Stress and cancer

Since the last years, our research team is involved in the characterization of the tumor microenvironment (TME), particularly in breast and ovarian cancer. Cancer Associated Fibroblasts (CAF) constitute one of the main tumor components. Their role in tumor progression has long been underestimated, while CAF display key pro-tumorigenic functions. They are involved in cancer progression and metastatic spread. They also highjack the immune system to prevent anti-tumor immune reaction and mediate resistance to treatments.

1. Tumor stiffness is correlated with myofibroblast enrichment and is linked to poor prognosis in ovarian cancer patients

High-grade serous ovarian cancers (HGSOC) are among the most aggressive gynecological cancers, in particular for patients suffering from the mesenchymal molecular subtype (Mateescu et al, Nature Medicine, 2011; Batista et al, Nature Communications, 2016; Kieffer et al, Frontiers in Genetics, 2020). These patients are characterized by high myofibroblast content and accumulation of extracellular matrix proteins (Givel et al, Nature Communications, 2018; Mieulet et al, Scientific Reports, 2021). Myofibroblasts are activated and contractile CAF which constitutively express the smooth muscle actin protein (SMA). We characterized the role of these CAF in tumor stiffness and aggressiveness. We observed a major difference between HGSOC molecular subtypes: mesenchymal HGSOC exhibit a greater stiffness than non-mesenchymal tumors upon tumor growth. This is directly associated with myofibroblast content and collagen density: the more HGSOC accumulate myofibroblasts, the denser collagens fibers are, and the stiffer tumors are. Our study also demonstrates that the stiffness induced by myofibroblastic stroma plays a central role in HGSOC aggressiveness as it correlates with the activation of the MAPK / MEK pathway and causes a glycolytic metabolic switch both in epithelial and stromal cells (Mieulet et al, Scientific Reports, 2021).

1. Discovery and characterization of CAF subpopulations in cancers

Given their important roles in cancer progression (Toullec et al, EMBO Molecular Medicine 2010; Costa et al, Seminars in Cancer Biology, 2014; Mhaidly et al, Seminars in Immunology, 2020; Mhaidly et al, Immunol Rev, 2021), we undergo to a deep characterization of CAF heterogeneity within the TME. Characterizing CAF heterogeneity is a major challenge in order to achieve an accurate and comprehensive tumor mapping. This is also key to understand their distinct functions during cancer progression, and their evolution throughout patient treatment. The precise identification of CAF subpopulations that play a key role in tumor escape and resistance to treatment will lead to new diagnostic and therapeutic approaches that could in term benefit cancer patients.

Thanks to patient samples analysis, we identified 4 CAF subpopulations (called CAF-S1, -S2, -S3, -S4) in both breast and ovarian cancers (Costa et al, Cancer Cell, 2018; Givel et al, Nature Communications, 2018). CAF-S2 and CAF-S3 are not only observed in tumors, but also in healthy tissue, suggesting that they may originate from normal fibroblasts. In contrast, the two myofibroblastic populations CAF-S1 and CAF-S4 accumulate in aggressive cancers, including triple-negative breast cancers and mesenchymal ovarian cancers, suggesting that these CAF-S1 and CAF-S4 play a major role in tumor progression.

These 4 CAF subsets are also detected in other cancers demonstrating their biological relevance (Kieffer et al, Cancer Discovery, 2020).

The 4 CAF subsets (CAF-S1 to CAF-S4) differentially accumulate between breast cancer subtypes. LumA BC = luminal A breast cancer, HER2 BC = HER2 positive breast cancer, TN BC: triple negative breast cancer (Costa et al, Cancer Cell, 2018).

1. Study of the functions of CAF-S1 and CAF-S4 fibroblasts in cancers

CAF-S1 fibroblasts display both immunosuppressive and pro-metastatic functions (Costa et al, Cancer Cell, 2018; Givel et al, Nature Communications, 2018; Bonneau et al, Breast Cancer Research, 2020; Pelon et al, Nature communications, 2020; Kieffer et al, Cancer Discovery, 2020).

