Inferring Intracellular Signal Transduction Circuitry from Molecular Perturbation Experiments

The development of network inference methodologies that accurately predict connectivity in dysregulated pathways may enable the rational selection of patient therapies. Accurately inferring an intracellular network from data remains a very challenging problem in molecular systems biology. Living cells integrate extremely robust circuits that exhibit significant heterogeneity, but still respond to external stimuli in predictable ways. This phenomenon allows us to introduce a network inference methodology that integrates measurements of protein activation from perturbation experiments. The methodology relies on logic-based networks to provide a predictive approximation of the transfer of signals in a network. The approach presented was validated in silico with a set of test networks and applied to investigate the epidermal growth factor receptor signaling of a breast epithelial cell line, MFC10A. In our analysis, we predict the potential signaling circuitry most likely responsible for the experimental readouts of several proteins in the mitogen-activated protein kinase and phosphatidylinositol-3 kinase pathways. The approach can also be used to identify additional necessary perturbation experiments to distinguish between a set of possible candidate networks.

The importance of accurately correcting for the natural abundance of stable isotopes

The use of isotopically labeled tracer substrates is an experimental approach for measuring in vivo and in vitro intracellular metabolic dynamics. Stable isotopes that alter the mass but not the chemical behavior of a molecule are commonly used in isotope tracer studies. Because stable isotopes of some atoms naturally occur at non-negligible abundances, it is important to account for the natural abundance of these isotopes when analyzing data from isotope labeling experiments. Specifically, a distinction must be made between isotopes introduced experimentally via an isotopically labeled tracer and the isotopes naturally present at the start of an experiment. In this tutorial review, we explain the underlying theory of natural abundance correction of stable isotopes, a concept not always understood by metabolic researchers. We also provide a comparison of distinct methods for performing this correction and discuss natural abundance correction in the context of steady state 13C metabolic flux, a method increasingly used to infer intracellular metabolic flux from isotope experiments.

Abstract 1040: Differential levels of glycogen in breast cancer cell lines: A potential new target

Cancer cells have been known to alter their metabolic processes in order to survive and proliferate. Normally in muscle and liver, excess glucose is stored within the cells as glycogen. Elevated levels of glycogen have also been found in various cancers, including breast cancers. Recent studies have implicated glycogen metabolism as important in promoting survival of cancer cells, suggesting targeting of glycogen metabolism as a possible treatment to inhibit cancer cell growth. In general, modulation of cancer metabolism is believed to be an attractive adjunct strategy to conventional or targeted therapies. Here we set out to investigate glycogen levels as well as levels of proteins involved in glycogen synthesis and degradation vary across different breast cancer cell lines.

A glucose metabolism qPCR array found differential levels of the alpha subunit of phosphorylase kinase 1, a key enzyme involved in glycogen degradation among three different breast cancer cell lines. Expression levels of glycogen synthesis and degradation enzymes were assessed using qPCR and immunoblot in various breast cancer cell lines. Glycogen levels in these breast cancer cell lines were quantified using an amyloglucosidase reaction coupled with other enzymatic reactions to produce a fluorescent product. It was found that MDA-MB-231, SUM149, and MCF7 cell lines had increased levels of glycogen, between 6.5 and 23.5 μg glycogen per mg protein, whereas SUM190 and normal-like breast epithelial cell line MCF10A had undetectable levels of glycogen. These findings demonstrate that glycogen metabolism can vary widely amongst cancer types, indicating that therapies targeted to disrupt glycogen degradation may produce differential results and that further study of the role of glycogen metabolism in cancer is warranted.

Citation Format: Joel A. Yates, Megan Altemus, Zhifen Wu, Michelle L. Wynn, Sofia D. Merajver. Differential levels of glycogen in breast cancer cell lines: A potential new target. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1040.

