Gautier Stoll

Chemotherapy-induced antitumor immunity requires formyl peptide receptor 1

How dying tumor cells get noticed Besides killing tumor cells directly, some chemotherapies, such as anthracyclines, also activate the immune system to kill tumors. Vacchelli et al. discovered that in mice, anthracycline-induced antitumor immunity requires immune cells to express the protein formyl peptide receptor 1 (FPR1). Dendritic cells (DCs) near tumors expressed especially high amounts of FPR1. DCs normally capture fragments of dying tumor cells and use them to activate nearby T cells to kill tumors, but DCs lacking FPR1 failed to do this effectively. Individuals with breast or colon cancer expressing a variant of FPR1 and treated with anthracyclines showed poor metastasis-free and overall survival. Thus, FPR1 may affect anti-tumor immunity in people, too. Science, this issue p. 972 Formyl peptide receptor 1 helps the immune system sense dying tumor cells. Antitumor immunity driven by intratumoral dendritic cells contributes to the efficacy of anthracycline-based chemotherapy in cancer. We identified a loss-of-function allele of the gene coding for formyl peptide receptor 1 (FPR1) that was associated with poor metastasis-free and overall survival in breast and colorectal cancer patients receiving adjuvant chemotherapy. The therapeutic effects of anthracyclines were abrogated in tumor-bearing Fpr1−/− mice due to impaired antitumor immunity. Fpr1-deficient dendritic cells failed to approach dying cancer cells and, as a result, could not elicit antitumor T cell immunity. Experiments performed in a microfluidic device confirmed that FPR1 and its ligand, annexin-1, promoted stable interactions between dying cancer cells and human or murine leukocytes. Altogether, these results highlight the importance of FPR1 in chemotherapy-induced anticancer immune responses.

CCL2/CCR2-Dependent Recruitment of Functional Antigen-Presenting Cells into Tumors upon Chemotherapy

The therapeutic efficacy of anthracyclines relies, at least partially, on the induction of a dendritic cell- and T-lymphocyte-dependent anticancer immune response. Here, we show that anthracycline-based chemotherapy promotes the recruitment of functional CD11b(+)CD11c(+)Ly6C(high)Ly6G(-)MHCII(+) dendritic cell-like antigen-presenting cells (APC) into the tumor bed, but not into lymphoid organs. Accordingly, draining lymph nodes turned out to be dispensable for the proliferation of tumor antigen-specific T cells within neoplastic lesions as induced by anthracyclines. In addition, we found that tumors treated with anthracyclines manifest increased expression levels of the chemokine Ccl2. Such a response is important as neoplasms growing in Ccl2(-/-) mice failed to accumulate dendritic cell-like APCs in response to chemotherapy. Moreover, cancers developing in mice lacking Ccl2 or its receptor (Ccr2) exhibited suboptimal therapeutic responses to anthracycline-based chemotherapy. Altogether, our results underscore the importance of the CCL2/CCR2 signaling axis for therapeutic anticancer immune responses as elicited by immunogenic chemotherapy.

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time.BackgroundThere exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature.ResultsHere, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions.ConclusionsApplications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.

BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats

BackgroundPublic repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret.ResultsWe have developed BiNoM (Biological Network Manager), a Cytoscape plugin, which provides functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM can be used to import and analyze files created with the CellDesigner software. BiNoM provides a set of functions allowing to import BioPAX files, but also to search and edit their content. As such, BiNoM is able to efficiently manage large BioPAX files such as whole pathway databases (e.g. Reactome). BiNoM also implements a collection of powerful graph-based functions and algorithms such as path analysis, decomposition by involvement of an entity or cyclic decomposition, subnetworks clustering and decomposition of a large network in modules.ConclusionsHere, we provide an in-depth overview of the BiNoM functions, and we also detail novel aspects such as the support of the BioPAX Level 3 format and the implementation of a new algorithm for the quantification of pathways for influence networks. At last, we illustrate some of the BiNoM functions on a detailed biological case study of a network representing the G1/S transition of the cell cycle, a crucial cellular process disturbed in most human tumors.

Systems biology of Ewing sarcoma: a network model of EWS-FLI1 effect on proliferation and apoptosis

Ewing sarcoma is the second most frequent pediatric bone tumor. In most of the patients, a chromosomal translocation leads to the expression of the EWS-FLI1 chimeric transcription factor that is the major oncogene in this pathology. Relative genetic simplicity of Ewing sarcoma makes it particularly attractive for studying cancer in a systemic manner. Silencing EWS-FLI1 induces cell cycle alteration and ultimately leads to apoptosis, but the exact molecular mechanisms underlying this phenotype are unclear. In this study, a network linking EWS-FLI1 to cell cycle and apoptosis phenotypes was constructed through an original method of network reconstruction. Transcriptome time-series after EWS-FLI1 silencing were used to identify core modulated genes by an original scoring method based on fitting expression profile dynamics curves. Literature data mining was then used to connect these modulated genes into a network. The validity of a subpart of this network was assessed by siRNA/RT-QPCR experiments on four additional Ewing cell lines and confirmed most of the links. Based on the network and the transcriptome data, CUL1 was identified as a new potential target of EWS-FLI1. Altogether, using an original methodology of data integration, we provide the first version of EWS-FLI1 network model of cell cycle and apoptosis regulation.

