The most prominent treatment for the serious cases of Crohn's disease (CD) are biological tumour necrosis factor (TNF) inhibitors. Unfortunately, therapy nonresponse is still a serious issue in ~1/3 of CD patients. Accurate prediction of responsiveness prior to therapy start would therefore be of great value. Clinical predictors have, however, proved insufficient. Here, we integrate genomic and expression data on potential pre-treatment biomarkers of anti-TNF nonresponse. We show that there is almost no overlap between genomic (annotated with tissue-specific expression quantitative trait loci data) and transcription (RNA and protein data) biomarkers. Furthermore, using interaction networks we demonstrate there is little direct interaction between the proposed biomarkers, though a majority do have common interactors connecting them into networks. Our gene ontology analysis shows that these networks have roles in apoptotic signalling, response to oxidative stress and inflammation pathways. We conclude that a more systematic approach with genome-wide search of genomic and expression biomarkers in the same patients is needed in future studies.
COBISS.SI-ID: 512899384
Response to anti-TNF therapy is crucial for life expectancy and life quality in patients with severe Crohn's disease. We investigated if a previously reported gene expression profile predictive for infliximab response could be also applied to adalimumab response in an independent cohort. Forty-seven Slovene Crohn's disease patients indicated for adalimumab therapy were enrolled in the study. Inflamed and non-inflamed colon biopsy samples were obtained during routine colonoscopy prior to adalimumab treatment. Response to adalimumab was measured with IBDQ. Gene expression in inflamed and non-inflamed colon biopsy samples was measured with RT-qPCR. Genotypes were extracted from previously available genotype data. Statistical analysis was performed with SPSS software. The R package e1071 was used to train bootstrap aggregated support vector machines (SVM).SVM prediction model analysis was used to analyze pooled, non-inflamed, and inflamed colon tissue datasets using IBDQ response after 4, 12, 20 and 30 weeks of adalimumab treatment. The bagging approach was used in an endeavor to obtain 100?% accuracy using 10?×?100 or 100?×?100 iterations. Average adalimumab response prediction accuracy is 75.5?% for pooled samples, 90.5?% for inflamed samples, and 100?% for non-inflamed samples. Moreover, models trained on selected SNPs from analyzed genes had an average accuracy of 92.8?%, confirming the involvement of genetic regions mapping the reported genes. Finally, using combined gene expression and SNP data we observed 100?% adalimumab response prediction accuracy for pooled, inflamed, and non-inflamed datasets. Our study supports the reported genetic anti-TNF response profile and extends it for adalimumab prediction.
COBISS.SI-ID: 6817599
We report two restriction enzyme-based approaches for generating clean locus-specific unmethylated controls for methylation-sensitive high-resolution melting (MS-HRM) analyses. These unmethylated standards are derived from DNA treated with the demethylating agent 5-aza-2-deoxycytidine (5-Aza-dc). By using them, we overcome a limitation of 5-Aza-dc treatment - incomplete demethylation at various genomic regions. When 5-Aza-dc-treated DNA is used directly as unmethylated MS-HRM standard, partially demethylated DNA can give false methylation results. MS-HRM assay differentiates between methylated and unmethylated bisulfite-treated DNA based on the different melting profiles of PCR products amplified from them. To estimate test sample methylation levels, test sample melting profiles are compared to those of methylation standards. With our pure unmethylated controls, adequate standards of known methylation levels can be prepared for single-locus MS-HRM.
COBISS.SI-ID: 22141718