Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
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 unde...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
eLife Sciences Publications Ltd
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/fb8dc9b236644627993affff85ba8824 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:fb8dc9b236644627993affff85ba8824 |
|---|---|
| record_format |
dspace |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
immunotherapy biomarkers patient stratification translational research Medicine R Science Q Biology (General) QH301-705.5 |
| spellingShingle |
immunotherapy biomarkers patient stratification translational research Medicine R Science Q Biology (General) QH301-705.5 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 Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling |
| 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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Joseph D Butner |
| title |
Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling |
| 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 |
| publisher |
eLife Sciences Publications Ltd |
| publishDate |
2021 |
| url |
https://doaj.org/article/fb8dc9b236644627993affff85ba8824 |
| work_keys_str_mv |
AT josephdbutner earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT geoffreyvmartin earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT zhihuiwang earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT brunacorradetti earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT mauroferrari earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT nestoresnaola earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT carolinechung earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT davidshong earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT jameswwelsh earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT naomihasegawa earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT elizabethamittendorf earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT stevenacurley earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT shuhsiachen earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT pingyingpan earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT stevenklibutti earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT shridarganesan earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT richardlsidman earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT renatapasqualini earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT wadiharap earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT eugenejkoay earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling AT vittoriocristini earlypredictionofclinicalresponsetocheckpointinhibitortherapyinhumansolidtumorsthroughmathematicalmodeling |
| _version_ |
1718407382575349760 |
| spelling |
oai:doaj.org-article:fb8dc9b236644627993affff85ba88242021-11-29T13:57:51ZEarly prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling10.7554/eLife.701302050-084Xe70130https://doaj.org/article/fb8dc9b236644627993affff85ba88242021-11-01T00:00:00Zhttps://elifesciences.org/articles/70130https://doaj.org/toc/2050-084XBackground: 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.Joseph D ButnerGeoffrey V MartinZhihui WangBruna CorradettiMauro FerrariNestor EsnaolaCaroline ChungDavid S HongJames W WelshNaomi HasegawaElizabeth A MittendorfSteven A CurleyShu-Hsia ChenPing-Ying PanSteven K LibuttiShridar GanesanRichard L SidmanRenata PasqualiniWadih ArapEugene J KoayVittorio CristinieLife Sciences Publications Ltdarticleimmunotherapybiomarkerspatient stratificationtranslational researchMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021) |