Predicting Cancer Immunotherapy Response Using T-Cell Ratios
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Solution Overview
Problem
Current cancer immunotherapy treatments, such as anti-PD-1 antibodies, have a significant portion of patients classified as 'ineffective' where the condition worsens within three months, lacking a reliable biomarker for predicting responsiveness.
Innovation Solution
The method utilizes the composition of CD4+ T-cells, dendritic cells, and/or CD8+ T-cells as biomarkers to predict responsiveness to cancer immunotherapy, specifically using ratios of subpopulations like CD62LlowCD4+ T-cells and dendritic cell subpopulations to identify effective and ineffective groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anti-PD-1 antibodies are administered to all cancer patients as standard therapy, then the highly effective group achieves significant clinical benefit, but the ineffective group experiences disease progression and wasted medical resources
Solution Approach 1:
The patent segments the complex immune system into specific measurable components: dendritic cell subpopulations (CD11c+, CD141+, CD123+), T-cell subpopulations (CD4+, CD8+), and their activation markers (HLA-DR, CD80, CD86, PD-L1). By dividing the immune response into these discrete measurable segments, the patent creates a practical biomarker panel that can predict therapeutic response without requiring analysis of the entire immune system complexity.
Solution Approach 2:
The patent transforms the qualitative assessment of immune response into quantitative parameters by measuring the ratios and frequencies of specific cell subpopulations (e.g., ratio of CD11c+CD141+ to CD11c+CD123+ dendritic cells, frequency of PD-L1+ dendritic cells). These parameter changes convert complex immunological states into measurable numerical values that can predict therapeutic effectiveness with high accuracy.
2Measurement precision
If conventional biomarkers are used to predict immunotherapy response, then the analysis is simple, but the sensitivity and specificity are insufficient to accurately identify effective and ineffective groups
Solution Approach 1:
The patent creates simplified models of the immune response by measuring surrogate markers (specific dendritic cell and T-cell subpopulation ratios) that copy and reflect the overall immune system state. Instead of directly measuring complex immune activation, the patent uses these cellular ratios as copies that accurately represent the functional immune status and predict therapeutic response.
Solution Approach 2:
The patent replaces conventional simple biomarker measurement (like PD-L1 expression alone) with a more sophisticated but measurable immunophenotyping approach using flow cytometry or mass cytometry. This substitution transitions from simple but imprecise measurements to more complex yet quantitatively precise measurements of multiple cell parameters simultaneously.
Data Source
AI summary
The present invention relates to the prediction of responsiveness to cancer immunotherapy of a subject based on the T-cell composition of the subject, and a therapeutic method using cancer immunotherapy based on the prediction. The present invention also provides a method for improving or maintaining responsiveness to cancer immunotherapy of a subject. Responsiveness to cancer immunotherapy is predicted by determining a relative value of a CD4+ T-cell subpopulation, dendritic cell subpopulation, and/or CD8+ T-cell subpopulation correlated with a dendritic cell stimulation in an anti-tumor immune response in a sample derived from a subject. A composition for treating or preventing cancer comprising cells such as CD62LlowCD4+ T-cells is also provided.