Dataset

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

Dataset ID Technology Samples
EGAD00001003883 HiSeq X Ten 69

Dataset 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, and copy number changes. 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.

Data Use Conditions

GRU RUO PUB US IS

See further information on Data Use Conditions

Label Code Version Modifier
obsolete general research use and clinical care DUO:0000005 2017-10-16
obsolete research use only DUO:0000014 2017-10-16
publication required DUO:0000019 2017-10-16
user specific restriction DUO:0000026 2017-10-16
institution specific restriction DUO:0000028 2017-10-16