CDR3 Pattern Identification for Immune Repertoire Diagnostics
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Solution Overview
Problem
Conventional methods are impractical for accurately assessing immune response changes associated with diseases due to a weak signal-to-noise ratio in immune repertoire analysis, making it difficult to develop effective diagnostic tests for conditions like cancer and autoimmune diseases.
Innovation Solution
A method involving the collection and sequencing of immune repertoires from patient and control groups to identify shared CDR3 sequences, ranking them by frequency, and using Positive Linklets to enrich the diagnostic signal and filter out noise, thereby generating a disease signature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional immune repertoire analysis methods are used, then the analysis can be performed with standard techniques, but the signal-to-noise ratio remains weak making accurate disease assessment difficult
Solution Approach 1:
The patent segments the immune repertoire analysis by focusing specifically on CDR3 sequences rather than analyzing entire receptor sequences. This segmentation isolates the most antigen-specific portion of the repertoire, enriching the diagnostic signal while filtering out redundant framework region information that contributes to noise.
Solution Approach 2:
The method extracts and analyzes only the CDR3 region from the complete V(D)J sequence, removing unnecessary framework regions. This extraction concentrates the diagnostic information in the most variable and antigen-specific portion of the receptor, thereby improving the signal-to-noise ratio for disease detection.
2Measurement precision
If the entire V(D)J sequence is analyzed, then complete receptor information is obtained, but the complexity of data processing increases and diagnostic accuracy decreases
Solution Approach 1:
The patent extracts only the CDR3 region from the complete V(D)J sequence for analysis. This extraction eliminates the need to process the entire receptor sequence including framework regions, thereby reducing data processing complexity while maintaining or improving diagnostic accuracy by focusing on the most informative region.
Solution Approach 2:
Instead of analyzing unique full-length V(D)J sequences which are extremely diverse, the method copies and analyzes the CDR3 region across multiple sequences from the same sample. This allows identification of recurrent CDR3 patterns that are more likely to be disease-associated, simplifying the analysis while improving diagnostic relevance.
3Reliability
If rare CDR3 sequences are included in analysis, then comprehensive repertoire coverage is achieved, but the reliability of disease signature identification decreases
Solution Approach 1:
The patent applies partial action by setting a frequency threshold (e.g., minimum 2 occurrences) for CDR3 sequences to be included in the disease signature analysis. This filters out extremely rare sequences that are likely noise while retaining sequences that appear with sufficient frequency to be biologically relevant, thereby improving the reliability of disease signature identification.
Solution Approach 2:
The method uses frequency information as feedback to filter CDR3 sequences. By counting occurrences of each CDR3 sequence and applying frequency-based filtering, the system automatically adjusts which sequences are included in analysis based on their recurrence patterns, improving the signal-to-noise ratio and reliability of disease-associated signature identification.
Data Source
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AI summary
The present disclosure generally pertains to a method for developing diagnostic tests that are based on the immune response and the resulting immune repertoire. The presently disclosed method increases the signal and reduces the background to allow the identification of shared CDR3s that can be used to produce a disease signature. The presently disclosed method may be used to develop a diagnostic test for different diseases including, but not limited to, cancer, autoimmune disease, inflammatory disease and infectious disease.