Scott M Haake1, Samira A Brooks2, Eric Welsh3, William J Fulp4, Dung-Tsa Chen3, Jasreman Dhillon5, Eric Haura6, Wade Sexton7, Philippe E Spiess6, Julio Pow-Sang7, W Kimryn Rathmell8, Mayer Fishman9. 1. Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN. Electronic address: Scott.M.Haake@Vanderbilt.edu. 2. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC. 3. Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 4. Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, WA. 5. Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL. 6. Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL. 7. Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL. 8. Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN. 9. Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL. Electronic address: Mayer.Fishman@Moffitt.org.
Abstract
PURPOSE: The 34-gene classifier, ClearCode34, identifies prognostically distinct molecular subtypes of clear cell renal cell carcinoma (ccRCC) termed clear cell A (ccA) and clear cell B (ccB). The primary objective of this study was to describe clinical characteristics and comorbidities of relevance in patients stratified by ClearCode34. PATIENTS AND METHODS: In this retrospective analysis, 282 patients from Moffitt Cancer Center with ccRCC with gene expression analyses of the primary tumor were identified and ClearCode34 was applied to identify tumors as ccA or ccB. The medical record and institutional databases were queried to define patient characteristics, comorbidities, and outcomes. RESULTS: We validated in this external cohort the superior overall survival, cancer-specific survival, and recurrence-free survival of ccA patients relative to ccB patients (P<0.001). Addressing other clinical characteristics, the ccA patients were more likely to be obese (48% vs. 34%, P = 0.021) and diabetic (26% vs. 13%, P = 0.035). The ccA patients also trended toward having been more frequent users of angiotensin system inhibitors (71% vs. 52%, P = 0.055). In multivariate analyses, ccB status is independently associated with inferior cancer-specific survival (hazard ratio = 3.26, 95% confidence interval: 1.84-5.79) and overall survival (hazard ratio = 2.50, 95% confidence interval: 1.53-4.08). CONCLUSIONS: ClearCode34, after considering distinct patterns of comorbidities in each molecular subtype, remains a strong prognostic tool in patients with ccRCC. Obesity and diabetes mellitus emerged as factors that may influence ccRCC phenotypes and further studies investigating the effect of these metabolic conditions functionally onto tumor biology are warranted. Additionally, use of angiotensin system inhibitors could be studied in the context of ccRCC molecular classification in future studies to better understand its effect on ccRCC outcomes.
PURPOSE: The 34-gene classifier, ClearCode34, identifies prognostically distinct molecular subtypes of clear cell renal cell carcinoma (ccRCC) termed clear cell A (ccA) and clear cell B (ccB). The primary objective of this study was to describe clinical characteristics and comorbidities of relevance in patients stratified by ClearCode34. PATIENTS AND METHODS: In this retrospective analysis, 282 patients from Moffitt Cancer Center with ccRCC with gene expression analyses of the primary tumor were identified and ClearCode34 was applied to identify tumors as ccA or ccB. The medical record and institutional databases were queried to define patient characteristics, comorbidities, and outcomes. RESULTS: We validated in this external cohort the superior overall survival, cancer-specific survival, and recurrence-free survival of ccApatients relative to ccBpatients (P<0.001). Addressing other clinical characteristics, the ccApatients were more likely to be obese (48% vs. 34%, P = 0.021) and diabetic (26% vs. 13%, P = 0.035). The ccApatients also trended toward having been more frequent users of angiotensin system inhibitors (71% vs. 52%, P = 0.055). In multivariate analyses, ccB status is independently associated with inferior cancer-specific survival (hazard ratio = 3.26, 95% confidence interval: 1.84-5.79) and overall survival (hazard ratio = 2.50, 95% confidence interval: 1.53-4.08). CONCLUSIONS: ClearCode34, after considering distinct patterns of comorbidities in each molecular subtype, remains a strong prognostic tool in patients with ccRCC. Obesity and diabetes mellitus emerged as factors that may influence ccRCC phenotypes and further studies investigating the effect of these metabolic conditions functionally onto tumor biology are warranted. Additionally, use of angiotensin system inhibitors could be studied in the context of ccRCC molecular classification in future studies to better understand its effect on ccRCC outcomes.
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