Biology subjects

Basanta, D.

Publications and source records attributed to Basanta, D..

At least 19 recordsLinked to original sources

bioRxiv2026

A reference-calibrated spatial-statistical framework for quantifying stroma-tumor cell spatial associations from histology and its geometric limits

Quantifying interactions in biological tissues requires determining whether an interaction exists, the distance over which it operates, and its strength. Existing spatial-statistical approaches can characterize spatial organization but often require interaction range or interaction models to be specified a priori, making them less suited to settings where these quantities are themselves unknown. Here, we present a practical framework that recovers the presence, range, and association strength of spatial bias toward a tissue structure directly from segmented histology. The framework combines four complementary statistics, the resource selection function, nearest-structure distance distributions, the cross J-function, and the radial distribution function, with specimen-specific reference states that preserve tissue stromal architecture while spanning a continuum of known spatial biases. Across controlled in silico tissues, we show that tissue stromal architecture imposes geometric limits on the range of recoverable interactions and that event fraction independently constrains statistical sensitivity. These spatial associations establish fundamental bounds on inference and demonstrate that spatial statistics must be interpreted relative to tissue-specific reference states rather than absolute scales. We apply the framework to proliferating tumor cells relative to CAF-rich stroma during tyrosine kinase inhibitor therapy in an ALK+ NSCLC model and identify a strong stroma-associated proliferation bias extending approximately 30-60 micrometers. The framework broadly applies to any histological setting where events and reference structures can be defined, providing a practical approach to quantify localized interactions directly from tissue stromal architecture.

cancer biology↗

bioRxiv2026

Pharmacokinetic Acceleration via CYP3A4 Hyperactivation as a Clinically Actionable Mechanism of Targeted Therapy Resistance in NSCLC

Resistance of cancers to targeted therapies is traditionally framed as a tumor-intrinsic phenomenon, mediated by tumor cell-intrinsic or microenvironmental mechanisms. Here, we identify a tumor-extrinsic, systemic resistance mechanism resulting from hyperactivation of the hepatic cytochrome P450 enzyme, CYP3A4. This tumor-extrinsic resistance mechanism can function independently of, or in tandem with, tumor-intrinsic resistance. Focusing on experimental mouse models of targetable lung cancer, we find that xenobiotic-mediated induction of CYP3A4 results in accelerated drug metabolism and a drastic reduction in systemic and tumor-drug exposure in vivo. CYP3A4 activation can be triggered by chemically unrelated xenobiotics, leading to resistance to a wide range of targeted therapies, including ALK, EGFR, and KRASG12C inhibitors. Retrospective analysis of clinical cohorts suggests that variability in CYP3A4 activity might be a major contributor to variability in clinical outcomes. While higher CYP3A4 activity leads to sub-therapeutic tumor drug exposure and shorter progression-free survival, reduced drug metabolism is expected to result in supratherapeutic exposure and increased systemic toxicity. To address the consequences of abnormal CYP3A4 activity, we utilized mathematical modeling to demonstrate that drug concentrations can be restored through the optimization of dosing amounts and intervals. Further, we show that tumor sensitivity to targeted therapies can be rescued through pharmacological inhibition of CYP3A4. Our findings establish systemic metabolic variability as a bona fide resistance and toxicity driver, providing a translational framework for personalized dosing to maximize both safety and efficacy.

cancer biology↗

bioRxiv2025

Rethinking the seven-day treatment-free interval in T-cell engager therapy using agent-based modeling

