Markers of sunitinib-resistance in clear cell renal cell carcinoma: A gene expression analysis.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 533-533
Author(s):  
Òscar Reig ◽  
Mercedes Marín-Aguilera ◽  
Juan José Lozano ◽  
Blanca Gonzalez ◽  
Carme Mallofré ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Expression Analysis ◽  
Renal Cell ◽  
Gene Expression Analysis ◽  
Predictive Biomarkers ◽  
Intrinsic Resistance ◽  
Primary Tumors ◽  
Novel Biomarkers

533 Background: Sunitinib is a standard first line treatment of metastatic renal cell carcinoma (ccRCC). Approximately 20% of patients experience primary resistance to therapy and no predictive biomarkers are available. The aim of our study is to discover novel biomarkers to predict response to sunitinib and to generate hypothesis about mechanisms of intrinsic resistance. Methods: Gene expression analysis was performed in formalin-fixed paraffin embedded (FFPE) samples from 44 patients with metastatic ccRCC treated with sunitinib in our institution. Affymetrix Human Gene 2.0 ST array was performed in primary tumors from 6 extremely sensitive (progression free survival (PFS) > 24 months) and 8 refractory (progression disease as best response) ccRCC. Technical validation with qPCR (Fluidigm Dynamic 96.96 Array) was performed in the whole cohort. The ΔΔCt method was used to quantify the relative amount of mRNA. Clinical and pathological data were correlated with gene expression. Results: 330 genes were differentially expressed between refractory and sensitive patients (p≤0.05). Network analysis showed 16 significant networks represented. Gene expression of 96 selected markers was tested in 47 primary tumors and 24 metastases. We found IL8, VEGF and NOTCH pathways upregulated on refractory patients. Survival analysis showed that the overexpression of IL8 correlated with a worse PFS (HR: 2.42, 95%CI1.27 – 4.63, p=0.0075) and overall survival (OS) (HR: 3.42, 95%CI 1.50 – 7.79, p=0.0034). Overexpression of VEGFBcorrelated with a prolonged PFS (HR 0.52, 95% CI 0.28 – 0.98, p=0.0415). Conclusions: Our results confirm the predictive value of IL8 expression in a cohort of ccRCC patients treated with sunitinib and suggests novel biomarkers of response to sunitinib. These data will be further validated in an independent cohort of patients.

scholarly journals Identification of Appropriate Housekeeping Genes for Gene Expression Studies in Human Renal Cell Carcinoma Under Hypoxic Conditions

2021 ◽  
Author(s):  
Luiz Felipe S. Teixeira ◽  
Rodrigo Gigliotti ◽  
Luana da Silva Ferreira ◽  
MARIA Helena Bellini
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Expression Analysis ◽  
Renal Cell ◽  
Gene Expression Analysis ◽  
Housekeeping Genes ◽  
Human Renal Cell Carcinoma ◽  
Von Hippel Lindau ◽  
Hypoxic Conditions

Abstract Background: Due to the loss of von Hippel-Lindau tumor suppressor function, clear renal cell carcinoma (ccRCC) deregulates hypoxia pathways. Quantitative PCR is a powerful tool for quantifying differential expression between normal and cancer cells. Reliable gene expression analysis requires the use of genes encoding housekeeping genes. Therefore, in this study, eight reference candidate genes were evaluated to determine their stability in 786-0 cells under normoxic and hypoxic conditions. Methods and Results: Four different tools were used to rank the most stable genes: GeNorm, NormFinder, BestKeeper, and Comparative Ct (ΔCt), and a general ranking was performed using the RankAggreg. According to the four algorithms, the TFRC reference gene was identified as the most stable, and therefore, no agreement was observed for the 2nd and 3rd positions. A general classification was then established using the RankAggreg tool. Finally, the three most suitable reference genes to be used in 786-0 cells under normoxic and hypoxic conditions were TFRC, RPLP0, and SDHA. Conclusions: To our knowledge, this is the first study to evaluate reliable genes that can be used in gene expression analysis in ccRcc under a hypoxic environment.

scholarly journals MET Expression in Primary and Metastatic Clear Cell Renal Cell Carcinoma: Implications of Correlative Biomarker Assessment to MET Pathway Inhibitors

