AML Prognosis Prediction via Leukemic Stem Cell Gene Expression
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Solution Overview
Problem
Current treatments for pediatric and adult Acute Myeloid Leukemia (AML) face challenges with drug resistance and relapse due to the persistence of leukemic stem cells, leading to poor treatment outcomes, particularly with standard chemotherapy regimens like ara-C, daunorubicin, and etoposide.
Innovation Solution
Development of a method involving the analysis of specific RNA transcripts from genes such as DNMT3B, GPR56, CD34, SOCS2, SPINK2, FAM30A, DCTD, CBR1, MPO, ABCC1, and TOP2A to calculate scores like pLSC6 and ADE-RS5, which predict prognosis and treatment outcomes by assessing leukemic stem cell activity and pharmacokinetics/pharmacodynamics of anti-cancer therapeutics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard chemotherapy regimens (ara-C, daunorubicin, etoposide) are used to treat AML, then treatment coverage is comprehensive, but drug resistance develops leading to relapse
Solution Approach 1:
The patent performs gene expression analysis before treatment to identify patients at high risk of drug resistance. By calculating the LSC17 score from 17 leukemic stem cell-related genes, the system predicts which patients will respond poorly to standard chemotherapy, allowing clinicians to adjust treatment plans in advance rather than waiting for resistance to develop
Solution Approach 2:
The patent uses gene expression profiling to provide feedback on predicted treatment response. The LSC17 score serves as a biomarker that feeds back into treatment decision-making, enabling dynamic adjustment of therapy based on molecular characteristics of the patient's leukemic cells
2Measurement precision
If gene expression analysis of multiple genes is performed to predict treatment outcome, then predictive accuracy improves, but test complexity increases
Solution Approach 1:
The patent segments the complex gene expression analysis into a standardized panel of 17 specific genes related to leukemic stem cell function. By focusing on this defined subset rather than analyzing all genes, the assay becomes more manageable while maintaining high predictive accuracy for treatment response
Solution Approach 2:
The patent transforms complex gene expression data into a simplified numerical score (LSC17 score) that can be easily interpreted. This parameter transformation converts multiple gene expression measurements into a single prognostic metric that guides clinical decision-making
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
These scores provide improved predictive power for event-free survival, overall survival, and treatment response, helping identify patients at higher risk for relapse and guiding personalized treatment decisions, including the potential for hematopoietic stem cell transplantation.
Implementation Method 1
reverse transcribing RNA transcripts of a set of genes consisting of DNMT3B, GPR56, CD34, SOCS2, SPINK2, and FAM30A, and at least one reference gene, to produce a set of cDNAs
Implementation Method 2
amplifying the cDNAs to produce amplification products
Data Source
AI summary
Aspects of the disclosure relate to compositions and methods for predicting prognosis and classifying risk of subjects having certain cancers, for example acute myeloid leukemia (AML). In some embodiments, methods described by the disclosure comprise a step of assessing the mRNA expression of certain leukemic stem cell (LSC)-enriched genes in a subject to produce a predictive score for pediatric AML. In some embodiments, methods described by the disclosure comprise a step of assessing the mRNA expression of certain genes of pharmacological relevance for standard chemotherapy consisting of Cytarabine (also known as Ara-C), daunorubicin and etoposide in a subject to produce a predictive score for pediatric AML.