DOI: 10.6084/m9.figshare.11558676
title: Metadata and data files supporting the published article: The therapeutic response of ER+/HER2- breast cancers differs according to the molecular Basal or Luminal subtype

description: Here, the authors performed an in-silico analysis on a meta-dataset including gene-expression data from 5,342 clinically defined estrogen receptor-positive/ human epidermal growth factor receptor 2-negative (ER+/HER2-) breast cancers (BC), and DNA copy number/mutational and proteomic data, to determine whether the therapeutic response of ER+/HER2- breast cancers differs according to the molecular basal or luminal subtype.<br><b>Data access</b>: The dataset Breast_cancer_classifications.csv supporting figure 1, table 1, and supplementary tables 1-3 is publicly available in the figshare repository as part of this data record. This study used and analysed 36 publicly available datasets that are all listed in Supplementary table 8 and are cited from the data availability statement of the published article.<br><b>Study aims and methodology</b>: To evaluate the response and/or potential vulnerability to hormone treatment (HT) and other systemic therapies of BC, and to assess the degree of difference between basal and luminal breast cancer subtypes, the authors performed an in-silico analysis of a meta-dataset including gene expression data from 8,982 non-redundant BCs and DNA copy number/mutational and proteomic data from TCGA. The aim was to compare the Basal versus Luminal samples. Out of the 8,982 samples of the database, 6,563 were defined as ER+ (5,342 according to immunohistochemistry (IHC) and 1,221 according to inferred stratus).The authors analysed breast cancer gene expression data pooled from 36 public datasets (the publicly available datasets are listed in supplementary table 8), comprising 8,982 invasive primary BCs. The pre-analytic data processing was done as described previously in <b>https://doi.org/10.1038/s41416-018-0309-1</b>. Please refer to the published article for more details on the methodology and statistical analysis.<br><b>Data supporting the figures, tables and supplementary tables in the published article</b>: Data supporting figure 1, table 1, and supplementary tables 1-3: Dataset<b> Breast_cancer_classifications.csv</b> is in <b>.csv</b> file format. The dataset includes histo-clinical and molecular data of the tumors analysed in study, and is part of this data record.<br>Data supporting supplementary table 4: Dataset <b>genome.wustl.edu_BRCA.IlluminaGA_DNASeq.Level_2.3.2.0.tar.gz.1</b> is a tar archive gz compressed of <b>maf format </b>files. This dataset was accessed through the Genomic Data Commons (GDC) Data Portal and can be downloaded directly here: <b>https://api.gdc.cancer.gov/data/afaf2790-04d4-453a-8c1b-75cf42ffd35f.</b><b><br></b><b>Data supporting supplementary table 5</b>: Dataset <b>gdc_manifest.txt</b> consists of gz archives of <b>txt format</b> files. The file was accessed through the GDC Data Portal here : <b>https://portal.gdc.cancer.gov/repository?facetTab=files&filters={"op":"and","content":[{"op":"in","content":{"field":"cases.project.project_id","value":["TCGA-BRCA"]}},{"op":"in","content":{"field":"files.access","value":["open"]}},{"op":"in","content":{"field":"files.analysis.workflow_type","value":["HTSeq - Counts"]}},{"op":"in","content":{"field":"files.experimental_strategy","value":["RNA-Seq"]}}]}&searchTableTab=files</b><b><br></b><b>Data supporting supplementary table 6</b>: Dataset <b>Table S5_Revised.xlsx</b> is in <b>.xlsx file format</b> and is part of the supplementary information files of the published article.<br><b>Data supporting supplementary table 7</b>: Dataset <b>BRCA.RPPA.Level_3.tar</b> is a tar archive of <b>txt format files</b>. The file was accessed through the GDC Data Portal and can be downloaded directly here: <b>https://api.gdc.cancer.gov/data/85988e1b-4f7d-493e-96ae-9eee61ac2833.</b><br>