Search

Previous studies have shown that approximately 40% of estrogen receptor positive (ER+) breast cancer (BC) patients harbor immune signaling defects in their blood at diagnosis that predicts overall survival. The aim of this work is to apply communication theory and signal processing methods to model cytokine-mediated signaling errors in immune cells in the peripheral blood. Our approach uses communication theory concepts to develop a communication system model consisting of a transmitter, receiver, and channel, applied to the JAK/STAT signaling pathway and tested using multi-color flow cytometry analysis of peripheral blood mononuclear cells stimulated with IFNγ, IL-2, IL-6, IL-4, IL-10, IL-12, or TGFβ in samples from 2 BC patients and 4 healthy controls. We use our model to estimate the detection error rate when one of the 7 cytokines is being transduced through the JAK/STAT signaling pathway, but its signal is being received on multiple phosphorylated signal transducers and activators of transcription (pSTAT) molecules. In addition, we extend the measured error rate value to an error rate curve by varying the signal-to-noise ratio available to the receiver in our system model. Our results show a trend towards higher detection error rates in immune cells from BC patients as compared to healthy controls, which may indicate altered immune signaling and incorrect cell decisions in these patients. Also, considering different cell subtypes, our results show two orders of magnitude increase in error rate in CD4+ memory T cells compared to CD4+ naïve T cells in BC patients. With further development, we believe that our communication theory model and quantification of optimal signal detection in cells will provide a better understanding into how immune cells can accurately “decode” information to respond properly to various signals and how the immune signaling is altered in BC patients, causing better or worse outcomes. The benefit of our novel approach is that it integrates all the transmitted (cytokines) and received signals (pSTATs) into a single communications framework that can be efficiently analyzed together. Citation Format: Adina Matache, Aleksandra Karolak, Sergio Branciamore, Andrei Rodin, Peter P. Lee, Russell C. Rockne. A communication theory framework for modeling cytokine-mediated signaling in healthy and breast cancer derived peripheral blood immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2741.

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

Targeted therapy has held promise to be a successful anticancer treatment due to its specificity towards tumor cells that express the target receptors. However, not all targeting drugs used in the clinic are equally effective in tumor eradication. To examine which biochemical and biophysical properties of targeted agents are pivotal for their effective distribution inside the tumor and their efficient cellular uptake, we combine mathematical micro-pharmacological modeling with in vivo imaging of targeted human xenograft tumors in SCID mice. The mathematical model calibrated to experimental data was used to explore properties of the targeting ligand (diffusion and affinity) and ligand release schemes (rates and concentrations) with a goal to identify the properties of cells and ligands that enable high receptor saturation. By accounting for heterogeneities typical of in vivo tumors, our model was able to identify cell- and tissue-level barriers to efficient drug uptake. This work provides a base for utilizing experimentally measurable properties of a ligand-targeted agent and patient-specific attributes of the tumor tissue to support the development of novel targeted imaging agents and for improvement in their delivery to individual tumor cells.

The successful delivery of toxic cargo directly to tumor cells is of primary importance in targeted (α) particle therapy. Complexes of radioactive atoms with the 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelating agent are considered as effective materials for such delivery processes. The DOTA chelator displays high affinity to radioactive metal isotopes and retains this capability after conjugation to tumor targeting moieties. Although the α-decay chains are well defined for many isotopes, the stability of chelations during the decay process and the impact of released energy on their structures remain unknown. The radioactive isotope 225Ac is an α-particle emitter that can be easily chelated by DOTA. However, 225Ac has a complex decay chain with four α-particle emissions during decay of each radionuclide. To advance our fundamental understanding of the consequences of α-decay on the stability of tumor-targeted 225Ac-DOTA conjugate radiopharmaceuticals, we performed first principles calculations of the structure, stability, and electronic properties of the DOTA chelator to the 225Ac radioactive isotope, and the initial daughters in the decay chain, 225Ac, 221Fr, 217At and 213Bi. Our calculations show that the atomic positions, binding energies, and electron localization functions are affected by the interplay between spin-orbit coupling, weak dispersive interactions, and environmental factors. Future empirical measurements may be guided and interpreted in light of these results.

