Localized Clear Cell Research Articles

Development of a CT-assessed adiposity nomogram for predicting outcome in localized ccRCC.

This study aimed to develop and validate a computed tomography-based nomogram assessing visceral and subcutaneous adiposity for predicting outcomes in localized clear cell renal cell carcinoma (ccRCC). A cohort of 364 patients with pathologically confirmed ccRCC participated in this retrospective study, with 254 patients assigned to the training set and 110 to the validation set (a 7:3 distribution ratio). The adipose score (AS) was generated using the least absolute shrinkage and selection operator Cox regression. Subsequently, a nomogram was constructed by integrating the clinical independent predictor with the AS to predict disease-free survival (DFS) in localized ccRCC after surgery. The performance of the nomogram was compared with the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade, and Necrosis (SSIGN) score. In both the training and validation cohorts, the nomogram exhibited superior discrimination compared to SSIGN and UISS (C-index: 0.897 vs. 0.781 vs. 0.776 in the training cohort, and 0.752 vs. 0.596 vs. 0.686 in the validation cohort; 5year AUC: 0.907 vs. 0.805 vs. 0.820 in the training cohort, and 0.832 vs. 0.577 vs. 0.726 in the validation cohort). Decision curve analysis (DCA) revealed a superior net benefit across a wider range of threshold probabilities for predicting 5year DFS compared to UISS and SSIGN scores. The developed prognostic nomogram demonstrated high accuracy and overall superior performance compared to existing prognostic models.

53 Associations between body composition, ClearCode34, and clinical outcomes in localized clear cell renal cell carcinoma

Abstract Background Emerging evidence suggests that pre-surgical body composition is a prognostic factor for cancer outcomes. Lower skeletal muscle quantity and radiodensity have been associated with renal cell carcinoma (RCC) outcomes in other studies, while reports of the relationship between visceral adipose tissue and RCC outcomes have been inconsistent. Mechanisms underlying these associations are unclear but may relate to tumor biology. We leveraged a large molecular epidemiology study of localized clear cell renal cell carcinoma (ccRCC) patients to examine how pre-surgical body composition features were associated with survival post-nephrectomy as well as ClearCode34 status, a previously validated prognostic gene expression signature. Methods The RESOLVE study at Memorial Sloan Kettering Cancer Center is a retrospective study of 1,239 patients with stage I-III ccRCC undergoing nephrectomy without systemic therapy. Pre-surgical CT scans at the third lumbar vertebrae were segmented for the cross-sectional areas and radiodensities of skeletal muscle, subcutaneous adipose and visceral adipose tissues using Automatica software. Formalin-fixed, paraffin-embedded tumor tissue samples were available for 929 patients, which were sent to the University of North Carolina at Chapel Hill and assayed for gene expression. RNA was extracted from these samples and analyzed using the Nanostring nCounter system on a custom panel of 356 genes, including the 34 genes used in the ClearCode34 assay. Of the 929 patients with tumor tissue available, gene expression data from 837 patients (90.1%) passed RNA extraction and data QC. Samples were classified into either ccA or ccB ClearCode34 classes using a reference data set and PAM (prediction analysis of microarrays) centroid-based classification. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for disease-free survival (DFS) within five years of diagnosis using Cox proportional hazards models for both body composition variables and ClearCode34 subtype. We also estimated odds ratios (ORs) and 95% CIs for associations between ClearCode34 subtype and body composition with logistic regression. All multivariable models were adjusted for age, sex, and all body composition variables. Results Of the 837 patients in our analytic sample, 219 (26%) had ccB tumors. Men, patients with high stage or grade tumors, and patients with chronic kidney disease prior to nephrectomy were more likely to present with ccB tumors. Lower skeletal muscle density (SMD) was significantly associated with lower five-year DFS [HR (95% CI) per 10 HU decrease in SMD: 1.7 (1.2, 2.3)]. While no other body composition features were significantly associated, we also observed non-significantly increased HRs for VATI and VATD [HR (95% CIs): 1.3 (0.8, 2.0) and 1.5 (0.9, 2.7), respectively]. ClearCode34 subtype was significantly associated with five-year DFS, where patients with ccB tumors had poorer DFS [HR (95% CI): 1.7 (1.1, 2.5)] compared to patients with ccA tumors. Notably, we observed no significant associations between any body composition feature and ClearCode34 in univariable analyses. However, in multivariable models that accounted for all body composition variables simultaneously as well as age and sex, lower SMD emerged as significantly associated with ccB subtype [OR (95% CI) per 10 HU decrease in SMD: 1.3 (1.0, 1.7)]. Conclusions Our findings suggest that the associations observed between body composition features and clinical outcomes in ccRCC may be related to tumor biology. Lower pre-surgical SMD, which may represent myosteatosis and metabolic dysregulation in ccRCC patients, was associated with both decreased five-year DFS and an increased likelihood of presenting with a more aggressive ccB tumor. Future studies should investigate body composition features related to ccRCC prognosis and additional markers of tumor biology, such as immune-related gene expression patterns, to extend these findings.

