PNNL

Abstract

In recent years, mouse models have proven to be invaluable in expanding our understanding of cancer biology. We have amassed a tremendous amount of proteomics and transcriptomics data profiling blood and tissues from a Her2-driven mouse model of breast cancer that closely recapitulates the pathology and natural history of human breast cancer. The purpose of this report is to make all of these data publicly available in raw and processed forms, as a resource to the community. Importantly, high quality biospecimens from this same mouse model are freely available through a sample repository that we established, so researchers can readily obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Specifically, six proteomics and six transcriptomics datasets are available, with the former encompassing 841 liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments of both plasma and tissue samples, and the latter including 255 individual microarray analyses of five different tissue types (thymus, spleen, liver, blood cells, and breast ± laser capture microdissection). A total of 18,880 unique peptides were identified with a PeptideProphet error rate =1%, with 3884 non-redundant protein groups identified in five plasma datasets, and 1659 non-redundant protein groups in a tissue dataset (4977 non-redundant protein groups in total). We anticipate that these data will be of use to the community for software tool development, investigations of analytical variation in MS/MS data, development of quality control tools (multiple technical replicates are provided for a subset of the data), empirical selection of proteotypic peptides for multiple reaction monitoring mass spectrometry, and for advancing our understanding of cancer biology.