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Record W2323536404 · doi:10.1158/1538-7445.am2015-5142

Abstract 5142: A naturally occurring model for gastric cancer

2015· article· en· W2323536404 on OpenAlexaboutno aff
Manar AbdelMageed, Monica Betancur-Boissel, Parthena Foltopoulou, Sureshkumar Muthupalani, James G. Fox, Elizabeth A. McNiel

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCancerPathologyCarcinogenesisMedicineCarcinomaDiseaseCancer cellSubmucosaBiologyCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Abstract Gastric cancer is the fourth most common cancer and the second leading cause of cancer related deaths. While there are increasing data characterizing the molecular features and defining potential targets for gastric carcinoma, preclinical animal models that recapitulate the human disease are limited. We have developed a number of resources to permit the characterization of spontaneously occurring canine gastric cancer which may provide a useful resource to understand gastric cancer carcinogenesis, its prevention and treatment. While gastric carcinoma is reportedly uncommon in dogs, certain breeds have significantly increased risk. To study this disease, we have established a database and tissue repository. Our goal has been to characterize canine gastric carcinoma clinically, histologically, and molecularly to determine its comparative value as a model for the human disease. Recently, we have performed a histologic review of 50 canine gastric carcinomas and have found that the canine disease is comprises largely of diffuse type gastric carcinoma and is enriched in the signet ring variant. We report for the first time a technique for culturing primary gastric cancer from affected dogs. Gastric cancer tissue specimens collected from spontaneously occurring tumors in dogs were disaggregated with collagenase and cells were grown in mammary epithelial growth media with or without 5% FBS. Four different cell lines were derived from tumors of a Labrador mix (GC1), a Basset hound (GC2), a Bouvier de Flandres (GC3) and a mixed breed (GC4). Early passages of all cell lines were able to form colonies when grown in soft agar. Different growth patterns were observed when cells were cultured with versus without serum. Serum treated cells tend to be fast growing, fibroblastic in appearance and senesce within 10 to 18 passages and these may represent cancer associated fibroblasts or mesenchymal differentiation of cancer cells. However, cells grown in mammary epithelial growth media without serum tend to form spheres that grow well when transferred to ultra-low attachment plate and form extensions when grown in matrigel. That semi-attached growth behavior is consistent with that reported for some human gastric cancer cell lines. Canine gastric cancer cell lines provide a new resource for the characterization of this spontaneous model that recapitulates diffuse, signet ring cell gastric carcinoma. Citation Format: Manar A. AbdelMageed, Monica Betancur-Boissel, Parthena Foltopoulou, Sureshkumar Muthupalani, James G. Fox, Elizabeth A. McNiel. A naturally occurring model for gastric 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 5142. doi:10.1158/1538-7445.AM2015-5142

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.156
GPT teacher head0.468
Teacher spread0.311 · 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 designBench or experimental
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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