Periodic Reporting for period 1 - SPACE (Precision oncology of spatial immune escape mechanisms in ovarian cancer)
Période du rapport: 2023-02-01 au 2025-07-31
Cancer progression relies on the ability of tumor cells to evade the immune system. Developing better cancer immunotherapies has been challenging due to the complexity of these immune escape mechanisms. I believe that the genetic drivers of the tumor determine these escape mechanisms, and by understanding them, we can improve treatments for patients with high-grade serous ovarian cancer (HGSC), the most common and deadly form of ovarian cancer.
My team has developed a method to detect DNA repair deficiencies in HGSC, allowing us to better understand the tumor's genetic makeup. We will study how different HGSC genotypes interact with the immune system using over 600 tumor samples, analyzing mutations, neoantigens, and immune cell activity. We’ll also examine the tumor's spatial structure using advanced imaging techniques, and apply machine learning to uncover immune escape mechanisms. By profiling hundreds of spatial regions, we aim to pinpoint the biological processes behind immune evasion in HGSC.
Additionally, we have created patient-derived organoids (iPDOs) that closely mimic real tumors. Using these models, we will test targeted immunotherapies and analyze how tumors respond at the single-cell level. Our work will uncover new immune escape mechanisms and provide a foundation for personalized immunotherapies in HGSC.
My team has developed a method to detect DNA repair deficiencies in HGSC, allowing us to better understand the tumor's genetic makeup. We will study how different HGSC genotypes interact with the immune system using over 600 tumor samples, analyzing mutations, neoantigens, and immune cell activity. We’ll also examine the tumor's spatial structure using advanced imaging techniques, and apply machine learning to uncover immune escape mechanisms. By profiling hundreds of spatial regions, we aim to pinpoint the biological processes behind immune evasion in HGSC.
Additionally, we have created patient-derived organoids (iPDOs) that closely mimic real tumors. Using these models, we will test targeted immunotherapies and analyze how tumors respond at the single-cell level. Our work will uncover new immune escape mechanisms and provide a foundation for personalized immunotherapies in HGSC.
Our work was highlighted in our publication in Cancer Cell (2024), where we analyzed 117 patient samples representing diverse HGSC genotypes. By integrating genomics, immune profiles, and spatial mapping, we identified how immune cells interact with tumor cells in both chemotherapy-sensitive and resistant tumors. This dataset, consisting of over 25 million single cells, revealed key immune interactions within the tumor microenvironment (TME).
We discovered "Myelonets," networks of myeloid cells near the tumor that drive T cell exhaustion after chemotherapy. The tumor-stroma interface (TSI) was crucial in the polarization of macrophages, influencing T cell exhaustion and immune exclusion. We also found that the NECTIN2-TIGIT axis plays a major role in immune suppression, which could be targeted to enhance immune checkpoint therapy in chemotherapy-treated HGSC patients.
Additionally, in our recent work, we created a high-resolution single-cell spatial atlas of 15.1 million cells from HGSC patients, revealing the pivotal role of MHC class II expression in driving immune hotspots. We also developed CEFIIRA, a machine learning tool that integrates spatial data with clinical outcomes to identify biomarkers and immune escape mechanisms associated with poor prognosis and chemoresistance.
By integrating spatial proteomics and transcriptomics, we have gained unprecedented insights into the immune escape processes in ovarian cancer, providing new targets for personalized immunotherapy strategies.
We discovered "Myelonets," networks of myeloid cells near the tumor that drive T cell exhaustion after chemotherapy. The tumor-stroma interface (TSI) was crucial in the polarization of macrophages, influencing T cell exhaustion and immune exclusion. We also found that the NECTIN2-TIGIT axis plays a major role in immune suppression, which could be targeted to enhance immune checkpoint therapy in chemotherapy-treated HGSC patients.
Additionally, in our recent work, we created a high-resolution single-cell spatial atlas of 15.1 million cells from HGSC patients, revealing the pivotal role of MHC class II expression in driving immune hotspots. We also developed CEFIIRA, a machine learning tool that integrates spatial data with clinical outcomes to identify biomarkers and immune escape mechanisms associated with poor prognosis and chemoresistance.
By integrating spatial proteomics and transcriptomics, we have gained unprecedented insights into the immune escape processes in ovarian cancer, providing new targets for personalized immunotherapy strategies.
This project has led to important discoveries that improve our understanding of how HGSC (high-grade serous ovarian cancer) escapes the immune system and offers new strategies for personalized treatments. By combining spatial proteomics, single-cell RNA sequencing, and advanced tissue imaging, we've identified previously unknown networks of immune suppression in the tumor microenvironment (TME). These networks can now be targeted using our new immuno-oncology platform.
Mapping Tumor-Immune Interactions: We used spatial transcriptomics and advanced imaging techniques to map how immune cells interact within the tumor. This allowed us to uncover how immune suppression is organized in HGSC, providing insights that weren’t possible before.
Discovery of Myelonets: We identified Myelonets, clusters of myeloid cells, as key players in causing T cell exhaustion. This finding changes our understanding of immune suppression in tumors and introduces a new target for immunotherapy.
Identifying Immune Evasion Mechanisms: We discovered that the NECTIN2-TIGIT signaling pathway plays a central role in immune evasion in HGSC. Targeting this pathway could offer new opportunities for immune checkpoint therapy.
Innovative Use of Multi-Omics Data: By combining multi-omics data with our machine learning tool, CEFIIRA, we’ve uncovered immune-escape mechanisms linked to treatment resistance and poor prognosis. This approach has helped us make sense of complex data and gain insights into the spatial TME that were not previously possible.
These discoveries push the limits of tumor immunology and could change how we treat HGSC by providing new therapies and personalized treatment options for patients.
Mapping Tumor-Immune Interactions: We used spatial transcriptomics and advanced imaging techniques to map how immune cells interact within the tumor. This allowed us to uncover how immune suppression is organized in HGSC, providing insights that weren’t possible before.
Discovery of Myelonets: We identified Myelonets, clusters of myeloid cells, as key players in causing T cell exhaustion. This finding changes our understanding of immune suppression in tumors and introduces a new target for immunotherapy.
Identifying Immune Evasion Mechanisms: We discovered that the NECTIN2-TIGIT signaling pathway plays a central role in immune evasion in HGSC. Targeting this pathway could offer new opportunities for immune checkpoint therapy.
Innovative Use of Multi-Omics Data: By combining multi-omics data with our machine learning tool, CEFIIRA, we’ve uncovered immune-escape mechanisms linked to treatment resistance and poor prognosis. This approach has helped us make sense of complex data and gain insights into the spatial TME that were not previously possible.
These discoveries push the limits of tumor immunology and could change how we treat HGSC by providing new therapies and personalized treatment options for patients.