RhoC GTPase Is a Potent Regulator of Glutamine Metabolism and N-Acetylaspartate Production in Inflammatory Breast Cancer Cells

Inflammatory breast cancer (IBC) is an extremely lethal cancer that rapidly metastasizes. Although the molecular attributes of IBC have been described, little is known about the underlying metabolic features of the disease. Using a variety of metabolic assays, including (13)C tracer experiments, we found that SUM149 cells, the primary in vitro model of IBC, exhibit metabolic abnormalities that distinguish them from other breast cancer cells, including elevated levels of N-acetylaspartate, a metabolite primarily associated with neuronal disorders and gliomas. Here we provide the first evidence of N-acetylaspartate in breast cancer. We also report that the oncogene RhoC, a driver of metastatic potential, modulates glutamine and N-acetylaspartate metabolism in IBC cells in vitro, revealing a novel role for RhoC as a regulator of tumor cell metabolism that extends beyond its well known role in cytoskeletal rearrangement.

Adipocytes promote pancreatic cancer cell proliferation via glutamine transfer

Adipocytes promote progression of multiple cancers, but their role in pancreatic intraepithelial neoplasia (PanIN) and ductal adenocarcinoma (PDAC) is poorly defined. Nutrient transfer is a mechanism underlying stromal cell-cancer crosstalk. We studied the role of adipocytes in regulating in vitro PanIN and PDAC cell proliferation with a focus on glutamine metabolism. Murine 3T3L1 adipocytes were used to model adipocytes. Cell lines derived from PKCY mice were used to model PanIN and PDAC. Co-culture was used to study the effect of adipocytes on PanIN and PDAC cell proliferation in response to manipulation of glutamine metabolism. Glutamine secretion was measured with a bioanalyzer. Western blotting was used to study the effect of PanIN and PDAC cells on expression of glutamine-related enzymes in adipocytes. Adipocytes promote proliferation of PanIN and PDAC cells, an effect that was amplified in nutrient-poor conditions. Adipocytes secrete glutamine and rescue PanIN and PDAC cell proliferation in the absence of glutamine, an effect that was glutamine synthetase-dependent and involved PDAC cell-induced down-regulation of glutaminase expression in adipocytes. These findings suggest glutamine transfer as a potential mechanism underlying adipocyte-induced PanIN and PDAC cell proliferation.

Abstract 1203: Metastasis-associated oncogene RhoC as a regulator of glutamine metabolism in the inflammatory breast cancer cell line SUM149

Metabolic reprogramming is increasingly recognized as a fundamental hallmark of cancer. While the Warburg effect and normal proliferative metabolism are similar, they are not equivalent. We hypothesize that there are key drivers of malignant metabolism that can be modulated to impede cancer proliferation without substantial effects on normal tissue growth. Using 13C-labeled glucose and glutamine tracers in combination with mass spectrometry and measurements of extracellular glucose, lactate, and glutamine flux, we have characterized system level differences in a series of breast cancer cell lines as well as normal-like breast epithelial cells. We observed an increase in the reductive carboxylation of glutamine-derived citrate and alpha-ketoglutarate in the triple-negative inflammatory breast cancer cell line SUM149. We also observed that the SUM149 exhibit high levels of HIF-1α and low levels of oxygen consumption under normoxia, suggesting that the cell line is highly adapted to hypoxia. Surprisingly, the stable depletion of HIF-1α via shRNA had no significant effect on the metabolic profile of these cells. Previous work by our lab and others has demonstrated that the GTPase RhoC is a driver of the metastatic phenotype exhibited by inflammatory breast cancer. Activation of RhoC is known to induce cytoskeletal rearrangements and increase invasive potential. The Rho GTPase family of proteins has also recently been linked to metabolism, specifically regulation of glutaminase activity. Here we show that stable knockdown of RhoC in SUM149 cells results in a marked decrease in the rate of both glutamine uptake and intracellular reductive carboxylation. This work reinforces the role of RhoC as an important driver of inflammatory breast cancer metastatic potential. We conclude that RhoC remains an important clinical target with the potential to alter patient outcomes.

Citation Format: Joel A. Yates, Michelle L. Wynn, ZhiFen Wu, Charles R. Evans, Charles Burant, Santiago D. Schnell, Sofia D. Merajver. Metastasis-associated oncogene RhoC as a regulator of glutamine metabolism in the inflammatory breast cancer cell line SUM149. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1203. doi:10.1158/1538-7445.AM2015-1203

Inferring the Effects of Honokiol on the Notch Signaling Pathway in SW480 Colon Cancer Cells

