Single-Cell Biophysical and Transcriptomic Analysis for Leukemia Relapse Prediction
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Solution Overview
Problem
Current methods for predicting and preventing relapse of hematopoietic malignant diseases, such as B-cell acute lymphoblastic leukemia, are inadequate due to insufficient enumeration and molecular characterization of cancer cells at the minimal residual disease phase, leading to insensitive detection of functionally significant subclones and limited understanding of cancer biology at the single-cell level.
Innovation Solution
An integrative approach combining biophysical measurements, like cellular mass and stiffness, with transcriptome analysis at the single-cell level to predict relapse risk, using MRD modules that include cellular mass, transcriptomic profiles, and mutation status of specific signaling pathways, and administering combinations of tyrosine kinase inhibitors, pre-BCR signaling pathway inhibitors, and p38 MAPK inhibitors to prevent relapse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for enumeration and molecular characterization of cancer cells at MRD are used, then treatment monitoring is possible, but the methods are insufficient for predicting therapeutic response and often insensitive for functionally significant subclones
Solution Approach 1:
The patent segments the cancer cell population into distinct subclones based on transcriptional profiles and biophysical properties. By dividing the heterogeneous tumor population into identifiable subgroups with different characteristics, the method enables detection of functionally significant subclones that traditional bulk methods miss, thereby improving both detection sensitivity and prediction accuracy.
Solution Approach 2:
The patent adds multiple dimensions to cancer cell characterization by combining transcriptional profiling with biophysical measurements (cellular mass, stiffness). This multi-dimensional approach provides a more comprehensive view of cancer cell states at MRD, enabling better prediction of therapeutic response and relapse risk compared to traditional single-method approaches.
2Loss of information
If single-cell level studies are performed to gain insight into treatment response and survival prognosis, then biological understanding is improved, but limited availability of cancer samples at MRD restricts the amount of information available
Solution Approach 1:
The patent replaces traditional mechanical/sample-based analysis with non-invasive biophysical measurements (cellular mass, stiffness) that can be obtained from minimal samples. These physical property measurements provide valuable biological information about cancer cell states without requiring large quantities of tissue samples, thus overcoming the limitation of sample availability at MRD.
Solution Approach 2:
The patent measures changes in biophysical parameters (cellular mass, stiffness) of cancer cells at different stages (MRD, relapse). By tracking these parameter changes, the method extracts meaningful biological information about treatment response and disease progression from minimal samples, maximizing the utility of limited MRD material.
3Measurement precision
If comprehensive molecular characterization of cancer cells is performed, then treatment monitoring is improved, but the complexity and cost of analysis increases
Solution Approach 1:
The patent segments the complex molecular characterization task into two main components: transcriptional profiling (identifying gene expression patterns) and biophysical measurements (cellular mass, stiffness). This segmentation makes the comprehensive characterization more manageable and interpretable, reducing the complexity burden while maintaining high accuracy.
Solution Approach 2:
The patent creates a multi-functional analysis platform that simultaneously performs transcriptional profiling and biophysical measurements on the same cancer cell population. This universal approach consolidates multiple analysis functions into a single integrated workflow, reducing overall complexity and cost compared to separate specialized analyses.
Data Source
AI summary
Described in exemplary embodiments herein are methods, compositions, and kits for diagnosing, prognosing, monitoring, treating and/or preventing a hemopoietic malignancy and/or relapse thereof in a subject. In some embodiments, the methods can include determining an average cellular mass of cells in a sample from the subject and/or detecting one or more molecular signatures in one or more of the cells. In some embodiments, treatment includes administering one or more BCR-ABL tyrosine kinase inhibitors or a pharmaceutical formulation thereof, one or more pre-BCR signaling pathway inhibitors or a pharmaceutical formulation thereof, one or more p38 MAPK inhibitors or a pharmaceutical formulation thereof; or any combination thereof.