Discovery of cancer biomarkers through the use of mouse models

Evidence-Based Severity Assessment of Animal Models for Pancreatic Cancer

Animal models are crucial to preclinical oncological research and drug development. Animal experiments must be performed in accordance with the 3R principles of replacement and reduction, if possible, and refinement where these procedures remain crucial. In addition, European Union legislations demand a continuous refinement approach, as well as pro- and retrospective severity assessment. In this study, an objective databased severity assessment was performed in murine models for pancreatic cancer induced by orthotopic, subcutaneous, or intravenous injection of Panc02 cells. Parameters such as body weight change, distress score, perianal temperature, mouse grimace scale, burrowing, nesting behavior, and the concentration of corticosterone in plasma and its metabolites in feces were monitored during tumor progression. The most important parameters were combined into a score and mapped against a reference data set by the Relative Severity Assessment procedure (RELSA) to obtain the maximum achieved severity for each animal (RELSAmax). This scoring revealed a significantly higher RELSAmax for the orthotopic model than for the subcutaneous and intravenous models. However, compared to animal models such as pancreatitis and bile duct ligation, the pancreatic cancer models are shown to be less severe. Data-based animal welfare assessment proved to be a valuable tool for comparing the severity of differently induced cancer models.

Emerging Preclinical Applications of Humanized Mouse Models in the Discovery and Validation of Novel Immunotherapeutics and Their Mechanisms of Action for Improved Cancer Treatment

Cancer therapeutics have undergone immense research over the past decade. While chemotherapies remain the mainstay treatments for many cancers, the advent of new molecular techniques has opened doors for more targeted modalities towards cancer cells. Although immune checkpoint inhibitors (ICIs) have demonstrated therapeutic efficacy in treating cancer, adverse side effects related to excessive inflammation are often reported. There is a lack of clinically relevant animal models to probe the human immune response towards ICI-based interventions. Humanized mouse models have emerged as valuable tools for pre-clinical research to evaluate the efficacy and safety of immunotherapy. This review focuses on the establishment of humanized mouse models, highlighting the challenges and recent advances in these models for targeted drug discovery and the validation of therapeutic strategies in cancer treatment. Furthermore, the potential of these models in the process of uncovering novel disease mechanisms is discussed.

Flavokawain C exhibits anti-tumor effects on in vivo HCT 116 xenograft and identification of its apoptosis-linked serum biomarkers via proteomic analysis

Chalcones and their derivatives belong to the flavonoid family. They have been extensively studied for their anticancer properties and some have been approved for clinical use. In this study, the in vivo anti-tumor activity of flavokawain C (FKC), a naturally occurring chalcone found in Kava (Piper methysticum Forst) was evaluated in HCT 116 cells (colon carcinoma). We also attempted to identify potential biomarkers and/or molecular targets in serum with applicability in predicting treatment outcome. The anti-tumor effects and toxicity of FKC were assessed using the xenograft nude mice model. Cisplatin was used as positive control. The anti-proliferative and apoptotic activities were then evaluated in tumor tissues treated with FKC. Furthermore, two-dimensional electrophoresis (2-DE) followed by protein identification using MALDI-TOF/TOF-MS/MS was performed to compare the serum proteome profiles between healthy nude mice and nude mice bearing HCT 116 tumor treated with vehicle solution and FKC, respectively. Our results showed that FKC treatment significantly inhibited HCT 116 tumor growth. In vivo toxicity studies showed that administration of FKC did not cause damage to major organs and had no significant effect on body weight. FKC was found to induce apoptosis in tumor, and this was associated with increased expression of cleaved caspase-3 and decreased expression of Ki67 in tumor tissues. Our proteomic analysis identified five proteins that changed in abundance - Ig mu chain C region (secreted form), GRP78, hemopexin, kininogen-1 and apolipoprotein E. Overall, our findings demonstrated the potential of FKC as an anti-cancer agent for the treatment of colon carcinoma.

Pancreatic preneoplastic lesions plasma signatures and biomarkers based on proteome profiling of mouse models

Background:

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with a mortality that is almost identical to incidence. Because early detected PDAC is potentially curable, blood-based biomarkers that could detect currently developing neoplasia would improve patient survival and management. PDAC develops from pancreatic intraepithelial neoplasia (PanIN) lesions, graded from low grade (PanIN1) to high grade (PanIN3). We made the hypothesis that specific proteomic signatures from each precancerous stage exist and are detectable in plasma.