In a tumor cell, the development of acquired therapeutic resistance and the ability to survive in extracellular environments that differ from the primary site are the result of molecular adaptations in potentially highly plastic molecular networks. The accurate prediction of intracellular networks in a tumor remains a difficult problem in cancer informatics. In order to make truly rational patient-driven therapeutic decisions, it will be critical to develop methodologies that can accurately infer the molecular circuitry in the cells of a specific tumor. Despite enormous heterogeneity, cellular networks elicit deterministic digital-like responses. We discuss the use and limitations of methodologies that model molecular networks in cancer cells as a digital circuit. We also develop a network model of Notch signaling in colon cancer using a novel reverse engineering logic-based method and published western blot data to elucidate the interactions likely present in the circuits of the SW480 colon cancer cell line. Within this framework, we make predictions related to the role that honokiol may be playing as an anti-cancer drug.

Follow-the-leader cell migration requires biased cell-cell contact and local microenvironmental signals

Directed cell migration often involves at least two types of cell motility that include multicellular streaming and chain migration. However, what is unclear is how cell contact dynamics and the distinct microenvironments through which cells travel influence the selection of one migratory mode or the other. The embryonic and highly invasive neural crest (NC) are an excellent model system to study this question since NC cells have been observed in vivo to display both of these types of cell motility. Here, we present data from tissue transplantation experiments in chick and in silico modeling that test our hypothesis that cell contact dynamics with each other and the microenvironment promote and sustain either multicellular stream or chain migration. We show that when premigratory cranial NC cells (at the pre-otic level) are transplanted into a more caudal region in the head (at the post-otic level), cells alter their characteristic stream behavior and migrate in chains. Similarly, post-otic NC cells migrate in streams after transplantation into the pre-otic hindbrain, suggesting that local microenvironmental signals dictate the mode of NC cell migration. Simulations of an agent-based model (ABM) that integrates the NC cell behavioral data predict that chain migration critically depends on the interplay of biased cell-cell contact and local microenvironment signals. Together, this integrated modeling and experimental approach suggests new experiments and offers a powerful tool to examine mechanisms that underlie complex cell migration patterns.

Abstract 5239: Unraveling the complex regulatory relationship between PI3K signaling and metabolic transformation in breast cancer

Cancer cells exhibit a metabolic phenotype characterized by high rates of glucose uptake and lactate production, known as the Warburg effect. While the Warburg effect and normal proliferative metabolism appear similar, important molecular differences exist. We hypothesize that molecular and metabolic drivers of the Warburg effect can be modulated to impede cancer proliferation without substantial effects on normal tissue growth. Intracellular networks exhibit a variety of emergent non-linear behaviors and, as a result, the use of experimental intuition alone will not be enough to identify these drivers. Using a combination of experimental and theoretical methods, we developed a model of breast cancer progression that includes metabolism and the phosphatidylinositol-3 kinase (PI3K) signaling pathway, an important regulator of carbon metabolism. A key component of our model is a detailed logic network of molecular interactions associated with PI3K signaling as well as regulatory connections to central carbon metabolism, including the ATP/AMP ratio, GLUT receptor activation, hexokinase activation, and changes in the catalytic activity of pyruvate kinase. To validate our model, a series of phospho Western blot analyses were performed using a normal-like breast cell line and a diverse set of breast cancer cell lines exposed to PI3K pathway inhibitors. From these data, a series of predictive network models were constructed representing distinct stages of breast cancer progression. We also generated detailed metabolic flux maps for each cell line using metabolic flux analysis (MFA), a method that relies on carbon-13 tracers, mass-spectrometry, and measurements of extracellular flux to infer intracellular flux. In agreement with recent studies, we found an increase in the reductive carboxylation of glutamine derived alpha-ketoglutarate in cells constitutively adapted to hypoxia. We also identified a potentially important metabolic vulnerability in aggressive breast cancers. Moreover, we found important PI3K network differences at the RNA and protein levels, some of which were isoform specific. Together our data indicate that very different system-level properties are associated with distinct stages of breast cancer progression and metabolic transformation. Our model is suitable for performing in silico molecular perturbations to predict a normal as well as tumor level response to a targeted therapy or combination of therapies. Our approach also serves as a prototype for the use of systems biology methods in personalized medicine where molecular and metabolic data collected from a patient's biopsied tumor is input into a predictive model designed to develop a strategic treatment plan for the patient. The use of predictive models to integrate data from an individual patient will have a profound impact on cancer care decisions and patient outcomes in the future.

