Chimeric Antigen Receptor (CAR) T-cell therapy has shown remarkable success in treating hematological cancers. However, CAR T-cell therapy for solid tumors has been challenging due to the complex and heterogeneous nature of the tumor microenvironment (TME). The spatial distribution of tumor cells, immune cells, blood vessels, and extracellular matrix elements within the TME plays a critical role in therapy efficacy.
Spatial AI Language for CAR T-Cell Therapy Optimization in Solid Tumors
Patent ID nr: 149114
The Tumor Microenvironment isn't just where cancer lives- it's how it survives. From immune evasion to therapy resistance
1. Analyzing CAR T-Cell Distribution in a Tumor Microenvironment. Basic
This command asks the system to locate CAR T-cells that are within 50 micrometers of tumor cells expressing a specific antigen (EGFRvIII). It could return a 3D visualization showing clusters of CAR T-cells close to tumor cells, highlighting areas where the therapy might be most effective.
2. Analyzing Spatial Density
This command instructs the AI to calculate how densely CAR T-cells are distributed in areas where tumor cells are particularly concentrated. This could help identify areas where CAR T-cells are underrepresented, signaling potential “cold” zones within the tumor that need more immune cells.
3. Tracking Movement Over Time
This temporal-spatial command monitors CAR T-cells as they move within the tumor environment, providing insights into their infiltration behavior. The output could be a 4D visualization (3D over time) showing the paths of CAR T-cells as they navigate the stroma and approach tumor cells, helping researchers identify any barriers.
4. Evaluating CAR T-Cell Persistence
In some parts of the tumor, low oxygen levels (hypoxia) make it difficult for immune cells to function. This command focuses on identifying CAR T-cells that can withstand these conditions, potentially highlighting engineered cells with enhanced survival capabilities.
5. Optimizing Delivery Locations
This command requests the AI to analyze the spatial configuration of the tumor and suggest points where CAR T-cells should be injected for maximum distribution. The system would evaluate factors like cell density and barriers, providing a strategic plan for CAR T-cell delivery.
6. Identifying Clusters of Immune Suppressive Cells
Regulatory T cells (Treg cells) can suppress immune responses, so their presence near CAR T-cells could impede the therapy’s effectiveness. This query identifies these suppressive clusters, allowing researchers to see where CAR T-cell activity might be compromised.
7. Simulating Synthetic CAR Modifications
This command tests how CAR T-cells engineered to follow a chemokine gradient (CCL2, for instance) would move within the tumor. The AI could simulate and visualize how these modified cells navigate toward high CCL2 concentrations, which might improve their ability to reach tumor cells hidden within the stroma.
8. Assessing Spatial Impact of Tumor Heterogeneity
Tumor cells often vary in the antigens they express, which can reduce CAR T-cell effectiveness. This command helps pinpoint areas where antigen diversity is high, which might require engineered CAR T-cells with broader specificity or combination therapies to target heterogeneous tumor regions.
9. Visualizing Tumor Microenvironment Dynamics
This comprehensive visualization maps out not only CAR T-cells and tumor cells but also blood vessels (providing nutrient access) and the extracellular matrix (ECM) that CAR T-cells must navigate. Watching these dynamics in 4D could reveal insights into the spatial challenges CAR T-cells face in reaching tumor cells.
10. Predicting Response Based on Spatial Features
This analytical command uses spatial data to forecast CAR T-cell performance in different regions of the tumor. It could highlight areas where CAR T-cell efficacy might be low due to immune suppression or poor vascularization, helping inform strategies to optimize treatment.
Possible Output
Each query could generate a combination of 3D visualizations, quantitative reports (e.g., cell densities, persistence times), and spatial analytics (e.g., heatmaps of CAR T-cell distribution) to assist researchers in understanding the tumor microenvironment and refining CAR T-cell therapies. This Spatial AI language enables precise, spatially aware instructions, facilitating the design of optimized and context-aware cancer therapies.
