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Zitation: Andreu Sanz, David und Gregor, Lisa und Carlini, Emanuele und Kobold, Sebastian: Supplemental Data: Predictive value of preclinical models for CAR-T cell therapy clinical trials: a systematic review and meta-analysis. 6. Juni 2025. Open Data LMU. 10.5282/ubm/data.626

Supplemental Data: Predictive value of preclinical models for CAR-T cell therapy clinical trials: a systematic review and meta-analysis
Supplemental Data: Predictive value of preclinical models for CAR-T cell therapy clinical trials: a systematic review and meta-analysis

The attached supplemental data has been used for the following preprint: https://doi.org/10.1101/2024.12.15.628103 Detailed description of data collection, aggregation and analysis can be found in the publication.

Abstract: Experimental mouse models are indispensable for the preclinical development of cancer immunotherapies, whereby complex interactions in the tumor microenvironment (TME) can be somewhat replicated. Despite the availability of diverse models, their predictive capacity for clinical outcomes remains largely unknown, posing a hurdle in the translation from preclinical to clinical success. This study systematically reviews and meta-analyzes clinical trials of chimeric antigen receptor (CAR)-T cell monotherapies with their corresponding preclinical studies. Adhering to PRISMA guidelines, a comprehensive search of PubMed and ClinicalTrials.gov was conducted, identifying 422 clinical trials and 3157 preclinical studies. From these, 105 clinical trials and 180 preclinical studies, accounting for 44 and 131 distinct CAR constructs, respectively, were included. Patients’ responses varied based on the target antigen, expectedly with higher efficacy and toxicity rates in hematological cancers. Preclinical data analysis revealed homogenous and antigen-independent efficacy rates. Our analysis revealed that only 4 % (n = 12) of mouse studies used syngeneic models, highlighting their scarcity in research. Three logistic regression models were trained on CAR structures, tumor entities, and experimental settings to predict treatment outcomes. While the logistic regression model accurately predicted clinical outcomes based on clinical or preclinical features (Macro F1 and AUC > 0.8), it failed in predicting preclinical outcomes from preclinical features (Macro F1 < 0.5, AUC < 0.6), indicating that preclinical studies may be influenced by experimental factors not accounted for in the model. These findings underscore the need to better understand the experimental factors enhancing the predictive accuracy of mouse models in preclinical settings.

Not available
Andreu Sanz, David
Gregor, Lisa
Carlini, Emanuele
Kobold, Sebastian
2025

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DOI: 10.5282/ubm/data.626

Dieser Datensatz steht unter der Creative Commons Lizenz
CC BY-NC 4.0

Be­schrei­bung

The attached supplemental data has been used for the following preprint: https://doi.org/10.1101/2024.12.15.628103 Detailed description of data collection, aggregation and analysis can be found in the publication. Abstract: Experimental mouse models are indispensable for the preclinical development of cancer immunotherapies, whereby complex interactions in the tumor microenvironment (TME) can be somewhat replicated. Despite the availability of diverse models, their predictive capacity for clinical outcomes remains largely unknown, posing a hurdle in the translation from preclinical to clinical success. This study systematically reviews and meta-analyzes clinical trials of chimeric antigen receptor (CAR)-T cell monotherapies with their corresponding preclinical studies. Adhering to PRISMA guidelines, a comprehensive search of PubMed and ClinicalTrials.gov was conducted, identifying 422 clinical trials and 3157 preclinical studies. From these, 105 clinical trials and 180 preclinical studies, accounting for 44 and 131 distinct CAR constructs, respectively, were included. Patients’ responses varied based on the target antigen, expectedly with higher efficacy and toxicity rates in hematological cancers. Preclinical data analysis revealed homogenous and antigen-independent efficacy rates. Our analysis revealed that only 4 % (n = 12) of mouse studies used syngeneic models, highlighting their scarcity in research. Three logistic regression models were trained on CAR structures, tumor entities, and experimental settings to predict treatment outcomes. While the logistic regression model accurately predicted clinical outcomes based on clinical or preclinical features (Macro F1 and AUC > 0.8), it failed in predicting preclinical outcomes from preclinical features (Macro F1 < 0.5, AUC < 0.6), indicating that preclinical studies may be influenced by experimental factors not accounted for in the model. These findings underscore the need to better understand the experimental factors enhancing the predictive accuracy of mouse models in preclinical settings.

Dokumententyp:Daten
Name der Kontakt­person:Kobold, Sebastian
E-Mail der Kontaktperson:sebastian.kobold at med.uni-muenchen.de
Fächer:Medizin
Dewey Dezimal­klassi­fikation:500 Naturwissenschaften und Mathematik
500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
ID Code:626
Eingestellt von: Lisa Gregor
Eingestellt am:06. Jun. 2025 14:18
Letzte Änderungen:06. Jun. 2025 14:19

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