Biology subjects

Altrock, P. M.

Publications and source records attributed to Altrock, P. M..

At least 19 recordsLinked to original sources

bioRxiv2024

Quantification of CAR T cell performance against acute myeloid leukemia using Bayesian inference

Chimeric Antigen Receptor (CAR) T-cell therapy has transformed cancer immunotherapy by genetically engineering T-cells to target tumor antigens. Acute myeloid leukemia (AML) presents unique challenges due to resistance mechanisms, especially in patients with TP53 loss mutations. The complex dynamics of CAR T-cell expansion remain poorly understood. The field lacks validated quantitative frameworks to systematically evaluate different CAR T-cell target constructs, such as CD33, CD123, and CD371, against resistant AML variants. We address this gap by combining mathematical modeling with in vitro assay data and Bayesian inference. We select, train, and validate a two-compartment deterministic mathematical model that describes the nonlinear dynamics of target AML and CAR T cells, accounting for expansion, killing, and exhaustion. Using Bayesian inference, we train and select the best-performing functional form for CAR T expansion and then validate it on unseen data. Our framework selects a CAR T-cell expansion model that accounts for handling time and T-cell self-interference, highlighting that expansion is a dynamic process in which target-cell handling time and T-cell crowding negatively affect T-cell expansion. Analysis of posterior parameter distributions reveals target-antigen-specific responses against TP53-deficient AML. For instance, CD33-targeting CARs have reduced attack rates against TP53-deficient cells, while CD123- and CD371-targeting CARs show moderately increased attack rates; however, the former exhibit higher death rates, and the latter have increased handling times, impeding efficacy. This target-dependent form of resistance challenges the assumption of uniform performance and reveals a unifying nonlinear expansion model for integrated, yet antigen-specific, preclinical predictions of efficacy. Short SummaryWe identified the target antigen-specific expansion properties and the efficacy of CAR T cells against TP53 wild-type and deficient AML by integrating experimental data from cytolytic assays, mechanistic mathematical modeling, and Bayesian inference.

cancer biology↗

bioRxiv2024

Deficient cell-cell cohesion is linked with lobular localization in simplified models of lobular carcinoma in situ (LCIS)

Lobular carcinoma in situ (LCIS) is a precursor of invasive lobular carcinoma of the breast. LCIS cells lack cell-cell cohesion due to the loss of E-cadherin. LCIS cells grow in mammary lobules rather than in ducts. The etiology of this pattern, especially its dependence on cellular cohesion, is incompletely understood. We simulated passive intra-glandular scattering of carcinoma in situ (CIS) cells in an ultra-simplified hollow mold tissue replica (HMTR) and a discrete-time mathematical model featuring particles of variable sizes representing single cells (LCIS-like particles) or groups of cohesive carcinoma cells (DCIS-like particles). The HMTR features structures reminiscent of a mammary duct with associated lobules. The discrete mathematical model characterizes spatial redistribution over time and includes transition probabilities between ductal or lobular localizations. Redistribution of particles converged toward an equilibrium depending on particle size. Strikingly, equilibrium proportions depended on particle properties, which we also confirm in a continuous-time mathematical model that considers controlling lobular properties such as crowding. Particles of increasing size, representing CIS cells with proficient cohesion, showed increasingly higher equilibrium ductal proportions. Our investigations represent two conceptual abstractions implying a link between loss of cell-cell cohesion and lobular localization of LCIS, which provide a much-needed logical foundation for studying the connections between collective cell behavior and cancer development in breast tissues. In light of the findings from our simplified modeling approach, we discuss multiple avenues for near-future research that can address and evaluate the redistribution hypothesis mathematically and empirically.

cancer biology↗

bioRxiv2024

Integration of immune cell-target cell conjugate dynamics changes the time scale of immune control of cancer