BackgroundThe CD3/CD19 bispecific T cell engager (TCE) blinatumomab has shown efficacy in relapsed/refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL), but response rates are often limited by T cell exhaustion. Recent preclinical studies suggest that incorporating treatment-free intervals (TFIs) into dosing schedules may enhance therapeutic outcomes. MethodsTo systematically evaluate alternative TFI strategies, we developed an agent-based model (ABM) of tumor-T cell interactions under various blinatumomab dosing regimens. The model was calibrated using published in vitro data and incorporated spatial, stochastic, and mechanistic rules governing T cell activation, cytotoxicity, proliferation, and exhaustion. ResultsOur ABM recapitulates experimental observations showing that a 7-day TFI improved T cell function over continuous dosing during the initial 28-day treatment period. However, when simulations were extended to a full 42-day cycle to mimic clinical regimen, this advantage was lost. In contrast, shorter TFIs consistently outperformed both 7-day and continuous schedules, leading to superior tumor control at all timepoints. A translationally oriented Monday-through-Friday (MO_FR) regimen also achieved comparable benefits. ConclusionsOur results indicate that the empirically tested 7-day TFI schedule may not be optimal. TFI with shorter intervals as well as translationally relevant schedules such as MO_FR, may offer greater therapeutic benefit. This work demonstrates the value of ABM in preclinical immunotherapy design and supports model-guided refinement of TCE dosing strategies prior to clinical translation. Future work will focus on validating these predictions in more complex in vivo models and leveraging patient-derived data to guide personalized TCE treatment design.

cancer biology↗

bioRxiv2025

A Mathematical Model of Persister Cell Plasticity and Its Impact on Adaptive Cancer Therapy

The emergence of resistance to therapy remains a significant obstacle to successful treatment in cancer that is driven by somatic evolution. Adaptive therapies represent a novel approach to manage the emergence of resistance, by leveraging the competition between different cell phenotypes to control tumor burden rather than aiming for complete eradication. However, the emergence of phenotypica ly plastic persister cells, exhibiting transient epigenetic resistance, poses a significant challenge to the efficacy of these approaches, and their specific impact is often overlooked in preclinical and mathematical models. This study investigates the role of persisters within adaptive therapy using a spatial agent-based model simulating sensitive, persistent, and genetically resistant cell populations. Our simulations reveal that persisters critically undermine treatment efficacy, significantly reducing progression-free survival (PFS) by approximately 40% (average 207 vs. 344 days in simulations) as they provide a reservoir for acquiring genetic resistance. Furthermore, tumors with higher levels of epigenetic resistance (more robust persisters) showed accelerated evolution towards resistance dominance phenotypes. The model showed treatment was most effective when sensitive cells initially dominated the tumor microenvironment. These findings highlight that persister dynamics are crucial determinants of adaptive therapy outcomes, suggesting that future strategies must account for epigenetic resistance, potentially informing approaches to assess tumor composition and sensitivity to better tailor treatments and improve patient outcomes.

systems biology↗

bioRxiv2025

Overcoming treatment resistance mediated by the bone marrow vascular niche in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy originating in the bone marrow, frequently progressing to extramedullary sites. Despite advances in molecularly targeted therapies and hematopoietic stem cell transplantation, clinical outcomes remain suboptimal. Tyrosine kinase inhibitors (TKIs) confer benefit in a subset of AML patients harboring FLT3-ITD mutations, yet relapse and resistance are common. These failures are driven by both intrinsic properties of leukemic stem cells (LSCs)--a quiescent, self-renewing population--and extrinsic cues from the tumor microenvironment. We previously demonstrated that arteriolar endothelial cells (ECs) produce miR-126, which is transferred to LSCs, promoting quiescence, treatment resistance, and niche retention. During disease progression, TNF- secreted by expanding blasts suppresses EC miR-126 production, enabling LSCs and their progeny to proliferate. Following TKI administration, blast reduction lowers TNF- levels, restoring EC miR-126 production and enabling LSCs to re-enter quiescence--thereby escaping therapy and facilitating relapse. To investigate this dynamic, we developed an agent-based computational model of the AML bone marrow microenvironment, parameterized with in vitro and in vivo data. The model captures vascular niche remodeling and the feedback between leukemic populations and endothelial signaling. Simulations reveal that LSC protection mediated by miR-126 can be overcome by combining TKIs with miRisten, a miR-126 inhibitor. When administered on a defined schedule, this combination disrupts the protective niche and enhances LSC eradication. These findings underscore the therapeutic potential of targeting microenvironmental feedback to overcome resistance and prevent AML relapse.

