Morpheus Framework for T-Cell Infiltration in Solid Tumors
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Solution Overview
Problem
Current cancer immunotherapies face challenges in effectively infiltrating solid tumors due to resistance from the tumor microenvironment, limiting their efficacy, as T cells struggle to penetrate and engage with tumor cells.
Innovation Solution
A deep learning framework, Morpheus, leverages spatial omics profiles to predict T-cell infiltration using a self-supervised machine learning approach and counterfactual optimization, generating tumor perturbations that enhance T-cell infiltration by adjusting signaling molecule intensities in the tumor microenvironment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current immunotherapies are administered, then T cells are introduced to attack tumor cells, but T cells are unable to effectively infiltrate the tumor microenvironment due to resistance
Solution Approach 1:
The system performs preliminary analysis of the tumor microenvironment using spatial omics data before administering immunotherapy. By pre-identifying resistance mechanisms and immune cell localization patterns through machine learning models, the system prepares targeted perturbation strategies that anticipate and counteract tumor resistance, thereby improving therapy efficacy before T cells are introduced
Solution Approach 2:
The system identifies and modifies key parameters in the tumor microenvironment that control T cell infiltration. By analyzing spatial proteomics data to determine which molecular signals (e.g., chemokine gradients, adhesion molecule expression) limit T cell penetration, the system generates counterfactual perturbations that change these parameters to create a more permissive environment for T cell infiltration
2Measurement precision
If spatial omics data is analyzed to determine immune cell localization, then T cell positioning can be identified, but the complexity of analyzing multi-channel molecular data increases
Solution Approach 1:
The system creates simplified representations (copies) of the complex spatial omics data by training machine learning models to recognize patterns in multi-channel molecular images. Once trained, these models can rapidly predict T cell localization and environmental signals without requiring complex real-time analysis of all molecular channels, thereby maintaining measurement precision while reducing analytical complexity
Solution Approach 2:
The system extracts only the most relevant features from the complex spatial omics data that are predictive of T cell infiltration. By using machine learning to identify and extract key molecular signals and spatial patterns from multi-channel data, the system separates essential information from redundant complexity, enabling accurate immune cell localization with simplified analysis
3Reliability
If counterfactual optimization is performed to determine tumor perturbations, then T cell infiltration can be enhanced, but computational resources and time are increased
Solution Approach 1:
The system performs counterfactual optimization on a subset of key molecular targets rather than exhaustively analyzing all possible perturbations. By identifying the most influential environmental signals through preliminary analysis and focusing optimization efforts on these critical targets, the system achieves sufficient T cell infiltration enhancement with reduced computational time and resources
Data Source
AI summary
Disclosed herein include systems, devices, and methods for counterfactual optimization. In some embodiments, spatial omics training data can comprise a plurality of training images each comprising a plurality of molecule channels. A training image label can be generated for each of a plurality of training images indicating presence of at least one T cell in the training image. A plurality of masked training images can be generated from a plurality of training images with any T cell present in a training image of the plurality of training images masked in a masked training image of the plurality of masked training images generated. A model comprising a classifier with the plurality of masked training images as input and the training image label as output can be generated. A counterfactual optimization can be performed to determine a tumor perturbation using a second plurality of images with no T cell present in each of the plurality of images.