The human immune system can recognize, attack, and eliminate cancer cells, but cancers can escape this immune surveillance. The dynamics of these cancer control mechanisms by cells of the adaptive immune system can be captured by variants of ecological predator-prey models. These dynamical systems can describe the interaction of cancer cells and, e.g., effector T cells to form tumor cell-immune cell conjugates, cancer cell killing, immune cell activation, and T cell exhaustion. Target (tumor) cell-T cell conjugation is integral to the adaptive immune systems cancer control or immunotherapy dynamics. However, it is incompletely understood whether conjugate dynamics should be explicitly included in mathematical models of cancer-immune interactions. Here, we analyze the dynamics of a cancer-effector T cell system regarding the impact of explicitly modeling the conjugate compartment to elucidate the role of cellular conjugate dynamics. We formulate a deterministic modeling framework to compare possible equilibria and their stability, such as tumor extinction, tumor-immune coexistence (tumor control), or tumor escape. We also formulate the stochastic analog of this system to analyze the impact of demographic fluctuations that arise when cell populations are small. We find that explicit consideration of a conjugate compartment can change long-term steady-state, critically change the time to reach an equilibrium, alter the probability of tumor escape, and lead to very different extinction time distributions. Thus, we demonstrate the importance of the conjugate compartment in defining tumor-effector interactions. Accounting for transitionary compartments of cellular interactions may better capture the dynamics of tumor control and progression.

immunology↗

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↗

bioRxiv2021

Heterogeneity, turn-over rate and karyotype space shape susceptibility to missegregation-induced extinction

The phenotypic efficacy of somatic copy number alterations (SCNAs) stems from their incidence per base pair of the genome, which is orders of magnitudes greater than that of point mutations. One mitotic event stands out in its potential to significantly change a cells SCNA burden-a chromosome missegregation. We present a general deterministic framework for modeling chromosome missegregations and use it to evaluate the possibility of missegregation-induced population extinction (MIE). The model predicts critical curves that separate viable from non-viable populations as a function of their turnover- and missegregation rates. Missegregation- and turnover rates estimated from a PAN-cancer scRNA-seq dataset of 15,464 cells are then compared to predictions. The majority of tumors across all cancer types had missegregation- and turnover rates that were within viable regions of the parameter space. When a dependency of missegregation rate on karyotype was introduced, karyotypes associated with low missegregation rates acted as a stabilizing refuge, rendering MIE impossible unless turnover rates are exceedingly high. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering MIE unlikely. Author SummaryWhen a cell missegregates a chromosome while dividing, the chance is high that its two daughter cells will behave drastically different from each other and from their parental cell. Chromosome missegregations are therefore one of the most powerful forces of phenotypic diversity. We developed a mathematical model of chromosome missegregations that allows for this cell-to-cell diversity to be accounted for. The model serves to help understand how selection acts upon cells with versatile chromosome contents, as a tool for genotype-to-phenotype mapping in various microenvironments. As a first application example we used the model to address whether there exists an upper limit on missegregation rate, beyond which cancer populations collapse. Chromosome missegregations are common. They occur in 1.2-2.3% per mitosis in normal cells [1] and in cancer cells their rate is between one and two orders of magnitudes higher [2]. The model revealed that the upper limit of missegregation rate is a function of the tumors turnover rate (i.e. how fast the tumor renews itself). In heterogenous populations however, cells with low missegregation rates protect the population from collapse. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering missegregation-induced extinction unlikely.

cancer biology↗

bioRxiv2021

Autocrine signaling explains the emergence of Allee effects in cancer cell populations

In many human cancers, the rate of cell growth depends crucially on the size of the tumour cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations of brain tumor cells grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy.

cancer biology↗

bioRxiv2021

Spatial Clustering of CD68+ Tumor Associated Macrophages with Tumor Cells is Associated with Worse Overall Survival in Metastatic Clear Cell Renal Cell Carcinoma

Immune infiltration is typically quantified using cellular density, not accounting for cellular clustering. Tumor-associated macrophages (TAM) activate oncogenic signaling through paracrine interactions with tumor cells, which may be better reflected by local cellular clustering than global density metrics. Using multiplex immunohistochemistry and digital pathologic analysis we quantified cellular density and cellular clustering for myeloid cell markers in 129 regions of interest from 55 samples from 35 patients with metastatic ccRCC. CD68+ cells were found to be clustered with tumor cells and dispersed from stromal cells, while CD163+ and CD206+ cells were found to be clustered with stromal cells and dispersed from tumor cells. CD68+ density was not associated with OS, while high tumor/CD68+ cell clustering was associated with significantly worse OS. These novel findings would not have been identified if immune infiltrate was assessed using cellular density alone, highlighting the importance of including spatial analysis in studies of immune cell infiltration of tumors. SIGNIFICANCEIncreased clustering of CD68+ TAMs and tumor cells was associated with worse overall survival for patients with metastatic ccRCC. This effect would not have been identified if immune infiltrate was assessed using cell density alone, highlighting the importance of including spatial analysis in studies of immune cell infiltration of tumors.