• CAF-S1 fibroblasts are immunosuppressive

Our studies on breast and ovarian cancer have shown that CAF-S1 fibroblasts are able to reduce the anti-tumor immune system response. CAF-S1 fibroblasts promote the attraction of T lymphocytes and stimulate their differentiation into regulatory T lymphocytes to the detriment of cytotoxic T lymphocytes, thus leading to an immunosuppressive microenvironment. CAF-S1 secrete the CXCL12 chemokine, leading to the attraction of CD4⁺ CD25⁺ T lymphocytes and retain them through OX40L, PD-L2 and JAM2. Finally, CAF-S1 induce the differentiation of these CD4⁺ CD25⁺ lymphocytes into regulatory T lymphocytes (FOXP3⁺) leading to the decreased proliferation of effector T cells (Costa et al, Cancer Cell, 2018; Givel, Nature Communications, 2018; Patent application number WO2019020728A1).

CAF-S1 fibroblasts induce an immunosuppressive microenvironment by attracting T lymphocytes in their vicinity, following by their sequestration and their differentiation into regulatory T lymphocytes characterized by a strong expression of FOXP3.

 

• • CAF-S1 and CAF-S4 fibroblasts stimulate metastatic spread through complementary mechanisms

  After having characterized the different CAF populations in primary tumors, we studied CAF composition in axillary lymphatic metastases in breast cancer patients. If the 4 CAF subpopulations (CAF-S1, -S2, -S3, -S4) previously identified in primary tumors can also be detected in metastatic lymph nodes, the proportions of CAF-S1 and CAF-S4 are correlated with invading tumor cells. Thus, CAF-S1 and CAF-S4 fibroblasts accumulate not only in aggressive tumors, but also in metastatic lymph nodes. Importantly, the content in CAF-S1 and CAF-S4 in metastatic lymph nodes is a new marker of poor prognosis, indicating - at diagnosis - a high risk of late recurrence and distant metastases (Pelon et al, Nature Communications, 2020).

   

  By functional analyzes combining interdisciplinary expertise in Biology and Physics, we demonstrated that CAF-S1 and CAF-S4 exert pro-metastatic activities and cooperate together through complementary mechanisms. CAF-S1 initiate an epithelial-to-mesenchymal transition (EMT) and stimulate their migration; CAF-S4 enhance their invasive capacity, thereby demonstrating the synergistic actions of these two CAF subsets in metastatic spread (Pelon et al, Nature Communications, 2020).

  

  CAF-S1 and CAF-S4 fibroblasts cooperate to induce the formation of metastases by acting on the proliferation, migration and epithelial-to-mesenshymal transition (EMT) on the one hand and on the invasive properties of cancer cells on the other hand (Pelon et al, Nature Communications, 2020).
  
   

The early luminal breast cancers T1N0 (tumors smaller than 2cm) are rarely studied, as their prognosis is generally favorable. Nevertheless, up to 5% of luminal T1N0 breast cancer patients relapse with distant metastases that ultimately prove fatal. From a clinical point of view, it is therefore essential to anticipate these responses in order to develop new therapeutic strategies. We compared a group of patients who relapsed and a "control" group who did not develop metastases. And we found that the patients who develop metastases show an enrichment in CAF-S1 within their primary tumors. This study shows, once again, that CAF-S1 fibroblasts are a marker of poor prognosis and associated with a high risk of recurrence. Metastases from luminal breast cancer predominantly occur within bones. CAF-S1 actively participate in the formation of metastases by inducing the migration of tumor cells through the osteoblastic cadherin CDH11 (Bonneau et al, Breast Cancer Research, 2020).

1. Characterization of CAF-S1 heterogeneity in breast cancer

• Identification of CAF-S1 cellular clusters in breast cancer

CAF-S1 fibroblasts display both pro-metastatic and immunosuppressive functions. Therefore, we undertook to characterize in depth CAF-S1 heterogeneity, thanks to single-cell sequencing analyses (single-cell RNAseq). The sequencing of more than 18,000 CAF-S1 fibroblasts isolated from breast cancers enabled us to identify the existence of 8 distinct CAF-S1 cell clusters, associated with a specific transcriptomic profile. Among these 8 subpopulations, 5 clusters are myofibroblastic CAFs (myCAF: ECM-myCAF, wound-myCAF, TGFβ-myCAF, INF⍺ / β-myCAF and acto-myCAF clusters). The 3 other CAF-S1 fibroblast clusters are related to “inflammatory” CAFs (iCAF: detox-iCAF, il-iCAF, IFN𝛾-iCAF), characterized by a strong expression of pro-inflammatory molecules and chemokines (Kieffer et al, Cancer Discovery, 2020).

