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Deciphering tumor micro-environment heterogeneity and plasticity

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).

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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).

 

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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).

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    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).

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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.

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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.