Conceived and designed the experiments: EI CL NCT AA. Performed the experiments: EI CL SM KF AM CAM RN KS NT NCT. Analyzed the data: EI CL AG JSRF NCT AA. Contributed reagents/materials/analysis tools: CL. Wrote the paper: EI CL JSRF NCT AA.
The authors have declared that no competing interests exist.
The majority of new drug approvals for cancer are based on existing therapeutic targets. One approach to the identification of novel targets is to perform high-throughput RNA interference (RNAi) cellular viability screens. We describe a novel approach combining RNAi screening in multiple cell lines with gene expression and genomic profiling to identify novel cancer targets. We performed parallel RNAi screens in multiple cancer cell lines to identify genes that are essential for viability in some cell lines but not others, suggesting that these genes constitute key drivers of cellular survival in specific cancer cells. This approach was verified by the identification of
Central to the design of novel therapeutic strategies for cancer is the identification of genes that are critical to the survival of tumour cells but which are largely redundant in normal cells
RNA interference (RNAi) is a naturally occurring mechanism that regulates gene expression at the post-transcriptional level. In mammalian cells, short-interfering RNAs (siRNAs) mediate the degradation of complementary messenger RNA (mRNA) transcripts in a sequence-dependent fashion
We demonstrate that RNAi screens can be used to identify genes that are differentially required for viability of cancer cell lines and, as proof of this principle, identify the known oncogene
To functionally identify important genes expressed in cancer cells, we used an RNAi screening approach. Using a diverse range of human cancer cell lines and a short interfering RNA (siRNA) library targeting 779 kinases, we performed five parallel viability screens using MCF7 (ER positive, luminal breast cancer), CAL51 (ER negative, microsatellite unstable breast cancer), A549 (lung cancer), NCI-H226 (lung cancer) and HeLa (cervical cancer) cell lines (
a. Scatter plots of Z scores from cell viability screens carried out in parallel in MCF7, CAL51, HeLa, A549 and NCI-H226 cancer cell lines. Black diamonds – individual siRNA SMARTpools targeting 779 kinase genes per cell line. Z scores≤−3 represent significant loss of viability effects. b. Distribution plots of Z scores from the parallel siRNA screens. Z scores≤−3 represent significant loss of viability effects. c. Kinases can be classified on the basis of the effect of silencing on cell viability across all five cancer cell lines. siRNAs that had no significant effect on cell viability in any of the cell lines studied likely target nonessential kinases (or the siRNA was not functional). siRNAs that cause significant loss of cell viability in all of the cell lines studied likely target kinases that are essential for viability in most tumour types or those that are essential for the viability of both normal and tumour cells. siRNAs that only cause significant lethality in some but not all cell lines likely target kinases that may not be critical for the viability of all cells but represent tumour-specific effects. d. Parallel RNAi screens identify a known oncogene,
We reasoned that siRNAs causing significant loss of cell viability (Z≤−3) in all of the cell lines assayed likely represented kinases that are essential for viability in most tumour types or more likely essential for the viability of both normal and tumour cells. Similarly, siRNAs that had no significant effect on viability in any of the cell lines were either not functional or targeted non-essential kinases. Finally, we hypothesised that siRNAs that only caused significant lethality in some but not all cell lines, identified kinases that represent tumour-specific effects potentially identifying new therapeutic targets (
GENE | AC. NO. | MCF7 Z SCORE | HeLa Z SCORE | CAL51 Z SCORE | A549 Z SCORE | H226 Z SCORE | Pearson r | P |
