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Biology subjects

McFarland, C. D.

Publications and source records attributed to McFarland, C. D..

5 recordsLinked to original sources

Can public good producing subclones invade a population of non-producers?

Cancer progression is increasingly understood as an evolutionary process shaped not only by competition but also by cooperative interactions including those mediated through diffusible "public goods" (PGs). Classical evolutionary game theory predicts that PG-producing (altruistic) subclones cannot invade well-mixed populations of non-producers, creating a paradox given their observed emergence in tumors. Here, we resolve this contradiction by combining stochastic spatial simulations with an analytically tractable Moran model to study the invasion dynamics of PG-producing cells in structured populations. Starting from a single producer cell, we explicitly model stochastic PG secretion, diffusion, binding/unbinding, and cell proliferation across biologically relevant parameter ranges. We demonstrate that spatial structure fundamentally alters invasion dynamics, enabling PG producers to invade and establish even when production incurs a fitness cost. Both numerical and analytical approaches converge on a key unifying parameter, a characteristic length scale{delta} , that captures the combined effects of diffusivity, binding kinetics, and degradation. This length scale determines the spatial extent of PG availability and thus the selective advantage of producers. We identify distinct regimes: when PGs are localized (small{delta} ), producers preferentially benefit and invasion is likely; when PGs are widely dispersed (large{delta} ), benefits are shared and invasion approaches neutrality or is suppressed by costs. Our results highlight that invasion of cooperative traits is governed by spatially mediated resource localization rather than intrinsic fitness alone. This framework provides a mechanistic basis for understanding the emergence of cooperative subclones in tumors and suggests that modulating biophysical transport properties of signaling molecules could influence tumor evolution, metastasis, and therapeutic resistance.

evolutionary biology↗

In Vivo Multiplexed Modeling Reveals Diverse Roles of the TBX2 Subfamily and Egr1 in Ras-Driven Lung Adenocarcinoma

The TBX2 subfamily of T-box transcription factors (including Tbx2, Tbx3, Tbx4, Tbx5) plays an essential role in lung development. Downregulation of these genes in human Lung adenocarcinoma (LUAD) suggests that these genes may be tumor suppressive, however because downregulation appears to occur primarily via epigenetic change, it remains unclear if these changes causally drive tumor progression or are merely the consequence of upstream events. Herein, we developed the first multiplexed mouse model to study the impact of TBX2 subfamily loss, alongside associated signaling genes Egr1, Chd2, Tnfaip3a, and Atf3, in Ras-driven lung cancer. Using TuBa-seq, a high-throughput tumor-barcoding system, we quantified the growth effects of these knockouts during early and late tumorigenesis. Chd2 loss consistently suppressed tumor progression, while Tbx2 loss exhibited stage-dependent effects. Notably, Egr1 emerged as a potent tumor suppressor, with its knockout increasing tumor size ([~]5x) at 20 weeks, surpassing Rb1 loss. Transcriptomic analyses of Egr1-deficient tumors suggested immune dysregulation, including heightened inflammation and potential markers of T cell exhaustion in the tumor microenvironment. These findings indicate that Egr1 may play a role in suppressing tumor growth through modulating immune dynamics, offering new insights into the interplay between tumor progression and immune regulation in LUAD.

cancer biology↗

Frequency-dependent ecological interactions increase the prevalence and shape the distribution of pre-existing drug resistance

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of pre-existing resistance.

evolutionary biology↗

Quantitative in vivo analyses reveal a complex pharmacogenomic landscape in lung adenocarcinoma

AbstractThe lack of knowledge about the relationship between tumor genotypes and therapeutic responses remains one of the most important gaps in enabling the effective use of cancer therapies. Here, we couple a multiplexed and quantitative platform with robust statistical methods to enable pharmacogenomic mapping of lung cancer treatment responses in vivo. We uncover a surprisingly complex map of genotype-specific therapeutic responses, with over 20% of possible interactions showing significant resistance or sensitivity. We validate one of these interactions - the resistance of Keap1 mutant tumors to platinum therapy - using a large patient response dataset. Our results highlight the importance of understanding the genetic determinants of treatment responses in the development of precision therapies and define a strategy to identify such determinants.

cancer biology↗

Most cancers carry a substantial deleterious load due to Hill-Robertson interference

Cancer genomes exhibit surprisingly weak signatures of negative selection1,2. This may be because selective pressures are relaxed or because genome-wide linkage prevents deleterious mutations from being removed (Hill-Robertson interference)3. By stratifying tumors by their genome-wide mutational burden, we observe negative selection (dN/dS ~ 0.47) in low mutational burden tumors, while remaining cancers exhibit dN/dS ratios ~1. This suggests that most tumors do not remove deleterious passengers. To buffer against deleterious passengers, tumors upregulate heat shock pathways as their mutational burden increases. Finally, evolutionary modeling finds that Hill-Robertson interference alone can reproduce patterns of attenuated selection and estimates the total fitness cost of passengers to be 40% per cell on average. Collectively, our findings suggest that the lack of observed negative selection in most tumors is not due to relaxed selective pressures, but rather the inability of selection to remove deleterious mutations in the presence of genome-wide linkage.

evolutionary biology↗