CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models
•A mathematical model of leukaemia response to CAR-T cell treatment is built and studied.•Tumour relapse is a dynamical phenomenon and can be controlled.•The mathematical model allows obtaining a formula for the relapse time.•Persistence of the disease depends on the generation of B-cells in the bon...
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Veröffentlicht in: | Communications in nonlinear science & numerical simulation 2021-03, Vol.94, p.105570, Article 105570 |
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creator | León-Triana, Odelaisy Sabir, Soukaina Calvo, Gabriel F. Belmonte-Beitia, Juan Chulián, Salvador Martínez-Rubio, Álvaro Rosa, María Pérez-Martínez, Antonio Ramirez-Orellana, Manuel Pérez-García, Víctor M. |
description | •A mathematical model of leukaemia response to CAR-T cell treatment is built and studied.•Tumour relapse is a dynamical phenomenon and can be controlled.•The mathematical model allows obtaining a formula for the relapse time.•Persistence of the disease depends on the generation of B-cells in the bone marrow.
Immunotherapies use components of the patient immune system to selectively target cancer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies –leukaemias and lymphomas– is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extracting the patient’s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19+, which is expressed in these haematological cancers.
Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compartments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell expansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19+ cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells. |
doi_str_mv | 10.1016/j.cnsns.2020.105570 |
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Immunotherapies use components of the patient immune system to selectively target cancer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies –leukaemias and lymphomas– is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extracting the patient’s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19+, which is expressed in these haematological cancers.
Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compartments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell expansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19+ cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.</description><identifier>ISSN: 1007-5704</identifier><identifier>EISSN: 1878-7274</identifier><identifier>DOI: 10.1016/j.cnsns.2020.105570</identifier><language>eng</language><publisher>AMSTERDAM: Elsevier B.V</publisher><subject>Antigens ; Biological activity ; Biological models (mathematics) ; Cancer ; Cancer dynamics ; Immune system ; Immunotherapy ; Leukemia ; Lymphocytes ; Lymphoma ; Mathematical analysis ; Mathematical modelling ; Mathematical models ; Mathematical oncology ; Mathematics ; Mathematics, Applied ; Mathematics, Interdisciplinary Applications ; Mechanics ; Physical Sciences ; Physics ; Physics, Fluids & Plasmas ; Physics, Mathematical ; Science & Technology ; Side effects ; T cell receptors ; Target recognition ; Technology ; Time response ; Tumour-immune system interactions</subject><ispartof>Communications in nonlinear science & numerical simulation, 2021-03, Vol.94, p.105570, Article 105570</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Mar 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>20</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000612166700004</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c331t-43b7e86efd9bd08073bc8a3db951ba50f79efa738ef638e4b3207f40d879a1373</citedby><cites>FETCH-LOGICAL-c331t-43b7e86efd9bd08073bc8a3db951ba50f79efa738ef638e4b3207f40d879a1373</cites><orcidid>0000-0001-5509-5236 ; 0000-0002-6575-495X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cnsns.2020.105570$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27928,27929,45999</link.rule.ids></links><search><creatorcontrib>León-Triana, Odelaisy</creatorcontrib><creatorcontrib>Sabir, Soukaina</creatorcontrib><creatorcontrib>Calvo, Gabriel F.</creatorcontrib><creatorcontrib>Belmonte-Beitia, Juan</creatorcontrib><creatorcontrib>Chulián, Salvador</creatorcontrib><creatorcontrib>Martínez-Rubio, Álvaro</creatorcontrib><creatorcontrib>Rosa, María</creatorcontrib><creatorcontrib>Pérez-Martínez, Antonio</creatorcontrib><creatorcontrib>Ramirez-Orellana, Manuel</creatorcontrib><creatorcontrib>Pérez-García, Víctor M.</creatorcontrib><title>CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models</title><title>Communications in nonlinear science & numerical simulation</title><addtitle>COMMUN NONLINEAR SCI</addtitle><description>•A mathematical model of leukaemia response to CAR-T cell treatment is built and studied.•Tumour relapse is a dynamical phenomenon and can be controlled.•The mathematical model allows obtaining a formula for the relapse time.•Persistence of the disease depends on the generation of B-cells in the bone marrow.