Mayo Clinic Validation of the AUA Risk Groups for Localized Renal Cell Carcinoma.

The AUA guidelines introduced a new risk group stratification system based primarily on tumor stage and grade to guide surveillance for patients treated surgically for localized renal cell carcinoma (RCC). We sought to evaluate the predictive ability of these risk groups using progression-free survival (PFS) and cancer-specific survival (CSS), and to compare their performance to that of our published institutional risk models. We queried our Nephrectomy Registry to identify adults treated with radical or partial nephrectomy for unilateral, M0, clear cell RCC, or papillary RCC from 1980 to 2012. The AUA stratification does not apply to other RCC subtypes as tumor grading for other RCC, such as chromophobe, is not routinely performed. PFS and CSS were estimated using the Kaplan-Meier method. Predictive abilities were evaluated using C indexes from Cox proportional hazards regression models. A total of 3191 patients with clear cell RCC and 633 patients with papillary RCC were included. For patients with clear cell RCC, C indexes for the AUA risk groups and our model were 0.780 and 0.815, respectively (P < .001) for PFS, and 0.811 and 0.857, respectively (P < .001), for CSS. For patients with papillary RCC, C indexes for the AUA risk groups and our model were 0.775 and 0.751, respectively (P = .002) for PFS, and 0.830 and 0.803, respectively (P = .2) for CSS. The AUA stratification is a parsimonious system for categorizing RCC that provides C indexes of about 0.80 for PFS and CSS following surgery for localized clear cell and papillary RCC.

Three-Dimensional (3D) deep learning model complements existing models for preoperative disease-free survival prediction (DFS) in localized clear cell renal cell carcinoma (ccRCC): A multicenter retrospective cohort study.

Current prognostic models have limited predictive abilities for the growing number of localized (stage I-III) ccRCCs. It is therefore crucial to explore novel preoperative recurrence prediction models to accurately stratify patients and optimize clinical decisions. This purpose of this study was to develop and externally validate a CT-based deep learning (DL) model for pre-surgical disease-free survival (DFS) prediction. Patients with localized ccRCC were retrospectively enrolled from six independent medical centers. Three-dimensional (3D) tumor regions from CT images were utilized as input to architect a ResNet 50 model, which outputted DL computed risk score (DLCR) of each patient for DFS prediction later. The predictive performance of DLCR was assessed and compared to the radiomics model (Rad-Score), clinical model we built and two existing prognostic models (UISS and Leibovich). The complementary value of DLCR to the UISS, Leibovich, as well as Rad-Score were evaluated by stratified analysis. 707 patients with localized ccRCC were finally enrolled for models' training and validating. The DLCR we established can perfectly stratify patients into low-, intermediate- and high-risks, and outperformed the Rad-Score, clinical model, UISS and Leibovich score in DFS prediction, with a C-index of 0.754 (0.689-0.821) in the external testing set. Furthermore, the DLCR presented excellent risk stratification capacity in subgroups defined by almost all clinic-pathological features. Moreover, patients in the UISS/Leibovich score/Rad-Score stratified low-risk but DLCR-defined intermediate- and high-risk groups were significantly more likely to experience ccRCC recurrences than those of intermediate- and high-risk in DLCR determined low-risk (all Log-rank P values＜0.05). Our deep learning model, derived from preoperative CT, is superior to radiomics and current models in precisely DFS predicting of localized ccRCC, and can provide complementary values to them, which may assist more informed clinical decisions and adjuvant therapies adoptions.

Abstract 5151: High serum kidney injury marker-1 and high baseline tumor PD-L1 protein expression levels are independently associated with treatment effect in adjuvant nivolumab plus ipilimumab vs placebo in localized clear cell renal cell carcinoma

