Biomarker development for gastrointestinal and ovarian cancer: a proteomic approach

The development of new biomarkers for cancer patients would be advantageous in population screening for the early detection of cancers, pathological diagnosis, assessment of prognosis, tailoring treatment to individuals, and assessment of treatment response. With this in mind different proteomic approaches were used to identify biomarkers which could potentially aid prognosis and predict response in gastrointestinal and ovarian cancer. Raf Kinase Inhibitor Protein (RKIP) was originally purified from bovine brain extracts and named phosphatidylethanolamine-binding protein (PEBP). It has subsequently been shown to be a widely expressed and highly conserved protein. Several recent studies have suggested that RKIP may suppress metastasis in melanoma, prostate, and breast cancer, as reduction or loss of RKIP expression was observed in metastatic cell lines and metastatic tissue. In this part of the project RKIP expression was assessed by immunohistochemistry in tissue microarrays (TMA) from patients with colorectal and ovarian cancer. The results confirmed the findings of earlier studies and suggest that the level of RKIP expression is significantly and inversely associated with metastatic disease and can predict the risk of metastatic relapse in patients with no evidence of metastases at presentation. The level of RKIP expression as a prognostic factor was independent of sex, age, tumour site, mitotic index, lymphovascular invasion and tumour stage. Cytokeratin 18 (CK18) is an epithelial-specific cytokeratin that undergoes cleavage by caspases during apoptosis. Measurement of caspase-cleaved (CK18-NE) or total cytokeratin 18 (CK18) from epithelial-derived tumours could be a simple, non-invasive way to monitor or predict responses to treatment. Soluble plasma CK18-NE and CK18 were measured by ELISA from 73 patients with advanced gastrointestinal adenocarcinomas before treatment and during chemotherapy, as well as 100 healthy volunteers. Both CK18-NE and total CK18 plasma levels were significantly higher in patients compared to the healthy volunteers (p=0.015, p<0.001). The total CK18 baseline plasma levels prior to treatment were significantly higher (p=0.009) in patients who develop progressive disease than those who achieve partial response or stable disease and this correlation was confirmed in an independent validation set. The peak plasma levels of CK18 occurring in any cycle following treatment were also found to be associated with tumour response, but peak levels of CK18-NE did not reach significance (p=0.01, and p=0.07, respectively). A surface-enhanced laser desorption-ionisation mass spectrometry (SELDI-MS) pilot study on serum from 8 oesophageal cancer patients and 8 healthy volunteers revealed a novel biomarker, ~4kDa, downregulated in patients (p=0.012). An expanded 30 tumour/normal study was performed for validation which confirmed the down-regulation of this potential biomarker (p<0.0001). Attempts to identify tentatively suggested that the peptide may be inter-alpha-trypsin inhibitor heavy chain H4 precursor, which was interesting as a cleavage fragment of inter-alpha -trypsin inhibitor heavy chain H4 had been previously found to be up-regulated in patients with ovarian cancer, and down-regulated in patients with breast cancer. However, it was not possible to confidently confirm this identification. In a further part of this study, haptoglobin was found to be significantly more abundant in the serum from patients with oesophageal cancer compared to healthy volunteers. It was straightforward to isolate and identify and would be amenable to immunoassay as there are good antibodies available for confirmation. In conclusion, with the current lack of effective markers of metastatic relapse in colorectal cancer, a straightforward test like RKIP expression in the primary tumour may be a very cost-effective way to identify which patients may derive greater benefit from adjuvant treatment and closer post-operative surveillance, and in patients with advanced gastrointestinal malignancy levels of plasma CK18 are a potential marker of tumour response

Contribution of oncoproteomics to cancer biomarker discovery

Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists

Ovarian cancer molecular pathology.

Peer reviewe

Computational diagnosis and risk evaluation for canine lymphoma

The canine lymphoma blood test detects the levels of two biomarkers, the acute phase proteins (C-Reactive Protein and Haptoglobin). This test can be used for diagnostics, for screening, and for remission monitoring as well. We analyze clinical data, test various machine learning methods and select the best approach to these problems. Three family of methods, decision trees, kNN (including advanced and adaptive kNN) and probability density evaluation with radial basis functions, are used for classification and risk estimation. Several pre-processing approaches were implemented and compared. The best of them are used to create the diagnostic system. For the differential diagnosis the best solution gives the sensitivity and specificity of 83.5% and 77%, respectively (using three input features, CRP, Haptoglobin and standard clinical symptom). For the screening task, the decision tree method provides the best result, with sensitivity and specificity of 81.4% and >99%, respectively (using the same input features). If the clinical symptoms (Lymphadenopathy) are considered as unknown then a decision tree with CRP and Hapt only provides sensitivity 69% and specificity 83.5%. The lymphoma risk evaluation problem is formulated and solved. The best models are selected as the system for computational lymphoma diagnosis and evaluation the risk of lymphoma as well. These methods are implemented into a special web-accessed software and are applied to problem of monitoring dogs with lymphoma after treatment. It detects recurrence of lymphoma up to two months prior to the appearance of clinical signs. The risk map visualisation provides a friendly tool for explanatory data analysis.Comment: 24 pages, 86 references in the bibliography, Significantly extended version with review of lymphoma biomarkers and data mining methods (Three new sections are added: 1.1. Biomarkers for canine lymphoma, 1.2. Acute phase proteins as lymphoma biomarkers and 3.1. Data mining methods for biomarker cancer diagnosis. Flowcharts of data analysis are included as supplementary material (20 pages

