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Record W2134871854 · doi:10.1186/s12885-015-1021-7

Retrospective review using targeted deep sequencing reveals mutational differences between gastroesophageal junction and gastric carcinomas

2015· article· en· W2134871854 on OpenAlexafffund
Hector Li-Chang, Ying Ng, Amy Lum, Esther Kong, Howard J. Lim, Steven J.M. Jones, David G. Huntsman, David F. Schaeffer, Stephen Yip

Bibliographic record

VenueBMC Cancer · 2015
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsCanada's Michael Smith Genome Sciences CentreVancouver General HospitalUniversity of British ColumbiaBC Cancer Agency
FundersCanadian Institutes of Health ResearchTerry Fox Foundation
KeywordsCancerAdenocarcinomaExome sequencingMissense mutationPathologyMedicineMassive parallel sequencingMutationCancer researchBiologyDNA sequencingInternal medicineGeneGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Adenocarcinomas of both the gastroesophageal junction and stomach are molecularly complex, but differ with respect to epidemiology, etiology and survival. There are few data directly comparing the frequencies of single nucleotide mutations in cancer-related genes between the two sites. Sequencing of targeted gene panels may be useful in uncovering multiple genomic aberrations using a single test. METHODS: DNA from 92 gastroesophageal junction and 75 gastric adenocarcinoma resection specimens was extracted from formalin-fixed paraffin-embedded tissue. Targeted deep sequencing of 46 cancer-related genes was performed through emulsion PCR followed by semiconductor-based sequencing. Gastroesophageal junction and gastric carcinomas were contrasted with respect to mutational profiles, immunohistochemistry and in situ hybridization, as well as corresponding clinicopathologic data. RESULTS: Gastroesophageal junction carcinomas were associated with younger age, more frequent intestinal-type histology, more frequent p53 overexpression, and worse disease-free survival on multivariable analysis. Among all cases, 145 mutations were detected in 31 genes. TP53 mutations were the most common abnormality detected, and were more common in gastroesophageal junction carcinomas (42% vs. 27%, p = 0.036). Mutations in the Wnt pathway components APC and CTNNB1 were more common among gastric carcinomas (16% vs. 3%, p = 0.006), and gastric carcinomas were more likely to have ≥3 driver mutations detected (11% vs. 2%, p = 0.044). Twenty percent of cases had potentially actionable mutations identified. R132H and R132C missense mutations in the IDH1 gene were observed, and are the first reported mutations of their kind in gastric carcinoma. CONCLUSIONS: Panel sequencing of routine pathology material can yield mutational information on several driver genes, including some for which targeted therapies are available. Differing rates of mutations and clinicopathologic differences support a distinction between adenocarcinomas that arise in the gastroesophageal junction and those that arise in the stomach proper.

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 score0.136
Threshold uncertainty score0.612

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 head0.110
GPT teacher head0.357
Teacher spread0.247 · 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

Citations46
Published2015
Admission routes2
Has abstractyes

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