Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Experimental Data
2.2. Clinical Data
2.3. Inferences of Proliferation and Death Rates
2.3.1. Spatial Statistics Analyses
2.3.2. Agent-Based Modeling
2.4. In-Silico Controls of Histological Point Patterns
3. Results
3.1. Approach
3.2. Quantitative Inferences of Experimentally Observed EMDR Effects
3.3. In Silico Model of EMDR
3.4. In Silico Inferences of the Impact of EMDR
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
- 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.; 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]
- Bozic, I.; Reiter, J.G.; Allen, B.; Antal, T.; Chatterjee, K.; Shah, P.; Moon, Y.S.; Yaqubie, A.; Kelly, N.; Le, D.T.; et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2013, 2, e00747. [Google Scholar] [CrossRef] [PubMed]
- Vander Velde, R.; Yoon, N.; Marusyk, V.; Durmaz, A.; Dhawan, A.; Miroshnychenko, D.; Lozano-Peral, D.; Desai, B.; Balynska, O.; Poleszhuk, J.; et al. Resistance to targeted therapies as a multifactorial, gradual adaptation to inhibitor specific selective pressures. Nat. Commun. 2020, 11, 2393. [Google Scholar] [CrossRef]
- Shaffer, S.M.; Dunagin, M.C.; Torborg, S.R.; Torre, E.A.; Emert, B.; Krepler, C.; Beqiri, M.; Sproesser, K.; Brafford, P.A.; Xiao, M.; et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 2017, 546, 431–435. [Google Scholar] [CrossRef] [PubMed]
- Ramirez, M.; Rajaram, S.; Steininger, R.J.; Osipchuk, D.; Roth, M.A.; Morinishi, L.S.; Evans, L.; Ji, W.; Hsu, C.H.; Thurley, K.; et al. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nat. Commun. 2016, 7, 10690. [Google Scholar] [CrossRef] [PubMed]
- França, G.S.; Baron, M.; King, B.R.; Bossowski, J.P.; Bjornberg, A.; Pour, M.; Rao, A.; Patel, A.S.; Misirlioglu, S.; Barkley, D.; et al. Cellular adaptation to cancer therapy occurs by progressive state transitions along a resistance continuum. Nature 2024, in press. [Google Scholar]
- Knoechel, B.; Roderick, J.E.; Williamson, K.E.; Zhu, J.; Lohr, J.G.; Cotton, M.J.; Gillespie, S.M.; Fernandez, D.; Ku, M.; Wang, H.; et al. An epigenetic mechanism of resistance to targeted therapy in T cell acute lymphoblastic leukemia. Nat. Genet. 2014, 46, 364–370. [Google Scholar] [CrossRef]
- Liau, B.B.; Sievers, C.; Donohue, L.K.; Gillespie, S.M.; Flavahan, W.A.; Miller, T.E.; Venteicher, A.S.; Hebert, C.H.; Carey, C.D.; Rodig, S.J.; et al. Adaptive Chromatin Remodeling Drives Glioblastoma Stem Cell Plasticity and Drug Tolerance. Cell Stem Cell 2017, 20, 233–246.e7. [Google Scholar] [CrossRef]
- Risom, T.; Langer, E.M.; Chapman, M.P.; Rantala, J.; Fields, A.J.; Boniface, C.; Alvarez, M.J.; Kendsersky, N.D.; Pelz, C.R.; Johnson-Camacho, K.; et al. Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat. Commun. 2018, 9, 3815. [Google Scholar] [CrossRef]
- Sharma, S.V.; Lee, D.Y.; Li, B.; Quinlan, M.P.; Takahashi, F.; Maheswaran, S.; McDermott, U.; Azizian, N.; Zou, L.; Fischbach, M.A.; et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 2010, 141, 69–80. [Google Scholar] [CrossRef]
