Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy

Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells...
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Gespeichert in:

Autor*in:	Can Zhang [verfasserIn] Changrong Shao [verfasserIn] Xiaopei Jiao [verfasserIn] Yue Bai [verfasserIn] Miao Li [verfasserIn] Hanping Shi [verfasserIn] Jinzhi Lei [verfasserIn] Xiaosong Zhong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	B‐cell acute lymphoblastic leukemia cancer immunotherapy CAR‐T cell plasticity immune escape individual cell‐based modeling

Übergeordnetes Werk:	In: Computational and Systems Oncology - Wiley, 2021, 1(2021), 3, Seite n/a-n/a
Übergeordnetes Werk:	volume:1 ; year:2021 ; number:3 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/cso2.1029

Katalog-ID:	DOAJ06271841X

Internformat


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520			\|a Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR‐T treatment through model simulation based on early‐stage observations of tumor burden and tumor cells analysis.
650		4	\|a B‐cell acute lymphoblastic leukemia
650		4	\|a cancer immunotherapy
650		4	\|a CAR‐T
650		4	\|a cell plasticity
650		4	\|a immune escape
650		4	\|a individual cell‐based modeling
653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
653		0	\|a Computer applications to medicine. Medical informatics
700	0		\|a Changrong Shao \|e verfasserin \|4 aut
700	0		\|a Xiaopei Jiao \|e verfasserin \|4 aut
700	0		\|a Yue Bai \|e verfasserin \|4 aut
700	0		\|a Miao Li \|e verfasserin \|4 aut
700	0		\|a Hanping Shi \|e verfasserin \|4 aut
700	0		\|a Jinzhi Lei \|e verfasserin \|4 aut
700	0		\|a Xiaosong Zhong \|e verfasserin \|4 aut
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allfields_unstemmed	10.1002/cso2.1029 doi (DE-627)DOAJ06271841X (DE-599)DOAJebaa24c0803b4f1d8833b7c316639ecf DE-627 ger DE-627 rakwb eng RC254-282 R858-859.7 Can Zhang verfasserin aut Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR‐T treatment through model simulation based on early‐stage observations of tumor burden and tumor cells analysis. B‐cell acute lymphoblastic leukemia cancer immunotherapy CAR‐T cell plasticity immune escape individual cell‐based modeling Neoplasms. Tumors. Oncology. Including cancer and carcinogens Computer applications to medicine. Medical informatics Changrong Shao verfasserin aut Xiaopei Jiao verfasserin aut Yue Bai verfasserin aut Miao Li verfasserin aut Hanping Shi verfasserin aut Jinzhi Lei verfasserin aut Xiaosong Zhong verfasserin aut In Computational and Systems Oncology Wiley, 2021 1(2021), 3, Seite n/a-n/a (DE-627)1753559456 (DE-600)3059855-2 26899655 nnns volume:1 year:2021 number:3 pages:n/a-n/a https://doi.org/10.1002/cso2.1029 kostenfrei https://doaj.org/article/ebaa24c0803b4f1d8833b7c316639ecf kostenfrei https://doi.org/10.1002/cso2.1029 kostenfrei https://doaj.org/toc/2689-9655 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 3 n/a-n/a
allfieldsGer	10.1002/cso2.1029 doi (DE-627)DOAJ06271841X (DE-599)DOAJebaa24c0803b4f1d8833b7c316639ecf DE-627 ger DE-627 rakwb eng RC254-282 R858-859.7 Can Zhang verfasserin aut Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR‐T treatment through model simulation based on early‐stage observations of tumor burden and tumor cells analysis. B‐cell acute lymphoblastic leukemia cancer immunotherapy CAR‐T cell plasticity immune escape individual cell‐based modeling Neoplasms. Tumors. Oncology. Including cancer and carcinogens Computer applications to medicine. Medical informatics Changrong Shao verfasserin aut Xiaopei Jiao verfasserin aut Yue Bai verfasserin aut Miao Li verfasserin aut Hanping Shi verfasserin aut Jinzhi Lei verfasserin aut Xiaosong Zhong verfasserin aut In Computational and Systems Oncology Wiley, 2021 1(2021), 3, Seite n/a-n/a (DE-627)1753559456 (DE-600)3059855-2 26899655 nnns volume:1 year:2021 number:3 pages:n/a-n/a https://doi.org/10.1002/cso2.1029 kostenfrei https://doaj.org/article/ebaa24c0803b4f1d8833b7c316639ecf kostenfrei https://doi.org/10.1002/cso2.1029 kostenfrei https://doaj.org/toc/2689-9655 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 3 n/a-n/a
allfieldsSound	10.1002/cso2.1029 doi (DE-627)DOAJ06271841X (DE-599)DOAJebaa24c0803b4f1d8833b7c316639ecf DE-627 ger DE-627 rakwb eng RC254-282 R858-859.7 Can Zhang verfasserin aut Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR‐T treatment through model simulation based on early‐stage observations of tumor burden and tumor cells analysis. B‐cell acute lymphoblastic leukemia cancer immunotherapy CAR‐T cell plasticity immune escape individual cell‐based modeling Neoplasms. Tumors. Oncology. Including cancer and carcinogens Computer applications to medicine. Medical informatics Changrong Shao verfasserin aut Xiaopei Jiao verfasserin aut Yue Bai verfasserin aut Miao Li verfasserin aut Hanping Shi verfasserin aut Jinzhi Lei verfasserin aut Xiaosong Zhong verfasserin aut In Computational and Systems Oncology Wiley, 2021 1(2021), 3, Seite n/a-n/a (DE-627)1753559456 (DE-600)3059855-2 26899655 nnns volume:1 year:2021 number:3 pages:n/a-n/a https://doi.org/10.1002/cso2.1029 kostenfrei https://doaj.org/article/ebaa24c0803b4f1d8833b7c316639ecf kostenfrei https://doi.org/10.1002/cso2.1029 kostenfrei https://doaj.org/toc/2689-9655 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2021 3 n/a-n/a