Uniform Manifold Approximation and Projection (UMAP) representation of the single cell sequencing (scRNA-seq) analysis of the CAF-S1 fibroblast population showing the presence of 8 distinct cellular clusters with a specific transcriptomic profile leading to specific properties (Kieffer et al, Cancer Discovery, 2020).

• • ECM-myCAF and TGFβ-myCAF clusters induce an immunosuppressive microenvironment

  Given the immunosuppressive functions of CAF-S1 fibroblasts, we found that these properties are strictly restricted to specific CAF-S1 clusters. Indeed, we showed that ECM-myCAF and TGFβ-myCAF clusters are correlated with regulatory T lymphocytes (CD4⁺ Treg) and inversely correlated with cytotoxic T lymphocytes (CD8⁺), demonstrating their association with an immunosuppressive microenvironment enriched in Treg. By studying the molecular mechanisms involved in this association, we have uncovered a reciprocal interaction between ECM-myCAF, TGFβ-myCAF clusters and Treg:

• ECM-myCAF fibroblasts directly increase the proportion of Treg (CD4⁺ CD25⁺ FOXP3⁺) by increasing the expression of PD-1 and CTLA-4 proteins at their surface.
• In return, Tregs promote the conversion of ECM-myCAF fibroblasts into TGFβ-myCAF fibroblasts (Kieffer et al, Cancer Discovery, 2020).

• The ECM-myCAF, TGFβ-myCAF and wound-myCAF clusters are indicative of resistance to immunotherapies

Given their role in PD-1 and CTLA-4 regulation, we investigated if these fibroblast clusters could be predictive of the response to immunotherapies, in patients with metastatic melanoma or lung cancers (NSCLC; non-small cell lung cancer). Very interestingly, the ECM-myCAF, TGFβ-myCAF and wound-myCAF clusters are enriched at diagnosis in patients who did not respond to immunotherapy, suggesting that the ECM-myCAF, TGFβ-myCAF and wound-myCAF fibroblasts are predictive of resistance to immunotherapies in cancer (Kieffer et al, Cancer Discovery, 2020; Patent application number EP20305454). Thus, our analyzes suggest that targeting these CAF-S1 clusters could be a relevant therapeutic strategy in combination with current treatments, in particular with immunotherapies.

Reciprocal interaction between ECM-myCAF, TGFβ-myCAF CAF-S1 clusters and T lymphocytes. (1) CAF-1S fibroblasts are heterogeneous, composed of 8 different fibroblast clusters. (2-3) ECM-myCAF and TGFβ-myCAF fibroblasts are associated with an immunosuppressive microenvironment enriched in Treg. ECM-myCAF fibroblasts increase the expression of PD-1 and CTLA-4 proteins at the surface of regulatory T cells which, in turn, induce the conversion of ECM-myCAF fibroblasts into TGFβ-myCAF fibroblasts. (4) These CAF-S1 clusters are indicative of immunotherapy resistance in cancer patients (Kieffer et al, Cancer Discovery, 2020).

The level of CAF characterization in our study was never reached before and showed the complexity of this cellular population within tumors. The interactions between these different CAF clusters with immune and cancer cells enabled us to develop a comprehensive view of the tumor biology. These discoveries put in light the major role of CAF in cancer progression and in resistance to treatments and allow to propose new diagnostic and therapeutic approaches, which could benefit cancer patients.

1. Research perspectives:

These recent findings lead us to explore and characterize in depth CAF-S1 heterogeneity. Determining CAF-S1 cell of origin and mechanisms by which the CAF-S1 cell diversity is generated is one of the main questions addressed in our current studies by using cutting-edge technologies, such as single cell RNAseq, spatial transcriptomics and multiplex immunohistochemistry. Carrying out a detailed mapping of the TME trough time and space will allow us to define interactions between the different cell types composing the tumor niche and thus determine their role in cancer progression and resistance to treatments. The complete characterization of the TME will allow us to offer new diagnostic and therapeutic protocols to treat cancer patients.

