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Record W1996092371 · doi:10.1158/1538-7445.chtme14-a79

Abstract A79: Mouse models of breast cancer identify oncogene-associated stroma subtypes

2015· article· en· W1996092371 on OpenAlexaff
Sadiq M.I. Saleh, Paul Savage, Julie Laferrière, Sean Cory, Nicholas Bertos, Margarita Souleimanova, Hong Zhao, Eldad Zacksenhaus, William J. Muller, Michael Hallett, Morag Park

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsToronto General HospitalMcGill University
Fundersnot available
KeywordsStromal cellStromaBiologyBreast cancerTumor microenvironmentOncogeneCancer researchCancerTumor progressionLaser capture microdissectionGenetically modified mousePathologyGeneTransgeneImmunologyGeneticsMedicineImmunohistochemistryGene expressionCell cycle

Abstract

fetched live from OpenAlex

Abstract Breast Cancer is a highly heterogeneous disease involving complex crosstalk between tumor cells and their surrounding microenvironment (stroma). Previous work in our lab utilized laser capture microdissection and gene expression analysis to separate Breast Cancer stromal samples into distinct subtypes. Although this and other data demonstrate the importance of the stroma in tumor progression, the cause of these stromal changes are still poorly understood. We hypothesize that changes within stroma are related to individual oncogenic events within the tumor epithelium. Analyzing human data does not allow us to draw causative conclusions due to the presence of compounding factors such as genetic variance and living conditions. In an attempt to circumvent this challenge we used transgenic mouse models of breast cancer in the same strain of mice (FVB) with oncogenes driven by a mammary specific promoter (MMTV). By keeping these variables constant and only varying the oncogene: using either Neu-NDL-2-5, PyVMT or Wnt1 - allowed us to test the aforementioned hypothesis. Class discovery and distinction confirmed that the epithelial and stromal samples from each mouse model were distinct, which validated our hypothesis. Pathway analysis of the genes that distinguish the stroma of the mouse models identifies genes linked to recruitment and activation of different stromal cells. Interestingly our findings corroborate with results from other labs using these mouse models. Citation Format: Sadiq M. Saleh, Paul Savage, Julie Laferrière, Sean Cory, Nicholas Bertos, Margarita Souleimanova, Hong Zhao, Eldad Zacksenhaus, William Muller, Michael Hallett, Morag Park. Mouse models of breast cancer identify oncogene-associated stroma subtypes. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr A79. doi:10.1158/1538-7445.CHTME14-A79

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 score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.004

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 head0.212
GPT teacher head0.458
Teacher spread0.245 · 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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