Predictive modeling of in vivo response to gemcitabine in pancreatic cancer

Modeling tumor dynamics and predicting response to chemo-, targeted-, and immune-therapies in a murine model of pancreatic cancer

We seek to establish a parsimonious mathematical framework for understanding the interaction and dynamics of the response of pancreatic cancer to the NGC triple chemotherapy regimen (mNab-paclitaxel, gemcitabine, and cisplatin), stromal-targeting drugs (calcipotriol and losartan), and an immune checkpoint inhibitor (anti-PD-L1). We developed a set of ordinary differential equations describing changes in tumor size (growth and regression) under the influence of five cocktails of treatments. Model calibration relies on three tumor volume measurements obtained over a 14-day period in a genetically engineered pancreatic cancer model (KrasLSLG12D-Trp53LSLR172H-Pdx1-Cre). Our model reproduces tumor growth in the control and treatment scenarios with an average concordance correlation coefficient (CCC) of 0.99±0.01. We conduct leave-one-out predictions (average CCC=0.74±0.06), mouse-specific predictions (average CCC=0.75±0.02), and hybrid, group-informed, mouse-specific predictions (average CCC=0.85±0.04). The developed mathematical model demonstrates high accuracy in fitting the experimental tumor data and a robust ability to predict tumor response to treatment. This approach has important implications for optimizing combination NGC treatment strategies.

Dynamic 2-deoxy-D-glucose-enhanced multispectral optoacoustic tomography for assessing metabolism and vascular hemodynamics of breast cancer

Clinical tools for measuring tumor vascular hemodynamics, such as dynamic contrast-enhanced MRI, are clinically important to assess tumor properties. Here we explored the use of multispectral optoacoustic tomography (MSOT), which has a high spatial and temporal resolution, to measure the intratumoral pharmacokinetics of a near-infrared-dye-labeled 2-Deoxyglucose, 2-DG-800, in orthotropic 2-LMP breast tumors in mice. As uptake of 2-DG-800 is dependent on both vascular properties, and glucose transporter activity - a widely-used surrogate for metabolism, we evaluate hemodynamics of 2-DG-MP by fitting the dynamic MSOT signal of 2-DG-800 into two-compartment models including the extended Tofts model (ETM) and reference region model (RRM). We showed that dynamic 2-DG-enhanced MSOT (DGE-MSOT) is powerful in acquiring hemodynamic rate constants, including Ktrans and Kep, via systemically injecting a low dose of 2-DG-800 (0.5 µmol/kg b.w.). In our study, both ETM and RRM are efficient in deriving hemodynamic parameters in the tumor. Area-under-curve (AUC) values (which correlate to metabolism), and Ktrans and Kep values, can effectively distinguish tumor from muscle. Hemodynamic parameters also demonstrated correlations to hemoglobin, oxyhemoglobin, and blood oxygen level (SO2) measurements by spectral unmixing of the MSOT data. Together, our study for the first time demonstrated the capability of DGE-MSOT in assessing vascular hemodynamics of tumors.

Inclusion of cancer-associated fibroblasts in drug screening assays to evaluate pancreatic cancer resistance to therapeutic drugs

Pancreatic ductal adenocarcinoma (PDAC) is characterised by a pro-inflammatory stroma and multi-faceted microenvironment that promotes and maintains tumorigenesis. However, the models used to test new and emerging therapies for PDAC have not increased in complexity to keep pace with our understanding of the human disease. Promising therapies that pass pre-clinical testing often fail in pancreatic cancer clinical trials. The objective of this study was to investigate whether changes in the drug-dosing regimen or the addition of cancer-associated fibroblasts (CAFs) to current existing models can impact the efficacy of chemotherapy drugs used in the clinic. Here, we reveal that gemcitabine and paclitaxel markedly reduce the viability of pancreatic cell lines, but not CAFs, when cultured in 2D. Following the use of an in vitro drug pulsing experiment, PDAC cell lines showed sensitivity to gemcitabine and paclitaxel. However, CAFs were less sensitive to pulsing with gemcitabine compared to their response to paclitaxel. We also identify that a 3D co-culture model of MIA PaCa-2 or PANC-1 with CAFs showed an increased chemoresistance to gemcitabine when compared to standard 2D mono-cultures a difference to paclitaxel which showed no measurable difference between the 2D and 3D models, suggesting a complex interaction between the drug in study and the cell type used. Changes to standard 2D mono-culture-based assays and implementation of 3D co-culture assays lend complexity to established models and could provide tools for identifying therapies that will match clinically the success observed with in vitro models, thereby aiding in the discovery of novel therapies.

Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC.

RESULTS

In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines.

CONCLUSION

Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery

BACKGROUND

Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.

METHODS

A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.

RESULTS

Our model shows that a tipping point-a sharp state transition between immune control and immune evasion-induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.

CONCLUSION

These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient's distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.

The neutral red assay can be used to evaluate cell viability during autophagy or in an acidic microenvironment in vitro

Harsh conditions within the tumor microenvironment, such as hypoxia and extracellular acidic pH (pHe), inactivate some chemotherapies, which results in limited or no cytotoxicity. Standard MTT, ATPlite and protease assays that are used to determine the potency of newly developed drugs often give erroneous results when applied under hypoxic or acidic conditions. Therefore, development of a cytotoxicity assay that does not yield false positive or false negative results under circumstances of both hypoxia and acidic pHe is needed. We evaluated currently used cell viability assays as well as neutral red staining to assess viability of ovarian and pancreatic cancer cells grown in an acidic pHe microenvironment after treatment with carboplatin, gemcitabine or chloroquine. We validated cell viability using western blotting of pro-caspase-9 and cleaved-caspase-9, and LC3-I and - II. Standard cell viability assays indicated cell viability accurately at pHe 7.4, but was not correlated with induction of apoptosis or autophagy at acidic pHe. By contrast, our modified neutral red assay detected cell viability accurately over a range of pHe as demonstrated by its correlation with induction of apoptosis and autophagy. Neutral red staining is effective for evaluating the effect of chemotherapeutic agents on cell viability under acidic pHe or hypoxic conditions.

Antitumor effect of VEGFR2-targeted microbubble destruction with gemcitabine using an endoscopic ultrasound probe: In vivo mouse pancreatic ductal adenocarcinoma model

BACKGROUND

Ultrasound-targeted microbubble destruction (UTMD) induces cellular inflow of drugs at low intensity, while high intensity eradicates tumor vessels. Since vascular endothelial growth factor receptor 2 (VEGFR2) is highly expressed in pancreatic ductal adenocarcinoma (PDAC), VEGFR2-targeted microbubble (MB) might additionally increase the tissue specificity of drugs and thus improve antitumor effects. In addition, fixing the dual pulse intensity could maximize MB properties. This study evaluated the one-off (experiment 1) and cumulative (experiment 2) treatment effect of UTMD by regulating the dual pulse output applied to PDAC using VEGFR2-targeted MB.

METHODS

C57BL/6 mice inoculated with Pan-02 cells were allocated to five groups: VEGFR2-targeted MB+ gemcitabine (GEM), VEGFR2-targeted MB, non-targeted MB+GEM, GEM, and control groups. After injection of GEM or GEM and either VEGFR2-targeted or non-targeted MB, UTMD was applied for several minutes at low intensity followed by high intensity application. In experiment 1, mice were treated by the protocol described above and then euthanized immediately or at the tumor diameter doubling time (TDT). In experiment 2, the same protocol was repeated weekly and mice were euthanized at TDT regardless of protocol completion. Histological analysis by CD31 and VEGFR2 staining provided microvascular density (MVD) and VEGFR2 expression along vessels (VEGFR2v) or intra/peripheral cells (VEGFR2c).

RESULTS

In experiment 1, TDT was significantly longer in the VEGFR2-targeted MB+GEM group compared to the non-targeted MB+GEM, GEM, and control groups, while the VEGFR2-targeted MB group showed no statistical significance. MVD and VEGFR2v in the immediate euthanasia was significantly lower in the VEGFR2-targeted MB+GEM and VEGFR2-targeted MB groups than other conditions. In experiment 2, the VEGFR2-targeted MB+GEM group produced significantly longer TDT than the GEM or control groups, whereas the VEGFR2-targeted MB group showed no significant difference. Histology revealed significantly reduced VEGFR2v and VEGFR2c in the VEGFR2-targeted and non-targeted MB+GEM groups, while only VEGFR2v was significantly less in the VEGFR2-targeted MB group.