language	English
source	In Computational and Systems Oncology 1(2021), 3, Seite n/a-n/a volume:1 year:2021 number:3 pages:n/a-n/a
sourceStr	In Computational and Systems Oncology 1(2021), 3, Seite n/a-n/a volume:1 year:2021 number:3 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	B‐cell acute lymphoblastic leukemia cancer immunotherapy CAR‐T cell plasticity immune escape individual cell‐based modeling Neoplasms. Tumors. Oncology. Including cancer and carcinogens Computer applications to medicine. Medical informatics
isfreeaccess_bool	true
container_title	Computational and Systems Oncology
authorswithroles_txt_mv	Can Zhang @@aut@@ Changrong Shao @@aut@@ Xiaopei Jiao @@aut@@ Yue Bai @@aut@@ Miao Li @@aut@@ Hanping Shi @@aut@@ Jinzhi Lei @@aut@@ Xiaosong Zhong @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	1753559456
id	DOAJ06271841X
language_de	englisch
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callnumber-first	R - Medicine
author	Can Zhang
spellingShingle	Can Zhang misc RC254-282 misc R858-859.7 misc B‐cell acute lymphoblastic leukemia misc cancer immunotherapy misc CAR‐T misc cell plasticity misc immune escape misc individual cell‐based modeling misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens misc Computer applications to medicine. Medical informatics Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy
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topic_title	RC254-282 R858-859.7 Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy B‐cell acute lymphoblastic leukemia cancer immunotherapy CAR‐T cell plasticity immune escape individual cell‐based modeling
topic	misc RC254-282 misc R858-859.7 misc B‐cell acute lymphoblastic leukemia misc cancer immunotherapy misc CAR‐T misc cell plasticity misc immune escape misc individual cell‐based modeling misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens misc Computer applications to medicine. Medical informatics
topic_unstemmed	misc RC254-282 misc R858-859.7 misc B‐cell acute lymphoblastic leukemia misc cancer immunotherapy misc CAR‐T misc cell plasticity misc immune escape misc individual cell‐based modeling misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens misc Computer applications to medicine. Medical informatics
topic_browse	misc RC254-282 misc R858-859.7 misc B‐cell acute lymphoblastic leukemia misc cancer immunotherapy misc CAR‐T misc cell plasticity misc immune escape misc individual cell‐based modeling misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens misc Computer applications to medicine. Medical informatics
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title	Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy
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title_full	Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy
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author_browse	Can Zhang Changrong Shao Xiaopei Jiao Yue Bai Miao Li Hanping Shi Jinzhi Lei Xiaosong Zhong
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title_sort	individual cell‐based modeling of tumor cell plasticity‐induced immune escape after car‐t therapy
callnumber	RC254-282
title_auth	Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy
abstract	Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR‐T treatment through model simulation based on early‐stage observations of tumor burden and tumor cells analysis.
abstractGer	Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR‐T treatment through model simulation based on early‐stage observations of tumor burden and tumor cells analysis.
abstract_unstemmed	Abstract Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B‐cell acute lymphoblastic leukemia (B‐ALL). The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR‐T treatment through model simulation based on early‐stage observations of tumor burden and tumor cells analysis.
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title_short	Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy
url	https://doi.org/10.1002/cso2.1029 https://doaj.org/article/ebaa24c0803b4f1d8833b7c316639ecf https://doaj.org/toc/2689-9655
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up_date	2024-07-03T13:39:36.630Z
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The majority of patients achieve a complete response following a single infusion of CD19‐targeted CAR‐modified T cells (CAR‐19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell‐based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR‐T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR‐T stress can lead to tumor relapse in B‐ALL. Model predictions were in agreement with experimental results of applying the second‐generation CAR‐T cells to mice injected with NALM‐6‐GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR‐T cell‐induced transition of tumor cells to hematopoietic stem‐like cells and myeloid‐like cells, which are resistant to the treatment. 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Individual cell‐based modeling of tumor cell plasticity‐induced immune escape after CAR‐T therapy

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