Neoantigen Burden Analysis for Immune Checkpoint Therapy Selection
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Solution Overview
Problem
Current approaches to immune checkpoint regulation in cancer treatment are largely based on guesswork and serendipity, lacking directed methods to identify which cancer patients will respond favorably to therapies targeting immune checkpoints like CTLA4, PD-1, and PDL1.
Innovation Solution
Determining the number of clonal neo-antigens, the ratio of clonal to sub-clonal neo-antigens, and the expression profile of immune checkpoint molecules in cancer cells to predict response to immune checkpoint interventions, allowing for more targeted and effective treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If immune checkpoint therapies are administered based on current approaches, then treatment coverage is provided, but treatment effectiveness is low due to lack of patient selection criteria
Solution Approach 1:
The patent applies preliminary action by determining neoantigen burden and clonality status before administering immune checkpoint therapy. This pre-treatment analysis allows clinicians to identify patients most likely to respond to therapy, thereby improving treatment effectiveness while avoiding unnecessary treatments in patients unlikely to benefit.
Solution Approach 2:
The patent replaces the mechanical/guesswork-based patient selection approach with a molecular biology-based system. Instead of relying on clinical guesswork or serendipity, the invention uses neoantigen burden determination and clonality analysis to objectively identify responsive patients, substituting empirical selection with science-based prediction.
2Measurement precision
If comprehensive neoantigen analysis is performed to identify responsive patients, then treatment precision is improved, but diagnostic complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex diagnostic process into distinct analytical components: (1) determining total neoantigen burden, (2) assessing clonality status of neoantigens, and (3) integrating these factors to predict therapy response. This segmentation makes the overall complex diagnostic task more manageable and systematic.
Solution Approach 2:
The patent uses parameter changes by focusing on specific quantifiable parameters (neoantigen burden levels, clonality ratios) that can be measured and compared against established thresholds. By translating complex biological information into discrete parameters with predictive value, the diagnostic complexity is reduced while maintaining high prediction accuracy.
Data Source
AI summary
The present invention relates to methods for identifying a subject with cancer who is suitable for treatment with an immune checkpoint intervention, and to methods of treatment of such subjects. The invention further relates to a method for predicting or determining the prognosis of a subject with cancer.