Abstract Introduction: CheckMate 914 (CM-914) Part A is a double-blind, phase III randomized trial of the Nivolumab (NIVO) plus Ipilimumab (IPI) vs placebo (PBO) in localized ccRCC. Our prior report from this study suggested a disease-free survival (DFS) benefit for NIVO+IPI among patients with Fuhrman grade 4, TNM stages PT2a and PT4, or sarcomatoid features, although the sample size was limited. It is known that high circulating KIM-1 is associated with worse DFS after nephrectomy. In this exploratory post hoc analysis, we investigated whether high KIM-1 may help identify a subset of patients who benefit from adjuvant NIVO+IPI. Methods: Patients (n=816) with RCC after nephrectomy were randomized in CM-914 Part A to receive NIVO+IPI or PBO as previously described. Assessment of KIM-1 levels was performed using enzyme linked immunoassay (ELISA) on pre-treatment (n=584) and matched on-treatment blood samples (n=584). We used pre-treatment tumor samples to assess PD-L1% tumor cell expression (%TC) in an PD-L1 IHC 28-8 pharm Dx assay. The association between biomarkers and DFS outcomes was investigated by Kaplan-Meier (KM) and Cox proportional hazards analysis. Results: Median baseline serum KIM-1 level was 102 (9.9 - 1055.7) pg/mL. Serum KIM-1 levels were higher in males vs females, ≥65 yrs vs &amp;lt;65 yrs, Asian vs white patients, and patients with partial vs radical nephrectomy. In the PBO arm, subjects with highest quartile of pre-treatment KIM-1 had significantly worse DFS than those from the three lower quartiles. In contrast, this DFS risk among the subjects within the highest KIM-1 quartile was mitigated with NIVO+IPI treatment. Among patients within the highest quartile of pre-treatment KIM-1, there was trend for better DFS for NIVO+IPI versus PBO, HR=0.6 (0.34-1.04). Increase in KIM-1 during study therapy was positively associated with higher DFS rate in both arms. Multivariable analysis showed that PD-L1 %TC was predictive at predefined PD-L1 cutoffs (&amp;gt;=1%, &amp;gt;=5%, and &amp;gt;=10%), associating with improved DFS compared to placebo. Subjects with high PD-L1 expression had a DFS benefit from NIVO+IPI independent of KIM-1. Conclusion: Circulating KIM-1 and tumor PD-L1 expression may enrich for benefit from IO therapy in adjuvant ccRCC and hence holds promise for informing risk stratification and patient inclusion in neoadjuvant or adjuvant clinical trials. Citation Format: Sai Vikram Vemula, Wenxin Xu, Yu Wang, Xiaowen Liu, Jorge Ruiz de Somocurcio, David McDermott, Jun Li, Rupal Bhatt, Chung-Wei Lee, Burcin Simsek, Saurabh Gupta, Robert Motzer. High serum kidney injury marker-1 and high baseline tumor PD-L1 protein expression levels are independently associated with treatment effect in adjuvant nivolumab plus ipilimumab vs placebo in localized clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5151.

Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study

ObjectiveTo develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC).MethodsA total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).ResultsThe AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809–0.914), 0.853 (95% CI: 0.785–0.921), and 0.837 (95% CI: 0.714–0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495–8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118–149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821–0.923), 0.865 (95% CI: 0.800–0.930), and 0.848 (95% CI: 0.728–0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups.ConclusionsThe radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients.Critical relevance statementThe triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC.Key points• The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature.• Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC.• This study provides a non-invasive method to stratify patients with localized ccRCC.Graphical

A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: A multicenter study

Background and purposeThe Stage, Size, Grade and Necrosis (SSIGN) score is the most commonly used prognostic model in clear cell renal cell carcinoma (ccRCC) patients. It is a great challenge to preoperatively predict SSIGN score and outcome of ccRCC patients. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting SSIGN score and outcome in localized ccRCC. MethodsA multicenter 784 (training cohort/ test 1 cohort / test 2 cohort, 475/204/105) localized ccRCC patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting SSIGN score. Model performance was evaluated with area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis was used to assess the association of the model-predicted SSIGN with cancer-specific survival (CSS). Harrell’s concordance index (C-index) was calculated to assess the CSS predictive accuracy of these models. ResultsThe DLRM achieved higher micro-average/macro-average AUCs (0.913/0.850, and 0.969/0.942, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did for the prediction of SSIGN score. The CSS showed significant differences among the DLRM-predicted risk groups. The DLRM achieved higher C-indices (0.827 and 0.824, respectively in test 1 cohort and test 2 cohort) than the RS and DLS did in predicting CSS for localized ccRCC patients. ConclusionThe DLRM can accurately predict the SSIGN score and outcome in localized ccRCC.

Unique molecular underpinnings by genetic ancestry in localized clear cell renal cell carcinoma.

