Abstract 873: Evaluating immunotherapy effects using preclinical molecular imaging tools for quantitative immune cell tracking
Notice bibliographique
Résumé
Abstract Immunotherapies are a promising class of cancer therapeutics, but clinical translation is often hampered by a lack of understanding regarding optimal therapy administration, combination, and reliable biomarkers of success. Traditional metrics such as RECIST, and modern metrics like irRC and PERCIST used for monitoring cancer therapy outcome, have limitations for immunotherapy evaluation and are not always reflective of the underlying immune mechanisms. We aim to better characterize and monitor these immunotherapies, with focus on combination therapy optimization, by tracking immune cell migration in response to these therapies using preclinical magnetic resonance imaging (MRI). MRI is used to obtain anatomical tumor changes, detect and quantify superparamagnetic iron oxide (SPIO)-labeled cells in vivo. Goal: To link immune cell migration to early prognostic biomarkers for immunotherapy success. Methods: C57/BL6 mice (n=40) received an implant of 5x105 C3 cancer cells in the left flank. Mice (n=10/group) were i) untreated or treated with ii) 200µg of anti-PD1/day on days 7, 9, 11, 21 and 25, iii) the peptide-based vaccine DepoVaxTM (DPX) on day 15, or iv) with anti-PD1 and DPX. CD8+ cytotoxic T cells (CD8) and suppressive regulatory T cells (Tregs) were isolated from diseased-matched & treated donor mice for expansion in culture before labeling with SPIO for adoptive cell transfer into mice receiving scans. PET/MRI Data: Anatomical and qualitative SPIO data is collected using a balanced steady-state free precession (bSSFP) sequence. Iron quantification is done using R2* maps from a multi-echo single point imaging sequence (TurboSPI). Tumor metabolism was assessed by 18F-fluorodeoxyglucose uptake during simultaneous acquisition of positron emission tomography (PET) with MRI. Imaging was done 21 and 28 days post-implant. Results: CD8 and Treg cells are consistently recruited to both the tumor and vaccine draining inguinal lymph nodes. CD8 T cells are primarily recruited to the tumor periphery and do not always penetrate the tumor core. Positive therapy outcomes are correlated with an increasing CD8/Treg ratio in the tumor, particularly at earlier time points (21 vs 28 days). In certain cases, CD8 T cells were found within the fat pad between the tumor and lymph node. As expected, DPX & anti-PD1 combination therapy resulted in the best prognosis. Simultaneous acquisition of PET/MRI demonstrated large areas of necrosis in tumor cores. Using TurboSPI, we have begun quantifying CD8 and Tregs cells, evaluating if volumetric tumor changes due to pseudoprogression correlate with Treg or CD8 T cell migration and comparing results to pre-existing biomarkers. Conclusions: Using MRI/PET with quantitative immune cell tracking results in more in-depth, longitudinal, characterization of immunotherapies at the preclinical level, which can be used to optimize therapy combinations. Citation Format: Marie-Laurence Tremblay, Zoe O'Brien-Moran, Christa Davis, Kimberly Brewer. Evaluating immunotherapy effects using preclinical molecular imaging tools for quantitative immune cell tracking [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 873. doi:10.1158/1538-7445.AM2017-873
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 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 | 0,002 | 0,002 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 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 ».