Related team publications:

• Mhaidly, R.; Mechta-Grigoriou, F. Role of Cancer-Associated Fibroblast Subpopulations in Immune Infiltration, as a New Means of Treatment in Cancer. Immunol Rev 2021. https://doi.org/10.1111/imr.12978.
• Mieulet, V.; Garnier, C.; Kieffer, Y.; Guilbert, T.; Nemati, F.; Marangoni, E.; Renault, G.; Chamming’s, F.; Vincent-Salomon, A.; Mechta-Grigoriou, F. Stiffness Increases with Myofibroblast Content and Collagen Density in Mesenchymal High Grade Serous Ovarian Cancer. Scientific Reports 2021, 11 (1), 4219. https://doi.org/10.1038/s41598-021-83685-0.
• Mhaidly, R.; Mechta-Grigoriou, F. Fibroblast Heterogeneity in Tumor Micro-Environment: Role in Immunosuppression and New Therapies. Seminars in Immunology 2020, 101417. https://doi.org/10.1016/j.smim.2020.101417.
• Kieffer, Y.; Hocine, H. R.; Gentric, G.; Pelon, F.; Bernard, C.; Bourachot, B.; Lameiras, S.; Albergante, L.; Bonneau, C.; Guyard, A.; Tarte, K.; Zinovyev, A.; Baulande, S.; Zalcman, G.; Vincent-Salomon, A.; Mechta-Grigoriou, F. Single-Cell Analysis Reveals Fibroblast Clusters Linked to Immunotherapy Resistance in Cancer. Cancer Discovery 2020, 10 (9), 1330–1351. https://doi.org/10.1158/2159-8290.CD-19-1384.
• Kieffer, Y.; Bonneau, C.; Popova, T.; Rouzier, R.; Stern, M.-H.; Mechta-Grigoriou, F. Clinical Interest of Combining Transcriptomic and Genomic Signatures in High-Grade Serous Ovarian Cancer. Frontiers in Genetics 2020, 11. https://doi.org/10.3389/fgene.2020.00219.
• Bonneau, C.; Eliès, A.; Kieffer, Y.; Bourachot, B.; Ladoire, S.; Pelon, F.; Hequet, D.; Guinebretière, J.-M.; Blanchet, C.; Vincent-Salomon, A.; Rouzier, R.; Mechta-Grigoriou, F. A Subset of Activated Fibroblasts Is Associated with Distant Relapse in Early Luminal Breast Cancer. Breast Cancer Research 2020, 22 (1), 76. https://doi.org/10.1186/s13058-020-01311-9.
• Pelon, F.; Bourachot, B.; Kieffer, Y.; Magagna, I.; Mermet-Meillon, F.; Bonnet, I.; Costa, A.; Givel, A.-M.; Attieh, Y.; Barbazan, J.; Bonneau, C.; Fuhrmann, L.; Descroix, S.; Vignjevic, D.; Silberzan, P.; Parrini, M. C.; Vincent-Salomon, A.; Mechta-Grigoriou, F. Cancer-Associated Fibroblast Heterogeneity in Axillary Lymph Nodes Drives Metastases in Breast Cancer through Complementary Mechanisms. Nature Communications 2020, 11 (1), 404. https://doi.org/10.1038/s41467-019-14134-w.
• Costa, A.; Kieffer, Y.; Scholer-Dahirel, A.; Pelon, F.; Bourachot, B.; Cardon, M.; Sirven, P.; Magagna, I.; Fuhrmann, L.; Bernard, C.; Bonneau, C.; Kondratova, M.; Kuperstein, I.; Zinovyev, A.; Givel, A.-M.; Parrini, M.-C.; Soumelis, V.; Vincent-Salomon, A.; Mechta-Grigoriou, F. Fibroblast Heterogeneity and Immunosuppressive Environment in Human Breast Cancer. Cancer Cell 2018, 33 (3), 463-479.e10. https://doi.org/10.1016/j.ccell.2018.01.011.
• Givel, A.-M.; Kieffer, Y.; Scholer-Dahirel, A.; Sirven, P.; Cardon, M.; Pelon, F.; Magagna, I.; Gentric, G.; Costa, A.; Bonneau, C.; Mieulet, V.; Vincent-Salomon, A.; Mechta-Grigoriou, F. MiR200-Regulated CXCL12β Promotes Fibroblast Heterogeneity and Immunosuppression in Ovarian Cancers. Nature Communications 2018, 9. https://doi.org/10.1038/s41467-018-03348-z.
• Batista, L.; Bourachot, B.; Mateescu, B.; Reyal, F.; Mechta-Grigoriou, F. Regulation of MiR-200c/141 Expression by Intergenic DNA-Looping and Transcriptional Read-Through. Nature Communications 2016, 7. https://doi.org/10.1038/ncomms9959.
• Costa, A.; Scholer-Dahirel, A.; Mechta-Grigoriou, F. The Role of Reactive Oxygen Species and Metabolism on Cancer Cells and Their Microenvironment. Seminars in Cancer Biology 2014, 25, 23–32. https://doi.org/10.1016/j.semcancer.2013.12.007.
• Mateescu, B.; Batista, L.; Cardon, M.; Gruosso, T.; de Feraudy, Y.; Mariani, O.; Nicolas, A.; Meyniel, J.-P.; Cottu, P.; Sastre-Garau, X.; Mechta-Grigoriou, F. MiR-141 and MiR-200a Act on Ovarian Tumorigenesis by Controlling Oxidative Stress Response. Nature Medicine 2011, 17 (12), 1627–1635. https://doi.org/10.1038/nm.2512.
• Toullec, A.; Gerald, D.; Despouy, G.; Bourachot, B.; Cardon, M.; Lefort, S.; Richardson, M.; Rigaill, G.; Parrini, M.-C.; Lucchesi, C.; Bellanger, D.; Stern, M.-H.; Dubois, T.; Sastre-Garau, X.; Delattre, O.; Vincent-Salomon, A.; Mechta-Grigoriou, F. Oxidative Stress Promotes Myofibroblast Differentiation and Tumour Spreading. EMBO Molecular Medicine 2010, 2 (6), 211–230. https://doi.org/10.1002/emmm.201000073.

