Cancer Associated Fibroblast Subsets Predict Immunotherapy Response
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Solution Overview
Problem
Current immunotherapy for solid tumors, such as head and neck squamous cell carcinoma, has low response rates due to unknown resistance mechanisms, making it difficult to predict patient response and optimize treatment.
Innovation Solution
Detecting and isolating specific subsets of cancer-associated fibroblasts (CAFs) from tumor biopsy samples, such as those enriched in certain gene clusters or differentially activated proteins, and administering them in combination with immunotherapy to enhance treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PD-1/PD-L1 immune checkpoint inhibitors are used as first line therapy for recurrent/metastatic head and neck squamous cell carcinoma, then treatment coverage is provided, but response rates remain low (as low as 20%) due to unknown resistance mechanisms
Solution Approach 1:
The patent segments the tumor microenvironment into distinct fibroblast subsets (CAFs, iCAFs, myCAFs) based on gene expression profiles and functional characteristics. This segmentation allows identification of specific cellular populations that drive resistance, transforming the undifferentiated problem of 'unknown resistance mechanisms' into targeted analysis of specific fibroblast subtypes with distinct molecular signatures
Solution Approach 2:
The patent changes the analytical parameters from general tumor characterization to specific gene expression profiling of fibroblast subsets. By measuring expression levels of specific gene signatures (e.g., collagen genes, activation markers) in different fibroblast populations, the patent transforms the inability to predict response into a quantifiable biomarker-based prediction system
2Adaptability or versatility
If immunotherapy is administered without patient selection, then all patients receive treatment, but it is difficult to predict who will respond and who will not
Solution Approach 1:
The patent performs preliminary characterization of fibroblast subsets in tumor biopsy samples before immunotherapy administration. By establishing baseline gene expression profiles of CAFs, iCAFs, and myCAFs pre-treatment, the patent enables prediction of response likelihood, allowing clinicians to select patients most likely to benefit from immunotherapy before treatment begins
Solution Approach 2:
The patent establishes a feedback mechanism where post-treatment changes in fibroblast subset composition and gene expression are measured and correlated with clinical response. This feedback loop allows validation of predictive models and refinement of biomarker signatures, transforming the lack of predictive information into a learnable, improving prediction system
Data Source
AI summary
Disclosed herein is a method for treating a solid tumor in a subject that involves detecting in a tumor biopsy sample from the subject enrichment of a cancer associated fibroblast (CAF) subset disclosed herein and then treating the subject with an immunotherapy. Also discussed is a method for treating a solid tumor in a subject that involves isolating cancer associated fibroblasts (CAFs) from the subject, isolating and expanding the disclosed subset of CAFs, and administering the expanded CAF subset to the subject in combination with an immunotherapy.