Genomic Prostate Score Algorithm for Clinical Endpoint Risk Stratification
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Solution Overview
Problem
Current prognostic tools for prostate cancer lack precision in distinguishing between patients who require immediate definitive therapy and those suitable for active surveillance, leading to over-treatment and potential loss of curative opportunities.
Innovation Solution
A multiple gene-expression based Genomic Prostate Score (GPS) test algorithm measures RNA transcripts of specific genes (BGN, COLIA1, SFRP4, FLNC, GSN, TPM2, GSTM2, FAM13C, KLK2, AZGP1, SRD5A2, and TPX2) to calculate a quantitative score, refining risk assessment and guiding personalized treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current prognostic tools are used for risk assessment, then treatment decisions can be made, but the precision in distinguishing between patients requiring immediate therapy and those suitable for active surveillance is insufficient
Solution Approach 1:
The prognostic assessment is segmented into multiple independent gene expression measurements (12 specific genes), each contributing to the overall GPS score. This segmentation allows for precise risk stratification by evaluating individual gene expressions separately and combining them systematically, thereby improving measurement precision without overwhelming complexity.
Solution Approach 2:
The GPS test algorithm serves multiple functions: it assesses risk of biochemical recurrence, evaluates suitability for active surveillance versus immediate therapy, and provides a unified quantitative score for clinical decision-making. This multi-functionality improves precision across various clinical scenarios while maintaining a single standardized tool.
2Reliability
If immediate definitive therapy is provided to all diagnosed patients, then recurrence risk is reduced, but over-treatment occurs with associated toxicity and loss of curative opportunities
Solution Approach 1:
The GPS test enables local quality assessment by identifying specific patient subgroups with distinct risk profiles. Patients with low GPS scores receive conservative management (active surveillance), while those with high GPS scores receive aggressive treatment (immediate therapy). This localized approach ensures appropriate treatment intensity for each patient's specific risk characteristics, reducing unnecessary toxicity while maintaining effectiveness for high-risk patients.
Solution Approach 2:
The GPS score transforms multiple gene expression parameters into a single quantitative risk parameter that directly guides treatment decisions. By changing from qualitative clinical assessment to a quantitative molecular parameter, the system reliably identifies which patients truly need immediate therapy versus those who can safely undergo surveillance, thereby reducing over-treatment toxicity.
3Object-affected harmful factors
If active surveillance is used for low-risk patients, then treatment toxicity is reduced, but 30-40% experience disease progression and may lose curative opportunities
Solution Approach 1:
The GPS test performs preliminary risk assessment before treatment decisions are made. By evaluating gene expression profiles upfront, the system identifies patients whose disease is likely to progress during surveillance, allowing these patients to receive immediate therapy before curative opportunities are lost. This preliminary molecular characterization ensures that active surveillance is reserved for truly low-risk patients while protecting high-risk patients from delayed treatment.
Solution Approach 2:
The GPS score provides feedback on the biological aggressiveness of the tumor based on gene expression patterns. This molecular feedback mechanism allows clinicians to adjust surveillance intensity or initiate treatment based on the patient's specific genomic risk profile, thereby maintaining curative opportunities for those at higher risk while minimizing unnecessary treatment for low-risk patients.
Data Source
AI summary
The present disclosure relates to uses of a multiple gene-expression based Genomic Prostate Score™ (GPS™) algorithm for assessment of various clinical endpoints in prostate cancer patients, such as risks of clinical recurrence (CR), biochemical recurrence (BCR), distant metastasis (Mets), and prostate cancer death (PCD). In some embodiments, GPS result is determined for low and intermediate risk prostate cancer patients in order to assist in determining treatment strategies for those patients.