Proteomic identification and profiling of canine lymphoma patients

Salivary proteomics in monitoring the therapeutic response of canine oral melanoma

Saliva biomarkers are suitable for monitoring the therapeutic response of canine oral melanoma (COM), because saliva directly contacts the tumor, and saliva collection is non-invasive, convenient and cost effective. The present study aimed to investigate novel biomarkers from the salivary proteome of COM treated with surgery and a chemotherapy drug, carboplatin, 1-6 times, using a liquid chromatography-tandem mass spectrometry approach. The expression of a potential salivary biomarker, ubiquitin D (UBD), was observed and verified by western blot analysis. A significantly increased ratio of free UBD (fUBD) to conjugated UBD (cUBD) was shown in the pre-surgery stage (PreS) in OM dogs with short-term survival (STS) (less than 12 months after surgery) compared with that with long-term survival (more than 12 months after surgery). In dogs with STS, the ratio was also shown to be augmented in PreS compared with that after surgery, followed by treatment with carboplatin twice, 4 and 5 times [After treatment (AT)2, AT4 and AT5]. In addition, the expression of fUBD was enhanced in PreS compared with that of AT2 in the STS group. In conclusion, this study revealed that a ratio of fUBD to cUBD in PreS was plausibly shown to be a potential prognostic biomarker for survival in dogs with OM.

Prospective evaluation of the lymph node proteome in dogs with multicentric lymphoma supplemented with sulforaphane

Background

Lymphoma (LSA) is a common malignancy in dogs. Epigenetic changes are linked to LSA pathogenesis and poor prognosis in humans, and LSA pathogenesis in dogs. Sulforaphane (SFN), an epigenetic‐targeting compound, has recently gained interest in relation to cancer prevention and therapy.

Objective

Examine the impact of oral supplementation with SFN on the lymph node proteome of dogs with multicentric LSA.

Animals

Seven client‐owned dogs with multicentric LSA.

Methods

Prospective, nonrandomized, noncontrolled study in treatment‐naïve dogs with intermediate or large cell multicentric LSA. Lymph node cell aspirates were obtained before and after 7 days of oral supplementation with SFN, and analyzed via label‐free mass spectrometry, immunoblots, and Gene Set Enrichment Analysis.

Results

There was no clinical response and no adverse events attributed to SFN. For individual dogs, the expression of up to 650 proteins changed by at least 2‐fold (range, 2‐100) after supplementation with SFN. When all dogs where analyzed together, 14 proteins were significantly downregulated, and 10 proteins were significantly upregulated after supplementation with SFN (P < .05). Proteins and gene sets impacted by SFN were commonly involved in immunity, response to oxidative stress, gene transcription, apoptosis, protein transport, maturation and ubiquitination.

Conclusions and Clinical Importance

Sulforaphane is associated with major changes in the proteome of neoplastic lymphocytes in dogs.

Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools

Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (H&E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R² = 0.8783; P < 0.0001). This simple approach is amenable to other similar analyses, including that of human tissues. Widespread adoption of these tools aims to democratize ML and improve precision in traditionally qualitative tasks in histopathology-based research.

Proteomic Differences in Feline Fibrosarcomas Grown Using Doxorubicin-Sensitive and -Resistant Cell Lines in the Chick Embryo Model

Proteomic analyses are rapid and powerful tools that are used to increase the understanding of cancer pathogenesis, discover cancer biomarkers and predictive markers, and select and monitor novel targets for cancer therapy. Feline injection-site sarcomas (FISS) are aggressive skin tumours with high recurrence rates, despite treatment with surgery, radiotherapy, and chemotherapy. Doxorubicin is a drug of choice for soft tissue sarcomas, including FISS. However, multidrug resistance is one of the major causes of chemotherapy failure. The main aim of the present study was to identify proteins that differentiate doxorubicin-resistant from doxorubicin-sensitive FISS using two-dimensional gel electrophoresis (2DE), followed by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) analysis. Using the three-dimensional (3D) preclinical in ovo model, which resembles features of spontaneous fibrosarcomas, three significantly (p ≤ 0.05) differentially expressed proteins were identified in tumours grown from doxorubicin-resistant fibrosarcoma cell lines (FFS1 and FFS3) in comparison to the doxorubicin-sensitive one (FFS5): Annexin A5 (ANXA5), Annexin A3 (ANXA3), and meiosis-specific nuclear structural protein 1 (MNS1). Moreover, nine other proteins were significantly differentially expressed in tumours grown from the high doxorubicin-resistant cell line (FFS1) in comparison to sensitive one (FFS5). This study may be the first proteomic fingerprinting of FISS reported, identifying potential candidates for specific predictive biomarkers and research targets for doxorubicin-resistant FISS.

