Multiplex Immunohistochemistry for Cancer Treatment Prediction
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Solution Overview
Problem
Current methods lack effective animal models and companion diagnostic evaluation techniques for immuno-oncology therapeutic agents, making it difficult to predict treatment responses to immune checkpoint inhibitors in cancer patients, leading to suboptimal drug prescriptions and increased side effects.
Innovation Solution
A method utilizing multiplex immunohistochemistry to measure the expression levels of immune checkpoint molecules like PD-L1, PD-1, and CTLA-4 in cancer tissue, employing automated algorithms for imaging and machine learning to accurately predict treatment responses by calculating Tumor Proportion Score (TPS) and Combined Positive Score (CPS) values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pathologist interpretation is used for immunohistochemistry staining, then flexibility in interpretation is maintained, but measurement precision and prediction accuracy deteriorate due to subjectivity and inter-observer variability
Solution Approach 1:
The patent replaces manual pathologist interpretation with an automated image analysis system that uses algorithms to quantify immune checkpoint molecule expression. The system automatically processes immunohistochemistry stains, segments tissue structures, counts positive cells, and calculates TPS/CPS scores, eliminating human subjectivity while maintaining measurement precision.
Solution Approach 2:
The automated system performs self-service by independently completing the entire analysis process without human intervention. It automatically acquires images, processes stains, identifies positive cells, and generates prediction results, allowing the system to serve itself in the diagnostic process.
2Measurement precision
If single-marker immunohistochemistry is used, then simplicity and ease of operation are maintained, but measurement precision deteriorates due to inability to capture multi-marker co-expression patterns
Solution Approach 1:
The patent merges multiple immunohistochemistry stains into a single multiplex analysis that simultaneously detects PD-L1, PD-1, and other immune checkpoint molecules. By combining multiple markers in one assay, the system captures co-expression patterns and cellular interactions that single-marker studies miss, improving prediction accuracy without requiring separate tests.
Solution Approach 2:
The multiplex immunohistochemistry system serves multiple functions simultaneously: it detects expression levels of multiple immune checkpoint molecules, identifies cell types, determines spatial relationships, and calculates predictive scores. This multi-functional approach consolidates what would otherwise require multiple separate assays into one comprehensive test.
3Productivity
If automated multiplex immunohistochemistry with machine learning is implemented, then productivity and measurement precision are improved, but device complexity and initial implementation difficulty increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images, automatically segmenting tissue structures, and pre-defining analysis parameters before final prediction. The machine learning models are trained in advance on reference data, allowing rapid automated analysis of new samples without requiring complex real-time decision-making during testing.
Solution Approach 2:
The patent introduces an intermediary software layer that bridges the automated imaging system and the prediction algorithm. This intermediary component handles image processing, cell segmentation, and data transformation, simplifying the overall system architecture by separating the hardware imaging function from the computational analysis function.
4Reliability
If comprehensive multi-marker analysis is performed, then reliability of treatment response prediction is improved, but loss of time and analysis duration increase
Solution Approach 1:
The automated system maintains continuous useful action by continuously processing images and calculating scores without interruption. The machine learning algorithms continuously analyze cell populations, track expression patterns, and generate predictions in real-time as images are acquired, eliminating the discontinuous nature of manual review and reducing total analysis time.
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
Enables accurate and rapid prediction of treatment responses to immune checkpoint inhibitors, reducing errors associated with manual pathologist interpretation and improving the effectiveness of companion diagnostics for personalized medicine.
Implementation Method 1
measuring an expression level of an immune checkpoint molecule by performing multiplex immunohistochemistry on tumor tissue
Implementation Method 2
the multiplex IHC assays enable imaging with equipment capable of spectrum unmixing using automated algorithms to separate the autofluorescence of tissues as well as the intrinsic fluorescence of each tissue
Data Source
AI summary
The present disclosure relates to a method of providing information for predicting a treatment response to an immune checkpoint inhibitor in a cancer patient by using multiplex immunohistochemistry, wherein, by performing multiplex immunohistochemistry on tumor tissue of a cancer patient to measure an expression level of an immune checkpoint molecule by an automated method, the treatment response to the immune checkpoint inhibitor in the cancer patient can be accurately and quickly predicted. In addition, unlike existing methods using single immunohistochemistry, the disclosed method can reduce errors of an inspector by analyzing markers simultaneously expressed in a single cell and evaluating the same by an automated method, and thus will be widely used as a companion diagnostic method for an immune checkpoint inhibitor.