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Record W4318045038 · doi:10.3389/fimmu.2023.1070492

Human antibodies targeting ENPP1 as candidate therapeutics for cancers

2023· article· en· W4318045038 on OpenAlexaff
Xiaojie Chu, Du-San Baek, Wei Li, Taras Shyp, Brian Mooney, Margaret G. Hines, Gregg B. Morin, Poul H. Sorensen, Dimiter S. Dimitrov

Bibliographic record

VenueFrontiers in Immunology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsCanada's Michael Smith Genome Sciences CentreTerry Fox Research InstituteUniversity of British Columbia
FundersNational Institutes of HealthNational Cancer InstituteStand Up To CancerUniversity of Pittsburgh
KeywordsAntibodyCancer researchCancerPhage displayAdeptCancer cellPhosphodiesteraseAvidityOvarian cancerImmunotherapyCancer immunotherapyChemistryMedicineImmunologyEnzymeBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) is a type II transmembrane glycoprotein expressed in many tissues. High expression levels of ENPP1 have been observed in many cancer types such as lung cancer, ovarian cancer, and breast cancer. Such overexpression has been associated with poor prognosis in these diseases. Hence, ENPP1 is a potential target for immunotherapy across multiple cancers. Here, we isolated and characterized two high-affinity and specific anti-ENPP1 Fab antibody candidates, 17 and 3G12, from large phage-displayed human Fab libraries. After conversion to IgG1, the binding of both antibodies increased significantly due to avidity effects. Based on these antibodies, we generated antibody-drug conjugates (ADCs), IgG-based bispecific T-cell engagers (IbTEs), and CAR T-cells which all exhibited potent killing of ENPP1-expressing cells. Thus, these various antibody-derived modalities are promising therapeutic candidates for cancers expressing human ENPP1.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0010.001

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 head0.014
GPT teacher head0.279
Teacher spread0.265 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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