UCLA Case No. 2018-916
SUMMARY
Researchers led by Dr. Xing from the Department of Microbiology, Immunology, and Molecular Genetics at UCLA have developed a big data approach to identify targets for personalized cancer immunotherapy.
BACKGROUND
Immunotherapies aim to use the patient’s own immune system to fight cancerous cells, but to be effective, immune cells need a target to identify cancer cells. Identifying optimal targets remains as the primary obstacle for developing personalized, effective cancer immunotherapies. Current techniques identify potential targets by predicting them from genetic mutations or through characterizing gene expression. However, these methodologies are limited to certain cancer types with high mutation rates and known differentially expressed targets. These methods do not take into account targets created through alternative splicing, the creation of many proteins from a single gene.
INNOVATION
UCLA researchers led by Dr. Xing have developed a big data approach to identify targets for personalized cancer immunotherapy. They have created a software named IRIS to predict peptide targets derived from alternative splicing for cancer immunotherapies. Their approach solves 2 major obstacles when predicting targets from alternative splicing: 1) the high computational cost of characterizing alternative splicing in large datasets and 2) the low detection specificity due to the lack of quality references for normal and cancerous tissue. IRIS predicts unique therapeutic targets by identifying the location of the alternative splicing events and then extrapolating the type of peptides that stem from them.
POTENTIAL APPLICATIONS
ADVANTAGES