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Overall tumor genomic instability: an important predictor of recurrence-free survival in patients with localized clear cell renal cell carcinoma.

ABSTRACT: Measurement of a tumor's overall genomic instability has gathered recent interest over the identification of specific genomic imbalances, as it may provide a more robust measure of tumor aggressiveness. Here we demonstrate the association of tumor genomic instability in the prediction of disease recurrence in patients with clinically localized clear cell renal cell carcinoma (ccRCC). Genomic copy number analysis was performed using SNP-based microarrays on tumors from 103 ccRCC patients. The number of copy number alterations (CNAs) for each tumor was calculated, and a genomic imbalance threshold (GIT) associated with high stage and high-grade disease was determined. Cox proportional hazards regression analyzes were performed to assess the effect of GIT on recurrence-free survival adjusting for known confounders. In the cohort, copy number losses in chromosome arms 3p, 14q, 6q, 9p, and 1p and gains of 5q and 7p/q were common. CNA burden significantly increased with increasing stage (p < .001) and grade (p < .001). The median CNA burden associated with patients presenting with advanced stage (IV) and high-grade (III/IV) tumors was ≥9, defining the GIT. On regression analysis, GIT was a superior predictor of recurrence (Hazard Ratio 4.44 [CI 1.36-14.48], p = .01) independent of stage, with similar results adjusting for grade. These findings were confirmed using an alternative measure of genomic instability, weighted Genomic Integrity Index. Our data support a key role for genomic instability in ccRCC progression. More importantly, we have identified a GIT (≥ 9 CNAs) that is a superior and independent predictor of disease recurrence in high-risk ccRCC patients.

SUBMITTER: Correa AF

PROVIDER: S-EPMC7515487 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.The subtypes were significantly associated with RFS (p<0.01), CSS (p<0.01), and OS (p<0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

Project description:BackgroundRisk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts.ObjectiveTo introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial.Design, setting, and participantsThe model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial.Outcome measurements and statistical analysisThe primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests.Results and limitationsA total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]).ConclusionsWe introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic.Patient summaryImportant decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.MethodsWe decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.ResultsWe developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.ConclusionOur study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.

Project description:BackgroundChromosomal instability (CIN) is a cancer hallmark and it is difficult to directly measure its phenotype, while a CIN25 gene signature was established to do so in several cancer types. However, it is currently unclear whether there exists this signature in clear cell renal cell carcinoma (ccRCC), and if so, which biological and clinical implications it has.MethodsTranscriptomic profiling was performed on 10 ccRCC tumors and matched renal non-tumorous tissues (NTs) for CIN25 signature analyses. TCGA and E-MBAT1980 ccRCC cohorts were analyzed for the presence of CIN25 signature, CIN25 score-based ccRCC classification, and association with molecular alterations and overall or progression-free survival (OS or PFS). IMmotion150 and 151 cohorts of ccRCC patients treated with Sunitinib were analyzed for the CIN25 impact on Sunitinib response and survival.ResultsThe transcriptomic analysis of 10 patient samples showed robustly upregulated expression of the CIN25 signature genes in ccRCC tumors, which were further confirmed in TCGA and E-MBAT1980 ccRCC cohorts. Based on their expression heterogeneity, ccRCC tumors were categorized into CIN25-C1 (low) and C2 (high) subtypes. The CIN25-C2 subtype was associated with significantly shorter patient OS and PFS, and characterized by increased telomerase activity, proliferation, stemness and EMT. The CIN25 signature reflects not only a CIN phenotype, but also levels of the whole genomic instability including mutation burden, microsatellite instability and homologous recombination deficiency (HRD). Importantly, the CIN25 score was significantly associated with Sunitinib response and survival. In IMmotion151 cohort, patients in the CIN25-C1 group exhibited 2-fold higher remission rate than those in the CIN25-C2 group (P = 0.0004) and median PFS in these two groups was 11.2 and 5.6 months, respectively (P = 7.78E-08). Similar results were obtained from the IMmotion150 cohort analysis. Higher EZH2 expression and poor angiogenesis, well characterized factors leading to Sunitinib resistance, were enriched in the CIN25-C2 tumors.ConclusionThe CIN25 signature identified in ccRCC serves as a biomarker for CIN and other genome instability phenotypes and predicts patient outcomes and response to Sunitinib treatment. A PCR quantification is enough for the CIN25-based ccRCC classification, which holds great promises in clinical routine application.

Project description:OBJECTIVES:To examine whether serum adiponectin or leptin level has the ability to differentiate clear cell renal cell carcinoma (ccRCC) from other subtypes of renal cell carcinoma (RCC) in a Chinese population. PATIENTS AND METHODS:We recruited 198 consecutive patients who were treated with radical or partial nephrectomy in our department from September 2011 to June 2013. Their histological types were all malignant, including clear cell, papillary, chromophobe and unclassified RCC. We also enrolled 86 people with no cancer or cancer-related diseases as normal controls. We measured patients' preoperative blood samples for plasma adiponectin and leptin concentrations using an enzyme-linked immunosorbent assay method. Statistical methods were used to analyze ccRCC and other subtypes as they relate to serum adiponectin/leptin level and other factors such as body mass index or visceral fat area. RESULTS:In our database, normal controls had significantly higher circulating adiponectin (p < 0.001) and leptin levels (p < 0.001) than patients with RCC. Among the 198 RCC patients, 156 patients had ccRCC while 42 patients had other histological types. Serum adiponectin levels were lower in ccRCC patients than in non-clear-cell RCC patients (p = 0.004). However, the plasma leptin level was not differently distributed between ccRCC and non-ccRCC patients (p = 0.940). In multivariate analysis, we found that serum adiponectin level may be an independent predictor for discriminating ccRCC patients from others (p = 0.004). Furthermore, in the ccRCC subgroup, we observed that men with ccRCC had lower leptin (p < 0.001) and adiponectin (p = 0.002) levels, and diabetic patients had lower plasma adiponectin levels (p = 0.001). CONCLUSIONS:Lower plasma adiponectin concentration was related to an increased incidence of ccRCC and may act as an independent predictor for ccRCC. Our study may help define the process from obesity to adipose tissue, to cytokines and finally to ccRCC.

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Overall tumor genomic instability: an important predictor of recurrence-free survival in patients with localized clear cell renal cell carcinoma.

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