CONCLUSIONS

UTMD-mediated GEM therapy with the dual pulse application using VEGFR2-targeted MB substantially suppresses PDCA growth.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

Actively Targeted Nanodelivery of Echinomycin Induces Autophagy-Mediated Death in Chemoresistant Pancreatic Cancer In Vivo

Pancreatic cancer remains a recalcitrant neoplasm associated with chemoresistance and high fatality. Because it is frequently resistant to apoptosis, exploiting autophagic cell death could offer a new treatment approach. We repurpose echinomycin, an antibiotic encapsulated within a syndecan-1 actively targeted nanoparticle, for treatment of pancreatic cancer. Tumor-specific uptake, biodistribution, efficacy of nanodelivered echinomycin, and mechanism of cell death were assessed in aggressive, metastatic models of pancreatic cancer. In these autophagic-dependent pancreatic cancer models, echinomycin treatment resulted in autophagic cell death noted by high levels of LC3 among other autophagy markers, but without hallmarks of apoptosis, e.g., caspase activation and chromatin fragmentation, or necrosis, e.g., plasma membrane degradation and chromatin condensation/degrading. In vivo, biodistribution of syndecan-1-targeted nanoparticles indicated preferential S2VP10 or S2CP9 tumor uptake compared to the liver and kidney (S2VP10 p = 0.0016, p = 0.00004 and S2CP9 p = 0.0009, p = 0.0001). Actively targeted nanodelivered echinomycin resulted in significant survival increases compared to Gemzar (S2VP10 p = 0.0003, S2CP9 p = 0.0017) or echinomycin only (S2VP10 p = 0.0096, S2CP9 p = 0.0073). We demonstrate that actively targeted nanodelivery of echinomycin results in autophagic cell death in pancreatic and potentially other high-autophagy, apoptosis-resistant tumors. Collectively, these findings support syndecan-1-targeted delivery of echinomycin and dysregulation of autophagy to induce cell death in pancreatic cancer.

Quantification of fluorophore distribution and therapeutic response in matched in vivo and ex vivo pancreatic cancer model systems

Therapeutic resistance plagues cancer outcomes, challenging treatment particularly in aggressive disease. A unique method to decipher drug interactions with their targets and inform therapy is to employ fluorescence-based screening tools; however, to implement productive screening assays, adequate model systems must be developed. Patient-derived pancreatic cancer models (e.g., cell culture, patient-derived xenograft mouse models, and organoids) have been traditionally utilized to predict personalized therapeutic response. However, cost, long read out times and the inability to fully recapitulate the tumor microenvironment have rendered most models incompatible with clinical decision making for pancreatic ductal adenocarcinoma (PDAC) patients. Tumor explant cultures, where patient tissue can be kept viable for up to weeks, have garnered interest as a platform for delivering personalized therapeutic prediction on a clinically relevant timeline. To fully explore this ex vivo platform, a series of studies were completed to quantitatively compare in vivo models with tumor explants, examining gemcitabine therapeutic efficacy, small molecule uptake and drug-target engagement using a novel fluorescently-labeled gemcitabine conjugate. This initial work shows promise for patient-specific therapeutic selection, where tumor explant drug distribution and response recapitulated the in vivo behavior and could provide a valuable platform for understanding mechanisms of therapeutic response and resistance.

Mathematical Modeling to Address Challenges in Pancreatic Cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.

Hyaluronidase-Expressing Salmonella Effectively Targets Tumor-Associated Hyaluronic Acid in Pancreatic Ductal Adenocarcinoma

In pancreatic ductal adenocarcinoma (PDAC), the extracellular matrix (ECM) surrounding cancer cells forms a barrier that often limits the ability of chemotherapeutic drugs and cytotoxic immune subsets to penetrate and eliminate tumors. The dense stromal matrix protecting cancer cells, also known as desmoplasia, results from the overproduction of major ECM components such as collagens and hyaluronic acid (HA). Although candidate drugs targeting ECM components have shown promise in increasing penetration of chemotherapeutic agents, severe adverse effects associated with systemic depletion of ECM in peripheral healthy tissues limits their use at higher, more effective doses. Currently, few strategies exist that preferentially degrade ECM in tumor tissue over healthy tissues. In light of this, we have developed an attenuated, tumor-targeting Salmonella typhimurium (ST) expressing functional bacterial hyaluronidase (bHs-ST), capable of degrading human HA deposited within PDAC tumors. Our data show that bHs-ST (i) targets and colonizes orthotopic human PDAC tumors following systemic administration and (ii) is efficiently induced in vivo to deplete tumor-derived HA, which in turn (iii) significantly increases diffusion of Salmonella typhimurium within desmoplastic tumors. BHs-ST represents a promising new tumor ECM-targeting strategy that may be instrumental in minimizing off-tumor toxicity while maximizing drug delivery into highly desmoplastic tumors.