Methods:

We explored the peptide profiles of microdissected PanIN cells and of plasma samples corresponding to the different PanIN grade from genetically engineered mouse models of PDAC using capillary electrophoresis coupled to mass spectrometry (CE-MS) and Chip-MS/MS.

Results:

We successfully characterised differential peptides profiles from PanIN microdissected cells. We found that plasma from tumor-bearing mice and age-matched controls exhibit discriminative peptide signatures. We also determined plasma peptide signatures corresponding to low- and high-grade precancerous step present in the mice pancreas using the two mass spectrometry technologies. Importantly, we identified biomarkers specific of PanIN3.

Conclusions:

We demonstrate that benign and advanced PanIN lesions display distinct plasma peptide patterns. This strongly supports the perspectives of developing a non-invasive screening test for prediction and early detection of PDAC.

Chemical compounds from anthropogenic environment and immune evasion mechanisms: potential interactions

An increasing number of studies suggest an important role of host immunity as a barrier to tumor formation and progression. Complex mechanisms and multiple pathways are involved in evading innate and adaptive immune responses, with a broad spectrum of chemicals displaying the potential to adversely influence immunosurveillance. The evaluation of the cumulative effects of low-dose exposures from the occupational and natural environment, especially if multiple chemicals target the same gene(s) or pathway(s), is a challenge. We reviewed common environmental chemicals and discussed their potential effects on immunosurveillance. Our overarching objective was to review related signaling pathways influencing immune surveillance such as the pathways involving PI3K/Akt, chemokines, TGF-β, FAK, IGF-1, HIF-1α, IL-6, IL-1α, CTLA-4 and PD-1/PDL-1 could individually or collectively impact immunosurveillance. A number of chemicals that are common in the anthropogenic environment such as fungicides (maneb, fluoxastrobin and pyroclostrobin), herbicides (atrazine), insecticides (pyridaben and azamethiphos), the components of personal care products (triclosan and bisphenol A) and diethylhexylphthalate with pathways critical to tumor immunosurveillance. At this time, these chemicals are not recognized as human carcinogens; however, it is known that they these chemicalscan simultaneously persist in the environment and appear to have some potential interfere with the host immune response, therefore potentially contributing to promotion interacting with of immune evasion mechanisms, and promoting subsequent tumor growth and progression.

Cancer biomarker discovery: current status and future perspectives

PURPOSE

Cancer is a multigene disease which arises as a result of mutational and epigenetic changes coupled with activation of complex signaling networks. The use of biomarkers for early cancer detection, staging and individualization of therapy might improve patient care. A few fundamental issues such as tumor heterogeneity, a highly dynamic nature of the intrinsic and extrinsic determinants of radio- and chemoresistance, along with the plasticity and diversity of cancer stem cells (CSC) make biomarker development a challenging task. In this review we outline the preclinical strategies of cancer biomarker discovery including genomic, proteomic, metabolomic and microRNomic profiling, comparative genome hybridization (CGH), single nucleotide polymorphism (SNP) analysis, high throughput screening (HTS) and next generation sequencing (NGS). Other promising approaches such as assessment of circulating tumor cells (CTC), analysis of CSC-specific markers and cell-free circulating tumor DNA (ctDNA) are also discussed.

CONCLUSIONS

The emergence of powerful proteomic and genomic technologies in conjunction with advanced bioinformatic tools allows the simultaneous analysis of thousands of biological molecules. These techniques yield the discovery of new tumor signatures, which are sensitive and specific enough for early cancer detection, for monitoring disease progression and for proper treatment selection, paving the way to individualized cancer treatment.

Circulating microRNAs as biomarkers for early detection of diffuse-type gastric cancer using a mouse model

Background:

Diffuse-type gastric cancer (DGC) exhibits rapid disease progression and a poor prognosis. There are no effective serum biomarkers for early detection of DGC. We have established an E-cadherin/p53 double conditional knockout (DCKO) mouse line that recapitulates human DGC morphologically and molecularly. In this study we tried to identify circulating microRNAs (miRNAs) as non-invasive biomarkers for DGC diagnosis using DCKO mice.

Methods:

We performed miRNA microarray and quantitative reverse transcription–PCR analyses of tissue and serum samples from DCKO mice with DGC and age-matched littermate controls.

Results:

Comparative analyses showed that mouse and human primary gastric cancers have similar miRNA expression patterns. Next, we selected some candidate miRNAs highly expressed in sera and cancer tissues of DCKO mice for further evaluation. TaqMan quantitative RT–PCR analyses indicated that four of them, miR-103, miR-107, miR-194 and miR-210, were significantly upregulated in sera of both early and advanced-stage DGC-bearing mice compared with in corresponding controls. Receiver-operating characteristic curve analyses demonstrated that these four miRNAs can discriminate DGC-positive cases from normal ones with high sensitivity and specificity.

