Use Case 13: Novel Cell Surface Targets for Individual Cancer Patients Analyzed with Common Fund Datasets

Version 1

A file was first uploaded. The file was parsed as a gene count matrix. Significantly over-expressed genes when compared to tissue expression in GTEx[1] were identified. RNA-seq-like LINCS L1000 Signatures[3] which mimick or reverse the the expression of IMP3 were visualized. Drugs which down-regulate the expression of IMP3 were identified from the RNA-seq-like LINCS L1000 Chemical Perturbagens[3]. Genes which down-regulate the expression of IMP3 were identified from the RNA-seq-like LINCS L1000 CRISPR Knockouts[3]. Genes were filtered by IDG Understudied Proteins[8]. The gene was searched with the MetGENE tool providing pathways, reactions, metabolites, and studies from the Metabolomics Workbench[9]. IMP3 was then searched in the Metabolomics Workbench[11] to identify associated metabolites. IMP3 was then searched in the Metabolomics Workbench[11] to identify relevant reactions. A list of regulatory elements in the vicinity of the gene were retrieved from the CFDE Linked Data Hub[14]. The GlyGen database[18] was searched to identify a relevant set of protein products that originate from IMP3.

1. Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nature Genetics vol. 45 580–585 (2013). doi:10.1038/ng.2653
2. Evangelista, J. E. et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Research vol. 50 W697–W709 (2022). doi:10.1093/nar/gkac328
3. IDG Protein List, https://druggablegenome.net/IDGProteinList
4. MetGENE, https://sc-cfdewebdev.sdsc.edu/MetGENE/metGene.php
5. The Metabolomics Workbench, https://www.metabolomicsworkbench.org/
6. CFDE Linked Data Hub, https://ldh.genome.network/cfde/ldh/
7. York, W. S. et al. GlyGen: Computational and Informatics Resources for Glycoscience. Glycobiology vol. 30 72–73 (2019). doi:10.1093/glycob/cwz080