Innovative Leukocyte Genomic CNV Analysis for Predicting Prostate Cancer Outcomes

Researchers at the University of Pittsburgh have developed a novel method for predicting prostate cancer clinical outcomes using genomic copy number variations (CNVs) in leukocytes. This innovative approach involves whole genome copy number analysis on leukocytes from prostate cancer patients, revealing that CNV size distributions are highly correlative with cancer aggressiveness. The developed prediction model significantly improves the accuracy of prostate cancer recurrence and progression predictions.

Description

The technology utilizes Affymetrix SNP6.0 chip technology to perform whole genome copy number analysis on leukocytes from 273 prostate cancer patients. An average of 152 CNV fragments per genome was identified, with size distributions correlating with prostate cancer aggressiveness. The prediction model, based on the large size ratio of CNVs, achieved an average prediction rate of 73.0% for prostate cancer recurrence, with sensitivity of 75.2% and specificity of 66.4%. When combined with Nomogram and fusion transcript status, the prediction rate improved to 82.5%, with sensitivity of 83.6% and specificity of 79.7%.

Applications

• Predicting prostate cancer recurrence and progression
• Enhancing clinical decision-making for prostate cancer treatment
• Developing personalized treatment plans based on genetic profiles

Advantages

This technology provides a novel and accurate method for predicting prostate cancer outcomes using leukocyte genomic data, improves prediction rates when combined with existing clinical tools, and offers a non-invasive approach to enhance clinical decision-making. The ability to predict cancer aggressiveness and recurrence can lead to better personalized treatment plans and improved patient outcomes.

Invention Readiness

The technology is currently at the in vivo data stage, with successful proof-of-concept studies demonstrating its predictive capabilities. The research team has developed and validated the prediction model using real clinical data from prostate cancer patients.

IP Status

https://patents.google.com/patent/US11008624B2