ADCK2 | NM_052853 | 0.74 | 0.34 | −0.59 | −1.99 | −0.93 | 0.02 | |
AURKB | NM_004217 | −1.42 | −1.33 | −0.93 | −0.56 | 0.32 | ||
BUB1B | NM_001211 | −1.60 | −1.96 | −1.78 | 0.06 | 0.93 | ||
CALM3 | NM_005184 | −1.01 | −2.71 | −1.88 | −1.23 | 0.2 | 0.74 | |
CDC2L2 | NM_024011 | −1.92 | 0.5 | 0.39 | ||||
CDK9 | NM_001261 | 0.91 | −1.80 | −1.35 | −0.18 | −0.46 | 0.44 | |
CHKA | NM_001277 | −0.27 | −0.38 | −0.59 | −2.00 | −0.18 | 0.77 | |
CIT | NM_007174 | −0.93 | −1.21 | −1.24 | 0.5 | 0.39 | ||
CNKSR1 | NM_006314 | 0.65 | 0.23 | |||||
COPB2 | NM_004766 | 0.1 | 0.87 | |||||
CSNK1G1 | NM_022048 | −1.06 | −2.81 | −1.98 | −1.06 | 0.04 | 0.94 | |
DGKE | NM_003647 | −0.42 | 0.30 | −2.50 | −1.40 | 0.34 | 0.58 | |
EXOSC10 | NM_002685 | −2.06 | −2.18 | −1.04 | −2.09 | −0.71 | 0.18 | |
FGFR3 | NM_000142 | −0.60 | −0.39 | −0.28 | −0.40 | 0.64 | 0.25 | |
GALK1 | NM_000154 | −1.13 | −1.50 | −0.92 | −1.03 | 0.18 | 0.77 | |
GALK2 | NM_002044 | −0.73 | −0.38 | −0.86 | −0.40 | 0.21 | 0.73 | |
GUCY2D | NM_000180 | −1.46 | −0.61 | −2.10 | −0.76 | −0.36 | 0.55 | |
GUK1 | NM_000858 | −1.33 | −2.34 | 0.41 | 0.26 | 0.68 | ||
LMTK3 | XM_055866 | −2.24 | −2.73 | −2.19 | −0.07 | 0.01 | 0.99 | |
MASTL | NM_032844 | −0.35 | 0.24 | −1.15 | −1.00 | 0.13 | 0.84 | |
MYLK2 | NM_033118 | −2.40 | −1.14 | −0.55 | −1.56 | 0.35 | 0.57 | |
NAGK | NM_017567 | −1.25 | −1.26 | −0.50 | −0.97 | 0.01 | ||
NME3 | NM_002513 | −0.71 | −1.71 | −1.07 | −0.19 | 0.83 | 0.08 | |
PANK4 | NM_018216 | −1.27 | −1.89 | −2.79 | −2.41 | −0.02 | 0.98 | |
PFKFB1 | NM_002625 | −0.95 | −2.41 | −0.67 | −0.75 | 0.8 | 0.1 | |
PIK3C2A | NM_002645 | −2.99 | −1.13 | −0.69 | −1.99 | −0.53 | 0.36 | |
PIK3CA | NM_006218 | 0.54 | −0.22 | −0.73 | −0.60 | −0.94 | 0.02 | |
PKN3 | NM_013355 | −0.28 | −0.98 | 0.11 | −0.13 | −0.7 | 0.19 | |
PLK1 | NM_005030 | −0.57 | 0.32 | |||||
PMVK | NM_006556 | −0.84 | −0.59 | −0.49 | −2.28 | 0.72 | 0.17 | |
PRKAG3 | NM_017431 | −2.04 | −0.09 | 0.2 | 0.74 | |||
RPS6KA2 | NM_021135 | −2.66 | −1.90 | −2.89 | 0.95 | 0.01 | ||
SYK | NM_003177 | −1.77 | −2.09 | −1.05 | −0.40 | 0.16 | 0.8 | |
TLR6 | NM_006068 | −0.71 | −0.77 | −0.04 | −0.19 | −0.97 | 0 | |
TTK | NM_003318 | −2.01 | −2.06 | −2.22 | −0.45 | 0.44 | ||
WEE1 | NM_003390 | −1.00 | 0.08 | −0.08 | −0.88 | 0.05 |
siRNAs causing loss of viability (where Z≤−3) are shown for five cell lines. Z scores of ≤−3 are shown in bold. Pearson correlation coefficient for correlation with gene expression, with P value.
Our initial analysis indicated that
Although cell-specific gene effects identified in the RNAi screen may be because of activating mutations, such as in
a–d. Z scores from the siRNA screens, Z scores≤−3 are highlighted with a red dotted box. e–h. Normalised expression levels calculated from Illumina expression profiling. High expression correlated with sensitivity to siRNA, highlighted with a red dotted box. Error bars represent the standard error of the mean (SEM). The significance of differences in genes expression between cell lines was assessed by one-way ANOVA and p value displayed for each cell lines. i–l. comparison of Z values with normalised expression levels. The dashed line represents the Z = −3 threshold for significant loss of viability effects (p<0.0015). For the four genes shown, an elevated level of expression is consistent with loss of viability after siRNA transfection. See
The Toll-like receptor 6, (TLR6) is known to activate nuclear factor kappa-B signalling, a candidate therapeutic target in cancer
We examined whether the relatively elevated expression of the four genes identified in our RNAi screen could be explained by changes in gene copy number (i.e. copy number gains and/ or gene amplification). Gene copy number was examined using microarray-based comparative genomic hybridisation (aCGH) analysis and overlayed on RNAi and gene expression data (
Scatter plots illustrating the relationship between gene copy number and gene expression. Vertical dashed lines represent the threshold for copy number gains (aws ratios>0.12).
