Abstract 2914: AI/ML-driven discovery of CTHRC1, collagen triple helix repeat-containing 1, a novel proteoglycan for stroma + tumor targeting and delivery of 4-1BB costimulation
Notice bibliographique
Résumé
Abstract Background: While checkpoint inhibitors have demonstrated efficacy in a number of solid tumor indications, those with high stromal presence have been difficult to treat with minimal objective responses observed. Phenomic has developed a proprietary machine learning/artificial intelligence platform to identify novel stromal targets with superior expression profiles that enable selective targeting of immune activating agents that will relieve these immunosuppressive barriers in difficult to treat indications. Methods: Using our single cell RNA Atlas, we assessed cancer-associated fibroblasts (CAFs) in several solid tumor indications for identification of novel targets, including proteoglycans. Our Atlas was also used to identify immune activating payloads whose cognate receptors were present in indications of interest. Antibodies were generated, and lead clones who demonstrated potent ligand binding and cell staining were used to generate fusion proteins to immune activating ligands. Efficacy and PD were assessed in multiple syngeneic tumor models. Results: Bioinformatic analysis identified a unique subset of pathogenic CAFs, which are TGF beta responsive, secrete several ECM proteins, and their presence tracks with poor outcome and resistance to immunotherapy in several solid tumor types. CTHRC1 was identified as a novel matrix protein highly expressed in this CAF subtype as well as tumor epithelium and is highly selective in a range of tumor types such as ovarian cancer, triple negative breast cancer, and pancreatic ductal adenocarcinoma. While secreted, CTHRC1 is complexed on the cell surface and affords the opportunity to drug this target in a variety of ways. We identified an absence of 4-1BBL expression across indications of interest, and fusion proteins were generated to deliver 4-1BBL via CTHRC1 targeting. In checkpoint-resistant tumor models, we observed significant increases in CD8 T cells and robust anti-tumor activity with anti-CTHRC1-targeted 4-1BBL. Biodistribution studies were conducted using our targeting mAb and demonstrate uptake only in sites of primary and metastatic tumors, even at doses 20-fold higher than those that achieve maximal therapeutic activity, suggesting the potential to minimize the toxicity that has been observed with other 4-1BB agonists. Conclusions: We have identified CTHRC1 as a novel proteoglycan expressed by both pathogenic CAFs and tumor cells that is highly selective for tumors, enabling the therapeutic targeting of immune activating payloads with the potential for safely delivering payloads while limiting toxicity. Given the specificity and selectivity afforded by CTHRC1 expression, ADC and CD3 engager approaches are also being pursued. These data represent novel approaches aimed at breaking down stromal barriers in tumors previously unresponsive to immunotherapies. Citation Format: Christopher Harvey, Elizabeth Koch, Amanda Hanson, Lindsey Rice, Amy Berkley, Kerry White, Reza Saberianfar, Nikolai Suslov, Sam Cooper, Michael Briskin. AI/ML-driven discovery of CTHRC1, collagen triple helix repeat-containing 1, a novel proteoglycan for stroma + tumor targeting and delivery of 4-1BB costimulation [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 2914.
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) | 0,001 | 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 ».