Identifying Therapeutically Targetable Tumour-Immune Interactions in Small Cell Lung Cancer
Description
Small cell lung cancer (SCLC) is a highly aggressive metastatic lung cancer, accounting for 15% of all cases with poor survival outcomes revealing the necessity to produce novel therapeutic strategies. Recent studies show that SCLC has significant tumour heterogeneity with varying gene expression, presenting an opportunity to use machine learning-driven algorithms with a promising route to uncover its underlying mechanisms. This study leverages single-cell RNA sequencing (scRNA-seq) datasets, and machine learning (ML) to explore tumour heterogeneity, identify biomarkers predictive of novel therapeutic targets, and generate graphical predictions of tumour-immune interactions in SCLC patients. We will use published datasets to identify the cellular basis of tumour-immune interactions and identify gene expression changes within SCLC cells. Pathway analysis and biological validation will extend the results to molecular signalling pathways. Then we will conduct a literature search for the selected genes that are known to disrupt cellular interactions distinctive of SCLC to be further tested by the lung cancer research team in pre-clinical models. Our preliminary analysis shows promising outcomes producing key biomarkers in SCLC stage and treatment groups across immune and epithelial cell subtypes. Genes including RBP1 and CD74 were identified with strong protective effects and further exploration of these genes can highlight specific-stage molecular drivers to guide the ML models. Incorporating advanced models may yield more accurate predictions and improved biomarker discovery with clinical significance. In summary, this study aims to identify novel biomarker targets and therapeutic strategies that can be validated in pre-clinical models and translated into clinical applications.
Identifying Therapeutically Targetable Tumour-Immune Interactions in Small Cell Lung Cancer
Small cell lung cancer (SCLC) is a highly aggressive metastatic lung cancer, accounting for 15% of all cases with poor survival outcomes revealing the necessity to produce novel therapeutic strategies. Recent studies show that SCLC has significant tumour heterogeneity with varying gene expression, presenting an opportunity to use machine learning-driven algorithms with a promising route to uncover its underlying mechanisms. This study leverages single-cell RNA sequencing (scRNA-seq) datasets, and machine learning (ML) to explore tumour heterogeneity, identify biomarkers predictive of novel therapeutic targets, and generate graphical predictions of tumour-immune interactions in SCLC patients. We will use published datasets to identify the cellular basis of tumour-immune interactions and identify gene expression changes within SCLC cells. Pathway analysis and biological validation will extend the results to molecular signalling pathways. Then we will conduct a literature search for the selected genes that are known to disrupt cellular interactions distinctive of SCLC to be further tested by the lung cancer research team in pre-clinical models. Our preliminary analysis shows promising outcomes producing key biomarkers in SCLC stage and treatment groups across immune and epithelial cell subtypes. Genes including RBP1 and CD74 were identified with strong protective effects and further exploration of these genes can highlight specific-stage molecular drivers to guide the ML models. Incorporating advanced models may yield more accurate predictions and improved biomarker discovery with clinical significance. In summary, this study aims to identify novel biomarker targets and therapeutic strategies that can be validated in pre-clinical models and translated into clinical applications.
https://scholar.uwindsor.ca/we-spark-conference/2025/postersessions/61