2020
DOI: 10.1101/2020.03.30.017186
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Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

Abstract: The authors declare no potential conflicts of interest. Running title: Stochastic profiling of luminal breast cancer by 10cRNA-seqThe heterogeneous composition of solid tumors is known to impact disease progression and response to therapy. Malignant cells coexist in different regulatory states that can be accessed transcriptomically by single-cell RNA sequencing, but these methods have many caveats related to sensitivity, noise, and sample handling. We revised a statistical fluctuation analysis called stochast… Show more

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Cited by 5 publications
(28 citation statements)
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References 76 publications
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“…The heterogeneity analysis uncovered differences in stem-progenitor markers, proneural-mesenchymal states, and cellfate assignments that were obscured in bulk averages. However, the 10-cell profiles also averaged over most cycling transcripts (19), many of which were detectably increased in bulk mutant OPCs compared to control. Other transcripts with bulk changes in expression did not fluctuate any more than expected given their measured abundance, implying that these alterations must occur somewhat uniformly in mutant OPCs.…”
Section: Discussionmentioning
confidence: 99%
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“…The heterogeneity analysis uncovered differences in stem-progenitor markers, proneural-mesenchymal states, and cellfate assignments that were obscured in bulk averages. However, the 10-cell profiles also averaged over most cycling transcripts (19), many of which were detectably increased in bulk mutant OPCs compared to control. Other transcripts with bulk changes in expression did not fluctuate any more than expected given their measured abundance, implying that these alterations must occur somewhat uniformly in mutant OPCs.…”
Section: Discussionmentioning
confidence: 99%
“…Stochastic profiling with 10cRNA-seq data was performed separately for the male and the female datasets exactly as described in the accompanying contribution (19).…”
Section: Overdispersion-based Stochastic Profilingmentioning
confidence: 99%
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