- Meads, M.B.; Gatenby, R.A.; Dalton, W.S. Environment-mediated drug resistance: A major contributor to minimal residual disease. Nat. Rev. Cancer 2009, 9, 665–674. [Google Scholar] [CrossRef] [PubMed]
- Klemm, F.; Joyce, J.A. Microenvironmental regulation of therapeutic response in cancer. Trends Cell Biol. 2015, 25, 198–213. [Google Scholar] [CrossRef] [PubMed]
- Kalluri, R. The biology and function of fibroblasts in cancer. Nat. Rev. Cancer 2016, 16, 582–598. [Google Scholar] [CrossRef] [PubMed]
- Ohlund, D.; Elyada, E.; Tuveson, D. Fibroblast heterogeneity in the cancer wound. J. Exp. Med. 2014, 211, 1503–1523. [Google Scholar] [CrossRef] [PubMed]
- Straussman, R.; Morikawa, T.; Shee, K.; Barzily-Rokni, M.; Qian, Z.R.; Du, J.; Davis, A.; Mongare, M.M.; Gould, J.; Frederick, D.T.; et al. Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion. Nature 2012, 487, 500–504. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; Li, Q.; Yamada, T.; Matsumoto, K.; Matsumoto, I.; Oda, M.; Watanabe, G.; Kayano, Y.; Nishioka, Y.; Sone, S.; et al. Crosstalk to stromal fibroblasts induces resistance of lung cancer to epidermal growth factor receptor tyrosine kinase inhibitors. Clin. Cancer Res. 2009, 15, 6630–6638. [Google Scholar] [CrossRef] [PubMed]
- Marusyk, A.; Tabassum, D.P.; Janiszewska, M.; Place, A.E.; Trinh, A.; Rozhok, A.I.; Pyne, S.; Guerriero, J.L.; Shu, S.; Ekram, M.; et al. Spatial Proximity to Fibroblasts Impacts Molecular Features and Therapeutic Sensitivity of Breast Cancer Cells Influencing Clinical Outcomes. Cancer Res. 2016, 76, 6495–6506. [Google Scholar] [CrossRef] [PubMed]
- Desai, B.; Miti, T.; Prabhakaran, S.; Miroshnychenko, D.; Henry, M.; Marusyk, V.; Gatenbee, C.; Bui, M.; Scott, J.; Altrock, P.M.; et al. Peristromal niches protect lung cancers from targeted therapies through a combined effect of multiple molecular mediators. bioRxiv 2024. [Google Scholar] [CrossRef]
- Miroshnychenko, D.; Miti, T.; Kumar, P.; Miller, A.; Laurie, M.; Giraldo, N.; Bui, M.M.; Altrock, P.M.; Basanta, D.; Marusyk, A. Stroma-mediated breast cancer cell proliferation indirectly drives chemoresistance by accelerating tumor recovery between chemotherapy cycles. Cancer Res. 2023, 83, 3681–3692. [Google Scholar] [CrossRef]
- Baddeley, A.; Rubak, E.; Turner, R. Spatial Point Patterns: Methodology and Applications with R; CRC Press: Boca Raton, FL, USA, 2015. [Google Scholar]
- Bull, J.A.; Macklin, P.S.; Quaiser, T.; Braun, F.; Waters, S.L.; Pugh, C.W.; Byrne, H.M. Combining multiple spatial statistics enhances the description of immune cell localisation within tumours. Sci. Rep. 2020, 10, 18624. [Google Scholar] [CrossRef]
- Nuske, R.; Sprauer, S.; Saborowski, J. Adapting the pair-correlation function for analysing the spatial distribution of canopy gaps. For. Ecol. Manag. 2009, 259, 107–116. [Google Scholar] [CrossRef]
- Bravo, R.R.; Baratchart, E.; West, J.; Schenck, R.O.; Miller, A.K.; Gallaher, J.; Gatenbee, C.D.; Basanta, D.; Robertson-Tessi, M.; Anderson, A.R.A. Hybrid Automata Library: A flexible platform for hybrid modeling with real-time visualization. PLoS Comput. Biol. 2020, 16, e1007635. [Google Scholar] [CrossRef]
