Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer
The goal of this study is to develop a mathematical model that captures the interaction between evofosfamide, immunotherapy, and the hypoxic landscape of the tumor in the treatment of tumors. Recently, we showed that evofosfamide, a hypoxia-activated prodrug, can synergistically improve treatment ou...
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AIMS Press
2023-09-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023783?viewType=HTML |
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| author | Ernesto A. B. F. Lima Patrick N. Song Kirsten Reeves Benjamin Larimer Anna G. Sorace Thomas E. Yankeelov |
| author_facet | Ernesto A. B. F. Lima Patrick N. Song Kirsten Reeves Benjamin Larimer Anna G. Sorace Thomas E. Yankeelov |
| author_sort | Ernesto A. B. F. Lima |
| collection | DOAJ |
| description | The goal of this study is to develop a mathematical model that captures the interaction between evofosfamide, immunotherapy, and the hypoxic landscape of the tumor in the treatment of tumors. Recently, we showed that evofosfamide, a hypoxia-activated prodrug, can synergistically improve treatment outcomes when combined with immunotherapy, while evofosfamide alone showed no effects in an <italic>in vivo</italic> syngeneic model of colorectal cancer. However, the mechanisms behind the interaction between the tumor microenvironment in the context of oxygenation (hypoxic, normoxic), immunotherapy, and tumor cells are not fully understood. To begin to understand this issue, we develop a system of ordinary differential equations to simulate the growth and decline of tumors and their vascularization (oxygenation) in response to treatment with evofosfamide and immunotherapy (6 combinations of scenarios). The model is calibrated to data from <italic>in vivo</italic> experiments on mice implanted with colon adenocarcinoma cells and longitudinally imaged with [<sup>18</sup>F]-fluoromisonidazole ([<sup>18</sup>F]FMISO) positron emission tomography (PET) to quantify hypoxia. The results show that evofosfamide is able to rescue the immune response and sensitize hypoxic tumors to immunotherapy. In the hypoxic scenario, evofosfamide reduces tumor burden by $ 45.07 \pm 2.55 $%, compared to immunotherapy alone, as measured by tumor volume. The model accurately predicts the temporal evolution of five different treatment scenarios, including control, hypoxic tumors that received immunotherapy, normoxic tumors that received immunotherapy, evofosfamide alone, and hypoxic tumors that received combination immunotherapy and evofosfamide. The average concordance correlation coefficient (CCC) between predicted and observed tumor volume is $ 0.86 \pm 0.05 $. Interestingly, the model values to fit those five treatment arms was unable to accurately predict the response of normoxic tumors to combination evofosfamide and immunotherapy (CCC = $ -0.064 \pm 0.003 $). However, guided by the sensitivity analysis to rank the most influential parameters on the tumor volume, we found that increasing the tumor death rate due to immunotherapy by a factor of $ 18.6 \pm 9.3 $ increases CCC of $ 0.981 \pm 0.001 $. To the best of our knowledge, this is the first study to mathematically predict and describe the increased efficacy of immunotherapy following evofosfamide. |
| first_indexed | 2024-03-11T18:45:54Z |
| format | Article |
| id | doaj.art-b4a6b342a4404b939a7de0f477b55c1f |
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| language | English |
| last_indexed | 2024-03-11T18:45:54Z |
| publishDate | 2023-09-01 |
| publisher | AIMS Press |
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| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-b4a6b342a4404b939a7de0f477b55c1f2023-10-12T01:21:37ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-09-012010176251764510.3934/mbe.2023783Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancerErnesto A. B. F. Lima0Patrick N. Song1Kirsten Reeves2 Benjamin Larimer3Anna G. Sorace 4Thomas E. Yankeelov 51. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX 78712, USA 2. Texas Advanced Computing Center, The University of Texas at Austin, 10100 Burnet Rd (R8700), Austin, TX 78758, USA3. Department of Radiology, The University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA 4. Graduate Biomedical Sciences, The University of Alabama at Birmingham, 1075 13th St S, Birmingham, AL 35294, USA3. Department of Radiology, The University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA 4. Graduate Biomedical Sciences, The University of Alabama at Birmingham, 1075 13th St S, Birmingham, AL 35294, USA3. Department of Radiology, The University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA5. O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, 1824 6th Ave S, Birmingham, AL 35233, USA3. Department of Radiology, The University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA5. O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, 1824 6th Ave S, Birmingham, AL 35233, USA6. Department of Biomedical Engineering, The University of Alabama at Birmingham, 1075 13th St S, Birmingham, AL 35294, USA1. