Dataset Information
Proteogenomic Integration Reveals Therapeutic Targets in Breast Cancer Xenografts
Ontology highlight
ABSTRACT: Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/ phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities. Additional supplementary data sets, provided below
Huang_Proteome_Peptide_Spectrum_Match_Results_SpectrumMill is the Proteome Peptide Spectrum Match reports exported from Spectrum Mill for each of the iTRAQ4 experiments, including the RefSeq FASTA file used for searches, and a Spectrum Mill quality metrics report.
INSTRUMENT(S): Q Exactive
ORGANISM(S): Homo Sapiens (ncbitaxon:9606) Mus Musculus (ncbitaxon:10090)
SUBMITTER: 
Steven A. Carr
PROVIDER: MSV000084249 | MassIVE | Wed Aug 28 17:56:00 BST 2019
SECONDARY ACCESSION(S): PXD015911
REPOSITORIES: MassIVE
Publications
Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.
Nature communications 20170328
Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analys ...[more]