The characterization of tumor cell diversity (transcriptomics, proteomics, metabolomics) in cancer represents a major challenge for better understanding tumor development, cell interactions and mechanisms of resistance to treatments. Historically, the "Stress and Cancer" laboratory has been interested in the link between oxidative stress, cell aging and tumor development. Indeed, cancer being one of the most frequent pathologies related to aging, we were interested in the impact of a persistent oxidative stress on tumor development. We have thus shown that:

• Oxidative stress stimulates tumor growth and metastatic dissemination, by profoundly modifying the tumor microenvironment through the action of the CXCL12 chemokine and the pro-angiogenic factor HIF in invasive HER2-positive breast cancers (Gerald et al, Cell, 2004; Toullec et al, EMBO Molecular Medicine, 2010; Dahirel et al, Cell cycle, 2013; Costa et al, Seminars Cancer Biology, 2014).
• Oxidative stress enhances chemotherapy response in patients with high-grade serous ovarian carcinoma (HGSOC). HGSOC patients are conventionally treated with taxanes and platinum salts, known to generate reactive oxygen species (ROS) and DNA damages. The positive impact of oxidative stress in the chemotherapy response are link to the miR-200 family of microRNAs. We highlighted two groups of HGSOC patients: the first one characterized by an oxidative stress signature and a better response to chemotherapy, and the “Fibrosis/Mesenchymal” subgroup with a worse prognosis (Mateescu et al, Nature Medicine, 2011; Batista et al, International Journal of Biochemistry and Cell Biology, 2013; Batista et al, Nature Communications, 2016).

The study of oxidative stress in patients with high-grade serous ovarian cancer (HGSOC) revealed two groups of patients: "oxidative stress" and "fibrous". Patients with a transcriptomic profile revealing an enrichment of genes involved in the response to oxidative stress have a better response to chemotherapy and thus a better prognosis (Mateescu et al, Natude Medicine, 2011).

1. HGSOC are among the most aggressive gynecologic diseases. Linked with the response to oxidative stress, we have performed two studies that demonstrate the tumor cell heterogeneity in these cancers, following two topics: cell metabolism and the signaling pathway induced by MAP3K8.

