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A phase 1/2 study of the TBL1 inhibitor, tegavivint (BC2059), in patients (pts) with advanced hepatocellular carcinoma (aHCC) with β-catenin activating mutations.

2024· article· en· W4400272940 on OpenAlexaffabout
Daneng Li, Eric Xueyu Chen, Zishuo Ian Hu, David Hsieh, Jimmy J. Hwang, Lynn G. Feun, Joseph W. Franses, Gentry Teng King, Thomas M Drake, Toshiyasu Suzuki, Thomas G. Bird, Stephen Horrigan, Jean Chang, David D. Stenehjem, Casey Cunningham

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsPrincess Margaret Cancer Centre
FundersIterion Therapeutics
KeywordsMedicineHepatocellular carcinomaInternal medicineCancer researchOncology

Abstract

fetched live from OpenAlex

TPS4192 Background: β-catenin mutations are present in up to 40% of aHCC pts. Elevated nuclear β-catenin expression levels correlate with poor responses to standard of care and β-catenin regulates both tumor metabolism and the immune microenvironment. Tegavivint is a first-in-class small molecule inhibitor of TBL1, a novel downstream Wnt-signaling pathway target. Tegavivint binds to TBL1 in the β-catenin pocket, disrupting the formation of the activation complex necessary for oncogenic activity, and enabling degradation of free nuclear β-catenin. The safety, clinical activity, and PK/PD of tegavivint was demonstrated through a proof-of-concept study in pts with desmoid tumors and is being studied through multiple Investigator Initiated Trials in AML (NCT04874480), pediatric solid tumors (NCT04851119), NSCLC (NCT04780568), and lymphoma (NCT05755087). Tegavivint was also studied in preclinical mouse models of β-cateninexon3 mutant HCC and the H22 syngeneic HCC model. In these models tegavivint decreased Wnt target gene expression and enhanced CD3+ T-cell infiltration in liver tumors. Tegavivint treatment of established β-cateninexon3 activated tumors resulted in reduced tumor growth and burden. Based on these promising preclinical results, the following phase 1/2 exploratory study was designed. Methods: This is a phase 1/2 study of tegavivint in pts with aHCC to characterize safety, PK/PD, and preliminary antitumor activity (NCT05797805). Eligibility includes aHCC pts ≥18 years old, with AXIN1or CTNNB1 mutation for all pts, except those in the single agent dose escalation; have BCLC Stage C or Stage B disease not amendable to local therapy or curative approaches, have Child-Pugh class A or B7 liver score, and must have received at least one prior line of systemic therapy. This study will be conducted in 2 parts. First, tegavivint will be administered as a single agent in a dose escalation/optimization and subsequent dose expansion cohort. Upon completion of the dose escalation via a 3+3 design, two dose levels will be selected for the dose selection optimization to determine the recommended phase 2 dose (RP2D) for use in the dose expansion. If sufficient clinical benefit is observed, the combination of tegavivint plus pembrolizumab will be explored in the second part of the study in aHCC pts previously treated with a PD-1/PD-L1 inhibitor. Tegavivint will be administered intravenously weekly. Primary objective is safety and secondary objectives are preliminary efficacy and PK/PD. The first patient began treatment in October of 2023 and the first dose escalation cohort was completed in January. Three institutions are open for enrollment; 5 other sites are pending site activation; all sites are in the United States and Canada. Clinical trial information: NCT05797805 .

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)0.001
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.373
Teacher spread0.328 · 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 designNon-randomized trial
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

Citations6
Published2024
Admission routes2
Has abstractyes

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