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1.  A Mouse Model Repository for Cancer Biomarker Discovery 
Journal of proteome research  2008;7(8):3613-3618.
Early detection of cancer using biomarkers obtained from blood or other easily accessible tissues would have a significant impact on reducing cancer mortality. However, identifying new blood-based biomarkers has been hindered by the dynamic complexity of the human plasma proteome, confounded by genetic and environmental variability, and the scarcity of high quality controlled samples. In this report we discuss a new paradigm for biomarker discovery through the use of mouse models. Inbred mouse models of cancer recapitulate many critical features of human cancer, while eliminating sources of environmental and genetic variability. The ability to collect samples from highly matched cases and controls under identical conditions further reduces variability which is critical for successful biomarker discovery. We describe the establishment of a repository containing tumor, plasma, urine, and other tissues from ten different mouse models of human cancer, including two breast, two lung, two prostate, two gastro-intestinal, one ovarian, and one skin tumor model. We present the overall design of this resource and its potential use by the research community for biomarker discovery.
doi:10.1021/pr800210b
PMCID: PMC3727967  PMID: 18624399
2.  Tumor Microenvironment-Derived Proteins Dominate the Plasma Proteome Response During Breast Cancer Induction and Progression 
Cancer research  2011;71(15):5090-5100.
Summary
Tumor development relies upon essential contributions from the tumor microenvironment and host immune alterations. These contributions may inform the plasma proteome in a manner that could be exploited for cancer diagnosis and prognosis. In this study, we employed a systems biology approach to characterize the plasma proteome response in the inducible HER2/neu mouse model of breast cancer during tumor induction, progression and regression. Mass spectrometry data derived from ∼ 1.6 million spectra identified protein networks involved in wound healing, microenvironment and metabolism that coordinately changed during tumor development. The observed alterations developed prior to cancer detection, increased progressively with tumor growth, and reverted toward baseline with tumor regression. Gene expression and immunohistochemical analyses suggested that the cancer-associated plasma proteome was derived from transcriptional responses in the non-cancerous host tissues as well as the developing tumor. The proteomic signature was distinct from a non-specific response to inflammation. Overall, the developing tumor simultaneously engaged a number of innate physiological processes, including wound repair, immune response, coagulation and complement cascades, tissue remodeling and metabolic homeostasis that were all detectable in plasma. Our findings offer an integrated view of tumor development with relevance to plasma-based strategies to detect and diagnose cancer.
doi:10.1158/0008-5472.CAN-11-0568
PMCID: PMC3148311  PMID: 21653680
3.  Proteome and Transcriptome Profiles of a Her2/Neu-driven Mouse Model of Breast Cancer 
Proteomics. Clinical applications  2011;5(3-4):179-188.
Purpose
We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens.
Experimental design
Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias.
Results
In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue.
Conclusions and clinical relevance
These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
doi:10.1002/prca.201000037
PMCID: PMC3069718  PMID: 21448875
Breast cancer; Her2; mouse; proteome; transcriptome
4.  A targeted proteomics–based pipeline for verification of biomarkers in plasma 
Nature biotechnology  2011;29(7):625-634.
High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.
doi:10.1038/nbt.1900
PMCID: PMC3232032  PMID: 21685906
5.  Mapping tissue-specific expression of extracellular proteins using systematic glycoproteomic analysis of different mouse tissues 
Journal of proteome research  2010;9(11):5837-5847.
