Harnessing Flex Point Symmetry to Estimate Logistic Tumor Population Growth.

The observed time evolution of a population is well approximated by a logistic growth function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential, then decelerates as the population approaches its limit size, i.e., the carrying capacity. In mathematical oncology, the tumor carrying capacity has been postulated to be dynamically evolving as the tumor overcomes several evolutionary bottlenecks and, thus, to be patient specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic function's rotation symmetry can help estimate the population's growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against published pan-cancer animal and human breast cancer data, achieving a 30% to 40% reduction in the time at which subsequent data collection is necessary to estimate the logistic growth rate and carrying capacity correctly. These results could improve tumor dynamics forecasting and augment the clinical decision-making process.

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 T 2 * 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. Summary: We 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 µg 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 T 2 relaxation times and tumor volume reduction, highlighting the predictive prognostic value of MENPs. Six of 17 mice in the confirmatory study's 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.

A gene for all seasons: The evolutionary consequences of HIF-1 in carcinogenesis, tumor growth and metastasis.

Oxygen played a pivotal role in the evolution of multicellularity during the Cambrian Explosion. Not surprisingly, responses to fluctuating oxygen concentrations are integral to the evolution of cancer-a disease characterized by the breakdown of multicellularity. Poorly organized tumor vasculature results in chaotic patterns of blood flow characterized by large spatial and temporal variations in intra-tumoral oxygen concentrations. Hypoxia-inducible growth factor (HIF-1) plays a pivotal role in enabling cells to adapt, metabolize, and proliferate in low oxygen conditions. HIF-1 is often constitutively activated in cancers, underscoring its importance in cancer progression. Here, we argue that the phenotypic changes mediated by HIF-1, in addition to adapting the cancer cells to their local environment, also "pre-adapt" them for proliferation at distant, metastatic sites. HIF-1-mediated adaptations include a metabolic shift towards anaerobic respiration or glycolysis, activation of cell survival mechanisms like phenotypic plasticity and epigenetic reprogramming, and formation of tumor vasculature through angiogenesis. Hypoxia induced epigenetic reprogramming can trigger epithelial to mesenchymal transition in cancer cells-the first step in the metastatic cascade. Highly glycolytic cells facilitate local invasion by acidifying the tumor microenvironment. New blood vessels, formed due to angiogenesis, provide cancer cells a conduit to the circulatory system. Moreover, survival mechanisms acquired by cancer cells in the primary site allow them to remodel tissue at the metastatic site generating tumor promoting microenvironment. Thus, hypoxia in the primary tumor promoted adaptations conducive to all stages of the metastatic cascade from the initial escape entry into a blood vessel, intravascular survival, extravasation into distant tissues, and establishment of secondary tumors.

Lung adenocarcinomas without driver genes converge to common adaptive strategies through diverse genetic, epigenetic, and niche construction evolutionary pathways.

Somatic evolution selects cancer cell phenotypes that maximize survival and proliferation in dynamic environments. Although cancer cells are molecularly heterogeneous, we hypothesized convergent adaptive strategies to common host selection forces can be inferred from patterns of epigenetic and genetic evolutionary selection in similar tumors. We systematically investigated gene mutations and expression changes in lung adenocarcinomas with no common driver genes (n = 313). Although 13,461 genes were mutated in at least one sample, only 376 non-synonymous mutations evidenced positive evolutionary selection with conservation of 224 genes, while 1736 and 2430 genes exhibited ≥ two-fold increased and ≥ 50% decreased expression, respectively. Mutations under positive selection are more frequent in genes with significantly altered expression suggesting they often "hardwire" pre-existing epigenetically driven adaptations. Conserved genes averaged 16-fold higher expression in normal lung tissue compared to those with selected mutations demonstrating pathways necessary for both normal cell function and optimal cancer cell fitness. The convergent LUAD phenotype exhibits loss of differentiated functions and cell-cell interactions governing tissue organization. Conservation with increased expression is found in genes associated with cell cycle, DNA repair, p53 pathway, epigenetic modifiers, and glucose metabolism. No canonical driver gene pathways exhibit strong positive selection, but extensive down-regulation of membrane ion channels suggests decreased transmembrane potential may generate persistent proliferative signals. NCD LUADs perform niche construction generating a stiff, immunosuppressive microenvironment through selection of specific collagens and proteases. NCD LUADs evolve to a convergent phenotype through a network of interconnected genetic, epigenetic, and ecological pathways.

Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. Here, we applied deep reinforcement learning (DRL) to guide adaptive drug scheduling and demonstrated that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. The DRL strategies were robust to patient variability, including both tumor dynamics and clinical monitoring schedules. The DRL framework could produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. The DRL framework had no knowledge of the underlying mathematical tumor model, demonstrating the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, a proposed five-step pathway, which combined mechanistic modeling with the DRL framework and integrated conventional tools to improve interpretability compared with traditional "black-box" DRL models, could allow translation of this approach to the clinic. Overall, the proposed framework generated personalized treatment schedules that consistently outperformed clinical standard-of-care protocols. SIGNIFICANCE: Generation of interpretable and personalized adaptive treatment schedules using a deep reinforcement framework that interacts with a virtual patient model overcomes the limitations of standardized strategies caused by heterogeneous treatment responses.

Modeling non-genetic information dynamics in cells using reservoir computing.

Virtually all cells use energy-driven, ion-specific membrane pumps to maintain large transmembrane gradients of Na+, K+, Cl-, Mg++, and Ca++, but the corresponding evolutionary benefit remains unclear. We propose that these gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize that environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion-specific membrane channels. The consequent changes in cytoplasmic ion concentration can generate a local response or orchestrate global/regional cellular dynamics through wire-like ion fluxes along pre-existing and self-assembling cytoskeleton to engage the endoplasmic reticulum, mitochondria, and nucleus.

Small extracellular vesicles from surviving cancer cells as multiparametric monitoring tools of measurable residual disease and therapeutic efficiency.

Although conventional anti-cancer therapies remove most cells of the tumor mass, small surviving populations may evolve adaptive resistance strategies, which lead to treatment failure. The size of the resistant population initially may not reach the threshold of clinical detection (designated as measurable residual disease/MRD) thus, its investigation requires highly sensitive and specific methods. Here, we discuss that the specific molecular fingerprint of tumor-derived small extracellular vesicles (sEVs) is suitable for longitudinal monitoring of MRD. Furthermore, we present a concept that exploiting the multiparametric nature of sEVs may help early detection of recurrence and the design of dynamic, evolution-adjusted treatments.

Experimental and phylogenetic evidence for correlated gene expression evolution in endometrial and skin fibroblasts.

Gene expression change is a dominant mode of evolution. Mutations, however, can affect gene expression in multiple cell types. Therefore, gene expression evolution in one cell type can lead to similar gene expression changes in another cell type. Here, we test this hypothesis by investigating dermal skin fibroblasts (SFs) and uterine endometrial stromal fibroblasts (ESFs). The comparative dataset consists of transcriptomes from cultured SF and ESF of nine mammalian species. We find that evolutionary changes in gene expression in SF and ESF are highly correlated. The experimental dataset derives from a SCID mouse strain selected for slow cancer growth leading to substantial gene expression changes in SFs. We compared the gene expression profiles of SF with that of ESF and found a significant correlation between them. We discuss the implications of these findings for the evolutionary correlation between placental invasiveness and vulnerability to metastatic cancer.

Immunomodulatory and pro-oncologic effects of ketamine and isoflurane anesthetics in a murine model.