The Current State of Clinical Application of Serum Biomarkers for Canine Lymphoma

Serum biomarkers of canine lymphoma activity for diagnosis, prognosis, and therapy monitoring have been of clinical interest for more than a decade. Tumor products, biochemical enzymes, cytokines, metabolic profiling, leakage enzymes, as well as serum proteins have been studied as biomarkers for lymphoma. Multiple biomarkers combined have been shown to be most sensitive and specific. C-reactive protein, thymidine kinase 1, and haptoglobin have been most extensively studied and commercialized in diagnostic tests, the TK Canine Cancer Panel and the Canine Lymphoma Blood Test. These tests have been evaluated either in cohorts of diseased and healthy dogs or in prospective studies of ill dogs, respectively, for application to clinical decision-making. Some evidence exists for application of these tests, but large-scale studies are lacking in a broad range of lymphoma forms. These biomarkers are commonly elevated at diagnosis and at relapse. Further study is necessary to determine if early intervention guided by biomarker elevation will improve quantity or quality of life for dogs with lymphoma.

The immunosignature of canine lymphoma: characterization and diagnostic application

Background

Cancer diagnosis in both dogs and humans is complicated by the lack of a non-invasive diagnostic test. To meet this clinical need, we apply the recently developed immunosignature assay to spontaneous canine lymphoma as clinical proof-of-concept. Here we evaluate the immunosignature as a diagnostic for spontaneous canine lymphoma at both at initial diagnosis and evaluating the disease free interval following treatment.

Methods

Sera from dogs with confirmed lymphoma (B cell n = 38, T cell n = 11) and clinically normal dogs (n = 39) were analyzed. Serum antibody responses were characterized by analyzing the binding pattern, or immunosignature, of serum antibodies on a non-natural sequence peptide microarray. Peptides were selected and tested for the ability to distinguish healthy dogs from those with lymphoma and to distinguish lymphoma subtypes based on immunophenotype. The immunosignature of dogs with lymphoma were evaluated for individual signatures. Changes in the immunosignatures were evaluated following treatment and eventual relapse.

Results

Despite being a clonal disease, both an individual immunosignature and a generalized lymphoma immunosignature were observed in each dog. The general lymphoma immunosignature identified in the initial set of dogs (n = 32) was able to predict disease status in an independent set of dogs (n = 42, 97% accuracy). A separate immunosignature was able to distinguish the lymphoma based on immunophenotype (n = 25, 88% accuracy). The individual immunosignature was capable of confirming remission three months following diagnosis. Immunosignature at diagnosis was able to predict which dogs with B cell lymphoma would relapse in less than 120 days (n = 33, 97% accuracy).

Conclusion

We conclude that the immunosignature can serve as a multilevel diagnostic for canine, and potentially human, lymphoma.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-657) contains supplementary material, which is available to authorized users.

Immunophenotypic characterization of canine malignant lymphoma: a retrospective study of cases diagnosed in Poland Lower Silesia, over the period 2011-2013

Lymphoma is the most frequently diagnosed cancer of the canine haematopoietic system. In this study, the flow cytometry and polymerase chain reaction (PCR) analysis were used to characterize a series of canine lymphomas in detail. The aim of this study was to determine the incidence of B- and T-cell high-grade lymphomas and their immunophenotypic characterization in Lower Silesia, Poland. The results show that the frequency of each type of lymphoma is 71% for B-cell and 17% for T-cell lymphomas. In two cases the PCR techniques confirmed the presence of simultaneous double gene rearrangements of the BCR and TCR receptors.

Proteogenomics of selective susceptibility to endotoxin using circulating acute phase biomarkers and bioassay development in sheep: a review

Scientists have injected endotoxin into animals to investigate and understand various pathologies and novel therapies for several decades. Recent observations have shown that there is selective susceptibility to Escherichia coli lipopolysaccharide (LPS) endotoxin in sheep, despite having similar breed characteristics. The reason behind this difference is unknown, and has prompted studies aiming to explain the variation by proteogenomic characterisation of circulating acute phase biomarkers. It is hypothesised that genetic trait, biochemical, immunological and inflammation marker patterns contribute in defining and predicting mammalian response to LPS. This review discusses the effects of endotoxin and host responses, genetic basis of innate defences, activation of the acute phase response (APR) following experimental LPS challenge, and the current approaches employed in detecting novel biomarkers including acute phase proteins (APP) and micro-ribonucleic acids (miRNAs) in serum or plasma. miRNAs are novel targets for elucidating molecular mechanisms of disease because of their differential expression during pathological, and in healthy states. Changes in miRNA profiles during a disease challenge may be reflected in plasma. Studies show that gel-based two-dimensional electrophoresis (2-DE) coupled with either matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) or liquid chromatography–mass spectrometry (LC-MS/MS) are currently the most used methods for proteome characterisation. Further evidence suggests that proteomic investigations are preferentially shifting from 2-DE to non-gel based LC-MS/MS coupled with data extraction by sequential window acquisition of all theoretical fragment-ion spectra (SWATH) approaches that are able to identify a wider range of proteins. Enzyme-linked immunosorbent assay (ELISA), quantitative real-time polymerase chain reaction (qRT-PCR), and most recently proteomic methods have been used to quantify low abundance proteins such as cytokines. qRT-PCR and next generation sequencing (NGS) are used for the characterisation of miRNA. Proteogenomic approaches for detecting APP and novel miRNA profiling are essential in understanding the selective resistance to endotoxin in sheep. The results of these methods could help in understanding similar pathology in humans. It might also be helpful in the development of physiological and diagnostic screening assays for determining experimental inclusion and endpoints, and in clinical trials in future.