    

2. The study of cell metabolism in HGSOCs:

Metabolic reprogramming is defined as one of the “Hallmark” of cancers (Gentric et al, Antioxidants and Redox Signaling, 2017, Gentric et Mechta-Grigoriou, Cancers, 2021). By combining proteomic, metabolomic and functional approaches (Seahorse, ¹³C-fluxomics, siRNA, xenografts), we identified a metabolic signature that distinguishes 2 HGSOC subgroups, low-OXPHOS and high-OXPHOS. Low-OXPHOS tumors are dependent of aerobic glycolysis or "Warburg Effect", while high-OXPHOS tumors use oxidative phosphorylation (OXPHOS). These results highlight, for the first time, the inter-tumor metabolic heterogeneity in ovarian cancers (Gentric et al, Cell Metabolism, 2019).

Interestingly, although no specific genomic alterations have been identified in high-OXPHOS tumors, this status appears to be associated with a deficiency in DNA repair mechanisms, homologous recombination type. As this deficiency in homologous recombination may be associated with chronic oxidative stress, we have demonstrated that “high-OXPHOS” HGSOC are characterized by a chronic oxidative stress, which promotes the accumulation and aggregation of PML (ProMyelocytic Leukemia) and activates the co-activator of mitochondrial biogenesis PGC1⍺ (Peroxisome proliferator-activated receptor gamma coactivator 1-alpha). The PML-PCG1⍺ axis promotes the expression of respiratory chain complex genes leading to an increased mitochondrial respiration in these cells (Gentric et al, Cell Metabolism, 2019).

Perspectives de recherche :

Nous souhaitons investiguer l’hétérogénéité métabolique intra-tumorale afin de faire le lien entre fonction moléculaire, propriété métabolique et localisation spatiale au sein de la tumeur. Nous souhaitons également déterminer les mécanismes à l’origine de la plasticité métabolique et les trajectoires des différents clones cellulaires. Enfin, nous caractériserons les interactions entre les différents clones cellulaires avec leur microenvironnement tumoral.

“High-OXPHOS” high-grade serous ovarian cancers are subjected to chronic oxidative stress by the presence of reactive oxygen species (ROS) which causes the activation of the PML-PCG1⍺ pathway leading to an increased expression of ETC genes and an increased mitochondrial respiration. Therefore, “high-OXPHOS” cells metabolism rely on the oxidative phosphorylation pathway (Gentric et al. Cell metabolism, 2019).

1. Research perspectives:

We wish to investigate intra-tumoral metabolic heterogeneity in order to establish the link between molecular function, metabolic property and spatial location within the tumor. We also want to determine the mechanisms behind metabolic plasticity and the trajectories of different cell clones. Finally, we will characterize the interactions between the different cell clones with their tumor microenvironment.

2. The study of the MAP3K8-ERK signaling pathway in HGSOCs:

In line with our studies concerning the response to oxidative stress in ovarian cancers, we have demonstrated, through proteomic studies on HGSOC tumor patients, the accumulation of the kinase protein MAP3K8, marker of poor prognosis, in 50% of HGSOC tumors (Gruosso et al, Nature Communications, 2015). MAP3K8 accumulation leads to a constitutive activation of the MEK/ERK signaling pathway in the absence of KRAS or BRAF mutation. There is therefore inter-tumor heterogeneity of the MAP3K8/MEK signaling pathway in HGSOC patients. We have shown that MAP3K8 exerts pro-tumorigenic functions mediated by MEK/ERK and that this protein kinase has predictive value for the efficacy of MEK inhibitors. Our work underlines the potential interest of the use of anti-MEKs in high-grade ovarian cancers for patients who present a strong MAP3K8 expression, whereas they are currently only tested in ovarian cancers of low-grade mutated for KRAS or BRAF oncogenes (Gruosso et al, Nature Communications, 2015; Patent application number: EP14306434, 2015).