International audience; Calcium is involved in important intracellular processes, such as intracellular signaling from cell membrane receptors to the nucleus. Typically, calcium levels are kept at less than 100 nM in the nucleus and cytosol, but some calcium is stored in the endoplasmic reticulum (ER) lumen for rapid release to activate intracellular calcium-dependent functions. Stromal interacting molecule 1 (STIM1) plays a critical role in early sensing of changes in the ER's calcium level, especially when there is a sudden release of stored calcium from the ER. Inactive STIM1, which has a bound calcium ion, is activated upon ion release. Following activation of STIM1, there is STIM1-assisted initiation of extracellular calcium entry through channels in the cell membrane. This extracellular calcium entering the cell then amplifies intracellular calcium-dependent actions. At the end of the process, ER levels of stored calcium are reestablished. The main focus of this work was to study the conformational changes accompanying homo- or heterodimerization of STIM1. For this purpose, the ER luminal portion of STIM1 (residues 58-236), which includes the sterile alpha motif (SAM) domain plus the calcium-binding EF-hand domains 1 and 2 attached to the STIM1 transmembrane region (TM), was modeled and embedded in a virtual membrane. Next, molecular dynamics simulations were performed to study the conformational changes that take place during STIM1 activation and subsequent protein-protein interactions. Indeed, the simulations revealed exposure of residues in the EF-hand domains, which may be important for dimerization steps. Altogether, understanding conformational changes in STIM1 can help in drug discovery when targeting this key protein in intracellular calcium functions.

SummaryDuctal Carcinoma in situ (DCIS) is an avascular disease characterized by profound acidosis. Pre-malignant cells within this niche must adapt to acidosis to survive and thrive. A component of this acid-adaptation involves extracellular matrix remodeling leading to niche construction and remodeling. Using discovery proteomics, we identified that collagen producing enzyme PLODs are upregulated in acid-adapted breast cancer cells. Second harmonic generation microscopy showed significant collagen deposition within DCIS lesions of patients. In vitro analyses identified that acid-adaptation involves production of rare collagens that can be regulated by Ras and SMAD pathway. Secretome analysis showed upregulation ECM remodeling enzymes such as TGM2 and LOXL2. Comparison of acid induced collagens in vitro and in patient data showed correlation between rare collagens production and survival of patients. We conclude acidosis induces collagen production by cancer cells and promote growth independent of basal membrane attachment. The independently produced collagen can be used for niche construction and engineering as an adaptation strategy of cancer cells to survive and evolve.

The dynamics of tumor progression is driven by multiple factors, which can be exogenous to the tumor (microenvironment) or intrinsic (genetic, epigenetic or due to intercellular interactions). While tumor heterogeneity has been extensively studied on the level of cell genetic profiles or cellular composition, tumor morphological diversity has not been given as much attention. The limited analysis of tumor morphophenotypes may be attributed to the lack of accurate models, both experimental and computational, capable of capturing changes in tumor morphology with fine levels of spatial detail. Using a three-dimensional, agent-based, lattice-free computational model, we generated a library of multicellular tumor organoids, the experimental analogues of in vivo tumors. By varying three biologically relevant parameters—cell radius, cell division age and cell sensitivity to contact inhibition, we showed that tumor organoids with similar growth dynamics can express distinct morphologies and possess diverse cellular compositions. Taking advantage of the high-resolution of computational modeling, we applied the quantitative measures of compactness and accessible surface area, concepts that originated from the structural biology of proteins. Based on these analyses, we demonstrated that tumor organoids with similar sizes may differ in features associated with drug effectiveness, such as potential exposure to the drug or the extent of drug penetration. Both these characteristics might lead to major differences in tumor organoid’s response to therapy. This indicates that therapeutic protocols should not be based solely on tumor size, but take into account additional tumor features, such as their morphology or cellular packing density., Author summary Primary tumors and tumor metastases grow as three-dimensional (3D) masses of cells. Depending on the surrounding stroma, they may acquire various shapes, more or less irregular. Tumor organoids are the 3D experimental cultures that mimic growth of in vivo tumors, as well as their response to treatments. However, it is difficult to assess experimentally in a reproducible and quantitative way, how tumor morphology influences treatment efficacy. Here, we used mathematical modeling and computer simulations to analyze the structure of the simulated organoids and to classify them with regards to two quantitative features: the tumor accessible surface area (ASA) describing organoid exposure to the drug and the extent of drug penetration through the tumor tissue (organoid compactness). We showed that organoids of similar sizes and growth dynamics can, in fact, be characterized by distinct values of compactness and ASA, and thus may respond differently to the drug treatment. We suggest that these tumor features should be taken into consideration in addition to tumor size, when the therapeutic interventions are designed.