INTRODUCTION: Volatile and intravenous anesthetics may worsen oncologic outcomes in basic science animal models. These effects may be related to suppressed innate and adaptive immunity, decreased immunosurveillance, and disrupted cellular signaling. We hypothesized that anesthetics would promote lung tumor growth via altered immune function in a murine model and tested this using an immunological control group of immunodeficient mice. METHODS: Lewis lung carcinoma cells were injected via tail vein into C57BL/6 immunocompetent and NSG immunodeficient mice during exposure to isoflurane and ketamine versus controls without anesthesia. Mice were imaged on days 0, 3, 10, and 14 post-tumor cell injection. On day 14, mice were euthanized and organs fixed for metastasis quantification and immunohistochemistry staining. We compared growth of tumors measured from bioluminescent imaging and tumor metastasis in ex vivo bioluminescent imaging of lung and liver. RESULTS: Metastases were significantly greater for immunocompromised NSG mice than immunocompetent C57BL/6 mice over the 14-day experiment (partial η2 = 0.67, 95% CI = 0.54, 0.76). Among immunocompetent mice, metastases were greatest for mice receiving ketamine, intermediate for those receiving isoflurane, and least for control mice (partial η2 = 0.88, 95% CI = 0.82, 0.91). In immunocompetent mice, significantly decreased T lymphocyte (partial η2 = 0.83, 95% CI = 0.29, 0.93) and monocyte (partial η2 = 0.90, 95% CI = 0.52, 0.96) infiltration was observed in anesthetic-treated mice versus controls. CONCLUSIONS: The immune system appears central to the pro-metastatic effects of isoflurane and ketamine in a murine model, with decreased T lymphocytes and monocytes likely playing a role.

Logistic tumor-population growth and ghost-points symmetry.

The observed time evolution of a population is well approximated by a logistic function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential at a constant rate and capped at a limit size, i.e., the carrying capacity. In mathematical oncology, the carrying capacity has been postulated to be co-evolving and thus patient-specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic function's rotation symmetry can help estimate the population's growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against a classic oncology database of logistic tumor growth, achieving a 30% to 40% reduction in the time necessary to correctly estimate the logistic growth rate and carrying capacity. Our results will improve tumor dynamics forecasting and augment the clinical decision-making process.

Converting genetic information to non-equilibrium cellular thermodynamics in enzyme-catalyzed reactions.

Living systems use genomic information to maintain a stable highly ordered state far from thermodynamic equilibrium but the specific mechanisms and general principles governing the interface of genetics and thermodynamics has not been extensively investigated. Genetic information is quantified in unitless bits termed "Shannon entropy", which does not directly relate to thermodynamic entropy or energy. Thus, it is unclear how the Shannon entropy of genetic information is converted into thermodynamic work necessary to maintain the non-equilibrium state of living systems. Here we investigate the interface of genetic information and cellular thermodynamics in enzymatic acceleration of a chemical reaction S+E→ES→E+P, where S and E are substrate and enzyme, ES is the enzyme substrate complex and P product. The rate of any intracellular chemical reaction is determined by probability functions at macroscopic (Boltzmann distribution of the reactant kinetic energies governed by temperature) or microscopic (overlap of reactant quantum wave functions) scales - described, respectively, by the Arrhenius and Knudsen equations. That is, the reaction rate, in the absence of a catalyst, is governed by temperature which determines the kinetic energy of the interacting molecules. Genetic information can act upon a when the encoded string of amino acids folds into a 3-deminsional structure that permits a lock/key spatial matching with the reactants. By optimally superposing the reactants' wave functions, the information in the enzyme increases the reaction rate by up to15 orders of magnitude under isothermal conditions. In turn, the accelerated reaction rate alters the intracellular thermodynamics environment as the products are at lower Gibbs free energy which permits thermodynamic work Wmax=-ΔG. Mathematically and biologically, the critical event that allows genetic information to produce thermodynamic work is the folding of the amino acid string specified by the gene into a 3-dimensional shape determined by its lowest energy state. Biologically, this allows the amino acid string to bind substrate and place them in an optimal spatial orientation. These key-lock are mathematically characterized by Kullback-Leibler Divergence and the interactions with the reaction channel now represent Fisher Information (the second derivative Kullback-Leibler divergence), which can take on the units of the process to which it is applied. Interestingly, Shannon is typically derived by "coarse graining" Shannon information. Thus, living system, by acting at a quantum level, "fine grain" Shannon information.

Updating the Definition of Cancer.

