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Record W4322625667 · doi:10.1016/j.isci.2023.106305

Development of an in vivo system to model breast cancer metastatic organotropism and evaluate treatment response using the chick embryo

2023· article· en· W4322625667 on OpenAlexafffund
Sumreen Javed, Sepideh Soukhtehzari, Nikki Salmond, Nazarine Fernandes, Karla C. Williams

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMetastasisMedicineMetastatic breast cancerIn vivoPrimary tumorEmbryoTropismMammary tumorBreast cancerCancerOncologyCancer researchBiologyImmunologyInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

Metastatic lesions produced through the process of systemic tumor cell dissemination and growth at distant sites are challenging to treat and the primary cause of patient mortality. Developing in vivo models of metastasis with utility in evaluating molecular targets and therapeutics in a timely manner would expedite the path to therapeutic discovery. Here, we evaluated breast cancer metastasis and metastatic organotropism using the chick embryo. We developed a method to evaluate metastasis using the MDA231 cell line. Then, using cell lines with demonstrated tropism for the bone, brain, and lung, we evaluated organotropism. Rapid and robust organ-specific metastasis was modeled in the chick embryo and, importantly, recapitulated metastatic organotropism congruent to what has been demonstrated in mice. Treatment response in the metastatic setting was also evaluated and quantified. This work establishes the chick embryo as a model for studies aimed at understanding organotropism and therapeutic response in the metastatic setting.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.073
GPT teacher head0.361
Teacher spread0.288 · 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 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

Citations6
Published2023
Admission routes2
Has abstractyes

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