cancer biology↗

bioRxiv2020

Branching and extinction in evolutionary public goods games

Public goods games (PGGs) describe situations in which individuals contribute to a good at a private cost, but others can free-ride by receiving their share of the public benefit at no cost. PGGs can be nonlinear, as often observed in nature, whereby either benefit, cost, or both are nonlinear functions of the available public good (PG): at low levels of PG there can be synergy whereas at high levels, the added benefit of additional PG diminishes. PGGs can be local such that the benefits and costs are relevant only in a local neighborhood or subset of the larger population in which producers (cooperators) and free-riders (defectors) co-evolve. Cooperation and defection can be seen as two extremes of a continuous spectrum of traits. The level of public good production, and similarly, the neighborhood size can vary across individuals. To better understand how distinct strategies in the nonlinear public goods game emerge and persist, we study the adaptive dynamics of production rate and neighborhood size. We explain how an initially monomorphic population, in which individuals have the same trait values, could evolve into a dimorphic population by evolutionary branching, in which we see distinct cooperators and defectors emerge, respectively characterized by high production and low neighborhood sizes, and low production and high neighborhood sizes. We find that population size plays a crucial role in determining the final state of the population, as smaller populations may not branch, or may observe extinction of a subpopulation after branching. Our work elucidates the evolutionary origins of cooperation and defection in nonlinear local public goods games, and highlights the importance of small population size effects on the process and outcome of evolutionary branching.

evolutionary biology↗

bioRxiv2020

Invasion of homogeneous and polyploid populations in nutrient-limiting environments

Breast cancer progresses in a multistep process from primary tumor growth and stroma invasion to metastasis. Progression is accompanied by a switch to an invasive cell phenotype. Nutrient-limiting environments promote chemotaxis with aggressive morphologies characteristic of invasion. It is unknown how co-existing cells differ in their response to nutrient limitations and how this impacts invasion of the metapopulation as a whole. We integrate mathematical modeling with microenvironmental perturbation-data to investigate invasion in nutrient-limiting environments inhabited by one or two cancer cell subpopulations. Hereby, subpopulations are defined by their energy efficiency and chemotactic ability. We estimate the invasion-distance traveled by a homogeneous population. For heterogeneous populations, our results suggest that an imbalance between nutrient efficacy and chemotactic superiority accelerates invasion. Such imbalance will spatially segregate the two populations and only one type will dominate at the invasion front. Only if these two phenotypes are balanced do the two subpopulations compete for the same space, which decelerates invasion. We investigate ploidy as a candidate biomarker of this phenotypic heterogeneity to discern circumstances when inhibiting chemotaxis amplifies internal competition and decelerates tumor progression, from circumstances that render clinical consequences of chemotactic inhibition unfavorable.Significance A better understanding of the nature of the double-edged sword of high ploidy is a prerequisite to personalize combination-therapies with cytotoxic drugs and inhibitors of signal transduction pathways such as MTOR-Is.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2020

The Harsh Microenvironment in Early Breast Cancer Select for a Warburg Phenotype

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected pre-malignant breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months, and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA-seq analysis of WE clones identified the transcription factors NF[kcy]B and KLF4 as potential inducers of the WE phenotype. NF[kcy]B was highly phosphorylated in the glycolytic clones. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a Warburg phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

