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Record W2032699899 · doi:10.1158/0008-5472.sabcs-4058

Combination effects of herceptin, pertuzumab and bevacizumab in a HER2-overexpressing breast cancer xenograft model.

2009· article· en· W2032699899 on OpenAlexaff
N. Alami, Yuliang Sun, Pradip De, Amine Benmassaoud, Y Wang, Brian Leyland‐Jones

Bibliographic record

VenueCancer Research · 2009
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsMontreal Clinical Research Institute
Fundersnot available
KeywordsPertuzumabTrastuzumabMedicineBevacizumabBreast cancerMetastatic breast cancerCombination therapyMetastasisInternal medicineChemotherapyOncologyCancerPharmacologyCancer research

Abstract

fetched live from OpenAlex

Abstract Abstract #4058 Background: The HER2 receptor is overexpressed in 20-25% of breast cancer patients and is associated with poor prognosis. Trastuzumab, a humanized monoclonal antibody directed against the HER2 receptor, used alone or in combination with chemotherapy, has shown significant clinical benefit in improving survival in metastatic patients, as well as improving survival in early breast cancer. However, resistance to trastuzumab often develops within 1 year of treatment initiation. Recent studies have shown that trastuzumab in combination with pertuzumab or bevacizumab may have clinical benefit in selected HER2 overexpressing breast cancer patients. In the present study, we investigated whether blockade of HER homo- and heterodimer pairs in combination with an anti-VEGF, could more effectively inhibit tumor growth in a HER2 overexpressing breast cancer model. Material and Methods: Nude mice bearing subcutaneous BT474 HER2-R (a resistant derivative cell line) xenograft tumors were treated with trastuzumab (T) (10 or 20 mg/kg), pertuzumab (P) (15 mg/kg once weekly after an initial 30 mg/kg loading dose), bevacizumab (B) (5mg/kg), as single agents or in doublets and triplets combination therapies with T at 10 mg/kg. Endpoints include tumor volume, HER signaling, angiogenic biomarkers and occurrence of metastasis. Results: T (at its highest dose) and B, as single courses, showed significant anti-tumor activity as compared with the non-treated group. P or T (at its lowest dose) were not effective at inhibiting tumor growth. Combined treatment with P and B significantly inhibited tumor growth as compared with either agent alone or T alone. Time to tumor progression (tumors reaching 2.5 times baseline size) was 26 days for T + B & T + P, and 30 days for P+B as compared with 15 for P, 20 for H (20mg/kg) and 25 days for B, as single agents. In the mice treated with the triple combination, tumor volumes decreased after treatment initiation achieving a complete tumor regression on day 47 with no tumor regrowth up to day 80. Preliminary mechanistic studies showed that P and T as single agents or in combination caused a decrease of HER2 phosphorylation and downstream AKT activation. No further decrease of HER2 signaling was observed with the triple combination. Discussion: Our results confirm that inhibiting tumour angiogenesis by targeting VEGF has anti-tumor effects. Time to progression was delayed in mice treated with T+P compared with mice treated with a double dose of T, confirming that increasing the dosage of a single anti-HER agent is not as effective as using a combination regimen of inhibitors with different mechanisms of action. Complete tumor regression was achieved when B was combined with P and T at its lower dose, suggesting crosstalk between the VEGF pathway with both the epidermal growth factor receptor (EGFR) and HER2 pathways. Ongoing experiments, IHC and angiogenic biomarker analysis, will provide additional insight into the mechanism of action of T+P+B in this tumor model. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4058.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.450
Teacher spread0.387 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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