Alu Element Bioinformatics for Early Cancer Detection
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Solution Overview
Problem
Current cancer detection technologies are limited in their ability to identify cancer-linked genes with driver mutations before tumor formation, leading to delayed detection and treatment, as they rely on identifying physical phenotypes rather than predicting genomic instability.
Innovation Solution
A bioinformatics methodology that predicts regions of high genome instability in an individual's genome, allowing for the monitoring of DNA damage in body tissues, wastes, and fluids to detect cancer-linked mutations early, using unique nucleotide signatures as biomarkers for diagnosis and treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If current cancer detection technologies rely on identifying physical phenotypes, then detection can be performed with existing methods, but detection occurs after tumor formation leading to delayed treatment
Solution Approach 1:
The patent applies preliminary action by predicting regions of high genome instability before cancer develops. The methodology identifies cancer-prone genomic regions in advance using bioinformatics tools and Alu element analysis, then monitors these specific regions for DNA damage over time. This allows detection of precancerous changes before tumors form, fundamentally shifting from reactive phenotype-based detection to proactive genotype-based prediction.
2Productivity
If the entire genome is searched for DNA damage, then comprehensive cancer detection is possible, but the search area is too large making detection inefficient
Solution Approach 1:
The patent applies segmentation by dividing the genome into specific cancer-prone regions based on Alu element landscapes and predicted genomic instability. Instead of searching the entire genome, the methodology segments and focuses monitoring on high-risk regions where cancer-linked mutations are most likely to occur. This segmentation dramatically reduces the search area while maintaining detection sensitivity for clinically relevant mutations.
Solution Approach 2:
The patent applies local quality by concentrating monitoring resources on specific regions with high predicted genomic instability rather than uniformly monitoring the entire genome. The methodology identifies local regions with abnormal Alu element patterns and focuses DNA damage detection on these specific loci, allocating detection sensitivity where it is most needed for early cancer detection.
Data Source
AI summary
The present invention relates generally to the field of human genetics. More specifically, this invention relates to the clinical application of a bioinformatics methodology described in patent application Ser. No. 14/154,303 for the early detection of cancer. Said clinical application relates to obtaining the genome sequence from an individual's healthy tissue and comparing it to the DNA sequence obtained from that same individual's body tissues, wastes and/or fluids. Said method inspects the DNA sequence obtained from body tissues, wastes and/or fluids for the presence of DNA damage at bioinformatically predicted genetically unstable loci within cancer-linked regions of the patients healthy DNA. The identification of DNA damage within a predicted locus is considered to be evidence of cancer. Said method then uses the unique signature of any damaged DNA sequence which has occurred at predicted unstable cancer-linked loci to construct patient-specific cancer biomarker(s). These biomarkers can be used for monitoring the progression of cancer and for treatment of the cancer in a patient.