Single-cell RNA sequencing for tumor microenvironment community characterization
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Solution Overview
Problem
Current treatments for pancreatic adenocarcinoma (PDAC) are ineffective due to reliance on bulk tumor sequencing, which fails to account for individual cell states and tumor microenvironment heterogeneity, leading to limited therapeutic options and poor prognosis.
Innovation Solution
A computer-implemented method using single-cell RNA sequencing (scRNA-seq) to characterize tumor microenvironmental communities by transforming scRNA-seq datasets into cell fraction datasets, assigning tumor microenvironment (TME) cell states, and selecting personalized treatments based on these states, including immune checkpoint blockade or molecular pathway targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bulk tumor sequencing is used to classify PDAC subtypes, then classification can be performed with existing technology, but individual cell states and tumor microenvironment heterogeneity cannot be resolved
Solution Approach 1:
The patent applies segmentation by transitioning from bulk sequencing (analyzing all cells together) to single-cell RNA sequencing (analyzing each cell individually). This divides the tumor sample into discrete cellular units, allowing resolution of individual cell states and microenvironment heterogeneity that were previously masked in bulk analysis.
Solution Approach 2:
The patent introduces a new dimension of analysis by moving from population-level bulk sequencing to single-cell resolution. This dimensional shift enables the detection of rare cell types, transcriptional states, and microenvironmental interactions that exist at the cellular level but are invisible in bulk data.
2Loss of information
If single-cell RNA sequencing is used to analyze tumor microenvironment, then granular cellular analysis is achieved, but data complexity and processing requirements increase
Solution Approach 1:
The patent employs computational algorithms and bioinformatics pipelines as intermediaries to process and interpret the complex single-cell RNA sequencing data. These computational tools transform raw high-dimensional data into meaningful biological insights, cell type classifications, and microenvironmental characterizations without losing cellular detail.
Solution Approach 2:
The patent creates digital representations (transcriptomic profiles) of individual cells that can be stored, analyzed, and compared without handling the physical cells again. These digital copies preserve all cellular information while enabling flexible computational analysis and integration with other datasets.
3Quantity of substance
If traditional NGS methodologies are used for PDAC analysis, then standard protocols can be applied, but only 20% of tumor cells can be effectively analyzed due to stromal dominance
Solution Approach 1:
The patent segments the tumor sample at the single-cell level, allowing individual tumor cells to be distinguished from stromal cells based on their unique transcriptomic profiles. This segmentation enables the analysis of all cell types separately, including the previously masked tumor cells that constitute only 20% of the bulk sample.
Solution Approach 2:
The patent applies local quality by characterizing different cell types (tumor cells, stromal cells, immune cells) with their specific molecular signatures. This allows tailored analysis of tumor cells using markers and pathways relevant to cancer biology, while separately analyzing stromal components, thereby improving tumor cell detection accuracy despite their low abundance.
Data Source
AI summary
Systems and methods for the selection of a treatment for pancreatic adenocarcinoma (PDAC) in a patient in need based on single-cell RNA sequencing data obtained from a tumor biopsy sample obtained prior to treatment are disclosed. Also disclosed are systems and methods for predicting a clinical outcome of a pancreatic adenocarcinoma (PDAC) patient based on the single-cell RNA sequencing data.