cancer biology↗

bioRxiv2024

The adaptive state determines the impact of mutations on evolving populations

Darwinian evolution results from an interplay between stochastic diversification of heritable phenotypes, impacting the chance of survival and reproduction, and fitness-based selection. The ability of populations to evolve and adapt to environmental changes depends on rates of mutational diversification and the distribution of fitness effects of random mutations. In turn, the distribution of fitness effects of stochastic mutations can be expected to depend on the adaptive state of a population. To systematically study the impact of the interplay between the adaptive state of a population on the ability of asexual populations to adapt, we used a spatial agent-based model of a neoplastic population adapting to a selection pressure of continuous exposure to targeted therapy. We found favorable mutations were overrepresented at the extinction bottleneck but depleted at the adaptive peak. The model-based predictions were tested using an experimental cancer model of an evolution of resistance to a targeted therapy. Consistent with the models prediction, we found that enhancement of the mutation rate was highly beneficial under therapy but moderately detrimental under the baseline conditions. Our results highlight the importance of considering population fitness in evaluating the fitness distribution of random mutations and support the potential therapeutic utility of restricting mutational variability. SIGNIFICANCE STATEMENTThe ability of a population to adapt and evolve is heavily influenced by the effects of random mutations on individuals. However, these effects can vary depending on the existing fitness level of the population. Using the development of cancer treatment resistance as an example, our research shows that populations nearing extinction can benefit from an increased rate of mutation. In contrast, mutations have a neutral or harmful effect on well-adapted populations. These findings suggest that new therapeutic strategies that manipulate mutation rates based on a populations current state of adaptation could be effective in preventing cancer and antimicrobial resistance.

evolutionary biology↗

bioRxiv2024

Peristromal niches protect lung cancers from targeted therapies through a combined effect of multiple molecular mediators

Targeted therapies directed against oncogenic signaling addictions, such as inhibitors of ALK in ALK+ NSCLC often induce strong and durable clinical responses. However, they are not curative in metastatic cancers, as some tumor cells persist through therapy, eventually developing resistance. Therapy sensitivity can reflect not only cell-intrinsic mechanisms but also inputs from stromal microenvironment. Yet, the contribution of tumor stroma to therapeutic responses in vivo remains poorly defined. To address this gap of knowledge, we assessed the contribution of stroma-mediated resistance to therapeutic responses to the frontline ALK inhibitor alectinib in xenograft models of ALK+ NSCLC. We found that stroma-proximal tumor cells are partially protected against cytostatic effects of alectinib. This effect is observed not only in remission, but also during relapse, indicating the strong contribution of stroma-mediated resistance to both persistence and resistance. This therapy-protective effect of the stromal niche reflects a combined action of multiple mechanisms, including growth factors and extracellular matrix components. Consequently, despite improving alectinib responses, suppression of any individual resistance mechanism was insufficient to fully overcome the protective effect of stroma. Focusing on shared collateral sensitivity of persisters offered a superior therapeutic benefit, especially when using an antibody-drug conjugate with bystander effect to limit therapeutic escape. These findings indicate that stroma-mediated resistance might be the major contributor to both residual and progressing disease and highlight the limitation of focusing on suppressing a single resistance mechanism at a time.

cancer biology↗

bioRxiv2023

Paracrine enhancement of tumor cell proliferation provides indirect stroma-mediated chemoresistance via acceleration of tumor recovery between chemotherapy cycles.

