Genome Copy Number Variation Analysis for Prostate Cancer Relapse Prediction
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Solution Overview
Problem
Current methods for diagnosing prostate cancer and predicting relapse are inadequate, as they rely heavily on Gleason's grading and serum prostate-specific antigen levels, which do not accurately differentiate between patients at high risk for relapse or rapid relapse, leading to inappropriate treatment decisions.
Innovation Solution
A comprehensive genome analysis method that identifies genome copy number variations (CNVs) in prostate cancer samples, adjacent tissues, and blood samples to predict the likelihood of relapse or rapid relapse, using specific thresholds for CNV numbers and sizes to determine treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Gleason's grading and serum prostate-specific antigen levels are used for diagnosis and relapse prediction, then the diagnostic process is simple and cost-effective, but the prediction accuracy is insufficient and cannot accurately differentiate between patients at high risk for relapse or rapid relapse
Solution Approach 1:
The genome is segmented into specific chromosomal regions and genes that are analyzed for copy number variations. The method focuses on specific genomic segments rather than analyzing the entire genome, thereby improving prediction accuracy while managing complexity through targeted analysis of predetermined genomic regions
Solution Approach 2:
The method changes the diagnostic parameters from traditional clinical metrics (Gleason grade, PSA levels) to genomic parameters (copy number variations, gene amplifications, deletions). This parameter transformation enables more accurate relapse prediction by detecting molecular-level changes that precede clinical relapse
2Reliability
If comprehensive genome analysis is performed to identify copy number variations, then relapse prediction accuracy is improved, but the cost and time required for diagnosis increases
Solution Approach 1:
The method performs preliminary genomic analysis on the primary tumor tissue to identify copy number variations and genetic alterations before treatment decisions are made. This preliminary action provides advance information about relapse risk, enabling proactive treatment planning rather than waiting for relapse to occur
Solution Approach 2:
The method extracts specific genomic information (copy number variations of predetermined genes) from the complex genome data. By isolating and analyzing only the most relevant genomic markers associated with relapse risk, the method reduces the time and computational resources needed while maintaining high prediction reliability
3Adaptability or versatility
If traditional diagnostic methods are used, then treatment decisions are made based on available clinical data, but inappropriate treatment decisions are made leading to either overtreatment or undertreatment
Solution Approach 1:
The method provides feedback about the patient's genomic profile and relapse risk level to guide treatment decisions. This feedback loop enables clinicians to adjust treatment intensity based on individual genomic characteristics, ensuring that high-risk patients receive more aggressive therapy while low-risk patients avoid unnecessary treatment
Data Source
AI summary
The present invention relates to methods and compositions for diagnosing prostate cancer and/or determining whether a prostate cancer patient is at increased risk of suffering a relapse, or a rapid relapse, of his cancer. It is based, at least in part, on the results of a comprehensive genome analysis on 241 prostate cancer samples (104 prostate cancer, 85 matched bloods, 49 matched benign prostate tissues adjacent to cancer, and 3 cell lines) which indicate that (i) genome copy number variation (CNV) occurred in both cancer and non-cancer tissues, and (ii) CNV predicts prostate cancer progression.