A new study has shown how computational approaches can drive important biomedical discoveries such as for head and neck cancers
Despite advances in defining the genomic characteristics of head and neck cancers, these malignancies continue to rank among the deadliest cancers with few targeted therapies available.
An important challenge in designing effective treatments is intra-tumour heterogeneity, the presence of multiple subpopulations of cells with distinct genomic and molecular alterations, with some cells inherently more resistant to certain treatments.
A new study from researchers at Boston University Chobanian & Avedisian School of Medicine applied advanced bioinformatics and machine learning approaches to the analysis of large multi-omics head and neck cancer datasets and found activation of mTORC1 by b-catenin/CBP as an upstream driver of the malignancy-associated partial epithelial-mesenchymal transition (p-EMT) phenotype.
EMT is a biological process that plays a crucial role in embryonic development, tissue repair and various disease processes, including cancer.
In cancer, EMT refers to the conversion of epithelial cells, which are typically found in the outer layers of organs and have strong cell-cell adhesion, into mesenchymal cells, which are more migratory and invasive.
Co-corresponding author Stefano Monti, PhD, associate professor of medicine at the School of Medicine, said: “This is of particular interest because both mTORC1 and b-catenin are important cancer hallmarks and p-EMT is a cellular process that is an early predictor of nodal metastasis, in which epithelial cells manifest characteristics of mesenchymal cells but do not fully undergo the complete transition.”
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Early steps in cancer
According to the researchers, the study aimed to better characterise oral tumour heterogeneity including the aggressive cell subpopulations more likely to drive the early steps in cancer progression and invasiveness, with the ultimate goal of identifying candidate vulnerabilities that could be targeted therapeutically.
Monti said: “Understanding and addressing the diverse characteristics within tumours can help optimise therapeutic strategies, improve treatment outcomes and ultimately enhance patient survival rates.”
This collaborative multi-disciplinary study applied novel computational methods to the analysis of single-cell data from primary oral cancer lesions.
Findings were first validated in independent multi-omics datasets, including The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE), then further validated through functional molecular and pharmacologic perturbations using cell line-based experiments, as well as through pharmacologic perturbation experiments in experimental models.
The study’s findings are of particularly timely significance, given the increasing evidence pointing to a crucial role of cells with a p-EMT phenotype in tumour progression to advanced disease and provide new information about additional therapeutic targets for this malignancy.
In particular, the study’s findings point to the potential of β-catenin/ CBP inhibition as a promising head and neck cancer treatment that distinctly targets more aggressive cells with elevated β catenin/ CBP activity.
While this study’s findings focus on head and neck cancer of the oral cavity, the researchers believe they are likely to be relevant to other cancer types, especially those that arise from mucosal tissues that line respiratory, gastrointestinal and genital tracts.
Image: Elements Of This World, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons.
