Periodic Reporting for period 1 - SpatialTMEMetabolism (Spatial Quantification of Cellular Metabolism in the Tumor Immune Microenvironment)
Reporting period: 2023-09-01 to 2026-02-28
Cancer cells do not grow in isolation. They exist in a complex local environment together with immune cells, stromal cells and blood vessels, all of which compete for nutrients and adapt to low oxygen and other forms of stress. These conditions can strongly influence whether the immune system is able to recognize and attack a tumor or instead becomes suppressed. Understanding these processes is essential for improving cancer treatment and for explaining why patients with apparently similar tumors can respond very differently to therapy.
This project investigates how tumor cells and immune cells use energy inside human cancers, how these metabolic interactions are organized in space, and how they shape disease progression and response to treatment. To address this, the project combines advanced spatial imaging technologies with computational analysis to map the location, identity and metabolic state of thousands of individual cells directly in tumor tissue. The work spans patient samples from different cancer types and is complemented by organoid-based model systems to study how tumor microenvironments are formed and how they may be manipulated.
The overall objective is to identify metabolic features that help explain immune dysfunction in cancer and to develop robust tools for measuring these features directly in clinical tissue samples. In the longer term, the project is expected to support more precise patient stratification and open new avenues for therapies that restore or strengthen anti-tumor immune responses.
This project investigates how tumor cells and immune cells use energy inside human cancers, how these metabolic interactions are organized in space, and how they shape disease progression and response to treatment. To address this, the project combines advanced spatial imaging technologies with computational analysis to map the location, identity and metabolic state of thousands of individual cells directly in tumor tissue. The work spans patient samples from different cancer types and is complemented by organoid-based model systems to study how tumor microenvironments are formed and how they may be manipulated.
The overall objective is to identify metabolic features that help explain immune dysfunction in cancer and to develop robust tools for measuring these features directly in clinical tissue samples. In the longer term, the project is expected to support more precise patient stratification and open new avenues for therapies that restore or strengthen anti-tumor immune responses.
So far, the project applied its spatial imaging and computational analysis framework to large collections of human tumor samples in order to understand how metabolism is organized within the tumor microenvironment. The work focused in particular on how metabolic programs differ between cell types, how they are arranged in space, and how they relate to disease progression and treatment response.
A first major line of work examined colorectal cancer. Using highly multiplexed tissue imaging, the project profiled more than 500 colorectal lesions at single-cell resolution and analyzed how tumor, immune and stromal cells are organized across disease stages. This revealed coordinated multicellular programs linked to tumor progression, including changes in immune-cell composition, stromal expansion and metabolic activity. The work also led to the development of a computational framework that integrates cellular, spatial and metabolic information to identify reproducible patterns in complex tumor tissues.
A second major line of work focused on metastatic melanoma and response to immune checkpoint inhibition. Here, the project identified a distinct metabolic signature in CD8 T cells that was associated with favorable therapeutic response. Additional analyses showed that this immune-cell state is linked to less suppressive cellular neighborhoods and to molecular programs associated with durable and therapeutically relevant T-cell function. Follow-up experiments further supported the biological relevance of this finding.
In parallel, the project continued to refine laboratory workflows, antibody validation, image analysis pipelines and quality-control procedures needed for robust spatial metabolic profiling in clinical samples. Together, these activities have moved the project from initial platform establishment toward concrete biological discoveries in human cancer and have created a strong foundation for the next phase of the work, including extension to further tumor types and mechanistic studies in organoid-based model systems.
A first major line of work examined colorectal cancer. Using highly multiplexed tissue imaging, the project profiled more than 500 colorectal lesions at single-cell resolution and analyzed how tumor, immune and stromal cells are organized across disease stages. This revealed coordinated multicellular programs linked to tumor progression, including changes in immune-cell composition, stromal expansion and metabolic activity. The work also led to the development of a computational framework that integrates cellular, spatial and metabolic information to identify reproducible patterns in complex tumor tissues.
A second major line of work focused on metastatic melanoma and response to immune checkpoint inhibition. Here, the project identified a distinct metabolic signature in CD8 T cells that was associated with favorable therapeutic response. Additional analyses showed that this immune-cell state is linked to less suppressive cellular neighborhoods and to molecular programs associated with durable and therapeutically relevant T-cell function. Follow-up experiments further supported the biological relevance of this finding.
In parallel, the project continued to refine laboratory workflows, antibody validation, image analysis pipelines and quality-control procedures needed for robust spatial metabolic profiling in clinical samples. Together, these activities have moved the project from initial platform establishment toward concrete biological discoveries in human cancer and have created a strong foundation for the next phase of the work, including extension to further tumor types and mechanistic studies in organoid-based model systems.
The project advances the state of the art by enabling metabolism to be studied directly in intact human tumor tissue at single-cell resolution. Until recently, most analyses of cancer metabolism relied on bulk measurements that average signals across many different cells and lose information about where these cells are located and how they interact. By combining high-dimensional spatial imaging with computational analysis, this project makes it possible to identify the metabolic states of individual tumor and immune cells within their natural tissue environment. This creates a new level of resolution for studying how tumors develop, how immune responses are suppressed, and why patients differ in their response to therapy.
A key advance of the project is that it moves beyond describing single cell types in isolation. Instead, it identifies coordinated spatial and metabolic patterns across tumor, immune and stromal cells, revealing how complex tumor microenvironments are organized and how these patterns relate to disease progression and treatment response. This has the potential to improve the biological interpretation of patient tissues and to provide a more robust basis for future biomarkers than approaches based on single markers alone.
The project also delivers broadly applicable methodological advances, including imaging workflows, quality-control procedures, antibody-based metabolic profiling strategies and computational tools for integrating cellular, spatial and metabolic information. These developments can support further research across multiple cancer types and may help accelerate future translational applications. Key needs for further uptake include continued validation in independent patient cohorts, extension to additional tumor types, and further development of model systems that allow the functional testing of candidate metabolic mechanisms under controlled conditions.
A key advance of the project is that it moves beyond describing single cell types in isolation. Instead, it identifies coordinated spatial and metabolic patterns across tumor, immune and stromal cells, revealing how complex tumor microenvironments are organized and how these patterns relate to disease progression and treatment response. This has the potential to improve the biological interpretation of patient tissues and to provide a more robust basis for future biomarkers than approaches based on single markers alone.
The project also delivers broadly applicable methodological advances, including imaging workflows, quality-control procedures, antibody-based metabolic profiling strategies and computational tools for integrating cellular, spatial and metabolic information. These developments can support further research across multiple cancer types and may help accelerate future translational applications. Key needs for further uptake include continued validation in independent patient cohorts, extension to additional tumor types, and further development of model systems that allow the functional testing of candidate metabolic mechanisms under controlled conditions.