Peptide Array Immunotherapy Response Classification
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Solution Overview
Problem
Current methods for predicting a patient's response to immunotherapeutic treatments, such as checkpoint inhibitors, are limited by their reliance on biomarkers like TMB, which are only weakly predictive of clinical response due to their indirect relationship with the immune response.
Innovation Solution
The use of peptide array formats, specifically immunosignature (IMS) and frameshift signature (FS), to classify subjects based on their antibody responses to tumor-associated peptides, allowing for a more direct assessment of the immune response and prediction of treatment outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If TMB (Total Mutation Burden) is used as a biomarker to predict response to immunotherapy, then a comprehensive measure of tumor mutational load is obtained, but the predictive value remains weak because TMB is indirectly related to the actual immune response
Solution Approach 1:
The patent extracts only the immunogenic subset of mutations from the total mutation burden. Instead of measuring all mutations (TMB), the method specifically identifies and measures mutations that are expressed at the RNA level, processed by the proteasome, and presented on MHC molecules—thereby extracting the biologically relevant portion that directly correlates with immune response and treatment outcome
Solution Approach 2:
The patent introduces neoantigen presentation as an intermediary step between mutation and immune response. The method measures the actual presentation of neoantigens on MHC molecules, which serves as a direct mediator linking tumor mutations to T-cell recognition and immune response, thereby providing a more accurate predictive biomarker than TMB alone
2Measurement precision
If DNA sequencing is performed on tumor biopsies to measure TMB, then mutation data is obtained, but the cost is high and not all tumors yield sufficient good DNA for sequencing
Solution Approach 1:
The patent uses circulating tumor DNA (ctDNA) in blood as an intermediary sample type to avoid the limitations of tissue biopsy. The method detects neoantigen-specific antibodies in the blood that recognize tumor-derived neoantigens, providing a non-invasive alternative that does not require sufficient tumor tissue and can be performed on readily available blood samples
Solution Approach 2:
The patent creates a functional copy of the tumor's immunogenic profile through antibody responses in the blood. Instead of directly sequencing tumor DNA, the method measures the immune system's recognition of tumor neoantigens via circulating antibodies, providing an indirect but equally informative representation of the tumor's mutational landscape
3Measurement precision
If only about 1% of non-synonymous mutations are potentially immunogenic, then the majority of mutations measured by TMB are irrelevant to immune response, but measuring only immunogenic mutations requires complex multi-step validation
Solution Approach 1:
The patent uses the patient's own immune system as a feedback mechanism to identify immunogenic mutations. By measuring which neoantigens the patient's T-cells actually recognize through antibody responses in the blood, the method automatically filters for the immunogenic subset without requiring complex in vitro validation, leveraging biological feedback to simplify the identification process
Solution Approach 2:
The patent allows the immune system to self-identify which mutations are immunogenic. The patient's own T-cells and antibodies serve as the screening mechanism, naturally selecting and highlighting the immunogenic neoantigens without external intervention or complex validation protocols, thereby simplifying the identification of relevant mutations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
These methods enable accurate classification of patients' likelihood of responding to immunotherapeutics, predicting clinical outcomes, and identifying potential adverse events, thereby improving treatment decision-making and patient prognosis.
Implementation Method 1
contacting a biological sample from the subject to a frameshift peptide array comprising a plurality of tumor-associated frameshift peptides; detecting the presence or absence of antibodies having affinity to one or more of the frameshift peptides
Data Source
AI summary
Provided herein are methods for classifying how a subject having a cancer will respond to immunotherapeutic (IT) therapy based on the subject's immunosignature or frameshift signature. Also provided herein are methods for classifying a subject having a cancer as having a good prognosis or a poor prognosis based on the subject's immunosignature or frameshift signature.