The ability of tumors to survive therapy reflects both cell-intrinsic and microenvironmental mechanisms. Across many cancers, including triple-negative breast cancer (TNBC), a high stroma/tumor ratio correlates with poor survival. In many contexts, this correlation can be explained by the direct reduction of therapy sensitivity by stroma-produced paracrine factors. We sought to explore whether this direct effect contributes to the link between stroma and poor responses to chemotherapies. Our in vitro studies with panels of TNBC cell line models and stromal isolates failed to detect a direct modulation of chemoresistance. At the same time, consistent with prior studies, we observed treatment-independent enhancement of tumor cell proliferation by fibroblast-produced secreted factors. Using spatial statistics analyses, we found that proximity to stroma is often associated with enhanced tumor cell proliferation in vivo. Based on these observations, we hypothesized an indirect link between stroma and chemoresistance, where stroma-augmented proliferation potentiates the recovery of residual tumors between chemotherapy cycles. To evaluate the feasibility of this hypothesis, we developed a spatial agent-based model of stroma impact on proliferation/death dynamics. The model was quantitatively parameterized using inferences from histological analyses and experimental studies. We found that the observed enhancement of tumor cell proliferation within stroma-proximal niches can enable tumors to avoid elimination over multiple chemotherapy cycles. Therefore, our study supports the existence of a novel, indirect mechanism of environment-mediated chemoresistance that might contribute to the negative correlation between stromal content and poor therapy outcomes.

cancer biology↗

bioRxiv2022

The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous proteasome inhibitor resistant disease

Multiple myeloma (MM) is an osteolytic plasma cell malignancy that, despite being responsive to therapies such as proteasome inhibitors, frequently relapses. Understanding the mechanism and the niches where resistant disease evolves remains of major clinical importance. Cancer cell intrinsic mechanisms and bone ecosystem factors are known contributors to the evolution of resistant MM but the exact contribution of each is difficult to define with current in vitro and in vivo models. However, mathematical modeling can help address this gap in knowledge. Here, we describe a novel biology-driven hybrid agent-based model that incorporates key cellular species of the bone ecosystem that control normal bone remodeling and, in MM, yields a protective environment under therapy. Critically, the spatiotemporal nature of the model captures two key features: normal bone homeostasis and how MM interacts with the bone ecosystem to induce bone destruction. We next used the model to examine how the bone ecosystem contributes to the evolutionary dynamics of resistant MM under control and proteasome inhibitor treatment. Our data demonstrates that resistant disease cannot develop without MM intrinsic mechanisms. However, protection from the bone microenvironment dramatically increases the likelihood of developing intrinsic resistance and subsequent relapse. The spatial nature of the model also reveals how the bone ecosystem provides a protective niche for drug sensitive MM cells under treatment, consequently leading to the emergence of a heterogenous and drug resistant disease. In conclusion, our data demonstrates a significant role for the bone ecosystem in MM survival and resistance, and suggests that early intervention with bone ecosystem targeting therapies may prevent the emergence of heterogeneous drug resistant MM.

cancer biology↗

bioRxiv2021

Integrated computational and in vivo models reveal Key Insights into Macrophage Be-havior during bone healing

