A mouse model repository for cancer biomarker discovery

MALDI-TOF MS Analysis of Serum Peptidome Patterns in Cervical Cancer

BACKGROUND

Cervical cancer is the fourth most common cancer among females worldwide. Identifying peptide patterns discriminating healthy individuals from those with diseases has gained interest in the early detection of cancers. Our study aimed to determine signature peptide patterns for cervical cancer screening.

METHODS

Our study focused on the serum peptidome analysis of 83 healthy women and 139 patients with cervical cancer. All spectra derived from matrix-assisted laser desorption/ionization time-of-flight mass spectrometry were analyzed using FlexAnalysis 3.0 and ClinProTools 2.2 software.

RESULTS

In the mass range of 1000-10,000 Da, the total average spectra were represented as the signature pattern. Principal component analysis showed that all the groups were separately distributed. Furthermore, the peaks at m/z 1466.91, 1898.01, 3159.09, and 4299.40 significantly differed among the investigated groups (Wilcoxon/Kruskal-Wallis test and ANOVA, p < 0.001).

CONCLUSIONS

Laboratory-based rapid mass spectrometry showed that serum peptidome patterns could serve as diagnostic tools for diagnosing cervical cancer; however, verification through larger cohorts and association with clinical data are required, and the use of externally validated samples, such as patients with other types of cancers, should be investigated to validate the specific peptide patterns.

Proteomic and transcriptomic profiling of Pten gene-knockout mouse model of prostate cancer

BACKGROUND

The prostate-specific phosphatase and tensin homolog deleted on chromosome 10 (Pten) gene-conditional knockout (KO) mouse carcinogenesis model is highly desirable for studies of prostate cancer biology and chemoprevention due to its close resemblance of primary molecular defect and many histopathological features of human prostate cancer including androgen response and disease progression from prostatic intraepithelial neoplasia to invasive adenocarcinoma. Here, we profiled the proteome and transcriptome of the Pten-KO mouse prostate tumors for global macromolecular expression alterations for signaling changes and biomarker signatures.

METHODS

For proteomics, four pairs of whole prostates from tissue-specific conditional knockout Pten-KO mice (12-15 weeks of age) and their respective wild-type littermates housed in the same cages were analyzed by 8-plex isobaric tags for relative and absolute quantitation iTRAQ. For microarray transcriptomic analysis, three additional matched pairs of prostate/tumor specimens from respective mice at 20 to 22 weeks of age were used. Real-time quantitative reverse transcription-polymerase chain reaction was used to verify the trends of protein and RNA expression changes. Gene Set Enrichment Analysis and Ingenuity Pathway Analysis were carried out for bioinformatic characterizations of pathways and networks.

RESULTS

At the macromolecular level, proteomic and transcriptomic analyses complement and cross-validate to reveal overexpression signatures including inflammation and immune alterations, in particular, neutrophil/myeloid lineage suppressor cell features, chromatin/histones, ion and nutrient transporters, and select glutathione peroxidases and transferases in Pten-KO prostate tumors. Suppressed expression patterns in the Pten-KO prostate tumors included glandular differentiation such as secretory proteins and androgen receptor targets, smooth muscle features, and endoplasmic reticulum stress proteins. Bioinformatic analyses identified immune and inflammation responses as the most profound macromolecular landscape changes, and the predicted key nodal activities through Akt, nuclear factor-kappaB, and P53 in the Pten-KO prostate tumor. Comparison with other genetically modified mouse prostate carcinogenesis models revealed notable molecular distinctions, especially the dominance of immune and inflammation features in the Pten-KO prostate tumors.

CONCLUSIONS

Our work identified prominent macromolecular signatures and key nodal molecules that help to illuminate the patho- and immunobiology of Pten-loss driven prostate cancer and can facilitate the choice of biomarkers for chemoprevention and interception studies in this clinically relevant mouse prostate cancer model.

Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

The discovery of biomarkers able to detect cancer at an early stage, to evaluate its aggressiveness, and to predict the response to therapy remains a major challenge in clinical oncology and precision medicine. In this review, we summarize recent achievements in the discovery and development of cancer biomarkers. We also highlight emerging innovative methods in biomarker discovery and provide insights into the challenges faced in their evaluation and validation.

