Discovery of new therapeutic targets in ovarian cancer through identifying significantly non-mutated genes

We obtained somatic mutation data from the TCGA Ovarian Cancer Project from the GDC Data Portal (https://​portal.​gdc.​cancer.​gov/​). We implemented a method to efficiently simulate mutations across a set of nucleotide sequences in Matlab as previously described in Malek, Halabi and Rafii [13]. The TCGA-OV data consisted of 316 patients, so we performed a simulation run 316 times. Since mutations were random, each simulated run of 316 patients was also repeated 100 times. In total, there were 31,600 simulated runs. Each simulation run consisted of simulating the mutagenesis of 140,362,938 nucleotide bases. Furthermore, since different bases undergo different mutation rates, it was necessary to implement a way to differentially mutate different sets of nucleotide bases. The sequence space was therefore divided into nucleotide bases that were (1) A or T (2) C or G (3) CG or GC. Mutations at these different sets were assigned different mutation rates. We used the background mutation rates (BMR) published in the TCGA study [4] in Additional file 1: Table S2.2b (A/T mutations: 8.54 × 10–7, C/G mutations: 1.2 × 10–6, CG/GC mutations: 4.31 × 10–6 and insertions-deletions at 2.2 × 10–7).  Since no information about insertions-deletion sequence specificity was available, we added the indel mutation rate to the other categories. Three different random mutation vectors were generated of a length equal to the number of bases in the A/T, C/G, and CG/GC vectors. Each random mutation vector consisted of 0’s and 1’s with the frequency of 1’s occurring randomly at a density equal to the TCGA published background mutation rate. The three different mutation vectors were then combined to form the final mutation vector that had within it all simulated mutations. We used a reduced sequence library corresponding to the sequences that overlapped with the Agilent SureSelect v2 probe sequences. Obtaining the chromosomal locations of each probe from Agilent generated this reduced library. We then identified the regions corresponding to those probes by detecting the overlaps between the exon coordinates and the probe coordinates. The final exon sequence library consisted of 40,362,938 bases.  After carrying out simulated mutagenesis, the total number of mutations per gene was calculated by identifying all the exons corresponding to a gene. All exons sharing the same gene symbol were considered the same gene.

With the ability to calculate the simulated mutation frequency for each gene, it is possible to compare the observed mutation frequency in a gene with the expected simulated mutation frequency. To prioritize the genes with the largest deviations from random simulation expectation, we looked at the top 50 genes where the observed mutation rate was lower or higher than the expected mutation rate, comparing the observed and simulated frequencies based on rank of the observed/expected from simulation mutation frequency ratio. To guarantee coverage in the TCGA-OV dataset, we restricted our analysis to genes that were mutated at least once in the TCGA data since the publicly available data only included a list of mutations per patient and not the coverage across all positions.

We also performed pathway analysis using Ingenuity Pathway Analysis software (IPA from Qiagen, content version March 12, 2022). IPA software consists of a database of published relationships between genes with tools to analyze and visualize pathways. A list of the top 50 non-mutated genes with the observed/expected ratio of each gene was generated and uploaded to IPA. Network diagrams were generated among the genes in the list with genes colored in shades of red based on their observed/expected ratio with the reddest indicating the lowest ratio. Networks were either built using the IPA tools (CONNECT, PATHWAY EXPLORER, TRIM, KEEP) or identified automatically by IPA software as indicated. Automatically generated network significance was assessed with an IPA generated score which represents the negative exponent of the right-tailed Fisher’s exact test result (described in the IPA documentation: http://​qiagen.​secure.​force.​com/​KnowledgeBase/​articles/​Basic_​Technical_​Q_​A/​Listing-of-Networks).

We used three ovarian cancer cell lines for silencing experiments: SKOV3, OVCAR3 and APOCC. SKOV3 and OVCAR3 were purchased from ATCC and APOCC was derived in-house from ascites of a patient with Stage III serous adenocarcinoma. These cell lines were all maintained in DMEM high glucose (Hyclone, Thermo Scientific), 10% FBS (Hyclone, Thermo Scientific), 1% Penicillin-Streptomycin-Amphotericin B solution (Sigma), 1X Non Essential Amino-Acid (Hyclone, Thermo Scientific). Additionally, one non-cancer primary ovarian epithelial cell line was purchased from Sciencell (Cat. No. 7310) and cultured in poly-L-lysine-coated culture vessel (2 μg/cm2, T-75 flask) following ScienCell recommendations in Ovarian Epithelial Cell Medium (OEpiCM, Cat. No. 7311), 1% Ovarian Epithelial Cell Growth Supplement (OEpiCGS, Cat. No.7352) and 1% penicillin/streptomycin solution (p/s, Cat.No 0503). All cultures were incubated in humidified 5% CO2 incubators and the media was replaced every three days. For RNA sequencing, we used ovarian cancer cell lines SKOV3, APOCC, GOC-2, and GOC-A2 [14, 15], non-cancer ovary derived fibroblasts (ScienCell, Cat. No. 7330) and non-cancer primary ovarian epithelial cell lines (ScienCell, Cat. No. 7310). GOC-2 cells were isolated from a papillary serous ovarian cancer obtained after neoadjuvant chemotherapy while GOC-A2 were derived from a stage IIIc serous ovarian cancer [14]. GOC-2, GOC-A2 and fibroblasts were cultured in DMEM high glucose as previously described.