Research perspectives:

Most oncogenic signaling pathways including MEK/ERK lead to translational reprogramming of the genome by activating eIF4F, a translation initiation complex. We have shown that constitutive activation of MEK/ERK, due to the accumulation of MAP3K8 in HGSOCs, controls the formation and activity of the eIF4F complex, thereby inducing translation of a set of specific mRNAs. We now wish to determine the causes and consequences of translational reprogramming induced by MAP3K8/MEK in HGSOCs on tumor development and response to treatments.

Related team publications:

• Gentric G., Mechta-Grigoriou F. Tumor Cells and Cancer-Associated Fibroblasts: An Updated Metabolic Perspective. Cancers 2021 Jan 22;13(3):399. doi: 10.3390/cancers13030399.
• Gentric, G.; Kieffer, Y.; Mieulet, V.; Goundiam, O.; Bonneau, C.; Nemati, F.; Hurbain, I.; Raposo, G.; Popova, T.; Stern, M.-H.; Lallemand-Breitenbach, V.; Müller, S.; Cañeque, T.; Rodriguez, R.; Vincent-Salomon, A.; de Thé, H.; Rossignol, R.; Mechta-Grigoriou, F. PML-Regulated Mitochondrial Metabolism Enhances Chemosensitivity in Human Ovarian Cancers. Cell Metabolism 2019, 29 (1), 156-173.e10. https://doi.org/10.1016/j.cmet.2018.09.002.
• Gentric, G.; Mieulet, V.; Mechta-Grigoriou, F. Heterogeneity in Cancer Metabolism: New Concepts in an Old Field. Antioxidants and Redox Signaling 2017, 26 (9), 462–485. https://doi.org/10.1089/ars.2016.6750.
• Batista, L.; Bourachot, B.; Mateescu, B.; Reyal, F.; Mechta-Grigoriou, F. Regulation of MiR-200c/141 Expression by Intergenic DNA-Looping and Transcriptional Read-Through. Nature Communications 2016, 7. https://doi.org/10.1038/ncomms9959.
• Gruosso, T.; Garnier, C.; Abelanet, S.; Kieffer, Y.; Lemesre, V.; Bellanger, D.; Bieche, I.; Marangoni, E.; Sastre-Garau, X.; Mieulet, V.; Mechta-Grigoriou, F. MAP3K8/TPL-2/COT Is a Potential Predictive Marker for MEK Inhibitor Treatment in High-Grade Serous Ovarian Carcinomas. Nature Communications 2015, 6. https://doi.org/10.1038/ncomms9583.
• Costa, A.; Scholer-Dahirel, A.; Mechta-Grigoriou, F. The Role of Reactive Oxygen Species and Metabolism on Cancer Cells and Their Microenvironment. . Seminars in Cancer Biology 2014, 25, 23–32. https://doi.org/10.1016/j.semcancer.2013.12.007.
• Batista, L.; Gruosso, T.; Mechta-Grigoriou, F. Ovarian Cancer Emerging Subtypes: Role of Oxidative Stress and Fibrosis in Tumour Development and Response to Treatment International Journal of Biochemistry & Cell Biology 2013, 45 (6), 1092–1098. https://doi.org/10.1016/j.biocel.2013.03.001.
• Scholer-Dahirel, A.; Costa, A.; Mechta-Grigoriou, F. Control of Cancer-Associated Fibroblast Function by Oxidative Stress: A New Piece in the Puzzle. Cell Cycle 2013, 12 (14), 2169. https://doi.org/10.4161/cc.25547.
• Mateescu, B.; Batista, L.; Cardon, M.; Gruosso, T.; de Feraudy, Y.; Mariani, O.; Nicolas, A.; Meyniel, J.-P.; Cottu, P.; Sastre-Garau, X.; Mechta-Grigoriou, F. MiR-141 and MiR-200a Act on Ovarian Tumorigenesis by Controlling Oxidative Stress Response. Nature Medicine 2011, 17 (12), 1627–1635. https://doi.org/10.1038/nm.2512.
• Toullec, A.; Gerald, D.; Despouy, G.; Bourachot, B.; Cardon, M.; Lefort, S.; Richardson, M.; Rigaill, G.; Parrini, M.-C.; Lucchesi, C.; Bellanger, D.; Stern, M.-H.; Dubois, T.; Sastre-Garau, X.; Delattre, O.; Vincent-Salomon, A.; Mechta-Grigoriou, F. Oxidative Stress Promotes Myofibroblast Differentiation and Tumour Spreading. EMBO Molecular Medicine 2010, 2 (6), 211–230. https://doi.org/10.1002/emmm.201000073.

