Automated brain tumor biopsy prediction using single-labeling cDNA microarrays-based gene expression profiling

Protein Tyrosine Phosphatase Receptor Zeta 1 as a Potential Target in Cancer Therapy and Diagnosis

Protein tyrosine phosphatase receptor zeta 1 (PTPRZ1) is a type V transmembrane tyrosine phosphatase that is highly expressed during embryonic development, while its expression during adulthood is limited. PTPRZ1 is highly detected in the central nervous system, affecting oligodendrocytes' survival and maturation. In gliomas, PTPRZ1 expression is significantly upregulated and is being studied as a potential cancer driver and as a target for therapy. PTPRZ1 expression is also increased in other cancer types, but there are no data on the potential functional significance of this finding. On the other hand, low PTPRZ1 expression seems to be related to a worse prognosis in some cancer types, suggesting that in some cases, it may act as a tumor-suppressor gene. These discrepancies may be due to our limited understanding of PTPRZ1 signaling and tumor microenvironments. In this review, we present evidence on the role of PTPRZ1 in angiogenesis and cancer and discuss the phenomenal differences among the different types of cancer, depending on the regulation of its tyrosine phosphatase activity or ligand binding. Clarifying the involved signaling pathways will lead to its efficient exploitation as a novel therapeutic target or as a biomarker, and the development of proper therapeutic approaches.

Proteomic analysis of minute amount of colonic biopsies by enteroscopy sampling

Colorectal cancer (CRC) is one of the most common types of malignant tumor worldwide. Currently, although many researchers have been devoting themselves in CRC studies, the process of locating biomarkers for CRC early diagnosis and prognostic is still very slow. Using a centrifugal proteomic reactor-based proteomic analysis of minute amount of colonic biopsies by enteroscopy sampling, 2620 protein groups were quantified between cancer mucosa and adjacent normal colorectal mucosa. Of which, 403 protein groups were differentially expressed with statistic significance between cancer and normal tissues, including 195 up-regulated and 208 down-regulated proteins in cancer tissues. Three proteins (SOD3, PRELP and NGAL) were selected for further Western blot validation. And the resulting Western blot experimental results were consistent with the quantitative proteomic data. SOD3 and PRELP are down-regulated in CRC mucosa comparing to adjacent normal tissue, while NGAL is up-regulated in CRC mucosa. In conclusion, the centrifugal proteomic reactor-based label-free quantitative proteomic approach provides a highly sensitive and powerful tool for analyzing minute protein sample from tiny colorectal biopsies, which may facilitate CRC biomarkers discovery for diagnoses and prognoses.

Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a dismal prognosis. However, while most patients die within the first year of diagnosis, very rarely, a few patients can survive for >10 years. Better understanding the molecular characteristics of the pancreatic adenocarcinomas from these very-long-term survivors (VLTS) may provide clues for personalized medicine and improve current pancreatic cancer treatment. To extend our previous investigation, we examined the proteomes of individual pancreas tumor tissues from a group of VLTS patients (survival ≥10 years) and short-term survival patients (STS, survival <14 months). With a given analytical sensitivity, the protein profile of each pancreatic tumor tissue was compared to reveal the proteome alterations that may be associated with pancreatic cancer survival. Pathway analysis of the differential proteins identified suggested that MYC, IGF1R and p53 were the top three upstream regulators for the STS-associated proteins, and VEGFA, APOE and TGFβ-1 were the top three upstream regulators for the VLTS-associated proteins. Immunohistochemistry analysis using an independent cohort of 145 PDAC confirmed that the higher abundance of ribosomal protein S8 (RPS8) and prolargin (PRELP) were correlated with STS and VLTS, respectively. Multivariate Cox analysis indicated that 'High-RPS8 and Low-PRELP' was significantly associated with shorter survival time (HR=2.69, 95% CI 1.46-4.92, P=0.001). In addition, galectin-1, a previously identified protein with its abundance aversely associated with pancreatic cancer survival, was further evaluated for its significance in cancer-associated fibroblasts. Knockdown of galectin-1 in pancreatic cancer-associated fibroblasts dramatically reduced cell migration and invasion. The results from our study suggested that PRELP, LGALS1 and RPS8 might be significant prognostic factors, and RPS8 and LGALS1 could be potential therapeutic targets to improve pancreatic cancer survival if further validated.

DISP3 promotes proliferation and delays differentiation of neural progenitor cells

DISP3 (PTCHD2), a sterol-sensing domain-containing protein, is highly expressed in neural tissue but its role in neural differentiation is unknown. In the present study we used a multipotent cerebellar progenitor cell line, C17.2, to investigate the impact of DISP3 on the proliferation and differentiation of neural precursors. We found that ectopically expressed DISP3 promotes cell proliferation and alters expression of genes that are involved in tumorigenesis. Finally, the differentiation profile of DISP3-expressing cells was altered, as evidenced by delayed expression of neural specific markers and a reduced capacity to undergo neural differentiation.

