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Record W2095720296 · doi:10.1200/jco.2008.16.3972

Dual-Agent Molecular Targeting of the ErbB2 Receptor: Killing One Bird With Two Stones

2008· letter· en· W2095720296 on OpenAlexaff
Hal W. Hirte, Sebastién J. Hotte

Bibliographic record

VenueJournal of Clinical Oncology · 2008
Typeletter
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsHamilton Health SciencesJuravinski Cancer CentreMcMaster University
Fundersnot available
KeywordsMedicineReceptorCancer researchPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

The phase I study by Storniolo et al in this issue of Journal of Clinical Oncology, which evaluates the combination of lapatinib (a selective small-molecule ErbB1 and ErbB2 tyrosine kinase inhibitor) and trastuzumab (an antibody blocking the extracellular domain of ErbB2) in patients with advanced ErbB2-positive breast cancer, is the first published study to our knowledge examining dual-agent molecular targeting of the ErbB2 receptor. The study demonstrated that there was clinical activity in a group of heavily pretreated patients and—using a toxicity end point—determined an optimally tolerated regimen for this combination of agents. The epidermal growth factor family of receptors plays a key role in regulating growth, survival, and resistance to chemotherapy in many epithelial malignancies. Approximately 25% of breast cancers overexpress ErbB2 or demonstrate ErbB2 gene amplification; ErbB2 is a key mediator of tumor cell growth and survival, and a predictor of poor outcome. Potential strategies to block peptide growth factor signaling pathways include (1) blocking or neutralizing the growth factor or its production, (2) blocking the receptor-binding site or prevention of receptor dimerization, (3) blocking the receptor tyrosine kinase domain, and (4) blocking downstream signaling pathways. The mechanism of action of trastuzumab is multifactorial and includes (1) antibody-dependent cellular cytotoxity, (2) modulation of ErbB2 signaling, (3) internalization of ErbB2 cell-surface protein, (4) reduction in proteolytic cleavage of the ErbB2 extracellular domain, and (5) interference with DNA damage repair. Small-molecule inhibitors such as lapatinib have an advantage over antibodies because they inhibit multiple signaling pathways at plasma concentrations achieved in the clinic. This is relevant because the survival of epithelial tumors is often dependent on the presence of redundant signaling pathways. Combining anti-ErbB2 antibodies with small-molecule tyrosine kinase inhibitors is an appealing strategy because both therapies exert their antitumor effects via distinct mechanisms of action and target ErbB2 at different sites. Combining distinct classes of inhibitors may not only potentiate cellular cytotoxicity, but may also assist in overcoming inherent or acquired resistance to one class of inhibitors. Mechanisms of resistance identified to date include activating mutations in the domain of the adenosine triphosphate–binding pocket for small-molecule tyrosine kinase inhibitors and the presence of truncated receptors for monoclonal antibodies. Dual-agent molecular targeting has been examined in preclinical models using the ErbB1 pathway as well as the ErbB2 pathway. With respect to the ErbB1 pathway, Huang et al and Matar et al studied the effect of cetuximab (an antibody that binds to the extracellular domain of ErbB1) in combination with either gefitinib or erlotinib (small-molecule inhibitors of the tyrosine kinase domain or ErbB1) against a panel of human cancer cell lines in culture and in tumor xenografts. The combination of cetuximab plus gefitinib or erlotinib enhances growth inhibition, in culture and in vivo, over that observed with either agent alone. Phosphorylation inhibition of downstream effector molecules (mitogen-activated protein kinase and AKT), and apoptosis was enhanced with the combination. In lung and head and neck tumor cells highly resistant to cetuximab, gefitinib, and erlotinib, but not cetuximab, retained the capacity to inhibit these tumor cells and were also able to further inhibit the activation of downstream effectors of ErbB1 signaling. When tested in vivo, single-agent therapy resulted in transient tumor regression only at the highest doses, whereas suboptimal doses of gefitinib and cetuximab in combination resulted in complete and permanent regression of large tumors. Contradictory results were obtained by Fishel et al who determined that gefitinib and cetuximab, when used in combination, were antagonistic when tested against two ErbB1expressing epidermoid cell lines. The combination caused less apoptosis than gefitinib alone, and this appeared to be the result of overexpression of functional ErbB1 associated with the combination. It is likely that these discordant results were a result of the particular cell lines studied. Most relevant to the study being reviewed are the preclinical models examining dual targeting of the ErbB2 pathway. Xia et al treated ErbB2-overexpressing breast cancer cell lines with a combination of lapatinib and either trastuzumab or a polyclonal anti-ErbB2 antibody. The combination treatments led to enhanced tumor cell apoptosis, whereas treatment with the same doses of lapatinib, trastuzumab, or polyclonal anti-ErbB2 antibody alone led to minimal or no increase in apoptosis. They demonstrated that the enhanced tumor cell apoptosis after combination ErbB2-targeted therapies was most closely associated with downregulation of survivin protein. Konecny et al studied the combination of lapatinib and trastuzumab in a panel of human breast cancer cell lines. Synergistic activity was seen when the combination was tested in ErbB2-overexpressing cell lines. Of JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 26 NUMBER 20 JULY 1

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.002

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 head0.202
GPT teacher head0.497
Teacher spread0.295 · 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".

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Citations12
Published2008
Admission routes1
Has abstractyes

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