Single-track sequencing for genotyping of multiple SNPs in the N-acetyltransferase 1 (NAT1) gene

BACKGROUND: Fast, cheap and reliable methods are needed to identify large populations, which may be at risk in relation to environmental exposure. Polymorphisms in NAT1 (N-acetyl transferase) may be suitable markers to identify individuals at risk. RESULTS: A strategy allowing to address simultaneously 24 various genetic variants in the NAT1 gene using the single sequencing reaction method on the same PCR product is described. A modified automated DNA sequencing using only one of the sequence terminators was used to genotype PCR products in single-track sequencing reactions of NAT1 and was shown to be universal for both DNA sequencing using labeled primers and labeled nucleotides. By this method we detected known SNPs at site T640G, which confers the NAT1*11 allele with frequency of 0.036, further T1088A and C1095A with frequency of 0.172 and 0.188, respectively and a deletion of TAATAATAA in the poly A signal area with a frequency 0.031. All observed frequencies were in Hardy Weinberg equilibrium and comparable to those in Caucasian population. The single-track signatures of the variant genotypes were verified on samples previously genotyped by RLFP. CONCLUSIONS: The method could be of great help to scientists in the field of molecular epidemiology of screening of large populations for known informative biomarkers of susceptibility, such as NAT1

Modeling of mouse experiments suggests that optimal anti-hormonal treatment for breast cancer is diet-dependent

Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.Comment: 44 pages, 21 figure

Intratumoural mRNA expression of genes from the oestradiol metabolic pathway and clinical and histopathological parameters of breast cancer

INTRODUCTION: The expression of the oestrogen receptor (ER) is one of the more important clinical parameters of breast cancer. However, the relationship between the ER and its ligand, oestradiol, and the enzymes that synthesise it are not well understood. The expression of mRNA transcripts of members of the oestradiol metabolic and signalling pathways including the ER was studied in detail. METHOD: mRNA transcripts for aromatase (CYP19), 17-β-hydroxysteroid dehydrogenase I, 17-β-hydroxysteroid dehydrogenase II, ERα, ERβ, steroid sulfatase (STS), oestradiol sulfotransferase (EST), cyclin D(1 )(CYCLD1) and ERBB2 were fluorometrically quantified by competitive RT-PCR using an internal standard in 155 breast carcinomas. In addition, the transcripts of CYP19 were analysed for alternative splicing/usage of exon 1 and an alternative poly A tail. RESULTS: A great variability of expression was observed, ranging from 0 to 2376 amol/mg RNA. The highest levels were observed for STS and EST, and the lowest levels (close to zero) were observed for the 17-β-hydroxysteroid dehydrogenase isoenzymes. The levels of mRNA expression were analysed with respect to clinical and histopathological parameters as well as for disease-free survival. High correlation of the mRNA expression of STS, EST and 17-β-hydroxysteroid dehydrogenase in the tumours suggested a common regulation, possibly by their common metabolite (oestradiol). Hierarchical clustering analysis in the 155 patients resulted in two main clusters, representing the ERα-negative and ERα-positive breast cancer cases. The mRNA expression of the oestradiol metabolising enzymes did not follow the expression of the ERα in all cases, leading to the formation of several subclasses of tumours. Patients with no expression of CYP19 and patients with high levels of expression of STS had significantly shorter disease-free survival time (P > 0.0005 and P < 0.03, respectively). Expression of ERβ mRNA was a better prognostic factor than that of ERα in this material. CONCLUSION: Our results indicate the importance of CYP19 and the enzymes regulating the oestrone sulfate metabolism as factors of disease-free survival in breast cancer, in addition to the well-known factors ER and ERBB2

Integrated study of copy number states and genotype calls using high-density SNP arrays

We propose a statistical framework, named genoCN, to simultaneously dissect copy number states and genotypes using high-density SNP (single nucleotide polymorphism) arrays. There are at least two types of genomic DNA copy number differences: copy number variations (CNVs) and copy number aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared with CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. GenoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue. We evaluated genoCN by applications to 162 HapMap individuals and a brain tumor (glioblastoma) dataset and showed that our method can successfully identify both types of copy number differences and produce high-quality genotype calls

MicroRNA in combination with HER2-targeting drugs reduces breast cancer cell viability in vitro

HER2-positive (HER2+) breast cancer patients that do not respond to targeted treatment have a poor prognosis. The effects of targeted treatment on endogenous microRNA (miRNA) expression levels are unclear. We report that responsive HER2+breast cancer cell lines had a higher number of miRNAs with altered expression after treatment with trastuzumab and lapatinib compared to poorly responsive cell lines. To evaluate whether miRNAs can sensitize HER2+cells to treatment, we performed a high-throughput screen of 1626 miRNA mimics and inhibitors in combination with trastuzumab and lapatinib in HER2+breast cancer cells. We identified eight miRNA mimics sensitizing cells to targeted treatment, miR-101-5p, mir-518a-5p, miR-19b-2-5p, miR-1237-3p, miR-29a-3p, miR-29c-3p, miR-106a-5p, and miR-744-3p. A higher expression of miR-101-5p predicted better prognosis in patients with HER2+breast cancer (OS: p=0.039; BCSS: p=0.012), supporting the tumor-suppressing role of this miRNA. In conclusion, we have identified miRNAs that sensitize HER2+breast cancer cells to targeted therapy. This indicates the potential of combining targeted drugs with miRNAs to improve current treatments for HER2+breast cancers.Peer reviewe

