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Record W2070129121 · doi:10.1093/jnci/djs007

Response: Re: Delta-Like Ligand 4-Notch Blockade and Tumor Radiation Response

2012· article· en· W2070129121 on OpenAlexaff
Stanley K. Liu, Ruth J. Muschel, Adrian L. Harris

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2012
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsBlockadeDeltaLigand (biochemistry)Cancer researchMedicineReceptorPhysicsInternal medicine

Abstract

fetched live from OpenAlex

We appreciate the opportunity to respond to Dr Masuda's questions. His first line of questioning pertains to potential resistance mechanisms. We are interested in identifying and testing potential resistance mediators, including alternate angiogenic pathways, such as the FGF2–FGFR and EphB4–EprinB2 pathways ( 1 , 2 ) and infiltrating vascular progenitor cells, such as CD11b-positive myelomonocytes (discussed in our article). Indeed, simultaneous blockade of EphB4–EphrinB2 and DLL4–Notch signaling has been shown to inhibit tumor growth more than blockade of either pathway alone ( 2 ). The discovery of resistance mediators is also important for the development of predictive biomarkers for DLL4–Notch inhibition therapy ( 3 ). We also agree that the potential contribution of cancer stem-like cell differentiation into tumor endothelial cells will be important to explore, especially given the demonstration by Ricci-Vitiani et al. ( 4 ) that Notch inhibition repressed the transition from CD133-positive CD144-negative cells to CD133-positive CD144-positive endothelial progenitor cells. Dr Masuda asks whether engraftment of tumors would be delayed or prevented if therapy was started immediately after tumor cell injection into mice. Although this experiment is potentially feasible, we do not believe that the information gained would be clinically relevant. Rather, we performed our experiments to mimic a common clinical situation in which therapies are initiated after patients have an established or advanced tumor, and the tumor has an established vasculature. Dr Masuda's concern that the gamma secretase inhibitor (GSI) dibenzazepine (DBZ) would be more effective on tumor growth delay if it was administered more frequently, or if the dosage was optimized, was addressed in the discussion: We were not able to use more frequent or prolonged dosing of DBZ due to gastrointestinal toxicity. It is important to highlight once again, however, that the DLL4 monoclonal antibody does not result in gastrointestinal toxicity and thus does not require dosing alterations to avoid this dose-limiting toxicity. Lastly, Dr Masuda questions whether a longer exposure of cells to Notch inhibitors might be required to observe growth suppression in vitro. Previous studies investigating the effects of GSIs on in vitro proliferation of carcinoma or glioma cell lines used a 5-day treatment period, such as that used in our study. Concerning NOTCH1 -activating mutations, to our knowledge, these have only be shown to be partly indicative of resistance to GSIs within the context of acute T-cell acute lymphoblastic leukemia and not for solid tumors. In addition, the complexity of resistance to Notch inhibitors has been demonstrated, and it is very likely that a “GSI-resistance signature” will be required to predict tumor response and resistance to GSIs ( 5 ). Dr Masuda also cites recent articles demonstrating that NOTCH1 is inactivated in 10%–15% of head and neck squamous cell carcinomas ( 6 , 7 ). However, once again, the NOTCH1 mutational status in FaDu cells is unlikely to be relevant to the response we saw because inactivation of NOTCH1 in these tumor cells would imply that the tumor radioresponse is due to DLL4–Notch inhibition in the stroma, which we have already posited.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0190.017
Insufficient payload (model declined to judge)0.0380.024

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 head0.044
GPT teacher head0.343
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreCommentary

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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