RNA Sequencing Immune-Oncology Profiles for Therapeutic Response Prediction
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Solution Overview
Problem
Current methods for understanding cancer immune and tumor profiles are inadequate for personalized treatment recommendations, particularly in predicting responses to therapeutic interventions and identifying cell types in tumor samples.
Innovation Solution
A method utilizing RNA sequencing data to determine gene expression levels, calculate mutational burden, and apply a deconvolution algorithm to identify and quantify cell types, generating an immune-oncology profile that predicts therapeutic responses and recommends interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used for understanding cancer immune and tumor profiles, then the analysis process is simple, but the predictive accuracy for therapeutic responses is insufficient
Solution Approach 1:
The patent segments the complex tumor sample analysis into distinct cell type components using deconvolution algorithms. By dividing the heterogeneous tumor profile into individual cell type contributions (e.g., immune cells, stromal cells, cancer cells), the method achieves more precise predictive accuracy for therapeutic responses while managing complexity through systematic breakdown of the analysis into manageable computational steps
Solution Approach 2:
The patent transforms the analysis from simple bulk tumor profiling to multi-parameter immune-oncology profiles that include gene expression levels, mutational burden, and cell type quantification. This parameter expansion enables accurate prediction of therapeutic responses by capturing multiple biological dimensions simultaneously, addressing the accuracy-complexity tradeoff through comprehensive yet structured measurement
2Measurement precision
If deconvolution algorithms are applied to identify and quantify cell types, then cell type identification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies deconvolution algorithms as a preliminary computational step to resolve cell type proportions before subsequent therapeutic response prediction. By performing this complex computational task upfront and storing the results as part of the immune-oncology profile, the method achieves high cell type identification accuracy while avoiding repeated computational complexity in later analysis stages
Solution Approach 2:
The patent uses deconvolution algorithms as an intermediary computational tool that translates raw sequencing data into meaningful cell type proportion estimates. This intermediary step bridges the gap between complex raw data and simpler interpretative profiles, enabling accurate cell type identification while managing computational complexity through a dedicated intermediate representation
3Measurement precision
If comprehensive immune-oncology profiles are generated including gene expression, mutational burden, and cell types, then therapeutic response prediction accuracy improves, but data processing time increases
Solution Approach 1:
The patent segments the comprehensive immune-oncology profile into three distinct analytical components: gene expression analysis, mutational burden calculation, and cell type quantification. By processing these components separately and integrating their results, the method achieves high therapeutic response prediction accuracy while optimizing data processing time through parallelizable and modular computational approaches
Solution Approach 2:
The patent performs preliminary calculations of gene expression levels, mutational burden, and cell type proportions during the initial sequencing data analysis phase. By preparing these comprehensive profile elements upfront and storing them as pre-processed features, the method enables accurate therapeutic response prediction without requiring repeated intensive processing during subsequent clinical decision-making
Data Source
AI summary
Provided herein are systems and methods for generating an immune-oncology profile from a biological sample. The immune-oncology profile can include the proportion or percentage of immune cells, expression of immune escape genes, and/or mutational burden. The immune-oncology profile may allow the generation of classifiers for making prognostic or diagnostic predictions.