Immunotherapies use components of the patient immune system to selectively target cancer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies –leukaemias and lymphomas– is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extracting the patient’s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19+, which is expressed in these haematological cancers.
Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compartments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell expansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19+ cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.</description><subject>Antigens</subject><subject>Biological activity</subject><subject>Biological models (mathematics)</subject><subject>Cancer</subject><subject>Cancer dynamics</subject><subject>Immune system</subject><subject>Immunotherapy</subject><subject>Leukemia</subject><subject>Lymphocytes</subject><subject>Lymphoma</subject><subject>Mathematical analysis</subject><subject>Mathematical modelling</subject><subject>Mathematical models</subject><subject>Mathematical oncology</subject><subject>Mathematics</subject><subject>Mathematics, Applied</subject><subject>Mathematics, Interdisciplinary Applications</subject><subject>Mechanics</subject><subject>Physical Sciences</subject><subject>Physics</subject><subject>Physics, Fluids & Plasmas</subject><subject>Physics, Mathematical</subject><subject>Science & Technology</subject><subject>Side effects</subject><subject>T cell receptors</subject><subject>Target recognition</subject><subject>Technology</subject><subject>Time response</subject><subject>Tumour-immune system interactions</subject><issn>1007-5704</issn><issn>1878-7274</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><recordid>eNqNkMFq3DAQhk1JoUmaJ-hF0GPwdmTJllzoITVJGwgUSnoMQpZHXW1tayPJDfv21cahx9LLaBj-b0Z8RfGOwoYCbT7sNmaOc9xUUB0ndS3gVXFKpZClqAQ_yT2AKPOYvynOYtxBptqanxYP3dV3ck8MjiNJWwx6fyBuJp_L54k2S0IyHqb91vejjskZMuLyS-Pk9EdyO0f3c5siscFPZNJ5QS7O6JFMfsAxvi1eWz1GvHh5z4sfN9f33dfy7tuX2-7qrjSM0VRy1guUDdqh7QeQIFhvpGZD39a01zVY0aLVgkm0TS68ZxUIy2GQotWUCXZevF_37oN_XDAmtfNLmPNJVXEpeEMZrXKKrSkTfIwBrdoHN-lwUBTU0aPaqWeP6uhRrR4zJVfqCXtvo3E4G_xLAkBDK9o0InfAO5eyAD93fplTRi__H83pT2s6i8PfDoN6IQYX0CQ1ePfPj_4BskidNQ</recordid><startdate>202103</startdate><enddate>202103</enddate><creator>León-Triana, Odelaisy</creator><creator>Sabir, Soukaina</creator><creator>Calvo, Gabriel F.</creator><creator>Belmonte-Beitia, Juan</creator><creator>Chulián, Salvador</creator><creator>Martínez-Rubio, Álvaro</creator><creator>Rosa, María</creator><creator>Pérez-Martínez, Antonio</creator><creator>Ramirez-Orellana, Manuel</creator><creator>Pérez-García, Víctor M.</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Science Ltd</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-5509-5236</orcidid><orcidid>https://orcid.org/0000-0002-6575-495X</orcidid></search><sort><creationdate>202103</creationdate><title>CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models</title><author>León-Triana, Odelaisy ; Sabir, Soukaina ; Calvo, Gabriel F. ; Belmonte-Beitia, Juan ; Chulián, Salvador ; Martínez-Rubio, Álvaro ; Rosa, María ; Pérez-Martínez, Antonio ; Ramirez-Orellana, Manuel ; Pérez-García, Víctor M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-43b7e86efd9bd08073bc8a3db951ba50f79efa738ef638e4b3207f40d879a1373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Antigens</topic><topic>Biological activity</topic><topic>Biological models (mathematics)</topic><topic>Cancer</topic><topic>Cancer dynamics</topic><topic>Immune system</topic><topic>Immunotherapy</topic><topic>Leukemia</topic><topic>Lymphocytes</topic><topic>Lymphoma</topic><topic>Mathematical analysis</topic><topic>Mathematical modelling</topic><topic>Mathematical