Epitope Ranking System for Immunotherapy via Multi-Level Data Fusion
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Solution Overview
Problem
Current methods for identifying and selecting immunogenic tumor-specific antigens for immunotherapy are suboptimal, leading to variable treatment responses, as they fail to accurately consider both peptide-level and sample-level information, resulting in inconsistent epitope prediction and low success rates in therapies like cancer vaccination and adoptive cell transfer.
Innovation Solution
A system and method that integrate peptide-level and sample-level information to identify, predict, and rank immunogenic epitopes, incorporating factors such as MHC Class I and II presentation, CD4 and CD8 activation, allele dosage, and tumor-specific characteristics, using next-generation sequencing data and machine learning models to assign weights and compute immunogenic scores for personalized treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for identifying tumor-specific antigens are used, then the process is simple, but the prediction accuracy and treatment success rate are low
Solution Approach 1:
The patent merges peptide-level information (MHC binding affinity, epitope sequences) with sample-level information (allele dosage, tumor mutational burden, immune checkpoint expression) into a unified predictive model. This integration combines multiple data dimensions and analytical approaches to achieve comprehensive epitope prediction accuracy while systematically managing the complexity through structured data fusion.
Solution Approach 2:
The predictive system is designed to evaluate multiple immunotherapy approaches simultaneously (cancer vaccination, adoptive cell transfer, immune checkpoint inhibitors) using a single integrated framework. The system universally processes different input data types and provides predictions applicable across various treatment modalities, enhancing versatility without proportionally increasing operational complexity.
2Reliability
If peptide-level information alone is considered, then the analysis is straightforward, but the immunogenicity prediction is inconsistent
Solution Approach 1:
The patent combines peptide-level features (MHC Class I and Class II binding predictions, epitope sequences) with sample-level features (allele dosage, tumor mutational burden, immune checkpoint expression) into an integrated predictive model. This merging of multiple information layers improves prediction consistency by considering both the intrinsic properties of peptides and the contextual characteristics of individual patient samples.
3Measurement precision
If comprehensive factors are integrated for epitope ranking, then the treatment selection accuracy improves, but the computational complexity increases
Solution Approach 1:
The system merges multiple factors including MHC binding affinity, allele dosage effects, tumor mutational burden, and immune checkpoint expression into a unified epitope ranking framework. This integration combines diverse biological and computational elements to achieve precise treatment selection while systematically managing computational complexity through structured data fusion and weighted evaluation.
Solution Approach 2:
The patent applies parameter changes by incorporating quantitative measures such as allele dosage (copy number variations of MHC alleles) and tumor mutational burden as weighted factors in the epitope ranking algorithm. These parameter transformations convert raw biological data into normalized scoring metrics that can be systematically integrated into the predictive model, improving accuracy while maintaining computational tractability.
Data Source
AI summary
This disclosure relates to systems and methods that identify, predict, and rank immunogenic T-cell epitopes. In particular, this disclosure identifies epitopes that arose from disease-associated mutations, wherein the epitopes are predicted to elicit immune response from T cells. Specifically, this disclosure simultaneously considers peptide-level information, including MHC Class I and II presentation, helper and cytotoxic T cell response and sample-level information, including mutation clonality and MHC allele dosage. In some embodiment, the systems and methods are used for personalized treatment of cancers.