Patients with ClearCode34-identified molecular subtypes of clear cell renal cell carcinoma represent unique populations with distinct comorbidities

Abstract

Purpose The 34-gene classifier, ClearCode34, identifies prognostically distinct molecular subtypes of clear cell renal cell carcinoma (ccRCC) termed ccA and 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 (OS), cancer specific survival (CSS), 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% versus 34%, p=0.021) and diabetic (26% versus 13%, p=0.035). The ccA patients also trended towards having been more frequent users of angiotensin system inhibitors (ASIs) (71% versus 52%, p=0.055). In multivariate analyses, ccB status is independently associated with inferior CSS (HR 3.26, 95% CI 1.84–5.79) and OS (HR 2.50, 95% CI 1.53–4.08). Conclusions ClearCode34, after considering distinct patterns of comorbidities in each molecular subtype, remains a strong prognostic tool in ccRCC patients. Obesity and DM emerged as factors that may influence ccRCC phenotypes and further studies investigating the impact of these metabolic conditions functionally onto tumor biology are warranted. Additionally, use of ASI could be studied in the context of ccRCC molecular classification in future studies to better understand its impact on ccRCC outcomes.

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups

“…34-gene classifier (ClearCode34), which was a authoritatively subtyped predictor for classifying clear cell RCC according to good risk (ccA) and poor risk (ccB) subtype for assigning clear cell RCC patient and built a subtype-inclusive model to analyze patient survival outcomes according to risk stratification [9, 32]. The ClearCode34 classifier was applied for RNA-sequencing of data from 380 nonmetastatic clear cell RCC samples from TCGA.…”

Clear Cell Type A and B Molecular Subtypes in Metastatic Clear Cell Renal Cell Carcinoma: Tumor Heterogeneity and Aggressiveness

“…Molecular subtyping of primary ccRCC tumors based on gene expression profiles has shown that ccRCC tumors can be classified into molecular subtypes that are significantly associated with outcome [6,7,11,12]. While this work was revolutionary and helps to understand the molecular underpinnings of ccRCC, there are important practical limitations.…”

Renal cancer subtypes: Should we be lumping or splitting for therapeutic decision making?

“…Perhaps most important, this classification represents clinically distinct subtypes, with ccB tumors demonstrating increased tumor size, grade, and rate of metastasis as well as decreased recurrence‐free and overall survival in multiple data sets . In addition, these molecular subtypes feature distinct metabolic patterns, with the aggressive ccB tumors demonstrating increased glucose uptake on imaging . However, the other clear finding from that study was that individual tumors can harbor both ccA and ccB components, making this classification strategy difficult for informing therapeutic selection, although it has emerged as a strong prognostic indicator in numerous studies .…”

“…41 However, the other clear finding from that study was that individual tumors can harbor both ccA and ccB components, making this classification strategy difficult for informing therapeutic selec-tion, although it has emerged as a strong prognostic indicator in numerous studies. 40,42 Other investigators have successfully merged gene expression data with metabolomics profiling to parse clear cell RCC into multiple molecular subtypes, including an aggressive cohort with increased glutathione metabolism. 43 Finally, building off the results from large-scale genome sequencing projects, another schema has emerged that focuses on 2 of the more commonly mutated genes in clear cell RCC: protein polybromo-1 (PBRM1 [also called BAP180]) and BRCA1-associated protein 1 (BAP1).…”