• Our research approach:

We treated three NRAS-mutated cell lines for 33 days until cells restored proliferation and observed very distinct responses. We acquired different types of omics data to understand the different behaviours.

Insights from omics data analysis:  

RNA-seq data revealed that all cell lines trigger at different time points an interferon and a senescent program. The senescent transcriptome is associated with enrichment in Epithelial-to-Mesenchymal (EMT)  and disappear in SKMEL30 and IPC298, cell lines which regain proliferative capacity. Interestingly only those cell lines display an enrichment in interferon gamma response.

Profiling the kinome activity of MELJUSO and SKMEL30 further confirmed the activation of innate immune receptors and network-based pathway enrichment identified pathways with active signaling. We found ErbB and neurotrophin active in both cell lines. On the other hand, insulin pathway was only enriched in the cytostatic MELJUSO which displays over time a constant decrease in activity of ERK5 kinase.  

Characterizing miRNA interactomes for adaptative cell lines, SKMEL30 and IPC298, led us to construct a resistant miRNA network further validated by qPCR. For most of the selected interactions, the miRNA and the targeted mRNA did not display an inverse expression pattern. mRNAs displayed across cell lines an upregulation of interferons related genes. We also observed that upregulation of miR-211-5p is associated with proliferative phenotypes.

Finally, integrating in-house bulk RNA-seq and matching single-cell data allowed us to reconstruct molecular networks for each condition using “iCell” methodology. We then applied topological analyses to investigate those networks. Using graphlet-based methods, we identified perturbed processes related to senescence, such as “OXPHOS”, “ADIPOGENESIS” and “COAGULATION” and predicted novel genes which could facilitating senescence-escape.

Role of IFN gamma in senescence escape:

All together our data show how NRAS-mutated melanoma adapt to a new therapeutic approach, combined inhibition of CDK4/6 and MEK.

At the transcriptomic level, interferon responses result from the effects of CDK4/6 inhibitors which indirectly inhibit E2F2 transcription factor leading to the downregulation of a DNA methylating enzyme, DNMT1 [1]. The associated hypomethylated state allows the expression of transposable elements (TEs). Their RNA and DNA intermediates activate innate immune sensors, a phenomenon called viral mimicry.
CDK4/6 inhibitors also trigger senescence which shares common transcription factors with EMT. Those transcription factors have opposite effects on both processes [2]. As interferon gamma was shown to induce EMT and is enriched in adaptative cell lines only, we hypothesized that IFN gamma may facilitate senescence escape through the induction of EMT.

At the kinome level, we observed the reactivation of different Receptor Tyrosine Kinases (RTKs) as previously reported after ERK1/2 inhibition. One of our inhibitors targets MEK, located  upstream ERK1/2, suppressing negative feedback loops on RTKs.
We also noticed that activity of ERK5 is restored in the adaptative SKMEL30, but not in the senescent MELJUSO. Interestingly, Tubita et al recently reported that ERK5 knock-down or inhibition triggers senescence in melanoma [3] and ERK5 has been shown to regulate EMT. In line with the previous hypothesis, interferon gamma has been reported to activate ERK5 in other cell types [4].

Focusing on miRNAs, we observed that miR-211-5p upregulation is associated with proliferative cell lines. Interestingly, this miRNA indirectly regulates ERK5 and is lowly expressed in the cytostatic MELJUSO [5].

The "iCell" Methodology and Network Analysis:

Finally, we integrated bulk and single-cell RNA-seq into molecular networks through an NMTF-based approach called “iCell” [6].

Comparing the overall networks' structure, the iCell networks recapitulated phenotypic observations with early adaptation of IPC298. We then characterized the local structure around genes in the network and compared the conditions two-by-two to identify genes which stayed stable or changed. We enriched those genes and identified perturbed biological processes linked to senescence such as “OXPHOS”, “ADIPOGENESIS” and “COAGULATION”. Interestingly, those processes have been reported after CDK4/6i or senescence induction and escape. Finally, we clustered genes based on their local structure to predict novel genes associated with both EMT and IFN gamma which could therefore facilitate senescence escape. Our top prediction is the famous cancer-related lncRNA NEAT1 which has been shown to physically interact with DNMT1 to epigenetically inhibit TP53, STING and cGAS [7].

Conclusion:

Overall, our study suggests a new role for interferon gamma in supporting senescence escape through the activation of ERK5 which leads to an EMT process reverting the associated cell-cycle arrest. Our data sheds a new light on three long-standing pathways: senescence, interferon and insulin. It associates insulin signaling with persistence of senescence and interferon gamma with senescence escape!

References:

1    Goel S, DeCristo MJ, Watt AC, BrinJones H, Sceneay J, Li BB, et al. CDK4/6 inhibition triggers anti-tumour immunity. Nature. 2017;548:471.

2    Faheem MM, Seligson ND, Ahmad SM, Rasool RU, Gandhi SG, Bhagat M, et al. Convergence of therapy-induced senescence (TIS) and EMT in multistep carcinogenesis: Current opinions and emerging perspectives. Cell Death Discov. 2020;6:51.

3    Tubita A, Lombardi Z, Tusa I, Lazzeretti A, Sgrignani G, Papini D, et al. Inhibition of ERK5 elicits cellular senescence in melanoma via the cyclin-dependent kinase inhibitor p21. Cancer Res. 2022;82:447–57.

4    Saleiro D, Blyth GT, Kosciuczuk EM, Ozark PA, Majchrzak-Kita B, Arslan AD, et al. IFN-γ–inducible antiviral responses require ULK1-mediated activation of MLK3 and ERK5. Sci Signal. 2018;11:eaap9921.

5    Lee B, Sahoo A, Sawada J, Marchica J, Sahoo S, Layng FI, et al. MicroRNA-211 modulates the DUSP6-ERK5 signaling axis to promote BRAFV600E-driven melanoma growth in vivo and BRAF/MEK inhibitor resistance. J Investig Dermatol. 2021;141:385–94.

6    Malod-Dognin N, Petschnigg J, Windels SF, Povh J, Hemingway H, Ketteler R, et al. Towards a data-integrated cell. Nature Commun. 2019;10:805.

7    Ma F, Lei YY, Ding MG, Luo LH, Xie YC, Liu XL. LncRNA NEAT1 interacted with DNMT1 to regulate malignant phenotype of cancer cell and cytotoxic T cell infiltration via epigenetic inhibition of p53, cGAS, and STING in lung cancer. Front Genet. 2020;11:250.