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX 78712, USA7. Department of Biomedical Engineering, The University of Texas at Austin, 1107 W. Dean Keeton St, Austin, TX 78712, USA 8. Department of Diagnostic Medicine, The University of Texas at Austin, 1601 Trinity St Bldg B, Austin, TX 78712, USA 9. Department of Oncology, The University of Texas at Austin, 1601 Trinity St Bldg B, Austin, TX 78712, USA 10. Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, 623 W. 38th St Ste 300, Austin, TX 78705, USA 11. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St Unit 1472, Houston, TX 77030, USAThe goal of this study is to develop a mathematical model that captures the interaction between evofosfamide, immunotherapy, and the hypoxic landscape of the tumor in the treatment of tumors. Recently, we showed that evofosfamide, a hypoxia-activated prodrug, can synergistically improve treatment outcomes when combined with immunotherapy, while evofosfamide alone showed no effects in an <italic>in vivo</italic> syngeneic model of colorectal cancer. However, the mechanisms behind the interaction between the tumor microenvironment in the context of oxygenation (hypoxic, normoxic), immunotherapy, and tumor cells are not fully understood. To begin to understand this issue, we develop a system of ordinary differential equations to simulate the growth and decline of tumors and their vascularization (oxygenation) in response to treatment with evofosfamide and immunotherapy (6 combinations of scenarios). The model is calibrated to data from <italic>in vivo</italic> experiments on mice implanted with colon adenocarcinoma cells and longitudinally imaged with [<sup>18</sup>F]-fluoromisonidazole ([<sup>18</sup>F]FMISO) positron emission tomography (PET) to quantify hypoxia. The results show that evofosfamide is able to rescue the immune response and sensitize hypoxic tumors to immunotherapy. In the hypoxic scenario, evofosfamide reduces tumor burden by $ 45.07 \pm 2.55 $%, compared to immunotherapy alone, as measured by tumor volume. The model accurately predicts the temporal evolution of five different treatment scenarios, including control, hypoxic tumors that received immunotherapy, normoxic tumors that received immunotherapy, evofosfamide alone, and hypoxic tumors that received combination immunotherapy and evofosfamide. The average concordance correlation coefficient (CCC) between predicted and observed tumor volume is $ 0.86 \pm 0.05 $. Interestingly, the model values to fit those five treatment arms was unable to accurately predict the response of normoxic tumors to combination evofosfamide and immunotherapy (CCC = $ -0.064 \pm 0.003 $). However, guided by the sensitivity analysis to rank the most influential parameters on the tumor volume, we found that increasing the tumor death rate due to immunotherapy by a factor of $ 18.6 \pm 9.3 $ increases CCC of $ 0.981 \pm 0.001 $. To the best of our knowledge, this is the first study to mathematically predict and describe the increased efficacy of immunotherapy following evofosfamide.https://www.aimspress.com/article/doi/10.3934/mbe.2023783?viewType=HTMLimmunotherapy modelmousemodel calibrationuncertainty quantificationtumor growth model[<sup>18</sup>f]fmiso-pethypoxia |
| spellingShingle | Ernesto A. B. F. Lima Patrick N. Song Kirsten Reeves Benjamin Larimer Anna G. Sorace Thomas E. Yankeelov Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer Mathematical Biosciences and Engineering immunotherapy model mouse model calibration uncertainty quantification tumor growth model [<sup>18</sup>f]fmiso-pet hypoxia |
| title | Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer |
| title_full | Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer |
| title_fullStr | Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer |
| title_full_unstemmed | Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer |
| title_short | Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer |
| title_sort | predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer |
| topic | immunotherapy model mouse model calibration uncertainty quantification tumor growth model [<sup>18</sup>f]fmiso-pet hypoxia |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023783?viewType=HTML |
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