HLA Diversity Score Calculation for Cancer Therapy Prediction
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Solution Overview
Problem
Current methods for quantifying HLA allele diversity are inadequate in predicting patient responsiveness to cancer therapies, particularly immune checkpoint inhibitors, due to oversimplification of HLA heterozygosity and lack of consideration for additional genetic parameters.
Innovation Solution
A method that calculates a diversity score for HLA allele pairs by comparing DNA sequences, incorporating alignment scores, expression levels, and other genetic parameters to determine a weighted percentile score, which guides treatment recommendations based on predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple HLA heterozygosity assessment is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the simple heterozygosity assessment into a multi-parameter diversity score that incorporates sequence alignment scores, expression levels, and multiple HLA loci. This changes the measurement parameters from a single binary heterozygosity state to a composite score with multiple contributing factors, thereby improving measurement precision while maintaining operational feasibility through automated calculation.
Solution Approach 2:
The diversity score is constructed as a composite measure integrating multiple components: sequence alignment scores from DNA sequencing, expression levels from RNA or protein data, and contributions from multiple HLA loci (A, B, C, E, F, G, H, J, K, L, DP, DQ, DR). This composite approach combines different types of data to create a more precise and comprehensive HLA diversity assessment.
2Measurement precision
If comprehensive genetic parameters are incorporated, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The calculation process is divided into distinct segments: obtaining DNA sequences of HLA alleles, comparing sequences to generate alignment scores, obtaining expression levels separately, and then combining these components into the diversity score. This segmentation allows each component to be processed independently using standard laboratory and computational methods, reducing overall system complexity.
Solution Approach 2:
The diversity score calculation system is designed to accept multiple types of input data (DNA sequences, RNA expression levels, protein expression levels) and process them through a unified framework. This multi-functional approach allows the same system to handle various genetic parameters without requiring separate specialized systems for each data type.
3Reliability
If HLA diversity score is used to guide treatment, then treatment efficacy is improved, but loss of time in assessment increases
Solution Approach 1:
The HLA typing and sequencing are performed as preliminary actions before treatment selection. By obtaining the DNA sequences and calculating the diversity score in advance, the system prepares the necessary information beforehand, allowing for rapid treatment decision-making when needed without repeating the entire assessment process.
Solution Approach 2:
The diversity score provides feedback that directly informs treatment recommendations, creating a closed-loop system where the assessment results immediately guide clinical decisions. This feedback mechanism ensures that the time invested in assessment translates directly into improved treatment reliability by selecting therapies based on quantified HLA diversity rather than arbitrary criteria.
Data Source
AI summary
Provided herein are systems and methods for quantitating the HLA diversity in a solid tissue or circulating tumor DNA sample that is predictive of a patient's responsiveness to immune checkpoint inhibitory therapies.