Abstract 388: AI/ML-driven discovery of a novel proteoglycan for precision targeting of ADCs for disruption of stromal barriers and direct anti-tumor activity
Notice bibliographique
Résumé
Abstract Background: While checkpoint inhibitors (CPIs) such as anti-CTLA-4 and anti-PD-1/L1 have demonstrated efficacy in a number of solid tumor indications, those with high stromal presence have been difficult to treat with minimal response observed. We aimed to use a proprietary machine learning/artificial intelligence platform to identify novel stromal targets to relieve this immunosuppressive barrier and increase CPI responsiveness in difficult to treat indications. Methods: Based on bioinformatic analysis using our single cell RNA atlas, we assessed cancer-associated fibroblasts (CAFs)/fibroblastic cells in cancer tissue for identification of novel targets, including proteoglycans. Antibodies were generated by immunization of humanized mice, and lead antibodies were tested for activity in inhibiting cell adhesion and were further characterized for staining of both CAFs as well as tumor cells. ADCs were developed and tested in vitro for selective tumor cell killing. Results: Bioinformatic analysis identified a unique subset of cancer-associated fibroblasts, termed ecmCAFs, which demonstrated selective expression of Collagen Triple Helix Repeat Containing 1 (CTHRC1). This highly-selective expression pattern suggests it may be ideal as a target for alternative modalities, including ADC targeting or specific T cell activation. In addition, we identified that in certain tumor types, such a triple negative breast cancer and pancreatic ductal adenocarcinoma (PDAC), CTHRC1 is also highly expressed by cancer cells within the tumor and shows a more favorable expression profile for ADC targeting when compared to other stromal proteins such as FAP and LRRC15. We have confirmed surface expression and binding of CTHRC1 by our lead antibodies and have observed robust internalization on both human and mouse cancer cell lines. In vitro killing of tumor cells by ADCs and in vivo PD and efficacy will be presented on both ADCs and naked antibodies. Conclusions: We have identified CTHRC1 as a novel proteoglycan expressed by both ecmCAFs and tumor cells that appears to be an ideal target for both direct inhibition of stromal barrier function as well as targeting of cytotoxic payloads as an ADC. CTHRC1 expression is more selective than the classical markers FAP and LRRC15, both of which have been previously developed as ADCs. Citation Format: Elizabeth Koch, Max London, Amy Berkley, Allison Nixon, Sean Phippen, Kerry White, Amanda Hanson, Samuel Cooper, Christopher Harvey, Michael Briskin. AI/ML-driven discovery of a novel proteoglycan for precision targeting of ADCs for disruption of stromal barriers and direct anti-tumor activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 388.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,000 | 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».