Cooperation among cancer cells as public goods games on Voronoi networks

Cancer cells produce growth factors that diffuse and sustain tumor proliferation, a form of cooperation among cancer cells that can be studied using mathematical models of public goods in the framework of evolutionary game theory. Cell populations, however, form heterogeneous networks that cannot be described by regular lattices or scale-free networks, the types of graphs generally used in the study of cooperation. To describe the dynamics of growth factor production in populations of cancer cells, I study public goods games on Voronoi networks, using a range of non-linear benefits that account for the known properties of growth factors, and different types of diffusion gradients. e results are surprisingly similar to those obtained on regular graphs and different from results on scale-free networks, revealing that network heterogeneity per se does not promote cooperation when public goods diffuse beyond one-step neighbours. e exact shape of the diffusion gradient is not crucial, however, whereas the type of non-linear benefit is an essential determinant of the dynamics. Public goods games on Voronoi networks can shed light on intra-tumor heterogeneity, the evolution of resistance to therapies that target growth factors, and new types of cell therapy

Hybrid data-based modelling in oncology: successes, challenges and hopes

International audienceIn this review we make the statement that hybrid models in oncology are required as a mean for enhanced data integration. In the context of systems oncology, experimental and clinical data need to be at the heart of the models developments from conception to validation to ensure a relevant use of the models in the clinical context. The main applications pursued are to improve diagnosis and to optimize therapies.We first present the Successes achieved thanks to hybrid modelling approaches to advance knowledge, treatments or drug discovery. Then we present the Challenges than need to be addressed to allow for a better integration of the model parts and of the data into the models. And Finally, the Hopes with a focus towards making personalised medicine a reality. Mathematics Subject Classification. 35Q92, 68U20, 68T05, 92-08, 92B05

Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy

Funding: German Research Foundation DFG (SFB 873; subproject B08) (T.S. and A.M.-C); Heidelberg Graduate School (T.L.).We present a stochastic individual-based model for the phenotypic evolution of cancer cell populations under chemotherapy. In particular, we consider the case of combination cancer therapy whereby a chemotherapeutic agent is administered as the primary treatment and an epigenetic drug is used as an adjuvant treatment. The cell population is structured by the expression level of a gene that controls cell proliferation and chemoresistance. In order to obtain an analytical description of evolutionary dynamics, we formally derive a deterministic continuum counterpart of this discrete model, which is given by a nonlocal parabolic equation for the cell population density function. Integrating computational simulations of the individual-based model with analysis of the corresponding continuum model, we perform a complete exploration of the model parameter space. We show that harsher environmental conditions and higher probabilities of spontaneous epimutation can lead to more effective chemotherapy, and we demonstrate the existence of an inverse relationship between the efficacy of the epigenetic drug and the probability of spontaneous epimutation. Taken together, the outcomes of the model provide theoretical ground for the development of anticancer protocols that use lower concentrations of chemotherapeutic agents in combination with epigenetic drugs capable of promoting the re-expression of epigenetically regulated genes.PostprintPeer reviewe

Discrete and continuum phenotype-structured models for the evolution of cancer cell populations under chemotherapy

We present a stochastic individual-based model for the phenotypic evolution of cancer cell populations under chemotherapy. In particular, we consider the case of combination cancer therapy whereby a chemotherapeutic agent is administered as the primary treatment and an epigenetic drug is used as an adjuvant treatment. The cell population is structured by the expression level of a gene that controls cell proliferation and chemoresistance. In order to obtain an analytical description of evolutionary dynamics, we formally derive a deterministic continuum counterpart of this discrete model, which is given by a nonlocal parabolic equation for the cell population density function. Integrating computational simulations of the individual-based model with analysis of the corresponding continuum model, we perform a complete exploration of the model parameter space. We show that harsher environmental conditions and higher probabilities of spontaneous epimutation can lead to more effective chemotherapy, and we demonstrate the existence of an inverse relationship between the efficacy of the epigenetic drug and the probability of spontaneous epimutation. Taken together, the outcomes of the model provide theoretical ground for the development of anticancer protocols that use lower concentrations of chemotherapeutic agents in combination with epigenetic drugs capable of promoting the re-expression of epigenetically regulated genes

Cancer evolution: mathematical models and computational inference.

