Biology subjects

Gatenby, R. A.

Publications and source records attributed to Gatenby, R. A..

15 recordsLinked to original sources

bioRxiv2026

Cancer Evolvability Determines Therapy Outcomes

Cancer progression following treatment failure is an evolutionary process in which therapy acts as a selection pressure driving Darwinian selection on heritable variation to favor resistant clones. This ability to generate variation, i.e., the cancers evolvability, is a key determinant of how rapidly tumors adapt to therapy. Here, we present an evolutionary game-theoretic model to evaluate how evolvability shapes resistance dynamics under two treatment modalities: targeted therapy and chemotherapy. We first compare cancer populations with fixed evolvabilities: low or high. Targeted therapy imposes a steep selection gradient, enabling rapid resistance evolution, while chemotherapy exerts a flatter gradient but drives tumors toward more extreme resistance strategies. We show that targeted therapy works better in low-evolvability cancers, whereas chemotherapy better controls high-evolvability populations. We then extend the model to incorporate facultative evolvability in which cancer cells dynamically adjust their evolvability in response to therapy-induced stress in which cells fine-tune the trade-off between acquiring higher resistance and limiting the costs of resistance and evolvability. The latter strategy sustains a higher tumor burden than fixed-evolvability populations. To address the challenges of facultative evolvability for therapy efficacy, we develop and simulate an evolutionary double bind using sequential cycles of chemotherapy and targeted therapy. With an appropriate sequence and timing, this strategy can drive cancer cells with facultative evolvability to extinction. Our results highlight the importance of evolvability in shaping treatment response and underscore the need to incorporate evolutionary principles into therapy design.

cancer biology↗

bioRxiv2026

Modeling VEGF and GLUT1 Expression as Coadapted Foraging Strategies in Cancer

Natural selection acting on cancer cells within their tumor microenvironment should favor cells with fast or efficient nutrient uptake strategies. Here, we develop and analyze a game-theoretic model focusing on the coadaptation between two foraging traits: vascular endothelial growth factor (VEGF) and glucose transporter 1 (GLUT1). Studies show that VEGF and GLUT1 are often co-expressed and are associated with more aggressive tumor phenotypes and poor clinical prognosis. VEGF is a diffusible paracrine factor that recruits blood vessels towards neighborhoods of cancer cells (angiogenesis). GLUT1 is a cell-surface transporter that enables the uptake of nutrients, especially glucose. We model these strategies operating at different scales: VEGF influences resource availability at the neighborhood level, while GLUT1 determines resource uptake at the cellular level. For VEGF, we introduce a resource-sharing continuum. With no resource sharing, cells access resources in proportion to their VEGF contribution. With uniform sharing, cells have equal access to resources, regardless of their VEGF contribution. The former leads to a tragedy of the commons and overproduction of VEGF. The latter yields a public goods game with moderate VEGF expression matching a group optimum. GLUT1 expression mediates uptake of resources recruited by VEGF and is largely independent of the degree of resource sharing. Therapeutically, both VEGF and GLUT1 inhibitors are more effective in high resource-sharing neighborhoods and less so as resource sharing declines. Overall, inhibition of GLUT1-mediated uptake emerges as more effective. The model, perhaps the first to consider VEGF and GLUT1 as coadaptations, emphasizes the need to consider cancer cell traits jointly.

cancer biology↗

bioRxiv2025

The impact of normal tissue density on tumor growth and evolution in a 3D whole-tumor model of lung cancer

Serial low-dose computed tomography (LDCT) scans in patients who are diagnosed with lung cancer during screening offer a history of the densities of tumors and the tissues that surround them during carcinogenesis and cancer progression. We built a CT-scan-resolution computational model to explore how variations in lung tissue density impact tumor growth and evolution in non-small cell lung cancer (NSCLC). Our findings indicate that tumors spread more rapidly through denser tissues when they upregulate glycolysis whilst tumors spread more rapidly through sparser tissues when they upregulate angiogenesis. We used data and images from the National Lung Screening Trial to calibrate our model for untreated lung cancer growth in patients and observed consistency with model predictions in low-density environments. SignificanceOur lung lesion model supports prior studies that find tumors tend to evolve toward angiogenic or glycolytic phenotypes. We demonstrate that these evolutionary strategies may be driven by the surrounding normal tissue density and may be observable on imaging.

