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.