Scandiatransplant acceptable mismatch program-10 years with an effective strategy for transplanting highly sensitized patients.

In March 2009, the Scandiatransplant acceptable mismatch program (STAMP) was introduced as a strategy toward improving kidney allocation to highly sensitized patients. Patients with a transplantability score ≤ 2% are potential candidates for the program. Samples are analyzed and acceptable antigens (HLA-A, B, C, DRB1, DRB3/4/5, DQB1, DQA1, DPB1, DPA1) are defined by the local tissue typing laboratory and finally evaluated by a steering committee. In the matching algorithm, patients have the highest priority when the donor's antigens are all among the recipient's own or acceptable HLA antigens. In the period from 2009 to 2020, we have transplanted 278 highly sensitized kidney patients through the program. The graft survival of the STAMP patients was compared with 9002 deceased donor kidney-transplanted patients, transplanted in the same time period. The 10-year graft survival was 73.4% (95% CI: 60.3-90.0) for STAMP and 82.9% (95% CI: 81.6-84.3) for the reference group. (p = .2). In conclusion, the 10-year allograft survival demonstrates that the STAMP allocation algorithm is immunological safe. The program is continuously monitored and evaluated, and the introduction of matching for all HLA loci is a huge improvement to the program and demonstrate technical adaptability as well as clinical flexibility in a de-centralized organization.

MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data.

AIM: To develop a web tool for survival analysis based on CpG methylation patterns. MATERIALS & METHODS: We utilized methylome data from 'The Cancer Genome Atlas' and used the Cox proportional-hazards model to develop an interactive web interface for survival analysis. RESULTS: MethSurv enables survival analysis for a CpG located in or around the proximity of a query gene. For further mining, cluster analysis for a query gene to associate methylation patterns with clinical characteristics and browsing of top biomarkers for each cancer type are provided. MethSurv includes 7358 methylomes from 25 different human cancers. CONCLUSION: The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.

Mutation analysis and copy number alterations of KIF23 in non-small-cell lung cancer exhibiting KIF23 over-expression.

KIF23 was recently suggested to be a potential molecular target for the treatment of lung cancer. This proposal is based on elevated expression of KIF23 in several tumors affecting breast, lung, brain, and liver, and also on the presence of KIF23 mutations in melanoma and colorectal cancer. Recently, we identified a mutation in the KIF23 gene causing a rare hereditary form of dyserythropoietic anemia (CDA III) with predisposition to blood cancer. We suggested that KIF23 overexpression in tumors might be due to the presence of activating somatic mutations, and therefore, mutation screening of the KIF23 in 15 non-small-cell lung cancer (NSCLC) cases with elevated expression level of KIF23 was undertaken. Eight sequence variants were found in all samples. Furthermore, one variant was present in two cases, and one variant was case specific. Nine variants were previously reported while one variant lacks frequency information. Nine of ten cases available for single nucleotide polymorphism-array analysis demonstrated aberrant karyotypes with additional copy of entire chromosome 15. Thus, no activating somatic mutations in coding regions of the KIF23 were found. Furthermore, no mutations were detected in cell cycle genes homology region in KIF23 promoter responsible for p53-dependent repression of KIF23 expression. We showed that the elevated level of KIF23 could be due to additional copy of chromosome 15 demonstrated in 90% of NSCLC cases analyzed in this study. Considering the crucial role of KIF23 in the final step of mitosis, the gene is a potential molecular marker, and for better understanding of its role in cancer development, more tumors should be analyzed.

Correction: Methylation Markers of Early-Stage Non-Small Cell Lung Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0039813.].

seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data.

MOTIVATION: One of the main goals of large scale methylation studies is to detect differentially methylated loci. One way is to approach this problem sitewise, i.e. to find differentially methylated positions (DMPs). However, it has been shown that methylation is regulated in longer genomic regions. So it is more desirable to identify differentially methylated regions (DMRs) instead of DMPs. The new high coverage arrays, like Illuminas 450k platform, make it possible at a reasonable cost. Few tools exist for DMR identification from this type of data, but there is no standard approach. RESULTS: We propose a novel method for DMR identification that detects the region boundaries according to the minimum description length (MDL) principle, essentially solving the problem of model selection. The significance of the regions is established using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches. AVAILABILITY AND IMPLEMENTATION: The methods have been implemented in R package seqlm that is available through Github: https://github.com/raivokolde/seqlm CONTACT: rkolde@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic and epigenetic regulation of gene expression in fetal and adult human livers.

