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Oncogene-induced senescence provides a barrier against malignant transformation. However, it can also promote cancer through the secretion of a plethora of factors released by senescent cells, called the senescence associated secretory phenotype (SASP). We have previously shown that in proliferating cells, nuclear lncRNA MIR31HG inhibits p16/CDKN2A expression through interaction with polycomb repressor complexes and that during BRAF-induced senescence, MIR31HG is overexpressed and translocates to the cytoplasm. Here, we show that MIR31HG regulates the expression and secretion of a subset of SASP components during BRAF-induced senescence. The SASP secreted from senescent cells depleted for MIR31HG fails to induce paracrine invasion without affecting the growth inhibitory effect. Mechanistically, MIR31HG interacts with YBX1 facilitating its phosphorylation at serine 102 (p-YBX1S102) by the kinase RSK. p-YBX1S102 induces IL1A translation which activates the transcription of the other SASP mRNAs. Our results suggest a dual role for MIR31HG in senescence depending on its localization and points to the lncRNA as a potential therapeutic target in the treatment of senescence-related pathologies., Senescence-associated secretory phenotype (SASP) involves secretion of factors such as pro-inflammatory cytokines. Here the authors show that MIR31HG regulates the expression and secretion of a subset of SASP components that induce paracrine invasion, through interaction with YBX1 and induction of IL1A translation.

Background Overexpression of epidermal growth factor receptor (EGFR), and downstream pathway activation appears to be a common oncogenic driver in the majority of head and neck squamous cell cancers (HNSCCs); yet targeting EGFR for the treatment of HNSCC has met with limited success. Apart from the anti-EGFR antibody cetuximab, no small molecule EGFR/tyrosine kinase inhibitors (TKIs) have progressed to routine clinical use. The aim of this study was to determine factors contributing to the lack of response to TKIs and identify alternative therapeutic vulnerabilities. Methods Genomic and transcriptomic sequencing, high-throughput compound screens, overexpression and siRNA knockdown, western blot, in vivo xenograft studies. Findings We derived three pairs of isogenic gefitinib (TKI)-sensitive and resistant patient-derived HNSCC cell lines. Genomic sequencing of gefitinib-resistant cell lines identified a lack of activating and resistance-associated EGFR mutations. Instead, transcriptomic sequencing showed upregulated EMT gene signature in the gefitinib-resistant cells with a corresponding increase in their migratory phenotype. Additionally, the resistant cell displayed reduced growth rate. Surprisingly, while gefitinib-resistant cells were independent of EGFR for survival, they nonetheless displayed activation of downstream ERK and AKT signalling. High-throughput screening (HTS) of druggable, small molecule libraries revealed that the gefitinib-resistant cells were particularly sensitive to inhibitors of genes involved in cell cycle and mitosis, such as Aurora kinase inhibitors (AKIs), cyclin-dependent kinase (CDK) inhibitors, and microtubule inhibitors. Notably our results showed that in the EGFR inhibited state, Aurora kinases are essential for cell survival. Interpretation Our study demonstrates that in the absence of activating EGFR mutations, HNSCCs may gain resistance to gefitinib through decreased cell proliferation, which makes them exceptionally vulnerable to cell-cycle inhibitors. Funding Agency for Science, Technology, and Research (A*STAR), National Medical Research Council (NMRC), and the National Institutes of Health (NIH)/National Cancer Institute (NCI).

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is a key to tumor progression. Here, we deconvoluted bulk tumor transcriptomes to infer cross-talk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. This approach recovered known transcriptional hallmarks of cancer and stromal cells and was concordant with single-cell, in situ hybridization and IHC data. Inferred autocrine cancer cell interactions varied between tissues but often converged on Ephrin, BMP, and FGFR-signaling pathways. Analysis of immune checkpoints nominated interactions with high levels of cancer-to-immune cross-talk across distinct tumor types. Strikingly, PD-L1 was found to be highly expressed in stromal rather than cancer cells. Overall, our study presents a new resource for hypothesis generation and exploration of cross-talk in the TME. Significance: This study provides deconvoluted bulk tumor transcriptomes across multiple cancer types to infer cross-talk in the tumor microenvironment.

Epithelial to mesenchymal transition (EMT) has implications in tumor progression and metastasis. Metabolic alterations have been described in cancer development but studies focused on the metabolic re-wiring that takes place during EMT are still limited. We performed metabolomics profiling of a breast epithelial cell line and its EMT derived mesenchymal phenotype to create genome-scale metabolic models descriptive of both cell lines. Glycolysis and OXPHOS were higher in the epithelial phenotype while amino acid anaplerosis and fatty acid oxidation fueled the mesenchymal phenotype. Through comparative bioinformatics analysis, PPAR-γ1, PPAR- γ2 and AP-1 were found to be the most influential transcription factors associated with metabolic re-wiring. In silico gene essentiality analysis predicts that the LAT1 neutral amino acid transporter is essential for mesenchymal cell survival. Our results define metabolic traits that distinguish an EMT derived mesenchymal cell line from its epithelial progenitor and may have implications in cancer progression and metastasis. Furthermore, the tools presented here can aid in identifying critical metabolic nodes that may serve as therapeutic targets aiming to prevent EMT and inhibit metastatic dissemination., Financial support for this work was provided by the Icelandic Research Council (Grants number: 130591-051, 130816-051 and 152358-051).

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend., Author Summary The epidermal growth factor receptor (EGFR) signaling cascade is one of the key signaling pathways that are involved in the induction of Epithelial Mesenchymal Transition (EMT) and tumor metastasis. These signaling cascades often affect metabolic fate in tumor cells and control their progression. Here we demonstrate a method to build a mathematical model of the EGFR signaling cascade and use it to study signaling in EMT and how signaling affects metabolism. The model was used to obtain a list of potential signaling and metabolic targets of EMT. These targets may aid in the understanding of the molecular mechanisms that underlie EMT and metastasis. Our results further highlight the heterogeneity of cell models used to study EMT and support the idea of cell specific anti-cancer interventions.