Shannon M. Mumenthaler

Pharmacologic inhibition of Pim kinases alters prostate cancer cell growth and resensitizes chemoresistant cells to taxanes

The serine/threonine family of Pim kinases function as oncogenes and have been implicated in prostate cancer progression, particularly in hormone-refractory prostate disease, as a result of their antiapoptotic function. In this study, we used a pharmacologic inhibitor targeting the Pim family members, SGI-1776, to determine whether modulation of Pim kinase activity could alter prostate cancer cell survival and modulate chemotherapy resistance. Extensive biochemical characterization of SGI-1776 confirmed its specificity for the three isoforms of the Pim family. Treatment of prostate cancer cells with SGI-1776 resulted in a dose-dependent reduction in phosphorylation of known Pim kinase substrates that are involved in cell cycle progression and apoptosis (p21Cip1/WAF1 and Bad). Consequently, SGI-1776 compromised overall cell viability by inducing G1 cell cycle arrest and triggering apoptosis. Overexpression of recombinant Pim-1 markedly increased sensitivity of SGI-1776–mediated prostate cancer cell apoptosis and p21Cip1/WAF1 phosphorylation inhibition, reinforcing the specificity of SGI-1776. An additional cytotoxic effect was observed when SGI-1776 was combined with taxane-based chemotherapy agents. SGI-1776 was able to reduce cell viability in a multidrug resistance 1 protein–based taxane-refractory prostate cancer cell line. In addition, SGI-1776 treatment was able to resensitize chemoresistant cells to taxane-based therapies by inhibiting multidrug resistance 1 activity and inducing apoptosis. These findings support the idea that inhibiting Pim kinases, in combination with a chemotherapeutic agent, could play an important role in prostate cancer treatment by targeting the clinical problem of chemoresistance. [Mol Cancer Ther 2009;8(10):2882–93]

Evolutionary modeling of combination treatment strategies to overcome resistance to tyrosine kinase inhibitors in non-small cell lung cancer

Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance.

The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells

Therapeutic resistance arises as a result of evolutionary processes driven by dynamic feedback between a heterogeneous cell population and environmental selective pressures. Previous studies have suggested that mutations conferring resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in non-small-cell lung cancer (NSCLC) cells lower the fitness of resistant cells relative to drug-sensitive cells in a drug-free environment. Here, we hypothesize that the local tumor microenvironment could influence the magnitude and directionality of the selective effect, both in the presence and absence of a drug. Using a combined experimental and computational approach, we developed a mathematical model of preexisting drug resistance describing multiple cellular compartments, each representing a specific tumor environmental niche. This model was parameterized using a novel experimental dataset derived from the HCC827 erlotinib-sensitive and -resistant NSCLC cell lines. We found that, in contrast to in the drug-free environment, resistant cells may hold a fitness advantage compared to parental cells in microenvironments deficient in oxygen and nutrients. We then utilized the model to predict the impact of drug and nutrient gradients on tumor composition and recurrence times, demonstrating that these endpoints are strongly dependent on the microenvironment. Our interdisciplinary approach provides a model system to quantitatively investigate the impact of microenvironmental effects on the evolutionary dynamics of tumor cells.

CD4+ NK cells can be productively infected with HIV, leading to downregulation of CD4 expression and changes in function.

NK cells mediate the innate immune response, and HIV-infected individuals demonstrate altered NK cell phenotype and function. We find that CD4+ NK cells are susceptible to HIV infection; this could account for the NK cell dysfunction seen in HIV-infected individuals. CD4+ NK cells express CXCR4 and can be infected with X4-tropic viruses and some primary R5-utilizing viral isolates. Treatment with the CXCR4 ligands AMD3100 and SDF-1alpha partially blocks infection with X4-tropic virus, treatment with anti-CCL Igs upregulates CCR5 surface expression and enables infection with HIV-Bal. HIV infection of NK cells results in CD4 downregulation and the production of infectious virus. HIV-infected CD4+ NK cells mediate NK cell cytotoxicity, however, HIV infection is associated with decreased chemotaxis towards IL-16. Thus, HIV infection of CD4+ NK cells could account for the NK cell dysfunction observed in HIV-infected individuals. Furthermore infected NK cells could serve as a viral reservoir of HIV in vivo.

Cancer as a moving target: understanding the composition and rebound growth kinetics of recurrent tumors.

