CD8+ T-Cell Subset Profiling for Anticancer Response Prediction
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Solution Overview
Problem
Current methods fail to accurately predict a cancer patient's responsiveness to anticancer treatment due to individual differences in resistance and patient characteristics, leading to variable therapeutic effects.
Innovation Solution
Measure the proportion of specific CD8+ T cell subsets, such as CD27+ CD28+ CCR7− CD45RA+ CD8+ T cells (DP Temra), in a biological sample to predict prognosis and responsiveness to anticancer treatment, using a method that includes measuring additional subsets like CD27+ CD28+ CCR7− CD45RA− CD8+ T cells (DP Tem), CD27− CD28− CCR7− CD45RA− CD8+ T cells (DN Tem), perforin+ CCR7− CD45RA− CD8+ T cells, granzyme B+ CCR7− CD45RA− CD8+ T cells, and IL-2+ CCR7− CD45RA− CD8+ T cells, and applying machine learning for rapid prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If IL-2 treatment is administered to cancer patients, then anti-tumor activity is enhanced through stimulation of CD8+ T cells and NK cells, but serious side effects occur and Treg cells are strongly expanded which suppress anti-tumor immunity
Solution Approach 1:
The patent applies local quality by differentiating between high-affinity IL-2 receptors (on Treg cells) and low-affinity IL-2 receptors (on CD8+ T cells and NK cells). By engineering IL-2 variants with modified receptor binding properties, the treatment selectively activates anti-tumor immune cells while avoiding excessive Treg cell expansion, thus achieving localized immunomodulation rather than global activation.
Solution Approach 2:
The patent employs parameter changes by modifying the amino acid sequence of IL-2 to create variants with altered receptor binding affinities. Specific mutations in the IL-2 molecule change its interaction parameters with different IL-2 receptor subtypes, enabling selective stimulation of CD8+ T cells and NK cells while reducing Treg cell activation, thereby improving the therapeutic index.
2Ease of operation
If conventional anticancer treatment is administered, then treatment is provided to cancer patients, but responsiveness varies due to individual resistance and patient characteristics making accurate prediction difficult
Solution Approach 1:
The patent applies preliminary action by measuring CD8+ T cell subset proportions in patient samples before administering anticancer treatment. This pre-treatment assessment allows clinicians to predict which patients are most likely to respond to IL-2-based therapies, enabling informed treatment selection and avoiding unnecessary treatment of patients unlikely to benefit.
Solution Approach 2:
The patent implements feedback by using measured CD8+ T cell subset proportions to guide treatment decisions. The biomarker data provides feedback on patient immune status, allowing clinicians to adjust treatment plans based on predicted responsiveness, thereby optimizing therapeutic outcomes and reducing unnecessary treatments.
Data Source
AI summary
The present invention relates to a method for predicting the prognosis or responsiveness to anticancer therapy of cancer patients, the method enabling accurate prediction of prognosis and prediction of responsiveness to anticancer therapy by measuring the proportion of a specific CD8+ T cells subset. Particularly, the present invention applies machine learning to the prediction method to provide a system capable of rapidly and simply calculating the probability of patients responding to anticancer therapy, and thus can more effectively predict anticancer therapy-responsiveness and the like. Especially, the prediction method according to the present invention uses a non-invasive sample, and thus can rapidly and accurately predict a prognosis without the involvement of cancer tissue analysis and the like.