Eleni Ch. Georgiadi

Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaptation of a multiscale cancer model.

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the models parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumors growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem.

The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context

This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

Prognostic significance of immunohistochemical expression of the angiogenic molecules vascular endothelial growth factor-A, vascular endothelial growth factor receptor-1 and vascular endothelial growth factor receptor-2 in patients with classical Hodgkin lymphoma.

Abstract Angiogenesis leads to new blood vessel formation and is implicated in both physiological and pathological situations. The vascular endothelial growth factor (VEGF) family is the major mediator of this process. The aim of our study was to evaluate the expression of VEGF-A, vascular endothelial growth factor receptor-1 (VEGFR-1) and VEGFR-2 and their correlation with clinicopathological parameters and prognosis in patients with classical Hodgkin lymphoma (cHL), since the role of angiogenesis in this tumor still remains unclear. The immunohistochemical expression of VEGF-A, VEGFR-1 and VEGFR-2 was examined in 194 patients with cHL. The neoplastic Hodgkin Reed–Sternberg (HRS) cells expressed VEGF-A, VEGFR-1 and VEGFR-2 in 90.3%, 97.2% and 94.1% of cases, respectively. Only the expression of VEGFR-2 was positively correlated with serum albumin levels ≥ 4 g/dL. No correlation with patient outcome was observed. All three molecules were statistically correlated with ramifications of blood vessels. Summarizing, our results are not sufficient to consider VEGF-A and/or VEGF receptors as prognosticators in cHL.

Towards in silico oncology: Adapting a four dimensional nephroblastoma treatment model to a clinical trial case based on multi-method sensitivity analysis

In the past decades a great progress in cancer research has been made although medical treatment is still widely based on empirically established protocols which have many limitations. Computational models address such limitations by providing insight into the complex biological mechanisms of tumor progression. A set of clinically-oriented, multiscale models of solid tumor dynamics has been developed by the In Silico Oncology Group (ISOG), Institute of Communication and Computer Systems (ICCS)-National Technical University of Athens (NTUA) to study cancer growth and response to treatment. Within this context using certain representative parameter values, tumor growth and response have been modeled under a cancer preoperative chemotherapy protocol in the framework of the SIOP 2001/GPOH clinical trial. A thorough cross-method sensitivity analysis of the model has been performed. Based on the sensitivity analysis results, a reasonable adaptation of the values of the model parameters to a real clinical case of bilateral nephroblastomatosis has been achieved. The analysis presented supports the potential of the model for the study and eventually the future design of personalized treatment schemes and/or schedules using the data obtained from in vitro experiments and clinical studies.

Translating multiscale cancer models into clinical trials: Simulating breast cancer tumor dynamics within the framework of the “Trial of Principle” clinical trial and the ACGT project.

The potential of cancer multilevel modeling has been particularly emphasized over the past years. Integration of multiscale experimental and clinical information pertaining to cancer via advanced computer models seems to considerably accelerate optimization of cancer treatment in the patient individualized context. However, a sine qua non prerequisite for such models to reach clinical practice is to be thoroughly tested through clinical trials for validation and optimization purposes. This is one of the major goals of the European Commission funded ldquoadvancing clinico-genomic trials on cancerrdquo (ACGT) project. This paper presents a discrete state based, four dimensional, multiscale tumor dynamics model that has been specially developed by the in silico oncology group in order to mimick the trial of principle (TOP) clinical trial concerning breast cancer treated with epirubicin. The TOP trial constitutes one of the ACGT clinical trials. A substantial part of the model can address other tumor types as well. The actual pseudoanonymized imaging, histopathological, molecular and clinical data of the patient are exploited. Special emphasis is put on the effect of cancer stem/clonogenic, progenitor, differentiated and dead cells, the cell category transition rates and the cell category relative populations within the tumor from the treatment baseline onwards. The importance of adaptation of the cell category relative populations to the cell category transition rates for free tumor growth is revealed and the concept of a pertinent nomogram is introduced. A method which ensures adaptation of these two sets of entities at the beginning of the simulation execution is proposed and subsequently successfully applied. Convergence and code checking issues are addressed. Indicative parametric/sensitivity studies are presented along with specific numerical findings. The modelpsilas behavior substantiates its potential to serve as the basis of a treatment optimization system following an eventually succesful completion of the clinical validation and optimization process.

