Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma

Abstract

The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.

Figure 1

Flow chart of identification of potential synthetic lethal interactions and construction of DM-DG-drug networks.

Figure 2

Identifying driver genes and subclass characteristics in clear cell renal cell carcinoma. (A). Overview of driver genes identification via DriverNet analysis in clinical cohorts. (B) The mutation profiles of subclasses classified by network-based stratification (NBS). Characteristics of clinical stage, histological grade, previously reported transcriptome-based molecular subclasses (MSKCC, Rini and Brooks) between two subclasses were presented simultaneously. (C) Difference in mutation frequency of driver genes, molecular characteristics stratified by Brooks and MSKCC between two subclasses. Fisher’s exact tests were applied to compared the statistical differences. (D) Kaplan–Meier survival curve of two subclasses. Statistical difference was calculated by log-rank test.

Figure 3

Exploring feasibility of druggable genes in treating driver mutation-specific clear cell renal cell carcinoma patients. (A) The bipartite network of representative DM-DG interactions. (B) Overall survival of distinct BRD4 expression profiles in BAP1 mutated patients. (C) Overall survival of distinct TYK2 expression profiles in VHL mutated patients. (D) The venn graph for summarizing the available cancer cell lines and compounds in CTRP, PRISM and GDSC pharmacogenomic datasets. (E) Comparing estimated drug sensitivity (LogAUC) of pazopanib between BAP1 mutated and wild-type samples.

Figure 4

Determining sensitivities of identified drugs on renal cancer cell lines. (A) Gene set enrichment analysis between BAP1 mutated and wild-type groups. Blue dots indicate BAP1 mutant-enriched pathway, while red dots indicate wild type-associated pathways. (B) The bipartite network of representative TSG-DT-drug interactions. (C) The DEMETER scores derived from RNAi screens of BI-2536 across 24 kidney CCLs. (D) The CERES scores derived from CRISPR knockout screens of BI-2536 across 26 kidney CCLs. (E) The DEMETER scores derived from RNAi screens of OTX015 across 24 kidney CCLs. (F) The CERES scores derived from CRISPR knockout screens of OTX015 across 26 kidney CCLs.

Figure 5

Estimating drug responses of BI-2536 and PI-103 across BAP1 mutated renal cancer patients. (A) Differential drug response analyses of identified 26 compounds with potential therapeutic efficacies on BAP1-mutant ccRCC. The BRD4 and PRKDC inhibitors with significant response differences between BAP1 mutant and wild type groups were labeled on the plot. (B) Summarizing the current evidences, target gene expression, drug dependency and CMap analysis of candidate drugs. (C) Estimating the drug responses of BI-2536 and PI-103 in treating BAP1 mutated and wild-type RCC patients.