Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy
Abstract
:1. Introduction
2. Results
2.1. Cisplatin-Sensitive and Tolerant Cells Demonstrate Different Behaviors in Monotypic and Heterotypic Cultures
2.2. Sensitive Cells Suppress Growth of Tolerant Cells in the Absence of Drug
2.3. Sensitive Cells Secrete a Factor(s) that Retards the Growth of Tolerant Cells
2.4. Intermittent Therapy Can Sustain a Population of Cisplatin-Sensitive Tumor Cells while Attenuating the Proliferation of Resistant Cells
2.5. Epigenetic Modulation Can Distinguish Drug Sensitivity, Tolerance and Resistance in Lung Cancer
2.6. Modeling Cancer Group Behavior Using Experimentally Derived Growth Curves
- Sensitive cells suppress the proliferation of the tolerant cells by secreting diffusible factors (can be overcome by increasing the frequency of tolerant cells)
- The suppressive effect is only prominent after co-culture of the two cell types for three weeks, but not if the cells are mixed and monitored immediately
- Competition by the sensitive cells is eliminated in the presence of cisplatin
- Epigenetic modifier SAHA can switch the tolerant cells to be drug-sensitive through non-genetic means, implying that these cells can switch their phenotypes in response to the environment.
- Sensitive cells generate one or more products that affect the proliferation of the tolerant cells (and possibly their own as well). We call this hypothetical product(s) ‘stress’ (we explain its significance in Supplementary Text Section S2).
- Since the cohabitation of the cells appear to change their phenotypes (e.g., stronger suppression of tolerant cells by the sensitive cells after three weeks of co-culture), and we do not have enough information to model this phenotypic change as function of the cohabitation conditions, every system (i.e., monotypic, heterotypic-12 h and heterotypic-3 weeks) must be treated as distinct with their own phenotypic parameters.
- Through mutual cooperation the tolerant cells can mitigate or neutralize the ‘stress’ generated by the sensitive cells in a frequency-dependent manner.
- Due to stochastic phenotypic switching (sensitive tolerant) by the two cellular species, a state of equilibrium exists between the two phenotypes at any point of time, where the equilibrium constant depends on the stress. As stress increases in the system, the equilibrium shifts to the right to increase the fraction of the tolerant phenotype.
- To keep the model conceptually tractable, we make the simplifying assumption that in relation to the observed growth dynamics, there is no significant difference between the true tolerant phenotype and the one produced through phenotypic switching of the sensitive cells. Likewise, the true sensitive phenotype and the one from phenotypic switching are identical as well (see caveats mentioned in the discussion).
2.7. PSMSR and Cisplatin Response
2.8. PSMSR in Monotypic and Heterotypic Cultures
2.9. Effects of Phenotypic Switching and Stress Give Rise to Diverse Game-Theoretical Strategies in Mixed Cell Populations
2.10. PSMSR Model Demonstrates the Effectiveness of the Intermittent Cisplatin Therapy
3. Discussion
4. Materials and Methods
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Alvarez-Arenas, A.; Podolski-Renic, A.; Belmonte-Beitia, J.; Pesic, M.; Calvo, G.F. Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer. Sci. Rep. 2019, 9, 9332. [Google Scholar] [CrossRef] [Green Version]
- Salgia, R.; Kulkarni, P. The Genetic/Non-genetic Duality of Drug ‘Resistance’ in Cancer. Trends Cancer 2018, 4, 110–118. [Google Scholar] [CrossRef] [PubMed]
