lifelong) androgen deprivation therapy (ADT). The efficacy of response to ADT is variable and may last from a few months to several years at which time chemotherapy is introduced. A lack of consensus with resultant ambiguity exists in medical practice as to what clinical features or tests predict response to ADT (""""""""Predictive factor""""""""- A factor determining which patients will do well with some types of treatment and not others). The most well known biomarker in prostate cancer, serum prostatic specific antigen (PSA) is useful in detecting early progressive disease after initial treatments but lacks evidence as a predictive factor for ADT. The inability to predict ADT treatment outcomes is an unmet critical gap in our fund of knowledge as its application in the clinic has a direct impact on patient management. For example, in hormonally unresponsive tumor type patients destined to fail ADT quickly, an earlier initiation of aggressive chemo-hormonal combination treatments could provide longer durations of meaningful clinical benefit. Conversely patients harboring a profile responsive to ADT may avoid long-term side effects of chronic ADT including osteoporosis and loss of sexual libido, the two most common and distressing side effects of ADT, by undergoing an intermittent schedule rather than continuous. This exploratory application will focus on an identification strategy for ADT predictive factors using a novel proteomics-based approach. The traditional approach in identifying biomarkers and developing predictive factors has relied on evaluation of a single peptide/protein in tissue/circulation in a cancer-specific stage. At best, this strategy has had limited success since multiple pathological tumor pathways are involved in ADT response which diminish the significance of any one candidate protein/peptide. We propose using two-dimensional electrophoresis coupled with mass spectrometry analysis as a platform for evaluating multiple serum-based biological variables representative of tumor-host-treatment interactions. For conducting this exploratory proteomic research we will collect a unique set of well annotated clinical research specimens obtained from prostate cancer patients. The PI (M Kohli) has previous experience in conducting clinical proteomic research studies, and is specifically attuned to collecting high quality clinical specimens for cancer proteomics for developing proteomic based predictive classifiers of ADT. The application aims include performing comparative analyses of the proteome in two main cohorts including;cancer patients before and three to four month post initiation of ADT (cohort-1);and a separate cohort of cancer patients consisting of a short duration response to ADT and a sustained and prolonged response duration to ADT (cohort -2). Consistently identified and characterized biomarker(s) associated with three to four month ADT response and short or sustained duration of ADT response will be then be evaluated in prospectively designe predictive factor modeling clinical trials in future.
Advanced prostate cancer is a significant and increasing public health burden. Current treatment practice for this stage of the disease is with hormonal therapy. This project attempts to focus on devising tools to predict the efficacy of hormonal treatments in prostate cancer patients, as this knowledge has potential to elevate cancer and treatment related morbidity in patient populations undergoing hormonal treatments.
| Oberg, Ann L; Mahoney, Douglas W (2012) Statistical methods for quantitative mass spectrometry proteomic experiments with labeling. BMC Bioinformatics 13 Suppl 16:S7 |
