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

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TLDR
A multi-omics survey of progressive compared to regressive carcinoma in situ lesions provides a molecular map of early lung cancer development and is expected to improve early detection, reduce overtreatment, and foster preventative therapies targeting early clonal events in lung cancer.
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.

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Integrative Genomics Viewer

TL;DR: The sheer volume and scope of data posed by this flood of data pose a significant challenge to the development of efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
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Molecular profiling for precision cancer therapies.

TL;DR: This review summarizes the current and upcoming approaches to implement precision cancer medicine, highlighting the challenges and potential solutions to facilitate the interpretation and to maximize the clinical utility of molecular profiling results.
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Context is everything: aneuploidy in cancer

TL;DR: The context dependency of aneuploidy in cancer is explained and its clinical potential is discussed, which may have clinical relevance as a prognostic marker and as a potential therapeutic target.
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Intratumoral heterogeneity in cancer progression and response to immunotherapy.

TL;DR: In this paper, the authors discuss the main sources of intratumoral heterogeneity and its impact on the natural history of the disease, including sensitivity to treatment, as well as delineate potential strategies to target such a detrimental feature of aggressive malignancies.
References
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TL;DR: A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination, and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake.
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KEGG: Kyoto Encyclopedia of Genes and Genomes

TL;DR: The Kyoto Encyclopedia of Genes and Genomes (KEGG) as discussed by the authors is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules.
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limma powers differential expression analyses for RNA-sequencing and microarray studies

TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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TL;DR: There are striking variations in the risk of different cancers by geographic area, most of the international variation is due to exposure to known or suspected risk factors related to lifestyle or environment, and provides a clear challenge to prevention.
Journal ArticleDOI

Integrative genomics viewer

TL;DR: In this article, the authors present an approach for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
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