- Bialic, M.; Al Ahmad Nachar, B.; Kozlak, M.; Coulon, V.; Schwob, E. Measuring S-Phase Duration from Asynchronous Cells Using Dual EdU-BrdU Pulse-Chase Labeling Flow Cytometry. Genes 2022, 13, 408. [Google Scholar] [CrossRef]
- Schutte, B.; Reynders, M.M.; van Assche, C.L.; Hupperets, P.S.; Bosman, F.T.; Blijham, G.H. An improved method for the immunocytochemical detection of bromodeoxyuridine labeled nuclei using flow cytometry. Cytometry 1987, 8, 372–376. [Google Scholar] [CrossRef]
- Law, R.; Illian, J.; Burslem, D.F.R.P.; Gratzer, G.; Gunatilleke, C.V.S.; Gunatilleke, I.A.U.N. Ecological information from spatial patterns of plants: Insights from point process theory. J. Ecol. 2009, 97, 616–628. [Google Scholar] [CrossRef]
- Younge, K.; Johnston, B.; Christenson, C.; Bohara, A.; Jacobson, J.; Butler, N.M.; Saulnier, P. The use of radial distribution and pair-correlation functions to analyze and describe biological aggregations. Limnol. Oceanogr. Methods 2006, 4, 382–391. [Google Scholar] [CrossRef]
- Seferbekova, Z.; Lomakin, A.; Yates, L.R.; Gerstung, M. Spatial biology of cancer evolution. Nat. Rev. Genet. 2023, 24, 295–313. [Google Scholar] [CrossRef]
- Lewis, S.M.; Asselin-Labat, M.L.; Nguyen, Q.; Berthelet, J.; Tan, X.; Wimmer, V.C.; Merino, D.; Rogers, K.L.; Naik, S.H. Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods 2021, 18, 997–1012. [Google Scholar] [CrossRef]
- Miroshnychenko, D.; Miti, T.; Miller, A.; Kumar, P.; Laurie, M.; Bui, M.M.; Altrock, P.M.; Basanta, D.; Marusyk, A. Paracrine enhancement of tumor cell proliferation provides indirect stroma-mediated chemoresistance via acceleration of tumor recovery between chemotherapy cycles. bioRxiv 2023. [Google Scholar] [CrossRef]
- Picco, N.; Sahai, E.; Maini, P.K.; Anderson, A.R.A. Integrating Models to Quantify Environment-Mediated Drug Resistance. Cancer Res. 2017, 77, 5409–5418. [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]
- Robertson-Tessi, M.; Gillies, R.J.; Gatenby, R.A.; Anderson, A.R. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res. 2015, 75, 1567–1579. [Google Scholar] [CrossRef] [PubMed]
- Araujo, A.; Cook, L.M.; Lynch, C.C.; Basanta, D. An integrated computational model of the bone microenvironment in bone-metastatic prostate cancer. Cancer Res. 2014, 74, 2391–2401. [Google Scholar] [CrossRef]
- Frankenstein, Z.; Basanta, D.; Franco, O.E.; Gao, Y.; Javier, R.A.; Strand, D.W.; Lee, M.; Hayward, S.W.; Ayala, G.; Anderson, A.R.A. Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression. Nat. Ecol. Evol. 2020, 4, 870–884. [Google Scholar] [CrossRef]
- Mumenthaler, S.M.; Foo, J.; Choi, N.C.; Heise, N.; Leder, K.; Agus, D.B.; Pao, W.; Michor, F.; Mallick, P. The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells. Cancer Inf. 2015, 14, 19–31. [Google Scholar] [CrossRef]
- M, M.A.; Kim, J.Y.; Pan, C.H.; Kim, E. The impact of the spatial heterogeneity of resistant cells and fibroblasts on treatment response. PLoS Comput. Biol. 2022, 18, e1009919. [Google Scholar] [CrossRef]