A Study Comparing the Effects of Targeted Intra-Arterial and Systemic Chemotherapy in an Orthotopic Mouse Model of Pancreatic Cancer

Systemic chemotherapy is the first line treatment for patients with unresectable pancreatic cancer, however, insufficient drug delivery to the pancreas is a major problem resulting in poor outcomes. We evaluated the therapeutic effects of targeted intra-arterial (IA) delivery of gemcitabine directly into the pancreas in an orthotopic mouse model of pancreatic cancer. Nude mice with orthotopic pancreatic tumors were randomly assigned into 3 groups receiving gemcitabine: systemic intravenous (IV) injection (low: 0.3 mg/kg and high: 100 mg/kg) and direct IA injection (0.3 mg/kg). Treatments were administered weekly for 2 weeks. IA treatment resulted in a significantly greater reduction in tumor growth compared to low IV treatment. To achieve a comparable reduction in tumor growth as seen with IA treatment, gemcitabine had to be given IV at over 300x the dose (high IV treatment) which was associated with some toxicity. After 2 weeks, tumor samples from animals treated with IA gemcitabine had significantly lower residual cancer cells, higher cellular necrosis and evidence of increased apoptosis when compared to animals treated with low IV gemcitabine. Our study shows targeted IA injection of gemcitabine directly into the pancreas, via its arterial blood supply, has a superior therapeutic effect in reducing tumor growth compared to the same concentration administered by conventional systemic injection.

Pharmacokinetic/pharmacodynamic modeling of combination-chemotherapy for lung cancer

Chemotherapy for non-small cell lung cancer (NSCLC) typically involves a doublet regimen for a number of cycles. For any particular patient, a course of treatment is usually chosen from a large number of combinational protocols with drugs in concomitant or sequential administration. In spite of newer drugs and protocols, half of patients with early disease will live less than five years and 95% of those with advanced disease survive for less than one year. Here, we apply mathematical modeling to simulate tumor response to multiple drug regimens, with the capability to assess maximum tolerated dose (MTD) as well as metronomic drug administration. We couple pharmacokinetic-pharmacodynamic intracellular multi-compartment models with a model of vascularized tumor growth, setting input parameters from in vitro data, and using the models to project potential response in vivo. This represents an initial step towards the development of a comprehensive virtual system to evaluate tumor response to combinatorial drug regimens, with the goal to more efficiently identify optimal course of treatment with patient tumor-specific data. We evaluate cisplatin and gemcitabine with clinically-relevant dosages, and simulate four treatment NSCLC scenarios combining MTD and metronomic therapy. This work thus establishes a framework for systematic evaluation of tumor response to combination chemotherapy. The results with the chosen parameter set indicate that although a metronomic regimen may provide advantage over MTD, the combination of these regimens may not necessarily offer improved response. Future model evaluation of chemotherapy possibilities may help to assess their potential value to obtain sustained NSCLC regression for particular patients, with the ultimate goal of optimizing multiple-drug chemotherapy regimens in clinical practice.

The influence of soluble fragments of extracellular matrix (ECM) on tumor growth and morphology

A major challenge in matrix-metalloproteinase (MMP) target validation and MMP-inhibitor-drug development for anti-cancer clinical trials is to better understand their complex roles (often competing with each other) in tumor progression. While there is extensive research on the growth-promoting effects of MMPs, the growth-inhibiting effects of MMPs has not been investigated thoroughly. So we develop a continuum model of tumor growth and invasion including chemotaxis and haptotaxis in order to examine the complex interaction between the tumor and its host microenvironment and to explore the inhibiting influence of the gradients of soluble fragments of extracellular matrix (ECM) density on tumor growth and morphology. Previously, it was shown both computationally (in one spatial dimension) and experimentally that the chemotactic pull due to soluble ECM gradients is anti-invasive, contrary to the traditional view of the role of chemotaxis in malignant invasion [1]. With two-dimensional numerical simulation and using a level set based tumor-host interface capturing method, we examine the effects of chemotaxis on the progression and morphology of a tumor growing in nutrient-rich and nutrient-poor microenvironments which was not investigated before. In particular we examine how the geometry of the growing tumor is affected when placed in different environments. We also investigate the effects of varying ECM degradation rate, the production rate of matrix degrading enzymes (MDE), and the conversion of ECM into soluble ECM. We find that chemotaxis due to ECM-fragment gradients strongly influences tumor growth and morphology, and that the instabilities caused by tumor cell proliferation and haptotactic movements can be prevented if chemotaxis is sufficiently strong. The influence of chemotaxis and the above factors on tumor growth and morphology are found to be more prominent in nutrient-poor environments than in nutrient-rich environments. So we extend our investigations of these antinvasive chemotactic influences by examining the effects of cell-cell and cell-ECM adhesion and low proliferation rate for tumors growing in low-nutrient environments. We find that as the extent of chemotaxis increases, the effects of adhesion on tumor growth and shape become negligible. Under conditions of low cell mitosis, chemotaxis may cause the tumor to shrink, as the extent of chemotaxis increases. Both stable and unstable tumor shrinkage are predicted by our model. Unexpectedly, in some cases chemotaxis may contribute toward developing instability where haptotaxis alone induces stable growth.