Citation Format: Michelle L. Wynn, Megan Egbert, Lauren D. Van Wassenhove, Zhi Fen Wu, Firas Midani, Charles Evans, Charles F. Burant, Santiago Schnell, Sofia D. Merajver. Unraveling the complex regulatory relationship between PI3K signaling and metabolic transformation in breast cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5239. doi:10.1158/1538-7445.AM2013-5239

Abstract 5223: Elucidating the complex cross-talk between the MAPK and PIK3 pathways

Targeted molecular inhibitors have emerged as a leading anti-cancer strategy; however, despite promising pre-clinical data, many targeted inhibitors induce undesirable off-target effects in the clinic. The large number of off-target effects associated with molecular inhibitors was recently termed the ‘‘whack a mole problem’’ because inhibiting one molecular target often unintentionally activates another molecule. It is increasingly clear that the high incidence of off-target effects associated with targeted inhibitors is related to the complex interactions and emergent behaviors inherent to the highly complex and dysregulated intracellular networks of cancer. Both the mitogen activated protein kinase (MAPK) and phosphatidylinositol-3 kinase (PI3K) pathways are known to be dysregulated in cancer. In previous work, we and others have demonstrated that the MAPK pathway promotes motility, invasion, and angiogenic factors while the PI3K pathway plays an important role in controlling anchorage independent growth. In addition, the PI3K pathway plays an essential role in stimulating glucose metabolism and the Warburg effect. We hypothesize that robust interactions exist between these two pathways that influence efficacy and potentially also acquired resistance to targeted therapies. Using a combination of experimental and theoretical techniques, we developed a predictive network model linking growth factor signaling to the MAPK and PI3K pathways as well as to glucose metabolism. Specifically, we constructed a logic-based network of the cross-talk between MAPK and PI3K signaling that relied on a detailed literature survey to identify known molecular interactions as well as proposed interactions and regulatory feedback connections in the literature. We next performed a set of experiments using a normal-like breast epithelial cell line and a series of pathway specific inhibitors with and without growth factor stimulation to validate our model. Finally, we repeated these experiments using a diverse set of breast cancer cell lines and integrated this data to produce a series of cancer networks representative of different stages of breast cancer progression. Our model was able to recapitulate both our own experimental data and published data in the literature using a smaller subset of regulatory feedback mechanisms than we started with. Together, our results suggest that some proposed interactions and feedback mechanisms attributed to MAPK and PI3K cross-talk in the literature may not be valid.

Citation Format: Megan E. Egbert, Michelle L. Wynn, Zhi Fen Wu, Santiago Schnell, Sofia D. Merajver. Elucidating the complex cross-talk between the MAPK and PIK3 pathways. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5223. doi:10.1158/1538-7445.AM2013-5223

Logic-based models in systems biology: a predictive and parameter-free network analysis method

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

Abstract 5164: Using metabolomic flux to uncover new targets for the modulation of breast cancer metastasis

Inflammatory breast cancer (IBC) is the most deadly breast cancer because of its ability to rapidly metastasize, often before the primary lesion is detected. To better understand what drives this highly aggressive form of cancer, we studied central carbon metabolism in IBC. The Warburg effect, which is characterized by high rates of glucose uptake and glycolysis even under aerobic conditions, occurs in most cancer cells and plays an important role in the tumorigenic capacity of breast cancer. Specifically, we examined metabolic changes that occur when breast cancer cells switch from a proliferating phenotype to a highly motile phenotype. In primary tumor formation rapid proliferation is crucial, while in metastasis motility to new sites is critical. We hypothesize that alterations in the regulation of metabolic pathways leads to the direction of energy from proliferation to motility, and that this change is important in the progression of breast cancer towards metastases. Understanding if a coincident metabolic shift occurs when malignant cells switch from a proliferative to a more aggressive motile form may help identify new therapeutics for highly aggressive breast cancers. For our studies, we are using “normal-like” MCF-10A breast cells, which proliferate quickly and move slower than cancer cells, and two highly metastatic breast cancer cell lines (MDA-MB-231 and the IBC-derived line SUM 149), which have the ability to move quickly and proliferate relatively slowly. Using these cell lines as a model, we have examined metabolic changes as cancer progresses. We are conducting targeted metabolomic studies to determine relative concentrations of metabolites in the glycolysis, tricarboxylic acid (TCA) cycle, and mevalonate pathway. In addition, we are using 13C-labeled pyruvate and glucose to measure the transient and steady state flux through these metabolic pathways. Our preliminary results indicate interesting and unexpected changes in both the transient and steady state metabolic fluxes across the different cell lines. In addition, the metabolic flux through the TCA cycle is very active in the metastatic cancer cells despite an increase in glycolysis. In agreement with flux studies of other cancer cell lines, our results suggest that the cancer cells are likely using intermediates from the TCA cycle to generate nucleotides and fatty acids needed for replication. The results from this work, once validated, can be used to identify new therapeutic metabolic targets to modulate breast cancer metastasis.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5164. doi:1538-7445.AM2012-5164