BACKGROUND: The liver plays a central role in the maintenance of homeostasis and health in general. However, there is substantial inter-individual variation in hepatic gene expression, and although numerous genetic factors have been identified, less is known about the epigenetic factors. RESULTS: By analyzing the methylomes and transcriptomes of 14 fetal and 181 adult livers, we identified 657 differentially methylated genes with adult-specific expression, these genes were enriched for transcription factor binding sites of HNF1A and HNF4A. We also identified 1,000 genes specific to fetal liver, which were enriched for GATA1, STAT5A, STAT5B and YY1 binding sites. We saw strong liver-specific effects of single nucleotide polymorphisms on both methylation levels (28,447 unique CpG sites (meQTL)) and gene expression levels (526 unique genes (eQTL)), at a false discovery rate (FDR) < 0.05. Of the 526 unique eQTL associated genes, 293 correlated significantly not only with genetic variation but also with methylation levels. The tissue-specificities of these associations were analyzed in muscle, subcutaneous adipose tissue and visceral adipose tissue. We observed that meQTL were more stable between tissues than eQTL and a very strong tissue-specificity for the identified associations between CpG methylation and gene expression. CONCLUSIONS: Our analyses generated a comprehensive resource of factors involved in the regulation of hepatic gene expression, and allowed us to estimate the proportion of variation in gene expression that could be attributed to genetic and epigenetic variation, both crucial to understanding differences in drug response and the etiology of liver diseases.

DNA methylome profiling of human tissues identifies global and tissue-specific methylation patterns.

BACKGROUND: DNA epigenetic modifications, such as methylation, are important regulators of tissue differentiation, contributing to processes of both development and cancer. Profiling the tissue-specific DNA methylome patterns will provide novel insights into normal and pathogenic mechanisms, as well as help in future epigenetic therapies. In this study, 17 somatic tissues from four autopsied humans were subjected to functional genome analysis using the Illumina Infinium HumanMethylation450 BeadChip, covering 486 428 CpG sites. RESULTS: Only 2% of the CpGs analyzed are hypermethylated in all 17 tissue specimens; these permanently methylated CpG sites are located predominantly in gene-body regions. In contrast, 15% of the CpGs are hypomethylated in all specimens and are primarily located in regions proximal to transcription start sites. A vast number of tissue-specific differentially methylated regions are identified and considered likely mediators of tissue-specific gene regulatory mechanisms since the hypomethylated regions are closely related to known functions of the corresponding tissue. Finally, a clear inverse correlation is observed between promoter methylation within CpG islands and gene expression data obtained from publicly available databases. CONCLUSIONS: This genome-wide methylation profiling study identified tissue-specific differentially methylated regions in 17 human somatic tissues. Many of the genes corresponding to these differentially methylated regions contribute to tissue-specific functions. Future studies may use these data as a reference to identify markers of perturbed differentiation and disease-related pathogenic mechanisms.

Methylation markers of early-stage non-small cell lung cancer.

BACKGROUND: Despite of intense research in early cancer detection, there is a lack of biomarkers for the reliable detection of malignant tumors, including non-small cell lung cancer (NSCLC). DNA methylation changes are common and relatively stable in various types of cancers, and may be used as diagnostic or prognostic biomarkers. METHODS: We performed DNA methylation profiling of samples from 48 patients with stage I NSCLC and 18 matching cancer-free lung samples using microarrays that cover the promoter regions of more than 14,500 genes. We correlated DNA methylation changes with gene expression levels and performed survival analysis. RESULTS: We observed hypermethylation of 496 CpGs in 379 genes and hypomethylation of 373 CpGs in 335 genes in NSCLC. Compared to adenocarcinoma samples, squamous cell carcinoma samples had 263 CpGs in 223 hypermethylated genes and 513 CpGs in 436 hypomethylated genes. 378 of 869 (43.5%) CpG sites discriminating the NSCLC and control samples showed an inverse correlation between CpG site methylation and gene expression levels. As a result of a survival analysis, we found 10 CpGs in 10 genes, in which the methylation level differs in different survival groups. CONCLUSIONS: We have identified a set of genes with altered methylation in NSCLC and found that a minority of them showed an inverse correlation with gene expression levels. We also found a set of genes that associated with the survival of the patients. These newly-identified marker candidates for the molecular screening of NSCLC will need further analysis in order to determine their clinical utility.