Karen S. Kelly-Spratt

A targeted proteomics–based pipeline for verification of biomarkers in plasma

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.

High Throughput Quantitative Analysis of Serum Proteins Using Glycopeptide Capture and Liquid Chromatography Mass Spectrometry

It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are N-linked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates.

Lung Cancer Signatures in Plasma Based on Proteome Profiling of Mouse Tumor Models

We investigated the potential of in-depth quantitative proteomics to reveal plasma protein signatures that reflect lung tumor biology. We compared plasma protein profiles of four mouse models of lung cancer with profiles of models of pancreatic, ovarian, colon, prostate, and breast cancer and two models of inflammation. A protein signature for Titf1/Nkx2-1, a known lineage-survival oncogene in lung cancer, was found in plasmas of mouse models of lung adenocarcinoma. An EGFR signature was found in plasma of an EGFR mutant model, and a distinct plasma signature related to neuroendocrine development was uncovered in the small-cell lung cancer model. We demonstrate relevance to human lung cancer of the protein signatures identified on the basis of mouse models.

p19 Arf Suppresses Growth, Progression, and Metastasis of Hras-Driven Carcinomas through p53-Dependent and -Independent Pathways

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.

Tumor Microenvironment-Derived Proteins Dominate the Plasma Proteome Response During Breast Cancer Induction and Progression

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 approximately 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 noncancerous host tissues as well as the developing tumor. The proteomic signature was distinct from a nonspecific response to inflammation. Overall, the developing tumor simultaneously engaged a number of innate physiologic 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 relevant to plasma-based strategies to detect and diagnose cancer.

Plasma proteome profiling of a mouse model of breast cancer identifies a set of up-regulated proteins in common with human breast cancer cells.

We have applied an in-depth quantitative proteomic approach, combining isotopic labeling extensive intact protein separation and mass spectrometry, for high confidence identification of protein changes in plasmas from a mouse model of breast cancer. We hypothesized that a wide spectrum of proteins may be up-regulated in plasma with tumor development and that comparisons with proteins expressed in human breast cancer cell lines may identify a subset of up-regulated proteins in common with proteins expressed in breast cancer cell lines that may represent candidate biomarkers for breast cancer. Plasma from PyMT transgenic tumor-bearing mice and matched controls were obtained at two time points during tumor growth. A total of 133 proteins were found to be increased by 1.5-fold or greater at one or both time points. A comparison of this set of proteins with published findings from proteomic analysis of human breast cancer cell lines yielded 49 proteins with increased levels in mouse plasma that were identified in breast cancer cell lines. Pathway analysis comparing the subset of up-regulated proteins known to be expressed in breast cancer cell lines with other up-regulated proteins indicated a cancer related function for the former and a host-response function for the latter. We conclude that integration of proteomic findings from mouse models of breast cancer and from human breast cancer cell lines may help identify a subset of proteins released by breast cancer cells into the circulation and that occur at increased levels in breast cancer.

p19/Arf and p53 suppress sentinel lymph node lymphangiogenesis and carcinoma metastasis

The ability of tumor cells to metastasize is increasingly viewed as an interaction between the primary tumor and host tissues. Deletion of the p19/Arf or p53 tumor suppressor genes accelerates malignant progression and metastatic spread of 7,12-dimethylbenz(a)anthracene (DMBA)/12-O-tetradecanoyl-phorbol-13-acetate (TPA)-induced squamous cell carcinomas, providing a model system to address mechanisms of metastasis. Here, we show that benign pre-metastatic papillomas from wild-type mice trigger lymphangiogenesis within draining lymph nodes, whereas there is no growth of primary tumor lymphatic vessels. Lymph node lymphangiogenesis is greatly accelerated in papilloma-bearing p19/Arf- or p53-deficient mice, which coincides with the greater propensity of these tumors to progress to carcinomas and to metastasize. The extent of accumulation of B cells within the tumor-draining lymph nodes of wild-type mice predicted the level of lymph node lymphangiogenesis and metastatic potential. Arf or p53 deficiency strongly accelerated lymph node immune cell accumulation, in a manner that was associated with the extent of lymph node lymphatic sinus growth. This immune cell accumulation and lymph node lymphangiogenesis phenotype identifies host anti-tumor responses that could drive metastatic spread of cancers via the lymphatics.

Inhibition of PI-3K restores nuclear p27Kip1 expression in a mouse model of Kras-driven lung cancer.

Reduced expression of the CDK inhibitor p27Kip1 (p27) in human lung cancer correlates with tumor aggressiveness and poor prognosis. However, the regulation of p27 expression and the role of p27 during lung cancer are poorly understood. Urethane-induced lung tumors in mice frequently harbor mutations in the Kras oncogene, and in this study, we use this model to address the regulation of p27 during tumorigenesis. The Ras effector Akt is known to regulate p27 mRNA abundance by phosphorylating and inactivating the FOXO transcription factors. Phosphorylated Akt and FOXO proteins were both increased in lung tumors, correlating with a reduction in p27 mRNA transcript. Akt also directly phosphorylates p27 and regulates its nuclear/cytoplasmic localization. Tumors showed a reduced nuclear/cytoplasmic ratio of p27 protein, together with an increase in phosphorylated Thr197 p27 in the cytoplasmic pool. Treatment of lung tumor-bearing mice with the phosphoinositol-3 kinase inhibitor LY294002 induced a rapid decrease in phosphorylated Akt and phosphorylated p27, concomitant with an increase in nuclear p27. Germline p27 deficiency accelerated both the growth and malignant progression of urethane-induced lung tumors, and did so in a cell autonomous manner, confirming a causal role of p27 in tumor suppression. These results show that p27 is a potent barrier to the growth and malignant progression of Kras-initiated lung tumors. Further, the reduction of nuclear p27 in tumors is mediated by oncogene signaling pathways, which can be reversed by pharmacological agents.

Mapping tissue-specific expression of extracellular proteins using systematic glycoproteomic analysis of different mouse tissues

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 them 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.

A mouse model repository for cancer biomarker discovery.

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 10 different mouse models of human cancer, including two breast, two lung, two prostate, two gastrointestinal, 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.