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Record W2329632577 · doi:10.1158/1538-7445.am2012-1375

Abstract 1375: Contrasting effects of VEGF pathway inhibitors in primary and metastatic disease using perioperative neoadjuvant mouse models

2012· article· en· W2329632577 on OpenAlexaff
John M.L. Ebos, Christina R. Lee, William Cruz‐Muñoz, Christopher Jedeszko, Robert S. Kerbel

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineSunitinibPrimary tumorOncologyMelanomaBevacizumabInternal medicineMetastasisNeoadjuvant therapyPerioperativeChemotherapyDiseaseCancer researchCancerBreast cancerSurgery

Abstract

fetched live from OpenAlex

Abstract Recent clinical trial failures with antiangiogenic therapy in patients with early-stage disease have raised the possibility that drug efficacy in preclinical studies, typically involving models of localized primary grown tumors, may not always predict for efficacy in treating metastatic disease, which is the most common cause of mortality in patients. Moreover, depending on disease stage and treatment circumstances, the efficacy of VEGF-pathway inhibitors administered as monotherapies may be offset in certain settings by increased aggressive invasiveness and augmented metastatic potential. Thus, with hundreds of trials underway in neoadjuvant and adjuvant settings to evaluate the efficacy of antiangiogenic therapy in blocking or slowing micrometastatic disease and eventual tumor recurrence, there is an urgent need clarify the utility of such drugs in all stages of tumor progression. Herein we describe a novel preclinical neoadjuvant therapeutic methodology used to compare the effects of short-term VEGF pathway inhibition in highly metastatic human breast, melanoma, and kidney tumor cells grown orthotopically in SCID mice before and after surgical resection. While cytotoxic chemotherapy administered in the maximum tolerated dose could slow tumor growth and lead to a prolongation of survival after resection, similar significant benefits by various VEGF inhibitors in primary tumor inhibition did not consistently translate into significant prevention of recurrent local and distant metastasis after treatment cessation. Furthermore, reductions in primary tumor growth by neutralizing antibodies to VEGF or VEGFR-2 were more predictive of modest survival benefits after tumor resection, compared to the VEGFR tyrosine kinase inhibitor (TKI) sunitinib, which had either no effect or decreased survival compared to control - despite significant reductions in primary tumor growth after neoadjuvant treatment. Potential tumor-independent ‘host’ drug responses could account for this difference as short-term treatment of immunocompromised nu/nu mice prior to intravenous tumor inoculation lead to modest or negligible benefits with VEGF or VEGFR2 antibodies compared to worse outcomes in VEGFR TKI pre-treated animals. Taken together, preclinical neoadjuvant therapy allows for the distinction of ‘anti-primary’ and ‘anti-metastatic’ effects and may serve as a predictive tool to model ongoing clinical trials as well as indicate potential drug combinations which may be used to overcome limitations when such drugs are used as a monotherapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1375. doi:1538-7445.AM2012-1375

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.060
GPT teacher head0.361
Teacher spread0.300 · 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
Published2012
Admission routes1
Has abstractyes

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