Unraveling the dynamic protein expression levels of Androgen Receptor Variants in Prostate Cancer
Abstract
Scientific objective: Prostate cancer (PCa) is the most common non-skin malignancy in men worldwide. Approximately 75 percent of PCa patients receive either surgery or radiotherapy. While potentially curative, these treatment modalities are associated with high morbidity and can have a profound negative impact on quality of life. Patients whose PCa does progress are often given androgen deprivation therapies (ADT) that block the function of the signaling protein, androgen receptor (AR). While effective initially, tumors typically transform into a more aggressive disease, termed castration resistant-prostate cancer (CRPC), while on ADT. There are newer therapies that target AR or androgen synthesis that can prolong the life of men with CRPC, but they are not curative. One major cause of resistance in CRPC is thought to be the emergence of androgen receptor variants (AR-Vs). AR-Vs are altered forms of AR that are not sensitive to these drugs. One such AR-V, ARv7, predicts resistance to therapy in certain CRPC patients, but there is a large subset of men lacking ARv7 that is also resistant to these therapies. Importantly, these considerations underscore a dire need for better predictive tests that will help identify which patients would benefit most from any given treatment choice. This proposal aims to develop a high-throughput platform that can identify which AR-Vs are present in clinical samples, including tumor tissues and circulating tumor cells (CTCs). Understanding which AR-Vs are present in tumors will help predict which treatments the tumors are more likely to respond to. The ultimate goal is to adapt this platform for use with CTCs, which can be collected with a simple blood draw as opposed to relying on biopsy tissue, thereby avoiding invasive and risky surgery to obtain critical diagnostic information. Moreover, this method would allow easier surveillance of the disease over time by detecting resistance earlier in the course to timely change the treatment. Research Applicability: This research will help men who are likely to develop resistance to anti-androgen therapy. Currently, a portion of CRPC patients might benefit from taxane therapy over the anti-androgen inhibitors that are commonly used; however, at present there is a lack of tests that can identify the patients who should make the switch. The current state-of-the-field is that the AR-V7 molecule is the only variant for which clinical assays have been developed, such that only patients who are positive for this variant will receive appropriate treatment. However, this leaves 25 percent of patients for whom it is currently impossible to predict optimal therapy. Therefore, it is of great urgency to develop novel predictive assays to better stratify patients to inform clinicians of the appropriate course of treatment. The development of the Androgen Receptor variants Targeted Mass Spectrometry (ARvT-MS) platform will allow us to identify all of the AR-Vs simultaneously, telling us whether certain variants are expressed in PCa at the protein level early on before treatment, which may help predict which treatment is likely to be more effective. The assay being developed in this work can be readily applied to patient samples, including blood and tumor tissues. The long-term potential of our work is that treatment optimization through the use of this assay will help improve quality of life for the survivors by minimizing unnecessary or ineffective therapies. Career Goals: My mission is to solve biological problems in order to improve the life of people with devastating diseases such as PCa. I strongly believe that the most important aspect of science is its ability to serve humanity. During my graduate training, I studied specific cellular changes during oncogenic viral infection that define the basic biology of this pathogenesis. While this training provided me with a molecular biology skill set that allowed me to become an independe
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010070
Entities
People
- Zoi Sychev
Organizations
- United States Army
- University of Minnesota