Dataset Information

Identification of tissue microRNAs predictive of sunitinib activity in patients with metastatic renal cell carcinoma.

ABSTRACT: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell-carcinoma (MRCC) and to evaluate in vitro their mechanism of action in sunitinib resistance.We screened 673 microRNAs using TaqMan Low-density-Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n = 41). The most relevant differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenotypes selected from an independent cohort (n = 101). In vitro experiments were conducted to study the role of miR-942 in sunitinib resistance.TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p = 0.0074). High expression of miR-942, miR-628-5p, miR-133a, and miR-484 was significantly associated with decreased time to progression and overall survival. These microRNAs were also overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive cell line. MiR-942 overexpression in Caki-2 up-regulates MMP-9 and VEGF secretion which, in turn, promote HBMEC endothelial migration and sunitinib resistance.We identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity or resistance to sunitinib. MiR-942 was the best predictor of efficacy. We describe a novel paracrine mechanism through which high miR-942 levels in MRCC cells up-regulates MMP-9 and VEGF secretion to enhance endothelial migration and sunitinib resistance. Our results support further validation of these miRNA in clinical confirmatory studies.

SUBMITTER: Prior C

PROVIDER: S-EPMC3901669 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of tissue microRNAs predictive of sunitinib activity in patients with metastatic renal cell carcinoma.

Prior Celia C Perez-Gracia Jose Luis JL Garcia-Donas Jesus J Rodriguez-Antona Cristina C Guruceaga Elizabeth E Esteban Emilio E Suarez Cristina C Castellano Daniel D del Alba Aránzazu González AG Lozano Maria Dolores MD Carles Joan J Climent Miguel Angel MA Arranz Jose Angel JA Gallardo Enrique E Puente Javier J Bellmunt Joaquim J Gurpide Alfonso A Lopez-Picazo Jose Maria JM Hernandez Alvaro Gonzalez AG Mellado Begoña B Martínez Esther E Moreno Fernando F Font Albert A Calvo Alfonso A

PloS one 20140124 1

<h4>Purpose</h4>To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell-carcinoma (MRCC) and to evaluate in vitro their mechanism of action in sunitinib resistance.<h4>Methods</h4>We screened 673 microRNAs using TaqMan Low-density-Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n = 41). The most relevant differentially expressed microRNAs were s ...[more]

PMID: 24475095

Similar Datasets

Project description:Purpose: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell carcinoma (MRCC) and to validate them in a cellular model. Selected microRNAs were studied in serum from MRCC patients and healthy individuals. Methods: We screened 673 microRNAs using TaqMan Low-density Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n=41). Differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenoytpes selected from an independent cohort (n=117). Results were validated in a cellular model of sunitinib resistance and studied in serum from healthy individuals and MRCC patients. Results: TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p=0.0074). High expression of miR-942, miR-133a, miR484, and miR-628-5p was significantly associated with decreased time-to-progression and overall survival. These microRNAs were overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive parental cell line. Serum levels of miR-942, miR-133a, miR-484, miR-146a-5p, miR-374a and miR-486-5p were significantly reduced in MRCC patients compared to healthy controls. Conclusions: Our strategy identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity and resistance to sunitinib. Mir-942 was the best predictor of efficacy. Results were confirmed in a cellular model of sunitinib resistance. We also identified exosome derived serum microRNAs differentially expressed in MRCC patients and healthy individuals. Taqman Low Density Array for 6 FFPE tissues obtained from extreme phenotype MRCC patients, (n=3 marked resistance to sunitinib treatment patients and n=3 marked sensitivity to sunitinib treatment patients), was performanced to screen 667 microRNAs.

Dataset Information

Identification of tissue microRNAs predictive of sunitinib activity in patients with metastatic renal cell carcinoma.

Publications

Identification of tissue microRNAs predictive of sunitinib activity in patients with metastatic renal cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets