Advanced Methods for Diagnosing Prostate Cancer and Predicting Relapse

The invention introduces novel methods for diagnosing prostate cancer and assessing the risk of relapse. These methods are based on identifying specific genomic variations, which offers a powerful tool for predicting cancer progression and relapse with higher accuracy.

Description

This technology utilizes a comprehensive genome analysis to identify and analyze genome copy number variation (CNV) in prostate cancer samples. CNV refers to alterations in the number of copies of a particular gene, and the invention is based on the discovery that these variations occur in both cancerous and non-cancerous tissues and can predict prostate cancer progression. The method involves analyzing a list of specific genes to determine a patient's risk profile. The analysis can be performed on prostate cancer tissue samples as well as adjacent benign prostate tissue and blood samples, providing a flexible diagnostic approach.

Applications

- Diagnostic testing services for prostate cancer.
- Predictive screening for patients at high risk of prostate cancer relapse.
- Personalized medicine platforms for guiding prostate cancer treatment decisions.
- Development of new therapeutic targets based on identified CNV biomarkers.
- Pharmaceutical and biotechnology research for drug development in oncology.

Advantages

- Provides accurate prediction of prostate cancer relapse and progression.
- Utilizes a comprehensive genome analysis, allowing for a detailed understanding of the disease.
- Can be performed on multiple types of samples, including prostate tissue and blood, offering flexibility in testing.
- Identifies specific biomarkers (CNV) that are directly linked to cancer behavior.
- Enables personalized treatment plans by better informing physicians of a patient's risk profile.

Invention Readiness

The invention is a method that has been developed and validated through a comprehensive genome analysis on 241 prostate cancer samples. This research generated data indicating that genome copy number variation (CNV) can predict prostate cancer progression. To further advance this technology, additional clinical studies are needed to validate its effectiveness on a larger and more diverse patient population.

IP Status

https://patents.google.com/patent/US10760132B2

Related Publication(s)

Yu, Y. P., Song, C., Tseng, G., Ren, B. G., LaFramboise, W., Michalopoulos, G., Nelson, J., & Luo, J.-H. (2012). Genome Abnormalities Precede Prostate Cancer and Predict Clinical Relapse. The American Journal of Pathology, 180(6), 2240–2248. https://doi.org/10.1016/j.ajpath.2012.03.008