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Record W2621545768 · doi:10.1002/hon.2439_20

Analysis of preclinical and clinical samples after treatment with a <scp>CD37</scp> targeting antibody drug conjugate (<scp>AGS67E</scp>) support a high level of <scp>CD37</scp> expression in <scp>NHL</scp>

2017· article· en· W2621545768 on OpenAlexaff
Fernando Doñate, Yang Pu, Kendall Morrison, Sher Karki, Héctor Aviña, Jacqueline Lackey, Ahmed Sawas, Kerry J. Savage, Raymond P. Perez, Ranjana H. Advani, Jasmine Zain, Owen A. O’Connor, Leonard Reyno

Bibliographic record

VenueHematological Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsBC Cancer FoundationBC Cancer Agency
Fundersnot available
KeywordsAntibodyFlow cytometryAntibody-drug conjugateMedicineCancer researchMonoclonal antibodyMolecular biologyImmunologyBiology

Abstract

fetched live from OpenAlex

Introduction: AGS67E is an antibody drug conjugate against CD37 bound to monomethyl auristatin E (MMAE) via a protease-cleavable linker currently being investigated in subjects with relapsed/refractory NHL in a phase I dose-escalation study. CD37 is expressed in normal white blood cells (WBCs: B and T lymphocytes, NK cells, monocytes, and neutrophils). CD37 is also highly expressed in CLL, AML, and NHL, including 80% expression in DLBCL, as detected in tumor microarrays (TMA) of formalin-fixed paraffin-embedded (FFPE) tumor samples using the proprietary vCD37-9a73.1 antibody (Pereira et al., 2015). In contrast, the percentage of CD37 expression in DLBCL was recently determined to be 40% using a commercial antibody (2B8) on FFPE samples (Xu-Monette et al., 2016). Clinical and preclinical results, including a comparison of vCD37-9a73.1 with commercially available anti-CD37 reagents, are presented here in order to clarify the CD37 expression in DLBCL and in NHL in general. Methods: CD37 expression was investigated on WBCs by flow cytometry and on archived FFPE tumor samples by immunohistochemistry (IHC) from subjects in the phase I trial using the proprietary anti-CD37 antibody (vCD37-9a73.1). This antibody was selected for flow cytometry since it could detect CD37 on the surface of cells in the presence of AGS67E and showed similar CD37 levels to other anti-CD37 proprietary antibodies tested. The vCD37-9a73.1 and 2B8 antibodies were evaluated by IHC in FFPE cells and xenografts with different levels of CD37 mRNA levels, as well as in TMA of NHL. Results: CD37 expression in patient's WBCs was downregulated after AGS67E dosing, presumably due to AGS67E-mediated, dose-dependent decreases in WBCs. Furthermore, samples were CD37 positive as detected by both AGS67E and vCD37-9a73.1. These data support vCD37-9a73.1 as a suitable reagent for CD37 detection. IHC data from FFPE tumor samples from subjects enrolled in the ongoing phase I clinical trial with a variety of NHL pathologies demonstrated that CD37 was expressed in 100% of the samples examined (23 subjects) with an average H-score of 270 (maximum is 300), including 7 subjects with DLBCL (average H-score 277). Overall, the level and degree of expression were slightly higher than previously seen in TMA samples of NHL (~80%) with the same antibody and significantly higher than those reported with the 2B8 antibody. Evaluation of CD37 expression using vCD37-9a73.1 side-by-side with 2B8 in FFPE cells, xenografts, and TMA of NHL indicated that vCD37-9a73.1 has better sensitivity and specificity compared to 2B8 under the conditions tested. Keywords: CD37; diffuse large B-cell lymphoma (DLBCL); non-Hodgkin lymphoma (NHL)

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 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)0.005
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.125
GPT teacher head0.430
Teacher spread0.305 · 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 teacher head, not a consensus.

Study designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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