cancer biology↗

bioRxiv2024

Evolutionary rescue model informs strategies for driving cancer cell populations to extinction

Cancers exhibit a remarkable ability to develop resistance to a range of treatments, often resulting in relapse following first-line therapies and significantly worse outcomes for subsequent treatments. While our understanding of the mechanisms and dynamics of the emergence of resistance during cancer therapy continues to advance, questions remain about how to minimize the probability that resistance will evolve, thereby improving long-term patient outcomes. Here, we present an evolutionary simulation model of a clonal population of cells that can acquire resistance mutations to one or more treatments. We leverage this model to examine the efficacy of a two-strike "extinction therapy" protocol, in which two treatments are applied sequentially to first contract the population to a vulnerable state and then push it to extinction, and compare it to a combination therapy protocol. We investigate how factors such as the timing of the switch between the two strikes, the rate of emergence of resistant mutations, the doses of the applied drugs, the presence of cross-resistance, and whether resistance is a binary or a quantitative trait affect the outcome. Our results show that the timing of switching to the second strike has a marked effect on the likelihood of driving the cancer to extinction, and that extinction therapy outperforms combination therapy when cross-resistance is present. We conduct an in silico trial that reveals when and why a second strike will succeed or fail. Finally, we demonstrate that our conclusions hold whether we model resistance as a binary trait or as a quantitative, multi-locus trait.

evolutionary biology↗

bioRxiv2024

Nanoparticles use magnetoelectricity to target and eradicate cancer cells

This study presents the first in vivo and in vitro evidence of an externally controlled, predictive, MRI-based nanotheranostic agent capable of cancer cell specific targeting and killing via irreversible electroporation (IRE) in solid tumors. The rectangular-prism-shaped magnetoelectric nanoparticle is a smart nanoparticle that produces a local electric field in response to an externally applied magnetic field. When externally activated, MENPs are preferentially attracted to the highly conductive cancer cell membranes, which occurs in cancer cells because of dysregulated ion flux across their membranes. In a pancreatic adenocarcinoma murine model, MENPs activated by external magnetic fields during magnetic resonance imaging (MRI) resulted in a mean three-fold tumor volume reduction (62.3% vs 188.7%; P < .001) from a single treatment. In a longitudinal confirmatory study, 35% of mice treated with activated MENPs achieved a durable complete response for 14 weeks after one treatment. The degree of tumor volume reduction correlated with a decrease in MRI T2* relaxation time (r = .351; P = .039) which suggests that MENPs have a potential to serve as a predictive nanotheranostic agent at time of treatment. There were no discernable toxicities associated with MENPs at any timepoint or on histopathological analysis of major organs. MENPs are a noninvasive alternative modality for the treatment of cancer. SummaryWe investigated the theranostic capabilities of magnetoelectric nanoparticles (MENPs) combined with MRI via a murine model of pancreatic adenocarcinoma. MENPs leverage the magnetoelectric effect to convert an applied magnetic field into local electric fields, which can induce irreversible electroporation of tumor cell membranes when activated by MRI. Additionally, MENPs modulate MRI relaxivity, which can be used to predict the degree of tumor ablation. Through a pilot study (n=21) and a confirmatory study (n=27), we demonstrated that, [≥]300 {micro}g of MRI-activated MENPs significantly reduced tumor volumes, averaging a three-fold decrease as compared to controls. Furthermore, there was a direct correlation between the reduction in tumor T2 relaxation times and tumor volume reduction, highlighting the predictive prognostic value of MENPs. Six of 17 mice in the confirmatory studys experimental arms achieved a durable complete response, showcasing the potential for durable treatment outcomes. Importantly, the administration of MENPs was not associated with any evident toxicities. This study presents the first in vivo evidence of an externally controlled, MRI-based, theranostic agent that effectively targets and treats solid tumors via irreversible electroporation while sparing normal tissues, offering a new and promising approach to cancer therapy.

bioengineering↗

bioRxiv2022

Distinct tumor-immune ecologies in NSCLC patientspredict progression and define a clinical biomarker of therapy response

