The claudin-low breast cancer subtype is defined by gene expression characteristics and encompasses a remarkably diverse range of breast tumors. Here, we investigate genomic, transcriptomic, and clinical features of claudin-low breast tumors. We show that claudin-low is not simply a subtype analogous to the intrinsic subtypes (basal-like, HER2-enriched, luminal A, luminal B and normal-like) as previously portrayed, but is a complex additional phenotype which may permeate breast tumors of various intrinsic subtypes. Claudin-low tumors are distinguished by low genomic instability, mutational burden and proliferation levels, and high levels of immune and stromal cell infiltration. In other aspects, claudin-low tumors reflect characteristics of their intrinsic subtype. Finally, we explore an alternative method for identifying claudin-low tumors and thereby uncover potential weaknesses in the established claudin-low classifier. In sum, these findings elucidate the heterogeneity in claudin-low breast tumors, and substantiate a re-definition of claudin-low as a cancer phenotype.
In my previous post, I gave a brief introduction to mutational signature and spectrum analysis using the deconstructSigs framework, including how to modify the source code to allow for the analysis of murine samples. deconstructSigs allows for the visualisation of mutational spectra as barcharts, giving a granular view of the trinucleotide context for variants in an individual sample. Unfortunately, this form of visualisation works poorly for the purpose of comparing the mutational spectra of multiple samples in a cohort. One solution to this is to display mutational spectra as a heatmap, as presented in PMK Westcott et al. 2015. This post will show how this can be done in R.
Mutational signatures were pioneered by Nik-Zainal et al. in 2012 and the concept has since become instrumental in the analysis of the etiology of cancer. This post shows how a mutational signature analysis can be performed using the deconstructSigs R package, and the adjustments to the source code that need to be made in order to be able to do so on sequence data from mice.
Hi, and welcome! If you've arrived here... you probably came here by accident. In brief, I'm a medical student doing an MD/PhDish program at the University of Oslo at the Department of Cancer Genetics. I'm currently working on sequence data from mouse cancer models, with a focus on tumor progression. Mainly I just made this website to teach myself some HTML/CSS. Recently, I've been spending far too much time figuring out what should be fairly standard sequence analyses, so I'll also be writing a few notes here in an attempt to publicly document some of my work, and hopefully be of help to to the next person working through the same issues as myself. For now, thanks for visiting!