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Tumor Immunotherapy

Tumor Immunotherapy

Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.

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Author Spotlight: Advancements in Hypoxia-Sensitive CAR-T Therapy for Enhanced Cancer Immunotherapy

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Unifying heterogeneous expression data to predict targets for CAR-T cell therapy.

Patrick Schreiner¹, Mireya Paulina Velasquez², Stephen Gottschalk²

¹The Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA.

|December 3, 2021

Summary

A new computational method identifies suitable tumor antigens for CAR-T cell therapy by analyzing gene expression data. This approach aids in developing targeted immunotherapies for cancers like acute myeloid leukemia (AML).

Keywords:

Acute myeloid leukemia (aml)b-cell acute lymphoblastic (b-all)bioinformatics car-t cell therapy data heterogeneity immunotherapy leukemia megakaryoblastic aml (amkl)microarray rna-seq (rna sequencing)

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Area of Science:

Immunology
Computational Biology
Oncology

Background:

Chimeric antigen receptor (CAR) T-cell therapy shows promise but requires specific tumor antigens for efficacy and safety.
Identifying suitable antigens for acute myeloid leukemia (AML) is challenging due to expression on normal cells.

Purpose of the Study:

To develop a computational method for identifying tumor-associated antigens (TAAs) for CAR-T cell therapy.
To address the challenge of finding AML-specific targets.

Main Methods:

Developed a computational method for data transformation to compare gene expression across datasets.
Utilized transformed expression values (TEVs) in an antigen prediction algorithm.
Validated the method using known B-cell acute lymphoblastic leukemia (B-ALL) antigens (CD19, CD22).

Main Results:

The algorithm successfully identified known B-ALL antigens.
Predicted TAAs currently under investigation for AML CAR-T therapy.
Identified novel TAAs for pediatric megakaryoblastic AML.

Conclusions:

The developed analytical approach is a promising strategy for mining diverse datasets to find TAAs for CAR-T immunotherapy.
This method can accelerate the identification of effective and safe CAR-T cell targets for various cancers, including AML.