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

Abstract

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.

The impact of estimated tumour purity on gene expression-based drug repositioning of Clear Cell Renal Cell Carcinoma samples

“…variation in sampling or processing of the tissue) as including all possible sources of batch effects (the full linear mixed model outputs presented in the Supplementary Tables S6 and S7) does not affect the direction or statistical significance of any of the variables of interest. It is of course possible that any hidden batch effects are still in the data, but principal component analysis of the gene expression data did not show any clustering to suggest any hidden batch effects 2,9 .…”

“…The tumour sample and drug signatures were calculated as described previously 2 . Briefly, the tumour sample signatures were generated by comparing 538 individual tumour gene expression profiles to 72 normal kidney tissue gene expression profiles, both generated by TCGA 9 .…”

“…Tumours of metastatic clear cell renal carcinoma (ccRCC) patients typically become resistant to available treatments within 1.5 years 1 . To discover new potentially therapeutic drugs against ccRCC within drugs already prescribed for diseases (drug repositioning), we previously developed an individualised drug repositioning approach based on the gene expression profiles of over 500 ccRCC tumours generated using bulk RNA-Seq by The Cancer Genome Atlas (TCGA) 2 .…”

Influence of batch effect correction methods on drug induced differential gene expression profiles

“…A gene expression signature is a set of genes that are significantly up- or down-regulated by certain biological process or pathological medical condition as compared to a control condition. A popular approach is to identify new indications for drugs based on their gene signature showing an opposite pattern of up−/down-regulation as compared to a disease signature [4]. This approach was piloted by the Connectivity Map (CMAP) project, in which a pattern matching algorithm was employed to rank the similarities between the query signature and the compound profiles called reference signatures [5].…”

“…Otherwise, mean centering was used to correct for batch effects (Noort et al [11]). Koudijs et al corrected for batch effects by blocking on batch id [4]. The impact of batch effect correction methods on computational drug repositioning efforts using these data resources, and their final impact on downstream drug repositioning pipelines has not been analysed.…”