Computational System Identifies Disease-Associated Antigens
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Solution Overview
Problem
Current cancer immunotherapy approaches face challenges in identifying patient-specific neoantigens and developing effective immunotherapies due to the rarity of somatic mutations and the need for personalized treatments, while driver mutations that affect protein function are often conserved across tumor sites, suggesting a potential for 'off-the-shelf' antigens. Additionally, aberrant splicing patterns in cancer cells complicate the identification of effective immunological targets.
Innovation Solution
The Immunotherapy Builder System (IBS) is a multi-modular platform that includes modules for Differential Expression Analysis, MHC Allele Affinity Determination, MHC Composite Feature Calculation, T-Cell Receptor Immunogenicity Assessment, and B-Cell Receptor Epitope Identification, which analyze amino acid sequences to predict disease-associated antigens and narrow down potential targets for immunotherapy, facilitating the development of resource-efficient therapies that can be effective across a broader population.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized neoantigen identification based on patient-specific somatic mutations is used, then treatment effectiveness is improved, but development time and cost increase
Solution Approach 1:
The patent segments the antigen identification process into modular computational steps: mutation detection, neoantigen prediction, MHC binding affinity calculation, and immunogenicity scoring. This segmentation allows parallel processing of multiple patient samples simultaneously, reducing overall development time while maintaining personalized treatment effectiveness.
Solution Approach 2:
The system performs preliminary computational screening of neoantigens in silico before experimental validation. By pre-filtering candidate antigens based on predicted MHC binding affinity and immunogenicity scores, the system reduces the number of antigens requiring time-consuming experimental testing, thereby accelerating the overall development process.
2Measurement precision
If comprehensive neoantigen screening is performed, then identification accuracy is improved, but computational resources required increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific high-priority regions: hot-spot mutations in driver genes and tumor-specific splice junctions. Rather than uniformly analyzing the entire exome, the system concentrates computational power on areas most likely to yield clinically relevant neoantigens, improving identification accuracy while reducing overall computational resource consumption.
Solution Approach 2:
The system dynamically adjusts computational parameters such as MHC binding affinity thresholds and immunogenicity score cutoffs based on the specific patient sample characteristics. This adaptive parameter adjustment optimizes the balance between identification accuracy and computational resource usage for each individual case.
3Adaptability or versatility
If hot-spot mutations are targeted for off-the-shelf antigens, then treatment accessibility is improved, but applicability to individual patients decreases
Solution Approach 1:
The patent identifies neoantigens that can serve dual purposes: they are patient-specific (derived from individual tumor mutations) yet shareable across multiple patients (when the same mutation occurs in different individuals). This universality enables development of semi-off-the-shelf antigen products that can be manufactured in advance and stocked, improving accessibility while maintaining patient-specific effectiveness through personalized HLA allele matching.
Solution Approach 2:
The system uses HLA allele typing as an intermediary to bridge between patient-specific neoantigens and off-the-shelf antigen products. By matching predicted neoantigens with the patient's specific HLA alleles, the system determines which pre-manufactured antigens will be effectively presented to the patient's immune system, thereby maintaining individualized treatment effectiveness while enabling broader access through standardized antigen products.
Data Source
AI summary
Disclosed here are methods for treating a condition (e.g., cancer) with an appropriate immunotherapeutic agent and/or regimen. Also disclosed are methods for the use of effective combinations of proteins encoded by hot-spot mutations and/or tumor-associated mRNA splice variants to optimize the targeting of a patient's condition (e.g., cancer) with immunotherapies.