- Kuznetsov, M.; Clairambault, J.; Volpert, V. Improving cancer treatments via dynamical biophysical models. Phys. Life Rev. 2021, 39, 1–48. [Google Scholar] [CrossRef]
- Altrock, P.M.; Liu, L.L.; Michor, F. The mathematics of cancer: Integrating quantitative models. Nat. Rev. Cancer 2015, 15, 730–745. [Google Scholar] [CrossRef] [PubMed]
- Gerlee, P.; Basanta, D.; Anderson, A.R. Evolving homeostatic tissue using genetic algorithms. Prog. Biophys. Mol. Biol. 2011, 106, 414–425. [Google Scholar] [CrossRef]
- Gerlee, P.; Anderson, A.R. A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype. J. Theor. Biol. 2008, 250, 705–722. [Google Scholar] [CrossRef]
- Bishop, R.T.; Miller, A.K.; Froid, M.; Nerlakanti, N.; Li, T.; Frieling, J.S.; Nasr, M.M.; Nyman, K.J.; Sudalagunta, P.R.; Canevarolo, R.R.; et al. The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous drug resistant disease. Nat. Commun. 2024, 15, 2458. [Google Scholar] [CrossRef] [PubMed]
- Mansury, Y.; Kimura, M.; Lobo, J.; Deisboeck, T.S. Emerging patterns in tumor systems: Simulating the dynamics of multicellular clusters with an agent-based spatial agglomeration model. J. Theor. Biol. 2002, 219, 343–370. [Google Scholar] [CrossRef] [PubMed]
- Alarcon, T.; Byrne, H.M.; Maini, P.K. A cellular automaton model for tumour growth in inhomogeneous environment. J. Theor. Biol. 2003, 225, 257–274. [Google Scholar] [CrossRef] [PubMed]
- Bonabeau, E. Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 2002, 99 (Suppl. S3), 7280–7287. [Google Scholar] [CrossRef] [PubMed]
- Glen, C.M.; Kemp, M.L.; Voit, E.O. Agent-based modeling of morphogenetic systems: Advantages and challenges. PLoS Comput. Biol. 2019, 15, e1006577. [Google Scholar] [CrossRef] [PubMed]
- Chamseddine, I.M.; Rejniak, K.A. Hybrid modeling frameworks of tumor development and treatment. Wiley Interdiscip. Rev. Syst. Biol. Med. 2020, 12, e1461. [Google Scholar] [CrossRef] [PubMed]
- Rejniak, K.A.; Anderson, A.R. Hybrid models of tumor growth. Wiley Interdiscip. Rev. Syst. Biol. Med. 2011, 3, 115–125. [Google Scholar] [CrossRef] [PubMed]
- Rasanen, K.; Vaheri, A. Activation of fibroblasts in cancer stroma. Exp. Cell Res. 2010, 316, 2713–2722. [Google Scholar] [CrossRef] [PubMed]
- Sahai, E.; Astsaturov, I.; Cukierman, E.; DeNardo, D.G.; Egeblad, M.; Evans, R.M.; Fearon, D.; Greten, F.R.; Hingorani, S.R.; Hunter, T.; et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer 2020, 20, 174–186. [Google Scholar] [CrossRef]
- Hu, H.; Piotrowska, Z.; Hare, P.J.; Chen, H.; Mulvey, H.E.; Mayfield, A.; Noeen, S.; Kattermann, K.; Greenberg, M.; Williams, A.; et al. Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. Cancer Cell 2021, 39, 1531–1547.e10. [Google Scholar] [CrossRef]
- Xi, K.X.; Wen, Y.S.; Zhu, C.M.; Yu, X.Y.; Qin, R.Q.; Zhang, X.W.; Lin, Y.B.; Rong, T.H.; Wang, W.D.; Chen, Y.Q.; et al. Tumor-stroma ratio (TSR) in non-small cell lung cancer (NSCLC) patients after lung resection is a prognostic factor for survival. J. Thorac. Dis. 2017, 9, 4017–4026. [Google Scholar] [CrossRef]
