Cancer Pathway Transcript Profiling for Multi-Type Diagnosis
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Solution Overview
Problem
Current diagnostic and prognostic methods for cancer are limited in their ability to provide reliable predictions across multiple cancer types, focusing mainly on specific cancer subtypes and lacking broader predictive capabilities.
Innovation Solution
The development of methods involving the analysis of RNA expression data to determine global cancer pathway transcript (CPT) expression profiles, which include pathways such as cell cycle, Notch, and Wnt, to provide diagnoses, monitor cancer progression, and offer prognosis across various cancer types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cancer diagnostic methods focus on specific cancer types or subtypes, then diagnostic accuracy for that specific type is improved, but the ability to provide reliable predictions across multiple cancer types deteriorates
Solution Approach 1:
The patent applies universality by developing a diagnostic method that uses global cancer pathway transcript (CPT) expression profiles to predict outcomes across multiple cancer types. The method analyzes expression patterns of CPTs from various pathways (cell cycle, Notch, Wnt, etc.) and identifies recurring patterns that are predictive of survival and progression in diverse cancer types, making a single diagnostic approach applicable universally rather than requiring separate tests for each cancer type.
2Ease of operation
If traditional pathological assessments are used, then diagnostic simplicity is maintained, but the ability to identify novel tumor subtypes and behaviors deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/pathological assessment with molecular transcript profiling. Instead of relying on morphological examination and manual pathology review, the method uses RNA expression data and computational analysis of CPT profiles to identify tumor subtypes and predict outcomes. This substitution enables the detection of molecular patterns that are not visible through traditional microscopy, thereby improving reliability while maintaining operational simplicity through automated analysis.
Data Source
AI summary
Disclosed herein t-SNE-assisted clustering revealed that the expression of certain cancer pathway transcripts are correlated with certain cancer types. In one aspect, disclosed herein are methods for diagnosis and prognosis of a cancer using cancer pathway transcript expression.