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Record W4409625028 · doi:10.1158/1538-7445.am2025-3503

Abstract 3503: ZW171, a differentiated 2+1 T cell-engaging bispecific antibody with antitumor activity in a range of mesothelin-expressing cancers

2025· article· en· W4409625028 on OpenAlexaff
Nicole Afacan, Patricia Zwierzchowski, Wingkie Wong, Maya C. Poffenberger, Chayne L. Piscitelli, Thomas Spreter von Kreudenstein, Nina E. Weisser

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsZymeworks (Canada)
Fundersnot available
KeywordsMesothelinCancer researchBispecific antibodyMedicineAntibodyOncologyCancerInternal medicineMonoclonal antibodyImmunology

Abstract

fetched live from OpenAlex

Abstract Mesothelin (MSLN) is a tumor associated antigen overexpressed in many cancer indications and is an attractive target for immunotherapies including bispecific T cell engagers (TCE) and chimeric antigen receptor T (CART) cells. While MSLN-targeting immunotherapies have shown signs of clinical activity, their success has been hindered by dose-limiting toxicities associated with on-target off-tumor effects and cytokine release syndrome (CRS). To overcome these issues, we engineered ZW171, a MSLN-targeting TCE, with enhanced safety and antitumor activity. ZW171 is a 2+1 IgG1-like antibody, built with AzymetricTM and EFECTTM technologies, consisting of two MSLN binding domains and one low affinity CD3ε binding domain. We previously showed that the unique geometry and 2+1 design of ZW171 facilitates tumor selective binding and potent preferential killing of MSLN-mid and -high target cells, while sparing MSLN-low expressing target cells, and enhanced antitumor activity compared to other 2+1 TCE formats and the clinical benchmark HPN536 in MSLN-expressing PBMC-engrafted CDX models. To address the evolving clinical landscape and assess antitumor activity in additional indications and more translationally relevant models, we assessed ZW171 activity in advanced patient-derived organoid and xenograft models and benchmarked to other MSLN-targeting TCE including AMG 305, JNJ-79032421 and CT95. Additionally, activity in expanded MSLN-positive indications including pancreatic, endometrial, and gastric cancer, and the in the presence of soluble MSLN (sMSLN), which is observed in the serum of patients and can impede MSLN-targeted antibody-based therapies, was assessed in vitro. Ex vivo, ZW171 mediated potent tumor cell killing and T cell activation in patient-derived ovarian cancer organoid models. ZW171 induced complete tumor regressions in established patient-derived in vivo MSLN-positive non-small cell lung cancer and pancreatic cancer models. Comparison of ZW171 to AMG 305, JNJ-79032421 and CT95 showed reduced binding to T cells and equivalent or greater antitumor activity against MSLN-overexpressing cells. In vitro, ZW171 demonstrated potent MSLN-dependent killing in MSLN-positive indications including pancreatic, endometrial, and gastric cancer, and maintained activity in the presence of clinically relevant sMSLN concentrations observed in patient serum. Overall, ZW171 demonstrates differentiated and potent antitumor activity in a range of MSLN-expressing cancers. ZW171 is being evaluated in a Phase 1 clinical trial in MSLN-expressing solid tumors (NCT06523803). Citation Format: Nicole J. Afacan, Patricia Zwierzchowski, Wingkie Wong, Maya Poffenberger, Chayne Piscitelli, Thomas Spreter von Kreudenstein, Nina E. Weisser. ZW171, a differentiated 2+1 T cell-engaging bispecific antibody with antitumor activity in a range of mesothelin-expressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3503.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 integrity0.0000.000
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 head0.071
GPT teacher head0.430
Teacher spread0.359 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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