Pyranocoumarin Tissue Distribution, Plasma Metabolome and Prostate Transcriptome Impacts of Sub-Chronic Exposure to Korean Angelica Supplement in Mice

Herbal products containing Korean Angelica gigas Nakai (AGN) root extract are marketed as dietary supplements for memory enhancement, pain killing, and female menopausal symptom relief. We have shown the anticancer activities of AGN supplements in mouse models. To facilitate human anticancer translational research, we characterized the tissue distribution of AGN marker pyranocoumarin compounds decursin (D) and decursinol angelate (DA) ([Formula: see text]% in AGN) and their metabolite decursinol (DOH), assessed the safety of sub-chronic AGN dietary exposure in mice, and explored its impact on plasma aqueous metabolites and the prostate transcriptome. The data show that after a gavage dose, plasma contained readily detectable DOH, but little D and DA, mirroring patterns in the liver. Extra-hepatic tissues retained greater levels of DA and D than the liver did. For sub-chronic exposures, male mice were provided ad libitum AIN93M-pellet diets with 0.5 and 1% AGN for six weeks. No adverse effects were observed on the plasma biochemistry markers of liver and kidney integrity in spite of their enlargement. Histopathological examinations of the liver, kidney and other visceral organs did not reveal tissue abnormalities. Metabolomic assessment of plasma from mice fed the 1%-AGN diet suggested metabolic shifts of key amino acids especially in the methionine-cysteine cycle, purine cycle, and glycolysis-citrate cycle. Prostate transcriptomic profiling identified gene signature changes in the metabolisms of drugs, lipids and cellular energetics, neuro-muscular features, immunity and inflammation, and tumor suppressor/oncogene balance. The safety profile was corroborated with a daily [Formula: see text] injection of AGN extract (100-300[Formula: see text]mg/kg) for four weeks, which resulted in much greater systemic pyranocoumarin exposure than the dietary route did.

Serial analysis of 38 proteins during the progression of human breast tumor in mice using an antibody colocalization microarray

Proteins in serum or plasma hold great potential for use in disease diagnosis and monitoring. However, the correlation between tumor burden and protein biomarker concentration has not been established. Here, using an antibody colocalization microarray, the protein concentration in serum was measured and compared with the size of mammary xenograft tumors in 11 individual mice from the time of injection; seven blood samples were collected from each tumor-bearing mouse as well as control mice on a weekly basis. The profiles of 38 proteins detected in sera from these animals were analyzed by clustering, and we identified 10 proteins with the greatest relative increase in serum concentration that correlated with growth of the primary mammary tumor. To evaluate the diagnosis of cancer based on these proteins using either an absolute threshold (i.e. a concentration cutoff) or self-referenced differential threshold based on the increase in concentration before cell injection, receiver operating characteristic curves were produced for 10 proteins with increased concentration, and the area under curve was calculated for each time point based on a single protein or on a panel of proteins, in each case showing a rapid increase of the area under curve. Next, the sensitivity and specificity of individual and optimal protein panels were calculated, showing high accuracy as early as week 2. These results provide a foundation for studies of tumor growth through measuring serial changes of protein concentration in animal models.

Divide and conquer: subproteomic approaches toward gastric cancer biomarker and drug target discovery

The discovery of biomarkers for early detection and treatment for gastric cancer are two important gaps that proteomics have the potential to fill. Advancements in mass spectrometry, sample preparation and separation strategies are crucial to proteomics-based discoveries and subsequent translations from bench to bedside. A great number of studies exploiting various subproteomic approaches have emerged for higher-resolution analysis (compared with shotgun proteomics) that permit interrogation of different post-translational and subcellular compartmentalized forms of the same proteins as determinants of disease phenotypes. This is a unique and key strength of proteomics over genomics. In this review, the salient features, competitive edges and pitfalls of various subproteomic approaches are discussed. We also highlight valuable insights from several subproteomic studies that have increased our understanding of the molecular etiology of gastric cancer and the findings that led to the discovery of potential biomarkers/drug targets that were otherwise not revealed by conventional shotgun expression proteomics.

