Fibroblasts and alectinib switch the evolutionary games that non-small cell lung cancer plays

Heterogeneity in strategies for survival and proliferation among the cells which constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumour’s strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-culures of alectinib-sensitive and alectinib-resistant non-small cell lung cancer. The games are not only quantitatively different between different environments, but targeted therapy and cancer associated fibroblasts qualitatively switch the type of game being played from Leader to Deadlock. This observation provides the first empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.

[1]  M. Archetti,et al.  Evolutionary game theory of growth factor production: implications for tumour heterogeneity and resistance to therapies , 2013, British Journal of Cancer.

[2]  P. Sen Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[3]  J. M. Smith,et al.  The Logic of Animal Conflict , 1973, Nature.

[4]  G. Heppner Tumor heterogeneity. , 1984, Cancer research.

[5]  Johannes G. Reiter,et al.  The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers , 2012, Nature.

[6]  P. Altrock,et al.  Extinction rates in tumour public goods games , 2017, Journal of The Royal Society Interface.

[7]  Jeffrey E. Barrick,et al.  Adaptation, Clonal Interference, and Frequency-Dependent Interactions in a Long-Term Evolution Experiment with Escherichia coli , 2015, Genetics.

[8]  H. Theil A Rank-Invariant Method of Linear and Polynomial Regression Analysis , 1992 .

[9]  R. Jain Normalizing tumor microenvironment to treat cancer: bench to bedside to biomarkers. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  S. Hilsenbeck,et al.  Spatial Proximity to Fibroblasts Impacts Molecular Features and Therapeutic Sensitivity of Breast Cancer Cells Influencing Clinical Outcomes. , 2016, Cancer research.

[11]  K. Polyak,et al.  Non-cell autonomous tumor-growth driving supports sub-clonal heterogeneity , 2014, Nature.

[12]  Alissa M. Weaver,et al.  Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment , 2006, Cell.

[13]  David Basanta,et al.  Edge effects in game-theoretic dynamics of spatially structured tumours , 2013, Journal of The Royal Society Interface.

[14]  I. Tomlinson,et al.  Game-theory models of interactions between tumour cells. , 1997, European journal of cancer.

[15]  D. Robinson,et al.  The topology of the 2x2 games : a new periodic table , 2005 .

[16]  Jacob G. Scott,et al.  Investigating prostate cancer tumour–stroma interactions: clinical and biological insights from an evolutionary game , 2011, British Journal of Cancer.

[17]  J. Engelman,et al.  ALK in lung cancer: past, present, and future. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  R. MacLean,et al.  Resource competition and social conflict in experimental populations of yeast , 2006, Nature.

[19]  Anatol Rapoport,et al.  Exploiter, leader, hero, and martyr: The four archetypes of the 2 × 2 game , 1967 .

[20]  Artem Kaznatcheev,et al.  Two conceptions of evolutionary games: reductive vs effective , 2017, bioRxiv.

[21]  Joel s. Brown,et al.  Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer , 2017, Nature Communications.

[22]  Michael Thomas,et al.  Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. , 2013, The New England journal of medicine.

[23]  David Basanta,et al.  The role of IDH1 mutated tumour cells in secondary glioblastomas: an evolutionary game theoretical view , 2011, Physical biology.

[24]  E. T. Gawlinski,et al.  Acid-mediated tumor invasion: a multidisciplinary study. , 2006, Cancer research.

[25]  R. Gillies,et al.  Evolutionary dynamics of carcinogenesis and why targeted therapy does not work , 2012, Nature Reviews Cancer.

[26]  Alexander G. Fletcher,et al.  Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance , 2015, PLoS Comput. Biol..

[27]  A. van Oudenaarden,et al.  Snowdrift game dynamics and facultative cheating in yeast , 2009, Nature.

[28]  Rafal Dziadziuszko,et al.  Alectinib versus Crizotinib in Untreated ALK‐Positive Non–Small‐Cell Lung Cancer , 2017, The New England journal of medicine.

[29]  Jacob G. Scott,et al.  Somatic clonal evolution: A selection-centric perspective. , 2017, Biochimica et biophysica acta. Reviews on cancer.

[30]  Vincent Conitzer,et al.  The Exact Computational Complexity of Evolutionarily Stable Strategies , 2013, WINE.

[31]  C. Maley,et al.  Cancer is a disease of clonal evolution within the body1–3. This has profound clinical implications for neoplastic progression, cancer prevention and cancer therapy. Although the idea of cancer as an evolutionary problem , 2006 .

[32]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[33]  Artem Kaznatcheev Computational complexity is an ultimate constraint on evolution , 2018 .

[34]  R. Govindan,et al.  Alectinib in Crizotinib-Refractory ALK-Rearranged Non-Small-Cell Lung Cancer: A Phase II Global Study. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[35]  W. F. Bodmer,et al.  Modelling the consequences of interactions between tumour cells. , 1997, British Journal of Cancer.

[36]  Laurent Lehmann,et al.  Gains from switching and evolutionary stability in multi-player matrix games. , 2013, Journal of theoretical biology.

[37]  David Basanta,et al.  Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature , 2016, British Journal of Cancer.

[38]  Shinji Takeuchi,et al.  Paracrine Receptor Activation by Microenvironment Triggers Bypass Survival Signals and ALK Inhibitor Resistance in EML4-ALK Lung Cancer Cells , 2012, Clinical Cancer Research.

[39]  A. Marusyk,et al.  Collateral sensitivity networks reveal evolutionary instability and novel treatment strategies in ALK mutated non-small cell lung cancer , 2016, bioRxiv.

[40]  Arne Traulsen,et al.  Which games are growing bacterial populations playing? , 2015, Journal of The Royal Society Interface.

[41]  M. Archetti,et al.  Heterogeneity for IGF-II production maintained by public goods dynamics in neuroendocrine pancreatic cancer , 2015, Proceedings of the National Academy of Sciences.

[42]  M. Feldman,et al.  Local dispersal promotes biodiversity in a real-life game of rock–paper–scissors , 2002, Nature.

[43]  S. Antonia,et al.  The anti-fibrotic agent pirfenidone synergizes with cisplatin in killing tumor cells and cancer-associated fibroblasts , 2016, BMC Cancer.

[44]  A. Shaw,et al.  Therapeutic Targeting of Anaplastic Lymphoma Kinase in Lung Cancer: A Paradigm for Precision Cancer Medicine , 2015, Clinical Cancer Research.

[45]  Y. Ohe,et al.  CH5424802 (RO5424802) for patients with ALK-rearranged advanced non-small-cell lung cancer (AF-001JP study): a single-arm, open-label, phase 1-2 study. , 2013, The Lancet. Oncology.

[46]  Robert J Gillies,et al.  Defining Cancer Subpopulations by Adaptive Strategies Rather Than Molecular Properties Provides Novel Insights into Intratumoral Evolution. , 2017, Cancer research.

[47]  Artem Kaznatcheev,et al.  Effective games and the confusion over spatial structure , 2018, Proceedings of the National Academy of Sciences.