PD-1 Immunotherapy Response Prediction Using Tumor Mutation Burden
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cancer immunotherapy methods lack effective predictors for determining which patients are likely to respond favorably to treatments with immune checkpoint modulators, such as anti-CTLA4 and anti-PD1 antibodies.
Innovation Solution
Identifying cancer patients through detection of high mutation burden, particularly nonsynonymous mutations and neoepitopes, which correlate with responsiveness to immune checkpoint modulators like pembrolizumab, using methods that include sequencing cancer samples and analyzing mutation characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If immunotherapy with immune checkpoint inhibitors is applied to cancer patients, then long-term disease control can be achieved in certain patients, but there is no effective method to predict which patients will respond favorably to the treatment
Solution Approach 1:
The patent performs preliminary genomic sequencing and mutation analysis on cancer samples before treatment to identify patients likely to respond to immunotherapy. By conducting these predictive tests in advance, the system determines treatment eligibility based on mutation burden and neoepitope presence, allowing clinicians to select appropriate patients for immune checkpoint inhibitor therapy before administering the treatment.
2Measurement precision
If cancer samples are sequenced to identify mutations and neoepitopes, then prediction of treatment response is improved, but the complexity of the detection method increases
Solution Approach 1:
The patent segments the complex genomic analysis into distinct components: identifying specific mutation types (nonsynonymous mutations), calculating mutation burden metrics, and detecting neoepitopes. By dividing the overall predictive process into these manageable segments, the system can implement targeted sequencing approaches and analysis methods for each component, reducing the overall complexity while maintaining comprehensive prediction capability.
Solution Approach 2:
The patent applies different analytical approaches to different aspects of the genomic data based on their specific requirements. For example, it uses specific algorithms for calculating transition-to-transversion ratios, separate methods for identifying neoepitopes, and distinct thresholds for determining high versus low mutation burden. This localized optimization of analysis methods for each specific measurement improves precision while avoiding the need for a single overly complex unified system.
Data Source
AI summary
Molecular determinants of cancer response to immunotherapy are described, as are systems and tools for identifying and/or characterizing cancers likely to respond to immunotherapy.