Multilevel cancer modeling in the clinical environment: Simulating the behavior of Wilms tumor in the context of the SIOP 2001/GPOH clinical trial and the ACGT project

Mathematical and computational tumor dynamics models can provide considerable insight into the relative importance and interdependence of related biological mechanisms. They may also suggest the existence of optimal treatment windows in the generic setting. Nevertheless, they cannot be translated into clinical practice unless they undergo a strict and thorough clinical validation and adaptation. In this context one of the major actions of the EC funded project ldquoAdvancing Clinico-Genomic Trials on Cancerrdquo (ACGT) is dedicated to the development of a patient specific four dimensional multiscale tumor model mimicking the nephroblastoma tumor response to chemotherapeutic agents according to the SIOP 2001/GPOH clinical trial. Combined administration of vincristine and dactinomycin is considered. The patient#x2019;s pseudoanonymized imaging, histopathological, molecular and clinical data are carefully exploited. The paper briefly outlines the basics of the model developed by the In Silico Oncology Group and particularly stresses the effect of stem/clonogenic, progenitor and differentiated tumor cells on the overall tumor dynamics. The need for matching the cell category transition rates to the cell category relative populations of free tumor growth for an already large solid tumor at the start of simulation has been clarified. A technique has been suggested and succesfully applied in order to ensure satisfaction of this condition. The concept of a nomogram matching the cell category transition rates to the cell category relative populations at the treatment baseline is introduced. Convergence issues are addressed and indicative numerical results are presented. Qualitative agreement of the modelpsilas behavior with the corresponding clinical trial experience supports its potential to constitute the basis for an optimization system within the clinical environment following completion of its clinical validation and optimization. In silico treatment experimentation in the patient individualized context is expected to constitute the primary application of the model.

Multi-level analysis and information extraction considerations for validating 4D models of human function

Recent research trends focus on how multiscale biomedical information can be modeled and transformed into knowledge, in order to lead to a less interfering but also more individualized diagnosis and therapy. In order to assess the clinical importance of models of human pathology (e.g. cancer), it is necessary to validate them with prior and post treatment clinical data which in turn requires the determination of the tumor size and shape with high resolution, accuracy and precision, as well as structural and physiological information. This paper discusses some of the most important image analysis challenges in order to define an optimal method for extracting more accurate and precise anatomical and functional information related to the underlying pathology, which can be used for initializing and validating models of pathophysiology as well as simulations/predictions of the response to therapeutical regimes.

In Silico oncology: a top-down multiscale simulator of cancer dynamics. Studying the effect of symmetric stem cell division on the cellular constitution of a tumour

The tremendous rate of accumulation of experimentally and clinically extracted knowledge concerning cancer at all levels of biocomplexity dictates the development of integrative in silico models of tumour dynamics in order to better understand and treat the disease. Since the eventual translation of biomodels into clinical practice presupposes successful clinical validation we have developed a number of multiscale cancer simulation models oriented towards patient individualized treatment optimization. A top-down modelling approach based primarily on discrete event/state simulation has been proposed, developed and implemented. The emerging simulators have been serving as the core simulation modules (oncosimulators) of both the EC funded research projects Contra- Cancrum and ACGT. In this paper a brief outline of the basics of the approach along with paradigmal results demonstrating the effect of symmetric division of cancer stem cells on the cellular constitution of a tumour are presented. The potential and extensibility of the models are discussed.

A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine:

The plethora of available disease prediction models and the ongoing process of their application into clinical practice – following their clinical validation – have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work. In addition, the end users should be provided with all the necessary interfaces for model execution and effortless result retrieval. We therefore propose a software infrastructure, based on a tool, model and data repository that handles the storage of models and pertinent execution-related data, along with functionalities for execution management, communication with third-party applications, user-friendly interfaces to access and use the infrastructure with minimal effort and basic security features.

Functional p53 can modulate the relationship between E2F-1 expression and tumor kinetics in Hodgkin lymphoma

Abstract E2F-1 is the best-described member of the E2F family of transcriptional factors and is particularly interesting in view of its often opposing roles. Our purpose was to examine the immunohistochemical expression of E2F-1 in Hodgkin lymphoma (HL) and to correlate it with proliferation and apoptosis of the tumor, clinicopathological parameters and patient outcome, as well as with expression of the downstream molecules p53 and p21. The median percentage of E2F-1-expressing Hodgkin Reed–Sternberg (HRS) cells was 80.2%. A significant positive correlation was found between expression of E2F-1 and p53 (p = 0.034). Following stratification of our cases, within the group harboring functional p53, a statistically significant inverse correlation was identified between E2F-1 and Topo IIa (p = 0.019). E2F-1 is up-regulated in the context of HL and its expression is inversely associated with proliferation. It seems that functional p53 can modulate the relationship between E2F-1 expression and tumor kinetics in HL.