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. 2022 May 26;20(1):244.
doi: 10.1186/s12967-022-03440-5.

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

Halema Al-Farsi  1 , Iman Al-Azwani  2 , Joel A Malek  3 , Lotfi Chouchane  4 , Arash Rafii  5 , Najeeb M Halabi  6
Affiliations

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

Halema Al-Farsi et al. J Transl Med. .

Abstract

Background: Mutated and non-mutated genes interact to drive cancer growth and metastasis. While research has focused on understanding the impact of mutated genes on cancer biology, understanding non-mutated genes that are essential to tumor development could lead to new therapeutic strategies. The recent advent of high-throughput whole genome sequencing being applied to many different samples has made it possible to calculate if genes are significantly non-mutated in a specific cancer patient cohort.

Methods: We carried out random mutagenesis simulations of the human genome approximating the regions sequenced in the publicly available Cancer Growth Atlas Project for ovarian cancer (TCGA-OV). Simulated mutations were compared to the observed mutations in the TCGA-OV cohort and genes with the largest deviations from simulation were identified. Pathway analysis was performed on the non-mutated genes to better understand their biological function. We then compared gene expression, methylation and copy number distributions of non-mutated and mutated genes in cell lines and patient data from the TCGA-OV project. To directly test if non-mutated genes can affect cell proliferation, we carried out proof-of-concept RNAi silencing experiments of a panel of nine selected non-mutated genes in three ovarian cancer cell lines and one primary ovarian epithelial cell line.

Results: We identified a set of genes that were mutated less than expected (non-mutated genes) and mutated more than expected (mutated genes). Pathway analysis revealed that non-mutated genes interact in cancer associated pathways. We found that non-mutated genes are expressed significantly more than mutated genes while also having lower methylation and higher copy number states indicating that they could be functionally important. RNAi silencing of the panel of non-mutated genes resulted in a greater significant reduction of cell viability in the cancer cell lines than in the non-cancer cell line. Finally, as a test case, silencing ANKLE2, a significantly non-mutated gene, affected the morphology, reduced migration, and increased the chemotherapeutic response of SKOV3 cells.

Conclusion: We show that we can identify significantly non-mutated genes in a large ovarian cancer cohort that are well-expressed in patient and cell line data and whose RNAi-induced silencing reduces viability in three ovarian cancer cell lines. _targeting non-mutated genes that are important for tumor growth and metastasis is a promising approach to expand cancer therapeutic options.

Keywords: Cancer somatic mutation; Epithelial ovarian cancer; Mutated genes; Non-mutated genes; RNA-Seq; RNAi; Simulated mutation; Unmutated genes.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Identifying non-mutated genes. a Schematic of mutagenesis simulation. To approximate the data used in the patient exome sequencing, a reduced exon library was used consisting of the exons approximating those used in the TCGA trial. Simulated mutagenesis depicted as red lines is subjected to repeated trials using the observed background mutation frequencies. The mutation frequencies are then compared between the simulated and observed data. b Log ratio distribution of observed/simulated data. The inset limits the y-axis to 25 to see the distribution at the tails more clearly; the genes that are most extreme are labelled. Gray dashed lines show the top 50 non-mutated genes and top 50 mutated genes that are used for subsequent analysis (corresponding to the values less than 0.007 quantile and greater than the 99.23 quantile of the dataset)
Fig. 2
Fig. 2
Pathway analysis of non-mutated genes. IPA generated pathway showing the interaction of genes within one network. The non-mutated gene with the highest difference from random expectation, TRAPPC9, is linked to a central NF-κB pathway along with other non-mutated genes. The shading as shown in the legend is proportional to the ratio of observed/simulated where genes that are redder have a lower ratio. Links between genes are either direct (solid lines) or indirect (dashed lines)
Fig. 3
Fig. 3
Gene expression distributions of non-mutated and mutated genes. Distribution of gene expression of non-mutated and mutated genes for our cell line data (a) and for patient data from TCGA-OV project (b). In blue is the expression distribution of top 50 non-mutated genes and in orange is the distribution of the top 50 mutated genes. Note that the expression of non-mutated genes is significantly higher than the expression of mutated genes in both the cell line and patient data. Distribution of gene expression of non-mutated genes (c) and mutated genes (d) in non-cancer cell lines and cancer lines. Note that there is no significant difference in gene expression difference between non-cancer and cancer cell lines in either mutated or non-mutated genes. The inset for each panel shows the cumulative density plots of the same data
Fig. 4
Fig. 4
Cell proliferation after selected gene knockdown in three different ovarian cancer cell lines: SKOV3 (a), OVCAR (b) and APOCC (c). Fraction of cell viability is standardized against the negative control (CTL-) proliferation levels and the error bars are the standard deviations from three replicates. A reference line is drawn through 0.5 showing that almost all genes in all tested cell lines reduced cell proliferation by at least 50%. Asterisks denote significant differences (p-value < 0. 05) between the gene knockdown and the negative control
Fig. 5
Fig. 5
ANKLE2 knockdown in SKOV3 cell line. a Representative fluorescent images of microscopic imaging of SKOV3 after ANKLE2 and CTL- knockdowns. b Migration assay for SKOV3 after knockdown with selected genes. Images at the top were taken just after the scratch while the lower images were taken after 48 h. The blue lines denote the margins of the scratch. The white horizontal line is 200 µm. The chart to the right shows quantification of the migration normalized to the negative control at two different time points. The p-value (P) of a pairwise t-test combining the differences at the 24HR and 48HR time points is shown in the chart. c Chemosensitivity of CTL- (negative control) and ANKLE2 knockdown in SKOV3 cells with and without paclitaxel/taxol (T) and carboplatin (C) in serum containing and serum free media. Errors bars are the standard error of the mean of three replicates. Values are normalized to the negative control (CTL-). Asterisks denote significant differences between the indicated pairs
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