npj Systems Biology and Applications

Exploring the role of EMT in ovarian cancer progression using a multiscale mathematical model

Abstract Epithelial-to-mesenchymal transition (EMT) plays a key role in the progression of cancer tumours, significantly reducing the success of treatment. EMT occurs when a cell undergoes phenotypical changes, resulting in enhanced drug resistance, higher cell plasticity, and increased metastatic abilities. Here, we employ a 3D agent-based multiscale modelling framework using PhysiCell to explore the role of EMT over time in two cell lines, OVCAR-3 and SKOV-3. This approach allows us to investigate the spatiotemporal progression of ovarian cancer and the impacts of the conditions in the microenvironment. OVCAR-3 and SKOV-3 cell lines possess highly contrasting tumour layouts, allowing a wide range of different tumour dynamics and morphologies to be tested and studied. Along with performing sensitivity analysis on the model, simulation results capture the biological observations and trends seen in tumour growth and development, thus helping to obtain further insights into OVCAR-3 and SKOV-3 cell line dynamics.

Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer

AbstractOvarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of ‘omics’ data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

Mesenchymal ovarian cancer cells promote CD8+ T cell exhaustion through the LGALS3-LAG3 axis

AbstractCancer cells often metastasize by undergoing an epithelial-mesenchymal transition (EMT). Although abundance of CD8+ T-cells in the tumor microenvironment correlates with improved survival, mesenchymal cancer cells acquire greater resistance to antitumor immunity in some cancers. We hypothesized the EMT modulates the immune response to ovarian cancer. Here we show that cancer cells from infiltrated/inflamed tumors possess more mesenchymal cells, than excluded and desert tumors. We also noted high expression of LGALS3 is associated with EMT in vivo, a finding validated with in vitro EMT models. Dissecting the cellular communications among populations in the tumor revealed that mesenchymal cancer cells in infiltrated tumors communicate through LGALS3 to LAG3 receptor expressed by CD8+ T cells. We found CD8+ T cells express high levels of LAG3, a marker of T cell exhaustion. The results indicate that EMT in ovarian cancer cells promotes interactions between cancer cells and T cells through the LGALS3 - LAG3 axis, which could increase T cell exhaustion in infiltrated tumors, dampening antitumor immunity.

A single-cell network approach to decode metabolic regulation in gynecologic and breast cancers

Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.

Modeling the metabolic response of A2780 ovarian cancer cells to gold-based cytotoxic drugs

Gold compounds are a promising class of experimental anticancer metallodrugs. Unlike platinum-based drugs, their antiproliferative effects are thought to result mainly from modulation of cancer cell metabolism rather than direct interaction with DNA. Previous NMR studies have shown that four cytotoxic gold compounds - auranofin, aurothiomalate and two gold N-heterocyclic carbenes - induce distinct metabolic changes in A2780 ovarian cancer cells, suggesting the occurrence of different mechanisms of action. To better understand these effects, we constructed a genome-scale metabolic model (GEM) of A2780 cells to analyze the NMR-detected metabolomic changes. The model successfully predicts the diverse metabolic responses induced by each gold compound and identifies common metabolic changes. These results confirm the potential of GEMs as a powerful tool for interpreting and predicting cellular responses to gold-based drugs, providing insights into their mechanisms of action and potential therapeutic applications.

CTCFL regulates the PI3K-Akt pathway and it is a target for personalized ovarian cancer therapy

AbstractHigh-grade serous ovarian carcinoma (HGSC) is the most lethal gynecologic malignancy due to the lack of reliable biomarkers, effective treatment, and chemoresistance. Improving the diagnosis and the development of targeted therapies is still needed. The molecular pathomechanisms driving HGSC progression are not fully understood though crucial for effective diagnosis and identification of novel targeted therapy options. The oncogene CTCFL (BORIS), the paralog of CTCF, is a transcriptional factor highly expressed in ovarian cancer (but in rarely any other tissue in females) with cancer-specific characteristics and therapeutic potential. In this work, we seek to understand the regulatory functions of CTCFL to unravel new target genes with clinical relevance. We used in vitro models to evaluate the transcriptional changes due to the presence of CTCFL, followed by a selection of gene candidates using de novo network enrichment analysis. The resulting mechanistic candidates were further assessed regarding their prognostic potential and druggability. We show that CTCFL-driven genes are involved in cytoplasmic membrane functions; in particular, the PI3K-Akt initiators EGFR1 and VEGFA, as well as ITGB3 and ITGB6 are potential drug targets. Finally, we identified the CTCFL targets ACTBL2, MALT1 and PCDH7 as mechanistic biomarkers to predict survival in HGSC. Finally, we elucidated the value of CTCFL in combination with its targets as a prognostic marker profile for HGSC progression and as putative drug targets.