Passenger Gene Mutation Burden Analysis for Immunotherapy Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to effectively identify passenger genes and their mutations to assess immunogenicity, which are crucial for determining the responsiveness of cancer patients to immunotherapy, as overall tumor mutational burden includes both driver and passenger mutations, with driver mutations potentially suppressing immunogenicity.
Innovation Solution
A method to identify passenger genes by establishing a Passenger Gene Index (PGI) based on correlation coefficients, categorize patients with high passenger gene mutation burden, and administer immunotherapy regimens targeting T cell receptors to enhance immune response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If overall tumor mutational burden is used to assess immunogenicity, then the number of neo-antigen presentations is increased, but driver gene mutations suppress immunogenicity and decrease treatment sensitivity
Solution Approach 1:
The patent segments the tumor mutational burden into two distinct components: driver mutations and passenger mutations. By separating these components, the method can selectively count only passenger mutations (which do not confer growth advantage) to assess immunogenicity, thereby avoiding the suppressive effect of driver mutations while still capturing the neo-antigen presentation potential from accumulated passenger mutations.
Solution Approach 2:
The patent extracts and removes driver mutations from the overall mutational burden calculation. By taking out the harmful component (driver mutations that suppress immunogenicity), the method isolates the relevant signal (passenger mutations that contribute to neo-antigen presentation), enabling accurate assessment of immunogenicity without the confounding suppressive effect of driver mutations.
2Quantity of substance
If driver gene mutations are included in tumor mutational burden, then the total number of mutations is increased, but immunogenicity is decreased due to suppression by driver mutations
Solution Approach 1:
The patent applies local quality by differentiating between types of mutations based on their functional characteristics. Passenger mutations are counted because they contribute to immunogenicity, while driver mutations are excluded because they suppress it. This selective counting based on local quality (mutation type) enables accurate assessment of immunogenicity without the suppressive confound of driver mutations.
3Ease of operation
If existing methods are used to assess tumor mutational burden, then the analysis is simplified, but passenger gene identification is not achieved
Solution Approach 1:
The patent performs preliminary classification of mutations into driver and passenger categories before calculating mutational burden. By pre-sorting mutations based on their functional impact (using databases like COSMIC Cancer Gene Census), the method enables accurate passenger gene identification while maintaining a systematic workflow that can be integrated into existing clinical pipelines.
Data Source
AI summary
Methods for selecting a cancer patient for immunotherapy comprise establishing a total passenger gene mutation burden from a tumor of a cancer patient, generating a background distribution for the mutational burden of the tumor, normalizing the total passenger gene mutation burden against the background distribution, and categorizing the cancer patient as an immunotherapy responder when the total passenger gene mutation burden is greater than the mean of the background distribution. When the cancer patient is an immunotherapy responder, the patient may be administered an immunotherapy regimen that comprises activation/inhibition of T cell receptors that promote T cell activation and/or prolong immune cytolytic activities.