Dataset Information

RRM1 single nucleotide polymorphism -37C-->A correlates with progression-free survival in NSCLC patients after gemcitabine-based chemotherapy.

ABSTRACT:

Background

The ribonucleotide reductase M1 (RRM1) gene encodes the regulatory subunit of ribonucleotide reductase, the molecular target of gemcitabine. The overexpression of RRM1 mRNA in tumor tissues is reported to be associated with gemcitabine resistance. Thus, single nucleotide polymorphisms (SNPs) of the RRM1 gene are potential biomarkers of the response to gemcitabine chemotherapy. We investigated whether RRM1 expression in peripheral blood mononuclear cells (PBMCs) or SNPs were associated with clinical outcome after gemcitabine-based chemotherapy in advanced non-small cell lung cancer (NSCLC) patients.

Methods

PBMC samples were obtained from 62 stage IIIB and IV patients treated with gemcitabine-based chemotherapy. RRM1 mRNA expression levels were assessed by real-time PCR. Three RRM1 SNPs, -37C-->A, 2455A-->G and 2464G-->A, were assessed by direct sequencing.

Results

RRM1 expression was detectable in 57 PBMC samples, and SNPs were sequenced in 56 samples. The overall response rate to gemcitabine was 18%; there was no significant association between RRM1 mRNA expression and response rate (P = 0.560). The median progression-free survival (PFS) was 23.3 weeks in the lower expression group and 26.9 weeks in the higher expression group (P = 0.659). For the -37C-->A polymorphism, the median PFS was 30.7 weeks in the C(-)37A group, 24.7 weeks in the A(-)37A group, and 23.3 weeks in the C(-)37C group (P = 0.043). No significant difference in PFS was observed for the SNP 2455A-->G or 2464G-->A.

Conclusions

The RRM1 polymorphism -37C-->A correlated with PFS in NSCLC patients treated with gemcitabine-based chemotherapy. No significant correlation was found between PBMC RRM1 mRNA expression and the efficacy of gemcitabine.

SUBMITTER: Dong S

PROVIDER: S-EPMC2855513 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Publications

RRM1 single nucleotide polymorphism -37C-->A correlates with progression-free survival in NSCLC patients after gemcitabine-based chemotherapy.

Dong Song S Guo Ai-Lin AL Chen Zhi-Hong ZH Wang Zhen Z Zhang Xu-Chao XC Huang Ying Y Xie Zhi Z Yan Hong-Hong HH Cheng Hua H Wu Yi-Long YL

Journal of hematology & oncology 20100313

<h4>Background</h4>The ribonucleotide reductase M1 (RRM1) gene encodes the regulatory subunit of ribonucleotide reductase, the molecular target of gemcitabine. The overexpression of RRM1 mRNA in tumor tissues is reported to be associated with gemcitabine resistance. Thus, single nucleotide polymorphisms (SNPs) of the RRM1 gene are potential biomarkers of the response to gemcitabine chemotherapy. We investigated whether RRM1 expression in peripheral blood mononuclear cells (PBMCs) or SNPs were as ...[more]

PMID: 20226083

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Project description:ObjectivesThis study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.Materials and methodsIn a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients).ResultsTNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40-3.67); PFS: (HR (95%), 2.23 (1.36-3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70-0.79) and 0.72 (0.67-0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68-0.81) and 0.72 (0.66-0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS.ConclusionBy integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.

Project description:Abstract Background: Glioblastoma (GBM) accounts for approximately 50% of all glioma and among these tumors, are the most malignant. The current standard of care for patients with newly diagnosed GBM includes temozolomide and radiotherapy. Melanocortins are peptides with well-recognized anti-inflammatory and neuroprotective activity. No data are currently available on MC4R gene polymorphisms and gliomas or their relationship with radiotherapy or chemotherapy. Given the association of MC4R with antiinflammatory activity, neuroprotection, induction of neural stem/progenitor cell proliferation in brain hypoxia, and prevention of astrocyte apoptosis, the aim of this study was to retrospectively evaluate the possible prognostic/predictive role of the MC4R SNPs on GBM therapy. Methods: Sixty-one patients with a proven diagnosis of GBM, ECOG PS 0–2, age greater than18 years, and treated with radiotherapy and temozolomide were enrolled. Blood samples (3 ml) were collected in EDTA tubes and stored at -80°C. Genomic DNA was extracted using QIAamp DNA Blood Mini Kit. MC4R gene SNPs (rs17782313, rs489693, rs8087522, rs17700633) were analyzed; the allelic discrimination was performed using an ABI PRISM 7900 SDS and with validated TaqMan® SNP genotyping assays. PCR reactions were carried out according to the manufacturer’s protocol. Kaplan Meier curves were performed for statistical association with genotypes. A P value less than 0.0125 (Bonferroni’s correction) was considered significant. Results: Fifty-six patients were clinically evaluated. The median  progression-free survival (PFS) and median overall survival (OS) were 10.8 months and 23 months, respectively. The distribution of genotypes in the 56 patients did not deviate from Hardy-Weinberg equilibrium. A relevant finding of our study was the identification of a MC4R genotype that was significantly associated with PFS. Indeed, with regard to PFS, patients harbouring the rs489693 AA genotype had a median PFS of 3 months whereas patients with AC/CC genotypes had a median PFS of 13.7 months (P=0.0088). Interestingly, the rs489693 AA patients also had a lower median OS, even though the difference was not sta[[Unsupported Character - Codename ]]tistically significant as compared with the median OS of the AC/CC genotypes (15.6 vs. 24.6 months, respectively, P=0.274). No significant differences in PFS and OS for the other genotypes of the investigated MC4R polymorphisms were found. Conclusion: The rs489693 AA genotype is significantly associated with a shorter PFS in GBM patients treated with radiotherapy and temozolomide schedule. The present pharmacogenetic, retrospective, pilot study may represent the stimulus to prospectively investigate the role of rs489693 MC4R polymorphism as a predictive pharmacogenetic marker of GBM radio-chemotherapy.

Dataset Information

RRM1 single nucleotide polymorphism -37C-->A correlates with progression-free survival in NSCLC patients after gemcitabine-based chemotherapy.

Background

Methods

Results

Conclusions

Publications

RRM1 single nucleotide polymorphism -37C-->A correlates with progression-free survival in NSCLC patients after gemcitabine-based chemotherapy.

Similar Datasets

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