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Tumor microenvironment landscapes supporting EGFR-mutant NSCLC are modulated at the single cell interaction level by Unesbulin treatment

ABSTRACT: Lung cancer is the leading cause of cancer deaths. Lethal pulmonary adenocarcinomas (ADCs) present with frequent mutations in the Epidermal Growth Factor Receptor (EGFR). Genetically-engineered murine models of lung cancer expedited comprehension of the molecular mechanisms driving tumorigenesis and drug response. Here, we systematically analyzed the evolution of tumor heterogeneity in the context of dynamic interactions occurring with the intermingled tumor microenvironment by high resolution transcriptomics. Our effort identified vulnerable tumor-specific epithelial cells, as well as their cross-talk with niche components (endothelial cells, fibroblasts and tumor-infiltrating immune cells), whose symbiotic interface shapes tumor aggressiveness and is almost completely abolished by treatment with Unesbulin, a tubulin binding agent that reduces BMI-1 activity. Simultaneous MRI analysis demonstrated decreased tumor growth, setting the stage for future investigations into the potential of novel therapeutic strategies for EGFR-mutant ADCs.

ORGANISM(S): Mus musculus

PROVIDER: GSE253658 | GEO | 2024/04/03

REPOSITORIES: GEO

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Project description:Pulmonary enteric adenocarcinoma (PEAD) is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas (ADCs) by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome wide methylation profiles of 600 primary pulmonary, colorectal and upper gastrointestinal ADCs obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal ADCs from TCGA and the GEO database, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed DNA methylation data of 15 PEADs as well as four pulmonary metastases and four primary colorectal ADCs with the algorithm. All 15 PEADs were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal ADC samples were identified as primary colorectal ADCs. In a t-distributed stochastic neighbor embedding analysis, the PEAD samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary ADCs. Additional characterization of the PEAD series using fluorescence in-situ hybridization, next generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (66.7%) the molecular data was mostly reminiscent of standard pulmonary ADCs. In conclusion, we provide sound evidence of the pulmonary origin of PEAD and in addition provide a publicly available machine learning based algorithm to reliably distinguish PEAD from metastatic colorectal cancer and upper gastrointestinal adenocarcinomas.

Dataset Information

Tumor microenvironment landscapes supporting EGFR-mutant NSCLC are modulated at the single cell interaction level by Unesbulin treatment

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