Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction

Xin Chen; Tengda Li; Will Cao; Xin Chen; Tengda Li; Will Cao

doi:10.3934/mbe.2023781

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 10: 17589-17607. doi: 10.3934/mbe.2023781

Previous Article Next Article

Research article Special Issues

Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction

Xin Chen ¹,
Tengda Li ^{2
,
,},
Will Cao ^{3
,
,}

1.
University of California, Los Angeles College of Letters and Science, Los Angeles, California, United States
2.
Wenzhou Medical University School of Laboratory Medicine and Life Sciences, Wenzhou, Zhejiang, China
3.
Duke University Department of Biomedical Engineering, Durham, North Carolina, United States

Academic Editor: Gianpaolo Ruocco

Received: 15 May 2023 Revised: 24 July 2023 Accepted: 15 August 2023 Published: 14 September 2023

Background and aim
Chemotherapy is a crucial component of cancer therapy, albeit with significant side effects. Chemotherapy either damages or inhibits the immune system; therefore, its efficacy varies according to the patient's immune state. Currently, there is no efficient model that incorporates tumor-immune-drug (TID) interactions to guide clinical medication strategies. In this study, we compared five different types of existing TID models with the aim to integrate them into a single, comprehensive model; our goal was to accurately reflect the reality of TID interactions to guide personalized cancer therapy.

Methods
We studied four different drug treatment profiles: direct function, normal distribution function, sine function, and trapezoid function. We developed a platform capable of plotting all combinations of parameter sets and their corresponding treatment efficiency scores. Subsequently, we generated 10,000 random parameter combinations for an individual case and plotted two polygon graphs using a seismic colormap to depict efficacy of treatment. Then, we developed a platform providing treatment suggestions for all stages of tumors and varying levels of self-immunity. We created polygons demonstrating successful treatments according to parameters related to tumor and immune status.

Results
The trapezoid drug treatment function achieved the best inhibitory effect on the tumor cell density. The treatment can be optimized with a high score indicating that the drug delivery interval had exceeded a specific value. More efficient parameter combinations existed when the immunity was strong compared to when it was weak, thus indicating that increasing the patient's self-immunity can make treatment much more effective.

Conclusions
In summary, we created a comprehensive model that can provide quantitative recommendations for a gentle, yet efficient, treatment customized according to the individual's tumor and immune system characteristics.
- dynamic analysis,
- immune response,
- mathematical modeling,
- personalized cancer therapy,
- tumor-immune-drug system interaction
Citation: Xin Chen, Tengda Li, Will Cao. Optimizing cancer therapy for individuals based on tumor-immune-drug system interaction[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 17589-17607. doi: 10.3934/mbe.2023781

Related Papers:

Abstract

Background and aim

Chemotherapy is a crucial component of cancer therapy, albeit with significant side effects. Chemotherapy either damages or inhibits the immune system; therefore, its efficacy varies according to the patient's immune state. Currently, there is no efficient model that incorporates tumor-immune-drug (TID) interactions to guide clinical medication strategies. In this study, we compared five different types of existing TID models with the aim to integrate them into a single, comprehensive model; our goal was to accurately reflect the reality of TID interactions to guide personalized cancer therapy.

Methods

We studied four different drug treatment profiles: direct function, normal distribution function, sine function, and trapezoid function. We developed a platform capable of plotting all combinations of parameter sets and their corresponding treatment efficiency scores. Subsequently, we generated 10,000 random parameter combinations for an individual case and plotted two polygon graphs using a seismic colormap to depict efficacy of treatment. Then, we developed a platform providing treatment suggestions for all stages of tumors and varying levels of self-immunity. We created polygons demonstrating successful treatments according to parameters related to tumor and immune status.

Results

The trapezoid drug treatment function achieved the best inhibitory effect on the tumor cell density. The treatment can be optimized with a high score indicating that the drug delivery interval had exceeded a specific value. More efficient parameter combinations existed when the immunity was strong compared to when it was weak, thus indicating that increasing the patient's self-immunity can make treatment much more effective.