The single-cell-based computational models treat the cells as separate independent entities with individually regulated cell processes of growth, death, quiescence, and motility. Each cell behavior is influenced by interactions with other cells and with the surrounding environment. In the hybrid single-cell-based models, the microenvironment is represented by continuous concentrations of oxygen, nutrients, matrix, and drugs. By design, such models can handle significant differences between individual cells and can trace dynamic changes arising in these cells over time. Therefore, in contrast to continuous models that relay on averaged values, the single-cell-based models are ideally suited to address the heterogeneities observable in various biological systems, and to test diverse biological mechanisms. In this article, we present the off-lattice single-cell-based hybrid model MultiCell-LF and discuss how this model can be applied to various aspects of tumor heterogeneity, both on cellular and microenvironment level. In particular, we focus on intra-, inter-, and extracellular variability in tumor tissue, and present several simulations that address these phenomena.

Non-small-cell lung cancer is the leading cause of cancer death worldwide. Although radiotherapy is an effective treatment choice for early-stage cases, the 5-year survival rate of patients diagnosed in late-stages remains poor. Increasing evidence suggests that the local and systemic effects of radiotherapy dependent on the induced anti-tumor immune responses. We believe that an educated adaptation of radiotherapy plans based not only on the induced immune responses, but also on the tumor-immune ecosystem composition at the beginning of treatment might increase local tumor control. We propose two different mathematical models to evaluate the potential of the tumor-immune context to inform adaptation of treatment plans with the aim of improving outcomes.

Oxidation of cytosine is a leading cause of mutations and can lead to cancer. Here we report molecular dynamics simulations that characterized the structure and flexibility of 5-hydroxycytosine damaged DNA. A total of four systems were studied: undamaged DNA, damaged DNA base paired to a matching guanine, damaged DNA base paired to a mismatching adenine, and the corresponding undamaged mismatched strand. The simulations showed high spatial similarity between undamaged and damaged DNA; however, the matched damaged strand had greater overtwisting flexibility, and for both the matched and unmatched strands sugar puckering was much more flexible at the damaged site. The mismatch introduced larger changes, notably a loss in hydrogen bonding and a gain in stacking interactions, as well as effects on base pair and step geometry and solvation. Implications for damage recognition are discussed.

In vivo tumor microenvironments are heterogeneous in their architecture, cellular contents, metabolic landscape, and extracellular matrix (ECM) composition. Moreover, these microenvironments can undergo temporal and spatial changes. This may alter tumor responses to anticancer therapies. To examine the complex and dynamic changes within tumors and in tumor microenvironments, we developed a computational single cell-based model of the three-dimensional multicellular organotypic cultures: Organoid3D. This model was calibrated to experimental data and reproduced growth dynamics and morphologies of four human breast cell lines: a nontumorigenic epithelial MCF-10A, a mildly tumorigenic MCF-10AT1, and metastatic MCF-10CA-1a and MCF-10CA-1d lines grown in various microenvironmental conditions. By explicitly including the fibril structure of the ECM in the model, we examined how ECM properties promote or suppress the growth of in silico tumor organoids. The quantitative integration of experimental data for organoids exposed to different concentrations of doxorubicin allowed us to derive hypotheses on relative importance of microenvironmental factors and chemotherapeutic treatments on organoid growth. Thus, the in silico organoid model interrogated with experimental data provides a tool for fast and broad hypotheses testing and presents an opportunity to explore experimental conditions beyond what is physically feasible in laboratory experiments. Citation Format: Katarzyna A. Rejniak, Sharan Poonja, Jessica Kingsley, Shreya Mathur, Ibrahim Chamseddine, Aleksandra Karolak, Dmitry A. Markov, Lisa J. McCawley. In silico organoids: A model for deconvolution of microenvironmental and drug effects on tumor growth [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr A47.

Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.

Both targeted and standard chemotherapy drugs are subject to various intratumoral barriers that impede their effectiveness. The tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, and nonuniform expression of cell membrane receptors hinder drug molecule transport and perturb its cellular uptake. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine these aspects of drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed themicroPKPDmodels and coupled them with patient-specific data to test personalized treatments.

The thermodynamics of ligand photorelease from Ru(II)bis(2,2′-bipyridine)(6,6′-dimethyl-2,2′-bipyridine) ([Ru(bpy)2(dmbpy)]2+) complex in aqueous solution have been examined using photoacoustic calorimetry (PAC). Photolysis of the [Ru(bpy)2(dmbpy)]2+ complex with a ∼5 ns laser pulse (FWHM) results in enthalpy changes of 42 ± 4 kcal mol−1, with a corresponding volume change of 6 ± 1 mL mol−1. Density functional theory calculations also demonstrate that the positive enthalpy values arise from N σ-donation to the Ru ion. It is noted that the LUMO-Ligand Field gap fails to track with photoaquation quantum yield.

Three-dimensional organoid and organoidal cell cultures can recreate certain aspects of in vivo tumors and tumor microenvironments, and thus can be used to test intratumoral interactions and tumor response to treatments. In silico organoid models, when based on biological or clinical data, are an invaluable tool for hypothesis testing, and provide an opportunity to explore experimental conditions beyond what is feasible experimentally. In this chapter, three different approaches to building in silico organoids are described together with methods for integration with experimental or clinical data. The first model will be used to determine the mechanisms of development of breast tumor acini, based on their in vitro morphology. The second model will be used to predict conditions for the most effective cellular uptake of therapies targeting pancreatic cancers that incorporate intravital microscopy data. The third model will provide a procedure for assessing patients’ response to chemotherapeutic treatments, based on the biopsy data. For each of the models, a protocol will be proposed indicating how it can be used to generate testable hypotheses or predictions. These models can help biologists in determining what experiments should be performed in the laboratory. They can also assist clinicians in assessing cancer patients’ response to a given therapy and their risk of tumor recurrence.

A novel approach for the selection of step parameters as reaction coordinates in enhanced sampling simulations of DNA is presented. The method uses three atoms per base and does not require coordinate overlays or idealized base pairs. This allowed for a highly efficient implementation of the calculation of all step parameters and their Cartesian derivatives in molecular dynamics simulations. Good correlation between the calculated and actual twist, roll, tilt, shift, and slide parameters is obtained, while the correlation with rise is modest. The method is illustrated by its application to the methylated and unmethylated 5′-CATGTGACGTCACATG-3′ double stranded DNA sequence. One-dimensional umbrella simulations indicate that the flexibility of the central CG step is only marginally affected by methylation. © 2014 Wiley Periodicals, Inc.

The poor immunogenicity of pancreatic tumors makes them particularly difficult to treat. Standard chemotherapies and single agent immunotherapies have had notoriously little success in this arena. Oncolytic virus therapy has the potential to enhance the penetration of immunotherapeutically-delivered CAR T cells into the tumor and improve treatment outcomes. We evaluate this potential by combining two different mathematical approaches: an ordinary differential equation model to simulate population level tumor response to cytotoxic activity of T cells, coupled with an agent-based model to simulate the enhancement of CAR T cell penetration by oncolytic virus therapy.