We introduce a stochastic branching process model of diversity in recurrent tumors whose growth is driven by drug resistance. Here, an initially declining population can escape certain extinction via the production of mutants whose fitness is drawn at random from a mutational fitness landscape. Using a combination of analytical and computational techniques, we study the rebound growth kinetics and composition of the relapsed tumor. We find that the diversity of relapsed tumors is strongly affected by the shape of the mutational fitness distribution. Interestingly, the model exhibits a qualitative shift in behavior depending on the balance between mutation rate and initial population size. In high mutation settings, recurrence timing is a strong predictor of the diversity of the relapsed tumor, whereas in the low mutation rate regime, recurrence timing is a good predictor of tumor aggressiveness. Analysis reveals that in the high mutation regime, stochasticity in recurrence timing is driven by the random survival of small resistant populations rather than variability in production of resistance from the sensitive population, whereas the opposite is true in the low mutation rate setting. These conclusions contribute to an evolutionary understanding of the suitability of tumor size and time of recurrence as prognostic and predictive factors in cancer.

A high-content image-based method for quantitatively studying context-dependent cell population dynamics

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.

Modeling Multiscale Necrotic and Calcified Tissue Biomechanics in Cancer Patients: Application to Ductal Carcinoma In Situ (DCIS)

Tissue necrosis and calcification significantly affect cancer progression and clinical treatment decisions. Necrosis and calcification are inherently multiscale processes, operating at molecular to tissue scales with time scales ranging from hours to months. This chapter details key insights we have gained through mechanistic continuum and discrete multiscale models, including the first modeling of necrotic cell swelling, lysis, and calcification. Among our key findings: necrotic volume loss contributes to steady tumor sizes but can destabilize tumor morphology; steady necrotic fractions can emerge even during unstable growth; necrotic volume loss is responsible for linear ductal carcinoma in situ (DCIS) growth; fast necrotic cell swelling creates mechanical tears at the perinecrotic boundary; multiscale interactions give rise to an age-structured, stratified necrotic core; and mechanistic, patient-calibrated DCIS modeling allows us to assess our working biological assumptions and better interpret pathology and mammography. We finish by outlining our integrative computational oncology approach to developing computational tools that we hope will one day assist clinicians and patients in their treatment decisions.

Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma

We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.

Anti-MET ImmunoPET for Non–Small Cell Lung Cancer Using Novel Fully Human Antibody Fragments

MET, the receptor of hepatocyte growth factor, plays important roles in tumorigenesis and drug resistance in numerous cancers, including non–small cell lung cancer (NSCLC). As increasing numbers of MET inhibitors are being developed for clinical applications, antibody fragment–based immunopositron emission tomography (immunoPET) has the potential to rapidly quantify in vivo MET expression levels for drug response evaluation and patient stratification for these targeted therapies. Here, fully human single-chain variable fragments (scFvs) isolated from a phage display library were reformatted into bivalent cys-diabodies (scFv-cys dimers) with affinities to MET ranging from 0.7 to 5.1 nmol/L. The candidate with the highest affinity, H2, was radiolabeled with 89Zr for immunoPET studies targeting NSCLC xenografts: low MET-expressing Hcc827 and the gefitinib-resistant Hcc827-GR6 with 4-fold MET overexpression. ImmunoPET at as early as 4 hours after injection produced high-contrast images, and ex vivo biodistribution analysis at 20 hours after injection showed about 2-fold difference in tracer uptake levels between the parental and resistant tumors (P < 0.01). Further immunoPET studies using a larger fragment, the H2 minibody (scFv-CH3 dimer), produced similar results at later time points. Two of the antibody clones (H2 and H5) showed in vitro growth inhibitory effects on MET-dependent gefitinib-resistant cell lines, whereas no effects were observed on resistant lines lacking MET activation. In conclusion, these fully human antibody fragments inhibit MET-dependent cancer cells and enable rapid immunoPET imaging to assess MET expression levels, showing potential for both therapeutic and diagnostic applications. Mol Cancer Ther; 13(11); 2607–17. ©2014 AACR.

Disruption of arginase II alters prostate tumor formation in TRAMP mice

Arginase II (AII) is involved in the polyamine synthetic pathway, and elevated levels of expression have been found in a high proportion of prostate cancer samples and patients. However, the biological function of arginase II in prostate cancer still remains to be elucidated. In this study, we utilized the TRAMP mouse prostate cancer model to better understand the contribution of AII on tumor development.