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

Chimeric antigen receptor (CAR) T-cell therapy combines antigen-specific properties of monoclonal antibodies with the lytic capacity of T cells. An effective and safe CAR-T cell therapy strategy relies on identifying an antigen that has high expression and is tumor specific. This strategy has been s...

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Autores principales:	Patrick Schreiner, Mireya Paulina Velasquez, Stephen Gottschalk, Jinghui Zhang, Yiping Fan
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	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) Immunologic diseases. Allergy RC581-607 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/bf7f740860484a71a617907389cbe23f
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spelling	oai:doaj.org-article:bf7f740860484a71a617907389cbe23f2021-11-26T11:19:49ZUnifying heterogeneous expression data to predict targets for CAR-T cell therapy2162-402X10.1080/2162402X.2021.2000109https://doaj.org/article/bf7f740860484a71a617907389cbe23f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/2162402X.2021.2000109https://doaj.org/toc/2162-402XChimeric antigen receptor (CAR) T-cell therapy combines antigen-specific properties of monoclonal antibodies with the lytic capacity of T cells. An effective and safe CAR-T cell therapy strategy relies on identifying an antigen that has high expression and is tumor specific. This strategy has been successfully used to treat patients with CD19+ B-cell acute lymphoblastic leukemia (B-ALL). Finding a suitable target antigen for other cancers such as acute myeloid leukemia (AML) has proven challenging, as the majority of currently targeted AML antigens are also expressed on hematopoietic progenitor cells (HPCs) or mature myeloid cells. Herein, we developed a computational method to perform a data transformation to enable the comparison of publicly available gene expression data across different datasets or assay platforms. The resulting transformed expression values (TEVs) were used in our antigen prediction algorithm to assess suitable tumor-associated antigens (TAAs) that could be targeted with CAR-T cells. We validated this method by identifying B-ALL antigens with known clinical effectiveness, such as CD19 and CD22. Our algorithm predicted TAAs being currently explored preclinically and in clinical CAR-T AML therapy trials, as well as novel TAAs in pediatric megakaryoblastic AML. Thus, this analytical approach presents a promising new strategy to mine diverse datasets for identifying TAAs suitable for immunotherapy.Patrick SchreinerMireya Paulina VelasquezStephen GottschalkJinghui ZhangYiping FanTaylor & Francis Grouparticleacute myeloid leukemia (aml)b-cell acute lymphoblastic (b-all)bioinformaticscar-t cell therapydata heterogeneityimmunotherapyleukemiamegakaryoblastic aml (amkl)microarrayrna-seq (rna sequencing)Immunologic diseases. AllergyRC581-607Neoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENOncoImmunology, Vol 10, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	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) Immunologic diseases. Allergy RC581-607 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	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) Immunologic diseases. Allergy RC581-607 Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Patrick Schreiner Mireya Paulina Velasquez Stephen Gottschalk Jinghui Zhang Yiping Fan Unifying heterogeneous expression data to predict targets for CAR-T cell therapy
description	Chimeric antigen receptor (CAR) T-cell therapy combines antigen-specific properties of monoclonal antibodies with the lytic capacity of T cells. An effective and safe CAR-T cell therapy strategy relies on identifying an antigen that has high expression and is tumor specific. This strategy has been successfully used to treat patients with CD19+ B-cell acute lymphoblastic leukemia (B-ALL). Finding a suitable target antigen for other cancers such as acute myeloid leukemia (AML) has proven challenging, as the majority of currently targeted AML antigens are also expressed on hematopoietic progenitor cells (HPCs) or mature myeloid cells. Herein, we developed a computational method to perform a data transformation to enable the comparison of publicly available gene expression data across different datasets or assay platforms. The resulting transformed expression values (TEVs) were used in our antigen prediction algorithm to assess suitable tumor-associated antigens (TAAs) that could be targeted with CAR-T cells. We validated this method by identifying B-ALL antigens with known clinical effectiveness, such as CD19 and CD22. Our algorithm predicted TAAs being currently explored preclinically and in clinical CAR-T AML therapy trials, as well as novel TAAs in pediatric megakaryoblastic AML. Thus, this analytical approach presents a promising new strategy to mine diverse datasets for identifying TAAs suitable for immunotherapy.
format	article
author	Patrick Schreiner Mireya Paulina Velasquez Stephen Gottschalk Jinghui Zhang Yiping Fan
author_facet	Patrick Schreiner Mireya Paulina Velasquez Stephen Gottschalk Jinghui Zhang Yiping Fan
author_sort	Patrick Schreiner
title	Unifying heterogeneous expression data to predict targets for CAR-T cell therapy
title_short	Unifying heterogeneous expression data to predict targets for CAR-T cell therapy
title_full	Unifying heterogeneous expression data to predict targets for CAR-T cell therapy
title_fullStr	Unifying heterogeneous expression data to predict targets for CAR-T cell therapy
title_full_unstemmed	Unifying heterogeneous expression data to predict targets for CAR-T cell therapy
title_sort	unifying heterogeneous expression data to predict targets for car-t cell therapy
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/bf7f740860484a71a617907389cbe23f
work_keys_str_mv	AT patrickschreiner unifyingheterogeneousexpressiondatatopredicttargetsforcartcelltherapy AT mireyapaulinavelasquez unifyingheterogeneousexpressiondatatopredicttargetsforcartcelltherapy AT stephengottschalk unifyingheterogeneousexpressiondatatopredicttargetsforcartcelltherapy AT jinghuizhang unifyingheterogeneousexpressiondatatopredicttargetsforcartcelltherapy AT yipingfan unifyingheterogeneousexpressiondatatopredicttargetsforcartcelltherapy
_version_	1718409496505614336

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

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