Automated analysis of co-localized protein expression in histologic sections of prostate cancer

An automated approach based on routinely-processed, whole-slide immunohistochemistry (IHC) was implemented to study co-localized protein expression in tissue samples. Expression of two markers was chosen to represent stromal (CD31) and epithelial (Ki-67) compartments in prostate cancer. IHC was performed on whole-slide sections representing low-, intermediate-, and high-grade disease from 15 patients. The automated workflow was developed using a training set of regions-of-interest in sequential tissue sections. Protein expression was studied on digital representations of IHC images across entire slides representing formalin-fixed paraffin embedded blocks. Using the training-set, the known association between Ki-67 and Gleason grade was confirmed. CD31 expression was more heterogeneous across samples and remained invariant with grade in this cohort. Interestingly, the Ki-67/CD31 ratio was significantly increased in high (Gleason ≥ 8) versus low/intermediate (Gleason ≤7) samples when assessed in the training-set and the whole-tissue block images. Further, the feasibility of the automated approach to process Tissue Microarray (TMA) samples in high throughput was evaluated. This work establishes an initial framework for automated analysis of co-localized protein expression and distribution in high-resolution digital microscopy images based on standard IHC techniques. Applied to a larger sample population, the approach may help to elucidate the biologic basis for the Gleason grade, which is the strongest, single factor distinguishing clinically aggressive from indolent prostate cancer.

Proteomic strategies in the search for novel pancreatic cancer biomarkers and drug targets: recent advances and clinical impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers; despite a low incidence rate it is the fourth leading cause of cancer-related death in the world. Improvement of the diagnosis, prognosis and treatment remains the main focus of pancreatic cancer research. Rapid developments in proteomic technologies has improved our understanding of the pancreatic cancer proteome. Here, the authors summarise the recent proteomic strategies undertaken in the search for: novel biomarkers for early diagnosis, pancreatic cancer-specific proteins which may be used for novel targeted therapies and proteins which may be useful for monitoring disease progression post-therapy. Recent advances and findings discussed here provide great promise of having a significant clinical impact and improving the outcome of patients with this malignancy.

In Silico Oncology: Quantification of the In Vivo Antitumor Efficacy of Cisplatin-Based Doublet Therapy in Non-Small Cell Lung Cancer (NSCLC) through a Multiscale Mechanistic Model

The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs’ cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions.

Less than 14% of medically treated patients with locally advanced and metastatic non-small cell lung cancer are expected to be alive 5 years after diagnosis. Standard therapeutic strategies include the administration of two drugs in combination, aiming at shrinking the tumor before surgery and improving overall survival. Knowing the sensitivity profile of each patient to different treatment strategies at diagnosis may help choose the most appropriate ones. We develop a methodology for the quantitative estimation of the cytotoxic efficacy of cisplatin-based doublets on cancer cells by applying a simulation model of cancer progression and response. The model incorporates the proliferation cycle, quiescence, differentiation and loss of tumor cells. We evaluate the effect of in vivo microenvironment of real tumors, as expressed by measurable tumor proliferation kinetics, such as how fast the tumor grows, the percentage of cells that are actively dividing, the resistance of stem cells, etc. on treatment outcome so as to derive more accurate estimates. A literature survey guides the selection of values. The methodology is applied to a real clinical dataset of patients. Correlation studies between the derived cytotoxicities and the patients’ molecular profile could lead to predictions of treatment response at the time of diagnosis.