- Wu, A.; Liao, D.; Tlsty, T.D.; Sturm, J.C.; Austin, R.H. Game theory in the death galaxy: Interaction of cancer and stromal cells in tumour microenvironment. Interface Focus 2014, 4, 20140028. [Google Scholar] [CrossRef]
- Kaznatcheev, A.; Peacock, J.; Basanta, D.; Marusyk, A.; Scott, J.G. Fibroblasts and alectinib switch the evolutionary games played by non-small cell lung cancer. Nat. Ecol. Evol. 2019, 3, 450–456. [Google Scholar] [CrossRef] [PubMed]
- Staňková, K. Resistance games. Nat. Ecol. Evol. 2019, 3, 336–337. [Google Scholar] [CrossRef] [Green Version]
- Mirnezami, R.; Nicholson, J.; Darzi, A. Preparing for precision medicine. N. Engl. J. Med. 2012, 366, 489–491. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cunningham, J.J.; Brown, J.S.; Gatenby, R.A.; Staňková, K. Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer. J. Theor. Biol. 2018, 459, 67–78. [Google Scholar] [CrossRef] [Green Version]
- Stanková, K.; Brown, J.S.; Dalton, W.S.; Gatenby, R.A. Optimizing Cancer Treatment Using Game Theory: A Review. JAMA Oncol. 2019, 5, 96–103. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Cunningham, J.J.; Brown, J.S.; Gatenby, R.A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 2017, 8, 1816. [Google Scholar] [CrossRef]
- Gatenby, R.A.; Silva, A.S.; Gillies, R.J.; Frieden, B.R. Adaptive therapy. Cancer Res. 2009, 69, 4894–4903. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hata, A.N.; Niederst, M.J.; Archibald, H.L.; Gomez-Caraballo, M.; Siddiqui, F.M.; Mulvey, H.E.; Maruvka, Y.E.; Ji, F.; Bhang, H.-e.C.; Krishnamurthy Radhakrishna, V.; et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat. Med. 2016, 22, 262–269. [Google Scholar] [CrossRef] [PubMed]
- Venkatesan, S.; Swanton, C.; Taylor, B.S.; Costello, J.F. Treatment-Induced Mutagenesis and Selective Pressures Sculpt Cancer Evolution. Cold Spring Harb. Perspect. Med. 2017, 7, a026617. [Google Scholar] [CrossRef]
- Bottery, M.J.; Pitchford, J.W.; Friman, V.-P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 2020, 15, 939–948. [Google Scholar] [CrossRef] [PubMed]
- Grolmusz, V.K.; Chen, J.; Emond, R.; Cosgrove, P.A.; Pflieger, L.; Nath, A.; Moos, P.J.; Bild, A.H. Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model. Cancer Cell Int. 2020, 20, 253. [Google Scholar] [CrossRef]
- Marusyk, A.; Tabassum, D.P.; Altrock, P.M.; Almendro, V.; Michor, F.; Polyak, K. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature 2014, 514, 54–58. [Google Scholar] [CrossRef] [Green Version]
- Mohanty, A.; Nam, A.; Pozhitkov, A.; Yang, L.; Srivastava, S.; Nathan, A.; Wu, X.; Mambetsariev, I.; Nelson, M.; Subbalakshmi, A.R.; et al. A Non-genetic Mechanism Involving the Integrin beta4/Paxillin Axis Contributes to Chemoresistance in Lung Cancer. iScience 2020, 23, 101496. [Google Scholar] [CrossRef]
- Boumahdi, S.; de Sauvage, F.J. The great escape: Tumour cell plasticity in resistance to targeted therapy. Nat. Rev. Drug Discov. 2020, 19, 39–56. [Google Scholar] [CrossRef] [PubMed]
- Mullard, A. Stemming the tide of drug resistance in cancer. Nat. Rev. Drug Discov. 2020, 19, 221–223. [Google Scholar] [CrossRef]
- Babbs, C.F. Predicting success or failure of immunotherapy for cancer: Insights from a clinically applicable mathematical model. Am. J. Cancer Res. 2012, 2, 204–213. [Google Scholar]
- Momeni, B.; Xie, L.; Shou, W. Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. Elife 2017, 6, e25051. [Google Scholar] [CrossRef] [PubMed]
- Vet, S.; de Buyl, S.; Faust, K.; Danckaert, J.; Gonze, D.; Gelens, L. Bistability in a system of two species interacting through mutualism as well as competition: Chemostat vs. Lotka-Volterra equations. PLoS ONE 2018, 13, e0197462. [Google Scholar] [CrossRef]
