Peacock, J. | Search bioRxiv

bioRxiv2017

Cancer associated 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 tumours 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-cultures of non-small cell lung cancer cells which are sensitive and resistant to the anaplastic lymphoma kinase inhibitor Alectinib. 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 by the in-vitro population from Leader to Deadlock. This observation provides 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.

cancer biology↗

bioRxiv2017

Using competition assays to quantitatively model cooperative binding by transcription factors and other ligands

BACKGROUNDThe affinities of DNA binding proteins for target sites can be used to model the regulation of gene expression. These proteins can bind to DNA cooperatively, strongly impacting their affinity and specificity. However, current methods for measuring cooperativity do not provide the means to accurately predict binding behavior over a wide range of concentrations.\n\nMETHODSWe use standard computational and mathematical methods, and develop novel methods as described in Results.\n\nRESULTSWe explore some complexities of cooperative binding, and develop an improved method for relating in vitro measurements to in vivo function, based on ternary complex formation. We derive expressions for the equilibria among the various complexes, and explore the limitations of binding experiments that model the system using a single parameter. We describe how to use single-ligand binding and ternary complex formation in tandem to determine parameters that have thermodynamic relevance. We develop an improved method for finding both single-ligand dissociation constants and concentrations simultaneously. We show how the cooperativity factor can be found when only one of the single-protein dissociation constants can be measured.\n\nCONCLUSIONSThe methods that we develop constitute an optimized approach to accurately model cooperative binding.\n\nGENERAL SIGNIFICANCEThe expressions and methods we develop for modeling and analyzing DNA binding and cooperativity are applicable to most cases where multiple ligands bind to distinct sites on a common substrate. The parameters determined using these methods can be fed into models of higher-order cooperativity to increase their predictive power.\n\nHIGHLIGHTSO_LIHill plots remain prominent in biology, but can mask cooperativity\nC_LIO_LIEffective modeling of binding by two ligands requires the use of 3 parameters\nC_LIO_LIWe develop novel ways to find these parameters for two cooperating ligands\nC_LIO_LIWe show how they can be used to enhance the power of established methods\nC_LIO_LIWe describe how this framework can be extended to multiple cooperating ligands\nC_LI

biochemistry↗