Conclusion:

These observations suggest that this mouse model of DGC is useful for identifying serum biomarkers, and we found circulating miRNAs that can accurately detect DGC at an early stage.

Identification of prostate cancer-associated microRNAs in circulation using a mouse model of disease

MicroRNAs (miRNAs) derived from the cell-free fractions of blood are emerging as useful noninvasive markers of cancer. However, many tumors display significant molecular heterogeneity, which is likely to be reflected in the circulating miRNA fingerprints associated with that pathology. One strategy to minimize such heterogeneity is to employ genetically engineered mouse models of human cancer. Here, we describe a method to profile miRNAs in the serum of a mouse model of prostate cancer, TRansgenic Adenocarcinoma of Mouse Prostate (TRAMP), and discuss practical considerations for translating these potential biomarkers into a clinical setting.

Sparc-like protein 1 is a new marker of human glioma progression

High-grade gliomas (glioblastomas) are the most common and deadly brain tumors in adults, currently with no satisfactory treatment available. Apart from de novo glioblastoma, it is currently accepted that these malignancies mainly progress from lower grade glial tumors. However, the molecular entities governing the progression of gliomas are poorly understood. Extracellular and membrane proteins are key biomolecules found at the cell-to-cell communication interface and hence are a promising proteome subpopulation that could help understand the development of glioma. Accordingly, the current study aims at identifying new protein markers of human glioma progression. For this purpose, we used glial tumors generated orthotopically with T98G and U373 human glioma cells in nude mice. This setup allowed also to discriminate the protein origin, namely, human (tumor) or mouse (host). Extracellular and membrane proteins were selectively purified using biotinylation followed by streptavidin affinity chromatography. Isolated proteins were digested and then identified and quantified employing 2D-nano-HPLC-MS/MS analysis. A total of 23 and 27 up-regulated extracellular and membrane proteins were identified in the T98G and U373 models, respectively. Approximately two-thirds of these were predominantly produced by the tumor, whereas the remaining proteins appeared to be mainly overexpressed by the host tissue. Following extensive validation, we have focused our attention on sparc-like protein 1. This protein was further investigated using immunohistochemistry in a large collection of human glioma samples of different grades. The results showed that sparc-like protein 1 expression correlates with glioma grade, suggesting the possible role for this protein in the progression of this malignancy.

Circulating microRNAs: macro-utility as markers of prostate cancer?

The realization that microRNAs (miRNAs) are frequently deregulated in malignancy has had a major impact on cancer research. In particular, the recent finding that highly stable forms of miRNAs can be accurately measured in body fluids, including blood, has generated considerable excitement. Here, we discuss the potential of blood-based circulating miRNAs as diagnostic, prognostic, and predictive biomarkers of prostate cancer. We also describe practical considerations that may influence identification and/or measurement of miRNA biomarkers in the circulation. Finally, evidence is prevented for the emerging concept that circulating miRNAs are actively released by their cells of origin and can modulate gene expression at distal sites. These mobile miRNAs, which we term 'hormomirs' because of their hormone-like characteristics, could act as local or long-range signals to maintain normal homeostasis or influence the development and progression of diseases such as cancer.

Discovery of circulating microRNAs associated with human prostate cancer using a mouse model of disease

Circulating microRNAs (miRNAs) are emerging as useful non-invasive markers of disease. The objective of this study was to use a mouse model of prostate cancer as a tool to discover serum miRNAs that could be assessed in a clinical setting. Global miRNA profiling identified 46 miRNAs at significantly altered levels (p ≤ 0.05) in the serum of TRansgenic Adenocarcinoma of Mouse Prostate (TRAMP) mice with advanced prostate cancer compared to healthy controls. A subset of these miRNAs with known human homologues were validated in an independent cohort of mice and then measured in serum from men with metastatic castration-resistant prostate cancer (mCRPC; n = 25) or healthy men (n = 25). Four miRNAs altered in mice, mmu-miR-141, mmu-miR-298, mmu-miR-346 and mmu-miR-375, were also found to be at differential levels in the serum of men with mCRPC. Three of these (hsa-miR-141, hsa-miR-298 and hsa-miR-375) were upregulated in prostate tumors compared with normal prostate tissue, suggesting that they are released into the blood as disease progresses. Moreover, the intra-tumoral expression of hsa-miR-141 and hsa-miR-375 were predictors of biochemical relapse after surgery. This study is the first to demonstrate that specific serum miRNAs are common between human prostate cancer and a mouse model of the disease, highlighting the potential of such models for the discovery of novel biomarkers.