4536 Background: Clinical studies evaluating the impact of race on outcomes in renal cell carcinoma (RCC) are inconsistent and conceivably influenced by both biological and socioeconomic factors. Self-reported African Americans (AA) are also largely underrepresented in clinical trials and molecular studies of clear cell (cc) RCC compared to their White (W) counterparts. Herein, we sought to comprehensively characterize molecular differences by genetic ancestry in localized ccRCC. Methods: We performed whole exome (WES) and transcriptome (RNAseq) sequencing on a case-matched cohort of AA and W patients (pts) who underwent nephrectomy for localized ccRCC at our institution. Germline single nucleotide polymorphisms were combined with reference panels from the 1000 Genomes Project, and ancestry was estimated using ADMIXTURE. Molecular subtypes were defined according to the IMmotion151 (IMm151) subtypes, and a random forest model was trained to predict subgroups. Results: From an initial selected cohort of 84 pts, WES and RNAseq data of adequate quality were available for 61 pts including 28 of African (AFR) and 33 of European (EUR) descent. We augmented our institutional cohort with a 3:1 EUR:AFR matched subset of the TCGA KIRC by stage, nuclear grade (NG), and gender. The final analytic cohort contained 75 pts of AFR and 177 pts of EUR descent. There were no significant differences in stage, NG, or gender between AFR and EUR groups. We observed that VHL [OR 0.22, CI [0.11-0.43], p &lt;0.001) and PBRM1 (OR 0.4, CI [0.19-0.79], p = 0.005) mutations are enriched in pts with EUR ancestry and identified a unique association of KMT2C somatic mutations (OR 3.7, CI [1.1-13.5], p = 0.027) with AFR ancestry. Consistent with a lower frequency of VHL mutations, gene-set variation analysis revealed lower enrichment for angiogenesis (-0.23 vs 0.045; p &lt; 0.001) in AFR vs EUR ancestry, respectively. AFR ancestry was associated with an increased proportion of the “Proliferative” IMm151 subtype (14.9% [n = 11/74] vs 3.4% [n = 6/177], p = 0.002). There was no association between overall survival (OS) and ancestry; however, IMm151 subtypes stratified OS with longest survival in the “Angiogenic/Stromal” group (median OS NR) and shortest OS in the T-Effector/Proliferative subtype (5.5yrs CI [2.3 - NR], p = 0.01). Conclusions: Our findings indicate that genetic ancestry is associated with distinct molecular subtypes of ccRCC. Biological drivers of ccRCC pathogenesis and therapeutic response may vary by ancestry, underscoring the need for racially diverse representation in future studies.

Influence of metabolic syndrome on survival of patients with localized renal clear cell carcinoma: A retrospective cohort study in China.

To assess the effects of metabolic syndrome (MetS) and its components on the survival and prognosis of patients with localized clear cell renal cell carcinoma (ccRCC). This retrospective cohort study in Chongqing, China, identified patients with localized ccRCC from two medical centers of Chongqing Medical University between January 1, 2011, and December 31, 2020. The Chinese Medical Association Diabetes Society criteria of 2004 were used to diagnose MetS. Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify independent risk factors for ccRCC. The outcomes were overall survival (OS), progression-free survival (PFS), and cancer-specific survival (CSS). In our cohort, 378 eligible patients with localized ccRCC were included (median age, 56; range, 30-85; 255 men [67.5%]), and 87 patients (23.0%) were diagnosed with MetS. The median follow-up time was 66 months (1-126 months). Kaplan-Meier and log-rank analyses showed shorter PFS (P = 0.043) and CSS (P = 0.009) in patients with MetS. Univariate and multivariate Cox regression analyses found that MetS and dyslipidemia were independent risk factors for CSS in patients with localized ccRCC (P = 0.047; P = 0.035). When we analyzed MetS separately, the 4 components of MetS (hypertension, hyperglycemia, overweight/obesity, and dyslipidemia) did not show significant differences in OS, PFS, and CSS. MetS and dyslipidemia are independent adverse prognostic factors for CSS in patients with localized ccRCC. It is suggested to assign comprehensive therapy and follow-up to this patient population.

Screening for important risk factors for cancer-specific mortality in patients with localized clear cell renal carcinoma

To study the risk factors for cancer-specific mortality (CSM) among patients with localized clear cell renal carcinoma (LCCRC) in the Chinese population. The clinical data of 1,376 LCCRC patients were postoperatively collected to analyze the correlations between CSM and multiple factors using Cox regression analysis. Receiver operating characteristic curves were constructed as per the screened risk factors to identify factors with optimal criticality judgment values, which were then used as the scoring standard for the stratification evaluation of LCCRC prognosis. The CSM rate was 5.6% (77/1,376 cases) and the median follow-up duration was 78.1 (60-105) months. Cox analysis revealed that age, tumor diameter, and nuclear grade were associated with CSM. The optimal criticality judgment values for age and tumor diameter using receiver operating characteristic curve analysis were 53 years and 5.8 cm, respectively. LCCRC prognosis divided into low-risk (≤ 2 points), intermediate-risk (3-4 points), and high-risk (5 points) showed CSM rates of 3.8%, 13.8%, and 58.3%, respectively, among patients with more than 5 years of follow-up. Age, tumor diameter, and nuclear grade were important risk factors for CSM in LCCRC patients. The scoring criteria including these three risk factors may be an important supplement to the prognostic model of LCCRC in the Chinese population.

