Spatial Transcriptomic Biomarkers for Immunotherapy Responsiveness
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Solution Overview
Problem
Current biomarkers for predicting immunotherapy responsiveness in cancer patients lack precision due to their reliance on average gene expression across all cells in a tissue, failing to consider the specific cellular composition of the tumor microenvironment, leading to inaccurate treatment decisions and potential adverse effects.
Innovation Solution
Development of biomarkers using spatial transcriptomic analysis to measure the mRNA or protein expression levels of specific genes (NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-β, TNF-β, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G) in distinct regions of the tumor microenvironment, such as tumor, immune, and stromal regions, to predict immunotherapy responsiveness and survival prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mRNA expression analysis from pathological cancer tissue slides is used to select biomarkers, then the measurement process is simple and widely applicable, but the biomarkers lack precision in predicting immunotherapy responsiveness because they only reflect average gene expression across all cells without considering tumor microenvironment composition
Solution Approach 1:
The tissue section is segmented into distinct regions (tumor region, immune region, stromal region) based on morphological markers, allowing separate analysis of gene expression in each microenvironment compartment. This segmentation enables precise measurement of biomarker expression in specific cellular contexts rather than averaging across all cells.
Solution Approach 2:
The analysis transitions from uniform whole-tissue analysis to region-specific gene expression measurement. Each microenvironment region (tumor, immune, stromal) is analyzed with its own expression profile, capturing local biological characteristics that determine immunotherapy responsiveness. This local quality approach reveals spatial heterogeneity in biomarker expression.
2Measurement precision
If spatial transcriptomic analysis based on GeoMx Digital Spatial Profiling system is used to measure gene expression in distinct tumor microenvironment regions, then the prediction precision of immunotherapy responsiveness is improved, but the measurement complexity and technical requirements increase
Solution Approach 1:
Morphological markers are used to pre-identify and delineate regions of interest (tumor, immune, stromal regions) before performing gene expression analysis. This preliminary spatial mapping simplifies the subsequent transcriptomic measurement by restricting analysis to predefined anatomical compartments, reducing the complexity of spatial coordinate management.
Solution Approach 2:
Morphological markers serve as intermediaries that bridge tissue morphology and gene expression data. These markers first label and define spatial regions, which then guide the extraction and analysis of region-specific transcriptomic profiles, facilitating the integration of spatial and molecular information.
Data Source
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AI summary
The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.