We investigated multiplexed histological images from nine patients (both pre- and on-treatment) with immunotherapy-refractory Non-Small Cell Lung Cancer (NSCLC) treated with an oral HDAC inhibitor (vorinostat) combined with a PD-1 inhibitor (pembrolizumab). Patient responses comprised of either stable disease (SD) or progressive disease (PD). We built an extensive multiplexed-image analysis pipeline involving both cell segmentation and quadrats, coupled with spatial statistics, machine learning, and deep learning to analyze the spatial and temporal features that predict disease progression and identify potential clinical biomarkers. We found that distinct spatial immune ecologies exist between SD and PD patients. We also demonstrate that tumors from PD patients are already characterized by an immune-suppressive environment prior to treatment. Finally, we show that the learned spatial ecologies can predict disease progression better than PD-L1 status alone, suggesting these ecologies can be used as potential companion biomarkers with PD-L1 in NSCLC. These findings will be investigated in a larger-cohort study generated from an ongoing clinical trial (NCT02638090) that includes a wider range of responses including complete and partial responders. Additionally, the computational infrastructure developed in this study can be generalized to any cancer type.

cancer biology↗

bioRxiv2022

The sum and the parts: dynamics of multiple and individual metastases during adaptive therapy

Evolutionary therapies, such as adaptive therapy, have shown promise in delaying treatment resistance in late-stage cancers. By alternating between drug applications and drug-free vacations, competition between sensitive and resistant cells can be exploited to maximize the time to progression. However, the optimal schedule of this dosing regimen depends on the properties of the tumor, which often are not directly measurable in clinical practice. In this work, we propose that the initial cycle of adaptive therapy can be used as a tool to probe the relevant tumor properties. We present a framework for estimating individual and collective components of a metastatic system through tumor response dynamics, which uses a system of off-lattice agent-based models to represent individual metastatic lesions within independent domains, all of which are subject to the same systemic therapy. We find that the first cycle of adaptive therapy delineates several features of the computational metastatic system: larger metastases have longer cycles; a higher proportion of drug resistant cells slows the cycles; and a faster cell turnover rate speeds up drug response time and slows the regrowth time. The number of metastases does not affect cycle times, as response dynamics are dominated by the largest tumors rather than the aggregate. In addition, the heterogeneity of the system is also a guide for therapeutic approaches: generally, systems with more between-tumor (intertumor) heterogeneity had better success with continuous therapy, while systems with more within-tumor (intratumor) heterogeneity responded better to adaptive therapy. Intertumor heterogeneity was found to correlate more with dynamics from patients with high and low Gleason scores while intratumor heterogeneity was correlated with dynamics from patients with intermediate Gleason scores.

systems biology↗

bioRxiv2021

Mistic: an open-source multiplexed image t-SNE viewer

Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs. Mistic is open-source and can be downloaded at: https://github.com/MathOnco/Mistic.

cancer biology↗

bioRxiv2021

Macrophage-derived cholesterol contributes to therapeutic resistance in prostate cancer

Tumor-associated macrophages are key immune cells associated with cancer progression. Here we sought to determine the role of macrophages in castration-resistant prostate cancer (CRPC) using a syngeneic model that reflected the mutational landscape of the disease. A transcriptomic analysis of CRPC tumors following macrophage depletion revealed lower molecular signatures for steroid and bile acid synthesis, indicating potential perturbation of cholesterol metabolism. Since cholesterol is the precursor of the five major classes of steroid hormones, we reasoned that macrophages were regulating androgen biosynthesis within the prostate tumor microenvironment. Indeed, macrophage depletion reduced the levels of androgens within prostate tumors and restricted androgen receptor (AR) nuclear localization in vitro and in vivo. Macrophages were cholesterol rich and had the ability to transfer cholesterol to tumor cells in vitro, and AR nuclear translocation was inhibited by activation of Liver X Receptor (LXR)-{beta}, the master regulator of cholesterol homeostasis. Finally, combining macrophage depletion with androgen deprivation therapy increased survival, supporting the therapeutic potential of targeting macrophages in CRPC.

cancer biology↗

bioRxiv2020

Frequency-dependent interactions determine outcome of competition between two breast cancer cell lines

