Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
TITLE: Predictive Genetic Risk Score for Prediction of Survival in Cancer INVENTORS: Matthew Porembka, Tae Hyun Hwang, Sam Wang, Sunho Paik, and Jae-Ho Cheong TECHNOLOGY: Biologicals UTSD: 2995 SUMMARY: The contributors have developed an innovative algorithm that integrates somatic genetic variants with known biologic pathways drawn from public databases, such as KEGG and BioCarta, to identified modular signaling pathways that are altered between phenotypes. Using this algorithm, the contributors have identified a 34 gene signature from the TCGA datasets that effectively stratifies patients by their risk of death for several different cancers. This genetic risk score has broad applicability and has been validated in gastric cancer and brain cancer. There is no other gene score that potentially has this broad applicability. There are currently no clinically relevant genetic risk stratification tools other than Oncotype DX, which is used in only a small subset of breast cancer patients. This risk score is developed through a novel method and has been shown to predict the risk of death for gastric cancer and brain cancer. The contributors are in the process of validating it in several other cancers. Current staging modalities are inadequate and may result in unnecessary neoadjuvant therapy (if overstaged) or missed opportunities for upfront chemotherapy (if understaged). Identifying a cancer specific genetic signature that accurately identified high-risk disease will improve staging, inform treatment, and aid in development of novel therapeutics. Long-term benefits of an accurate risk score could include more informed treatment decisions, reduced cost, enhanced patient compliance/quality of life, and fewer missed treatment opportunities secondary to the use of ineffective first-line therapies. Please contact the Office for Technology Development for more details: Phone: 214-648-1888 Email: [email protected] Please reference UT Southwestern Case Number: 2995