Breast Functional Genomics

Manuscript

"Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities and Resistance"

Richard Marcotte*, Azin Sayad*, Kevin R. Brown, Felix Sanchez-Garcia, Jüri Reimand, Maliha Haider, Carl Virtanen, James E. Bradner, Gary D. Bader, Gordon B. Mills, Dana Pe’er, Jason Moffat and Benjamin G. Neel
Cell, Volume 164, Issues 1–2, 14 January 2016, Pages 293–309

(* Equal Contributions)

Cell | PubMed | Science Direct
siMEM Algorithm Website

News/Updates

• 2016-08-08: Fixed download link for breast RNA-Seq per-gene FPKM file without floor values, log2-transformation or quantile-normalization. You can find a link to the file below, under the "Functional, Genomics and Proteomics Data Files" header "RNA-Seq", or click here to download the file directly.
   
  
• 2016-03-18: Breast miRNA data link fixed and file available for download. You can find a link to the file below, under the "Functional, Genomics and Proteomics Data Files" header "miRNA", or click here to download the file directly.
   
  
• 2016-01-21: Breast per-gene zGARP scores file added for download. You can find a link to the file below, under the "Functional, Genomics and Proteomics Data Files" header, or click here to download the file directly.
   
  
• 2016-01-14: Manuscript, data and siMEM code published.
   
  

Links to Data on GEO

• shRNA screens (GSE74702)
• RNAseq (GSE73526)

Functional, Genomics and Proteomics Data Files

shRNA screens

• Per-gene zGARP scores
• Normalized ExpressionSet: File used in manuscript analyses. RData format file containing a Bioconductor ExpressionSet object combining
  • Per-sample (cell line + time-point + replicate), per-hairpin log2 measurements matrix. Accessed using the exprs() R function.
  • Per-hairpin, per-gene annotations data frame, with the same number of rows as the measurement matrix. Accessed using the fData() R function.
  • Per sample annotation data frame, including information such as cell line and time-point. This number of rows in this data frame matches the number of columns in the measurements matrix. Accessed using the pData() R function.
  • Loaded into R using the load() function.

RNASeq

• Per-gene FPKM: Breast cell line FPKM values without floor values set, quantile-normalization or log2-transformation.
• Per-gene normalized log2 FPKM: As above, but also Quantile-Normalized, and a floor value set at log2(0.1) FPKM. This version was used in the manuscript analyses.

CNA

• Per-gene Log-R Ratios: CBS-segmented SNP array Log R-Ratios, mapped to genes.

miRNA

• miRNA

RPPA

• RPPA

Annotation Files

Cell line subtypes: sub-typing of each cell line according to

• Receptor expression and status (AR, ERBB2, ESR1, PGR).
• Three-receptor classification (ER, HER2, TNBC).
• Curtis Integrative Subtypes (IntClust 1 through 10).
• Intrinsic classification (Luminal A, Luminal B, Her2, Basal, Normal-like) with Claudin Low status.
• Lehmann TNBC subtypes (Basal Like 1, Basal Like 2, Immunomodulatory, Luminal Androgen Receptor, Mesenchymal, Mesenchymal Stem-Like).
• Neve classification (luminal, basal A, basal B) with luminal cell lines further classified as her2 based on high ERBB2 expression status.
• Unsupervised clustering of RNASeq, RPPA and shRNA screen data.

updated shRNA annotations: Update to Entrez gene ids and symbols, to account for changed symbols, deprecated Entrez ids and the like. Approximately 300 gene ids from the original TRC II annotations no longer exist, leading to a slightly reduced overall gene id and shRNA count.