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2023 World Conference on Lung Cancer (Posters)
EP02.02. Machine Learning for Multi-Omics Data Ide ...
EP02.02. Machine Learning for Multi-Omics Data Identifies Vulnerabilities in a Subset of Squamous Cell Lung Cancers - PDF(Slides)
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A study conducted at the University of Cambridge has used machine learning to analyze multi-omics data to identify vulnerabilities in a subset of squamous cell lung cancers (LUSC). The study found that while most targetable alterations are not present in LUSC, many patients exhibit SOX2 gene amplification as a driver of the disease. <br /><br />The researchers generated a SOX2-associated protein network using a computational pipeline and identified potential drug target candidates in SOX2-driven LUSC. These candidates, including the PI3K/AKT/mTOR pathway, KLF5, and FOXM1, were validated through target inhibition and gene knockout studies. <br /><br />Based on these findings, the study suggests that SOX2 is a driver of LUSC and that multi-omics data has the potential to identify new drug target candidates for the disease. The PI3K/AKT/mTOR pathway, KLF5, and FOXM1 are highlighted as potential targetable vulnerabilities in SOX2-driven LUSC. <br /><br />The study also recommends that clinical trials for LUSC should consider the SOX2 status of patients in order to identify subgroups who might benefit from targeted therapies. Overall, this research provides insights into the molecular mechanisms underlying LUSC and identifies potential therapeutic targets for this subset of lung cancers.
Asset Subtitle
Daniel Kottmann
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Speaker
Daniel Kottmann
Topic
Tumor Biology: Preclinical Biology - Omics Approaches
Keywords
University of Cambridge
machine learning
multi-omics data
squamous cell lung cancers
LUSC
SOX2 gene amplification
SOX2-associated protein network
PI3K/AKT/mTOR pathway
KLF5
FOXM1
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