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Brian Shuch ◽  
Ryan Falbo ◽  
Fabio Parisi ◽  
Adebowale Adeniran ◽  
Yuval Kluger ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Predictive Biomarkers ◽  
Cell Renal Cell Carcinoma ◽  
Primary Tumors ◽  
Distant Tissue ◽  
Significant Expression ◽  
Metastatic Sites

Aims. Inhibitors of the MET pathway hold promise in the treatment for metastatic kidney cancer. Assessment of predictive biomarkers may be necessary for appropriate patient selection. Understanding MET expression in metastases and the correlation to the primary site is important, as distant tissue is not always available.Methods and Results. MET immunofluorescence was performed using automated quantitative analysis and a tissue microarray containing matched nephrectomy and distant metastatic sites from 34 patients with clear cell renal cell carcinoma. Correlations between MET expressions in matched primary and metastatic sites and the extent of heterogeneity were calculated. The mean expression of MET was not significantly different between primary tumors when compared to metastases (P=0.1). MET expression weakly correlated between primary and matched metastatic sites (R=0.5) and a number of cases exhibited very high levels of discordance between these tumors. Heterogeneity within nephrectomy specimens compared to the paired metastatic tissues was not significantly different (P=0.39).Conclusions. We found that MET expression is not significantly different in primary tumors than metastatic sites and only weakly correlates between matched sites. Moderate concordance of MET expression and significant expression heterogeneity may be a barrier to the development of predictive biomarkers using MET targeting agents.

scholarly journals Comprehensive analysis of DNA methylation and gene expression reveals specific predictive biomarkers for renal cell carcinoma

2020 ◽  
Author(s):  
Yuan Zhang ◽  
Meng Xu ◽  
Jianmei Yin ◽  
Chunnv Li ◽  
Zhuang Ye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Clustering Analysis ◽  
Renal Cell ◽  
Cox Regression ◽  
Predictive Biomarkers ◽  
Unsupervised Clustering ◽  
Aberrant Methylation ◽  
Predictive Efficiency

Abstract Background We aimed to identify methylation-driven genes (MDGs) and predict the prognosis of clear cell renal cell carcinoma (ccRCC) based on several cohorts with high-throughput data.Methods The transcriptome profiles, 450K methylated data and corresponding clinical information were extracted from the cancer genome atlas (TCGA) database. MethylMix package was used to screen aberrant methylation events. Next, the Cox regression models were applied via survival package. Functional analysis was mainly performed based on ConsensusPathDB database. Besides, “mimifi” R package were adopted to analyze methylated alterations from three Gene Expression Omnibus (GEO) datasets. The predictive efficiency of constructed MDGs signature was assessed and validated in TCGA-KIRC and ICGC-RCC cohort, respectively. Unsupervised clustering analysis was conducted via ConsensusClusterPlus package. Moreover, MDGs-nomogram for OS prediction was conducted via glm and survival packages. Results Totally, we collected We finally identified 761 samples from TCGA-KIRC, ICGC-RCC, GSE61441, GSE105260 and GSE105261. We combined the expression data and 450K methylated data to find 5 hub prognostic MDGs (TAGLN2, PDK2, HHLA2, HOXA2, XAF1). We used the TCGA-KIRC cohort as the training dataset to verify the superior predictive significance of the 5 MDGs signature (AUC = 0.713), and validated it in ICGC-RCC cohort with AUC = 0.769. Kaplan-Meier analysis suggested that patients with high MDGs levels suffered from worse suvival outcomes. Besides, we further conducted the unsupervised clustering analysis in the whole ccRCC patients and identified the sub-cluster with the worst prognosis, indicating the MDGs could be used as efficient molecular classifier for ccRCC. We also identified 32 prognostic risk loci associated with hub MDGs in KIRC. Superior predictive efficiency was found in the MDGs-nomogram [Area Under Curve (AUC) of 3-year: 0.842, AUC of 5-year: 0.862], compared with traditional independent feature such as TNM stage (AUC of 3-year: 0.759, AUC of 5-year: 0.717). The 5 MDGs signature were mainly associated with oxygen metabolism, glycometabolism, even the HIF-1 signaling pathway. Conclusions Collectively, this study indicated several hub-MDGs and explored the prognostic value in ccRCC, which would provide new insights on the exploration of epigenetic pathogenesis and therapeutic targets for ccRCC.

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