Biology subjects

Maltas, J.

Publications and source records attributed to Maltas, J..

11 recordsLinked to original sources

bioRxiv2026

Selection for targeted therapy resistance leads to an indirect selection for higher phenotypic plasticity and enhanced evolvability to orthogonal stressors

Acquired resistance to targeted therapies is a primary barrier to durable cancer control. Resistance frequently coincides with stem-like features and EMT-associated cell-state changes (partial EMT), yet whether these programs directly cause resistance or instead enable escape by increasing phenotypic plasticity remains debated. Integrating computational modeling, functional experimental assays, and lineage tracing, we investigated how cell-state plasticity shapes acquired resistance to ALK inhibition in ALK+ lung cancer models. Our results support a model in which drug exposure selects for cells with higher phenotypic plasticity, progressively increasing their representation as resistance emerges. Consequently, resistant populations show enhanced capacity to adapt to orthogonal therapeutic and environmental stressors and exhibit heightened metastatic potential. Bulk ATAC-seq showed that highly plastic cells have increased chromatin accessibility at regulators of EMT and stemness. Consistent with this, boosting plasticity via Yamanaka-factor induction or EMT-factor expression reduced ALKi sensitivity over time. In contrast, constraining plasticity (SOX2 knockdown or epigenetic inhibition) reduced long-term resistance outgrowth and prolonged ALKi response. Together, our results indicate that targeted therapy indirectly selects for cells with increased phenotypic plasticity, providing the substrate from which multifactorial resistance and metastatic competence evolve. Further, it suggests that constraining plasticity could delay resistance and extend response durability.

cancer biology↗

bioRxiv2025

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↗

bioRxiv2023

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↗

bioRxiv2023

Reinforcement Learning informs optimal treatmentstrategies to limit antibiotic resistance.

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 {beta} -lactam antibiotics with which to treat the simulated E. coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

microbiology↗

bioRxiv2022

Drug dependence in cancer is exploitable by optimally constructed treatment holidays

Recent work in cell culture models, animal models, and human patients indicates that cancers with acquired resistance to a drug can become simultaneously dependent upon the presence of that drug for survival. This drug dependence offers a potential avenue for improving treatments aimed at slowing resistance, yet relatively little is known about the frequency with which drug dependence arises, the mechanisms underlying that dependence, and how drug schedules might be tuned to optimally exploit drug dependence. In this work, we address these open questions using a combination of laboratory evolution, in vitro experiments, and simple mathematical models. First, we used laboratory evolution to select more than 100 resistant BRAF mutant melanoma cell lines with acquired resistance to BRAF, MEK, or ERK inhibitors. We found that nearly half of these lines exhibit drug dependence, and the dependency response is associated with EGFR-driven senescence induction, but not apoptosis, following drug withdrawal. Then, using melanoma populations with evolved resistance to the BRAF inhibitor PLX4720, we showed that drug dependence can be leveraged to dramatically reduce population growth when treatment strategies include optimally chosen drug-free "holidays". On short timescales, the duration of these holidays depends sensitively on the composition of the population, but for sufficiently long treatments it depends only on a single dimensionless parameter ({gamma}) that describes how the growth rates of each cell type depend on the different treatment environments. Experiments confirm that the optimal holiday duration changes in time-with holidays of different durations leading to optimized treatments on different timescales. Furthermore, we find that the presence of "non-dependent" resistant cells does not change the optimal treatment schedule but leads to a net increase in population size. Finally, we show that even in the absence of detailed information about the composition and growth characteristics of cellular clones within a population, a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size, at least when these measurements are sufficiently frequent. As a whole, these results may provide a stepping-stone toward the eventual development of evolution-inspired treatment strategies for drug dependent cancers.

cancer biology↗

bioRxiv2021

Dynamic collateral sensitivity profiles highlight challenges and opportunities for optimizing antibiotic sequences

As failure rates for traditional antimicrobial therapies escalate, recent focus has shifted to evolution-based therapies to slow resistance. Collateral sensitivity-the increased susceptibility to one drug associated with evolved resistance to a different drug-offers a potentially exploitable evolutionary constraint, but the manner in which collateral effects emerge over time is not well understood. Here, we use laboratory evolution in the opportunistic pathogen E. faecalis to phenotypically characterize collateral profiles through evolutionary time. Specifically, we measure collateral profiles for 400 strain-antibiotic combinations over the course of 4 evolutionary time points as strains are selected in increasing concentrations of antibiotic. We find that at a global level-when results from all drugs are combined-collateral resistance dominates during early phases of adaptation, when resistance to the selecting drug is lower, while collateral sensitivity becomes increasingly likely with further selection. At the level of individual populations, however, the trends are idiosyncratic; for example, the frequency of collateral sensitivity to ceftriaxone increases over time in isolates selected by linezolid but decreases is isolates selected by ciprofloxacin. Finally, we show experimentally how dynamic collateral sensitivity relationships can lead to time-dependent dosing windows that depend on finely timed switching between drugs.

evolutionary biology↗

bioRxiv2020

Dose dependent evolutionary game dynamics modulate competitive release in cancer therapy

Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies attempt to delay resistance via judicious treatments that maintain a significant treatable subpopulation. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. In this study we experimentally measure the frequency-dependent interactions between a gefitinib resistant non-small cell lung cancer (NSCLC) population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. In addition, we show that frequency-dependent growth rate changes may ultimately result in a safe harbor for resistant populations to safely accumulate, even those with significant cost of resistance. Using frequency-dependent growth rate data we then show that gefitinib treatment results in competitive exclusion of the ancestor, while absence of treatment results in a likely, but not guaranteed exclusion of the resistant strain. Finally, using our empirically derived growth rates to constrain simulations, we demonstrate that incorporating ecological growth effects can dramatically change the predicted time to sensitive strain extinction. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Taken together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and the clinic.

cancer biology↗

bioRxiv2019

Evolution in alternating environments with tunable inter-landscape correlations

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, though understanding the effects of varying selection pressures remains challenging. Here we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the inter-landscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anti-correlated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anti-correlated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.

evolutionary biology↗

bioRxiv2019

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and cross resistance. For example, selection in a dual-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and -lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations correspond to (nearly) optimized linear combinations of those selected by the component drugs. Our results highlight trade-offs between drug interactions and collateral effects during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

evolutionary biology↗

bioRxiv2019

Using selection by non-antibiotic stressors to sensitize bacteria to antibiotics

O_SCPCAPABSTRACTC_SCPCAPBacterial resistance to one antibiotic is frequently accompanied by crossresistance to other drugs. Similarly, non-antibiotic selective forces, from biocides to osmotic stress, have been shown to decrease antibiotic susceptibility, often the result of shared, non-specific resistance mechanisms. On the other hand, evolved resistance to particular antibiotics may also be associated with increased sensitivity to other drugs, highlighting evolutionary constraints that could form the basis for novel anti-resistance strategies. While recent studies indicate this collateral sensitivity is common between antibiotics, much less is known about potentially sensitizing effects of non-antibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of E. faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that non-antibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multi-drug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and non-antibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and non-drug environments can be used to sensitize bacterial to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.

microbiology↗

bioRxiv2017

Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

Evolved resistance to one antibiotic may be associated with \"collateral\" sensitivity to other drugs. Here we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict, as independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. While phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in four-drug uniform cycles.

evolutionary biology↗