RNA from six different cell lines were isolated using Qiagen Allprep DNA/RNA miniprep kit as per manufacturer instructions. Library preparation was done with Nugen’s Ovation Single Cell RNA-Seq System. Sequencing (Illumina 100 bp paired-end reads) was done on Illumina HiSeq 2500. Alignment was done with RNA Star to GRCH37 [16]. Mapping to genes was done with Rsubread using the FeatureCounts function [17]. Normalization and quantification of gene expression was done with edgeR [18]. All genes with any read count were included. The reads per kilobase of transcript per million mapped reads (RPKM) measure was calculated for all genes in all cell lines and used for distribution comparison.

Publicly available gene expression data from the TCGA-OV project was downloaded from the GDC data portal (https://​portal.​gdc.​cancer.​gov/​legacy-archive) using the following filters: Primary-site = Ovary, Data-category = Gene expression and Platform = HT_HG-U133A. This data consisted of gene-level, robust multiarray analysis (RMA) normalized and background-corrected expression values for 12,042 genes from primary ovarian cancer biopsies. The RMA values were used as provided. The gene expression data files were further filtered to include only those that had somatic mutation data. Somatic mutation data was similarly obtained from the GDC data portal (https://​portal.​gdc.​cancer.​gov). The intersection between gene expression data and somatic mutation data files resulted in 315 samples for further expression analysis.

Custom scripts in Matlab software (Mathworks) were used for further analysis and visualization. To analyze the distribution of gene expression of both cell lines and TCGA-OV we used the non-parametric, two-sample Kolmogorov–Smirnov test as implemented in Matlab software (version 2019a).

We downloaded from the GDC data portal (data release 32) methylation Beta value data obtained from Illumina human methylation 27 chip from 605 samples. Beta values represent the fraction of methylation at a specific site with 0 representing no methylation and 1 representing complete methylation. We then excluded from the analysis non-primary and non-cancer samples which resulted in 582 samples for further analysis. We aggregated the Beta value data from all patients for the top 50 non-mutated (41 matches) and top 50 mutated genes (33 matches) and compared their distribution using the two-sample Kolmogorov–Smirnov test implemented in Matlab software (version 2021a). When one gene matched multiple methylation sites, the beta values were aggregated across the gene.

Similarly, for copy number analysis we downloaded from the GDC data portal (data release 32) ‘Gene Level Copy Number’ data obtained from the Affymetrix snp 6.0 array from 589 samples. We excluded non-primary cancer samples to obtain 562 samples for comparison. We matched 48 of the top 50 non-mutated genes and 43 of the top 50 mutated genes and aggregated all the copy number data across all samples. Distributions were compared using the non-parametric, two- sample Kolmogorov–Smirnov test as implemented in Matlab software (version 2021a).

Double-stranded siRNAs targeting each gene were obtained from Invitrogen (Silencer^® Select Pre-Designed siRNA LPP gene, Cat. No 4392420, siRNA ID: s8270, Silencer^® Select Pre-Designed siRNA TRAPPC9 gene, Cat. No 4392420, siRNA ID: s38115, Silencer^® Select Pre-Designed siRNA ELFN2 gene, Cat. No 4392420, siRNA ID: s41621, Silencer^® Select Pre-Designed siRNA ANKLE2 gene, Cat. No 4392420, siRNA ID: s23124, Silencer^® Select Pre-Designed siRNA PGR gene, Cat. No 4392420, siRNA ID: s10415, Silencer^® Select Pre-Designed siRNA MAP1B gene, Cat. No 4392420, siRNA ID: s8499, Silencer^® Select Pre-Designed siRNA VEGFA gene, Cat. No 4392420, siRNA ID: s461, Silencer^® Select Pre-Designed siRNA SLC12A9 gene, Cat. No 4392420, siRNA ID: s224445, Silencer^® Select Pre-Designed siRNA CELSR1 gene, Cat. No 4392420, siRNA ID: s18485). We also selected from Qiagen RNAi Human/Mouse starter kit Cat. No 301799, positive siRNA targeted against the protein kinase MAPK1, also called ERK2, and a non-targeting negative or non-silencing control siRNA that exhibits minimal nonspecific effects on gene expression and phenotype. Both the positive and negative controls were included in each 96-well plate.

