Biomedical subjects

Jennifer Dufour

Publications and source records attributed to Jennifer Dufour.

3 recordsLinked to original sources

PubMed2006

Association of DNA repair and steroid metabolism gene polymorphisms with clinical late toxicity in patients treated with conformal radiotherapy for prostate cancer.

OBJECTIVE: To explore the possible relationship between single nucleotide polymorphisms (SNP) in candidate genes encoding DNA damage recognition/repair/response and steroid metabolism proteins with respect to clinical radiation toxicity in a retrospective cohort of patients previously treated with three-dimensional conformal radiotherapy (3-DCRT) for prostate cancer. EXPERIMENTAL DESIGN: One hundred twenty-four patients with prostate cancer underwent 3-DCRT at our institution between September 1996 and December 2000. Of these, 83 consented for follow-up of blood sampling and SNP analysis. Twenty-eight patients were documented as having experienced grade >/=2 late bladder or rectal toxicity (scoring system of Radiation Therapy Oncology Group) on at least one follow-up visit. We analyzed 49 SNPs in BRCA1, BRCA2, ESR1, XRCC1, XRCC2, XRCC3, NBN, RAD51, RAD52, LIG4, ATM, BCL2, TGFB1, MSH6, ERCC2, XPF, NR3C1, CYP1A1, CYP2C9, CYP2C19, CYP3A5, CYP2D6, CYP11B2, and CYP17A1 genes using the Pyrosequencing technique. RESULTS: Significant univariate associations with late rectal or bladder toxicity (grade >/=2) were found for XRCC3 (A>G 5' untranslated region NT 4541), LIG4 (T>C Asp(568)Asp), MLH1 (C>T, Val(219)Ile), CYP2D6*4 (G>A splicing defect), mean rectal and bladder dose, dose to 30% of rectum or bladder, and age <60 years. On Cox multivariate analysis, significant associations with toxicity were found for LIG4 (T>C, Asp(568)Asp), ERCC2 (G>A, Asp(711)Asp), CYP2D6*4 (G>A, splicing defect), mean bladder dose >60 Gy, and dose to 30% of rectal volume >75 Gy. CONCLUSIONS: In this study, we identified SNPs in LIG4, ERCC2, and CYP2D6 genes as putative markers to predict individuals at risk for complications arising from radiation therapy in prostate cancer.

Aged↗

PubMed2005

Identification and functional characterization of variants in human concentrative nucleoside transporter 3, hCNT3 (SLC28A3), arising from single nucleotide polymorphisms in coding regions of the hCNT3 gene.

INTRODUCTION: Human concentrative nucleoside transporter 3, hCNT3 (SLC28A3), which mediates transport of purine and pyrimidine nucleosides and a variety of antiviral and anticancer nucleoside drugs, was investigated to determine if there are single nucleotide polymorphisms in the coding regions of the hCNT3 gene. METHODS AND RESULTS: Ninety-six DNA samples from Caucasians (Coriell Panel) were sequenced and sixteen variants in exons and flanking intronic regions were identified, of which five were coding variants; three of these were non-synonymous (S5N, L131F, Y513F) and were further investigated for functional alterations of the resulting recombinant proteins in Saccharomyces cerevisiae and Xenopus laevis oocytes. In yeast, immunostaining and fluorescence quantitation of the reference (wild-type) and variant CNT3 proteins showed similar levels of expression. Kinetic studies were undertaken in yeast with a high through-put semi-automated assay process; reference hCNT3 exhibited Km values of 1.7+/-0.3, 3.6+/-1.3, 2.2+/-0.7, and 2.1+/-0.6 muM and Vmax values of 1402+/-286, 1310+/-113, 1020+/-44, and 1740+/-114 pmol/mg/min, respectively, for uridine, cytidine, adenosine and inosine. Similar Km and Vmax values were obtained for the three variant proteins assayed in yeast under identical conditions. All of the characterized hCNT3 variants produced in oocytes retained sodium and proton dependence of uridine transport based on measurements of radioisotope flux and two-electrode voltage-clamp studies. CONCLUSION: These results suggested a high degree of conservation of function for hCNT3 in the Caucasian population.

Adenosine↗

PubMed2004

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

Algorithms↗