Expression Data Integration: Advancing Immuno-Oncology Target Discovery

Abstract
Although genome-wide RNA expression analysis has become a routine tool in biomedical research, extracting valuable biological insight from thousands of published studies and underlying data remains a major challenge for two main reasons: the heterogeneity in annotations and technology, and the unreliable quality levels. GENEVESTIGATOR is an analysis tool and database, containing manually curated gene expression data from public studies, making use of controlled vocabularies for several biological dimensions such as tissues, genotypes, diseases and treatments. To avoid bias in the results a strict quality control ensures to only integrate high quality samples and experiments into the database. In this example study, we used GENEVESTIGATOR for 1. indication finding, 2. novel target predictions and 3. target validation for cancer immunotherapy as follows:
1. Profiled the expression of selected immune checkpoint surface molecules across hundreds of tumors and subtypes to indicate which cancer types could be suitable for similar immunotherapies.
2. Identified novel target genes that are co-regulated with known immune checkpoint targets across immune-oncology studies, for insights to which immunological pathways are affected.
3. Compared spatial expression profiles of targets, to validate their combinative advantage.
These studies show how GENEVESTIGATOR can effectively take advantage of the world’s high-quality expression data, and help identifying new targets and characterize expression patterns of targets across cancers.