Adjuvant nivolumab plus ipilimumab versus placebo for localised renal cell carcinoma after nephrectomy (CheckMate 914): a double-blind, randomised, phase 3 trial

Effective adjuvant therapy for patients with resected localised renal cell carcinoma represents an unmet need, with surveillance being the standard of care. We report results from part A of a phase 3, randomised trial that aimed to assess the efficacy and safety of adjuvant nivolumab plus ipilimumab versus placebo. The double-blind, randomised, phase 3 CheckMate 914 trial enrolled patients with localised clear cell renal cell carcinoma who were at high risk of relapse after radical or partial nephrectomy between 4-12 weeks before random assignment. Part A, reported herein, was done in 145 hospitals and cancer centres across 20 countries. Patients were randomly assigned (1:1) to nivolumab (240 mg) intravenously every 2 weeks for 12 doses plus ipilimumab (1 mg/kg) intravenously every 6 weeks for four doses, or matching placebo, via an interactive response technology system. The expected treatment period was 24 weeks, and treatment could be continued until week 36, allowing for treatment delays. Randomisation was stratified by TNM stage and nephrectomy (partial vs radical). The primary endpoint was disease-free survival according to masked independent central review; safety was a secondary endpoint. Disease-free survival was analysed in all randomly assigned patients (intention-to-treat population); exposure, safety, and tolerability were analysed in all patients who received at least one dose of study drug (all-treated population). This study is registered with ClinicalTrials.gov, NCT03138512. Between Aug 28, 2017, and March 16, 2021, 816 patients were randomly assigned to receive either adjuvant nivolumab plus ipilimumab (405 patients) or placebo (411 patients). 580 (71%) of 816 patients were male and 236 (29%) patients were female. With a median follow-up of 37·0 months (IQR 31·3-43·7), median disease-free survival was not reached in the nivolumab plus ipilimumab group and was 50·7 months (95% CI 48·1 to not estimable) in the placebo group (hazard ratio 0·92, 95% CI 0·71-1·19; p=0·53). The number of events required for the planned overall survival interim analysis was not reached at the time of the data cutoff, and only 61 events occurred (33 in the nivolumab plus ipilimumab group and 28 in the placebo group). 155 (38%) of 404 patients who received nivolumab plus ipilimumab and 42 (10%) of 407 patients who received placebo had grade 3-5 adverse events. All-cause adverse events of any grade led to discontinuation of nivolumab plus ipilimumab in 129 (32%) of 404 treated patients and of placebo in nine (2%) of 407 treated patients. Four deaths were attributed to treatment with nivolumab plus ipilimumab and no deaths were attributed to treatment with placebo. Adjuvant therapy with nivolumab plus ipilimumab did not improve disease-free survival versus placebo in patients with localised renal cell carcinoma at high risk of recurrence after nephrectomy. Our study results do not support this regimen for the adjuvant treatment of renal cell carcinoma. Bristol Myers Squibb and Ono Pharmaceutical.

Serum C-NLR score, a new inflammatory marker, predicts tumor histopathology and oncological outcomes of localized clear cell renal carcinoma after nephrectomy: A single center retrospective analysis

