Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Vitor H. Teixeira, Christodoulos P. Pipinikas, Adam Pennycuick, Henry Lee-Six, Deepak Chandrasekharan, Jennifer Beane, Tiffany J. Morris, Anna Karpathakis, Andrew Feber, Charles E. Breeze, Paschalis Ntolios, Robert E. Hynds, Mary Falzon, Arrigo Capitanio, Bernadette Carroll, Pascal F. Durrenberger, Georgia Hardavella, James M. Brown, Andy G. Lynch, Henry Farmery, Dirk S. Paul, Rachel C. Chambers, Nicholas McGranahan, Neal Navani, Ricky M. Thakrar, Charles Swanton, Stephan Beck, Phillip Jeremy George, Avrum Spira, Peter J. Campbell, Christina Thirlwell, Sam M. Janes

ABSTRACT

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. More... »

PAGES

517-525

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41591-018-0323-0

DOI

http://dx.doi.org/10.1038/s41591-018-0323-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111348293

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30664780


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HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41591-018-0323-0'

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curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41591-018-0323-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41591-018-0323-0'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41591-018-0323-0'


 

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