Abstract 4925: The natural enzyme sequestration in signaling cascades provides inherent opportunities for off-target effects induced by kinase inhibitors

Off-target effects from targeted therapies are often attributed to cross-talk, which usually refers to inter-pathway molecular interactions that occur because of explicit regulatory feedback connections between two pathways. Recent experimental and theoretical studies have demonstrated, however, that covalently modified cascades naturally exhibit bidirectional signal propagation via a phenomenon termed retroactivity. This phenomenon arises due to enzyme sequestration where each cycle is coupled, not only to the next cycle, but also to the previous cycle. While retroactivity occurs naturally in covalently modified cascades, signaling pathways likely evolved to propagate information in a downstream manner. An important consequence of retroactivity, however, is that a downstream perturbation can induce an upstream response without the presence of regulatory feedback connections. We hypothesize that kinase inhibitors can produce off-target effects as a consequence of retroactivity alone via the following mechanism: a signal travels upstream from the site of a downstream perturbation through retroactivity and, upon reaching a shared upstream component, is delivered to an independent parallel pathway. To test the hypothesis we used a computational model to simulate the targeted inhibition of a specific kinase in a series signaling networks using physiologically and therapeutically relevant ranges for all parameters. Surprisingly, our results suggest that an off-target effect due to retroactive signaling is more likely when the first cycle in a non-inhibited cascade is “off” and not consuming large amounts of a shared up-stream activator. Our results also suggest that the kinetics governing covalently modified cycles in a cascade are more important for propagating an upstream off-target effect than the binding affinity of the drug to the targeted protein, which is a commonly optimized property in drug development. Finally, our results suggest that a single mutation has the capacity to produce a large spontaneous off-target effect without any direct regulatory connections between the targeted protein and the effected protein. Together, our results suggest that retroactivity may play an important role in the dysregulated signaling networks of cancer cells as well as the cellular response to targeted therapies. These findings have important implications for somatic evolution in cancer and the onset of therapeutic resistance, which has been widely reported for many targeted cancer therapeutics, including kinase inhibitors.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4925. doi:1538-7445.AM2012-4925

Computational modelling of cell chain migration reveals mechanisms that sustain follow-the-leader behaviour

Follow-the-leader chain migration is a striking cell migratory behaviour observed during vertebrate development, adult neurogenesis and cancer metastasis. Although cell–cell contact and extracellular matrix (ECM) cues have been proposed to promote this phenomenon, mechanisms that underlie chain migration persistence remain unclear. Here, we developed a quantitative agent-based modelling framework to test mechanistic hypotheses of chain migration persistence. We defined chain migration and its persistence based on evidence from the highly migratory neural crest model system, where cells within a chain extend and retract filopodia in short-lived cell contacts and move together as a collective. In our agent-based simulations, we began with a set of agents arranged as a chain and systematically probed the influence of model parameters to identify factors critical to the maintenance of the chain migration pattern. We discovered that chain migration persistence requires a high degree of directional bias in both lead and follower cells towards the target. Chain migration persistence was also promoted when lead cells maintained cell contact with followers, but not vice-versa. Finally, providing a path of least resistance in the ECM was not sufficient alone to drive chain persistence. Our results indicate that chain migration persistence depends on the interplay of directional cell movement and biased cell–cell contact.

