Abstract 4792: Highly specific GLUT-1 inhibiting antibodies for the treatment of pancreatic cancer
Bibliographic record
Abstract
Abstract Background: Cancer cells often exhibit upregulated glucose metabolism to support their abnormal growth and division. Targeting the cancer-specific glucose transporter GLUT-1 offers a promising strategy to limit glucose uptake and disrupt tumor metabolism. Unlike small molecule inhibitors, monoclonal antibodies provide a unique opportunity to selectively target complex multi-pass membrane transporters. This study presents the development of highly specific antibodies that recognize GLUT-1 glycovariants, effectively blocking GLUT-1 function in cancer cells while preserving glucose transport across the blood-brain barrier. Methods: GLUT-1-specific monoclonal antibodies were generated using GLUT-1-displaying virus-like particles (VLPs) as immunogens. Chickens were chosen for immunization due to their ability to produce antibodies against highly conserved multi-transmembrane proteins. Antibody discovery was conducted using HybriFree B cell cloning technology and phage display. Functional screens included assays for 2-deoxyglucose uptake inhibition and cancer cell proliferation. Results: The identified antibodies bind GLUT-1 with nanomolar EC50 values and exhibit no cross-reactivity with other glucose transporters. The lead candidate, ICO-33, inhibits glucose uptake and reprograms GLUT-1-dependent cancer cells to rely on oxidative phosphorylation (OXPHOS). This metabolic shift synergizes with OXPHOS inhibitors, resulting in significant cancer cell growth inhibition at doses subtherapeutic as monotherapies. In vivo, the combination of ICO-33 and an OXPHOS inhibitor was well-tolerated and led to substantial tumor growth suppression in colorectal and pancreatic cancer models. Importantly, ICO-33 preserves glucose uptake in the brain, minimizing systemic toxicities. Conclusions: This study highlights the discovery of GLUT-1-targeting monoclonal antibodies with high specificity and efficacy. The lead antibody, ICO-33, effectively restricts glucose uptake in cancer cells and enhances sensitivity to OXPHOS inhibitors, demonstrating its potential as a candidate for clinical development in cancer therapy. Citation Format: Siret Tahk, Paule Hermet, Kyumhyuk Kim, Takefumi Morizumi, Anu Ustav, Kai Virumäe, Robin Pau, Korneelia Anton, Alastair J. King, Francisca Neethling, Andres Männik, Joan Teyra, Oliver Ernst, Mart Ustav. Highly specific GLUT-1 inhibiting antibodies for the treatment of pancreatic cancer [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 4792.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".