Most definitions of cancer broadly conform to the current NCI definition: "Cancer is a disease in which some of the body's cells grow uncontrollably and spread to other parts of the body." These definitions tend to describe what cancer "looks like" or "does" but do not describe what cancer "is" or "has become." While reflecting past insights, current definitions have not kept pace with the understanding that the cancer cell is itself transformed and evolving. We propose a revised definition of cancer: Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection. We believe this definition captures the essence of the majority of previous and current definitions. To the simplest definition of cancer as a disease of uncontrolled proliferation of cells, our definition adds in the adjective "transformed" to capture the many tumorigenic processes that cancer cells adopt to metastasize. To the concept of uncontrolled proliferation of transformed cells, our proposed definition then adds "subject to evolution by natural selection." The subject to evolution by natural selection modernizes the definition to include the genetic and epigenetic changes that accumulate within a population of cancer cells that lead to the lethal phenotype. Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection.

Back to basic: Trials and tribulations of alkalizing agents in cancer.

"Dysregulated" metabolism is a characteristic of the cancer cell phenotype. This includes persistent use of glycolytic metabolism in normoxic environments (Warburg effect) leading to increased acid production and accumulation of protons in the interstitial space. Although often thought to be disordered, altered cancer metabolism is the outcome of intense Darwinian selection and, thus, must have evolved to maximize cancer cell fitness. In an evolutionary context, cancer-induced acidification of the microenvironment represents a niche construction strategy to promote proliferation. Ecological advantages conferred on the cancer population included remodeling of the extracellular matrix to promote local invasion, suppression of potential competitive proliferation of fibroblasts, and suppression of host immune response. Preclinical data demonstrates that increasing the serum buffering capacity (through, for example, oral sodium bicarbonate and TRIS) can neutralize the acidic tumor microenvironment with inhibition local invasion and proliferation which can be synergistic with the effects of chemotherapy and immunotherapy agents. Here, we describe the proton dynamics in cancer and their influence on tumor progression and metastasis. Additionally, we will discuss targeting the tumor acidosis with alkalizing agents including our bicarbonate clinical trial results. Clinical Trial Registration: clinicaltrials.gov, identifier NCT01350583, NCT01198821 and NCT01846429.

A Phase 1b Adaptive Androgen Deprivation Therapy Trial in Metastatic Castration Sensitive Prostate Cancer.

Background: We hypothesize that cancer survival can be improved through adapting treatment strategies to cancer evolutionary dynamics and conducted a phase 1b study in metastatic castration sensitive prostate cancer (mCSPC). Methods: Men with asymptomatic mCSPC were enrolled and proceeded with a treatment break after achieving > 75% PSA decline with LHRH analog plus an NHA. ADT was restarted at the time of PSA or radiographic progression and held again after achieving >50% PSA decline. This on-off cycling of ADT continued until on treatment imaging progression. Results: At data cut off in August 2022, only 2 of the 16 evaluable patients were off study due to imaging progression at 28 months from first dose of LHRH analog for mCSPC. Two additional patients showed PSA progression at 12.4 and 20.5 months and remain on trial. Since none of the 16 patients developed imaging progression at 12 months, the study succeeded in its primary objective of feasibility. The secondary endpoints of median time to PSA progression and median time to radiographic progression have not been reached at a median follow up of 26 months. Conclusions: It is feasible to use an individual's PSA response and testosterone levels to guide intermittent ADT in mCSPC.

Interdisciplinary approaches to metastasis.

Interdisciplinary research is making a significant contribution to understanding metastasis - one of the grand challenges in cancer research. Examples drawn from apparently unconnected areas of physics, and described at a recent workshop on metastasis, illustrate the value of interdiscplinary thinking.

A simulation of parental and glycolytic tumor phenotype competition predicts observed responses to pH changes and increased glycolysis after anti-VEGF therapy.