Unraveling the complex regulatory relationships between metabolism and signal transduction in cancer

Cancer cells exhibit an altered metabolic phenotype, known as the Warburg effect, which is characterized by high rates of glucose uptake and glycolysis, even under aerobic conditions. The Warburg effect appears to be an intrinsic component of most cancers and there is evidence linking cancer progression to mutations, translocations, and alternative splicing of genes that directly code for or have downstream effects on key metabolic enzymes. Many of the same signaling pathways are routinely dysregulated in cancer and a number of important oncogenic signaling pathways play important regulatory roles in central carbon metabolism. Unraveling the complex regulatory relationship between cancer metabolism and signaling requires the application of systems biology approaches. Here we discuss computational approaches for modeling protein signal transduction and metabolism as well as how the regulatory relationship between these two important cellular processes can be combined into hybrid models.

Kinase inhibitors can produce off-target effects and activate linked pathways by retroactivity

BACKGROUND

It has been shown in experimental and theoretical work that covalently modified signaling cascades naturally exhibit bidirectional signal propagation via a phenomenon known as retroactivity. An important consequence of retroactivity, which arises due to enzyme sequestration in covalently modified signaling cascades, is that a downstream perturbation can produce a response in a component upstream of the perturbation without the need for explicit feedback connections. Retroactivity may, therefore, play an important role in the cellular response to a targeted therapy. Kinase inhibitors are a class of targeted therapies designed to interfere with a specific kinase molecule in a dysregulated signaling pathway. While extremely promising as anti-cancer agents, kinase inhibitors may produce undesirable off-target effects by non-specific interactions or pathway cross-talk. We hypothesize that targeted therapies such as kinase inhibitors can produce off-target effects as a consequence of retroactivity alone.

RESULTS

We used a computational model and a series of simple signaling motifs to test the hypothesis. Our results indicate that within physiologically and therapeutically relevant ranges for all parameters, a targeted inhibitor can naturally induce an off-target effect via retroactivity. The kinetics governing covalent modification cycles in a signaling network were more important for propagating an upstream off-target effect in our models than the kinetics governing the targeted therapy itself. Our results also reveal the surprising and crucial result that kinase inhibitors have the capacity to turn "on" an otherwise "off" parallel cascade when two cascades share an upstream activator.

CONCLUSIONS

A proper and detailed characterization of a pathway's structure is important for identifying the optimal protein to target as well as what concentration of the targeted therapy is required to modulate the pathway in a safe and effective manner. We believe our results support the position that such characterizations should consider retroactivity as a robust potential source of off-target effects induced by kinase inhibitors and other targeted therapies.

Abstract 4907: Elucidating the role of retroactive signaling and kinase inhibitors on off-target drug effects

The primary objective of targeted cancer therapies is to modulate cancer progression by perturbing specific molecules involved in aberrant proliferation and invasion. Kinase inhibitors are targeted therapies which are designed to interfere with a specific kinase molecule in a dysregulated oncogenic signaling cascade. While extremely promising as anti-cancer agents, such inhibitors may have undesirable off-target effects, whether by non-specific interactions or by effects from pathway cross-talk. We have shown in published experimental and theoretical work that covalently modified signaling cascades naturally exhibit bidirectional signal propagation. This phenomenon is termed retroactivity and challenges the widespread notion that information in cascades only flows from the cell surface to the nucleus. Previous work has demonstrated that increasing the concentration of a phosphatase in the terminal cycle of a covalently modified cascade may result in a measurable decrease in the concentration of the previous cycle's activated kinase. Thus, a downstream perturbation in a signaling cascade can produce a reverse (or retroactive) response without the need for direct negative feedback connections. This led us to hypothesize that the use of an inhibitory drug in a signaling network may cause an upstream off-target effect simply by inhibiting the activation or deactivation of a downstream kinase. To test the hypothesis that retroactivity contributes to off-target effects, we extended our previous work to a computational model that tested a series of signaling networks. The objective of our approach was two-fold: (1) to probe the effect of retroactivity on a kinase inhibitor in a signaling network and (2) to test whether retroactivity is likely to produce a measurable off-target effect under physiologically realistic conditions. Specifically, our model simulates the targeted inhibition of an activated kinase in a series of multi-cycle networks. The results of our work indicate that at physiologically and therapeutically relevant concentrations, a targeted inhibitor may induce a measurable off-target effect via retroactivity. We also performed local sensitivity analyses to predict the kinetic parameters that most affect the off-target response. Surprisingly, the drug disassociation constant is predicted to have very little effect while parameters such as the enzyme saturation and maximum velocity of some cycles are predicted to be very important. A proper characterization of a pathway's structure is important for identifying which protein in the pathway represents the optimal drug target as well as what concentration of the targeted therapy is likely to modulate the pathway in the manner desired. We believe our results support the position that such characterizations should consider the role of retroactivity as a source of a potential off-target effects by kinase inhibitors.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4907. doi:10.1158/1538-7445.AM2011-4907