models</topic><topic>Mathematical oncology</topic><topic>Mathematics</topic><topic>Mathematics, Applied</topic><topic>Mathematics, Interdisciplinary Applications</topic><topic>Mechanics</topic><topic>Physical Sciences</topic><topic>Physics</topic><topic>Physics, Fluids & Plasmas</topic><topic>Physics, Mathematical</topic><topic>Science & Technology</topic><topic>Side effects</topic><topic>T cell receptors</topic><topic>Target recognition</topic><topic>Technology</topic><topic>Time response</topic><topic>Tumour-immune system interactions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>León-Triana, Odelaisy</creatorcontrib><creatorcontrib>Sabir, Soukaina</creatorcontrib><creatorcontrib>Calvo, Gabriel F.</creatorcontrib><creatorcontrib>Belmonte-Beitia, Juan</creatorcontrib><creatorcontrib>Chulián, Salvador</creatorcontrib><creatorcontrib>Martínez-Rubio, Álvaro</creatorcontrib><creatorcontrib>Rosa, María</creatorcontrib><creatorcontrib>Pérez-Martínez, Antonio</creatorcontrib><creatorcontrib>Ramirez-Orellana, Manuel</creatorcontrib><creatorcontrib>Pérez-García, Víctor M.</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><jtitle>Communications in nonlinear science & numerical simulation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>León-Triana, Odelaisy</au><au>Sabir, Soukaina</au><au>Calvo, Gabriel F.</au><au>Belmonte-Beitia, Juan</au><au>Chulián, Salvador</au><au>Martínez-Rubio, Álvaro</au><au>Rosa, María</au><au>Pérez-Martínez, Antonio</au><au>Ramirez-Orellana, Manuel</au><au>Pérez-García, Víctor M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models</atitle><jtitle>Communications in nonlinear science & numerical simulation</jtitle><stitle>COMMUN NONLINEAR SCI</stitle><date>2021-03</date><risdate>2021</risdate><volume>94</volume><spage>105570</spage><pages>105570-</pages><artnum>105570</artnum><issn>1007-5704</issn><eissn>1878-7274</eissn><abstract>•A mathematical model of leukaemia response to CAR-T cell treatment is built and studied.•Tumour relapse is a dynamical phenomenon and can be controlled.•The mathematical model allows obtaining a formula for the relapse time.•Persistence of the disease depends on the generation of B-cells in the bone marrow.
Immunotherapies use components of the patient immune system to selectively target cancer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies –leukaemias and lymphomas– is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extracting the patient’s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19+, which is expressed in these haematological cancers.
Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compartments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell expansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19+ cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.</abstract><cop>AMSTERDAM</cop><pub>Elsevier B.V</pub><doi>10.1016/j.cnsns.2020.105570</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0001-5509-5236</orcidid><orcidid>https://orcid.org/0000-0002-6575-495X</orcidid></addata></record> |
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subjects | Antigens Biological activity Biological models (mathematics) Cancer Cancer dynamics Immune system Immunotherapy Leukemia Lymphocytes Lymphoma Mathematical analysis Mathematical modelling Mathematical models Mathematical oncology Mathematics Mathematics, Applied Mathematics, Interdisciplinary Applications Mechanics Physical Sciences Physics Physics, Fluids & Plasmas Physics, Mathematical Science & Technology Side effects T cell receptors Target recognition Technology Time response Tumour-immune system interactions |
title | CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models |
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