Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Bulletin of mathematical biology 2022, Vol.84 (11), p.130-130, Article 130
Hauptverfasser: El Wajeh, Mohammad, Jung, Falco, Bongartz, Dominik, Kappatou, Chrysoula Dimitra, Ghaffari Laleh, Narmin, Mitsos, Alexander, Kather, Jakob Nikolas
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 130
container_issue 11
container_start_page 130
container_title Bulletin of mathematical biology
container_volume 84
creator El Wajeh, Mohammad
Jung, Falco
Bongartz, Dominik
Kappatou, Chrysoula Dimitra
Ghaffari Laleh, Narmin
Mitsos, Alexander
Kather, Jakob Nikolas
description Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.
doi_str_mv 10.1007/s11538-022-01075-7
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9522842</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2719458722</sourcerecordid><originalsourceid>FETCH-LOGICAL-c355t-d116dfefb43c994c1a73398701c21882e060b1f4b0b26c34873d1812411999983</originalsourceid><addsrcrecordid>eNp9kUFPFTEUhRuigQfyB1iYJm7cjN7bdqbtRkNeBIwQjJF10-l0eEPmtc92BiO_3uJDUBd20zT3O-fe20PIEcIbBJBvM2LNVQWMVYAg60rukAXW5akbYM_IAkCzSjEBe2Q_5xsoIs31LtnjDcpaQr0gJ0sb6LTy9NN8F_yU4y29iJ0f6Re_GQdnJ09t6Ojn5LvBTbTQzid6muL3aUWHQM_mtQ35_QvyvLdj9ocP9wG5OvnwdXlWnV-eflwen1eO1_VUdYhN1_u-FdxpLRxayblWEtAxVIp5aKDFXrTQssZxoSTvUCETiLocxQ_Iu63vZm7XvnM-TMmOZpOGtU0_TLSD-bsShpW5jrdGl29RghWD1w8GKX6bfZ7MesjOj6MNPs7ZMMlAsAYUFPTVP-hNnFMo6xUKtaiVZPeGbEu5FHNOvn8cBsHcx2S2MZkSk_kVk5FF9PLPNR4lv3MpAN8CuZTCtU9Pvf9j-xOnOZtW</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2719458722</pqid></control><display><type>article</type><title>Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>El Wajeh, Mohammad ; Jung, Falco ; Bongartz, Dominik ; Kappatou, Chrysoula Dimitra ; Ghaffari Laleh, Narmin ; Mitsos, Alexander ; Kather, Jakob Nikolas</creator><creatorcontrib>El Wajeh, Mohammad ; Jung, Falco ; Bongartz, Dominik ; Kappatou, Chrysoula Dimitra ; Ghaffari Laleh, Narmin ; Mitsos, Alexander ; Kather, Jakob Nikolas</creatorcontrib><description>Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.</description><identifier>ISSN: 0092-8240</identifier><identifier>EISSN: 1522-9602</identifier><identifier>DOI: 10.1007/s11538-022-01075-7</identifier><identifier>PMID: 36175705</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Cancer ; Cancer immunotherapy ; Cell Biology ; Cell Count ; Data points ; Effector cells ; Humans ; Immunotherapy ; Life Sciences ; Mathematical and Computational Biology ; Mathematical Concepts ; Mathematical models ; Mathematics ; Mathematics and Statistics ; Medical treatment ; Models, Biological ; Neoplasms - therapy ; Original ; Original Article ; Parameter estimation ; Parameter identification ; Patients ; Tumor cells ; Tumors</subject><ispartof>Bulletin of mathematical biology, 2022, Vol.84 (11), p.130-130, Article 130</ispartof><rights>The Author(s) 2022. corrected publication 2023</rights><rights>2022. The Author(s).</rights><rights>The Author(s) 2022. corrected publication 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c355t-d116dfefb43c994c1a73398701c21882e060b1f4b0b26c34873d1812411999983</cites><orcidid>0000-0002-3730-5348</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11538-022-01075-7$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11538-022-01075-7$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36175705$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>El Wajeh, Mohammad</creatorcontrib><creatorcontrib>Jung, Falco</creatorcontrib><creatorcontrib>Bongartz, Dominik</creatorcontrib><creatorcontrib>Kappatou, Chrysoula Dimitra</creatorcontrib><creatorcontrib>Ghaffari Laleh, Narmin</creatorcontrib><creatorcontrib>Mitsos, Alexander</creatorcontrib><creatorcontrib>Kather, Jakob Nikolas</creatorcontrib><title>Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?</title><title>Bulletin of mathematical biology</title><addtitle>Bull Math Biol</addtitle><addtitle>Bull Math Biol</addtitle><description>Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.