Clinical cancers are typically spatially and temporally heterogeneous, containing multiple microenvironmental habitats and diverse phenotypes and/or genotypes, which can interact through resource competition and direct or indirect interference. A common intratumoral evolutionary pathway, probably initiated as adaptation to hypoxia, leads to the "Warburg phenotype" which maintains high glycolytic rates and acid production, even in normoxic conditions. Since individual cancer cells are the unit of Darwinian selection, intraspecific competition dominates intratumoral evolution. Thus, elements of the Warburg phenotype become key "strategies" in competition with cancer cell populations that retain the metabolism of the parental normal cells. Here we model the complex interactions of cell populations with Warburg and parental phenotypes as they compete for access to vasculature, while subject to direct interference by Warburg-related acidosis. In this competitive environment, vasculature delivers nutrients, removes acid and necrotic detritus, and responds to signaling molecules (VEGF and TNF-α). The model is built in a nested fashion and growth parameters are derived from monolayer, spheroid, and xenograft experiments on prostate cancer. The resulting model of in vivo tumor growth reaches a steady state, displaying linear growth and coexistence of both glycolytic and parental phenotypes consistent with experimental observations. The model predicts that increasing tumor pH sufficiently early can arrest the development of the glycolytic phenotype, while decreasing tumor pH accelerates this evolution and increases VEGF production. The model's predicted dual effects of VEGF blockers in decreasing tumor growth while increasing the glycolytic fraction of tumor cells has potential implications for optimizing angiogenic inhibitors.

Ecoevolutionary biology of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC), the most common histological subtype of pancreatic cancer, is an aggressive disease predicted to be the 2nd cause of cancer mortality in the US by 2040. While first-line therapy has improved, 5-year overall survival has only increased from 5 to ∼10%, and surgical resection is only available for ∼20% of patients as most present with advanced disease, which is invariably lethal. PDAC has well-established highly recurrent mutations in four driver genes including KRAS, TP53, CDKN2A, and SMAD4. Unfortunately, these genetic drivers are not currently therapeutically actionable. Despite extensive sequencing efforts, few additional significantly recurrent and druggable drivers have been identified. In the absence of targetable mutations, chemotherapy remains the mainstay of treatment for most patients. Further, the role of the above driver mutations on PDAC initiation and early development is well-established. However, these mutations alone cannot account for PDAC heterogeneity nor discern early from advanced disease. Taken together, management of PDAC is an example highlighting the shortcomings of the current precision medicine paradigm. PDAC, like other malignancies, represents an ecoevolutionary process. Better understanding the disease through this lens can facilitate the development of novel therapeutic strategies to better control and cure PDAC. This review aims to integrate the current understanding of PDAC pathobiology into an ecoevolutionary framework.

Mistic: An open-source multiplexed image t-SNE viewer.

Understanding the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immuno-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. Here, 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 t-SNE or UMAP coordinates. This grouped view of all images allows 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, and can help select images for subsequent downstream analysis. Currently, there is no freely available tool to generate such image t-SNEs.

CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation.

MOTIVATION: Time-lapse microscopy is a powerful technique that relies on images of live cells cultured ex vivo that are captured at regular intervals of time to describe and quantify their behavior under certain experimental conditions. This imaging method has great potential in advancing the field of precision oncology by quantifying the response of cancer cells to various therapies and identifying the most efficacious treatment for a given patient. Digital image processing algorithms developed so far require high-resolution images involving very few cells originating from homogeneous cell line populations. We propose a novel framework that tracks cancer cells to capture their behavior and quantify cell viability to inform clinical decisions in a high-throughput manner. RESULTS: The brightfield microscopy images a large number of patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to 6 days. We developed a robust and user-friendly pipeline CancerCellTracker that detects cells in co-culture, tracks these cells across time and identifies cell death events using changes in cell attributes. We validated our computational pipeline by comparing the timing of cell death estimates by CancerCellTracker from brightfield images and a fluorescent channel featuring ethidium homodimer. We benchmarked our results using a state-of-the-art algorithm implemented in ImageJ and previously published in the literature. We highlighted CancerCellTracker's efficiency in estimating the percentage of live cells in the presence of bone marrow stromal cells. AVAILABILITY AND IMPLEMENTATION: https://github.com/compbiolabucf/CancerCellTracker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.