- Zhang, T.; Xu, J.; Shen, H.; Dong, W.; Ni, Y.; Du, J. Tumor-stroma ratio is an independent predictor for survival in NSCLC. Int. J. Clin. Exp. Pathol. 2015, 8, 11348–11355. [Google Scholar] [PubMed]
- Box, G.E.P. Robustness in the Strategy of Scientific Model Building. In Robustness in Statistics; Launer, R.L., Wilkinson, G.N., Eds.; Academic Press: Cambridge, MA, USA, 1979; pp. 201–236. [Google Scholar]
- Ge, W.; Yue, M.; Wang, Y.; Wang, Y.; Xue, S.; Shentu, D.; Mao, T.; Zhang, X.; Xu, H.; Li, S.; et al. A Novel Molecular Signature of Cancer-Associated Fibroblasts Predicts Prognosis and Immunotherapy Response in Pancreatic Cancer. Int. J. Mol. Sci. 2022, 24, 156. [Google Scholar] [CrossRef]
- Graizel, D.; Zlotogorski-Hurvitz, A.; Tsesis, I.; Rosen, E.; Kedem, R.; Vered, M. Oral cancer-associated fibroblasts predict poor survival: Systematic review and meta-analysis. Oral Dis. 2020, 26, 733–744. [Google Scholar] [CrossRef]
- Irvine, A.F.; Waise, S.; Green, E.W.; Stuart, B.; Thomas, G.J. Characterising cancer-associated fibroblast heterogeneity in non-small cell lung cancer: A systematic review and meta-analysis. Sci. Rep. 2021, 11, 3727. [Google Scholar] [CrossRef] [PubMed]
- Jamieson, N.B.; Carter, C.R.; McKay, C.J.; Oien, K.A. Tissue biomarkers for prognosis in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis. Clin. Cancer Res. 2011, 17, 3316–3331. [Google Scholar] [CrossRef]
- Kramer, C.J.H.; Vangangelt, K.M.H.; van Pelt, G.W.; Dekker, T.J.A.; Tollenaar, R.; Mesker, W.E. The prognostic value of tumour-stroma ratio in primary breast cancer with special attention to triple-negative tumours: A review. Breast Cancer Res. Treat. 2019, 173, 55–64. [Google Scholar] [CrossRef] [PubMed]
- Xu, J.; Fang, Y.; Chen, K.; Li, S.; Tang, S.; Ren, Y.; Cen, Y.; Fei, W.; Zhang, B.; Shen, Y.; et al. Single-Cell RNA Sequencing Reveals the Tissue Architecture in Human High-Grade Serous Ovarian Cancer. Clin. Cancer Res. 2022, 28, 3590–3602. [Google Scholar] [CrossRef]
- Wilson, T.R.; Fridlyand, J.; Yan, Y.; Penuel, E.; Burton, L.; Chan, E.; Peng, J.; Lin, E.; Wang, Y.; Sosman, J.; et al. Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature 2012, 487, 505–509. [Google Scholar] [CrossRef]
Tissue Quadrant | I | II | III | IV | V | VI | VII | VIII |
---|---|---|---|---|---|---|---|---|
Stroma abundance | 15% | 6% | 6% | 6% | 22% | 22% | 10% | 10% |
Stroma dispersal | 70% | 50% | 70% | 90% | 90% | 67% | 89% | 67% |
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Miti, T.; Desai, B.; Miroshnychenko, D.; Basanta, D.; Marusyk, A. Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses. Cancers 2024, 16, 2405. https://doi.org/10.3390/cancers16132405
Miti T, Desai B, Miroshnychenko D, Basanta D, Marusyk A. Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses. Cancers. 2024; 16(13):2405. https://doi.org/10.3390/cancers16132405
Chicago/Turabian StyleMiti, Tatiana, Bina Desai, Daria Miroshnychenko, David Basanta, and Andriy Marusyk. 2024. "Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses" Cancers 16, no. 13: 2405. https://doi.org/10.3390/cancers16132405