Due to their easy accessibility, proteins outside of the plasma membrane represent an ideal but untapped resource for potential drug targets or disease biomarkers. They constitute the major biochemical class of current therapeutic targets and clinical biomarkers. Recent advances in proteomic technologies have fueled interest in analysis of extracellular proteins such as membrane proteins, cell surface proteins, and secreted proteins. However, unlike the gene expression analyses from a variety of tissues and cells using genomic technologies, quantitative proteomic analysis of proteins from various biological sources is challenging due to the high complexity of different proteomes, and the lack of robust and consistent methods for analyses of different tissue sources, especially for specific enrichment of extracellular proteins. Since most extracellular proteins are modified by oligosaccharides, the population of glycoproteins therefore represents the majority of extracellular proteomes. Here, we quantitatively analyzed glycoproteins and determined the expression patterns of extracellular proteins from 12 mouse tissues using solid-phase extraction of N-linked glycopeptides and liquid chromatography tandem mass spectrometry. We identified peptides enclosing 1231 possible N-linked glycosites from 826 unique proteins. We further determined the expression pattern of formerly N-linked glycopeptides and identified extracellular glycoproteins specifically expressed in each tissue. Furthermore, the tissue specificities of the overexpressed glycoproteins in a mouse skin tumor model were determined by comparing to the quantitative protein expression from the different tissues. These skin tumor-specific extracellular proteins might serve as potential candidates for cell surface drug targets or disease-specific protein markers.
doi:10.1021/pr1006075
PMCID: PMC2988866  PMID: 20828161
Extracellular proteins; glycosylation; solid-phase extraction of glycopeptides; tissue specificity; different tissues; skin tumor; proteomics; and mass spectrometry
6.  Plasma Proteome Profiles Associated with Inflammation, Angiogenesis, and Cancer 
PLoS ONE  2011;6(5):e19721.
Tumor development is accompanied by a complex host systemic response, which includes inflammatory and angiogenic reactions. Both tumor-derived and systemic response proteins are detected in plasma from cancer patients. However, given their non-specific nature, systemic response proteins can confound the detection or diagnosis of neoplasia. Here, we have applied an in-depth quantitative proteomic approach to analyze plasma protein changes in mouse models of subacute irritant-driven inflammation, autoreactive inflammation, and matrix associated angiogenesis and compared results to previously described findings from mouse models of polyoma middle T-driven breast cancer and Pdx1-Cre KrasG12D Ink4a/Arf lox/lox -induced pancreatic cancer. Among the confounding models, approximately 1/3 of all quantified plasma proteins exhibited a significant change in abundance compared to control mice. Of the proteins that changed in abundance, the majority were unique to each model. Altered proteins included those involved in acute phase response, inflammation, extracellular matrix remodeling, angiogenesis, and TGFβ signaling. Comparison of changes in plasma proteins between the confounder models and the two cancer models revealed proteins that were restricted to the cancer-bearing mice, reflecting the known biology of these tumors. This approach provides a basis for distinguishing between protein changes in plasma that are cancer-related and those that are part of a non-specific host response.
doi:10.1371/journal.pone.0019721
PMCID: PMC3093388  PMID: 21589862
7.  Identification of glycoproteins from mouse skin tumors and plasma 
Clinical proteomics  2008;4(3-4):117-136.
Plasma has been the focus of testing different proteomic technologies for the identification of biomarkers due to its ready accessibility. However, it is not clear if direct proteomic analysis of plasma can be used to discover new marker proteins from tumor that are associated with tumor progression. Here, we reported that such proteins can be detected in plasma in a chemical induced skin cancer mouse model. We analyzed glycoproteins from both benign papillomas and malignant carcinomas from mice using our recently developed platform, solid-phase extraction of glycopeptides (SPEG) and mass spectrometry, and identified 463 unique N-linked glycosites from 318 unique glycoproteins. These include most known extracellular proteins that have been reported to play roles in skin cancer development such as thrombospondin, cathepsins, epidermal growth factor receptor, cell adhesion molecules, cadherins, integrins, tuberin, fibulin, TGFβ receptor, etc. We further investigated whether these tumor proteins could be detected in plasma from tumor bearing mice using isotope labeling and 2D-LC-MALDI-MS/MS. Two tumor glycoproteins, Tenascin-C and Arylsulfatase B, were identified and quantified successfully in plasma from tumor bearing mice. This result indicates that analysis of tumor associated proteins in tumors and plasma by method using glycopeptide capture, isotopic labeling, and mass spectrometry can be used as a discovery tool to identify candidate tumor proteins that may be detected in plasma.
doi:10.1007/s12014-008-9014-z
PMCID: PMC2976030  PMID: 21072318
8.  Differential Plasma Glycoproteome of p19ARF Skin Cancer Mouse Model Using the Corra Label-Free LC-MS Proteomics Platform 
Clinical proteomics  2008;4(3-4):105.
A proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented here, using a mouse model for skin cancer as an example. Blood plasma was collected from 10 control mice, and 10 mice having a mutation in the p19ARF gene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the modified tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists. We next assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localisation, trasport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application. These results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.
doi:10.1007/s12014-008-9018-8
PMCID: PMC2821048  PMID: 20157627
Skin cancer; LC-MS; Label-free protein quantification; Biomarker discovery; Systems biology; Targeted peptide sequencing; Glycoproteomics; Plasma
9.  Tumor suppression by p53 in the absence of Atm 
Molecular cancer research : MCR  2008;6(7):1185-1192.
Oncogenes can induce p53 through a signaling pathway involving p19/Arf. It was recently proposed that oncogenes can also induce DNA damage and this can induce p53 through the Atm DNA damage pathway. To assess the relative roles of Atm, Arf, and p53 in suppression of Ras- driven tumors we examined susceptibility to skin carcinogenesis in DMBA/TPA treated Atm and p53 deficient mice and compared these results to previous studies on Arf deficient mice. Mice with epidermal specific deletion of p53 showed increased papilloma number and progression to malignant invasive carcinomas compared to wild type littermates. In contrast, Atm deficient mice showed no increase in papilloma number, growth, or malignant progression. γ-H2AX and p53 levels were increased in both Atm+/+ and Atm−/− papillomas, while Arf−/− papillomas showed much lower p53 expression. Thus although there is evidence of DNA damage, signaling through Arf appears to regulate p53 in these Ras-driven tumors. In spontaneous and radiation induced lymphoma models, tumor latency was accelerated in Atm−/−p53−/− compound mutant mice compared to the single mutant Atm−/− or p53−/− mice, indicating cooperation between loss of Atm and loss of p53. Although p53 mediated apoptosis was impaired in irradiated Atm−/− lymphocytes, p53 loss was still selected for during lymphomagenesis in Atm−/− mice. In conclusion, in these models of oncogene or DNA damage induced tumors, p53 retains tumor suppressor activity in the absence of Atm.
doi:10.1158/1541-7786.MCR-07-2009
PMCID: PMC2680228  PMID: 18583527
Squamous cell carcinoma; Trp53; DNA damage; Hras; apoptosis
10.  p19 Arf Suppresses Growth, Progression, and Metastasis of Hras-Driven Carcinomas through p53-Dependent and -Independent Pathways 
PLoS Biology  2004;2(8):e242.
Ectopic expression of oncogenes such as Ras induces expression of p19Arf, which, in turn, activates p53 and growth arrest. Here, we used a multistage model of squamous cell carcinoma development to investigate the functional interactions between Ras, p19Arf, and p53 during tumor progression in the mouse. Skin tumors were induced in wild-type, p19Arf-deficient, and p53-deficient mice using the DMBA/TPA two-step protocol. Activating mutations in Hras were detected in all papillomas and carcinomas examined, regardless of genotype. Relative to wild-type mice, the growth rate of papillomas was greater in p19Arf-deficient mice, and reduced in p53-deficient mice. Malignant conversion of papillomas to squamous cell carcinomas, as well as metastasis to lymph nodes and lungs, was markedly accelerated in both p19 Arf- and p53-deficient mice. Thus, p19Arf inhibits the growth rate of tumors in a p53-independent manner. Through its regulation of p53, p19Arf also suppresses malignant conversion and metastasis. p53 expression was upregulated in papillomas from wild-type but not p19 Arf-null mice, and p53 mutations were more frequently seen in wild-type than in p19 Arf-null carcinomas. This indicates that selection for p53 mutations is a direct result of signaling from the initiating oncogenic lesion, Hras, acting through p19Arf.
A squamous cell carcinoma model shows Ras mutation not only initiates tumor development but, through Arf and p53, directly influences the subsequent evolutionary trajectory of the tumors
doi:10.1371/journal.pbio.0020242
PMCID: PMC509304  PMID: 15314658

Results 1-10 (10)