MétaCan
Menu
Back to cohort
Record W2113402870 · doi:10.1016/j.neo.2015.02.001

Characterizing the Prevalence of Chromosome Instability in Interval Colorectal Cancer

2015· article· en· W2113402870 on OpenAlexafffundabout
Amy L. Cisyk, Signe Penner-Goeke, Zelda Lichtensztejn, Zoann Nugent, Robert Wightman, Harminder Singh, Kirk J. McManus

Bibliographic record

VenueNeoplasia · 2015
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsHealth Sciences CentreUniversity of WinnipegCancerCare ManitobaResearch Institute in Oncology and HematologyUniversity of Manitoba
FundersCanadian Institutes of Health ResearchStrongCancerCare Manitoba Foundation
KeywordsMicrosatellite instabilityColorectal cancerFluorescence in situ hybridizationPopulationOncologyChromosome instabilityCancerInternal medicineCohortMedicineComparative genomic hybridizationTissue microarrayChromosomeBiologyPathologyGeneticsMicrosatelliteGene

Abstract

fetched live from OpenAlex

A substantial proportion of colorectal cancers (CRCs) are interval CRCs (I-CRCs; i.e., CRCs diagnosed soon after a colonoscopy). Chromosomal instability (CIN) is defined as an increase in the rate of which whole chromosomes/large chromosomal fragments are gained or lost and is observed in 85% of non-hereditary CRCs. The contribution of CIN to the etiology of I-CRCs remains unknown. We established a fluorescence in situ hybridization (FISH) approach to characterize CIN by enumerating specific chromosomes and determined the prevalence of numerical CIN in a population-based cohort of I-CRCs and control (sporadic) CRCs. Using the population-based Manitoba Health administrative databases and Manitoba Cancer Registry, we identified an age, sex, and colonic site of CRC matched cohort of I-CRCs and controls and retrieved their archived paraffin-embedded tumor samples. FISH chromosome enumeration probes specifically recognizing the pericentric regions of chromosomes 8, 11, and 17 were first used on cell lines and then CRC tissue microarrays to detect aneusomy, which was then used to calculate a CIN score (CS). The 15th percentile CS for control CRC was used to define CIN phenotype. Mean CSs were similar in the control CRCs and I-CRCs; 82% of I-CRCs exhibited a CIN phenotype, which was similar to that in the control CRCs. This study suggests that CIN is the most prevalent contributor to genomic instability in I-CRCs. Further studies should evaluate CIN and microsatellite instability (MSI) in the same cohort of I-CRCs to corroborate our findings and to further assess concomitant contribution of CIN and MSI to I-CRCs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.300
Teacher spread0.266 · 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 teacher head, 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

Citations34
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueNeoplasiaSame topicGenetic factors in colorectal cancerFrench-language works237,207