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Record W2564386366 · doi:10.1158/1538-7445.am2015-1058

Abstract 1058: Novel insights into the genetic and epigenetic regulation of the MLH1 CpG island and shore in colorectal cancer

2015· article· en· W2564386366 on OpenAlexaff
Andrea J. Savio, Mathieu Lemire, Miralem Mrkonjic, Steven Gallinger, Brent W. Zanke, Thomas J. Hudson, Bharati Bapat

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsOttawa HospitalLunenfeld-Tanenbaum Research InstituteOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsDNA methylationCpG siteMLH1BiologyEpigeneticsMethylationSingle-nucleotide polymorphismCancer epigeneticsCancer researchGeneticsCancerColorectal cancerGenotypeDNA mismatch repairGeneGene expression

Abstract

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Abstract Gene silencing via CpG island hypermethylation contributes to colorectal cancer (CRC). We previously demonstrated that genetic variants (single nucleotide polymorphisms, or SNPs) in the MLH1 gene promoter region are associated with MLH1 CpG island hypermethylation, MLH1 protein loss, and DNA mismatch repair deficiency in tumors. Recently, CpG-rich “shore” regions were identified flanking many CpG islands which exhibiting distinct methylation profiles among different tissues and in normal versus tumor states. To explore the role of MLH1 CpG island shore methylation, we performed global methylation profiling (Illumina 450K microarrays) to investigate DNA methylation in peripheral blood cell (PBC) DNA of over 800 CRC cases and 800 controls (Savio et al., 2012). We found that MLH1 CpG island shore hypomethylation occurred in PBC DNA of both cases and controls. Intriguingly, individuals carrying variant alleles of the MLH1 promoter SNP rs1800734 incur significant hypomethylation at the MLH1 CpG island shore, located 1.5 kb upstream irrespective of disease status. Based on these observations we sought to determine whether these SNP-associated epigenetic changes were apparent in other tissues, including CRC and normal colonic tissue. To address this, we investigated MLH1 CpG island shore methylation in matched PBC, normal colon, and tumor DNA of 349 CRC cases using the real-time PCR-based technique, MethyLight. Methylation was significantly lower in PBC DNA of CRC cases with variant SNP genotypes (both heterozygous and homozygous variant carriers) and this association was also observed in normal colonic DNA. The association between genotype and epigenotype was lost in tumor DNA, as no differences in methylation were seen among different genotypes. Hypermethylation in tumor DNA compared to normal tissues (PBC and colon) was also observed. This hypermethylation and lack of association between SNP genotype and shore methylation in tumors may indicate an epigenetic switch occurring. Bisulfite sequencing of DNA from fresh frozen tumors and paired normal colonic mucosa have recapitulated these results, with hypomethylation incurred in variant SNP carriers. Hypermethylation in tumor compared to normal DNA was also observed. Bisulfite sequencing of four CRC cell lines has also revealed SNP-dependent hypermethylation at the MLH1 CpG island shore. Taken together, we have integrated methylation data from a microarray platform, sensitive real-time PCR, and bisulfite sequencing for a comprehensive and systematic analysis of DNA methylation at the CpG island shore of MLH1 in multiple tissues of CRC cases, controls, and cell lines. These results indicate that static genetic variants can modulate dynamic epigenetic regulation at the MLH1 gene region, and may play a role in colorectal tumorigenesis. Citation Format: Andrea J. Savio, Mathieu Lemire, Miralem Mrkonjic, Steven Gallinger, Brent W. Zanke, Thomas J. Hudson, Bharati Bapat. Novel insights into the genetic and epigenetic regulation of the MLH1 CpG island and shore in colorectal cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1058. doi:10.1158/1538-7445.AM2015-1058

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.380
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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