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.FM would like to acknowledge the support of The University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.This is the final published version. It first appeared at http://sysbio.oxfordjournals.org/content/early/2014/10/07/sysbio.syu081.short?rss=1

Metastatic Progression and Tumour Heterogeneity

Improved understanding of the cellular and molecular makeup of tumors in the last 30 years has unraveled a previously unexpected level of heterogeneity among tumor cells as well as within the tumor microenvironment. The concept of tumor heterogeneity underlines the realization that different tumors can display significant differences in their genomic content as well as in their overall behavior. Our capacity to better understand the heterogeneous make up of tumors has very important consequences on our ability to design efficient therapeutic strategies to improve patient survival. This book highlights several aspects of tumor heterogeneity in the context of metastatic development and summarize some of the challenges posed by heterogeneity for tumor diagnostics and therapeutic management of tumors

The role of extracellular vesiclesin multiple myeloma

Abstract: Multiple myeloma (MM) is a hematological malignancy of clonal antibody-secreting plasma-cells(PC). MM diagnosis and risk stratification rely on tumoral assessment via bone marrow (BM) biopsy, which is an invasive procedure prone to sample bias. Liquid biopsies, such as extracellular vesicles (EV) in peripheral blood(PB), hold promise as new minimallyinvasive tools. Real-world studies analyzing patient-derived EV proteome are rare. Here, we characterizePB and BMEV protein content from acohort of 102monoclonal gammopathiespatients routinely followed in theclinic. A total of 223 PB and 111BM samples were included.We investigated whether EV protein and particle concentrationcould predict MM patient prognosisandfoundthat high EVprotein/particle (EVc> 0.6 μg/108particles) levelis related to poorer survival and immune dysfunction.These results were supportedat protein level by mass spectrometry.We report a set of PB EV-proteins (PDIA3, C4BPA, BTN1A1, APRIL, PSMB8 and PDE8B) with new biomarker potential for myeloma patientoutcomes. High proteomic expression similarity between PB and BM matched pairs were found, suggestingthe use of circulating EV as a personalized counterpart of BM EV proteome. Overall, we found that EV protein content couldberelated to patient outcomes, suchassurvival,immune dysfunction,andpossiblyspecific treatment response.Resumo: O mieloma múltiplo (MM) é uma doença hematológica maligna dos plasmócitos(PC), células produtoras de anticorpos. O diagnóstico e a estratificação de risco no MM dependem da avaliação das células tumorais na medula óssea (BM)por biópsia osteomedular, um procedimento invasivo e propenso a viés de amostragem. As biópsias líquidas, como por exemplo as vesículas extracelulares (EV) no sangue periférico(PB), são ferramentas diagnósticas minimamente invasivas promissoras. Estudos clínicos com dados de vida real em doentes com MMainda são escassos. Neste trabalho apresentamos a caracterizaçãode EVno PB e na BMnuma coorte de102doentescom gamapatias monoclonais, durante oacompanhamento habitual dasuadoença na prática clínica.No total foram incluídas223 amostras de PB e 111 de BM.Identificamos queacarga proteica (proteína/partículasou EVc) destas vesiculas pode predizer o prognóstico dos doentes com MM. Descobrimos queumnível alto de EVc(> 0,6 μg/108partículas)está relacionado com umasobrevivênciainferiore disfunção imunológicanos doentes com MM.Estes resultados foram confirmadosa nível proteicopor espectrometria de massa.Neste trabalhoidentificamos um conjunto de proteínasem EV no PB (PDIA3, C4BPA, BTN1A1, APRIL, PSMB8 e PDE8B) comopotenciais novos biomarcadores em doentescom MM. Encontrou-se ainda uma elevada similaridade na expressão proteica entre amostras dePB e BM, sugerindo o uso de EV no PB como contraparte personalizada doproteoma das EV naBM.Concluímosque o conteúdoproteico das EV está relacionado com os resultados clínicos dosdoentescom MM, incluindo sobrevivência, disfunção imunológica e possivelmentede resposta a determinados tratamentos

