Personalised drug repositioning for Clear Cell Renal Cell Carcinoma using gene expression

• Published in: Scientific reports Vol. 8; no. 1; pp. 5250 - 7
• Main Authors: Koudijs, Karel K M, Terwisscha van Scheltinga, Anton G T, Böhringer, Stefan, Schimmel, Kirsten J M, Guchelaar, Henk-Jan
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 27.03.2018; Nature Publishing Group UK
• Subjects: Carcinoma, Renal Cell - drug therapy; Carcinoma, Renal Cell - genetics; Clear cell-type renal cell carcinoma; Drug Repositioning - methods; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic - drug effects; Humans; Kidney cancer; Kidney Neoplasms - drug therapy; Kidney Neoplasms - genetics; Precision Medicine - methods; Rapamycin; Transcriptome - drug effects; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-23195-8

Reversal of cancer gene expression is predictive of therapeutic potential and can be used to find new indications for existing drugs (drug repositioning). Gene expression reversal potential is currently calculated, in almost all studies, by pre-aggregating all tumour samples into a single group signature or a limited number of molecular subtype signatures. Here, we investigate whether drug repositioning based on individual tumour sample gene expression signatures outperforms the use of tumour group and subtype signatures. The tumour signatures were created using 534 tumour samples and 72 matched normal samples from 530 clear cell renal cell carcinoma (ccRCC) patients. More than 20,000 drug signatures were extracted from the CMAP and LINCS databases. We show that negative enrichment of individual tumour samples correlated (Spearman's rho = 0.15) much better with the amount of differentially expressed genes in drug signatures than with the tumour group signature (Rho = 0.08) and the 4 tumour subtype signatures (Rho 0.036-0.11). Targeted drugs used against ccRCC, such as sirolimus and temsirolimus, which could not be identified with the pre-aggregated tumour signatures could be recovered using individual sample analysis. Thus, drug repositioning can be personalized by taking into account the gene expression profile of the individual's tumour sample.