Myeloid-derived monocyte and macrophages are key cells in the bone that contribute to remodeling and injury repair. However, their temporal polarization status and control of boneresorbing osteoclasts and bone-forming osteoblasts responses is largely unknown. In this study, we focused on two aspects of monocyte/macrophage dynamics and polarization states over time: 1) the injury-triggered pro- and anti-inflammatory monocytes/macrophages temporal profiles, 2) the contributions of pro-versus anti-inflammatory monocytes/macrophages in coordinating healing response. Bone healing is a complex multicellular dynamic process. While traditional in vitro and in vivo experimentation may capture the behavior of select populations with high resolution, they cannot simultaneously track the behavior of multiple populations. To address this, we have used an integrated a coupled ordinary differential equations (ODEs)-based framework describing multiple cellular species to in vivo bone injury data in order to identify and test various hypotheses regarding bone cell populations dynamics. Our approach allowed us to infer several biological insights including, but not limited to,: 1) anti-inflammatory macrophages are key for early osteoclast inhibition and pro-inflammatory macrophage suppression, 2) pro-inflammatory macrophages are involved in osteoclast bone resorptive activity, whereas osteoblasts promote osteoclast differentiation, 3) Pro-inflammatory monocytes/macrophages rise during two expansion waves, which can be explained by the anti-inflammatory macrophages-mediated inhibition phase between the two waves. In addition, we further tested the robustness of the mathematical model by comparing simulation results to an independent experimental dataset. Taken together, this novel comprehensive mathematical framework allowed us to identify biological mechanisms that best recapitulate bone injury data and that explain the coupled cellular population dynamics involved in the process. Furthermore, our hypothesis testing methodology could be used in other contexts to decipher mechanisms in complex multicellular processes. Author SummaryMyeloid-derived monocytes/macrophages are key cells for bone remodeling and injury repair. However, their temporal polarization status and control of bone-resorbing osteoclasts and boneforming osteoblasts responses is largely unknown. In this study, we focused on two aspects of monocyte/macrophage population dynamics: 1) the injury-triggered pro- and anti-inflammatory monocytes/macrophages temporal profiles, 2) the contributions of pro-versus anti-inflammatory monocytes/macrophages in coordinating healing response. In order to test various hypotheses regarding bone cell populations dynamics, we have integrated a coupled ordinary differential equations-based framework describing multiple cellular species to in vivo bone injury data. Our approach allowed us to infer several biological insights including: 1) anti-inflammatory macrophages are key for early osteoclast inhibition and pro-inflammatory macrophage suppression, 2) pro-inflammatory macrophages are involved in osteoclast bone resorptive activity, whereas osteoblasts promote osteoclast differentiation, 3) Pro-inflammatory monocytes/macrophages rise during two expansion waves, which can be explained by the anti-inflammatory macrophages-mediated inhibition phase between the two waves. Taken together, this mathematical framework allowed us to identify biological mechanisms that recapitulate bone injury data and that explain the coupled cellular population dynamics involved in the process.

immunology↗

bioRxiv2020

Understanding the Evolutionary Games in NSCLC Microenvironment

While initially highly successful, targeted therapies eventually fail as populations of tumor cells evolve mechanisms of resistance, leading to resumption of tumor growth. Historically, cell-intrinsic mutational changes have been the major focus of experimental and clinical studies to decipher origins of therapy resistance. While the importance of these mutational changes is undeniable, a growing body of evidence suggests that non-cell autonomous interactions between sub-populations of tumor cells, as well as with non-tumor cells within tumor microenvironment, might have a profound impact on both short term sensitivity of cancer cells to therapies, as well as on the evolutionary dynamics of emergent resistance. In contrast to well established tools to interrogate the functional impact of cell-intrinsic mutational changes, methodologies to understand non-cell autonomous interactions are largely lacking. Evolutionary Game Theory (EGT) is one of the main frameworks to understand the dynamics that drive frequency changes in interacting competing populations with different phenotypic strategies. However, despite a few notable exceptions, the use of EGT to understand evolutionary dynamics in the context of evolving tumors has been largely confined to theoretical studies. In order to apply EGT towards advancing our understanding of evolving tumor populations, we decided to focus on the context of the emergence of resistance to targeted therapies, directed against EML4-ALK fusion gene in lung cancers, as clinical responses to ALK inhibitors represent a poster child of limitations, posed by evolving resistance. To this end, we have examined competitive dynamics between differentially labelled therapy-naive tumor cells, cells with cell-intrinsic resistance mechanisms, and cells with cell-extrinsic resistance, mediated by paracrine action of hepatocyte growth factor (HGF), within in vitro game assays in the presence or absence of front-line ALK inhibitor alectinib. We found that producers of HGF were the fittest in every pairwise game, while also supporting the proliferation of therapy-naive cells. Both selective advantage of these producer cells and their impact on total population growth was a linearly increasing function of the initial frequency of producers until eventually reaching a plateau. Resistant cells did not significantly interact with the other two phenotypes. These results provide insights on reconciling selection driven emergence of subpopulations with cell non-cell autonomous resistance mechanisms, with lack of evidence of clonal dominance of these subpopulations. Further, our studies elucidate mechanisms for co-existence of multiple resistance strategies within evolving tumors. This manuscript serves as a technical report and will be followed up with a research paper in a different journal.