High-throughput screen in vitro identifies dasatinib as a candidate for combinatorial treatment with HER2-targeting drugs in breast cancer

Peer reviewe

Multilocus analysis of SNP and metabolic data within a given pathway

BACKGROUND: Complex traits, which are under the influence of multiple and possibly interacting genes, have become a subject of new statistical methodological research. One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common multifactorial diseases and their association to different quantitative phenotypic traits. RESULTS: Two types of data from the same metabolic pathway were used in the analysis: categorical measurements of 18 SNPs; and quantitative measurements of plasma levels of several steroids and their precursors. Using the combinatorial partitioning method we tested various thresholds for each metabolic trait and each individual SNP locus. One SNP in CYP19, 3UTR, two SNPs in CYP1B1 (R48G and A119S) and one in CYP1A1 (T461N) were significantly differently distributed between the high and low level metabolic groups. The leave one out cross validation method showed that 6 SNPs in concert make 65% correct prediction of phenotype. Further we used pattern recognition, computing the p-value by Monte Carlo simulation to identify sets of SNPs and physiological characteristics such as age and weight that contribute to a given metabolic level. Since the SNPs detected by both methods reside either in the same gene (CYP1B1) or in 3 different genes in immediate vicinity on chromosome 15 (CYP19, CYP11 and CYP1A1) we investigated the possibility that they form intragenic and intergenic haplotypes, which may jointly account for a higher activity in the pathway. We identified such haplotypes associated with metabolic levels. CONCLUSION: The methods reported here may enable to study multiple low-penetrance genetic factors that together determine various quantitative phenotypic traits. Our preliminary data suggest that several genes coding for proteins involved in a common pathway, that happen to be located on common chromosomal areas and may form intragenic haplotypes, together account for a higher activity of the whole pathway

DNA methylation profiling in doxorubicin treated primary locally advanced breast tumours identifies novel genes associated with survival and treatment response

<p>Abstract</p> <p>Background</p> <p>Breast cancer is the most frequent cancer in women and consists of a heterogeneous collection of diseases with distinct histopathological, genetic and epigenetic characteristics. In this study, we aimed to identify DNA methylation based biomarkers to distinguish patients with locally advanced breast cancer who may benefit from neoadjuvant doxorubicin treatment.</p> <p>Results</p> <p>We investigated quantitatively the methylation patterns in the promoter regions of 14 genes (<it>ABCB1</it>, <it>ATM</it>, <it>BRCA1</it>, <it>CDH3</it>, <it>CDKN2A</it>, <it>CXCR4</it>, <it>ESR1</it>, <it>FBXW7</it>, <it>FOXC</it>1, <it>GSTP1</it>, <it>IGF2</it>, <it>HMLH1</it>, <it>PPP2R2B</it>, and <it>PTEN</it>) in 75 well-described pre-treatment samples from locally advanced breast cancer and correlated the results to the available clinical and molecular parameters. Six normal breast tissues were used as controls and 163 unselected breast cancer cases were used to validate associations with histopathological and clinical parameters.</p> <p>Aberrant methylation was detected in 9 out of the 14 genes including the discovery of methylation at the <it>FOXC1 </it>promoter. Absence of methylation at the <it>ABCB1 </it>promoter correlated with progressive disease during doxorubicin treatment. Most importantly, the DNA methylation status at the promoters of <it>GSTP1</it>, <it>FOXC1 </it>and <it>ABCB1 </it>correlated with survival, whereby the combination of methylated genes improved the subdivision with respect to the survival of the patients. In multivariate analysis <it>GSTP1 </it>and <it>FOXC1 </it>methylation status proved to be independent prognostic markers associated with survival.</p> <p>Conclusions</p> <p>Quantitative DNA methylation profiling is a powerful tool to identify molecular changes associated with specific phenotypes. Methylation at the <it>ABCB1 </it>or <it>GSTP1 </it>promoter improved overall survival probably due to prolonged availability and activity of the drug in the cell while <it>FOXC1 </it>methylation might be a protective factor against tumour invasiveness. <it>FOXC1 </it>proved to be general prognostic factor, while <it>ABCB1 </it>and <it>GSTP1 </it>might be predictive factors for the response to and efficacy of doxorubicin treatment. Pharmacoepigenetic effects such as the reported associations in this study provide molecular explanations for differential responses to chemotherapy and it might prove valuable to take the methylation status of selected genes into account for patient management and treatment decisions.</p