WEE1 has a well-defined role in cell cycle checkpoint control, with WEE1 activity limiting the pro-mitotic effects of CDC2 (aka CDK1)
a. Western blot analysis of lysates prepared from HeLa, CAL51, MCF7, A549, NCI-H226, PC3, DU145 and MCF10A cells. An antibody recognising WEE1 was used with β-tubulin as a loading control. WEE1 expression is significantly increased in HeLa and CAL51 cells compared to MCF7, A549, NCI-H226, PC3, DU145 and MCF10A cells. b. Left panel: Cell viability assay in cells transfected with WEE1 ONTARGETplus SMARTpool, or ONTARGETplus siControl. WEE1 silencing was selectively lethal to WEE1 overexpressing HeLa and CAL51 cells. Error bars represent the SEM from triplicate transfections. Right panel: Western blot analysis of lysates prepared from CAL51 cells transfected with WEE1 ONTARGETplus SMARTpool or ONTARGETplus siControl. An antibody recognising WEE1 was used with β-tubulin as a loading control. WEE1 ONTARGETplus SMARTpool significantly reduced WEE1 protein expression compared to siControl transfected cells. c. Cell viability assay in cells treated with WEE1 inhibitor. WEE1 inhibition was selectively lethal to WEE1 overexpressing HeLa and CAL51 cells. Error bars represent the SEM from triplicate cell treatments. d. Left hand panel: Western blot analysis of lysates prepared from cells treated with 5 µM WEE1 inhibitor for 0, 6, 24 and 48 hours. An antibody recognising PARP was used with β-tubulin as a loading control. After 24 hours WEE1 inhibition induced PARP cleavage (Clvd PARP) in WEE1 overexpressing HeLa and CAL51 cells but did not induce PARP cleavage in MCF7 and NCI-H226 cells which express WEE1 at normal levels. Right hand panel: Caspase 3,7 activity in cells treated with 5 µM WEE1 inhibitor for 24 hours. WEE1 inhibition induced caspase 3,7 activation in WEE1 overexpressing HeLa and CAL51 cells but did not induce caspase 3,7 activation in MCF7 and NCI-H226 cells which express WEE1 at normal levels. Error bars represent the SEM from triplicate cell treatments. e. Left hand panel: Western blot analysis of lysates prepared from cells transfected with WEE1 ONTARGETplus SMARTpool or ONTARGETplus siControl. An antibody recognising PARP was used with β-tubulin as a loading control. Silencing of WEE1 induced PARP cleavage in WEE1 overexpressing HeLa and CAL51 cells but did not induce PARP cleavage in MCF7 and NCI-H226 cells which express WEE1 at normal levels. Right hand panel: Caspase 3,7 activity in cells transfected with WEE1 ONTARGETplus SMARTpool or ONTARGETplus siControl. Silencing of WEE1 induced caspase 3,7 activation in WEE1 overexpressing HeLa and CAL51 cells but did not induce caspase 3,7 activation in MCF7 and NCI-H226 cells which express WEE1 at normal levels. Error bars represent the SEM from triplicate transfections.
