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Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Joseph D Butner, Geoffrey V Martin, Zhihui Wang, Bruna Corradetti, Mauro Ferrari, Nestor Esnaola, Caroline Chung, David S Hong, James W Welsh, Naomi Hasegawa, Elizabeth A Mittendorf, Steven A Curley, Shu-Hsia Chen, Ping-Ying Pan, Steven K Libutti, Shridar Ganesan, Richard L Sidman, Renata Pasqualini, Wadih Arap, Eugene J Koay, Vittorio Cristini

eLife, Volume: 10, Start page: e70130

Swansea University Author: Bruna Corradetti

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DOI (Published version): 10.7554/elife.70130

Abstract

Background:: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better und...

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ISSN: 2050-084X
Published: eLife Sciences Publications, Ltd 2021
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-12-31T13:07:16.5106159</datestamp><bib-version>v2</bib-version><id>58891</id><entry>2021-12-06</entry><title>Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling</title><swanseaauthors><author><sid>aa6a235c9e53c5b9b00e751422db5277</sid><firstname>Bruna</firstname><surname>Corradetti</surname><name>Bruna Corradetti</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-12-06</date><deptcode>FGMHL</deptcode><abstract>Background:: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods:: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (&#x3B1;), tumor-immune infiltration (&#x39B;), and immunotherapy-mediated amplification of anti-tumor response (&#xB5;). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results:: The derived parameters &#x39B; and &#xB5; were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions:: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding:: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Sk&#x142;odowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</abstract><type>Journal Article</type><journal>eLife</journal><volume>10</volume><journalNumber/><paginationStart>e70130</paginationStart><paginationEnd/><publisher>eLife Sciences Publications, Ltd</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2050-084X</issnElectronic><keywords/><publishedDay>9</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-11-09</publishedDate><doi>10.7554/elife.70130</doi><url/><notes/><college>COLLEGE NANME</college><department>Medicine, Health and Life Science - Faculty</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>FGMHL</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center; Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey); National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC); SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Sk&#x142;odowskaCurie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF); Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING); NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute.</funders><lastEdited>2021-12-31T13:07:16.5106159</lastEdited><Created>2021-12-06T10:29:25.4249825</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Joseph D</firstname><surname>Butner</surname><order>1</order></author><author><firstname>Geoffrey V</firstname><surname>Martin</surname><order>2</order></author><author><firstname>Zhihui</firstname><surname>Wang</surname><order>3</order></author><author><firstname>Bruna</firstname><surname>Corradetti</surname><order>4</order></author><author><firstname>Mauro</firstname><surname>Ferrari</surname><order>5</order></author><author><firstname>Nestor</firstname><surname>Esnaola</surname><order>6</order></author><author><firstname>Caroline</firstname><surname>Chung</surname><order>7</order></author><author><firstname>David S</firstname><surname>Hong</surname><order>8</order></author><author><firstname>James W</firstname><surname>Welsh</surname><order>9</order></author><author><firstname>Naomi</firstname><surname>Hasegawa</surname><order>10</order></author><author><firstname>Elizabeth A</firstname><surname>Mittendorf</surname><order>11</order></author><author><firstname>Steven A</firstname><surname>Curley</surname><order>12</order></author><author><firstname>Shu-Hsia</firstname><surname>Chen</surname><order>13</order></author><author><firstname>Ping-Ying</firstname><surname>Pan</surname><order>14</order></author><author><firstname>Steven K</firstname><surname>Libutti</surname><order>15</order></author><author><firstname>Shridar</firstname><surname>Ganesan</surname><order>16</order></author><author><firstname>Richard L</firstname><surname>Sidman</surname><order>17</order></author><author><firstname>Renata</firstname><surname>Pasqualini</surname><order>18</order></author><author><firstname>Wadih</firstname><surname>Arap</surname><order>19</order></author><author><firstname>Eugene J</firstname><surname>Koay</surname><order>20</order></author><author><firstname>Vittorio</firstname><surname>Cristini</surname><order>21</order></author></authors><documents><document><filename>58891__21793__1970855eadb640558d4052586079be72.pdf</filename><originalFilename>58891.pdf</originalFilename><uploaded>2021-12-06T10:32:05.8371884</uploaded><type>Output</type><contentLength>6672898</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2021-12-31T13:07:16.5106159 v2 58891 2021-12-06 Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling aa6a235c9e53c5b9b00e751422db5277 Bruna Corradetti Bruna Corradetti true false 2021-12-06 FGMHL Background:: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods:: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results:: The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions:: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding:: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Journal Article eLife 10 e70130 eLife Sciences Publications, Ltd 2050-084X 9 11 2021 2021-11-09 10.7554/elife.70130 COLLEGE NANME Medicine, Health and Life Science - Faculty COLLEGE CODE FGMHL Swansea University Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center; Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey); National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC); SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie SkłodowskaCurie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF); Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING); NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. 2021-12-31T13:07:16.5106159 2021-12-06T10:29:25.4249825 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Joseph D Butner 1 Geoffrey V Martin 2 Zhihui Wang 3 Bruna Corradetti 4 Mauro Ferrari 5 Nestor Esnaola 6 Caroline Chung 7 David S Hong 8 James W Welsh 9 Naomi Hasegawa 10 Elizabeth A Mittendorf 11 Steven A Curley 12 Shu-Hsia Chen 13 Ping-Ying Pan 14 Steven K Libutti 15 Shridar Ganesan 16 Richard L Sidman 17 Renata Pasqualini 18 Wadih Arap 19 Eugene J Koay 20 Vittorio Cristini 21 58891__21793__1970855eadb640558d4052586079be72.pdf 58891.pdf 2021-12-06T10:32:05.8371884 Output 6672898 application/pdf Version of Record true This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. true eng http://creativecommons.org/licenses/by/4.0/
title Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
spellingShingle Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
Bruna Corradetti
title_short Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
title_full Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
title_fullStr Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
title_full_unstemmed Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
title_sort Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
author_id_str_mv aa6a235c9e53c5b9b00e751422db5277
author_id_fullname_str_mv aa6a235c9e53c5b9b00e751422db5277_***_Bruna Corradetti
author Bruna Corradetti
author2 Joseph D Butner
Geoffrey V Martin
Zhihui Wang
Bruna Corradetti
Mauro Ferrari
Nestor Esnaola
Caroline Chung
David S Hong
James W Welsh
Naomi Hasegawa
Elizabeth A Mittendorf
Steven A Curley
Shu-Hsia Chen
Ping-Ying Pan
Steven K Libutti
Shridar Ganesan
Richard L Sidman
Renata Pasqualini
Wadih Arap
Eugene J Koay
Vittorio Cristini
format Journal article
container_title eLife
container_volume 10
container_start_page e70130
publishDate 2021
institution Swansea University
issn 2050-084X
doi_str_mv 10.7554/elife.70130
publisher eLife Sciences Publications, Ltd
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description Background:: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods:: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results:: The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions:: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding:: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
published_date 2021-11-09T04:15:46Z
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score 11.013148

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