A maximum Likelihood Approach to Analyzing Incomplete Longitudinal Data in Mammary Tumor Development Experiments with Mice

Longitudinal mammary tumor development studies using mice as experimental units are affected by i) missing data towards the end of the study by natural death or euthanasia, and ii) the presence of censored data caused by the detection limits of instrumental sensitivity. To accommodate these characteristics, we investigate a test to carry out K-group comparisons based on maximum likelihood methodology. We derive a relevant likelihood ratio test based on general distributions, investigate its properties of based on theoretical propositions, and evaluate the performance of the test via a simulation study. We apply the results to data extracted from a study designed to investigate the development of breast cancer in mice.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

Tumor-derived secretory clusterin induces epithelial-mesenchymal transition and facilitates hepatocellular carcinoma metastasis

Hepatocellular carcinoma (HCC) is the third most common cause of cancer mortality. Metastasis is the major concern that causes death in HCC. The goal of this study was to identify tumor-derived proteins in serum during HCC metastasis using an orthotopic xenograft tumor model and explore the role of key protein in HCC metastasis. Serum samples collected from HCCLM3-R metastatic HCC tumor model at specific stages of metastasis (1 wk, 3 wks and 6 wks) were subjected to iTRAQ labeling followed by 2DLC-ESI-MS/MS analysis. Twenty tumor-derived proteins were identified through human specific peptides. Secretory clusterin (sCLU), which was significantly upregulated during cancer progression and metastasis, was chosen for further study. The expression of sCLU was significantly higher in metastatic HCC cell lines and samples from metastatic HCC patients. ShRNA-mediated down-regulation of sCLU resulted in a reduced migratory capacity in HCC cell lines, as well as a reduction in pulmonary metastasis in vivo. Overexpression of sCLU in HepG2 cell line showed increased cell migratory ability. Further study found that sCLU contributed to HCC migration and epithelial-mesenchymal transition (EMT) in vitro, and metastasis in vivo. In addition, sCLU also plays an important role in the regulation of TGF-β1-smad3 signaling. These findings suggest that sCLU may promote HCC metastasis via the induction of EMT process and may be a candidate target for HCC therapy.

2D-DIGE analysis of sera from transgenic mouse models reveals novel candidate protein biomarkers for human gastric cancer

The gp130(F/F) genetically engineered mouse (GEM) model reproducibly and predictably develops a gastric adenoma phenotype resembling the primary lesions of human intestinal-type gastric cancer (GC). Accordingly, changes to the serum proteome of gp130(F/F) mice may uncover early-stage GC biomarkers. Here, we have employed several double and compound mutant GEM strains that display distinct phenotypes with respect to gastric tumour load and inflammatory response, thereby mimicking different states of inflammation-associated early-stage GC in humans. This allowed us to distinguish between proteomic changes associated with tumourigenesis rather than confounding systemic inflammation. The comparative proteomic workflow involved depletion of high abundance proteins, 2D-DIGE analysis and protein identification by LC-MS/MS. The differential expression of 112 2D-DIGE spots specifically correlated with the tumour-bearing phenotype, corresponding to 31 murine proteins and their 28 human orthologues. Eight proteins were selected for validation in GC patient sera versus healthy controls. Significant increases in serum apolipoprotein E and haptoglobin, and decreases in afamin and clusterin, were confirmed by ELISA. Receiver operating characteristic analysis revealed that these proteins may be more sensitive and specific discriminators of GC than the existing clinical marker CA72-4.

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 profiles associated with inflammation, angiogenesis, and cancer

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 Kras(G12D) 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.

Proteome and transcriptome profiles of a Her2/Neu-driven mouse model of breast cancer

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 multiple reaction monitoring-MS. The availability of these datasets will contribute positively to clinical proteomics.

Mouse phenogenomics, toolbox for functional annotation of human genome

Mouse models are crucial for the functional annotation of human genome. Gene modification techniques including gene targeting and gene trap in mouse have provided powerful tools in the form of genetically engineered mice (GEM) for understanding the molecular pathogenesis of human diseases. Several international consortium and programs are under way to deliver mutations in every gene in mouse genome. The information from studying these GEM can be shared through international collaboration. However, there are many limitations in utility because not all human genes are knocked out in mouse and they are not yet phenotypically characterized by standardized ways which is required for sharing and evaluating data from GEM. The recent improvement in mouse genetics has now moved the bottleneck in mouse functional genomics from the production of GEM to the systematic mouse phenotype analysis of GEM. Enhanced, reproducible and comprehensive mouse phenotype analysis has thus emerged as a prerequisite for effectively engaging the phenotyping bottleneck. In this review, current information on systematic mouse phenotype analysis and an issue-oriented perspective will be provided.

Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer

Despite widespread interest, few serum biomarkers have been introduced to the clinic over the past 20 years. Each approach to ovarian cancer biomarker discovery has its own advantages and disadvantages and it seems likely that a global biomarker discovery platform that mines all possible sources for biomarkers might be more useful. Such data could be combined with information from relevant microarray data, bioinformatic analyses and literature searches. This proposed integrated systems biology approach has the potential to yield promising ovarian cancer markers for diagnosis, prognosis and monitoring of patients during therapy.