Taken together, our results provide evidence to suggest that cell lines displaying higher levels of WEE1 expression are sensitive to WEE1 inhibition. To investigate the mechanism of sensitivity in these cell lines, we examined levels of apoptosis following WEE1 inhibition. Chemical inhibition of WEE1 caused apoptosis only in cell lines with higher levels of WEE1 expression (
Our data suggest that WEE1 overexpression may be essential for tumour cell viability. Therefore, we interrogated the expression of WEE1 in publicly available datasets that detail the expression profiles of human breast tumour cell lines and tumours
a. WEE1 immunohistochemical staining in formalin-fixed, paraffin-embedded breast cancer cell lines and invasive breast cancers. Note the low levels of WEE1 expression in H226 cells and a basal-like breast cancer and the high levels of WEE1 expression in CAL51 cells and luminal and HER2 breast cancers. (Harris Haematoxylin/DAB staining; original magnification ×200). b. High levels of WEE1 expression are preferentially expressed in luminal breast cancers. Cases were scored according to the Allred scoring system
The majority of new drug approvals for cancer treatment are based on existing targets. Rather than reflecting an absence of targets, this is perhaps indicative of the cost and time involved in identifying novel therapeutic approaches. Parallel RNAi screening may allow a simple, high-throughput, approach to the functional identification of targets, and others have also used parallel RNAi screening to identify potential drivers of tumourigenesis and candidate targets
MCF7, CAL51, HeLa, A549, NCI-H226, PC3, DU145 and MCF10A cells were obtained from ATCC (USA) and maintained according to the supplier's instructions. WEE1 inhibitor (681637) was obtained from Calbiochem (UK). MCF7 and HeLa cells were transfected with SMARTpool siRNAs using Dharmafect 3 transfection reagent; A549 and NCI-H226 cells were transfected with SMARTpool siRNAs using Dharmafect 1 transfection reagent according to manufacturer's instructions (Dharmacon). CAL51 cells were transfected with SMARTpool siRNAs using Oligofectamine transfection reagent according to manufacturer's instructions (Invitrogen). DU145 cells were transfected with SMARTpool siRNAs using Lipofectamine 2000 transfection reagent according to manufacturer's instructions (Invitrogen). The kinase siRNA library (siARRAY – targeting 779 known and putative human protein kinase genes) was obtained in ten 96 well plates from Dharmacon (USA). Each well in this library contained a SMARTpool of four distinct siRNA species targeting different sequences of the target transcript. Each plate was supplemented with siCONTROL (ten wells, Dharmacon (USA)). The WEE1 ONTARGETplus SMARTpool and ONTARGETplus siControl were obtained from Dharmacon (USA).
Antibodies targeting the following epitopes were used: WEE1 (4936, Cell Signaling, UK), PARP (9542, Cell Signaling, UK) and β-tubulin (T4026, Sigma, UK). All secondary antibodies used for western blot analysis were HRP conjugated.
Cells plated in 96 well plates were transfected 24 hours later with siRNA (final concentration 100 nM), as per manufacturer's instructions. Each siRNA plate was supplemented with 10 wells of siControl. Twenty four hours following transfection, cells were trypsinised and divided into three identical replica plates. Media was replenished after 48 hours and 96 hours, and cell viability was assessed after seven days using CellTiter Glo Luminescent Cell Viability Assay (Promega, USA) as per manufacturer's instructions. Data from each cell line was processed as follows: the luminescence reading for each well on a plate was log2 transformed and expressed relative to the median luminescence value of all wells on the same plate (plate centering). This data was then normalised according to the median of the entire screen data, using the median absolute deviation (MAD) to estimate the true variation within each screen
RNA was extracted from cell lines with Trizol and phenol/chloroform extraction followed by isopropanol precipitation. For reach cell line, triplicate extractions and profiles were performed. Biotin-labeled cRNA was produced by means of a linear amplification kit (IL1791; Ambion, Austin, TX,
The correlation between siRNA Z score and normalised gene expression was examined for genes where siRNA caused significant loss of viability (Z<−3). Z score was compared to normalised gene expression using Pearson correlation coefficient. A gene was taken as being significantly correlated if the Pearson correlation coefficient was significantly different to the null hypothesis, the correlation was inverse, and the variation in gene expression between cells lines were significantly different as assessed by one-way ANOVA.
Genomic DNA was extracted from cell lines using the QIAamp DNA Blood Mini Kit (51104, Qiagen), according to manufacturer's instructions. Microarray-based CGH analysis was performed on an in-house 32K tiling path BAC array platform as previously described
Cells plated in 96 well plates were transfected 24 hours later with WEE1 ONTARGETplus SMARTpool or ONTARGETplus siControl (final concentration 100 nM), as per manufacturer's instructions. Twenty four hours following transfection, cells were trypsinised and divided into three identical replica plates. Media was replenished after 48 hours and 96 hours, and cell viability was assessed after seven days using CellTiter Glo Luminescent Cell Viability Assay (Promega, USA) and expressed relative to mean luminescence in the wells transfected with siControl.