Tumors are highly dynamic ecosystems in which diverse cancer cell subpopulations compete for space and resources. These complex, often non-linear interactions govern continuous spatial and temporal changes in the size and phenotypic properties of these subpopulations. Because intra-tumoral blood flow is often chaotic, competition for resources may be a critical selection factor in progression and prognosis. Here, we quantify resource competition using 3D spheroid cultures with MDA-MB-231 and MCF-7 breast cancer cells. We hypothesized that MCF-7 cells, which primarily rely on efficient aerobic glucose metabolism, would dominate the population under normal pH and low glucose conditions; and MDA-MB-231 cells, which exhibit high levels of glycolytic metabolism, would dominate under low pH and high glucose conditions. In spheroids with single populations, MCF-7 cells exhibited equal or superior intrinsic growth rates (density-independent measure of success) and carrying capacities (density-dependent measure of success) when compared to MDA-MB-231 cells under all pH and nutrient conditions. Despite these advantages, when grown together, MCF-7 cells do not always outcompete MDA-MB-231 cells. MDA-MB-231 cells outcompete MCF-7 cells in low glucose conditions and coexistence is achieved in low pH conditions. Under all conditions, MDA-MB-231 has a stronger competitive effect (frequency-dependent interaction) on MCF-7 cells than vice-versa. This, and the inability of growth rate or carrying capacity when grown individually to predict the outcome of competition, suggests a reliance on frequency-dependent interactions and the need for competition assays. We frame these results in a game-theoretic (frequency-dependent) model of cancer cell interactions and conclude that competition assays can demonstrate critical density-independent, density-dependent and frequency-dependent interactions that likely contribute to in vivo outcomes. HighlightsDemonstrate how mixed-culture spheroids must be used to characterize competition between two cancer cell lines. Competition alters growth dynamics of cancer cells. Competition growth models can be used to quantify density-independent, density-dependent and frequency-dependent effects on competition. Competition affects tumor progression and structure, making it key to understanding tumor development and evolution.

cancer biology↗

bioRxiv2019

A mathematical dissection of the adaptation of cell populations to fluctuating oxygen levels

The disordered network of blood vessels that arises from tumour angiogenesis results in variations in the delivery of oxygen into the tumour tissue. This brings about regions of chronic hypoxia (i.e. sustained low oxygen levels) and regions with alternating phases of low and relatively higher oxygen levels within vascularised tumours, and makes it necessary for cancer cells to adapt to fluctuating environmental conditions. We use a phenotype-structured model to dissect the evolutionary dynamics of cell populations exposed to fluctuating oxygen levels. In this model, the phenotypic state of every cell is described by a continuous variable that provides a simple representation of its metabolic phenotype, ranging from fully oxidative to fully glycolytic, and cells are grouped into two competing populations that undergo heritable, spontaneous phenotypic variations at different rates. Model simulations indicate that, depending on the rate at which oxygen is consumed by the cells, nonlinear dynamic interactions between cells and oxygen can stimulate chronic hypoxia and cycling hypoxia. Moreover, the model supports the idea that under chronic-hypoxic conditions lower rates of phenotypic variation lead to a competitive advantage, whereas higher rates of phenotypic variation can confer a competitive advantage under cycling-hypoxic conditions. In the latter case, the numerical results obtained show that bet-hedging evolutionary strategies, whereby cells switch between oxidative and glycolytic phenotypes, can spontaneously emerge. We explain how these results can shed light on the evolutionary process that may underpin the emergence of phenotypic heterogeneity in vascularised tumours.

cancer biology↗

bioRxiv2019

Intermittent hormonal therapy shows similar outcome than SOC in ER+ breast cancer preclinical model.

Clinical breast cancers in which at least 10% of cells express the estrogen receptor are labeled as \"ER positive.\" First line therapy for these patients is typically continuous administration of anti-estrogen drugs at maximum tolerated dose (MTD) until progression. In the vast majority of patients, resistance to hormone therapy evolves in the breast cancer cells within 2 years leading to treatment failure and tumor progression. In prior studies, we have demonstrated continuous application of MTD chemotherapy results in evolutionary dynamics (termed \"competitive release\") that accelerates proliferation of treatment-resistance populations. In contrast, evolution-informed application of treatment reduces drug administration to maintain substantial populations of therapy-sensitive cells to reduce proliferation of resistant phenotypes. Prior pre-clinical and clinical studies have shown this strategy can delay or prevent proliferation of resistant cells and prolong time to progression (TTP). We hypothesize that similar dynamics may be observed in hormonal therapy of ER+ breast cancers. Here we address two important dynamics. First, we consider a clinical scenario in which symptoms are sufficiently severe or life-threatening to require rapid and substantial tumor reduction. Can this be achieved while retaining evolutionary dynamics to subsequently delay proliferation of resistance? A second, related question is defining the cost of resistance to anti-estrogen therapy. Here, we investigated the evolutionary dynamics of resistance to anti-estrogen therapy using ER+ MCF-7 orthotropic xenografts treated with both continuous Tamoxifen as well as cycles in which estrogen stimulation is combined with estrogen suppression. As expected, continuous administration of anti-estrogen drugs successfully suppressed tumor growth. However we found that brief interruptions in drug administration permitted equal tumor control while administering up to 50% less drug and maintaining cell phenotypes that retained high levels of ER expression and lower levels of MDR1 expression. In follow-on experiments combining hormonal and chemo-therapies; we obtained similar tumor control to hormonal therapy alone but with more necrosis and significantly lower ER expression in the surviving population.