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↗

bioRxiv2019

Modeling adaptive therapy in non-muscle invasive bladder cancer

Bladder cancer is the 9th most commonly diagnosed cancer. Nearly half of patients with early stage bladder cancer treated with the immune-stimulating agent BCG have disease recurrence, while 13% progress to invasive bladder cancer. Here we explored the potential of tumor mutational heterogeneity and the role of pro- and anti-inflammatory cytokines to identify different subtypes of bladder cancer that may predict therapeutic response to BCG. Further, we used mathematical modeling of dosing strategies to infer tumor response to varying doses and time schedules f BCG administration. As a proof-of-concept, present adaptive therapy scheduling of BCG as a viable strategy to control tumor size and minimize recurrence.

cancer biology↗

bioRxiv2019

Evolutionary Dynamics of CAR T Cell Therapy

Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR T cells, after lymphodepletion by chemotherapy. The laws underlying this expansion and subsequent tumor eradication remain unknown. Still, about 60% of CAR T-treated patients are likely to progress; their tumors are not eradicated. Here we seek to understand and disentangle the multiple processes that contribute to CAR expansion and tumor eradication. We developed a mathematical model of T cell-tumor cell interactions, and demonstrate that CAR T cell expansion is shaped by immune reconstitution dynamics after lymphodepletion and predator prey-like dynamics. Our cell population model was parameterized using patient population-level data over time and recapitulates progression free survival. As an intrinsic property, we find that tumor eradication is a stochastic event. Our cell population-based approach renders CAR T cell therapy as an ecological dynamic process that drives tumors toward an extinction vortex. Even if a clinical event, such as progression, is likely, its timing can be highly variable. We predict how clinical interventions that increase CAR T memory populations could improve the likelihood of tumor eradication and improve progression free survival. Our model can be leveraged to propose new CAR composition and dosing strategies, assess the need for multiple doses, and identify patient populations most likely to benefit from CAR T with or without additional interventions.

cancer biology↗

bioRxiv2019

Predictive modeling of co-evolving growing populations

How can we best explore and fit density and frequency dependence in the evolutionary and ecological dynamics of growing tumors? Here, we present an introduction to recent developments in our lab, and give two examples of complex interaction-driven tumor growth combined with statistical outcomes of treatment.

cancer biology↗

bioRxiv2018

Leveraging single cell RNA sequencing experiments to model intra-tumor heterogeneity

PURPOSEMany cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from preexistence or by evolving new genotypes and traits. Intra-tumor heterogeneity serves as a reservoir for resistance, which often occurs due to selection of minor cellular sub-clones. On the level of gene expression, the clonal heterogeneity can only be revealed by high-dimensional single cell methods. We propose to use a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution.\n\nMETHODSWe focused on individual patient samples probed with single cell RNA sequencing to describe heterogeneity. We developed a pipeline to analyze single cell data, via sample normalization, clustering and mathematical interpretation using a generalized diversity measure, and exemplify the utility of this platform using single cell data.\n\nRESULTSWe focused on three sources of RNA sequencing data: two healthy bone marrow (BM) samples, two acute myeloid leukemia (AML) patients, each sampled before and after BM transplant (BMT), four samples of pre-sorted lineages, and six lung carcinoma patients with multi-region sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified in which respect these samples differed. While a widely used Shannon diversity index sometimes reveals less differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparing pre and post BMT AML samples did not reveal differences in heterogeneity, although they can be very different biologically.\n\nCONCLUSIONGDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach offers wide applications to quantify heterogeneity across samples and conditions.

cancer biology↗

bioRxiv2018

Finite size effects in diffusive public goods games

A particular challenge in the co-evolutionary dynamics of growing populations is to determine the effects of population assortment on growth advantage of cooperative traits. Here we study the coevolution of cooperators that are public good producers and consumers, and free riders that are only public good consumers. Using an individual-based model and its continuum limit we show that the differential growth rate between producers and free-riders depends on the spatial Fourier decomposition of their respective densities. Further, we derive a finite-size correction, which scales as the cell size, which accurately describes the dynamics of a randomly assorted population. Using this result we are also able to derive an effective benefit to cost relation, which provides a criterion that relates the parameters of the model (e.g. diffusion constant of the public good, decay rate and cost) to the long-term persistence of cooperation. Our findings provide a powerful tool for the analysis of diffusive public goods games, and can explain commonly observed patterns of cooperation, rather than the tragedy of the commons, based on the derived finite-size correction.

ecology↗