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Record W1984199328 · doi:10.1016/j.breast.2013.07.011

Preclinical recapitulation of antiangiogenic drug clinical efficacies using models of early or late stage breast cancer metastatis

2013· review· en· W1984199328 on OpenAlex
Robert S. Kerbel, Éric Guérin, Giulio Francia, Ping Xu, Christina R. Lee, John M.L. Ebos, Shan Man

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Breast · 2013
Typereview
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsSunnybrook Health Science Centre
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsMedicineSunitinibBevacizumabBreast cancerMetastatic breast cancerOncologyCancerInternal medicineContext (archaeology)Clinical trialPaclitaxelChemotherapy

Abstract

fetched live from OpenAlex

Historically, preclinical tumor therapy models in mice have frequently been deficient in predicting subsequent clinical activity; over-prediction of clinical anti-tumor efficacy is common. Several approaches are being made in an attempt to improve the clinical relevance of preclinical models, and include the use of genetically engineered mouse models (GEMMs) of cancer or patient derived xenografts (PDXs). Here we summarize, in the context of breast cancer, another approach, namely, the development of postsurgical models of either macroscopic or microscopic metastatic disease to mimic metastatic or adjuvant therapy. To do so we used in vivo selected metastatic variants of established human breast cancer cell lines such as MDA-MB-231. Testing antiangiogenic drugs such as the oral tyrosine kinase inhibitor (TKI) sunitinib alone or combined with chemotherapy in models involving treatment of established primary tumors invariably resulted in demonstrable anti-tumor activity. In contrast, identical treatments of postsurgical mice with advanced metastatic disease did not: survival times were not prolonged. This reflects multiple failed phase III trials of sunitinib based therapies in metastatic breast cancer patients. However, using a VEGF pathway targeting antibody drug instead of a TKI, with (paclitaxel) chemotherapy, resulted in anti-tumor activity in the metastatic setting, partially reflecting prior clinical results of the E2100 phase III trial of weekly paclitaxel plus bevacizumab. Other experiments involving postsurgical adjuvant treatment of early stage disease foreshadowed the phase III clinical trial failures of adjuvant bevacizumab in colorectal or breast cancer. In contrast, some investigational metronomic oral chemotherapy protocols alone or in combination with an antiangiogenic drug demonstrated potent activity in the advanced metastatic setting; these encouraging results have yet to be validated in randomized phase III clinical trials, which are underway based on some encouraging phase II clinical trial results. We have also observed circumstances where mice with advanced systemic disease, when successfully treated so as to prolong survival, sometimes relapse with brain metastases, reflecting a similar clinical phenomenon. Given our overall findings, we suggest that using preclinical mouse tumor models which mimic postsurgical adjuvant or metastatic therapy may be a promising strategy to help improve the ability to predict subsequent clinical outcomes.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.301
GPT teacher head0.501
Teacher spread0.200 · 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