CD8 Localization Signature for Immunotherapy Selection
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Solution Overview
Problem
Current cancer treatments using immune checkpoint inhibitors are not effective for all patients due to variability in immune response, necessitating targeted strategies to identify suitable candidates for therapies targeting the PD-1/PD-L1 pathway, particularly in tumors with excluded CD8 localization and negative PD-L1 expression.
Innovation Solution
A method involving the use of an anti-PD-1/PD-L1 antagonist, administered alone or in combination with an anti-CTLA-4 antagonist, specifically for patients with tumors exhibiting an excluded CD8 localization phenotype and negative PD-L1 expression, utilizing machine learning algorithms for image analysis and classification of CD8+ T-cell abundance in tumor samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If immune checkpoint inhibitors are used for all cancer patients, then broad cancer treatment coverage is achieved, but treatment effectiveness varies significantly due to individual immune response variability
Solution Approach 1:
The patent segments the cancer patient population into distinct subgroups based on CD8+ T-cell localization patterns (excluded, inflamed, desert phenotypes) and PD-L1 expression levels. This segmentation allows for targeted immunotherapy assignment, where patients with excluded phenotype and negative PD-L1 receive anti-PD-1/PD-L1 therapy, while other phenotypes receive different treatments, thereby improving overall treatment effectiveness while maintaining broad coverage
Solution Approach 2:
The patent implements preliminary classification of patients using machine learning algorithms that analyze histology images to determine CD8+ T-cell localization and PD-L1 expression status before administering immunotherapy. This preliminary action enables proactive identification of suitable candidates for anti-PD-1/PD-L1 therapy, ensuring that treatment is administered to the right patients from the outset, thus improving treatment reliability
2Reliability
If anti-PD-1/PD-L1 therapy is administered to patients with excluded CD8 localization and negative PD-L1 expression, then treatment efficacy is improved, but requires complex classification of tumor samples
Solution Approach 1:
The patent replaces manual pathological assessment of tumor samples with an automated machine learning system that processes histology images. The system uses trained algorithms to objectively classify CD8+ T-cell localization patterns and PD-L1 expression levels, eliminating subjectivity and reducing the complexity burden on pathologists while maintaining high classification accuracy for identifying suitable immunotherapy candidates
Solution Approach 2:
The machine learning system performs self-training using labeled histology image data, automatically learning to distinguish between excluded, inflamed, and desert phenotypes as well as PD-L1 positive and negative status. This self-service capability allows the system to continuously improve its classification accuracy without requiring constant manual recalibration, thereby managing classification complexity autonomously
3Measurement precision
If machine learning algorithms are used for image analysis and classification, then patient identification accuracy is enhanced, but computational resources and analysis time are increased
Solution Approach 1:
The machine learning system is pre-trained on extensive datasets of labeled histology images before deployment, performing the computationally intensive learning phase in advance. This preliminary action allows the trained model to subsequently classify new patient samples rapidly with high accuracy, reducing the analysis time for actual clinical use while maintaining precise patient identification
Solution Approach 2:
The patent creates a digital copy of the pathological assessment process through the machine learning system, which replicates and enhances human expert classification capabilities. The system learns from numerous example cases and creates an internal model that can quickly evaluate new samples, achieving high identification accuracy without requiring proportional increases in analysis time
Data Source
AI summary
The present disclosure provides methods of identifying a subject suitable for an anti-PD-⅟PD-L1 antagonist therapy comprising measuring assay CD8 localization and PD-L1 expression in a tumor sample obtained from the subject. In some aspects, method further comprises administering (i) an anti-PD-⅟PD-L1 antagonist therapy or (ii) an anti-PD-⅟PD-L1 antagonist and anti-CT-LA-4 antagonist combination therapy to a subject identified as having a tumor exhibiting an excluded CD8 localization phenotype, wherein the tumor is PD-L1 negative.