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Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis II

ABSTRACT: Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis II

PROVIDER: PRJNA422415 | ENA |

REPOSITORIES: ENA

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Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

Project description:Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

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Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis III

Project description:Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis III

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Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-01-01 | GSE108124 | GEO

Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis I

Project description:Rationale: Lung carcinoma-in-situ (CIS) lesions are the pre-malignant precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those preinvasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. We performed gene expression microarrays on CIS lesions, with a view to deriving a molecular signature predictive of future progression to invasive cancer.

2019-01-01 | GSE94611 | GEO

Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis II

Project description:Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, insertions and deletions. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.

2019-01-01 | GSE108082 | GEO

Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis III

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Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions.

Project description:The molecular alterations that occur in cells before cancer is manifest are largely uncharted. Lung carcinoma in situ (CIS) lesions are the pre-invasive precursor to squamous cell carcinoma. Although microscopically identical, their future is in equipoise, with half progressing to invasive cancer and half regressing or remaining static. The cellular basis of this clinical observation is unknown. Here, we profile the genomic, transcriptomic, and epigenomic landscape of CIS in a unique patient cohort with longitudinally monitored pre-invasive disease. Predictive modeling identifies which lesions will progress with remarkable accuracy. We identify progression-specific methylation changes on a background of widespread heterogeneity, alongside a strong chromosomal instability signature. We observed mutations and copy number changes characteristic of cancer and chart their emergence, offering a window into early carcinogenesis. We anticipate that this new understanding of cancer precursor biology will improve early detection, reduce overtreatment, and foster preventative therapies targeting early clonal events in lung cancer.

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