Citations

Abstract Background Radiation therapy is used to treat half of the all cancer patients. Response to radiation therapy varies widely, in terms of both response and toxicity related events. Previous studies suggested almost 80% of the variation in normal tissue response to radiation treatment was likely to be due to genetic factors. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers that might help predict radiation response using a the “Human Variation Panel” lymphoblastoid cell lines (LCLs) that consists 287 ethnically defined cell lines. Method Basal gene expression level, 1.3 million genome-wide SNP data were obtained for 287 human LCLs using Affymetrix U133 plus 2.0 GeneChip, Illumina HumanHap 550K, 510S BeadChips and Affymetix SNP Array 6.0. Radiation cytotoxicity assay was also performed to obtain the area under radiation dose response curve (AUC), a summary measure for cytotoxicity for the same cell lines. Functional validation of candidate genes, selected based on the integrated analysis of SNP, expression and AUC data, was performed in multiple cancer cell lines using specific siRNAs followed by MTS and colony formation assays. Results 27 loci, containing at least 2 SNPs of p values <10−4 within 50kb were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC (p< 10−3). The integrated analysis of 27 loci, 54,000 basal expression probe sets and radiation AUC identified 50 unique SNPs associated with AUC were also associated with gene expression of 39 unique annotated genes. In addition, these 39 genes were associated with radiation AUC with p values <10−3. Based on the GWAS and integrated analysis of SNP, expression array and radiation AUC data, 22 genes were selected for functional validation studies using siRNA knock down followed by MTS and colony formation assays in multiple tumor cell lines. C13orf34 (Bora), MAD2L1, PLK4, TPD52 significantly altered the radiation sensitivity in at least 2 cancer cell lines for both MTS and colony formation assays (p<0.05). Knock down of DEPDC1B, a gene containing 2 SNPs at the 3′-end that was significantly related with Bora gene expression level (p<10−4) significantly desensitized cancer cells to radiation treatment. Both microarray data and QRT-PCR analysis indicated that expression levels for those two genes were significantly correlated. Conclusion Our results using the lymphoblastoid cell lines might help identify novel biomarkers that contribute to variation in response to radiation therapy and help understand the mechanisms underlying the association. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3602.

Abstract Two cytidine analogues, gemcitabine (dFdC) and 1-β-d-arabinofuranosylcytosine (AraC), show significant therapeutic effect in a variety of cancers. However, response to these drugs varies widely. Evidence from tumor biopsy samples shows that expression levels for genes involved in the cytidine transport, metabolism, and bioactivation pathway contribute to this variation in response. In the present study, we set out to test the hypothesis that variation in gene expression both within and outside of this “pathway” might influence sensitivity to gemcitabine and AraC. Specifically, Affymetrix U133 Plus 2.0 GeneChip and cytotoxicity assays were performed to obtain basal mRNA expression and IC50 values for both drugs in 197 ethnically defined Human Variation Panel lymphoblastoid cell lines. Genes with a high degree of association with IC50 values were involved mainly in cell death, cancer, cell cycle, and nucleic acid metabolism pathways. We validated selected significant genes by performing real-time quantitative reverse transcription-PCR and selected two representative candidates, NT5C3 (within the pathway) and FKBP5 (outside of the pathway), for functional validation. Those studies showed that down-regulation of NT5C3 and FKBP5 altered tumor cell sensitivity to both drugs. Our results suggest that cell-based model system studies, when combined with complementary functional characterization, may help to identify biomarkers for response to chemotherapy with these cytidine analogues. [Cancer Res 2008;68(17):7050–8]

Abstract Purpose: The proteasome is a multisubunit cellular organelle that functions as a nonlysosomal threonine protease. Proteasomes play a critical role in the degradation of proteins, regulating a variety of cellular processes, and they are also the target for antineoplastic proteasome inhibitors. Genetic variation in proteasome subunits could influence both proteasome function and response to drug therapy. Experimental Design: We resequenced genes encoding the three active proteasome β subunits using 240 DNA samples from four ethnic groups and the β5 subunit gene in 79 DNA samples from multiple myeloma patients who had been treated with the proteasome inhibitor bortezomib. Resequencing was followed by functional studies of polymorphisms identified in the coding region and 3′-flanking region (3′-FR) of PSMB5, the gene encoding the target for clinically useful proteasome inhibitors. Results: Resequencing of 240 DNA samples identified a series of novel ethnic-specific polymorphisms that are not represented in public databases. The PSMB5 3′-FR 1042 G allele significantly increased transcription during reporter gene studies, observations confirmed by genotype-phenotype correlations between single nucleotide polymorphisms (SNP) in PSMB5 and mRNA expression in the 240 lymphoblastoid cell lines from which the resequenced DNA was obtained. Studies with patient DNA samples identified additional novel PSMB5 polymorphisms, including a SNP and an insertion in the 3′-FR. Reporter-gene studies indicated that these two novel polymorphisms might decrease transcription. Conclusions: These results show that nonsynonymous coding SNPs in the PSMB5 gene did not show significant effects on proteasome activity, but SNPs did influence transcription. Future studies might focus on regulatory region polymorphisms.