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Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Biomarkers For Prediction Of Car T Therapy Outcomes: Current And Future Perspectives.

Home
Research Domains
Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Biomarkers For Prediction Of Car T Therapy Outcomes: Current And Future Perspectives.

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Published on: February 17, 2022

Biomarkers for prediction of CAR T therapy outcomes: current and future perspectives.

Lucija Levstek¹, Larisa Janžič¹, Alojz Ihan¹

¹Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.

Frontiers in Immunology

|April 1, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This review explores predictive biomarkers for chimeric antigen receptor (CAR) T cell therapy, aiming to improve treatment outcomes and reduce side effects. Identifying key markers could enhance this immunotherapy for hematologic malignancies.

Keywords:

CAR T cells adoptive cell immunotherapy cytokine release syndrome immune effector cell-associated neurotoxicity syndrome

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Area of Science:

Immunotherapy
Oncology
Biomarker Discovery

Background:

Chimeric antigen receptor (CAR) T cell therapy shows promise for hematologic malignancies but faces challenges with side effects and unresponsiveness.
Current predictive biomarkers for CAR T cell therapy have limited success in capturing complex immune dynamics.

Purpose of the Study:

To provide a comprehensive overview of current predictive biomarkers for CAR T cell therapy.
To identify six major groups of predictive biomarkers for improved prediction models.
To enhance the efficacy and patient experience of CAR T cell therapy.

Main Methods:

Literature review of existing research on predictive biomarkers for CAR T cell therapy.
Categorization of biomarkers into six key groups: mitochondrial dynamics, endothelial activation, CNS impairment, immune markers, extracellular vesicles, and tumor microenvironment.

predictive biomarkers

therapeutic response

Proposal for integrating these markers into advanced prediction models.

Main Results:

Existing predictive metrics offer partial correlation with treatment outcomes but do not fully represent immune system complexity.
Six distinct groups of predictive biomarkers have been identified as crucial for enhancing prediction models.
A deeper understanding of factors influencing therapeutic efficacy is essential.

Conclusions:

Improved prediction models incorporating diverse biomarkers can significantly enhance CAR T cell therapy outcomes.
Identifying predictive biomarkers will lead to more effective and patient-friendly immunotherapy.
This approach aims to make CAR T cell therapy more appealing and manageable for patients.