Algorithm Identifying Non-Classical Cancer Therapeutic Targets
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Solution Overview
Problem
Current methods struggle to identify therapeutic target genes in individual cancer patients that are not oncogenes or tumor suppressor genes but are influenced by the constraints of the cancer genome architecture on somatic cancer evolution, limiting effective targeted cancer therapies.
Innovation Solution
An algorithm that analyzes genome-wide molecular data from multiple samples of the same patient at different progression stages to construct a phylogenetic tree of cancer subclones, identifying putative therapeutic targets by determining essential genes, non-essential fitness modifying genes, and their impact on cancer evolution, allowing for personalized treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to identify therapeutic target genes, then only classical oncogenes or tumor suppressor genes can be identified, but therapeutic targets influenced by genome architecture constraints on somatic cancer evolution cannot be identified
Solution Approach 1:
The patent segments the identification process into distinct analytical components: constructing phylogenetic trees from genome-wide molecular data, identifying genes under positive selection, and classifying genes by their functional categories (oncogenes, tumor suppressor genes, and other therapeutic targets). This segmentation enables the systematic identification of diverse therapeutic targets beyond classical categories.
Solution Approach 2:
The patent introduces a new dimension of analysis by incorporating phylogenetic tree construction and positive selection detection into the therapeutic target identification process. This dimensional addition allows the method to identify genes that are not traditional oncogenes or tumor suppressor genes but are still critical therapeutic targets due to their role in cancer evolution and genome architecture constraints.
2Measurement precision
If genome-wide molecular data from multiple samples at different progression stages is analyzed, then personalized therapeutic targets can be identified, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent applies preliminary action by first constructing phylogenetic trees from genome-wide molecular data before identifying therapeutic targets. This preliminary structuring of data into evolutionary relationships simplifies subsequent analysis by organizing genetic variations in a hierarchical framework, making it easier to identify genes under positive selection and classify them functionally.
Solution Approach 2:
The patent incorporates feedback mechanisms through iterative analysis of genome-wide molecular data from multiple samples at different progression stages. The phylogenetic tree construction and positive selection detection processes continuously refine the identification of therapeutic targets based on evolutionary patterns, allowing the system to adapt and improve accuracy as more data is analyzed.
Data Source
AI summary
Most clinically distinguishable malignant tumors are characterized by specific mutations, specific patterns of chromosomal rearrangements and a predominant mechanism of genetic instability. It has been suggested that the internal dynamics of genomic modifications as opposed to the external evolutionary forces have a significant and complex impact on Darwinian species evolution. A similar situation can be expected for somatic cancer evolution as the key mechanisms encountered in species evolution such as duplications, rearrangements or deletions of genes also constitute prevalent mutation mechanisms in cancers with chromosomal instability. The invention is an algorithm which is based on a systems concept describing the putative constraints of the cancer genome architecture on somatic cancer evolution. The algorithm allows the identification of therapeutic target genes in individual cancer patients which do not represent oncogenes or tumor suppressor genes but have become putative therapeutic targets due to constraints of the cancer genome architecture on individual somatic cancer evolution. Target genes or regulatory elements may be identified by their designation as essential genes or regulatory elements in cancer cells of the patient but not in normal tissue cells or they may be identified by their impact on the process of somatic cancer evolution in individual patients based on phylogenetic trees of somatic cancer evolution and on the constructed multilayered cancer genome maps. The algorithm can be used for delivering personalized cancer therapy as well as for the industrial identification of novel anti-cancer drugs. The algorithm is essential for designing software programs which allow the prediction of the natural history of cancer disease in individual patients.