Background/Aim: Several blood and serum-based parameters have been described as prognostic markers of clear cell renal cell carcinoma (ccRCC). But most of these markers have inconsistent results and are not used in routine clinical practice. Therefore, novel potential predictor biomarkers are needed for the management of ccRCC patients in clinical practice. Here, we investigate the predictive value of a novel marker, serum C-NLR score, for pathological characteristics and oncological outcomes of ccRCC. Methods: A total of 162 RCC patients who underwent radical or partial nephrectomy between January 2015 and January 2021 were evaluated in a retrospective cohort study setting. The serum C-NLR score was compared according to the tumor histopathology-associated parameters. The predictive role of those parameters and C-NLR score on the oncological outcomes of ccRCC was also investigated. Results: The median serum C-NLR scores exhibited statistically significant increases in ccRCC patients with pathological necrosis, lymphovascular invasion, and variant differentiation. Among histopathological characteristics, only tumor necrosis and variant differentiation were associated with overall survival (OS) and tumor grade with metastasis-free survival (MFS) (no metastasis detected in grade 1–2 tumors) in Kaplan Meier analyses. Serum C-NLR score was also associated with OS but not MFS. In the univariate analyses, tumor necrosis, variant differentiation, and C-NLR score were associated with OS of localized RCC patients who underwent nephrectomy (HR: 0.29; 95% CI: 0.08–1.01; P=0.04, HR: 6.01; 95% CI: 1.66–21.82; P=0.006 and, HR: 1.21; 95% CI: 0.20–5.16; P=0.04). However, in the multivariate analysis, only variant differentiation and C-NLR score were associated with OS (HR: 1.43; 95% CI: 0.82–2.98; P=0.03 and HR: 1.21; 95% CI: 0.20–5.16; P=0.04). Tumor grade was directly associated with MFS because grade 1–2 tumors did not exhibit any metastasis. Conclusion: Serum C-NLR score was higher in worse histopathological entities. Moreover, it predicts the OS for patients with ccRCC as an independent factor.

Proteomics Characterization of Clear Cell Renal Cell Carcinoma.

To explore the tumor proteome of patients diagnosed with localized clear cell renal cancer (ccRCC) and treated with surgery. A total of 165 FFPE tumor samples from patients diagnosed with ccRCC were analyzed using DIA-proteomics. Proteomics ccRCC subtypes were defined using a consensus cluster algorithm (CCA) and characterized by a functional approach using probabilistic graphical models and survival analyses. We identified and quantified 3091 proteins, including 2026 high-confidence proteins. Two proteomics subtypes of ccRCC (CC1 and CC2) were identified by CC using the high-confidence proteins only. Characterization of molecular differences between CC1 and CC2 was performed in two steps. First, we defined 514 proteins showing differential expression between the two subtypes using a significance analysis of microarrays analysis. Proteins overexpressed in CC1 were mainly related to translation and ribosome, while proteins overexpressed in CC2 were mainly related to focal adhesion and membrane. Second, a functional analysis using probabilistic graphical models was performed. CC1 subtype is characterized by an increased expression of proteins related to glycolysis, mitochondria, translation, adhesion proteins related to cytoskeleton and actin, nucleosome, and spliceosome, while CC2 subtype showed higher expression of proteins involved in focal adhesion, extracellular matrix, and collagen organization. ccRCC tumors can be classified in two different proteomics subtypes. CC1 and CC2 present specific proteomics profiles, reflecting alterations of different molecular pathways in each subtype. The knowledge generated in this type of studies could help in the development of new drugs targeting subtype-specific deregulated pathways.

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma

PurposeTo develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes. MethodIn this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created. ResultsThe four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model. ConclusionThe CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.

A signature based on chromatin regulation and tumor microenvironment infiltration in clear cell renal cell carcinoma.

Aims: This research aimed to construct a signature based on chromatin regulation in localized clear cell renal cell carcinoma (ccRCC). Materials & methods: Non-negative matrix factorization clustering was performed on 438localized ccRCC cases. The immune infiltration was generated by the single-sample gene set enrichment analysis algorithm. Survival analyses were performed using the Kaplan-Meier method, and the significance of the differences was determined using the log-rank test. The risk score was constructed based on the expression of chromatin regulators to quantify chromatin modification. Results: A score system based on chromatin modification was established. The high-risk subtype was characterized by increased tumor mutation burden, whereas a low-risk score was characterized by an increase in chromatin regulator expression and better overall survival. Conclusion: This research has constructed a signature based on chromatin regulation in localized ccRCC.

1473P A prognostic microRNA-based signature for relapse risk prediction and definition of therapeutic targets in patients with high-risk localized clear cell renal cell carcinoma