cancer biology↗

bioRxiv2020

Computational modeling reveals a key role for polarized myeloid cells in controlling osteoclast activity during bone injury repair

Bone-forming osteoblasts and -resorbing osteoclasts control bone injury repair, and myeloid-derived cells such as monocytes and macrophages are known to influence their behavior. However, precisely how these multiple cell types coordinate and regulate each other over time to repair injured bone is difficult to dissect using biological approaches. Conversely, mathematical modeling lends itself well to this challenge. Therefore, we generated an ordinary differential equation (ODE) model powered by experimental data (osteoblast, osteoclast, bone volume, pro- and anti-inflammatory myeloid cells) obtained from intra-tibially injured mice. Initial ODE results using only osteoblast/osteoclast populations demonstrated that bone homeostasis could not be recovered after injury, but this issue was resolved upon integration of pro- and anti-inflammatory myeloid population dynamics. Surprisingly, the ODE revealed temporal disconnects between the peak of total bone mineralization/resorption, and osteoblast/osteoclast numbers. Specifically, the model indicated that osteoclast activity must vary greatly (>17-fold) to return the bone volume to baseline after injury and suggest that osteoblast/osteoclast number alone is insufficient to predict bone the trajectory of bone repair. Importantly, the values of osteoclast activity fall within those published previously. These data underscore the value of mathematical modeling approaches to understand and reveal new insights into complex biological processes.

physiology↗

bioRxiv2020

What is the storage effect, why should it occur in cancers, and how can it inform cancer therapy?

Intratumor heterogeneity is a feature of cancer that is associated with progression, treatment resistance, and recurrence. However, the mechanisms that allow diverse cancer cell lineages to coexist remain poorly understood. The storage effect is a coexistence mechanism that has been proposed to explain the diversity of a variety of ecological communities, including coral reef fish, plankton and desert annual plants. Three ingredients are required for there to be a storage effect: 1) temporal variability in the environment, 2) buffered population growth, and 3) species-specific environmental responses. Here, we argue that these conditions are observed in cancers and that it is likely that the storage effect contributes to intratumor diversity. Data that show the temporal variation within the tumor microenvironment is needed to quantify how cancer cells respond to fluctuations in the tumor microenvironment and what impact this has on interactions among cancer cell types. The presence of a storage effect within a patients tumors could have substantial impact on how we understand and treat cancer.

cancer biology↗

bioRxiv2020

Spontaneous Cell Fusions as a Mechanism of Parasexual Recombination in Tumor Cell Populations

Initiation and progression of cancers reflect the underlying process of somatic evolution, which follows a Darwinian logic, i.e., diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination - a key diversification mechanism throughout all strata of life. Based on observations of spontaneous fusions involving cancer cells, reported genetic instability of polypoid cells, and precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions could serve as a source of parasexual recombination in cancer cell populations. Using differentially labelled tumor cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modeling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumor cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape, relative to strictly asexual populations, which may substantially accelerate a tumors ability to adapt to new selective pressures.

cancer biology↗

bioRxiv2020

High school Internship Program in Integrated Mathematical Oncology (HIP IMO) - five-year experience at Moffitt Cancer Center

Modern cancer research, and the wealth of data across multiple spatial and temporal scales, has created the need for researchers that are well-versed in the life sciences (cancer biology, developmental biology, immunology), medical sciences (oncology) and natural sciences (mathematics, physics, engineering, computer sciences). College undergraduate education is traditionally provided in disciplinary silos, which creates a steep learning curve at the graduate and postdoctoral levels that increasingly bridge multiple disciplines. Numerous colleges have begun to embrace interdisciplinary curricula, but students who double-major in mathematics (or other quantitative sciences) and biology (or medicine) remain scarce. We identified the need to educate junior and senior high school students about integrating mathematical and biological skills, through the lens of mathematical oncology, to better prepare students for future careers at the interdisciplinary interface. The High school Internship Program in Integrated Mathematical Oncology (HIP IMO) at Moffitt Cancer Center has so far trained 59 students between 2015 and 2019. We report here on the program structure, training deliverables, curriculum, and outcomes. We hope to promote such interdisciplinary educational activities early in a students career.

