5 research outputs found

    Transcriptional analysis highlights three distinct immune profiles of high-risk oral epithelial dysplasia

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    Oral potentially malignant disorders (OPMD) are precursors of oral squamous cell carcinoma (OSCC), and the presence of oral epithelial dysplasia (OED) in OPMD confers an increased risk of malignant transformation. Emerging evidence has indicated a role for the immune system in OPMD disease progression; however, the underlying immune mechanisms remain elusive. In this study, we used immune signatures established from cancer to delineate the immune profiles of moderate and severe OED, which are considered high-risk OPMD. We demonstrated that moderate and severe OEDs exhibit high lymphocyte infiltration and upregulation of genes involved in both immune surveillance (major histocompatibility complex-I, T cells, B cells and cytolytic activity) and immune suppression (immune checkpoints, T regulatory cells, and tumor-associated macrophages). Notably, we identified three distinct subtypes of moderate and severe OED: immune cytotoxic, non-cytotoxic and non-immune reactive. Active immune surveillance is present in the immune cytotoxic subtype, whereas the non-cytotoxic subtype lacks CD8 immune cytotoxic response. The non-immune reactive subtype showed upregulation of genes involved in the stromal microenvironment and cell cycle. The lack of T cell infiltration and activation in the non-immune reactive subtype is due to the dysregulation of CTNNB1, PTEN and JAK2. This work suggests that moderate and severe OED that harbor the non-cytotoxic or non-immune reactive subtype are likely to progress to cancer. Overall, we showed that distinct immune responses are present in high-risk OPMD, and revealed targetable pathways that could lead to potential new approaches for non-surgical management of OED

    Mining and integration of oral cancer genomics for predictive therapeutics / Bernard Lee Kok Bang

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    Global oral cancer incidence and mortality rates are increasing rapidly, with more than 350 000 new cases and 170 000 deaths recorded in 2018. Depressingly, standard treatments for oral cancer such as surgery, chemotherapy, and radiotherapy are associated with significant morbidity and a relatively static 5-year survival rate of around 50 – 60%. To date, three drugs - cetuximab, pembrolizumab, and nivolumab, are available for treating oral cancer. However, only a small fraction of oral cancer patients respond to these drugs. Discovery of further efficacious drugs in a cost-effective way through drug repurposing can potentially uncover the best combinatorial drug therapy against oral cancer. In this thesis, I aimed to create, using computational and statistical approaches, an integrative digital resource that can be mined to identify drug candidates that could be repurposed for oral cancer treatment. To this end, two bioinformatics tools were developed. The first tool – GENIPAC (Genomic Information Portal on Cancer Cell Lines), is a web resource for exploring, visualising, and analysing genomics information from 44 head and neck cancer cell lines. The second tool – DeSigN (Differentially Expressed Gene Signatures - Inhibitors), linksthe gene expression of oral cancer cell lines to the publicly available gene expression databases that have drug sensitivity data. To validate the efficacy of drug candidate shortlisted by DeSigN on a panel of oral cancer cell lines, several in vitro experiments were performed. Using gene expression signatures retrieved from the ORL Series in GENIPAC, DeSigN predicted bosutinib, an Src/Abl kinase inhibitor used for treating leukemia, to have inhibitory effect on oral cancer cell lines. Subsequent in vitro drug sensitivity validation showed that these oral cancer cell lines were susceptible to bosutinib treatment at IC50 of 0.8 – 1.2 µM. Later, antiproliferative experiments confirmed the efficacy of bosutinib in controlling tumour iv growth in oral cancer cell lines. Technical evaluation of performance reliability of six gene signature similarity scoring algorithms showed that the Weighted Connectivity Score or the statistically significant Connectivity Map, are prime candidates for upgrading the current core algorithm of DeSigN, which is based on the Kolmogorov-Smirnov statistic. In conclusion, the present work has demonstrated that cancer genomics data mining and integration through GENIPAC and DeSigN is a viable approach to accelerating the drug development process for oral cancer. Importantly, application of these two tools led to the discovery of bosutinib as a new, promising drug candidate to be repurposed for treating oral cancer in the future

    The 4717C > G polymorphism in periplakin modulates sensitivity to EGFR inhibitors

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    Abstract The use of EGFR inhibitors on oral squamous cell carcinoma (OSCC) as monotherapy yielded modest clinical outcomes and therefore would benefit from biomarkers that could predict which patient subsets are likely to respond. Here, we determined the efficacy of erlotinib in OSCC cell lines, and by comparing sensitive and resistant lines to identify potential biomarkers. We focused on the 4717C > G polymorphism in periplakin (PPL) where the CC genotype was associated with erlotinib resistance. To validate this, erlotinib-resistant cell lines harbouring CC genotype were engineered to overexpress the GG genotype and vice versa. Isogenic cell lines were then studied for their response to erlotinib treatment. We demonstrated that overexpression of the GG genotype in erlotinib-resistant lines sensitized them to erlotinib and inhibition of AKT phosphorylation. Similarly, the expression of the CC genotype conferred resistance to erlotinib with a concomitant increase in AKT phosphorylation. We also demonstrated that cell lines with the CC genotype generally are more resistant to other EGFR inhibitors than those with the GG genotype. Overall, we showed that a specific polymorphism in the PPL gene could confer resistance to erlotinib and other EGFR inhibitors and further work to evaluate these as biomarkers of response is warranted
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