Proteomic Studies on the Management of High-Grade Serous Ovarian Cancer Patients: A Mini-Review

Teixit cancerígen; Càncer d'ovaris; ProteòmicaTejido canceroso; Cáncer de ovarios; ProteómicaCancer tissue; Ovarian cancer; ProteomicsHigh-grade serous ovarian cancer (HGSC) remains the most common and deadly subtype of ovarian cancer. It is characterized by its late diagnosis and frequent relapse despite standardized treatment with cytoreductive surgery and platinum-based chemotherapy. The past decade has seen significant advances in the clinical management and molecular understanding of HGSC following the publication of the Cancer Genome Atlas (TCGA) researchers and the introduction of targeted therapies with anti-angiogenic drugs and poly(ADP-ribose) polymerase inhibitors in specific subgroups of patients. We provide a comprehensive review of HGSC, focusing on the most important molecular advances aimed at providing a better understanding of the disease and its response to treatment. We emphasize the role that proteomic technologies are now playing in these two aspects of the disease, through the identification of proteins and their post-translational modifications in ovarian cancer tumors. Finally, we highlight how the integration of proteomics with genomics, exemplified by the work performed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), can guide the development of new biomarkers and therapeutic targets.This work was supported by the PhD4MD collaborative research program between the Vall d’Hebron Research Institute (VHIR) and the Centre for Genomic Regulation (CRG). The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium which is supported by grant PT17/0019 of the PE I + D + i 2013–2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science and Innovation (CTQ2016-80364-P) and “Centro de Excelencia Severo Ochoa 2013–2017”, SEV-2012-0208; the “Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya” (2017SGR595 and 2017SGR1661), from the Instituto de Salud Carlos III (PI15/02238, PI18/01017, CPII18/00027) and from the Ministerio de Economia y Competitividad y Fondos FEDER (RTC-2015-3821). We also acknowledge the support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya

Clinical value of bioelectrical properties of cancerous tissue in advanced epithelial ovarian cancer patients

Currently, there are no valid pre-operatively established biomarkers or algorithms that can accurately predict surgical and clinical outcome for patients with advanced epithelial ovarian cancer (EOC). In this study, we suggest that profiling of tumour parameters such as bioelectrical-potential and metabolites, detectable by electronic sensors, could facilitate the future development of devices to better monitor disease and predict surgical and treatment outcomes. Biopotential was recorded, using a potentiometric measurement system, in ex vivo paired non-cancerous and cancerous omental tissues from advanced stage EOC (n = 36), and lysates collected for metabolite measurement by microdialysis. Consistently different biopotential values were detected in cancerous tissue versus non-cancerous tissue across all cases (p < 0.001). High tumour biopotential levels correlated with advanced tumour stage (p = 0.048) and tumour load, and negatively correlated with stroma. Within our EOC cohort and specifically the high-grade serous subtype, low biopotential levels associated with poorer progression-free survival (p = 0.0179, p = 0.0143 respectively). Changes in biopotential levels significantly correlated with common apoptosis related pathways. Lactate and glucose levels measured in paired tissues showed significantly higher lactate/glucose ratio in tissues with low biopotential (p < 0.01, n = 12). Our study proposes the feasibility of biopotential and metabolite monitoring as a biomarker modality profiling EOC to predict surgical and clinical outcomes

Discovery of Prognostic Markers for Early-Stage High-Grade Serous Ovarian Cancer by Maldi-Imaging

With regard to relapse and survival, early-stage high-grade serous ovarian (HGSOC) patients comprise a heterogeneous group and there is no clear consensus on first-line treatment. Currently, no prognostic markers are available for risk assessment by standard targeted immunohistochemistry and novel approaches are urgently required. Here, we applied MALDI-imaging mass spectrometry (MALDI-IMS), a new method to identify distinct mass profiles including protein signatures on paraffin-embedded tissue sections. In search of prognostic biomarker candidates, we compared proteomic profiles of primary tumor sections from early-stage HGSOC patients with either recurrent (RD) or non-recurrent disease (N = 4; each group) as a proof of concept study. In total, MALDI-IMS analysis resulted in 7537 spectra from the malignant tumor areas. Using receiver operating characteristic (ROC) analysis, 151 peptides were able to discriminate between patients with RD and non-RD (AUC > 0.6 or 0.7). These results confirm that in using IMS, we could identify new candidates to predict clinical outcome and treatment extent for patients with early-stage HGSOC