Prostate cancer is the most frequently diagnosed male cancer. Currently, treatment of patients with advanced-stage disease relies on hormone-like drugs such as Abiraterone, a recently approved new generation of hormonal therapy that targets an enzyme involved in making androgen in the body. Even with the significant therapeutic advancement that has been brought into the clinic, advanced prostate cancer is still incurable and fatal. Approximately 40,000 men die from prostate cancer every year; most of them from advanced and metastatic disease. Therefore, it is critical to find new solutions via selective biomarkers that can enhance the effectiveness of the current treatments such as abiraterone.
Approach: Our proposal will take advantage of a prospective clinical study, PROMOTE, which is designed to specifically address the above-mentioned challenges. We investigators from UIUC and Mayo, who include computer scientists, a cancer biologist, and a pharmacologist, will make use of the data sets generated from PROMOTE to develop novel computational methods to identify resistance mechanisms and potential therapies to overcome resistance. The PROMOTE study enrolled 91 patients with castration-resistant prostate cancer (CRPC), a stage at which patients have failed therapy with conventional hormonal treatment. Biopsy samples were collected before and after 12 weeks of abiraterone treatment. Genetic material (DNA and RNA) extracted from biopsy tissues were used for next-generation sequencing studies. Animal models were also generated using these biopsy samples to represent an individual patient's tumor. We have successfully obtained a dozen models that uniquely represent diversity of tumor biology. Specifically, we plan to develop, refine, and apply two novel computational methods to help understand how genes are regulated differently between responders and non-responders and to use that information to help prioritize and select appropriate drugs to overcome abiraterone resistance. Our strength is also significantly enhanced by having animal models generated from PROMOTE patients' tumors to experimentally test the hypothesis generated from the computational analyses.
Applicability: The outcome of this research has the potential to help patients who suffer from prostate cancer and especially those who likely may not respond to standard hormonal therapy. Our proposed studies will result in clinically meaningful biomarkers to predict abiraterone response, as well as drugs that might help overcome abiraterone resistance. Furthermore, these results would help to build future clinical trials based on an individual patient's tumor biology so that we can select the most appropriate drugs for these patients.
Advancing the Field of Prostate Cancer: Our work will provide additional novel insights into our understanding of why and how patients become resistant to abiraterone. This knowledge is expected to identify many novel druggable targets and drugs that can be further tested in the clinic to help patients who do not respond to abiraterone, all of which could lead additional drug development and novel discovery science.
|Effective start/end date||1/1/19 → …|
- Congressionally Directed Medical Research Programs: $1,042,692.00