- Li, X.Y.; Pietschke, C.; Fraune, S.; Altrock, P.M.; Bosch, T.C.G.; Traulsen, A. Which games are growing bacterial populations playing? J. R. Soc. Interface 2015, 12, 20150121. [Google Scholar] [CrossRef] [Green Version]
- Carvalho, G.; Guilhen, C.; Balestrino, D.; Forestier, C.; Mathias, J.-D. Relating switching rates between normal and persister cells to substrate and antibiotic concentrations: A mathematical modelling approach supported by experiments. Microb. Biotechnol. 2017, 10, 1616–1627. [Google Scholar] [CrossRef] [Green Version]
- Ozawa, S.; Sugiyama, Y.; Mitsuhashi, Y.; Kobayashi, T.; Inaba, M. Cell killing action of cell cycle phase-non-specific antitumor agents is dependent on concentration--time product. Cancer Chemother. Pharm. 1988, 21, 185–190. [Google Scholar] [CrossRef]
- Nagai, N.; Ogata, H. Quantitative relationship between pharmacokinetics of unchanged cisplatin and nephrotoxicity in rats: Importance of area under the concentration-time curve (AUC) as the major toxicodynamic determinant in vivo. Cancer Chemother. Pharm. 1997, 40, 11–18. [Google Scholar] [CrossRef]
- El-Kareh, A.W.; Secomb, T.W. A mathematical model for cisplatin cellular pharmacodynamics. Neoplasia 2003, 5, 161–169. [Google Scholar] [CrossRef] [Green Version]
- Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning; Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, USA, 1989. [Google Scholar]
- Scrucca, L. GA: A Package for Genetic Algorithms in R. J. Stat. Software 2013, 53, 37. [Google Scholar] [CrossRef] [Green Version]
- Kareva, I.; Karev, G. Natural Selection Between Two Games with Applications to Game Theoretical Models of Cancer. Bull. Math. Biol. 2019, 81, 2117–2132. [Google Scholar] [CrossRef] [PubMed]
- Bomze, I.M. Lotka-Volterra equation and replicator dynamics: A two-dimensional classification. Biol. Cybern. 1983, 48, 201–211. [Google Scholar] [CrossRef]
- Bhattacharya, S.; Mohanty, A.; Achuthan, S.; Kotnala, S.; Jolly, M.K.; Kulkarni, P.; Salgia, R. Group Behavior and Emergence of Cancer Drug Resistance. Trends Cancer 2021, 7, 323–334. [Google Scholar] [CrossRef] [PubMed]
- You, L.; Brown, J.S.; Thuijsman, F.; Cunningham, J.J.; Gatenby, R.A.; Zhang, J.; Staňková, K. Spatial vs. non-spatial eco-evolutionary dynamics in a tumor growth model. J. Theor. Biol. 2017, 435, 78–97. [Google Scholar] [CrossRef] [Green Version]
- Swierniak, A.; Krzeslak, M.; Borys, D.; Kimmel, M. The role of interventions in the cancer evolution-an evolutionary games approach. Math. Biosci. Eng. MBE 2018, 16, 265–291. [Google Scholar] [CrossRef]
- West, J.; Robertson-Tessi, M.; Luddy, K.; Park, D.S.; Williamson, D.F.K.; Harmon, C.; Khong, H.T.; Brown, J.; Anderson, A.R.A. The Immune Checkpoint Kick Start: Optimization of Neoadjuvant Combination Therapy Using Game Theory. JCO Clin. Cancer Inform. 2019, 3, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Wu, A.; Liao, D.; Austin, R. Evolutionary game theory in cancer: First steps in prediction of metastatic cancer progression? Future Oncol. 2015, 11, 881–883. [Google Scholar] [CrossRef]
- Mooney, S.M.; Jolly, M.K.; Levine, H.; Kulkarni, P. Phenotypic plasticity in prostate cancer: Role of intrinsically disordered proteins. Asian J. Androl. 2016, 18, 704–710. [Google Scholar] [CrossRef] [PubMed]
- Goldman, A.; Majumder, B.; Dhawan, A.; Ravi, S.; Goldman, D.; Kohandel, M.; Majumder, P.K.; Sengupta, S. Temporally sequenced anticancer drugs overcome adaptive resistance by targeting a vulnerable chemotherapy-induced phenotypic transition. Nat. Commun. 2015, 6, 6139. [Google Scholar] [CrossRef] [Green Version]