Abstract 37: Alterations in the metabolism of highly motile cells: Implications for cancer metastasis

It has been known for some time that cancer cells have aberrant metabolism. Our lab observed that highly proliferative cells are not very motile, and likewise, highly motile cells do not proliferate rapidly. Thus, we hypothesized that cells direct their metabolism toward rapidly dividing or toward moving, but not both processes. This may be important in the progression of cancer from primary tumor to end-stage metastasis. By understanding how the metabolism and signaling pathways change in these very different situations, it may be possible to discover new drug targets to prevent cancers from progressing.

Traditionally, studies of cellular metabolism and proliferation have been measured in fixed cells. In order to truly understand a living system, quantitative measurements in live cells are necessary. To this end, we utilized the Fluorescent Ubiquitination-based Cell Cycle Indicator or FUCCI, developed by Sakaue-Sawano et al. This system involves two vectors, one of which encodes Geminin fused to a green fluorescent probe, and the other which encodes Cdt1 fused to a red fluorescent probe. The levels of these proteins are regulated inversely during the cell cycle, with Geminin at its highest level during S, G2, and M phase, while Cdt1 is highest during G1. Therefore, when our stable transfectants are dividing (in S, G2, or M phases), their nuclei fluoresce green, while in the G1 phase of the cell cycle, their nuclei fluoresce red.

Utilizing this system in our aggressive breast cancer cell lines enabled us to measure proliferation and metabolism in live cells. This also allowed us to directly test our hypothesis that actively motile cells are not proliferating (red), and proliferating cells (green) are not actively motile. By treating with different anti-proliferative and metabolic agents, we measured what percentage of cells cease dividing, and what percentage continued to grow. We used flow cytometry to separate these different populations and to determine genetic differences that may make them more or less resistant to these drugs.

We also examined known genes involved in both metabolism and motility. To do this, we measured protein and mRNA expression of several proteins in the glycolysis, lipid metabolism, and cholesterol synthesis pathways, as well as the small motility-related GTPases. From our findings, a preliminary model of the interaction between metabolism and metastasis is emerging. The proteins likely involved in metastasis are small GTPases. These proteins are modified by prenylation, allowing them to localize to the cell membrane and direct motility. Our results suggest that MDA-MB-231 cells may have a slower rate of glycolysis, but display increased cholesterol synthesis to prenylate proteins, while SUM 149 cells have an increased rate of glycolysis. This may help explain the differences in the phenotypes of these cancers, and may allow for the prediction of future drug targets to prevent cancer metastasis.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 37.

Temporal Patterns of Conditions and Symptoms Potentially Associated with Ovarian Cancer

BACKGROUND

The late stage at which ovarian cancer is typically diagnosed and its subsequent high mortality have been attributed to a lack of symptoms in its early stages. This study examined the temporal patterns of prediagnostic ovarian cancer symptoms and conditions among women with and without ovarian cancer.

METHODS

We identified 920 ovarian cancer cases from 1998-2002 claims and encounters from Thomson Healthcare's Medstat MarketScan Commercial Claims and Encounters and Medicare Supplemental Databases. These were matched with 2760 comparison women based on age, geographic region, Medicare eligibility, and health plan type. The rates of ovarian cancer-related symptoms, conditions, and procedures recorded in the claims data were compared between the two groups using chi-square and Student's t tests.

RESULTS

In the 270 to 31 days prior to the case diagnosis dates, cases had nearly five times more recorded abdominal symptoms (36.2% vs. 7.5%), 3.5 times more recorded female genital symptoms (9.8% vs. 2.7%), and 1.5-2 times more recorded gastrointestinal symptoms (7.7% vs. 3.5%), urethra/urinary tract disorders (12.7% vs. 6.4%), and menopausal disorders (12.4% vs. 7.5%) than the comparison women. However, when the data were examined in 30-day increments for these five diagnosed conditions, the rates for cases and comparison women only started to diverge as the cases' diagnosis drew closer-60-90 days prior.