Immunogenic stress and death of cancer cells: Contribution of antigenicity vs adjuvanticity to immunosurveillance

Cancer cells are subjected to constant selection by the immune system, meaning that tumors that become clinically manifest have managed to subvert or hide from immunosurveillance. Immune control can be facilitated by induction of autophagy, as well as by polyploidization of cancer cells. While autophagy causes the release of ATP, a chemotactic signal for myeloid cells, polyploidization can trigger endoplasmic reticulum stress with consequent exposure of the “eat‐me” signal calreticulin on the cell surface, thereby facilitating the transfer of tumor antigens into dendritic cells. Hence, both autophagy and polyploidization cause the emission of adjuvant signals that ultimately elicit immune control by CD8+ T lymphocytes. We investigated the possibility that autophagy and polyploidization might also affect the antigenicity of cancer cells by altering the immunopeptidome. Mass spectrometry led to the identification of peptides that were presented on major histocompatibility complex (MHC) class I molecules in an autophagy‐dependent fashion or that were specifically exposed on the surface of polyploid cells, yet lost upon passage of such cells through immunocompetent (but not immunodeficient) mice. However, the preferential recognition of autophagy‐competent and polyploid cells by the innate and cellular immune systems did not correlate with the preferential recognition of such peptides in vivo. Moreover, vaccination with such peptides was unable to elicit tumor growth‐inhibitory responses in vivo. We conclude that autophagy and polyploidy increase the immunogenicity of cancer cells mostly by affecting their adjuvanticity rather than their antigenicity.

MaBoSS 2.0: an environment for stochastic Boolean modeling

Motivation: Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto‐immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time‐dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model. Results: We present a new version of MaBoSS (2.0), including an updated version of the core software and an environment. With this environment, the needs for modeling signaling pathways are facilitated, including model construction, visualization, simulations of mutations, drug treatments and sensitivity analyses. It offers a framework for automated production of theoretical predictions. Availability and Implementation: MaBoSS software can be found at https://maboss.curie.fr, including tutorials on existing models and examples of models. Contact: gautier.stoll@upmc.fr or laurence.calzone@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Representing perturbed dynamics in biological network models.

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy H and the perturbation size Delta , a quantity that reflects the distance between the set of fixed points of the perturbed network and that of the unperturbed network. Applying our approach to the yeast-cell-cycle network introduced by Li [Proc. Natl. Acad. Sci. U.S.A. 101, 4781 (2004)] provides a low-dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoint functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.

Practical Use of BiNoM: A Biological Network Manager Software

The Biological Network Manager (BiNoM) is a software tool for the manipulation and analysis of biological networks. It facilitates the import and conversion of a set of well-established systems biology file formats. It also provides a large set of graph-based algorithms that allow users to analyze and extract relevant subnetworks from large molecular maps. It has been successfully used in several projects related to the analysis of large and complex biological data, or networks from databases. In this tutorial, we present a detailed and practical case study of how to use BiNoM to analyze biological networks.

Stabilizing patterning in the Drosophila segment polarity network by selecting models in silico

The segmentation of Drosophila is a prime model to study spatial patterning during embryogenesis. The spatial expression of segment polarity genes results from a complex network of interacting proteins whose expression products are maintained after successful segmentation. This prompted us to investigate the stability and robustness of this process using a dynamical model for the segmentation network based on Boolean states. The model consists of intra-cellular as well as inter-cellular interactions between adjacent cells in one spatial dimension. We quantify the robustness of the dynamical segmentation process by a systematic analysis of mutations. Our starting point consists in a previous Boolean model for Drosophila segmentation. We define mathematically the notion of dynamical robustness and show that the proposed model exhibits limited robustness in gene expression under perturbations. We applied in silico evolution (mutation and selection) and discover two classes of modified gene networks that have a more robust spatial expression pattern. We verified that the enhanced robustness of the two new models is maintained in differential equations models. By comparing the predicted model with experiments on mutated flies, we then discuss the two types of enhanced models. Drosophila patterning can be explained by modelling the underlying network of interacting genes. Here we demonstrate that simple dynamical considerations and in silico evolution can enhance the model to robustly express the expected pattern, helping to elucidate the role of further interactions.