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.

A proteomic analysis of serum from dogs before and after a controlled weight-loss program

The objective of this study was to investigate how weight-loss program would alter the proteome of the serum of Beagle dogs. For this purpose, serum samples from 5 Beagle dogs, before and after weight loss, were analyzed using 2-dimensional electrophoresis. Protein profiles of all samples were obtained, divided into 2 classes (obese and lean), and compared using specific 2-dimensional software, giving a total of 144 spot matches. Statistical analysis revealed 3 spot matches whose expressions were modulated in response to weight loss: 2 protein spots were upregulated and 1 protein spot was downregulated in the obese state in comparison with the lean state of the dogs. Mass spectrometric identification of differentially regulated spots revealed that these protein spots corresponded to retinol-binding protein 4, clusterin precursor, and α-1 antitrypsin, respectively, which could be considered potential markers of obesity and obesity-related disease processes in dogs.

Proteomic evaluation of sheep serum proteins

BACKGROUND

The applications of proteomic strategies to ovine medicine remain limited. The definition of serum proteome may be a good tool to identify useful protein biomarkers for recognising sub-clinical conditions and overt disease in sheep. Findings from bovine species are often directly translated for use in ovine medicine. In order to characterize normal protein patterns and improve knowledge of molecular species-specific characteristics, we generated a two-dimensional reference map of sheep serum. The possible application of this approach was tested by analysing serum protein patterns in ewes with mild broncho-pulmonary disease, which is very common in sheep and in the peripartum period which is a stressful time, with a high incidence of infectious and parasitic diseases.

RESULTS

This study generated the first reference 2-DE maps of sheep serum. Overall, 250 protein spots were analyzed, and 138 identified.Compared with healthy sheep, serum protein profiles of animals with rhino-tracheo-bronchitis showed a significant decrease in protein spots identified as transthyretin, apolipoprotein A1 and a significant increase in spots identified as haptoglobin, endopin 1b and alpha1B glycoprotein.In the peripartum period, haptoglobin, alpha-1-acid glycoprotein, apolipoprotein A1 levels rose, while transthyretin content dropped.

CONCLUSIONS

This study describes applications of proteomics in putative biomarker discovery for early diagnosis as well as for monitoring the physiological and metabolic situations critical for ovine welfare.

Recent advances in computational analysis of mass spectrometry for proteomic profiling

The proteome, defined as an organism's proteins and their actions, is a highly complex end-effector of molecular and cellular events. Differing amounts of proteins in a sample can be indicators of an individual's health status; thus, it is valuable to identify key proteins that serve as 'biomarkers' for diseases. Since the proteome cannot be simply inferred from the genome due to pre- and posttranslational modifications, a direct approach toward mapping the proteome must be taken. The difficulty in evaluating a large number of individual proteins has been eased with the development of high-throughput methods based on mass spectrometry (MS) of peptide or protein mixtures, bypassing the time-consuming, laborious process of protein purification. However, proteomic profiling by MS requires extensive computational analysis. This article describes key issues and recent advances in computational analysis of mass spectra for biomarker identification.

Biomarkers in veterinary cancer screening: Applications, limitations and expectations

The identification of biomarkers that distinguish diseased from normal individuals is of intense interest in many health-related fields. Potential applications for biomarkers in veterinary oncology include diagnosis, staging, prognosis and monitoring responses to therapy. By definition, effective biomarkers for cancer screening facilitate disease identification in sub-clinically affected patients and lead to subsequent improvements in clinical outcome. Although the methods and techniques of biomarker discovery and clinical application are translatable from humans to animals, veterinary medicine has lagged behind its human counterpart in several areas. Veterinarians have previously had a flawed understanding of how to use biomarker assays appropriately and have not had the positive influence on product research and development that could advance this field. The controversies, potentials biases, and considerations relative to the clinical application of biomarker assays for cancer screening are discussed in this review.