The membrane-bound mucins: From cell signalling to transcriptional regulation and expression in epithelial cancers

The membrane-bound mucins belong to an ever-increasing family of O-glycoproteins. Based on their structure and localization at the cell surface they are thought to play important biological roles in cell-cell and cell-matrix interactions, in cell signalling and in modulating biological properties of cancer cells. Among them, MUC1 and MUC4 mucins are best characterized. Their altered expression in cancer (overexpression in the respiratory, gastro-intestinal, urogenital and hepato-biliary tracts) indicates an important role for these membrane-bound mucins in tumour progression, metastasis, cancer cell resistance to chemotherapeutics drugs and as specific markers of epithelial cancer cells. Some mechanisms responsible for MUC1 and MUC4 role in tumour cell properties have been deciphered recently. However, much remains to be done in order to understand the molecular mechanisms and signalling pathways that control the expression of membrane-bound mucins during the different steps of tumour progression toward adenocarcinoma and evaluate their potential as prognostic/diagnostic markers and as therapeutic tools. In this review we focus on the molecular mechanisms and signalling pathways known to control the expression of membrane-bound mucins in cancer. We will discuss the mechanisms of regulation at the promoter level (including genetic and epigenetic modifications) that may be responsible for the mucin altered pattern of expression in epithelial cancers.

Targeted proteomic strategy for clinical biomarker discovery

The high complexity and large dynamic range of blood plasma proteins currently prohibit the sensitive and high-throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two-stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N-linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two-stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N-glycoproteome not only reduces the complexity of the analytes, but also targets an information-rich subproteome which is relevant for remote sensing of diseases in the plasma. The N-glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non-invasive and quantitative fashion.

A simple and reliable protocol for mouse serum proteome profiling studies by use of two-dimensional electrophoresis and MALDI TOF/TOF mass spectrometry

Background

Unravelling the serum proteome is the subject of intensified research. In this regard, two-dimensional electrophoresis coupled with MALDI MS analysis is still one of the most commonly used method. Despite some improvements, there is the need for better protocols to enable comprehensive identification of serum proteins.

Here we report a combination of two proteomic strategies, zoom in acidic and neutral part of 2-D gels and an application of two optimised matrix preparations for MALDI-MS analyses to simplify serum proteome mapping.

Results

Mouse serum proteins were separated by 2-D electrophoresis at the pH ranges 3–10 and 4–7, respectively. Then in gel tryptic digests were analysed by MALDI-MS. Notably, sample-matrix preparations consisted of either a thin-layer α-ciano-4-hydroxycinnamic acid (CHCA) matrix deposition or a matrix-layer 2,5-dihydroxybenzoic acid (DHB). This enabled an identification of 90 proteins. The herein reported method enhanced identification of proteins by 32% when compared with previously published studies of mouse serum proteins, using the same approaches. Furthermore, experimental improvements of matrix preparations enabled automatic identification of mouse proteins, even when one of the two matrices failed.

Conclusion

We report a simple and reliable protocol for serum proteome analysis that combines an optimized resolution of 2-D gels spots and improved sample-matrix preparations for MALDI-MS analysis. The protocol allowed automated data acquisition for both CHCA and DHB and simplified the MS data acquisition therefore avoiding time-consuming procedures. The simplicity and reliability of the developed protocol may be applied universally.

Proteomic contributions to personalized cancer care

Cancer impacts each patient and family differently. Our current understanding of the disease is primarily limited to clinical hallmarks of cancer, but many specific molecular mechanisms remain elusive. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies that improve patient prognosis are not widely available for most cancers. Individualized care plans, also described as personalized medicine, still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Here we describe how biological mass spectrometry and proteome analysis interact with other major patient care and research initiatives and present vignettes illustrating efforts in discovery of diagnostic biomarkers for ovarian cancer, development of treatment strategies in lung cancer, and monitoring prognosis and relapse in multiple myeloma patients.

A mouse plasma peptide atlas as a resource for disease proteomics

We present an in-depth analysis of mouse plasma leading to the development of a publicly available repository composed of 568 liquid chromatography-tandem mass spectrometry runs. A total of 13,779 distinct peptides have been identified with high confidence. The corresponding approximately 3,000 proteins are estimated to span a 7 logarithmic range of abundance in plasma. A major finding from this study is the identification of novel isoforms and transcript variants not previously predicted from genome analysis.

