Immune Scoring for Personalized Cancer Therapy Response Prediction
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Solution Overview
Problem
Existing methods for predicting patient response to immunotherapies, particularly in malignancies other than melanoma and non-small cell lung cancer, are limited by subjective and analytically robust assays, such as FDA-approved IHC assays for PD-L1 protein status, which fail to provide comprehensive characterization of the immunological tumor microenvironment.
Innovation Solution
A method involving qualitative and quantitative assessments of tumor infiltrating lymphocytes, T-cell receptor signaling, and mutational burden, combined with a predictive algorithm, to generate an immune score for determining cancer cell sensitivity to personalized oncology therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If FDA-approved IHC assays measuring PD-L1 protein status are used, then the evaluation process is simple and standardized, but the measurement precision and analytical robustness are insufficient for comprehensive tumor microenvironment characterization
Solution Approach 1:
The patent segments the comprehensive immune profiling into multiple independent assay modules: TIL assessment, TCR signaling analysis, mutational burden quantification, and immune score generation. Each module can be performed independently using specialized techniques (IHC, flow cytometry, sequencing) and then integrated through the predictive algorithm, allowing optimization of each component while maintaining overall system robustness
Solution Approach 2:
The patent creates a composite assessment system that combines multiple biomarkers and assay types into a unified immune score. This composite approach integrates data from different sources (protein expression, cellular infiltration, genetic mutations) through a predictive algorithm, achieving superior measurement precision compared to any single assay while maintaining clinical applicability
2Reliability
If comprehensive characterization of immunological tumor microenvironment is performed, then the prediction accuracy for patient response is improved, but the device complexity and assay complexity increase
Solution Approach 1:
The comprehensive immune profiling is divided into distinct analytical modules: TIL quantification, TCR signaling assessment, mutational burden measurement, and immune score calculation. Each module uses optimized specialized techniques, and the results are integrated through a predictive algorithm that generates personalized treatment recommendations, making the complex system manageable and reproducible
Solution Approach 2:
The patent introduces a predictive algorithm as an intermediary that processes and integrates data from multiple complex assays into a unified immune score. This intermediary layer simplifies the relationship between complex input data and clinical decisions, allowing comprehensive characterization without proportionally increasing operational complexity
3Loss of information
If multiple biomarkers and assay types are integrated, then the comprehensive characterization capability is improved, but the loss of time for analysis increases
Solution Approach 1:
The patent performs preliminary data processing and normalization of each biomarker assay independently before integration. Quality control metrics are established in advance for each assay type, and the predictive algorithm is pre-trained on comprehensive datasets. This preliminary preparation enables rapid integration of multiple biomarkers while maintaining comprehensive characterization, reducing overall analysis time without sacrificing information completeness
Data Source
AI summary
A method for generating an immune score, the method comprising the steps of: (i) determining a qualitative and/or quantitative assessment of tumor infiltrating lymphocytes in a sample; (ii) determining a qualitative and/or quantitative assessment of T-cell receptor signaling in the sample; (iii) determining a qualitative and/or quantitative assessment of mutation burden in the sample; (iv) generating, using a predictive algorithm, an immune score based on the determined qualitative and/or quantitative assessment of tumor infiltrating lymphocytes, the determined qualitative and/or quantitative assessment of T-cell receptor signaling, and the determined qualitative and/or quantitative assessment of mutation burden.