RNA Expression Analysis for Cancer Pathway Dysregulation Detection
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Solution Overview
Problem
Current methods struggle to accurately detect pathway disruption in cancer cells, particularly when DNA analysis is inconclusive, as they rely solely on genetic mutations and cannot distinguish between benign and pathogenic variants, leading to potential misidentification of treatment responders.
Innovation Solution
A system and method utilizing RNA expression level information to determine cellular pathway disruption through machine-learning models, trained with data from positive and negative control groups, to identify genetic variants impacting pathway activity and correlate them with disease state and therapeutic effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DNA analysis is used to identify genetic variants, then genetic mutations can be detected, but the ability to distinguish between benign and pathogenic variants is insufficient
Solution Approach 1:
The patent introduces RNA expression data as an intermediary layer between DNA sequence and phenotypic outcome. By measuring transcript levels of genetic variants and comparing them against reference ranges, the system mediates the interpretation of variant pathogenicity. This intermediary approach allows differentiation between benign variants (normal expression) and pathogenic variants (abnormal expression) without directly observing the phenotypic effect.
Solution Approach 2:
The patent changes the measurement parameter from static DNA sequence information to dynamic RNA expression levels. Instead of merely detecting the presence of a genetic variant, the system measures the quantitative expression level of the variant's transcript, providing a functional readout that indicates whether the variant is likely pathogenic. This parameter change transforms genetic data into functional information.
2Reliability
If DNA-based methods are used for pathway disruption detection, then genetic mutations can be identified, but false positives occur when mutations do not alter pathway activity
Solution Approach 1:
The patent implements a feedback mechanism where RNA expression data provides information about the actual functional outcome of genetic variants. By measuring whether the variant's transcript is expressed at abnormal levels, the system receives feedback on whether the genetic mutation actually impacts pathway activity. This feedback loop eliminates false positives by confirming that the detected mutation is functionally relevant before recommending targeted therapy.
3Loss of information
If only genetic mutation data is analyzed, then DNA variants can be detected, but pathway disruption cannot be accurately determined
Solution Approach 1:
The patent adds another dimension to genetic analysis by transitioning from one-dimensional DNA sequence data to two-dimensional data that includes both genetic variant presence and RNA expression level. This dimensional expansion provides functional context to genetic mutations, enabling accurate determination of pathway disruption status by combining genotypic and transcriptomic information.
Data Source
AI summary
Disclosed herein are systems, methods, and compositions useful for determining cellular pathway disruption comprising the use of RNA expression level information. This determined level of disruption can assist in the identification of genetic variants that alter pathway activity, to correlate these variants with disease state and disease progression, and to identify those therapeutics most likely to be effective and which should be avoided.