Increased throughput and reduced carryover of mass spectrometry-based proteomics using a high-efficiency nonsplit nanoflow parallel dual-column capillary HPLC system

We report a new design of a fully automated, high-efficiency parallel nonsplit nanoflow capillary HPLC system, coupled on-line with linear ion trap (LTQ) and high performance nanoelectrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (nanoESI LTQ-FTICR MS). The system, intended for high-throughput proteome analysis of complex protein mixtures, notably serum and plasma, consists of two reversed-phase trap columns for large volume sample injection with high speed sample loading and desalting and two reversed-phase analytical capillary columns. Through a nanoscale two-position, 10-port switching valve, the whole system is terminated by a 10 microm i.d. of nanoemitter mounted on the nanoelectrospray source in front of the sampling cone of the LTQ-FTICR MS. Gradient elution to both nanoflow-rate capillary columns is simultaneously delivered by a single HPLC system via two independent binary gradient pump systems. The parallel capillary column approach eliminates the time delays for column regeneration/equilibration since one capillary column is used for separating the sample mixtures and delivering the separated fractions to the MS, while the other capillary column is being regenerated and equilibrated. The reproducibility of retention time and peak intensity of the present automated parallel nanoflow-rate capillary HPLC system is comparable to that obtained using a single column configuration. Replicate injections of tryptic digests indicated that this system provided good reproducibility of retention time and peak area on both columns with average CV values of less than 1.08% and 7.04%, respectively. Throughput was increased to 100% for 2-h LC-MS analysis compared to the single capillary column LC-MS pipeline. Application of this system is demonstrated in a plasma proteomic study. A total of 312 868 MSMS events were acquired and 1564 proteins identified with high confidence (Protein Prophet > or = 0.9, and peptides matched > or = 2). Comparison of a series of plasma fractions run using the single-column LC-MS versus the parallel-column LC-MS demonstrated that parallel-column LC-MS system significantly reduced the sample carryover, improved MS data quality and increased the number of MS/MS sequence scan events.

Innovative proteomic approaches for cancer biomarker discovery

Substantial technological advances in proteomics and related computational science have been made in the past few years. These advances overcome in part the complexity and heterogeneity of the human proteome, permitting quantitative analysis and identification of protein changes associated with tumor development. Here, we discuss some of these advances that are uncovering new cancer biomarkers that have potential to detect cancer at early and curable stages and address remaining challenges.

Low-volume, high-throughput sandwich immunoassays for profiling plasma proteins in mice: identification of early-stage systemic inflammation in a mouse model of intestinal cancer

Mouse models of human cancers may provide a valuable resource for the discovery of cancer biomarkers. We have developed a practical strategy for profiling specific proteins in mouse plasma using low-volume sandwich-immunoassays. We used this method to profile the levels of 14 different cytokines, acute-phase reactants, and other cancer markers in plasma from a mouse models of intestinal tumors and their wild-type littermates, using as little as 1.5 microliters of diluted plasma per assay. Many of the proteins were significantly and consistently up-regulated in the mutant mice. The mutant mice could be distinguished nearly perfectly from the wild-type mice based on the combined levels of as few as three markers. Many of the proteins were up-regulated even in the mutant mice with few or no tumors, suggesting the presence of a systemic host response at an early stage of cancer development. These results have implications for the study of host responses in mouse models of cancers and demonstrate the value of a new low-volume, high-throughput sandwich-immunoassay method for sensitively profiling protein levels in cancer.

Quantitative proteomics for identification of cancer biomarkers

Quantitative proteomics can be used for the identification of cancer biomarkers that could be used for early detection, serve as therapeutic targets, or monitor response to treatment. Several quantitative proteomics tools are currently available to study differential expression of proteins in samples ranging from cancer cell lines to tissues to body fluids. 2-DE, which was classically used for proteomic profiling, has been coupled to fluorescence labeling for differential proteomics. Isotope labeling methods such as stable isotope labeling with amino acids in cell culture (SILAC), isotope-coded affinity tagging (ICAT), isobaric tags for relative and absolute quantitation (iTRAQ), and (18) O labeling have all been used in quantitative approaches for identification of cancer biomarkers. In addition, heavy isotope labeled peptides can be used to obtain absolute quantitative data. Most recently, label-free methods for quantitative proteomics, which have the potential of replacing isotope-labeling strategies, are becoming popular. Other emerging technologies such as protein microarrays have the potential for providing additional opportunities for biomarker identification. This review highlights commonly used methods for quantitative proteomic analysis and their advantages and limitations for cancer biomarker analysis.