A tumor vasculature that is functionally abnormal results in irregular gradients of metabolites and drugs within the tumor tissue. Recently, significant efforts have been committed to experimentally examine how cellular response to anti-cancer treatments varies based on the environment in which the cells are grown. In vitro studies point to specific conditions in which tumor cells can remain dormant and survive the treatment. In vivo results suggest that cells can escape the effects of drug therapy in tissue regions that are poorly penetrated by the drugs. Better understanding how the tumor microenvironments influence the emergence of drug resistance in both primary and metastatic tumors may improve drug development and the design of more effective therapeutic protocols. This chapter presents a hybrid agent-based model of the growth of tumor micrometastases and explores how microenvironmental factors can contribute to the development of acquired resistance in response to a DNA damaging drug. The specific microenvironments of interest in this work are tumor hypoxic niches and tumor normoxic sanctuaries with poor drug penetration. We aim to quantify how spatial constraints of limited drug transport and quiescent cell survival contribute to the development of drug resistant tumors.

This study quantitatively evaluates the tumor cell-targeting capabilities and intracellular uptake pattern of chlorotoxin (CTX) when delivered intratumorally (IT) and intravenously (IV) using subcutaneous (SC) U87 human glioblastoma mouse model. CTX is a scorpion-derived polypeptide that selectively binds to tumor cells of neuroectodermal origin, such as glioma and a wide range of other tumor cells, but not normal cells. The quantification of CTX cellular uptake presented here will assist in the further development of our tumor-targeting drug delivery system (DDS) consisting of a chemotherapeutic encapsulated in nonionic surfactant vesicles embedded in a thermosensitive cross-linked chitosan hydrogel, which has affinity for MUC1 receptor overexpressed on many tumor cells, including low-differentiated glioma. CTX addition to the DDS will serve as a second tumor-targeting molecule. In this study, U87 human glioma cells were implanted SC in the flank of nude athymic/ncr/nus mice. When tumors reached approximately the volume of 0.25 cm3, mice were injected either IT or IV (tail vein) with 50 μl of 100 μg CTX or phosphate buffer saline (PBS) as a control. The tumors were harvested at 5 minutes post IT injection or 7 hours post IV injection, fixed, sectioned at 5 μm, and followed by either immunofluorescence or immunocytochemistry using anti-CTX primary antibody and AlexaFluor-594 conjugated secondary antibody or immunoperoxidase- AEC kit, and evaluated by confocal or scanning microscopy, respectively. CTX levels were compared between three different tumor regions (A, B, and C; A = injection site marked by methylene blue dye, C = most distant from injection site, B = equidistant between these two) after direct IT injection and compared to CTX levels in tumor tissues following IV injection. Quantitative evaluation of CTX levels was performed with ImageJ software, while further intracellular analysis of CTX presence utilized MATLAB system. CTX was found to be evenly distributed between cells within each region post IT injection, but decreased in intensity from the injection site (A) toward the tumor edge. With region A set at 100% CTX intensity, the levels decreased to 23.7% (in region B), and 2.1% (in region C), compared to 1.8% post IV injection. Our previous in vitro studies showed CTX localization near the nucleus in U87 glioma cells but not in normal human astrocytes. Current evaluation of tumor z-sections quantified levels of CTX inside U87 glioma cells in vivo. These results confirm the specificity of CTX uptake by U87 human glioma cells in vitro and in vivo with CTX tissue levels post IV injection, which is comparable to those post IT injection in the distant C tumor region. This proves CTX is an important second tumor-targeting molecule for our DDS system. Citation Format: Alan D. Roberts, Joseph O. Johnson, Aleksandra Karolak, Norma Alcantar, Katarzyna A. Rejniak, Marzenna Wiranowska. Quantitative assessment of the intracellular uptake of chlorotoxin in a U87 human glioma mouse model for the targeted drug delivery system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4650.