This demonstration showcases the potential of a Spatial AI language to capture, analyze, and visualize complex spatial relationships and dynamics in ways that could lead to more effective treatments. The spatial insights provided by such a language could be game-changing for fields like CAR T-cell therapy, where spatial positioning and movement are critical to therapeutic success.
Field of Invention
This invention relates to artificial intelligence and computational biology, specifically a Spatial AI language designed to facilitate the analysis, optimization, and implementation of CAR T-cell therapies in solid tumors. This system allows for the precise modeling, visualization, and modification of CAR T-cell behavior in complex 3D tumor microenvironments, aiding in the development of more effective cancer immunotherapies.
Background of the Invention
Chimeric Antigen Receptor (CAR) T-cell therapy has shown remarkable success in treating hematological cancers. However, CAR T-cell therapy for solid tumors has been challenging due to the complex and heterogeneous nature of the tumor microenvironment (TME). The spatial distribution of tumor cells, immune cells, blood vessels, and extracellular matrix elements within the TME plays a critical role in therapy efficacy.
Existing computational systems are limited in their ability to represent, analyze, and optimize spatial relationships in 3D environments. To overcome these limitations, this invention introduces a Spatial AI language that enables researchers and clinicians to specify, simulate, and optimize CAR T-cell interactions within solid tumors. This language allows for intuitive, spatially aware queries and commands, facilitating the development of spatially optimized CAR T-cell therapies.
Summary of the Invention
The invention is a Spatial AI language designed to enhance CAR T-cell therapy by modeling, analyzing, and optimizing spatial relationships within solid tumors. This language enables users to define, track, and manipulate spatial features of the TME, such as cell clusters, tissue density, antigen distribution, and cell movement over time. By providing a structured syntax and vocabulary, this Spatial AI language supports the development of CAR T-cell therapies that can more effectively navigate and respond to complex tumor environments.
The Spatial AI language can be implemented on various computational platforms and integrated with digital twin models or 3D visualization tools. Key features of this language include spatial primitives, operators, modifiers, and dynamic spatial terms that facilitate the modeling of 3D and 4D tumor environments.
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Use Cases and Applications
- Optimizing CAR T-Cell Therapy: The Spatial AI language enables the design of CAR T-cell therapies that are spatially adapted to specific tumor types, improving targeting and infiltration.
- Digital Twin Development: Facilitates the creation of digital twin models of individual patients’ tumors, providing a personalized framework for therapy design.
- Enhanced Simulation of CAR T-Cell Modifications: Allows researchers to test CAR T-cell modifications in silico, such as enhanced chemokine receptor expression, to improve migration and retention within tumors.
Example Workflow
- A researcher initiates a query to identify CAR T-cells within proximity to high antigen-expressing regions in the tumor.
- The Spatial AI language processes the query, analyzes the spatial data, and outputs results, including a visualization of CAR T-cell distribution and a report on cell density.
- Based on the output, the researcher may simulate different CAR T-cell modifications or injection strategies to optimize therapy for the target tumor environment.
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Claims
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Claim 1: A Spatial AI language system that enables analysis and optimization of CAR T-cell therapy for solid tumors by facilitating spatial queries and commands within a three-dimensional tumor microenvironment.
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Claim 2: The system of claim 1, wherein the Spatial AI language comprises spatial primitives, spatial operators, spatial modifiers, and temporal-spatial terms to accurately model and analyze the spatial relationships and dynamics of CAR T-cells within the TME.
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Claim 3: The system of claim 1, wherein the Spatial AI language includes commands for calculating cell densities, tracking cell proximity, evaluating persistence, and simulating CAR T-cell modifications in response to spatial characteristics of the tumor.
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Claim 4: A method for optimizing CAR T-cell therapy using the Spatial AI language system, wherein researchers analyze CAR T-cell distribution and movement within solid tumors and adjust therapeutic strategies accordingly.
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Claim 5: The integration of the Spatial AI language with digital twin platforms and 3D visualization tools, enabling real-time modeling, simulation, and visualization of CAR T-cell interactions within the TME.
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Claim 6: A method for identifying optimal CAR T-cell injection points based on spatial density and antigen distribution in the TME, utilizing the Spatial AI language for precise localization.
