Over the last several decades, there has been a steady increase in the number of people who are diagnosed with kidney cancers each year in the United States. Perhaps more troubling, the number of people dying from kidney cancer in the U.S. each year is slowly rising as well. While removal of the kidney cancer by surgery can be an effective treatment, this depends primarily on how early the cancer is detected and whether or not the tumor is completely confined to the kidney. Unfortunately , even for kidney cancer patients who have tumors that appear to be treatable by surgery, there is roughly a 30% risk that their localized cancer will progress to metastatic disease after surgery, indicating that the disease was much more advanced than originally determined. As such, there is a clear need to better understand kidney cancer aggressiveness at the molecular level so that we can improve early detection, provide better prediction of who has aggressive forms of the disease, enhance treatment options for these patients, and, ultimately, stem the tide of deaths from this increasingly common cancer.
The goals of our proposed study are to improve the understanding of the underlying mechanisms of aggressiveness for the most common form of kidney cancer, clear cell renal cancer (ccRCC). In particular, the research knowledge gained will be used to develop a biomarker-based prediction model that will help clinicians more accurately predict which ccRCC patients are at greatest risk for return of the cancer after the initial surgery.
In addition, development of this biomarker panel will also provide new evidenced-based targets for the development of novel therapeutics to use in conjunction with surgery to slow ccRCC progression and improve patient survival. To do this, we propose to combine the unique biospecimen and patient data resources available at our institutions with state-of-the-art protein tissue imaging mass spectrometry and metabolomic techniques. Indeed, our application of direct imaging of proteins and analysis of cellular metabolites from frozen kidney cancer tissues is a novel approach. The metabolite analysis will be performed by a company with the most advanced resources available to derive meaningful information from these tissue samples. We hypothesize that identification of novel proteins and metabolites that are specific to aggressive forms of ccRCC can be linked to an existing panel of validated biomarkers of ccRCC aggressiveness already developed at the Mayo Clinic to create a robust, multi-biomarker panel for predicting outcome following surgery for localized ccRCC. This new test will improve identification of those ccRCC patients at greatest risk of experiencing cancer progression and death following surgery.
Such a test that can better predict this recurrence is needed, but what is also lacking are effective treatments to offer individuals at risk for cancer recurrence. The second phase of the study is designed to address this by examining advanced disease kidney cancer samples with the same methods used in the first sample group.
We expect to develop a novel biomarker prognostic panel within the 3-year grant period that can be applied clinically to enhance outcome prediction for patients with ccRCC. Moreover, given the freestanding nature of this new biomarker panel, an added benefit will be that this biomarker panel can be used in conjunction with a wide variety of existing pathologic features and algorithms currently used by clinicians to direct surveillance and manage care for ccRCC patients following surgery (i.e., broad applicability).
Finally, our plans to analyze paired primary metastatic ccRCC tumors is a longer-range study that could lead to new targets for therapy in patients with advanced ccRCC. We believe new targets can be identified because the methods of tissue imaging mass spectrometry and metabolite profiling have not been applied to renal cancer tissues. The strong existing clinical resources of the Mayo Clinic and the large urological clinical practice at EVMS is another unique combination that will facilitate moving these new diagnostics for renal cancers forward into wider clinical use.
|Effective start/end date||1/1/09 → 12/31/09|
- U.S. Department of Defense: $503,664.00