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. 2022 Nov 1;23(21):13365.
doi: 10.3390/ijms232113365.

Optimized Transcriptional Signature for Evaluation of MEK/ERK Pathway Baseline Activity and Long-Term Modulations in Ovarian Cancer

Mikhail S Chesnokov  1 , Anil Yadav  1 , Ilana Chefetz  1   2   3
Affiliations

Optimized Transcriptional Signature for Evaluation of MEK/ERK Pathway Baseline Activity and Long-Term Modulations in Ovarian Cancer

Mikhail S Chesnokov et al. Int J Mol Sci. .

Abstract

Ovarian cancer is the most aggressive and lethal of all gynecologic malignancies. The high activity of the MEK/ERK signaling pathway is tightly associated with tumor growth, high recurrence rate, and treatment resistance. Several transcriptional signatures were proposed recently for evaluation of MEK/ERK activity in tumor tissue. In the present study, we validated the performance of a robust multi-cancer MPAS 10-gene signature in various experimental models and publicly available sets of ovarian cancer samples. Expression of four MPAS genes (PHLDA1, DUSP4, EPHA2, and SPRY4) displayed reproducible responses to MEK/ERK activity modulations across several experimental models in vitro and in vivo. Levels of PHLDA1, DUSP4, and EPHA2 expression were also significantly associated with baseline levels of MEK/ERK pathway activity in multiple human ovarian cancer cell lines and ovarian cancer patient samples available from the TCGA database. Initial platinum therapy resistance and advanced age at diagnosis were independently associated with poor overall patient survival. Taken together, our results demonstrate that the performance of transcriptional signatures is significantly affected by tissue specificity and aspects of particular experimental models. We therefore propose that gene expression signatures derived from comprehensive multi-cancer studies should be always validated for each cancer type.

Keywords: MAPK; chemoresistance; ovarian cancer.

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Conflict of interest statement

The authors indicated no potential conflict of interest.

Figures

Figure 1
Figure 1
Changes in expression levels of MPAS genes induced by MEK/ERK pathway activity modulation in PEO4 cells. (A) Immunoblotting analysis of phosphorylation of MEK/ERK pathway components in response to treatment with FGF4 (100 ng/mL). Numbers under the bands represent relative intensity normalized to GAPDH levels and control samples. (B) Changes in expression of MPAS genes in FGF4-treated cells. Heatmap represents gene expression levels normalized to control samples. Unsupervised hierarchical clustering was performed using 1-minus Spearman’s rank correlation metrics. (C) Experimental design for enrichment of cultured cells in different phases of cell cycle (see Methods for details). Aph—aphidicolin. (D) Immunoblotting analysis of ERK1/2 phosphorylation changes during cell cycle progression in synchronized cells. Numbers under the bands represent relative intensity normalized to GAPDH levels and control samples. (E) Changes in expression of MPAS genes during cell cycle progression. Heatmap represents gene expression levels normalized to control samples. Unsupervised hierarchical clustering was performed using 1-minus Spearman’s rank correlation metrics.
Figure 2
Figure 2
Baseline MEK/ERK pathway activity and PHLDA1, DUSP4, EPHA2, and SPRY4 expression levels in ovarian cancer cell lines. (A) Immunoblotting analysis of phosphorylation of MEK/ERK pathway components in 20 human ovarian cancer cell lines. The image represents data obtained from several separate membranes. An A2780 sample was used in each membrane as a reference sample. Numbers under the bands represent relative intensity normalized to GAPDH levels and the A2780 sample. (B) Cluster analysis of associations between baseline expression levels of MEK/ERK responder genes and levels of phosphorylated proteins involved in MEK/ERK pathway. Seventeen cell lines displaying consistent activity of several MEK/ERK pathway elements were analyzed. Gene expression levels estimated via RT-qPCR and protein levels estimated via immunoblotting were subjected to Z-score transformation. Unsupervised hierarchical clustering was performed using 1-minus Spearman’s rank correlation metrics.
Figure 3
Figure 3
Temporal dynamics of sensitivity to MEK inhibition, ERK1/2 phosphorylation, and MEK/ERK responders’ expression during cell cycle progression. (A) Numbers of viable PEO4 cells enriched in different cell cycle phases after subsequent treatment with trametinib. Data are normalized to control samples and presented as mean ± SD. n = 3. * p < 0.05, Kruskal–Wallis H test). Aph—aphidicolin. (B) Dynamics of cell number reduction (after trametinib treatment), ERK1/2 phosphorylation (prior to trametinib addition), and COMS genes expression (prior to trametinib addition) across different stages of cell cycle. Data are normalized to “0 h” samples and presented as mean values. Relative pERK1/2 levels were calculated by normalization to total ERK1/2 levels.
Figure 4
Figure 4
Associations between MEK/ERK pathway activity, patient survival, and MEK/ERK responder genes’ expression in HGSOC patient samples from TCGA. (A) Cluster analysis of phosphorylated ERK1/2 protein levels in samples from TCGA “PanCancer Atlas” dataset (n = 414). Unsupervised hierarchical clustering was performed using Euclidean distance metrics. (B) Kaplan–Meyer analysis of overall patient survival differences between three pERK1/2-defined cohorts from TCGA “PanCancer Atlas” dataset (n = 414). Statistical significance was evaluated using log-rank tests. (C) Kaplan–Meyer analysis of overall patient survival differences between the “High pERK” cohort and combined “Low pERK”+”Moderate pERK” cohort from the TCGA “PanCancer Atlas” dataset. Statistical significance was evaluated using log-rank test. (D) Expression levels of COMS genes in samples from the TCGA “Nature 2011” dataset with available pERK1/2 protein data (n = 337). Boxes represent median and quartile values, whiskers represent minimum and maximum values, and squares represent sample mean values. Statistical significance was evaluated using two-tailed T-test with Welch’s correction.
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