Prognosis of localized clear cell renal cell carcinoma (ccRCC) patients is estimated by nomograms based on clinico-pathological factors. However, clinical guidelines do not recommend their use. Recently, pembrolizumab has demonstrated benefit as adjuvant therapy only in disease-free survival (DFS) in high-risk localized ccRCC, increasing the need for better patient stratification tools. miRNA expression was analyzed in FFPE nephrectomy tissues in the discovery and validation cohorts (Leeds-UK and Spain) including untreated, localized (stage Ib-III) ccRCC patients. 9 miRNAs for predict risk of relapse were defined and combined into Bio-miR. Molecular characterization of risk-based groups was done using proteomics and probabilistic graphical models (PGM). In the discovery cohort (n=71), DFS at 5 years was 93.9% in Bio-miR low-risk and 61.6% in high-risk patients (HR=6.9, p<0.001). Bio-miR compared favorably with UISS and Karakiewicz´s nomograms. In the Leeds-UK cohort (n=75), low-risk patients had a 5-year DFS rate of 94% vs 62% in high-risk, dividing the Leibovich intermediate-risk population into two risk groups (5-year DFS 100% vs 71%). In the Spanish cohort (n=180), DFS rates at 5 years were 82.9% in the low-risk and 58.7% in the high-risk group (HR=2.4; p<0.005). Amongst patients excluded from the KEYNOTE-564 due to low-risk features, Bio-miR defines a high-risk population who should be prioritized for adjuvant therapy. 124 proteins were differentially expressed between Bio-miR low and high-risk groups, mainly related to focal adhesion, metabolism and angiogenesis. PGM defined differences between low- and high-risk tumors in complement activation and translation functional nodes. We defined and validated a Bio-miR, dichotomizing patients with localized ccRCC into low- and high-risk relapse groups. Bio-miR acts independently of tumor stage and grade and could refine the selection of patients for adjuvant therapy and improve design of future adjuvant ccRCC trials. Molecular characterization of risk groups highlighted some processes that could be useful in tailored treatments.

Bio-miR: A prognostic microRNA-based signature for localized clear cell renal cell carcinoma.

e16519 Background: Prognosis of localized clear cell renal cell carcinoma (ccRCC) patients is estimated by nomograms based on clinico-pathological factors. However, clinical guidelines do not recommend their use. Recently, pembrolizumab has demonstrated benefit in disease-free survival (DFS) in medium-high risk localized ccRCC, increasing the need for better patient stratification tools. In this study, we have defined and validated a molecular signature, Bio-miR, based on the expression of nine microRNAs, with prognostic value in ccRCC. Methods: The discovery and two validation cohorts (Leeds-UK and Spain) consisted of patients with resected localized (stage Ib-III) ccRCC and no adjuvant therapy. miRNA expression was analyzed using microarrays and validated using qPCR in FFPE nephrectomy tissues. Cox regression was used to define the best microRNAs combination to predict risk of relapse. Results: In the discovery cohort (n = 71), DFS at 5 years was 93.9% amongst Bio-miR-defined low-risk patients and 61.6% in high-risk patients (HR = 6.9 (3.4-42.9), p &lt; 0.001). Cancer-specific survival at 5 years was 95.7% and 86.4% in low- and high-risk patients, respectively (HR = 7.7 (1.7-35.1), p &lt; 0.01). Bio-miR compared favorably with different histopathological factors and UISS and Karakiewicz´s nomograms. In the Leeds validation cohort (n = 75/95 passing qPCR quality control), patients defined as low-risk had a 5-year DFS rate of 94% versus 62% in high-risk defined disease. In this cohort Bio-miR was able to divide the Leibovich intermediate-risk population into two groups with divergent five-year DFS rates (100% vs 71%). In the Spanish validation cohort (n = 180), DFS rates at 5 years were 82.9% in the low-risk group and 58.7% in the high-risk group (HR = 2.4 (1.4-4.4); p &lt; 0.005). Applying the inclusion criteria from the phase III KEYNOTE-564 trial, Bio-miR identifies a small low-risk population who could be spared adjuvant treatment. Conversely, amongst patients excluded from the study due to low-risk features, Bio-miR defines a high-risk population (DFS at five years of 50%) who should be prioritized for adjuvant therapy. Conclusions: We define and validate a nine-microRNA based signature capable of dichotomizing patients with localized ccRCC into low- and high-risk groups based on risk of relapse. Importantly, Bio-miR acts independently of tumor stage and grade and could, therefore, help refine the selection of patients for adjuvant therapy as well as inform the design of future adjuvant ccRCC trials.

The Four-Feature Prognostic Models for Cancer-Specific and Overall Survival after Surgery for Localized Clear Cell Renal Cancer: Is There a Place for Inflammatory Markers?