cancer biology↗

bioRxiv2018

The Goldilocks Window of Personalized Chemotherapy: An Immune Perspective

The immune system is increasingly being recognized for its untapped potential in being recruited to attack tumors in cancer therapy. The main challenge, however, is that most tumors exist in a state of immune tolerance where the patients immune system has become insensitive to the cancer cells. In order to investigate the ability to use chemotherapy to break immune tolerance, we created a mathematical modeling framework for tumor-immune dynamics. Our results suggest that optimal chemotherapy scheduling must balance two opposing objectives: maximal tumor reduction and preserving patient immune function. Successful treatment requires therapy to operate in a Goldilocks Window where patient immune health is not overly compromised. By keeping therapy just right, we show that the synergistic effects of immune activation and chemotherapy can maximize tumor reduction and control.\n\nStatement of SignificanceIn order to maximize the synergy between chemotherapy and anti-tumor immune response, lymphodepleting therapy must be balanced in a Goldilocks Window of optimal dosing.

cancer biology↗

bioRxiv2018

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: a Multi-disciplinary Team-based, Multifactorial Analytical Approach

Although glioblastoma is a fatal primary brain cancer with a short median survival of 15 months, a small number of patients survive more than 5 years after diagnosis; they are known as extreme survivors (ES). Due to their rarity, very little is known about what differentiates these outliers from other glioblastoma patients. For the purpose of identifying unknown drivers of extreme survivorship in glioblastoma, we developed the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of glioblastoma). This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histological parameters. Leveraging our combined resources, the goals of the ENDURES consortium are two-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with glioblastoma and (2) to leverage the ENDURES repository for new insights on tumor behavior and novel targets for prolonging survival for all glioblastoma patients. In this article, we review the available literature and discuss what is already known about ES. We then describe the creation of our consortium and some of our preliminary results.\n\nFundingThis review was financially supported by a grant from the James S. McDonnell Foundation\n\nConflicts of InterestThe authors have declared that no conflicts of interest exist.\n\nAuthorshipConceptualized consortium: LW, RG, KME, PC, and KRS. Built consortium: SKJ, PK, NR, JS, KME, PC, and KRS. Wrote the manuscript: SKJ, PW, SCM, PK, AP, and KME. Reviewed and edited the manuscript: LFGC, MMM, AHD, PRJ, and LSH. Contributed to writing, provided feedback, and approved of final manuscript: All authors.\n\nLink to website for ENDUREShttp://mathematicalneurooncology.org/?page_id=2125

cancer biology↗

bioRxiv2017

Adaptive Therapy For Heterogeneous Cancer: Exploiting Space And Trade-Offs In Drug Scheduling

Treatment of advanced cancers has benefited from new agents that supplement or bypass conventional therapies. However, even effective therapies fail as cancer cells deploy a wide range of resistance strategies. We propose that evolutionary dynamics ultimately determine survival and proliferation of resistant cells, therefore evolutionary strategies should be used with conventional therapies to delay or prevent resistance. Using an agent-based framework to model spatial competition among sensitive and resistant populations, we apply anti-proliferative drug treatments to varying ratios of sensitive and resistant cells. We compare a continuous maximum tolerated dose schedule with an adaptive schedule aimed at tumor control through competition between sensitive and resistant cells. We find that continuous treatment cures mostly sensitive tumors, but with any resistant cells, recurrence is inevitable. We identify two adaptive strategies that control heterogeneous tumors: dose modulation controls most tumors with less drug, while a more vacation-oriented schedule can control more invasive tumors.

cancer biology↗