Conclusions

In summary, we created a comprehensive model that can provide quantitative recommendations for a gentle, yet efficient, treatment customized according to the individual's tumor and immune system characteristics.

References

[1]	H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, et al., Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin., 71 (2021), 209–249. https://doi.org/10.3322/caac.21660 doi: 10.3322/caac.21660
[2]	B. Hassannia, P. Vandenabeele, T. Vanden Berghe, Targeting ferroptosis to iron out cancer, Cancer Cell, 35 (2019), 830–849. https://doi.org/10.1016/j.ccell.2019.04.002 doi: 10.1016/j.ccell.2019.04.002
[3]	D. L. Hertz, D. S. Childs, S. B. Park, S. Faithfull, Y. Ke, N. T. Ali, et al., Patient-centric decision framework for treatment alterations in patients with chemotherapy-induced peripheral neuropathy (cipn), Cancer Treat. Rev., 99 (2021), 102241. https://doi.org/10.1016/j.ctrv.2021.102241 doi: 10.1016/j.ctrv.2021.102241
[4]	P. M. Glassman, J. P. Balthasar, Physiologically-based modeling of monoclonal antibody pharmacokinetics in drug discovery and development, Drug Metab. Pharmacokinet., 34 (2019), 3–13. https://doi.org/10.1016/j.dmpk.2018.11.002 doi: 10.1016/j.dmpk.2018.11.002
[5]	B. P. Solans, M. J. Garrido, I. F. Trocóniz, Drug exposure to establish pharmacokinetic-response relationships in oncology, Clin. Pharmacokinet., 59 (2020), 123–135. https://doi.org/10.1007/s40262-019-00828-3 doi: 10.1007/s40262-019-00828-3
[6]	O. Nave, M. Elbaz, Artificial immune system features added to breast cancer clinical data for machine learning (ml) applications, Biosystems, 202 (2021), 104341. https://doi.org/10.1016/j.biosystems.2020.104341 doi: 10.1016/j.biosystems.2020.104341
[7]	S. Abreu, F. Silva, R. Mendes, T. F. Mendes, M. Teixeira, V. E. Santo, et al., Patient-derived ovarian cancer explants: Preserved viability and histopathological features in long-term agitation-based cultures, Sci. Rep., 10 (2020), 19462. https://doi.org/10.1038/s41598-020-76291-z doi: 10.1038/s41598-020-76291-z
[8]	K. Harrington, D. J. Freeman, B. Kelly, J. Harper, J. C. Soria, Optimizing oncolytic virotherapy in cancer treatment, Nat. Rev. Drug Discovery, 18 (2019), 689–706. https://doi.org/10.1038/s41573-019-0029-0 doi: 10.1038/s41573-019-0029-0
[9]	H. Eriksson, K. E. Smedby, Immune checkpoint inhibitors in cancer treatment and potential effect modification by age, Acta Oncol., 59 (2020), 247–248. https://doi.org/10.1080/0284186x.2020.1724329 doi: 10.1080/0284186x.2020.1724329
[10]	T. Wu, Y. Dai, Tumor microenvironment and therapeutic response, Cancer Lett., 387 (2017), 61–68. https://doi.org/10.1016/j.canlet.2016.01.043 doi: 10.1016/j.canlet.2016.01.043
[11]	O. Nave, A mathematical model for treatment using chemo-immunotherapy, Heliyon, 8 (2022), e09288. https://doi.org/10.1016/j.heliyon.2022.e09288 doi: 10.1016/j.heliyon.2022.e09288
[12]	R. A. Ku-Carrillo, S. E. Delgadillo-Aleman, B. M. Chen-Charpentier, Effects of the obesity on optimal control schedules of chemotherapy on a cancerous tumor, J. Comput. Appl. Math., 309 (2017), 603–610. https://doi.org/https://doi.org/10.1016/j.cam.2016.05.010 doi: 10.1016/j.cam.2016.05.010
