Abstract 1869: The AI-powered KisoSeek™ platform identifies functional anti-TROP2 heavy-chain antibodies with potent anti-cancer activity
Notice bibliographique
Résumé
Abstract Functional heavy-chain antibodies (HCAbs) are promising therapeutic agents for targeted cancer therapy. Since they do not have a light chain, HCAbs have a small antigen-binding domain that can unlock new functionalities by reaching epitopes that are inaccessible to classical antibodies. HCAbs can be obtained by immunizing camelids, but this approach is expensive, time-consuming, and results in a limited antibody repertoire. Previously, we engineered the KisoMouse™- a transgenic mouse with multi-camelid species VHH/VH genes and the complete alpaca D and J gene segments integrated into the mouse IgH locus, along with a functional loss of the heavy chain CH1 domain to eliminate light chain pairing. KisoMouse™ are healthy and generate a robust serum HCAb response against a diverse range of immunogens. Trophoblast cell surface antigen-2 (TROP2) is a membrane protein overexpressed in numerous solid tumor types such as breast, colon, and lung cancers. TROP2 is associated with tumor aggressiveness and decreased survival and has emerged as an attractive therapeutic target. An anti-TROP2 antibody-drug conjugate has been approved for breast and bladder cancer, but challenges related to payload toxicity and resistance highlight the need for alternative therapeutics. We had previously identified an anti-TROP2 HCAb with high potency in in vivo preclinical models of human breast and colorectal cancer. Conventional antibody discovery approaches are resource-intensive and can fail to identify rare antibodies. To overcome such limitations, we developed the KisoSeek™ platform, a proprietary AI-powered toolkit that categorizes antibodies in an immune response based on their structural and biophysical features. With KisoSeek™, we analyzed the entire antibody repertoire generated from KisoMouse™ immunized with TROP2 and rapidly identified HCAbs with similar features as the high potency reference antibody described above. We selected three additional antibodies based on their in vitro TROP2 affinity, competitiveness for the reference antibody’s epitope, and yield in CHO cells. These candidates showed comparable potent efficacy in inducing regression in human breast and colorectal xenograft models, akin to the original reference antibody. In summary, the KisoSeek™ platform identified novel anti-TROP2 HCAbs out of the KisoMouse™ antibody repertoire that were similar to our original reference antibody. This demonstrates how KisoMouse™ and KisoSeek™ are two powerful complementary platforms for novel clinical antibody discovery. By categorizing the entire antibody repertoire and identifying antibodies with similar characteristics as known functional antibodies, KisoSeek™ has the potential to leverage existing discovery efforts to uncover many more rare yet potent therapeutic antibodies - the proverbial needles in a haystack. Citation Format: Sophie E. Cousineau, Amit Subedi, Richard Wargachuk, Juliana The, Xiaowei Wang, Wenyang Hou, Amogh Nair, Wansu Qiu, Alex Zhou, Tiffany Cheng, Morteza Babaie, Benyamin Gojogh, Marinieve Montero, Hiba Zahreddine, Elijus Undzys, Lucy Lai, Gordon Ngan, Luis Da Cruz, David Young. The AI-powered KisoSeek™ platform identifies functional anti-TROP2 heavy-chain antibodies with potent anti-cancer activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1869.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».