RNA sequencing identifies lung cancer lineage and facilitates drug repositioning
Longjin Zeng, Longyao Zhang, Lingchen Li, Xingyun Liao, Chenrui Yin, Lincheng Zhang, Xiewan Chen, Jianguo Sun

TL;DR
This study uses RNA sequencing to identify lung cancer subtypes and suggests new drug treatments based on these subtypes.
Contribution
The study introduces a 42-gene classifier and identifies drug repositioning opportunities for lung cancer subtypes.
Findings
Three lung cancer phenotypes (bronchioid, neuroendocrine, squamoid) were confirmed with distinct prognostic outcomes.
MEK inhibitors showed resistance in bronchioid but sensitivity in squamoid subtypes.
Dinaciclib and alvocidib showed activity in the neuroendocrine cluster, and KLF5 was identified as a potential target.
Abstract
Recent breakthrough therapies have improved survival rates in non-small cell lung cancer (NSCLC), but a paradigm for prospective confirmation is still lacking. Patientdatasets were mainly downloaded from TCGA, CPTAC and GEO. We conducted downstream analysis by collecting metagenes and generated 42-gene subtype classifiers to elucidate biological pathways. Subsequently, scRNA, eRNA, methylation, mutation, and copy number variation were depicted from a phenotype perspective. Enhancing the clinical translatability of molecular subtypes, preclinical models including CMAP, CCLE, and GDSC were utilized for drug repositioning. Importantly, we verified the presence of previously described three phenotypes including bronchioid, neuroendocrine, and squamoid. Poor prognosis was seen in squamoid and neuroendocrine clusters for treatment-naive and immunotherapy populations. The neuroendocrine…
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Taxonomy
TopicsRNA modifications and cancer · Lung Cancer Treatments and Mutations · Cancer Genomics and Diagnostics
