Cancer Driver Gene Signatures for Precision Oncology and Immunotherapy

University of Pittsburgh researchers have developed a comprehensive knowledgebase of molecular signatures of cancer driver genes and their impact on cancer immune responses. This innovative technology builds upon the previously disclosed Tumor-specific Driver Identification (TDI) framework, providing a detailed database of differentially expressed genes (DEGs) associated with driver somatic genome alterations (SGAs). The knowledgebase aims to guide molecularly targeted cancer therapy and immunotherapy, enhancing precision oncology practices.

Description

The technology involves the application of the TDI algorithm to approximately 5,000 tumors from The Cancer Genome Atlas (TCGA) project, identifying DEGs caused by driver SGAs in each tumor. By pooling results, researchers compiled molecular signatures for about 500 driver SGAs, creating a relational database. These signatures serve as biomarkers for guiding targeted cancer therapies and predicting immune responses. The knowledgebase includes DEGs that reflect the immune status of tumors, which may aid in the selection of appropriate cancer immunotherapies.

Applications

• Precision oncology for targeted cancer therapy
• Cancer immunotherapy guidance
• Biomarker discovery for cancer treatment
• Research on cancer immune evasion mechanisms

Advantages

This technology offers several key advantages, including the ability to identify driver SGAs and their molecular signatures, providing a more accurate basis for targeted cancer therapies. The knowledgebase enhances the understanding of tumor immune microenvironments and their evasion mechanisms, supporting the development of effective immunotherapies. The comprehensive database allows for the prediction of drug sensitivity and immune response, improving clinical decision-making in cancer treatment.

Invention Readiness

The technology is currently at the in vitro data stage. Initial studies have demonstrated the effectiveness of the TDI algorithm in identifying driver SGAs and their associated DEGs. The knowledgebase has been validated using large-scale drug sensitivity screening and immune response studies. Ongoing research aims to further refine the predictive models and validate the clinical utility of the molecular signatures in guiding cancer therapies.

IP Status

https://patents.google.com/patent/US11990209B2