</description><subject>Cancer</subject><subject>Cancer immunotherapy</subject><subject>Cell Biology</subject><subject>Cell Count</subject><subject>Data points</subject><subject>Effector cells</subject><subject>Humans</subject><subject>Immunotherapy</subject><subject>Life Sciences</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematical Concepts</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Medical treatment</subject><subject>Models, Biological</subject><subject>Neoplasms - therapy</subject><subject>Original</subject><subject>Original Article</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Patients</subject><subject>Tumor cells</subject><subject>Tumors</subject><issn>0092-8240</issn><issn>1522-9602</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><recordid>eNp9kUFPFTEUhRuigQfyB1iYJm7cjN7bdqbtRkNeBIwQjJF10-l0eEPmtc92BiO_3uJDUBd20zT3O-fe20PIEcIbBJBvM2LNVQWMVYAg60rukAXW5akbYM_IAkCzSjEBe2Q_5xsoIs31LtnjDcpaQr0gJ0sb6LTy9NN8F_yU4y29iJ0f6Re_GQdnJ09t6Ojn5LvBTbTQzid6muL3aUWHQM_mtQ35_QvyvLdj9ocP9wG5OvnwdXlWnV-eflwen1eO1_VUdYhN1_u-FdxpLRxayblWEtAxVIp5aKDFXrTQssZxoSTvUCETiLocxQ_Iu63vZm7XvnM-TMmOZpOGtU0_TLSD-bsShpW5jrdGl29RghWD1w8GKX6bfZ7MesjOj6MNPs7ZMMlAsAYUFPTVP-hNnFMo6xUKtaiVZPeGbEu5FHNOvn8cBsHcx2S2MZkSk_kVk5FF9PLPNR4lv3MpAN8CuZTCtU9Pvf9j-xOnOZtW</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>El Wajeh, Mohammad</creator><creator>Jung, Falco</creator><creator>Bongartz, Dominik</creator><creator>Kappatou, Chrysoula Dimitra</creator><creator>Ghaffari Laleh, Narmin</creator><creator>Mitsos, Alexander</creator><creator>Kather, Jakob Nikolas</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SS</scope><scope>7TK</scope><scope>JQ2</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3730-5348</orcidid></search><sort><creationdate>2022</creationdate><title>Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?</title><author>El Wajeh, Mohammad ; Jung, Falco ; Bongartz, Dominik ; Kappatou, Chrysoula Dimitra ; Ghaffari Laleh, Narmin ; Mitsos, Alexander ; Kather, Jakob Nikolas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-d116dfefb43c994c1a73398701c21882e060b1f4b0b26c34873d1812411999983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cancer</topic><topic>Cancer immunotherapy</topic><topic>Cell Biology</topic><topic>Cell Count</topic><topic>Data points</topic><topic>Effector cells</topic><topic>Humans</topic><topic>Immunotherapy</topic><topic>Life Sciences</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematical Concepts</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Medical treatment</topic><topic>Models, Biological</topic><topic>Neoplasms - therapy</topic><topic>Original</topic><topic>Original Article</topic><topic>Parameter estimation</topic><topic>Parameter identification</topic><topic>Patients</topic><topic>Tumor cells</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>El Wajeh, Mohammad</creatorcontrib><creatorcontrib>Jung, Falco</creatorcontrib><creatorcontrib>Bongartz, Dominik</creatorcontrib><creatorcontrib>Kappatou, Chrysoula Dimitra</creatorcontrib><creatorcontrib>Ghaffari Laleh, Narmin</creatorcontrib><creatorcontrib>Mitsos, Alexander</creatorcontrib><creatorcontrib>Kather, Jakob Nikolas</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bulletin of mathematical biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>El Wajeh, Mohammad</au><au>Jung, Falco</au><au>Bongartz, Dominik</au><au>Kappatou, Chrysoula Dimitra</au><au>Ghaffari Laleh, Narmin</au><au>Mitsos, Alexander</au><au>Kather, Jakob Nikolas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?</atitle><jtitle>Bulletin of mathematical biology</jtitle><stitle>Bull Math Biol</stitle><addtitle>Bull Math Biol</addtitle><date>2022</date><risdate>2022</risdate><volume>84</volume><issue>11</issue><spage>130</spage><epage>130</epage><pages>130-130</pages><artnum>130</artnum><issn>0092-8240</issn><eissn>1522-9602</eissn><abstract>Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>36175705</pmid><doi>10.1007/s11538-022-01075-7</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-3730-5348</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0092-8240
ispartof Bulletin of mathematical biology, 2022, Vol.84 (11), p.130-130, Article 130
issn 0092-8240
1522-9602
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9522842
source MEDLINE; SpringerLink Journals - AutoHoldings
subjects Cancer
Cancer immunotherapy
Cell Biology
Cell Count
Data points
Effector cells
Humans
Immunotherapy
Life Sciences
Mathematical and Computational Biology
Mathematical Concepts
Mathematical models
Mathematics
Mathematics and Statistics
Medical treatment
Models, Biological
Neoplasms - therapy
Original
Original Article
Parameter estimation
Parameter identification
Patients
Tumor cells
Tumors
title Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T06%3A18%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Can%20the%20Kuznetsov%20Model%20Replicate%20and%20Predict%20Cancer%20Growth%20in%20Humans?&rft.jtitle=Bulletin%20of%20mathematical%20biology&rft.au=El%20Wajeh,%20Mohammad&rft.date=2022&rft.volume=84&rft.issue=11&rft.spage=130&rft.epage=130&rft.pages=130-130&rft.artnum=130&rft.issn=0092-8240&rft.eissn=1522-9602&rft_id=info:doi/10.1007/s11538-022-01075-7&rft_dat=%3Cproquest_pubme%3E2719458722%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2719458722&rft_id=info:pmid/36175705&rfr_iscdi=true