We aimed at a determination of the relevance of comorbidities and selected inflammatory markers to the survival of patients with primary non-metastatic localized clear cell renal cancer (RCC). We retrospectively analyzed data from a single tertiary center on 294 patients who underwent a partial or radical nephrectomy in the years 2012–2018. The following parameters were incorporated in the risk score: tumor stage, grade, size, selected hematological markers (SIRI—systemic inflammatory response index; SII—systemic immune-inflammation index) and a comorbidities assessment tool (CCI—Charlson Comorbidity Index). For further analysis we compared our model with existing prognostic tools. In a multivariate analysis, tumor stage (p = 0.01), tumor grade (p = 0.03), tumor size (p = 0.006) and SII (p = 0.02) were significant predictors of CSS, while tumor grade (p = 0.02), CCI (p = 0.02), tumor size (p = 0.01) and SIRI (p = 0.03) were significant predictors of OS. We demonstrated that our model was characterized by higher accuracy in terms of OS prediction compared to the Leibovich and GRANT models and outperformed the GRANT model in terms of CSS prediction, while non-inferiority to the VENUSS model was revealed. Four different features were included in the predictive models for CSS (grade, size, stage and SII) and OS (grade, size, CCI and SIRI) and were characterized by adequate or even superior accuracy when compared with existing prognostic tools.

PD33-03 IS ANGIOTENSIN SYSTEM INHIBITOR USE ASSOCIATED WITH IMPROVED OUTCOMES IN PATIENTS WITH LOCALIZED CLEAR CELL RCC?

You have accessJournal of UrologyCME1 May 2022PD33-03 IS ANGIOTENSIN SYSTEM INHIBITOR USE ASSOCIATED WITH IMPROVED OUTCOMES IN PATIENTS WITH LOCALIZED CLEAR CELL RCC? Paige Nichols, Andrew Zganjar, Christine Lohse, Sounak Gupta, John Cheville, Cameron Britton, Aaron Potretzke, Stephen Boorjian, Houston Thompson, Bradley Leibovich, and Vidit Sharma Paige NicholsPaige Nichols More articles by this author , Andrew ZganjarAndrew Zganjar More articles by this author , Christine LohseChristine Lohse More articles by this author , Sounak GuptaSounak Gupta More articles by this author , John ChevilleJohn Cheville More articles by this author , Cameron BrittonCameron Britton More articles by this author , Aaron PotretzkeAaron Potretzke More articles by this author , Stephen BoorjianStephen Boorjian More articles by this author , Houston ThompsonHouston Thompson More articles by this author , Bradley LeibovichBradley Leibovich More articles by this author , and Vidit SharmaVidit Sharma More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002584.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Preclinical studies demonstrate VEGF expression is regulated by angiotensin II. Increased angiotensin II receptor expression in clear cell renal cell carcinoma (ccRCC) may be associated with higher tumor grade and poorer survival. It is unknown if angiotensin system inhibitors, in the form of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), are associated with improved outcomes for hypertensive patients with ccRCC. METHODS: A large nephrectomy registry was queried to identify adults treated with radical or partial nephrectomy for localized ccRCC from 2005-2012. Patients were divided into three groups: (1) patients without HTN, (2) patients with HTN but not treated with ACEIs or ARBs, and (3) patients with HTN treated with ACEIs or ARBs. The primary outcome of disease progression-free survival (PFS) and secondary outcomes of cancer-specific survival and CKD stage PFS were estimated using the Kaplan-Meier method. Associations with disease progression, death from RCC, and CKD stage progression were evaluated using multivariable Cox models. We adjusted for a disease progression score and a death from RCC score in the multivariable models for disease progression and death from RCC, respectively. RESULTS: Among 1,203 patients, there were 432 (36%), 359 (30%), and 412 (34%) in groups 1, 2, and 3, respectively, with a median follow-up of 10.1 (IQR 7.9-12.5) years. Disease PFS for the three groups is shown in Figure 1. On multivariable analysis, group 3 was associated with improved disease PFS compared to group 2 (HR 0.76, 95% CI 0.57-1.00, p=0.05) but not group 1 (HR 0.89, 95% CI 0.68-1.17, p=0.40). A subset multivariable analysis of group 3 treated with ACEIs alone relative to group 2 demonstrated improved disease PFS (HR 0.68, 95% CI 0.49-0.93, p=0.02). There were no significant differences between groups for either cancer specific survival or CKD stage PFS on multivariable analysis. CONCLUSIONS: The use of ACEIs or ARBs is associated with improved disease PFS in patients with HTN and localized ccRCC after radical or partial nephrectomy. No appreciable differences in cancer specific survival or CKD stage PFS were identified. Further studies are warranted to validate this finding and elucidate underlying mechanisms of angiotensin blockade in ccRCC patients. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e553 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Paige Nichols More articles by this author Andrew Zganjar More articles by this author Christine Lohse More articles by this author Sounak Gupta More articles by this author John Cheville More articles by this author Cameron Britton More articles by this author Aaron Potretzke More articles by this author Stephen Boorjian More articles by this author Houston Thompson More articles by this author Bradley Leibovich More articles by this author Vidit Sharma More articles by this author Expand All Advertisement PDF DownloadLoading ...

The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study.

Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.