Cells were plated in 96 well plates and exposed to various doses of WEE1 inhibitor (Calbiochem, Cat. No. 681637, 4-(2-Phenyl)-9-hydroxypyrrolo[3,4-c]carbazole-1,3-(2H,6H)-dione (PHCD))
Protein lysates were prepared using RIPA lysis buffer (50 nM Tris pH 8.0, 150 mM NaCl, 0.1% SDS, 0.1% DOC, 1% TritonX-100, 50 mM NaF, 1 mM Na3VO4 and protease inhibitors). 100 µg of total cell lysate was loaded onto prefabricated 4–12% Bis-Tris gels (Invitrogen), with full range rainbow molecular weight marker (GE Healthcare, UK) as a size reference, and resolved by SDS-PAGE electrophoresis. Proteins were transferred to nitrocellulose membrane (Bio-rad, USA), blocked and probed with primary antibody diluted 1 in 1000 in 1×TBS-T with 5% BSA overnight at 4°C. Secondary antibodies were diluted 1 in 5000 in 1×TBS-T with 5% skim milk and incubated for one hour at room temperature. Protein bands were visualised using ECL (GE Healthcare, UK) and MR or XAR film (Kodak).
Validation of RNAi gene silencing was determined by western blotting. Cells were transfected with WEE1 ONTARGETplus SMARTpool or ONTARGETplus siControl, and protein lysates were made 48 hours later and western blotted for WEE1 expression with β-tubulin as a loading control.
Cells were transfected with WEE1 ONTARGETplus SMARTpool or ONTARGETplus siControl, and total cell lysates were made 48 hours later and western blotted for PARP with β-tubulin as a loading control. Cells were treated with 5 µM Wee1 inhibitor for 0, 6, 24 and 48 hours. Total cell lysates were made at the time points and western blotted for PARP with β-tubulin as a loading control.
Cells were transfected with WEE1 ONTARGETplus SMARTpool or ONTARGETplus siControl, and caspase 3,7 activation was measured 48 hours later using Caspase-Glo 3/7 Assay (G8091, Promega) and expressed relative to mean luminescence in the wells transfected with siControl. Cells were treated with 5 µM Wee1 inhibitor and caspase 3,7 activation was measured 24 hours later using Caspase-Glo 3/7 Assay (G8091, Promega) and expressed relative to mean luminescence in the wells treated with vehicle.
Immunohistochemistry for WEE1 was performed with a rabbit polyclonal antibody (Cell Signalling; 4936) at a dilution of 1/20 and developed with the dual Envision kit (Dako®, Glostrup, Denmark). Details of this cohort of patients are described elsewhere
Title of dataset: Z scores for 779 siRNA SMARTpools in five cancer cell lines Description of dataset: The sensitivity of five cancer cell lines to siRNA SMARTpools is shown. Analysis was carried out as in the
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Title of dataset: Combined RNAi and expression data from five cancer cell lines Description of dataset: Z scores are shown for SMARTpools for all 779 gene. For each of the corresponding genes, transcript expression data is shown, generated by Illumina profiling
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Title of dataset: Array Comparative Genomic Hybridisation (aCGH) data from five cancer cell lines Description of dataset: Genome-wide aCGH profiling was performed as described in the
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Correlation between gene copy number and Z score. Scatter plots illustrating the relationship between gene copy number and sensitivity to siRNA. Horizontal dashed lines represent the threshold for copy number gains (aws ratios>0.12) and vertical dashed lines represent the threshold for significant loss of viability effects.
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Gene silencing of WEE1. a. Cells were transfected with SMARTPool siRNA or one of the component siRNAs from each SMARTPool as shown. Forty-eight hours after transfection, RNA was extracted and quantitative real-time PCR performed. Specific gene expression in each sample was normalised to that of a house keeping gene (GAPDH) and standardised according to gene expression in cells transfected with a control, non-targeting siRNA (siCONTROL). Each bar represents data from triplicate transfections, with error bars representing SEM. * = p<0.05 vs siCONTOL, Student's t test. B. Multiple WEE1 siRNAs cause selective killing of CAL51 cells, when compared to MCF7 cells. Cells were transfected with SMARTPool siRNA or one of the component siRNAs from each SMARTPool as shown. Cell viability measurements were performed and surviving fractions calculated as in the
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