Computational pipeline for identifying kinase drug targets in cancer

This technology is a computational pipeline designed to identify major kinases in different cancer subtypes for the development of precision cancer therapeutics.

Unmet Need: Accurate tumor subtype classification for precision medicine

Proteogenomic characterization of human tumors has yet to demonstrate value for precision cancer medicine. There is a need for more accurate classifiers due to the urgency of precision oncology and drug development targeting homogeneous tumor subsets. When kinases are mutated, their altered behaviors can drive cancer genesis and pathology; thus, understanding which specific kinases are the key players in different cancers can lead to more effective cancer treatment. However, currently there is no tool available to identify and target pathogenic kinases.

The Technology: Identification and targeting of master protein kinases for precision oncology

This technology is an integrative multi-omics computational method uses a machine-learning network to identify master kinases responsible for affecting phenotypic hallmarks of cancers. This pipeline can be applied to any cancer type to identify the kinases associated with individual tumors, which will allow for targeted therapies against specific kinases extending the current possibilities for precision oncology.

This technology has been validated in vivo with patient tumor samples.

Applications:

• Development tool for cancer therapeutics
• Research model for probing kinase function in cancers
• Classifier for identifying common proteins across tissue types

Advantages:

• High-throughput computational platform for disease analysis
• Efficiently identifies protein kinase subtypes with unique therapeutic vulnerabilities
• Can use frozen or paraffin-embedded tissues
• Cost-effective

Lead Inventor:

Antonio Iavarone, MD

Patent Information:

Patent Pending

Related Publications:

• Migliozzi S, Oh YT, Hasanain M, Garofano L, D'Angelo F, Najac RD, Picca A, Bielle F, Di Stefano AL, Lerond J, Sarkaria JN, Ceccarelli M, Sanson M, Lasorella A, Iavarone A. “Integrative multi-omics networks identify PKCδ and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy.” Nat Cancer. 2023 Feb; 4(2): 181-202.

Tech Ventures Reference:

• IR CU23037

• Licensing Contact: Kristin Neuman