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Enregistrement W2561995968 · doi:10.1158/1538-7445.panca16-b47

Abstract B47: Nanoliposomal irinotecan (nal-IRI) is an active treatment and reduces hypoxia as measured through longitudinal imaging using [18F]FAZA-PET in an orthotopic patient-derived model of pancreatic cancer

2016· article· en· W2561995968 sur OpenAlexaff
Stephan G. Klinz, Jinzi Zheng, Raquel De Souza, Manuela Ventura, Nancy Paz, David W. Hedley, David A. Jaffray, Jonathan B. Fitzgerald

Notice bibliographique

RevueCancer Research · 2016
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer, Hypoxia, and Metabolism
Établissements canadiensOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésIrinotecanHypoxia (environmental)MedicineTumor hypoxiaPancreatic cancerPharmacodynamicsPancreatic tumorPositron emission tomographyColorectal cancerCancerNuclear medicineActive metabolitePharmacokineticsInternal medicineCancer researchChemistryRadiation therapy

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Tumor hypoxia has been strongly linked to aggressive disease progression and resistance to therapy, especially in pancreatic cancer where the desmoplastic reaction is thought to also interfere with deposition of both small molecule drugs and nanotherapeutics. [18F]fluoroazomycin arabinoside (FAZA) is a radioactive tracer that allows for non-invasive quantification of tumor hypoxia during treatment by positron emission tomography (PET). We have previously shown that in the HT-29 cell-line derived xenograft model of colorectal cancer, nanoliposomal irinotecan (nal-IRI) achieves improved tumor growth control and is able to maintain a significantly lower level of tumor hypoxia as compared to non-liposomal irinotecan. Here, we evaluate the effects of nal-IRI on the kinetics and magnitude of hypoxia changes in an orthotopic patient-derived tumorgraft model of a pancreatic cancer (OCIP51) that is highly hypoxic. Experimental Procedures: Tumor growth of orthotopically implanted OCIP51 tumors was monitored using magnetic resonance imaging. Longitudinal FAZA-PET imaging of tumor hypoxia changes was performed over a 21-day period following weekly administration of nal-IRI at 20 mg/kg (n = 10) and compared to untreated controls (n = 5). Mean tumor FAZA uptake (%ID/g) and hypoxic fractions were calculated. In addition [18F]-fluorothymidine (FLT-) PET was conducted before treatment initiation and after the 3rd dosing cycle to assess tumor cell proliferation. Tumor levels of irinotecan and its active metabolite SN-38 were evaluated using an HPLC method in samples harvested 24 h after the last administration of nal-IRI and in a separate pharmacodynamic study component (n = 10) at 24 h and 72 h after administration of a single dose of nal-IRI at 10 mg/kg. Nal-IRI induced DNA damage was assessed using γH2AX immunohistochemistry. Results: nal-IRI treatment resulted in tumor growth inhibition of 71.6% compared to controls at study end. Tumor growth control was observable at Day 5 post treatment initiation. FAZA uptake in treated tumors decreased by 36% within the first treatment cycle, while average FAZA levels in control tumors remained unchanged during this period. Nal-IRI treatment resulted in statistically significant decreases in the FLTmax and FLTmean values compared to pre-treatment values. 100% of nal-IRI treated mice survived to study end compared to only 40% of controls. Tumor weights at study end were almost 4 times smaller in nal-IRI-treated mice compared to the controls. Tumors from treated mice were fluid-filled and showed extensive blood pooling, while tumors from untreated mice appeared to be much less vascularized. Irinotecan levels detected in the OCIP51 tumors were 8 times lower at 72 h after nal-IRI administration, while SN-38 levels were ~28 times lower when compared to previous findings in HT-29 tumors. Treatment with nal-IRI in the OCIP51 tumors significantly increased the frequency and intensity of γH2AX staining across tumor cell areas compared to that observed in the untreated tumors, which were characterized by only a scattered and sporadic γH2AX staining. Importantly, the stromal areas did not show γH2AX staining in either the treated or the control group. Conclusions: This study demonstrated the feasibility of performing longitudinal tumor hypoxia and proliferation assessments using FAZA- and FLT-PET imaging in a highly hypoxic orthotopic model of pancreatic cancer. Although this model showed reduced levels of liposomal drug deposition compared to cell-line derived xenograft models, treatment with nal-IRI led to effective tumor growth control, as well as significant changes in the tumor microenvironment as measured by reduced hypoxia levels compared to baseline and control tumors. Results from this study support the utility of FAZA-PET for evaluation of tumor hypoxia after anti-cancer therapy with nal-IRI as a means to provide early assessment of treatment activity. Citation Format: Stephan Klinz, Jinzi Zheng, Raquel De Souza, Manuela Ventura, Nancy Paz, David Hedley, David Jaffray, Jonathan Fitzgerald.{Authors}. Nanoliposomal irinotecan (nal-IRI) is an active treatment and reduces hypoxia as measured through longitudinal imaging using [18F]FAZA-PET in an orthotopic patient-derived model of pancreatic cancer. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr B47.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,109
Tête enseignante GPT0,391
Écart entre enseignants0,282 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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