Modelling timing in blood cancers

Dysregulation of biological processes in normal cells can lead to the abnormal growth of tumours. Oncogenesis requires the acquisition of advantageous mutations to expand in a fluctuating environment. Cancer cells gain these genetic and epigenetic alterations at different timing in their development, resulting in the formation of heterogeneous cell populations which interact and compete with each others inside tumours. At later stages, by escaping the immune system and acquiring malignant properties, some cancer cells manage to evade the primary tumour and spread in different organs to form metastases. Hence, tumour development in healthy tissues endure several biological changes whilst progressing and the order between these molecular and cellular events may modify prognosis. This thesis addresses the influence of biological event timing on blood cancer progression and clinical outcomes. It first investigates the therapeutic efficacy of p53 restoration in a lymphoma mouse model. While several therapy schedules are tested, all fail due to resistance emergence. Computational modelling establishes the cell dynamics in these tumours and how to use it to propose alternative treatment strategies. Data availability leads this work to explore the impact of molecular evolution in myeloid malignancies. Notably, one study has found that Myeloproliferative Neoplasms patients with both JAK2 and TET2 mutations have different disease characteristics with distinct mutation order. My analyses identify HOXA9 as a potential prognosis marker and biological switch responsible for patient stratification in these patients and in Acute Myeloid Leukemia. Additionally, a molecular network identifies the hematopoietic regulators involved in the branching evolution of Myeloproliferative Neoplasms. Further investigations of the Acute Myeloid Leukemia data show the possible involvement of APP, a gene associated to Alzheimer disease, in early cell fate commitment in hematopoiesis and in poor survival prognosis in undifferentiated leukemia when lowly expressed. Finally, this thesis examines the regulatory dynamics behind three clusters of Acute Myeloid Leukemia patients with distinct levels of HOXA9 and APP expression. By building a program inferring molecular motifs from biological observations, genes which may interact with HOXA9 and APP are identified.Microsoft Research and the MRC Cancer Unit

Mathematical Modeling Reveals That the Administration of EGF Can Promote the Elimination of Lymph Node Metastases by PD-1/PD-L1 Blockade

In the advanced stages of cancers like melanoma, some of the malignant cells leave the primary tumor and infiltrate the neighboring lymph nodes (LNs). The interaction between secondary cancer and the immune response in the lymph node represents a complex process that needs to be fully understood in order to develop more effective immunotherapeutic strategies. In this process, antigen-presenting cells (APCs) approach the tumor and initiate the adaptive immune response for the corresponding antigen. They stimulate the naive CD4+ and CD8+ T lymphocytes which subsequently generate a population of helper and effector cells. On one hand, immune cells can eliminate tumor cells using cell-cell contact and by secreting apoptosis inducing cytokines. They are also able to induce their dormancy. On the other hand, the tumor cells are able to escape the immune surveillance using their immunosuppressive abilities. To study the interplay between tumor progression and the immune response, we develop two new models describing the interaction between cancer and immune cells in the lymph node. The first model consists of partial differential equations (PDEs) describing the populations of the different types of cells. The second one is a hybrid discrete-continuous model integrating the mechanical and biochemical mechanisms that define the tumor-immune interplay in the lymph node. We use the continuous model to determine the conditions of the regimes of tumor-immune interaction in the lymph node. While we use the hybrid model to elucidate the mechanisms that contribute to the development of each regime at the cellular and tissue levels. We study the dynamics of tumor growth in the absence of immune cells. Then, we consider the immune response and we quantify the effects of immunosuppression and local EGF concentration on the fate of the tumor. Numerical simulations of the two models show the existence of three possible outcomes of the tumor-immune interactions in the lymph node that coincide with the main phases of the immunoediting process: tumor elimination, equilibrium, and tumor evasion. Both models predict that the administration of EGF can promote the elimination of the secondary tumor by PD-1/PD-L1 blockade