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

The effect of N6-adenine methylation on the flexibility and shape of palindromic GATC sequences has been investigated by molecular dynamics simulations. Variations in DNA backbone geometry were observed, which were dependent on the degree of methylation and the identity of the bases. While the effect was small, more frequent BI to BII conversions were observed in the GA step of hemimethylated DNA. The increased BII population of the hemimethylated system positively correlated with increased stacking interactions between methylated adenine and guanine, while stacking interactions decreased at the TC step for the fully methylated strand. The flexibility of the AT and TC steps was marginally affected by methylation, in a fashion that was correlated with stacking interactions. The facilitated BI to BII conversion in hemimethylated strands might be of importance for SeqA selectivity and binding.

Nickel complexes of general formula [Ni(PR 3 ) 2 (SiMe n Cl 3− n ) 2 ], where R=Bu, Ph; n =0–2, were synthesised from isothiocyanato–phosphine nickel complexes and fully characterised spectroscopically. They are the first chlorosilyl nickel complexes with at least one alkyl (methyl) group at silicon atom.

This study reports on the quantitative analysis of the diffusion and localization of a targeted drug delivery system (DDS) consisting of fluorescent labeled 0.01 µM paclitaxel-BODIPY 564/570 encapsulated in non-ionic surfactant vesicles embedded in a thermosensitive cross-linked chitosan hydrogel. This is a multi-tiered DDS that allows for enhanced stability, sustained and controlled delivery of embedded drugs to tumor sites, and reduced toxicity. We showed that cancer cells of epithelial origin, such as human ovarian epithelial carcinoma OV2008 and highly migratory mouse glioma G-26, overexpress MUC1, a mucin surface antigen, which effectively enhances the specific targeting capacity of chitosan. Utilizing this DDS we found that OV2008 carcinoma cells had ~2 times higher fluorescence intensity level than normal ovarian epithelial IMCC3 cells with a statistical significance at 5 min and 24 h incubation times suggesting that this DDS had a higher affinity for tumor cells. Therefore, we hypothesized that there is a difference in PTX diffusion towards epithelial origin tumor cells when compared to normal cells in both distance/location and time. Diffusion and localization of fluorescent labeled PTX was evaluated in vitro using confocal microscopy. The fluorescence intensity captured on the images was quantified with ImageJ software in OV2008 carcinoma cells and compared to IMCC3 normal cells at time intervals 5 min, 1 h, 24 h, 48 h, and 72 h. The fluorescence intensity image data was analyzed in multiple radial line segments separated into three different zones of 24 µm each to represent multiple diffusion distances. Our results showed that fluorescence intensity levels in the zones around the IMCC3 normal cells at 5 min and 1 h were significantly higher (p This PTX-BODIPY 564/570 diffusion data from in vitro studies is currently being used for computational modeling in the in vivo intracerebral model of G-26 glioma. It will be integrated with mathematical model simulations that describe the pharmacokinetic and pharmacodynamic (PK/PD) properties of this drug delivery system for the evaluation of its efficacy and to optimize drug delivery in vivo. Computational modelling is being done in Matlab using the treatment scenario of the post-surgical late-stage glioma. The simulation studies will be used to determine optimal drug concentrations, chitosan density, and localization. Citation Format: Rupin Singh, Rana Falahat, Eva Williams, Joseph O. Johnson, Norma Alcantar, Aleksandra Karolak, Katarzyna Rejniak, Marzenna Wiranowska. Cross-disciplinary optimization of nano-drug delivery to ovarian carcinoma and glioma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2185. doi:10.1158/1538-7445.AM2017-2185

