Biomedical subjects

Vessela Kristensen

Publications and source records attributed to Vessela Kristensen.

5 recordsLinked to original sources

PubMed2007

Somatic sequence alterations in twenty-one genes selected by expression profile analysis of breast carcinomas.

INTRODUCTION: Genomic alterations have been observed in breast carcinomas that affect the capacity of cells to regulate proliferation, signaling, and metastasis. Re-sequence studies have investigated candidate genes based on prior genetic observations (changes in copy number or regions of genetic instability) or other laboratory observations and have defined critical somatic mutations in genes such as TP53 and PIK3CA. METHODS: We have extended the paradigm and analyzed 21 genes primarily identified by expression profiling studies, which are useful for breast cancer subtyping and prognosis. This study conducted a bidirectional re-sequence analysis of all exons and 5', 3', and evolutionarily conserved regions (spanning more than 16 megabases) in 91 breast tumor samples. RESULTS: Eighty-seven unique somatic alterations were identified in 16 genes. Seventy-eight were single base pair alterations, of which 23 were missense mutations; 55 were distributed across conserved intronic regions or the 5' and 3' regions. There were nine insertion/deletions. Because there is no a priori way to predict whether any one of the identified synonymous and noncoding somatic alterations disrupt function, analysis unique to each gene will be required to establish whether it is a tumor suppressor gene or whether there is no effect. In five genes, no somatic alterations were observed. CONCLUSION: The study confirms the value of re-sequence analysis in cancer gene discovery and underscores the importance of characterizing somatic alterations across genes that are related not only by function, or functional pathways, but also based upon expression patterns.

Breast Neoplasms↗

PubMed2006

ATM variants and cancer risk in breast cancer patients from Southern Finland.

BACKGROUND: Individuals heterozygous for germline ATM mutations have been reported to have an increased risk for breast cancer but the role for ATM genetic variants for breast cancer risk has remained unclear. Recently, a common ATM variant, ATMivs38 -8T>C in cis with the ATMex39 5557G>A (D1853N) variant, was suggested to associate with bilateral breast cancer among familial breast cancer patients from Northern Finland. We have here evaluated the 5557G>A and ivs38-8T>C variants in an extensive case-control association analysis. We also aimed to investigate whether there are other ATM mutations or variants contributing to breast cancer risk in our population. METHODS: Two common ATM variants, 5557G>A and ivs38-8T>C, previously suggested to associate with bilateral breast cancer, were genotyped in an extensive set of 786 familial and 884 unselected breast cancer cases as well as 708 healthy controls. We also screened the entire coding region and exon-intron boundaries of the ATM gene in 47 familial breast cancer patients and constructed haplotypes of the patients. The identified variants were also evaluated for increased breast cancer risk among additional breast cancer cases and controls. RESULTS: Neither of the two common variants, 5557G>A and ivs38-8T>C, nor any haplotype containing them, was significantly associated with breast cancer risk, bilateral breast cancer or multiple primary cancers in any of the patient groups or subgoups. Three rare missense alterations and one intronic change were each found in only one patient of over 250 familial patients studied and not among controls. The fourth missense alteration studied further was found with closely similar frequencies in over 600 familial cases and controls. CONCLUSION: Altogether, our results suggest very minor effect, if any, of ATM genetic variants on familial breast cancer in Southern Finland. Our results do not support association of the 5557G>A or ivs38-8T>C variant with increased breast cancer risk or with bilateral breast cancer.

Ataxia Telangiectasia Mutated Proteins↗

PubMed2006

A quality assessment survey of SNP genotyping laboratories.

To survey the quality of SNP genotyping, a joint Nordic quality assessment (QA) round was organized between 11 laboratories in the Nordic and Baltic countries. The QA round involved blinded genotyping of 47 DNA samples for 18 or six randomly selected SNPs. The methods used by the participating laboratories included all major platforms for small- to medium-size SNP genotyping. The laboratories used their standard procedures for SNP assay design, genotyping, and quality control. Based on the joint results from all laboratories, a consensus genotype for each DNA sample and SNP was determined by the coordinator of the survey, and the results from each laboratory were compared to this genotype. The overall genotyping accuracy achieved in the survey was excellent. Six laboratories delivered genotype data that were in full agreement with the consensus genotype. The average accuracy per SNP varied from 99.1 to 100% between the laboratories, and it was frequently 100% for the majority of the assays for which SNP genotypes were reported. Lessons from the survey are that special attention should be given to the quality of the DNA samples prior to genotyping, and that a conservative approach for calling the genotypes should be used to achieve a high accuracy.

Clinical Laboratory Techniques↗

PubMed2006

Analysis of SNP-expression association matrices.

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 50 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Algorithms↗

PubMed2005

Analysis of SNP-expression association matrices.

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 49 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Algorithms↗