EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

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.

CD73 Inhibits cGAS-STING and Cooperates with CD39 to Promote Pancreatic Cancer.

The ectonucleotidases CD39 and CD73 catalyze extracellular ATP to immunosuppressive adenosine, and as such, represent potential cancer targets. We investigated biological impacts of CD39 and CD73 in pancreatic ductal adenocarcinoma (PDAC) by studying clinical samples and experimental mouse tumors. Stromal CD39 and tumoral CD73 expression significantly associated with worse survival in human PDAC samples and abolished the favorable prognostic impact associated with the presence of tumor-infiltrating CD8+ T cells. In mouse transplanted KPC tumors, both CD39 and CD73 on myeloid cells, as well as CD73 on tumor cells, promoted polarization of infiltrating myeloid cells towards an M2-like phenotype, which enhanced tumor growth. CD39 on tumor-specific CD8+ T cells and pancreatic stellate cells also suppressed IFNγ production by T cells. Although therapeutic inhibition of CD39 or CD73 alone significantly delayed tumor growth in vivo, targeting of both ectonucleotidases exhibited markedly superior antitumor activity. CD73 expression on human and mouse PDAC tumor cells also protected against DNA damage induced by gemcitabine and irradiation. Accordingly, large-scale pharmacogenomic analyses of human PDAC cell lines revealed significant associations between CD73 expression and gemcitabine chemoresistance. Strikingly, increased DNA damage in CD73-deficient tumor cells associated with activation of the cGAS-STING pathway. Moreover, cGAS expression in mouse KPC tumor cells was required for antitumor activity of the CD73 inhibitor AB680 in vivo. Our study, thus, illuminates molecular mechanisms whereby CD73 and CD39 seemingly cooperate to promote PDAC progression.

A pan-cancer metabolic atlas of the tumor microenvironment.

Tumors are heterogeneous cellular environments with entwined metabolic dependencies. Here, we use a tumor transcriptome deconvolution approach to profile the metabolic states of cancer and non-cancer (stromal) cells in bulk tumors of 20 solid tumor types. We identify metabolic genes and processes recurrently altered in cancer cells across tumor types, highlighting pan-cancer upregulation of deoxythymidine triphosphate (dTTP) production. In contrast, the tryptophan catabolism rate-limiting enzymes IDO1 and TDO2 are highly overexpressed in stroma, raising the hypothesis that kynurenine-mediated suppression of antitumor immunity may be predominantly constrained by the stroma. Oxidative phosphorylation is the most upregulated metabolic process in cancer cells compared to both stromal cells and a large atlas of cancer cell lines, suggesting that the Warburg effect may be less pronounced in cancer cells in vivo. Overall, our analysis highlights fundamental differences in metabolic states of cancer and stromal cells inside tumors and establishes a pan-cancer resource to interrogate tumor metabolism.

A Randomized Phase 2 Trial of Nivolumab Versus Nivolumab-Ipilimumab Combination in EGFR-Mutant NSCLC.

Introduction: Although immune checkpoint inhibitors (ICIs) have dramatically improved outcomes for nononcogene-addicted NSCLC, monotherapy with programmed cell death protein-1 (PD1) inhibition has been associated with low efficacy in the EGFR-mutant setting. Given the potential for synergism with combination checkpoint blockade, we designed a trial to test the activity of combination nivolumab (N)-ipilimumab (NI) in EGFR-mutant NSCLC. Methods: This is a randomized phase 2 study (NCT03091491) of N versus NI combination in EGFR tyrosine kinase inhibitor (TKI)-resistant NSCLC, with crossover permitted on disease progression. The primary end point was the objective response rate, and the secondary end points included progression-free survival, overall survival, and safety of ICI after EGFR TKI. Results: Recruitment ceased owing to futility after 31 of 184 planned patients were treated. A total of 15 patients received N and 16 received NI combination. There were 16 patients (51.6%) who had programmed death-ligand (PDL1) 1 greater than or equal to 1%, and 15 (45.2%) harbored EGFR T790M. Five patients derived clinical benefits from ICI with one objective response (objective response rate 3.2%), and median progression-free survival was 1.22 months (95% confidence interval: 1.15-1.35) for the overall cohort. None of the four patients who crossed over achieved salvage response by NI. PDL1 and tumor mutational burden (TMB) were not able to predict ICI response. Rates of all grade immune-related adverse events were similar (80% versus 75%), with only two grade 3 events. Conclusions: Immune checkpoint inhibition is ineffective in EGFR TKI-resistant NSCLC. Whereas a small subgroup of EGFR-mutant NSCLC may be immunogenic and responsive to ICI, better biomarkers are needed to select appropriate patients.

