Tumor Microenvironment Signatures for CAR-T Response Prediction
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Solution Overview
Problem
Current methods for assessing patient response to adoptive cell therapies like CAR-T therapy lack the ability to characterize the tumor microenvironment comprehensively, leading to uncertain therapeutic outcomes and inefficient clinical trial design.
Innovation Solution
Processing gene expression data to generate molecular functional signatures that reflect the tumor microenvironment, allowing categorization into four main types indicative of prognosis and therapeutic response, using a combination of gene groups specific to various biological processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gene expression data is processed to generate molecular functional signatures reflecting tumor microenvironment, then the ability to characterize TME comprehensively is improved, but the complexity of the assessment method increases
Solution Approach 1:
The patent segments the tumor microenvironment assessment into multiple discrete gene groups, each representing specific biological processes (e.g., immune response, angiogenesis, fibrosis). By dividing the complex TME into these manageable segments, the method achieves comprehensive characterization while maintaining analytical tractability. Each gene group can be independently analyzed and combined to form the overall TME profile.
Solution Approach 2:
The patent creates a universal molecular functional signature framework that can assess multiple aspects of the tumor microenvironment simultaneously. This multi-functional approach allows a single assessment system to evaluate diverse TME characteristics (immune infiltration, vascularization, stromal composition) using integrated gene expression analysis, thereby improving comprehensiveness without proportionally increasing complexity.
2Reliability
If molecular functional signatures are used to categorize patients into TME types, then the ability to predict therapeutic response is improved, but the time required for assessment increases
Solution Approach 1:
The patent performs preliminary categorization of tumor samples into distinct TME types based on molecular functional signatures before therapeutic decision-making. By pre-establishing TME classifications (e.g., immune-rich, fibrotic, vascularized types) and their associated therapeutic responses, the system enables rapid prediction without requiring time-consuming individualized analysis for each patient.
Solution Approach 2:
The patent transforms complex gene expression data into simplified categorical parameters (TME types) that capture the essential characteristics relevant to therapeutic response. This parameter transformation reduces the dimensionality of the assessment output, enabling faster clinical decision-making while preserving predictive reliability through the use of biologically meaningful categories.
Data Source
AI summary
Aspects of the disclosure relate to methods for determining whether or a subject is likely to respond to certain adoptive cell therapies (e.g., chimeric antigen receptor (CAR) T-cell therapy, etc.). In some embodiments, the methods comprise the steps of identifying a subject as having a tumor microenvironment (TME) type based upon a molecular-functional (MF) expression signature of the subject, and determining whether or not the subject is likely to respond to a chimeric antigen receptor (CAR) T-cell therapy based upon the TME type. In some embodiments, the methods comprise determining the lymphoma microenvironment (LME) type of a lymphoma (e.g., Diffuse Large B cell lymphoma (DLBCL)) subject and identifying the subjects prognosis based upon the LME type determination.