scientific communication and education↗

bioRxiv2020

Selection driven tumor evolution involving non-cell autonomous environmental engineering leads to patterns of clonal expansion consistent with neutral evolution

Cancers are the result of eco-evolutionary processes fueled by heritable phenotypic diversification and driven by environmentally dependent selection. As space represents a key growth-limiting ecological resource, the ability to gain and explore this resource is likely to be under strong selection. Using agent-based computational modeling, we explored the consequences of the interplay between phenotypic strategies centered on gaining access to new space through cell-extrinsic degradation of extracellular matrix barriers and the exploitation of this resource through maximizing cell proliferation. While cell proliferation is a cell-intrinsic property, newly accessed space represents a public good, which can benefit both producers and non-producers. We found that that this interplay results in ecological succession, enabling emergence of large, heterogenous, and highly proliferative populations. Even though in our simulations both remodeling and proliferation strategies were under strong positive selection, their interplay led to sub-clonal architecture that could be interpreted as evidence for neutral evolution, warranting cautious interpretation of inferences from sequencing of cancer genomes.

cancer biology↗

bioRxiv2019

Modelling Osteocyte Network Formation: Healthy and Cancerous Environments

Advanced cancers, such as prostate and breast cancers, commonly metastasize to bone. In the bone matrix, dendritic osteocytes form a spatial network allowing communication between osteocytes and the osteoblasts located on the bone surface. This communication network facilitates coordinated bone remodelling. In the presence of a cancerous microenvironment, the morphology of this network changes. Commonly osteocytes appear to be either overdifferentiated (i.e., there are more dendrites than healthy bone) or underdeveloped (i.e., dendrites do not fully form). In addition to structural changes, histological sections from metastatic breast cancer xenografted mice show that number of osteocytes per unit area is different between healthy bone and cancerous bone. We present a stochastic agent-based model for bone formation incorporating osteoblasts and osteocytes that allows us to probe both network structure and density of osteocytes in bone. Our model both allows for the simulation of our spatial network model and analysis of mean-field equations in the form of integro-partial differential equations. We considered variations of our model to study specific physiological hypotheses related to osteoblast differentiation; for example predicting how changing biological parameters, such as rates of bone secretion, rates of cancer formation and rates of osteoblast differentiation can allow for qualitatively different network morphologies. We then used our model to explore how commonly applied therapies such as bisphosphonates (e.g. zoledronic acid) impact osteocyte network formation.

physiology↗

bioRxiv2018

Hypoxia increases the tempo of evolution in the peri-necrotic niche in glioblastoma

BackgroundLow oxygen in tumours have long been associated with poor prognosis and metastatic disease, precise reasons for which remain poorly understood. Somatic evolution drives cancer progression and treatment resistance. This process is fuelled not only by genetic and epigenetic mutation, but by selection resulting from the interactions between tumour cells, normal cells and physical microenvironment. The ecological habitat tumour cells inhabit influences evolutionary dynamics but impact on tempo of evolution is less clear. MethodsWe explored this complex dialogue with a combined clinical-theoretical approach. Using an agent-based-model, we simulated proliferative hierarchy under heterogeneous oxygen availability. Predictions were compared against clinical data derived from histology samples taken from glioblastoma patients, stained to elucidate areas of hypoxia / necrosis, and p53 expression heterogeneity. ResultsSimulation results indicate cell division in hypoxic environments is effectively upregulated, and that low-oxygen niches provide new avenues for tumour cells to spread. Analysis of human data indicates cell division isnt decreased in low-oxygen regions, despite evidence of significant physiological stress. This is consistent with simulation, suggesting hypoxia is a crucible that effectively warping evolutionary velocity, making deleterious mutations more likely than in well-oxygenated regions. ConclusionsResults suggest hypoxic regions alter evolutionary tempo, driving mutations which fuel tumour heterogeneity..

cancer biology↗