To assess the degree of knockdown, cells were seeded in 96-well culture plates at a density of 5000 cells/well. cDNA synthesis was carried out 72 h after cell siRNA using TaqMan Gene Expression Cells-to-Ct kit (Thermo-Fisher). Normalization was done using the included B-actin probe in the Cells-to-Ct Control kit (Thermo-Fisher). All qPCR reactions were performed in triplicate and Cq values were averaged.

We used Promega’s CellTiter-Glo^® assay in 96 well plates. Briefly, cells were seeded at 5000 cells per well in 96-well plates and allowed to attach overnight at 37 °C. Twenty-four hours after attachment, cells were transfected with individual siRNAs at 10 nM using Lipofectamine Max (Thermo Fisher). Twenty-four hours after siRNA treatment the transfection media was replaced with serum-free media. We used the same siRNA concentrations and transfection reagents in all cell lines and experiments. In addition, positive and negative siRNA controls were added in different wells. Seventy-two hours after transfection, 100 μl of CellTiter-Glo^® reagent was added to 100 μl of medium containing cells in a 96-well plate and viability was evaluated using EnVision Workstation version 1.12 from PerkinElmer. All experiments were performed in triplicates. Student’s t-test was used to compare the proliferation fraction of the knockdowns with that of the negative control. A p-value less than 0.05 was considered significant.

Cells were incubated 72 h after transfection with Invitrogen’s Live Cell stain CellMask Orange/Red for the plasma membrane and Hoechst 33342. Both the cell morphology of the cells and the nuclear morphology were visualized by confocal microscopy (Zeiss LSM 510).

Cancer cells (50000 cells/well) were plated in 24-well plates in triplicate. Twenty-four hours after siRNA transfection, cells were starved from serum. A scratch was made in all the wells with a 1 μL pipette tip forty-eight hours after siRNA transfection. Images were taken directly after the scratch (0H) and again after 24 h (24H) and 48 h (48H). Edges were identified with manual inspection and wound healing was quantified as the ratio of the pixel distance at the timepoints relative to the 0H distance. Student’s t-test was used to calculate significance of differences between the ANKLE2 knockdown and the control by combining the 24H and 48H data.

Paclitaxel/taxol and carboplatin were purchased from National Center for Cancer Care and Research (NCCCR; Doha, Qatar) pharmacy. Briefly, cancer cells (5000 cells/well) were plated in 96-well plates in triplicate for each condition. Twenty-four hours after siRNA treatment, the cells were starved from serum. Forty-eight hours after siRNA transfection, each drug suspended in phosphate buffered saline (PBS) was added to each well at a concentration of 50 µM and viability was analyzed after 24 h. Student’s t-test was used to compare the proliferation fraction of different pairs. A p-value less than 0.05 was considered significant.

We obtained the publicly available mutation data from the TCGA-OV project as described in the methods. The mutation data consisted of somatic mutations from ovarian cancer tissues in 316 patients. To determine which, if any, genes were potentially significantly non-mutated, we performed simulated mutagenesis on a reduced exon sequence library (Fig. 1a). We then compared the simulated mutagenesis results to the observed mutation data. Our comparison of simulated mutations to the observed mutations showed that most genes had a mutation rate similar to what is expected randomly, as the observed/simulated ratio was close to 1:1 for the vast majority of genes (Fig. 1b). However, a few genes were observed with mutatio n rates both higher and lower than expected (Fig. 1b, Table 1, Additional file 1: Table S1a). Among well-known genes, TP53 and MMP8 are mutated the most (Additional file 1: Table S1b). Notably, among the genes that mutated the least is vascular endothelial growth factor (VEGFA), a molecule that plays an established role in tumor angiogenesis [12].

We then conducted a detailed literature search on the top 20 genes mutated less than expected to understand if they could be playing an important role in tumor biology (Table 1). Although we found no relevant cancer literature for several of these genes, others were found to be highly interesting in terms of cell biology including TRAPPC9, which plays a role in NF-κB signaling, ANKLE2, which plays a role in mitosis, and VEGFA, which plays a role in angiogenesis (references provided in Table 1).

To understand further if these genes were acting independently or could be part of pathways, we performed network analysis on the set of 50 top non-mutated genes. We observed, with a constructed pathway, that ten of these genes interact directly or indirectly through the AKT and NF-κB pathways (Fig. 2). The activation of the AKT/ NF-κB pathways is associated with resistance to therapy in advanced ovarian cancer [14, 96]. IPA also automatically identified a set of genes that significantly interact with the NF-κB pathway shown as network 2 in Additional file 1: Figure S1a. Furthermore, three additional networks were automatically identified by IPA among the Top 50 non-mutated genes (Additional file 1: Figure S1a). We show one network consisting of 12 genes including ANKLE2, SHROOM3 and ELFN2 interacting through direct connections with VIRMA, a nuclear/cytosolic protein involved in RNA methylation/adenylation and implicated in different cancers [97, 98]. These results show that the identified non-mutated genes interact in cancer related biological pathways.