Tumor development and its response to therapies are highly affected by heterogeneous microenvironments, including fluctuations in chemical and physical cues, as well as in the composition of the surrounding extracellular matrix. For example, the abnormal topology of tumor vasculature and tumor tissue leads to disruption in oxygen transport and in the emergence of regions of low oxygen (hypoxic conditions), and in accumulation of acidic products and decreased pH (acidic conditions). In addition, the elevated mechanical tension is commonly observed in the tumor connective tissue (high tension conditions). All these factors affect tumor growth and efficacy of anticancer drugs. Moreover, the microenvironmental conditions can dynamically evolve after treatment that is difficult to investigate in vivo in a systematic way. To represent the complexity and dynamics of tumor microenvironment we developed a computational model for the growth of 3D organotypic cultures integrated with experimental organ-on-the-chip 3D cultures. The computational model was validated based on the data collected from cultured breast spheroids, including the non-tumorigenic epithelial MCF-10A cell line, mildly tumorigenic c-Ha-ras oncogene transfected MCF-10AT1 cell line, and a metastatic line MCF-10CA-1a. Based on the differences in these cell lines genetic profiles, cell mechanical properties, and image-guided variations in organotypic morphologies, the computational model was able to reproduce in a quantitative way the dynamics of 3D tumor spheroids’ growth under various microenvironmental conditions. This calibrated model was subsequently applied to predict tumor growth and its response to chemotherapy in heterogeneous microenvironments consisting of dynamically changing gradients of oxygen and pH, as well as tension of the surrounding microenvironment. The overarching aim of our project is to use this integrated (in silico-organ and organ-on-the-chip) approach to target the treatment of tumor 3D organotypic cultures with drugs that display different mechanism of action, such as cell cycle arrest, initiation of apoptosis, or treatment combinations. Furthermore, this approach will allow us to propose new treatment schedules and dosages that will be tested in the organ-on-the-chip experiments. The progress on this integrated model development will be presented.

We have developed and efficient, sequence-specific coarse-grained model to simulate the conformational motion of large double stranded DNA in molecular dynamics simulations. The DNA backbone is modeled by beads with DNA bases represented by sets of three beads, and bead positions are used to calculate the shift, slide, rise, roll, twist and tilt conformational step parameters of DNA. With these parameters, elastic deformation energies are calculated following Olson's harmonic model (Olson et. al., Proc. Natl. Acad. Sci. USA 95, 11163 (1998)). Specifics of the model and sample simulations will be discussed.

The pancreatic adenocarcinoma is one of the most deadly cancers with only 6% overall 5-year survival rate. Since the current therapies fail to provide successful results, the improvement of techniques for early detection, predicting of treatment efficacy and monitoring of tumor spread during and after surgical procedures remain under focused research. The purpose of this study is to develop a novel method to assess behavior (diffusion and binding within the tissues) of imaging agents targeted to pancreatic cancer cells. Our long-term goal is to design a prospective tool for the prediction of targeted imaging agent peak concentration and cellular uptake based on individual patient data. The toll-like receptor 2 (TLR2) is known to play an important role in the immune system response. In addition, our team reported that TLR2 is a bona fide cell-surface marker for targeting pancreatic cancer. TLR2 recognizes a vast number of biomolecules, including lipoproteins, such as designed by our team, novel TLR2 ligand (TLR2L). Recent development of an intravital fluorescence microscopy method allowed for the real time in vivo imaging of the TLR2L conjugated to near-infrared fluorescent dye, Cyanine 5 (TLR2L-Cy5) and its penetration through the tissue of pancreatic adenocarcinoma tumor xenografts in mice with endogenous expression of TLR2. In order to quantify the space- and time-dependent dynamics of TLR2L we combined intravital dorsal window chamber experiments with computational modeling of TLR2L-Cy5 diffusion and internalization following intravenous administration. Our computational model accounts for an explicitly defined tissue morphology composed of individual tumor cells, extracellular matrix interpenetrated by the interstitial fluid, and tumor vasculature. We also model individual molecules of a fluorescent imaging agent that extravasate via transmural influx from blood capillaries, spread through the tumor and become internalized by the cells. The process of agonist binding to the receptor is modeled by trapping the particle by the near-by cell boundary receptor, which mimics the distance dependent electrostatic and hydrophobic interactions initiating the recognition and binding in vivo. Our studies led to quantification of the TLR2L-Cy5 intratumoral transport including agent extravasation, diffusion and intracellular accumulation in relation to tumor tissue structure and vascular architecture. The performed computational simulations allowed for detailed continuous in time assessment of the targeted imaging agent penetration on the microscopic (cell-to-tissue) level. The calibrated in silico model revealed the time-dependent dynamics of TLR2L-Cy5 agent binding, internalization and intracellular distribution. Our quantitative approach also showed a non-uniform spatial saturation of the TLR2L-Cy5 on the plasma membrane and inside the cell. Moreover, continuous simulations allowed for prediction of the maximum concentration peak in the extracellular matrix and for the uptake peak in individual cells. In addition to quantitative analysis, we provide novel (two- and three-dimensional) visualization tools for imaging agent dynamical penetration, membrane binding and receptor-mediated endocytosis process in pancreatic cells. In conclusion, we present an interdisciplinary approach to quantify diffusion and cellular uptake of an imaging agent targeted to pancreatic cancer cell lines expressing the TLR2 receptor. This integrated approach can be used in the future for the development of other targeted imaging and therapeutic agents, for other solid tumors, and for optimizing the administration schedules and time points for data collection from individual human tumor xenografts in order to improve treatment efficacy. Citation Format: Aleksandra Karolak, Veronica Estrella, Tingan Chen, Amanda Huynh, David Morse, Katarzyna Rejniak. Using computational modeling to quantify targeted agent binding and internalization in pancreatic cancers. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr B21.