Novel Holistic Approaches for Overcoming Therapy Resistance in Pancreatic and Colon Cancers

Gastrointestinal (GI) cancers, such as of the colon and pancreas, are highly resistant to both standard and targeted therapeutics. Therapy-resistant and heterogeneous GI cancers harbor highly complex signaling networks (the resistome) that resist apoptotic programming. Commonly used gemcitabine or platinum-based regimens fail to induce meaningful (i.e. disease-reversing) perturbations in the resistome, resulting in high rates of treatment failure. The GI cancer resistance networks are, in part, due to interactions between parallel signaling and aberrantly expressed microRNAs (miRNAs) that collectively promote the development and survival of drug-resistant cancer stem cells with epithelial-to-mesenchymal transition (EMT) characteristics. The lack of understanding of the resistance networks associated with this subpopulation of cells as well as reductionist, single protein-/pathway-targeted approaches have made 'effective drug design' a difficult task. We propose that the successful design of novel therapeutic regimens to target drug-resistant GI tumors is only possible if network-based drug avenues and agents, in particular 'natural agents' with no known toxicity, are correctly identified. Natural agents (dietary agents or their synthetic derivatives) can individually alter miRNA profiles, suppress EMT pathways and eliminate cancer stem-like cells that derive from pancreatic cancer and colon cancer, by partially targeting multiple yet meaningful networks within the GI cancer resistome. However, the efficacy of these agents as combinations (e.g. consumed in the diet) against this resistome has never been studied. This short review article provides an overview of the different challenges involved in the understanding of the GI resistome, and how novel computational biology can help in the design of effective therapies to overcome resistance.

Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor

Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model implements protein production, transport, and shedding based on tumor vascularization, cell proliferation, hypoxia, and necrosis, thus quantitatively relating the tumor and circulating proteomes. The results highlight the dynamics of shedding as a function of protein diffusivity and production. Linking the simulated tumor parameters to clinical tumor and vascularization measurements could potentially enable this approach to reveal the tumor-specific conditions based on the protein detected in circulation and thus help to more accurately manage cancer diagnosis and treatment.

Treatment of non-muscle invasive bladder cancer with Bacillus Calmette-Guerin (BCG): Biological markers and simulation studies

Intravesical Bacillus Calmette-Guerin (BCG) vaccine is the preferred first line treatment for non-muscle invasive bladder carcinoma (NMIBC) in order to prevent recurrence and progression of cancer. There is ongoing need for the rational selection of i) BCG dose, ii) frequency of BCG administration along with iii) synergistic adjuvant therapy and iv) a reliable set of biochemical markers relevant to tumor response. In this review we evaluate cellular and molecular markers pertinent to the immunological response triggered by the BCG instillation and respective mathematical models of the treatment. Specific examples of markers include diverse immune cells, genetic polymorphisms, miRNAs, epigenetics, immunohistochemistry and molecular biology 'beacons' as exemplified by cell surface proteins, cytokines, signaling proteins and enzymes. We identified tumor associated macrophages (TAMs), human leukocyte antigen (HLA) class I, a combination of Ki-67/CK20, IL-2, IL-8 and IL-6/IL-10 ratio as the most promising markers for both pre-BCG and post-BCG treatment suitable for the simulation studies. The intricate and patient-specific nature of these data warrants the use of powerful multi-parametral mathematical methods in combination with molecular/cellular biology insight and clinical input.

Salmonella-Based Therapy Targeting Indoleamine 2,3-Dioxygenase Coupled with Enzymatic Depletion of Tumor Hyaluronan Induces Complete Regression of Aggressive Pancreatic Tumors

Bacterial-based therapies are emerging as effective cancer treatments and hold promise for refractory neoplasms, such as pancreatic ductal adenocarcinoma (PDAC), which has not shown significant improvement in therapy for more than 25 years. Using a novel combination of shIDO-ST, a Salmonella-based therapy targeting the immunosuppressive molecule indoleamine 2,3-dioxygenase (IDO), with an enzyme, PEGPH20, which depletes extracellular matrix hyaluronan, we observed extended survival with frequent total regression of autochthonous and orthotopic PDAC tumors. This observation was associated with migration and accumulation of activated polymorphonuclear neutrophils (PMN) from spleens into tumors, which was not seen using a scrambled control (shScr-ST). Purified splenic PMNs from PEGPH20/shIDO-ST-treated mice exhibited significant IDO knockdown and were able to kill tumor targets ex vivo through mechanisms involving FasL and serine proteases. In addition, CD8(+) T cells were observed to contribute to late control of pancreatic tumors. Collectively, our data demonstrate that entry of shIDO-ST and PMNs into otherwise impermeable desmoplastic tumors is facilitated by PEGPH20-mediated HA removal, further highlighting an important component of effective treatment for PDAC.