Identification of breast cancer biomarkers in transgenic mouse models: A proteomics approach

PURPOSE

Transgenic mouse models for cancer circumvent many challenges that hamper human studies aimed at biomarker discovery. Lower biological variances among mice combined with controllable factors such as food uptake and health status may enable the detection of more subtle protein expression differences. This is envisioned to result in the identification of biomarkers better discriminating cancer cases from controls.

EXPERIMENTAL DESIGN

The current study used two innovative mouse models for breast-cancer to identify new serum biomarkers. Multi-analyte profiling technique was used to analyze 70 proteins in individual serum samples of non-tumor and mammary tumor-bearing Tg.NK (MMTV/c-neu) mice.

RESULTS

A small set of proteins fully differentiated tumor samples from controls. These comprised osteopontin, interleukin-18, cystatin C and CD40 antigen. Comparison of protein expression in another breast-cancer mouse model, the humanized p53.R270H mice, showed common discriminatory expression of osteopontin. However, other biomarkers showed distinct expression in the two different breast-cancer models, indicating that different mammary tumor sub-types with respect to molecular and estrogen receptor status reveal divergent serum biomarker sets.

CONCLUSIONS AND CLINICAL RELEVANCE

The current study supports the concept that serum proteins can discriminate mammary tumor cases from controls, and yielded interesting biomarkers that need further testing and validation in human studies.

Mouse-specific tandem IgY7-SuperMix immunoaffinity separations for improved LC-MS/MS coverage of the plasma proteome

We report on a mouse specific SuperMix immunoaffinity separation system for separating low-abundance proteins from high and moderate abundance proteins in mouse plasma. When applied in tandem with a mouse IgY7 column that removes the seven most abundant proteins in plasma, the SuperMix column captures more than 100 additional moderate abundance proteins, thus allowing significant enrichment of low-abundance proteins in the flow-through fraction. A side-by-side comparison of results obtained from 2D-LC-MS/MS analyses of flow-through samples from IgY7 and SuperMix columns revealed a nearly 2-fold improvement in the overall proteome coverage. Detection of low-abundance proteins was also enhanced, as evidenced by a more than 2-fold increase in the coverage of cytokines, growth factors, and other low-abundance proteins. Moreover, the tandem separations are automated, reproducible, and allow effective identification of protein abundance differences from LC-MS/MS analyses. Considering the overall reproducibility and increased sensitivity using the IgY7-SuperMix separation system, we anticipate broad applications of this strategy for biomarker discovery using mouse models.

Serum S100A6 concentration predicts peritoneal tumor burden in mice with epithelial ovarian cancer and is associated with advanced stage in patients

BACKGROUND

Ovarian cancer is the 5th leading cause of cancer related deaths in women. Five-year survival rates for early stage disease are greater than 94%, however most women are diagnosed in advanced stage with 5 year survival less than 28%. Improved means for early detection and reliable patient monitoring are needed to increase survival.

METHODOLOGY AND PRINCIPAL FINDINGS

Applying mass spectrometry-based proteomics, we sought to elucidate an unanswered biomarker research question regarding ability to determine tumor burden detectable by an ovarian cancer biomarker protein emanating directly from the tumor cells. Since aggressive serous epithelial ovarian cancers account for most mortality, a xenograft model using human SKOV-3 serous ovarian cancer cells was established to model progression to disseminated carcinomatosis. Using a method for low molecular weight protein enrichment, followed by liquid chromatography and mass spectrometry analysis, a human-specific peptide sequence of S100A6 was identified in sera from mice with advanced-stage experimental ovarian carcinoma. S100A6 expression was documented in cancer xenografts as well as from ovarian cancer patient tissues. Longitudinal study revealed that serum S100A6 concentration is directly related to tumor burden predictions from an inverse regression calibration analysis of data obtained from a detergent-supplemented antigen capture immunoassay and whole-animal bioluminescent optical imaging. The result from the animal model was confirmed in human clinical material as S100A6 was found to be significantly elevated in the sera from women with advanced stage ovarian cancer compared to those with early stage disease.

CONCLUSIONS

S100A6 is expressed in ovarian and other cancer tissues, but has not been documented previously in ovarian cancer disease sera. S100A6 is found in serum in concentrations that correlate with experimental tumor burden and with clinical disease stage. The data signify that S100A6 may prove useful in detecting and/or monitoring ovarian cancer, when used in concert with other biomarkers.