To investigate further the biological relevance of our computational findings, we assayed the gene expression of multiple ovarian cancer and normal cell lines using RNA sequencing (Additional file 1: Figures S2 and S3). The set of genes that were mutated less than expected were found to be expressed at a higher level than genes mutated more than expected (Fig. 3a). We confirmed similar findings using the TCGA-OV patient data (Fig. 3b) which consists of gene expression data from 315 ovarian cancer biopsies from 315 different patients. To determine if the mutation itself can affect gene expression we looked at expression distributions when a gene is both mutated and non-mutated in the TCGA-OV data and no significant difference was observed between mutation state and expression state in 25 out of 28 genes (Additional file 1: Figure S4). The best example of this is seen in the TP53 gene which has the most mutations; the expression level of the mutated and non-mutated TP53 samples have a similar distribution (Additional file 1: Figure S4).

We also compared the expression of non-mutated genes between cancer cell lines and normal cell lines (Fig. 3c) and the expression of mutated genes between cancer cell lines and normal cell lines (Fig. 3d). We found no significant difference in either of these comparisons in contrast to the significant differences across the gene sets. Moreover, we searched across the top 50 non-mutated genes shown in Additional file 1: Figure S2 for genes whose expression is low in non-cancer cells and high across all cancer cells. We could not identify such genes but we did identify several non-mutated genes whose expression was low in both non-cancer cell lines and high in three out of the four cancer cell lines. These genes include ELFN2, CELSR1 and TRPV6. We therefore conclude that non-mutated genes could play a role in tumor biology due to their relatively high expression when compared to the expression of the most mutated genes.

We then performed an analysis of methylation states comparing the aggregated methylation beta value of non-mutated and mutated genes using the TCGA-OV data as described in the Methods. As shown in Additional file 1: Figure S5a, non-mutated genes are overall significantly less methylated than mutated genes. Non-mutated genes show higher peaks than mutated genes at low beta values (0 to 0.2) while mutated genes show higher peaks than non-mutated genes at high beta values (0.8–1). Following the methylation analysis, we also performed copy-number analysis on the same TCGA-OV dataset as shown in Additional file 1: Figure S5b. Here, we also find a significant difference between non-mutated and mutated genes with non-mutated genes having overall more genes with copy number greater than 2 while mutated genes having more genes with copy number less than 2. The methylation and copy number results are consistent with the gene expression results. Lower methylation and higher copy number is associated with greater gene expression which is what we observe when comparing non-mutated genes to mutated genes.

To determine if non-mutated genes could directly impact cancer cell line growth we selected nine genes for proof-of-concept in-vitro gene silencing experiments. We selected seven genes among the top 10 ranked genes (rank 1–4, 6–7, and 9), VEGFA (rank 14), as it is known to be involved in angiogenesis, and SLC12A9 (rank 41), as it is a plasma membrane-embedded cation transporter which may be more easily targeted. We first determined that the transcripts were successfully knocked down at levels greater or equal to 50 percent of the negative control (Additional file 1: Figure S7). We then performed silencing experiments on these nine genes with three different ovarian cancer cell lines (Fig. 4) and one non-cancer ovarian epithelial cell line (Additional file 1: Figure S6). We found a significant reduction of viability following silencing of 7 out of 9 genes in SKOV3, 9 out of 9 genes in OVCAR, 9 out of 9 genes in APOCC and 2 out of 9 genes in the non-cancer ovarian epithelial cell line. We therefore conclude that silencing of these genes affects cancer cell lines significantly more than the non-cancer cell line.

To examine further functional effects, we selected ANKLE2 as it was interestingly found to play a role in cell division [26, 99, 100]. Silencing of ANKLE2 resulted in significant morphologic changes in SKOV3 (Fig. 5a) where cells were growing in packed scattered colonies with a fibroblast-like shape before the knockdown. We also observed a significant reduction in migration in SKOV3^{ANKLE2−SiRNA} using a scratch assay (Fig. 5b). Finally, we evaluated the impact of ANKLE2 knockdown on chemoresistance. SKOV3 cells are paclitaxel/taxol-resistant [101, 102] but we found that ANKLE2 knockdown enhanced the cytotoxic effects of paclitaxel compared with negative controls as shown in Fig. 5c. In contrast to paclitaxel effects, no chemotherapeutic sensitivity was observed with carboplatin (Fig. 5c).