The long non-coding RNA MIR31HG regulates the senescence associated secretory phenotype.

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.

Pan-Cancer Analysis of Ligand-Receptor Cross-talk in the Tumor Microenvironment.

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.

A chemical genetic screen identifies Aurora kinases as a therapeutic target in EGFR T790M negative, gefitinib-resistant head and neck squamous cell carcinoma (HNSCC).

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).

Integrative Profiling of T790M-Negative EGFR-Mutated NSCLC Reveals Pervasive Lineage Transition and Therapeutic Opportunities.

PURPOSE: Despite the established role of EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutated NSCLC, drug resistance inevitably ensues, with a paucity of treatment options especially in EGFR T790M-negative resistance. EXPERIMENTAL DESIGN: We performed whole-exome and transcriptome analysis of 59 patients with first- and second-generation EGFR TKI-resistant metastatic EGFR-mutated NSCLC to characterize and compare molecular alterations mediating resistance in T790M-positive (T790M+) and -negative (T790M-) disease. RESULTS: Transcriptomic analysis revealed ubiquitous loss of adenocarcinoma lineage gene expression in T790M- tumors, orthogonally validated using multiplex IHC. There was enrichment of genomic features such as TP53 alterations, 3q chromosomal amplifications, whole-genome doubling and nonaging mutational signatures in T790M- tumors. Almost half of resistant tumors were further classified as immunehot, with clinical outcomes conditional on immune cell-infiltration state and T790M status. Finally, using a Bayesian statistical approach, we explored how T790M- and T790M+ disease might be predicted using comprehensive genomic and transcriptomic profiles of treatment-naïve patients. CONCLUSIONS: Our results illustrate the interplay between genetic alterations, cell lineage plasticity, and immune microenvironment in shaping divergent TKI resistance and outcome trajectories in EGFR-mutated NSCLC. Genomic and transcriptomic profiling may facilitate the design of bespoke therapeutic approaches tailored to a tumor's adaptive potential.

Metabolic re-wiring of isogenic breast epithelial cell lines following epithelial to mesenchymal transition.

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.

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications.

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.

Kinetic analysis of gluconate phosphorylation by human gluconokinase using isothermal titration calorimetry.

Gluconate is a commonly encountered nutrient, which is degraded by the enzyme gluconokinase to generate 6-phosphogluconate. Here we used isothermal titration calorimetry to study the properties of this reaction. ΔH, KM and kcat are reported along with substrate binding data. We propose that the reaction follows a ternary complex mechanism, with ATP binding first. The reaction is inhibited by gluconate, as it binds to an Enzyme-ADP complex forming a dead-end complex. The study exemplifies that ITC can be used to determine mechanisms of enzyme catalyzed reactions, for which it is currently not commonly applied.

Biochemical characterization of human gluconokinase and the proposed metabolic impact of gluconic acid as determined by constraint based metabolic network analysis.

The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role of gluconate in humans. Here we report the recombinant expression, purification and biochemical characterization of isoform I of human gluconokinase alongside substrate specificity and kinetic assays of the enzyme catalyzed reaction. The enzyme, shown to be a dimer, had ATP dependent phosphorylation activity and strict specificity towards gluconate out of 122 substrates tested. In order to evaluate the metabolic impact of gluconate in humans we modeled gluconate metabolism using steady state metabolic network analysis. The results indicate that significant metabolic flux changes in anabolic pathways linked to the hexose monophosphate shunt (HMS) are induced through a small increase in gluconate concentration. We argue that the enzyme takes part in a context specific carbon flux route into the HMS that, in humans, remains incompletely explored. Apart from the biochemical description of human gluconokinase, the results highlight that little is known of the mechanism of gluconate metabolism in humans despite its widespread use in medicine and consumer products.