Functional Neoantigen Identification for Low-Mutational-Burden Cancers
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Solution Overview
Problem
Current methods for identifying tumor-specific neoantigens are inefficient, particularly for cancers with low mutational burden, leading to low success rates in personalized cancer immunotherapies due to challenges in detecting immunogenic neoantigens and the reliance on in silico prediction algorithms that may miss biologically relevant targets.
Innovation Solution
A functional neoantigen identification pipeline that involves generating tumor and normal sequence reads, identifying exome variants, selecting peptides with mutated amino acids, and evaluating immunogenicity through T cell response assays, bypassing in silico epitope binding prediction, to identify both MHC class I and class II neoantigens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If in silico prediction algorithms are used to identify neoantigens, then the process can handle high mutation rate malignancies with thousands of mutations, but the algorithms miss biologically relevant neoantigens due to low predicted binding affinity and cannot reliably detect immunogenic neoantigens in cancers with low mutational burden
Solution Approach 1:
The patent replaces in silico computational prediction algorithms with an in vitro functional assay system. Instead of relying on computer-based epitope binding predictions, the invention uses T cell response assays to directly detect and validate immunogenic neoantigens, substituting the mechanical/computational approach with a biological/experimental approach that actually measures immune recognition.
Solution Approach 2:
The patent implements a feedback mechanism where T cell response data from in vitro assays is used to validate and refine neoantigen candidates. The functional assays provide direct feedback on whether predicted neoantigens are actually immunogenic, allowing iterative improvement of the identification pipeline and correction of false negatives from in silico predictions.
2Ease of manufacture
If in silico epitope binding prediction is performed, then neoantigen candidates can be filtered and prioritized, but the method introduces false negatives where de facto neoantigens are missed due to low predicted binding affinity
Solution Approach 1:
The patent uses T cell response assays as a feedback mechanism to validate neoantigen candidates. By measuring actual immune recognition in vitro, the system can confirm whether low-affinity predicted neoantigens are still biologically relevant, correcting the reliability issues introduced by in silico prediction thresholds.
Solution Approach 2:
The patent substitutes computational binding affinity predictions with experimental T cell response measurements. This replacement eliminates the reliability problems of in silico methods by directly assessing whether neoantigens elicit functional immune responses, regardless of predicted binding affinity.
3Adaptability or versatility
If targeted cancer immunotherapies focus on tumor-associated antigens or fetal antigens, then broadly applicable off-the-self products can be developed, but adverse immune related events and autoimmune toxicity occur
Solution Approach 1:
The patent applies local quality by identifying and targeting specific neoantigens that are unique to individual tumors rather than using generic tumor-associated or fetal antigens. Each patient receives personalized therapy targeting their specific tumor's unique neoantigens, creating localized immune responses that avoid attacking normal self-tissues.
Solution Approach 2:
The patent uses in vitro T cell response assays as feedback to identify and select only those neoantigens that are truly tumor-specific and immunogenic. This feedback mechanism ensures that therapies are designed to target only malignant cells, avoiding off-target effects and autoimmune toxicity by validating tumor specificity before therapy administration.
4Reliability
If tumor-specific somatic mutations are used to generate neoantigens, then robust antigen-specific responses can be generated without off-target toxicity, but the immense genetic diversity across and within tumors makes neoantigen identification difficult
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of identifying neoantigens in the context of immense genetic diversity into manageable steps: (1) sequencing tumor and normal tissue to identify somatic mutations, (2) filtering mutations based on criteria, (3) predicting MHC binding, (4) validating with in vitro T cell response assays. This segmented approach makes the complex identification process systematic and manageable.
Solution Approach 2:
The patent replaces complex in silico prediction algorithms with simpler in vitro functional assays for validation. By using T cell response measurements, the system can directly assess neoantigen immunogenicity without needing to navigate the complexity of predicting binding affinities for thousands of potential neoantigens, simplifying the validation step.
Data Source
AI summary
Systems and methods for identifying neoantigen peptide candidates are described. Such systems and methods can be used to generate engineered immune cells that target neoantigen peptides. In certain embodiments, the systems and methods do not require in silico epitope binding prediction algorithms. In certain embodiments, neoantigens are identified for a cancer or tumor with a low mutational burden.