Single-Cell Transcriptomics Predicting Cancer Treatment Response
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Solution Overview
Problem
Current genomic and transcriptomic approaches for predicting cancer treatment response are limited by their reliance on bulk tumor data, which fails to account for the heterogeneity of tumors composed of multiple clones, leading to resistance and diminished treatment efficacy.
Innovation Solution
A method involving single-cell gene expression profiling, where the gene expression profiles of single cancer cells are clustered, and the mean expression profile for each cluster is compared to predictive gene expression profiles associated with specific cancer treatments, to predict the response of each cluster and subsequently the subject to various cancer treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bulk tumor data is used for predicting treatment response, then the analysis is simpler and more cost-effective, but the prediction accuracy is reduced due to tumor heterogeneity and clonal selection
Solution Approach 1:
The patent applies segmentation by transitioning from bulk tumor data analysis to single-cell resolution analysis. The method segments the tumor into individual cells, profiles their gene expression profiles, and clusters them into subpopulations. This segmentation enables the detection of heterogeneous responses across different cell types and clones, thereby improving prediction accuracy while maintaining manageable complexity through computational clustering algorithms.
2Reliability
If single-cell profiling is performed to capture tumor heterogeneity, then the prediction accuracy improves, but the cost and complexity of the analysis increases
Solution Approach 1:
The patent applies universality by developing a multi-functional computational framework that handles multiple tasks through a unified single-cell analysis approach. The same single-cell gene expression data is used to: (1) profile individual cell characteristics, (2) identify clusters of similar cells, (3) predict treatment responses for each cluster, and (4) determine overall patient response. This multi-functionality improves reliability while managing complexity through integrated analysis.
Solution Approach 2:
The patent uses copying by creating a computational model that replicates tumor cell behavior and treatment responses in silico. The method generates predictive gene expression profiles for each cluster based on single-cell data, effectively copying the biological behavior of tumor cells and their responses to treatments. This computational copying enables reliable predictions without requiring actual experimentation on all possible treatment combinations.
3Productivity
If treatments target multiple tumor clones simultaneously, then the overall patient response is enhanced, but the likelihood of resistance emerging increases due to clonal selection
Solution Approach 1:
The patent applies feedback by continuously monitoring and adapting treatment strategies based on identified resistant clones. The method provides feedback by: (1) identifying specific resistant clones through single-cell analysis, (2) predicting which clones are most likely to develop resistance, and (3) suggesting adaptive treatment modifications. This feedback mechanism enables the treatment to evolve in response to emerging resistance, maintaining overall efficacy while managing the reliability challenge.
Data Source
AI summary
Provided herein are methods of predicting the response of a subject's cancer to one or more cancer treatments by using gene expression data obtained from single cells of the cancer, and methods of treating the cancer.