A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin
Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Cancer Biology & Therapy |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15384047.2024.2321769 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850062984112504832 |
|---|---|
| author | Hugo J. M. Miniere Ernesto A. B. F. Lima Guillermo Lorenzo David A. Hormuth II Sophia Ty Amy Brock Thomas E. Yankeelov |
| author_facet | Hugo J. M. Miniere Ernesto A. B. F. Lima Guillermo Lorenzo David A. Hormuth II Sophia Ty Amy Brock Thomas E. Yankeelov |
| author_sort | Hugo J. M. Miniere |
| collection | DOAJ |
| description | Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting. |
| format | Article |
| id | doaj-art-0e280a58802b47fcb80eb711f4c69ab3 |
| institution | DOAJ |
| issn | 1538-4047 1555-8576 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cancer Biology & Therapy |
| spelling | doaj-art-0e280a58802b47fcb80eb711f4c69ab32025-08-20T02:49:47ZengTaylor & Francis GroupCancer Biology & Therapy1538-40471555-85762024-12-0125110.1080/15384047.2024.2321769A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicinHugo J. M. Miniere0Ernesto A. B. F. Lima1Guillermo Lorenzo2David A. Hormuth II3Sophia Ty4Amy Brock5Thomas E. Yankeelov6Department of Biomedical Engineering, The University of Texas at Austin, Austin, USAOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USAOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USAOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, USADepartment of Biomedical Engineering, The University of Texas at Austin, Austin, USADepartment of Biomedical Engineering, The University of Texas at Austin, Austin, USADepartment of Biomedical Engineering, The University of Texas at Austin, Austin, USATumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.https://www.tandfonline.com/doi/10.1080/15384047.2024.2321769Time-resolved microscopydata assimilationmechanism-based modelingmathematical oncology |
| spellingShingle | Hugo J. M. Miniere Ernesto A. B. F. Lima Guillermo Lorenzo David A. Hormuth II Sophia Ty Amy Brock Thomas E. Yankeelov A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin Cancer Biology & Therapy Time-resolved microscopy data assimilation mechanism-based modeling mathematical oncology |
| title | A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin |
| title_full | A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin |
| title_fullStr | A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin |
| title_full_unstemmed | A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin |
| title_short | A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin |
| title_sort | mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin |
| topic | Time-resolved microscopy data assimilation mechanism-based modeling mathematical oncology |
| url | https://www.tandfonline.com/doi/10.1080/15384047.2024.2321769 |
| work_keys_str_mv | AT hugojmminiere amathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT ernestoabflima amathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT guillermolorenzo amathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT davidahormuthii amathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT sophiaty amathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT amybrock amathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT thomaseyankeelov amathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT hugojmminiere mathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT ernestoabflima mathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT guillermolorenzo mathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT davidahormuthii mathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT sophiaty mathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT amybrock mathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin AT thomaseyankeelov mathematicalmodelforpredictingthespatiotemporalresponseofbreastcancercellstreatedwithdoxorubicin |