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Record W124472537 · doi:10.1007/978-1-60327-945-1_7

Modeling Human Breast Cancer

2009· book-chapter· en· W124472537 on OpenAlexaff
Rachelle L. Dillon, William J. Muller

Bibliographic record

VenueHumana Press eBooks · 2009
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsBreast cancerTransgeneGenetically modified mouseBiologyContext (archaeology)CancerGeneCancer researchLoss of heterozygosityOrganismModel organismComputational biologyGeneticsAllele

Abstract

fetched live from OpenAlex

The advances in genomic technologies have made it possible to examine the effects of altered gene expression in the context of specific cellular compartments within the whole organism. As such, transgenic mice have proven to be an invaluable tool to investigate genes involved in many human diseases, including genes implicated in the induction and progression of breast cancer. Human breast cancer is heterogeneous and no single mouse model recapitulates all aspects of the disease. In this regard, various mouse models are necessary to investigate specific characteristics of human breast cancer. In this chapter, we discuss various transgenic mouse strains that have been developed for the purpose of modeling breast cancer and will address their relevance to observations made in human breast tumors. Breast cancer is the most commonly diagnosed form of cancer and it is estimated that one in eight women will develop breast cancer in her lifetime. Once initiated, cancer progresses as a result of an accumulation of genetic abnormalities within cells, the most frequently observed lesions of which can be divided into two categories: (a) DNA amplification and/or overexpression of genes responsible for the generation of proliferative and survival signals, and (b) loss of heterozygosity (LOH), in genes involved in preventing unrestrained cell growth. Genetically modified animals generated by transgenic and gene-targeting knockout technology have contributed immensely to our understanding of gene function and regulation at the molecular level in the context of the whole organism. Since the first transgenic mouse model describing mammary tumors in 1984 (1), a wealth of transgenic mice for modeling breast cancer have been reported. Transgenic models encompassing a wide array of targets including growth factors, receptors, cell cycle regulators, oncogenes, and tumor suppressor genes have been generated for use in breast cancer research. In addition to conventional transgenic overexpression and germline knockouts, the advent of increasingly complex technology has allowed for the generation of more elaborate mouse models including conditional knockouts, conditional activating mutations, and inducible oncogenes or knockouts. Studies of the pathology of mammary carcinomas in genetically modified mice have demonstrated neoplasms that are morphologically similar to human breast cancer (2). While this chapter will describe a variety of genetically engineered mouse models of human breast cancer, it is by no means comprehensive and will not cover the entire spectrum of transgenic mouse models generated to date.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.048
GPT teacher head0.279
Teacher spread0.231 · 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 designSimulation or modeling
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
Published2009
Admission routes1
Has abstractyes

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