Download Document 8369243

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
Transcript
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