Design and Validation of a Biomarker-enhanced System to Predict RCC Progression

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DESCRIPTION (provided by applicant): Incidence rates for renal cell carcinoma (RCC) have risen steadily over the past three decades, with the majority of this increase seen among localized tumors. Hallmark features of RCC include a predominance of clear cell subtype (ccRCC), a variable clinical course, and limited treatment options beyond surgical excision. Of interest, approximately 35% of patients treated surgically for localized ccRCC will experience disease progression (i.e. develop distant metastases) and most of these will occur within one year of surgery. Related to this, in 2003 we published the Mayo Clinic Progression (PROG) Score, an algorithm that is used to help predict which ccRCC patients will progress after surgery. While the PROG score has demonstrable prognostic value for patients with localized ccRCC, it is based entirely on information from pathologic indices and therefore represents only a surrogate measure of the underlying molecular characteristics that ultimately determine tumor aggressiveness. As such, the PROG score does not provide complete patient stratification nor does it inform on the biology of ccRCC aggressiveness or identify potential targets for therapeutic intervention. These limitations underscore the need to identify molecular prognostic factors that, in isolation or in combination with existing prognostic tools, not only improve prediction of ccRCC progression but also provide potential targets for clinical intervention. In direct response to this need, members of our investigative team employed a variety of discovery methods to identify a panel of seven tumor-based biomarkers of ccRCC aggressiveness (survivin, B7-H1, B7-H4, Ki-67, IGF-IR, IMP3 and CA-IX). More importantly, we have published individual preliminary investigations showing that tumor expression levels of each of these biomarkers are associated with an increased risk of ccRCC progression following surgery for localized disease. Herein, we propose to continue the translation of our biomarker discovery efforts by (1) generating a novel biomarker-based scoring algorithm to predict ccRCC progression, which when integrated with our existing PROG score will result in a more robust and accurate scoring system (BioPROG); (2) externally validating the prognostic value of this new scoring system in two independent populations of ccRCC patients and (3) exploring for the first time the expression of our seven biomarkers in metastatic ccRCC tissues and examining their ability to predict time to death following diagnosis of metastatic disease. To do this, we propose to harness high-quality clinical data and biospecimen resources available through ongoing large patient registries at our institutions. In summation, our overarching goal is to improve prognostic stratification following surgery for patients with localized ccRCC as well as inform on the underlying biology of ccRCC progression. This effort will ultimately enhance patient management/surveillance, allow for more appropriate clinical trial design, inform the molecular underpinnings of ccRCC pathogenesis, provide the rationale for novel therapeutic strategies, and represent a logical platform for the evaluation of patient response to emerging adjuvant therapeutics. PUBLIC HEALTH RELEVANCE: In our proposed application, we will build upon our published work with seven individual biomarkers of ccRCC aggressiveness to develop and externally validate a second generation, biomarker-enhanced scoring system for predicting progression among patients with localized ccRCC (BioPROG). We will then extend the scope of our previous work by examining for the first time the expression of our seven biomarkers in paired samples of primary and metastatic ccRCC and estimating their association with time to death. The short-term clinical benefits of this effort include more appropriate ccRCC patient surveillance/management and clinical trial design, while more long-term benefits include a biologically relevant platform for evaluating response to emerging adjuvant therapeutics and for designing novel combinatorial therapies.