Inhibitory helix 1 (HI-1) of the Ets-1 human transcription factor unfolds upon binding the target DNA sequence. To identify the interactions that stabilize HI-1 in the apo state, we performed replica exchange and molecular dynamics simulations of various apo Ets-1 constructs. The simulations indicate the importance of local interactions for the stability of HI-1. The HI-2 and H4 helices stabilize the helical state of HI-1 through specific residue-residue contacts and macrodipolar interactions. The amount of stabilization in small length HI-1+H2 and HI-1+H4 constructs was similar to that in the protein. The studies suggest that the partial unfolding of Ets-1 upon DNA binding can be achieved by the removal of just a few specific local contacts.

The thermodynamics associated with small-protein interactions essentially define how drugs bind to target receptor proteins. As proteins are dynamic, it is of importance to understand the early time (sub-millisecond) thermodynamics and kinetics associated with small molecules binding. This time regime presents experimental challenges in both the measurement of the thermodynamics as well as in the introduction of the small molecule ligands on fast time scales. An attractive strategy is to ‘cage’ the protein ligand with a photochemically cleavable protecting grou which, upon photolysis, releases the ligand on nanosecond timescales allowing for the examination of fast time dynamics associated with ligand-protein interactions. Here, a new class of photocage is presented based upon the photophysics of Ru(II)bis(2,2′bipyridine)L2 type complexes. Specifically, we present the thermodynamics of ligand photodetachment from both Ru(II)bis(2,2′-bipyridine)(acetonitrile)2, Ru(II)bis(2,2′-bipyridine)(6,6′-dimethyl-2,2′-bipyridine), Ru(II)bis(2,2′-bipyridine)(seratonin)2 and Ru(II)bis(2,2′-bipyridine)(tyramine)2 complexes in aqueous solution using photoacousctic calorimetry (PAC) and density functional theory. Our results are consistent with N donation form the ligands to the metal dominating the interaction energies and positive reaction entahlpies. In addition, the relative small changes in molar volume change suggest the each complex is highly solvated in aqueous solutions.

We present a novel approach for the selection of DNA step parameters as reaction coordinates in umbrella sampling simulations. Simplified representation of DNA that uses only three atoms per base, allowed for highly efficient calculations of the step parameters and their Cartesian derivatives in the molecular dynamics simulations. Good correlation between the actual and calculated twist, roll, tilt, rise, slide and shift was obtained. The method is illustrated through its application to the unmethylated and methylated DNA systems. Impact of the methylation on the shape and flexibility of DNA depending on the location of the methyl group is discussed.