Abstract 1751: MSC-1 is a first-in-class humanized monoclonal antibody that modulates the tumor microenvironment by inhibiting a novel cancer immunotherapy target, LIF
Notice bibliographique
Résumé
Abstract Leukemia Inhibitory Factor (LIF) is a member of the IL-6 family of cytokines and is involved in many physiological and pathological processes including the promotion of an immunosuppressive environment to support embryo implantation, down-regulation of autoimmune processes and the regulation of stem cell homeostasis and differentiation. In cancer, LIF is hypothesized to have a complex role tumor development and progression, creating an immunosuppressive tumor microenvironment as well as promoting the activity of cancer initiating cells (CICs). LIF is highly expressed in a subset of tumors across multiple tumor types, e.g. glioblastoma multiforme (GBM), non-small cell lung cancer (NSCLC), colon, ovarian, prostate, and pancreatic cancer, and correlates with poor prognosis. Given the pleiotropic role LIF is hypothesized to play in cancer, inhibition of LIF represents an exciting new therapeutic concept sitting at the intersection of two key therapeutic approaches in oncology: blockade of tumor evasion of the immune system and blockade of tumor growth via inhibition of CICs. We have identified and developed MSC-1, a first-in-class humanized IgG1 monoclonal antibody that is a potent and selective inhibitor of LIF. MSC-1 cross reacts with mouse and non-human primate LIF and inhibits LIF signaling by blocking the recruitment of gp130 to the LIF-LIFR-gp130 signaling complex. The efficacy of MSC-1 was evaluated in multiple mouse tumor models and the mechanism of action investigated. LIF inhibition with MSC-1 or shRNAs reduced tumor growth in multiple syngeneic tumor models (NSCLC, ovarian and colon), and clear target engagement was shown for MSC-1. Investigations into the mechanism of action identified that inhibition of LIF with MSC-1 reprogrammed the tumor microenvironment by decreasing immunosuppressive M2 macrophages and increasing the number of intratumoral NK cells and total/activated T cells. MSC-1 also decreased immunosuppressive M2 macrophages in an orthotopic GBM xenograft model and human GBM organotypic tumor slices in an ex vivo model. Similarly, immunosuppressive macrophage genes were decreased when monocytes were co-cultured with supernatants from a GBM cell line in which LIF expression had been knocked-down. Given the effects of MSC-1 on intratumoral immune cells, we hypothesized that MSC-1 could be effectively combined with checkpoint inhibitors and we are currently evaluating MSC-1/checkpoint inhibitor combination therapy. Taken together, these findings form the basis of a robust therapeutic hypothesis, whereby MSC-1 treatment will lead to clinical activity in multiple cancer indications. A Phase I dose-escalation and expansion study of MSC-1 is planned to initiate early 2018 in patients with advanced solid tumors that will incorporate target engagement and PD biomarkers, as well as safety and efficacy endpoints. Citation Format: Angus Sinclair, Robin Hallett, Patricia Giblin, Isabel Huber-Ruano, Judit Anido, Naimish Pandya, Kimberly Hoffman, Ada Sala, Monica Pascual, Vanessa Chigancas, Swetha Raman, Johan Fransson, Jean-Philippe Julien, Robert Wasserman, Jeanne Magram, Joan Seoane. MSC-1 is a first-in-class humanized monoclonal antibody that modulates the tumor microenvironment by inhibiting a novel cancer immunotherapy target, LIF [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1751.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 tête enseignante, 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 ».