Abstract 2102: Assessing glioma-protein inhibition in sunitinib-resistance using a 3-dimentional tissue-engineered renal cell carcinoma patient derived model
Notice bibliographique
Résumé
Objective: To identify the role of glioma-proteins (Gli) in sunitinib-resistant metastatic renal cell carcinoma and the effect of Gli-inhibition using a 3D tissue-engineered patient-derived model.Background: Renal cell carcinoma (RCC) is the 6th most common malignancy with 2.3% annual increase in Canada. Metastatic RCC (mRCC) patients are treated with anti-angiogenic tyrosine kinase inhibitors (TKIs, sunitinib) as the first-line of treatment because mRCC tumor is heavily depend on VEGF pathway. Therefore, identifying the factor(s) responsible for sunitinib-resistance development and disease advancement in mRCC is imperative. Cancer reflect aspects of developmental patterning and the Hedgehog family proteins are known to play a central role. Among other proteins in the family, the inappropriate activation and/or maintenance of Sonic hedgehog (Shh)-Gli pathway results in various tumors. However, the potential of Shh-Gli pathway inhibitors in sunitinib-resistant mRCC have never been tested. To screen for effective Gli-inhibitors, we will use a 3D tissue-engineered patient-derived tumor model utilizing decellularized kidneys as matrices. Organ decellularization is the process of complete cell removal from the organ and maximum preservation of the extracellular matrix (ECM) microarchitecture, leaving some reported growth factors in which tumor cells from patients can be reseeded, regenerating the organ. This 3D-model is an economic and reliable platform to screen the Gli-inhibitors, and identify tumour-specific optimal therapies.Results: We established sunitinib-conditioned cell-line (Caki-1DC) from the parental cell-line (Caki-1WT) and a mouse model of acquired sunitinib-resistance. Proteomics and microarray results from these two models showed transcriptional and translational changes between the sensitive and resistant phenotypes, particularly in the Hedgehog signalling. In Caki-1DC, an upregulation of Gli-2 was observed, which could be abrogated with Shh pathway inhibitor, cyclopamine. Furthermore, in our decellularization and reseeding techniques of kidney, we have assessed cell viability for up to a month exhibiting adequate cell viability. This 3D platform will be used to assess the effectiveness of Gli-inhibitors.Conclusion: Our data from in vitro and in vivo models established Shh-Gli pathway activation in sunitinib-resistant mRCC. Testing the effectiveness of Gli-inhibitors will help tailor drugs for the patients to improve progression free survival. The 3D patient-derived mRCC platform will enable us to generate many in vitro avatars to accurately recreate the tumor of a patient, and simultaneously screen for suitable drugs and dose for the patient. The identification of the Shh-Gli driver pathway and the effective therapy will translate into improving patient’s quality of life and survival.Citation Format: Ninadh M. D'Costa, Zheng Tan, Claudia Chavez-Munoz, Alan So. Assessing glioma-protein inhibition in sunitinib-resistance using a 3-dimentional tissue-engineered renal cell carcinoma patient derived model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2102.
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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 | 0,000 | 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) | 0,000 | 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 ».