The tumor cellular heterogeneity of both stromal and tumor cells has to be taken into account in the treatment of cancer patients, since it is involved, at least in part in the treatment resistance mechanisms and therapeutic relapses. By characterizing tumor and stromal heterogeneity, we uncovered differences in response to treatments currently used in clinics.

1. Metabolic heterogeneity of tumor cells in ovarian cancers (HGSOC) is associated with a difference in sensitivity to chemotherapy

In HGSOC, we found two tumor cell populations "low-OXPHOS" and "high-OXPHOS", revealing inter-patient tumor metabolic heterogeneity (Gentric et al, Cell Metabolism, 2019). We evaluated the impact of tumor metabolism on the response to conventional chemotherapy. By the study of preclinical models, we demonstrated that the high-OXPHOS signature is associated with an increased chemosensitivity to conventional treatments (platinum salts and taxanes), independent of the BRCAness. In patients with HGSOC, the high expression of ETC proteins in tumors is associated with better survival. In collaboration with Dr. Raphael Rodriguez team at the Institut Curie, we have shown that the tumor cell sensitivity can be explained, in part by ferroptosis; an iron-dependent programmed cell death. Thus, “high-OXPHOS” tumors, subjected to chronic oxidative stress, are characterized by an increased sensitivity to chemotherapy based on the accumulation of ROS, alteration of mitochondria and induction of ferroptosis (Gentric et al, Cell Metabolism, 2019).

Research perspectives:

Although high-OXPHOS status is associated with better response to treatment, the vast majority of ovarian cancer patients relapse after treatment, underscoring the need to identify mechanisms of resistance to treatment. We therefore wish to investigate intra-tumoral metabolic heterogeneity as well as the metabolic plasticity induced after chemotherapy. We wish to investigate the molecular mechanisms of resistance to treatment (conventional chemotherapy, immunotherapies).

1. Development of a 3D co-culture model to study the cellular interactions involved in resistance to treatments: Tumor-On-Chip

Our work shows the importance of considering the tumor as a whole in order to understand how the responses to the different treatments used in the clinic are mediated. In the laboratory, we want to develop new technologies to get closer to the physiopathology and physiognomy of tumors. 3D culture techniques allowing the incorporation of different cell types are booming and very promising. Indeed, they permit to reconstruct the complexity of the tumors from a structural, biochemical and biophysical point of view, and to question the relationships existing between the different cell populations in basal conditions or in consequence of a treatment. In collaboration with Dr Stéphanie Descroix's team, our research team is developing a 3D cell co-culture model: the Tumor-on -Chip (ToC). This approach, based on a microfluidic device, allows to reconstitute ex-vivo the complexity of the tumor ecosystem. In combination with a video-microscopy system, we can visualize cell dynamics in real time. We are thus able to study, in an integrated manner, the cellular mechanisms involved in responses to treatments and to characterize all the spatio-temporal relationships existing between stromal, immune and tumor cells.

Schematic representation of the “Tumor-on-Chip” ex-vivo culture system allowing the co-culture of several cell types, in 3D.

 

Related team publications:

• Parrini MC. Models for Immuno-oncology research. Tumour-on-chip breakthrough. Cancer Cell. 2020, 10;38(2):145-147. https://doi.org/10.1016/j.ccell.2020.07.010.
• Gentric, G.; Kieffer, Y.; Mieulet, V.; Goundiam, O.; Bonneau, C.; Nemati, F.; Hurbain, I.; Raposo, G.; Popova, T.; Stern, M.-H.; Lallemand-Breitenbach, V.; Müller, S.; Cañeque, T.; Rodriguez, R.; Vincent-Salomon, A.; de Thé, H.; Rossignol, R.; Mechta-Grigoriou, F. PML-Regulated Mitochondrial Metabolism Enhances Chemosensitivity in Human Ovarian Cancers. Cell Metabolism 2019, 29 (1), 156-173.e10. https://doi.org/10.1016/j.cmet.2018.09.002.
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