Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines

Abstract

One approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.

Graphical abstract

Figure 1

Screening Overview (A) Schematic of siRNA screening, data processing, and genomic data integration. (B) Piechart illustrating histotypes for 117 cell lines that passed QC (CNS). (C) Frequency plot depicting the number of cell lines in which each kinase siRNA caused a significant growth defect (Z ≤ −2). (D) Clustered heatmap summarizing the KGDs of 117 cell lines. The average linkage hierarchical clustering was used with Pearson’s correlation as the similarity metric. Only the 20% most variable siRNA Z scores were used for the calculation of correlations. The histotype of each cell line is indicated by the color blocks to the left of the heatmap and corresponds to the scheme shown in (B).

Figure 2

Kinase Dependencies Associated with Histotypes (A) Radar plot summarizing the KGDs associated with the osteosarcoma histotype. The concentric circles indicate the statistical significance and the depth of color indicates the separation of Z scores between the osteosarcoma histotype and the non-osteosarcoma group of cell lines. A set of six kinases annotated as involved in skeletal system morphogenesis in the Gene Ontology are annotated with asterisks. (B) Heatmap of KGDs enriched in osteosarcoma cell lines are shown as a heatmap representing siRNA Z scores. The asterisks indicate kinases involved in skeletal system morphogenesis as in (A). (C and D) Box plots of area under curve (AUC) estimates for 58 cell lines exposed to the FGFR inhibitor AZD4547 (C) and PD173074 (D) at eight different concentrations. FGFR1 and FGFR2-amplified cell lines are indicated with black and green circles, respectively. The non-tumor epithelial cell lines MCF10A and MCF12A are indicated with gray arrows. (E) Box plot of AUC estimates for a panel of cell lines exposed to the FGFR inhibitor PD173074 (Garnett et al., 2012). In each box plot (C–E), the top and bottom of the box represents the third and first quartiles and the box band represents the median (second quartile); whiskers extend to 1.5 times the interquartile distance from the box. See also Figures S1 and S2.

Figure 3

KGDs Associated with Cancer Driver Mutations (A) Bar chart indicating the frequency of driver gene alterations observed in the cell line panel. The colored segments in each bar indicate the histotypes in which alterations were detected. (B) Radar plot summarizing the KGDs associated with ERBB2 amplification (the scheme as per Figure 2A). (C) Box plot showing the ERBB2 Z scores for cell lines grouped according to ERBB2 amplification status. The colors indicate cell line histotypes as in (A). (D) Box plots showing additional KGDs associated with ERBB2 amplification. (E) Box plots summarizing CCND1 KGDs upon CIT. (F) Examples of KGDs that are supported by protein-protein interactions. (G) Examples of KGDs that are supported by kinase-substrate relationships. (H) Examples of KGDs that are supported by gene regulatory relationships. (I) Examples of KGDs associated with ERBB2 amplification status in esophageal cancer models supported by kinase-substrate relationships that form a shortest path between the mutated driver gene and kinases. In each box plot (C–I), the top and bottom of the box represents the third and first quartiles and the box band represents the median (second quartile); whiskers extend to 1.5 times the interquartile distance from the box. See also Figures S3 and S4 and Tables S1I and S1K.

Figure 4

Driver Gene KGDs and Functional Interaction Networks (A) Functional interaction network showing interactions between ERBB2 amplification-associated KGDs. The nodes correspond to kinases that are identified as KGDs in ERBB2 amplified cell lines. The nodes are scaled to indicate the significance of the KGD association p value. The blue edges correspond to experimentally determined protein-protein interactions, the pink arrows indicate the direction of experimentally determined kinase-substrate interactions, and the gray edges reflect high-confidence STRING functional interactions. Only KGDs that interact with at least one other ERBB2 dependency are shown. (B) Functional interaction network showing interactions among KGDs identified in SMAD4 mutated cancer cell lines. Details as for ERBB2 network in (A). (C) Box plot showing AUC values of a panel of cell lines exposed to compounds targeting microtubules (paclitaxel and epothilone B) or Aurora Kinases (VX680) and classified into SMAD4 mutant or wild-type groups. The top and bottom of the box represents the third and first quartiles and the box band represents the median (second quartile); whiskers extend to 1.5 times the interquartile distance from the box. See also Figure S5 and Table S1L.

Figure 5

Pathway Mutations Associated with KGDs (A) Heatmap showing increased dependency on TWF2 in cell lines with loss-of-function mutations in members of the SWI-SNF complex. (B) Heatmap showing increased dependency on UCK2 in ovarian cancer cell lines with loss-of-function mutations in members of the SWI-SNF complex. (C) Heatmap showing increased dependency on DAPK1 in esophageal cancer cell lines with loss-of-function mutations in members of the SWI-SNF complex. (D) Heatmap showing increased dependency upon CDK6 in cell lines bearing mutations in KRAS, HRAS, NRAS, or BRAF. See also Figure S5 and Tables S1M–S1O.