Multi-task Model for Organoid-to-Patient Drug Response Prediction
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Solution Overview
Problem
Current methods struggle to accurately model the effects of therapies on personalized patient genomes due to technical biases between tumor organoid and human RNA-expression datasets, and the need to account for confounders such as histology and stage in predicting drug responses.
Innovation Solution
A multi-task model is developed to address these biases and confounders, learning a transferable drug-response mapping from organoids to patients, which integrates organoid and human molecular datasets to predict drug responses and identify drug-specific biomarkers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If tumor organoid models are used to predict drug responses, then the prediction can be made using simplified in vitro systems, but technical biases exist between organoid and human RNA-expression datasets that reduce prediction accuracy
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that bridges the gap between organoid data and human patient predictions. The model learns transferable features from organoid RNA-expression data while correcting for technical biases through systematic adjustments, enabling accurate prediction of human drug responses without direct human testing.
Solution Approach 2:
The patent applies parameter changes by transforming and normalizing RNA-expression data from organoids to account for technical biases. The machine learning model adjusts expression levels, fold-changes, and statistical parameters to align organoid data with human tissue characteristics, thereby improving prediction accuracy.
2Measurement precision
If confounders such as histology and stage are accounted for in the prediction model, then the prediction becomes more accurate, but the model complexity increases
Solution Approach 1:
The patent segments the prediction problem into multiple independent tasks: predicting drug response, classifying histology, and determining stage. Each task is handled by specialized components within the machine learning model, allowing confounders to be accounted for systematically without overwhelming complexity.
Solution Approach 2:
The machine learning model is designed with multi-functionality, serving both as a drug response predictor and as a classifier for histological and stage information. This universal approach allows the model to handle multiple confounders simultaneously while maintaining a unified, manageable structure.
Data Source
AI summary
The disclosure provides methods and systems for predicting an effect of a pharmaceutical agent in a test subject of a first species. Information about the test subject is input into a multi-task model comprising a plurality of parameters. The model applies the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs including a predicted effect of the pharmaceutical agent in the test subject and, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification. The information about the test subject includes a plurality of abundance values including, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.