CONCLUSIONS

The presence of ovarian cancer-related symptoms and conditions prior to diagnosis among cases was documented in claims data; however, this increase was most pronounced in the 2-3 months prior to diagnosis. It is likely that physicians will see similar symptoms and conditions for women with and without ovarian cancer during most of the 9 months prior to the cases' diagnosis.

Safety and efficacy of percutaneous transluminal angioplasty for intracranial atherosclerotic stenosis

BACKGROUND AND PURPOSE

Percutaneous transluminal angioplasty (PCTA) is increasingly used to treat extracerebral arterial stenosis. The present study evaluates the safety and efficacy of PCTA treatment of symptomatic intracranial atherosclerotic stenosis.

METHODS

A series of 22 vessels in 17 patients were treated with PCTA. All patients had recurrent neurological symptoms referable to the stenotic vessel despite optimal medical therapy. Critical (> 70%) arterial stenosis was confirmed by angiogram, and angioplasty was performed with a 3.0- to 3.5-mm Stealth balloon.

RESULTS

The average preangioplasty stenosis (North American Symptomatic Carotid Endarterectomy Trial criteria) was 72 +/- 8% (mean +/- SD), with a significant improvement seen after angioplasty; the best angiographic stenosis (after healing of intimal injury, if any) was 43 +/- 24% (P < .001). Overall PCTA was successful in 82% of the vessels. There were two strokes during angioplasty for a 30-day morbidity rate of 9.1% per treated vessel and 11.7% per case. The other 15 patients were clinically evaluated at 3 and 6 months; all cases were without further events. Restenosis was evaluated in 8 patients (12 vessels) with an angiogram at 6 months showing further improvement compared with the initial post-PCTA stenosis (51 +/- 10% versus 37 +/- 21% [P = .05]).

CONCLUSIONS

PCTA may be a beneficial therapy in selected cases of symptomatic intracranial atherosclerotic stenosis. Further study using a randomized trial is needed.

Safety and efficacy of delayed intraarterial urokinase therapy with mechanical clot disruption for thromboembolic stroke

PURPOSE

To evaluate safety and efficacy of delayed intraarterial urokinase therapy with mechanical disruption of clot to treat thromboembolic stroke.

METHODS

Thirteen patients with cerebral thrombolic disease (10 carotid territory, 3 basilar territory) were treated with catheter-directed intraarterial urokinase therapy with mechanical disruption of the clots. All patients were excluded from a 6-hour multicenter thrombolytic trial by either time, recent surgery, age, seizure, or myocardial infarction. Time elapsed before treatment ranged from 3.5 to 48 hours (12 +/- 13 hours), with 200,000 to 900,000 U of urokinase used.

RESULTS

Ten patients had successful vessel recanalization, confirmed by repeat angiography. Cases with distal branch vessel occlusions were less likely to recanalize. Asymptomatic hemorrhagic conversion occurred in 2 patients on repeat scans. Both acute neurologic and functional outcomes were assessed with significant improvement occurring in 9 (69%) of 13 patients at 48 hours (greater than four-point change on the National Institutes of Health scale) and in 100% of 3-month survivors. All patients who improved had normal initial CT scans.

CONCLUSIONS

Intraarterial cerebral thrombolysis with mechanical disruption of clot seems to be a useful therapy in selected stroke cases even after 6 hours.

Pseudoaneurysm of the thoracic aorta: a late complication of umbilical artery catheterization

Pseudoaneurysm of the thoracic aorta developed in an infant eight months following neonatal catheterization of the umbilical artery. Infection and placement of a stiff polyvinyl chloride catheter in the thoracic aorta appear to be the etiological factors. Preoperative diagnosis was posterior mediastinal tumor, and pseudoaneurysm was not included in the differential. Dacron graft patch angioplasty repair using partial cardiopulmonary bypass was successful. Postoperatively the patient has done well with no pressure gradient. Pseudoaneurysm should be suspected whenever a mediastinal mass appears in children who have had thoracic placement of umbilical artery catheters.