Mots-Clés

computational systems biology, Boolean networks, disease maps, cancer associated fibroblasts

Description

Fibroblasts as therapeutic targets in cancer: computational modeling of the metabolic reprogramming in Cancer-Associated Fibroblasts (CAFs)

Introduction: Cancer-Associated Fibroblasts (CAFs) are the main stromal cell type of solid tumor microenvironment. They undergo an activation process associated with secretion of growth factors, cytokines, and paracrine interactions (1). Studies in human lung, breast, colon, and prostate cancer have unequivocally demonstrated a key role for CAFs in initiation and progression of disease (2). Recently, an increasing number of studies have shown that CAFs activation and the subsequent tumoral phenotype are associated with an altered metabolism which may be therapeutically targetable (3-4). Indeed, activation of CAFs by hypoxia, platelet-derived growth factor (PDGF), tumor necrosis factor (TNF), and other inflammatory mediators increase glucose metabolism and transform CAFs from quiescent to aggressive cells.

Working hypothesis: Metabolic reprogramming plays a role in the regulation of the phenotypic changes in CAFs leading these cells to acquire an activated phenotype (5). The tumoral micro-environment creates toxic conditions favoring the fibroblasts’ transformation to adapt and survive, leading progressively to the amplification of the disastrous characteristics of the disease as these cells transform from passive responders to key disease effectors.

Objectives & Methodology:

1. Study the molecular and signaling pathways involved in CAFs’ regulation of metabolism by constructing cell specific networks: The student will use a publicly available CAF molecular map (6), and enrich it with metabolic markers linking signaling and gene regulation with metabolism from literature and resources such as the Metabolic Atlas https://www.metabolicatlas.org/, and the Virtual Metabolic Human (https://www.vmh.life/) among others. We are also going to use public datasets with expression data of CAFs (microarrays, RNAseq, RNAseq single cell) and data concerning metabolic pathways of fibroblasts from MetaCyc (https://metacyc.org/), to enrich and expand existing pathway resources for CAFs.

2. Dynamical modelling of intracellular networks: The student will create cell specific, executable Boolean models using the map to model conversion framework and the tool CaSQ (https://lifeware.inria.fr/~soliman/post/casq/), (7). The CAFs qualitative model will be used to decipher the input-output relationships in a cellular level. Specific focus will be given to pathways related to glycolysis. The evaluation of the coherence of the proposed models will be tested against published data to reproduce the dynamics and temporal behavior of fibroblasts. The models will also be used to evaluate possible targets that could impact the metabolic reprogramming of CAFs to a less aggressive phenotype.

Supervision: Dr Anna Niarakis, Dr Sylvain Soliman, Sahar Aghakhani Msc

Prerequisites:

• A penchant for Computational Systems Biology, Integrative Biology, Network Medicine
• A solid background in cellular and molecular biology concepts, and an interest in signalling pathways in cancer
• Basic bioinformatics skills (and an interest to develop them further)
• Interest in computational modelling and omics data integration

Modalities:

- The internship will start February 2022 until July 2022.

- CVs with a motivation letter and at least one reference should be sent to the following address: anna.niaraki@univ-evry.fr with the indication: “Application for the M2 post” in the subject.

Host team: GenHotel – Laboratoire de Recherche Européen pour la Polyarthrite Rhumatoïde, Université Évry Val d’Essonne – Paris-Saclay, 2 rue Gaston Crémieux 91000 Évry-Courcouronnes.

References:

(1) Powell DW, Mifflin RC, Valentich JD, Crowe SE, Saada JI, West AB. Myofibroblasts. I. Paracrine cells important in health and disease. Am J Physiol. (1999).

(2) De WO, Mareel M. Role of tissue stroma in cancer cell invasion. J Pathol. (2003).

(3) Yu L, Chen X, Sun X, Wang L, Chen S. The Glycolytic Switch in Tumors: How Many Players Are Involved? J Cancer. (2017).

(4) Karagiannis GS, Poutahidis T, Erdman SE, Kirsch R, Riddell RH, Diamandis EP. Cancer-associated fibroblasts drive the progression of metastasis through both paracrine and mechanical pressure on cancer tissue. Mol Cancer Res. (2012).

(5) Avagliano A, Granato G, Ruocco MR, et al. Metabolic Reprogramming of Cancer Associated Fibroblasts: The Slavery of Stromal Fibroblasts. Biomed Res Int. (2018).

(6) Kuperstein, I., Bonnet, E., Nguyen, HA. et al. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps. Oncogenesis (2015).

(7) S.S. Aghamiri, V. Singh, A. Naldi, T. Helikar, S. Soliman and A. Niarakis. Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics (2020).