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Record W2396118549 · doi:10.1158/1538-7445.fbcr15-ia13

Abstract IA13: Clonal dynamics of normal and malignant human mammary cell growth in xenografts

2016· article· en· W2396118549 on OpenAlexaff
Connie J. Eaves, Long Nguyen, Davide Pellacani, Nagarajan Kannan, Sylvan Lefort, Sneha Balani, Claire Cox, Tomo Osako, Samuel Aparício, Martin Hirst

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsBiologyCarcinogenesisTranscriptomeIn vivoCancerCancer researchTransplantationMammary tumorOncogeneCellMalignant transformationBreast cancerGeneticsCell cycleGeneMedicineGene expressionInternal medicine

Abstract

fetched live from OpenAlex

Abstract Most human breast cancers have diversified genomically and biologically by the time they become clinically evident and little is known about their origin from normal human mammary cells, or the cellular and molecular mechanisms that lead to their genesis and evolution. We have developed methods to quantify, purify and characterize different subsets of normal human mammary cells and have used these to identify properties that may influence their propensity for transformation. We have also developed methods for inducing the rapid transformation in vivo of these purified subsets following their transplantation into immunodeficient mice. The results demonstrate the ability of a single oncogene (KRASG12D) to induce the formation of serially transplantable, polyclonal, invasive ductal carcinomas within 8 weeks of being introduced either subrenally or subcutaneously into immunodeficient mice. Both primary and secondary tumors are phenotypically heterogeneous and transcriptome analyses of primary tumors assign them to a “normal-like” category. DNA barcoding of the cells at the time of their initial transduction with KRASG12D has revealed a dramatic change in the numbers and sizes of clones they generate after 2 weeks in vivo. DNA barcoding also showed the unexpected appearance of many “new” clones in tumors generated upon passage into secondary recipients, thus recapitulating some features of in vivo passaged human breast cancer cell lines and patients’ tumor xenografts. This system challenges previous concepts about the process of human mammary oncogenesis and provides a new system for analyzing factors that can influence its speed, efficiency and heterogeneity of outcomes. Citation Format: Connie J. Eaves, Long Nguyen, Davide Pellacani, Nagarajan Kannan, Sylvan Lefort, Sneha Balani, Claire Cox, Tomo Osako, Samuel Aparicio, Martin Hirst. Clonal dynamics of normal and malignant human mammary cell growth in xenografts. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr IA13.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.063
GPT teacher head0.404
Teacher spread0.341 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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