- Gupta, P.B.; Fillmore, C.M.; Jiang, G.; Shapira, S.D.; Tao, K.; Kuperwasser, C.; Lander, E.S. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 2011, 146, 633–644. [Google Scholar] [CrossRef] [Green Version]
- Jolly, M.K.; Kulkarni, P.; Weninger, K.; Orban, J.; Levine, H. Phenotypic Plasticity, Bet-Hedging, and Androgen Independence in Prostate Cancer: Role of Non-Genetic Heterogeneity. Front. Oncol. 2018, 8, 50. [Google Scholar] [CrossRef]
- Brauner, A.; Fridman, O.; Gefen, O.; Balaban, N.Q. Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat. Rev. Microbiol. 2016, 14, 320–330. [Google Scholar] [CrossRef] [PubMed]
- Dawson, C.C.; Intapa, C.; Jabra-Rizk, M.A. “Persisters”: Survival at the cellular level. PLoS Pathog. 2011, 7, e1002121. [Google Scholar] [CrossRef] [Green Version]
- Vallette, F.M.; Olivier, C.; Lézot, F.; Oliver, L.; Cochonneau, D.; Lalier, L.; Cartron, P.F.; Heymann, D. Dormant, quiescent, tolerant and persister cells: Four synonyms for the same target in cancer. Biochem. Pharmacol. 2019, 162, 169–176. [Google Scholar] [CrossRef] [Green Version]
Condition | K | Kb | KGs0 | KGt0 | Kstr | Kstr,d | a | b | g | Sdrug | AUCs5 | AUCs95 | SCALEs | AUCt5 | AUCt95 | SCALEt |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Heterotypic | 0.049 ± 0.0003 | 3.57 ± 0.02 | 0.713 ± 0.001 | 0.687 ± 0.004 | 7.14 × 10−4 ± 3 × 10−6 | 5.64 × 10−3 ± 4 × 10−5 | 0.046 ± 0.0004 | 0.038 ± 0.0002 | 0.018 ± 0.0006 | |||||||
Heterotypic, 3 weeks | 0.052 ± 0.0007 | 2.6 ± 0.05 | 0.708 ± 0.002 | 0.189 ± 0.016 | 6.74 × 10−4 ± 8 × 10−6 | 5.52 × 10−3 ± 1 × 10−4 | 0.05 ± 0.001 | 0.038 ± 0.0006 | 0.02 ± 0.0006 | |||||||
Heterotypic, cisplatin 5 μM | 0.033 ± 0.0017 | 0 | 1.033 ± 0.046 | 0.976 ± 0.054 | 5.9 × 10−4 ± 2.8 × 10−5 | 0 | NA | 0.04 ± 0.003 | 0.034 ± 0.003 | 108 ± 14.5 | 239 ± 12 | 1153 ± 58 | 8.61 ± 0.5 | 233 ± 17 | 1201 ± 49 | 5.79 ± 0.6 |
Heterotypic, 3 weeks, cisplatin 5 μM | 0.033 ± 0.0015 | 0 | 0.966 ± 0.05 | 0.967 ± 0.05 | 5.8 × 10−4 ± 3.3 × 10−5 | 0 | NA | 0.039 ± 0.002 | 0.036 ± 0.003 | 96.8 ± 9.95 | 271 ± 14 | 1098 ± 44 | 8.91 ± 0.5 | 250 ± 14 | 1098 ± 47 | 6.89 ± 0.7 |
Parameter search range | 0–0.1 | 0–5 | 0–2 | 0–2 | 0–0.001 | 0–0.02 | 0–0.1 | 0–0.1 | 0–0.1 | 1–500 | 1–500 | 10–1500 | 0.1–20 | 1–500 | 10–1500 | 0.1–20 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Nam, A.; Mohanty, A.; Bhattacharya, S.; Kotnala, S.; Achuthan, S.; Hari, K.; Srivastava, S.; Guo, L.; Nathan, A.; Chatterjee, R.; et al. Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy. Biomolecules 2022, 12, 8. https://doi.org/10.3390/biom12010008
Nam A, Mohanty A, Bhattacharya S, Kotnala S, Achuthan S, Hari K, Srivastava S, Guo L, Nathan A, Chatterjee R, et al. Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy. Biomolecules. 2022; 12(1):8. https://doi.org/10.3390/biom12010008
Chicago/Turabian StyleNam, Arin, Atish Mohanty, Supriyo Bhattacharya, Sourabh Kotnala, Srisairam Achuthan, Kishore Hari, Saumya Srivastava, Linlin Guo, Anusha Nathan, Rishov Chatterjee, and et al. 2022. "Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy" Biomolecules 12, no. 1: 8. https://doi.org/10.3390/biom12010008
APA StyleNam, A., Mohanty, A., Bhattacharya, S., Kotnala, S., Achuthan, S., Hari, K., Srivastava, S., Guo, L., Nathan, A., Chatterjee, R., Jain, M., Nasser, M. W., Batra, S. K., Rangarajan, G., Massarelli, E., Levine, H., Jolly, M. K., Kulkarni, P., & Salgia, R. (2022). Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy. Biomolecules, 12(1), 8. https://doi.org/10.3390/biom12010008