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Creating a ‘Timeline’ of ductal carcinoma in situ to identify processes and biomarkers for progression towards invasive ductal carcinoma

TLDR
A ‘Timeline’ of disease progression is generated, utilising the variability within patients and combining >2,000 individually micro-dissected ductal lesions from 145 patients into one continuous trajectory, showing there is a progressive loss in basal layer integrity, coupled with two epithelial to mesenchymal transitions (EMT) early in the timeline.
Abstract
Ductal carcinoma in situ (DCIS) is considered a non-invasive precursor to breast cancer, and although associated with an increased risk of developing invasive disease, many women with DCIS will never progress beyond their in situ diagnosis. The path from normal duct to invasive disease is not well understood, and efforts to do so are hampered by the substantial heterogeneity that exists between patients and even within patients. Using gene expression analysis, we have generated a ‘Timeline’ of disease progression, utilising the variability within patients and combining >2,000 individually micro-dissected ductal lesions from 145 patients into one continuous trajectory. Using this Timeline we show there is a progressive loss in basal layer integrity, coupled with two epithelial to mesenchymal transitions (EMT), one early in the timeline and a second just prior to cells leaving the duct. We identify early processes and potential biomarkers, including CAMK2N1, MNX1, ADCY5, HOXC11 and ANKRD22, whose reduced expression is associated with the progression of DCIS to invasive breast cancer.

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References
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Journal ArticleDOI

STAR: ultrafast universal RNA-seq aligner

TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Journal ArticleDOI

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

TL;DR: The latest version of STRING more than doubles the number of organisms it covers, and offers an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input.
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GSVA: gene set variation analysis for microarray and RNA-seq data.

TL;DR: This work introduces Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner and constitutes a starting point to build pathway-centric models of biology.
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voom: precision weights unlock linear model analysis tools for RNA-seq read counts

TL;DR: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments, and the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.
Journal ArticleDOI

xCell: digitally portraying the tissue cellular heterogeneity landscape

TL;DR: This work presents xCell, a novel gene signature-based method, and uses it to infer 64 immune and stromal cell types and shows that xCell outperforms other methods.
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