[13]	S. Sameen, R. Barbuti, P. Milazzo, A. Cerone, M. Del Re, R. Danesi, Mathematical modeling of drug resistance due to kras mutation in colorectal cancer, J. Theor. Biol., 389 (2016), 263–273. https://doi.org/https://doi.org/10.1016/j.jtbi.2015.10.019 doi: 10.1016/j.jtbi.2015.10.019
[14]	S. Sharma, G. P. Samanta, Dynamical behaviour of a tumor-immune system with chemotherapy and optimal control, J. Nonlinear Dyn., 2013 (2013), 608598. https://doi.org/10.1155/2013/608598 doi: 10.1155/2013/608598
[15]	S. Sharma, G. P. Samanta, Analysis of the dynamics of a tumor–immune system with chemotherapy and immunotherapy and quadratic optimal control, Differ. Equations Dyn. Syst., 24 (2016), 149–171. https://doi.org/10.1007/s12591-015-0250-1 doi: 10.1007/s12591-015-0250-1
[16]	C. Zeng, S. Ma, Dynamic analysis of a tumor-immune system under allee effect, Math. Probl. Eng., 2020 (2020), 4892938. https://doi.org/10.1155/2020/4892938 doi: 10.1155/2020/4892938
[17]	U. Ledzewicz, M. S. F. Mosalman, H. Schättler, Optimal controls for a mathematical model of tumor-immune interactions under targeted chemotherapy with immune boost, Discrete Contin. Dyn. Syst. - Ser. B, 18 (2013), 1031–1051. https://doi.org/10.3934/dcdsb.2013.18.1031 doi: 10.3934/dcdsb.2013.18.1031
[18]	I. Bashkirtseva, L. Ryashko, Analysis of noise-induced phenomena in the nonlinear tumor–immune system, Physica A, 549 (2020), 123923. https://doi.org/https://doi.org/10.1016/j.physa.2019.123923 doi: 10.1016/j.physa.2019.123923
[19]	P. Das, S. Mukherjee, P. Das, S. Banerjee, Characterizing chaos and multifractality in noise-assisted tumor-immune interplay, Nonlinear Dyn., 101 (2020), 675–685. https://doi.org/10.1007/s11071-020-05781-6 doi: 10.1007/s11071-020-05781-6
[20]	H. Yang, Y. Tan, J. Yang, Z. Liu, Extinction and persistence of a tumor-immune model with white noise and pulsed comprehensive therapy, Math. Comput. Simul., 182 (2021), 456–470. https://doi.org/10.1016/j.matcom.2020.11.014 doi: 10.1016/j.matcom.2020.11.014
[21]	D. Kirschner, J. C. Panetta, Modeling immunotherapy of the tumor-immune interaction, J. Math. Biol., 37 (1998), 235–252. https://doi.org/10.1007/s002850050127 doi: 10.1007/s002850050127
[22]	A. Arabameri, D. Asemani, J. Hadjati, A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications, Math. Biosci., 304 (2018), 48–61. https://doi.org/10.1016/j.mbs.2018.07.006 doi: 10.1016/j.mbs.2018.07.006
[23]	B. Dhar, P. K. Gupta, A numerical approach of tumor‐immune model with b cells and monoclonal antibody drug by multi‐step differential transformation method, Math. Methods Appl. Sci., 44 (2020), 4058–4070. https://doi.org/10.1002/mma.7009 doi: 10.1002/mma.7009
[24]	U. Ledzewicz, M. S. F. Mosalman, H. Schättler, Optimal controls for a mathematical model of tumor-immune interactions under targeted chemotherapy with immune boost, Discrete Contin. Dyn. Syst. - Ser. B, 18 (2013), 1031–1051. https://doi.org/10.3934/dcdsb.2013.18.1031 doi: 10.3934/dcdsb.2013.18.1031
[25]	H. R. Meredith, A. J. Lopatkin, D. J. Anderson, L. You, Bacterial temporal dynamics enable optimal design of antibiotic treatment, PLoS Comput. Biol., 11 (2015), e1004201. https://doi.org/10.1371/journal.pcbi.1004201 doi: 10.1371/journal.pcbi.1004201

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)