Identification of GPM6A and GPM6B as potential new human lymphoid leukemia-associated oncogenes

BACKGROUND

Previously, we found that the Graffi murine leukemia virus (MuLV) is able to induce a wide spectrum of hematologic malignancies in vivo. Using high-density oligonucleotide microarrays, we established the gene expression profiles of several of these malignancies, thereby specifically focusing on genes deregulated in the lymphoid sub-types. We observed over-expression of a variety of genes, including Arntl2, Bfsp2, Gfra2, Gpm6a, Gpm6b, Nln, Fbln1, Bmp7, Etv5 and Celsr1 and, in addition, provided evidence that Fmn2 and Parm-1 may act as novel oncogenes. In the present study, we assessed the expression patterns of eight selected human homologs of these genes in primary human B-cell malignancies, and explored the putative oncogenic potential of GPM6A and GPM6B.

METHODS

The gene expression levels of the selected human homologs were tested in human B-cell malignancies by semi-quantitative RT-PCR. The protein expression profiles of human GPM6A and GPM6B were analyzed by Western blotting. The localization and the effect of GPM6A and GPM6B on the cytoskeleton were determined using confocal and indirect immunofluorescence microscopy. To confirm the oncogenic potential of GPM6A and GPM6B, classical colony formation assays in soft agar and focus forming assays were used. The effects of these proteins on the cell cycle were assessed by flow cytometry analysis.

RESULTS

Using semi-quantitative RT-PCR, we found that most of the primary B-cell malignancies assessed showed altered expression patterns of the genes tested, including GPM6A and GPM6B. Using confocal microscopy, we found that the GPM6A protein (isoform 3) exhibits a punctate cytoplasmic localization and that the GPM6B protein (isoform 4) exhibits a peri-nuclear and punctate cytoplasmic localization. Interestingly, we found that exogenous over-expression of both proteins in NIH/3T3 cells alters the actin and microtubule networks and induces the formation of long filopodia-like protrusions. Additionally, we found that these over-expressing NIH/3T3 cells exhibit anchorage-independent growth and enhanced proliferation rates. Cellular transformation (i.e., loss of contact inhibition) was, however, only observed after exogenous over-expression of GPM6B.

CONCLUSIONS

Our results indicate that several human homologs of the genes found to be deregulated in Graffi MuLV experimental mouse models may serve as candidate biomarkers for human B-cell malignancies. In addition, we found that GPM6A and GPM6B may act as novel oncogenes in the development of these malignancies.

Discovery and validation of DNA hypomethylation biomarkers for liver cancer using HRM-specific probes

Poor prognosis of hepatocellular carcinoma (HCC) associated with late diagnosis necessitates the development of early diagnostic biomarkers. We have previously delineated the landscape of DNA methylation in HCC patients unraveling the importance of promoter hypomethylation in activation of cancer- and metastasis-driving genes. The purpose of the present study was to test the feasibility that genes that are hypomethylated in HCC could serve as candidate diagnostic markers. We use high resolution melting analysis (HRM) as a simple translatable PCR-based method to define methylation states in clinical samples. We tested seven regions selected from the shortlist of genes hypomethylated in HCC and showed that HRM analysis of several of them distinguishes methylation states in liver cancer specimens from normal adjacent liver and chronic hepatitis in the Shanghai area. Such regions were identified within promoters of neuronal membrane glycoprotein M6-B (GPM6B) and melanoma antigen family A12 (MAGEA12) genes. Differences in HRM in the immunoglobulin superfamily Fc receptor (FCRL1) separated invasive tumors from less invasive HCC. The identified biomarkers differentiated HCC from chronic hepatitis in another set of samples from Dhaka. Although the main thrust in DNA methylation diagnostics in cancer is on hypermethylated genes, our study for the first time illustrates the potential use of hypomethylated genes as markers for solid tumors. After further validation in a larger cohort, the identified DNA hypomethylated regions can become important candidate biomarkers for liver cancer diagnosis and prognosis, especially in populations with high risk for HCC development.

Development of robust discriminant equations for assessing subtypes of glioblastoma biopsies

Background:

In the preceding decade, various studies on glioblastoma (Gb) demonstrated that signatures obtained from gene expression microarrays correlate better with survival than with histopathological classification. However, there is not a universal consensus formula to predict patient survival.

Methods:

We developed a gene signature using the expression profile of 47 Gbs through an unsupervised procedure and two groups were obtained. Subsequent to a training procedure through leave-one-out cross-validation, we fitted a discriminant (linear discriminant analysis (LDA)) equation using the four most discriminant probesets. This was repeated for two other published signatures and the performance of LDA equations was evaluated on an independent test set, which contained status of IDH1 mutation, EGFR amplification, MGMT methylation and gene VEGF expression, among other clinical and molecular information.

Results:

The unsupervised local signature was composed of 69 probesets and clearly defined two Gb groups, which would agree with primary and secondary Gbs. This hypothesis was confirmed by predicting cases from the independent data set using the equations developed by us. The high survival group predicted by equations based on our local and one of the published signatures contained a significantly higher percentage of cases displaying IDH1 mutation and non-amplification of EGFR. In contrast, only the equation based on the published signature showed in the poor survival group a significant high percentage of cases displaying a hypothesised methylation of MGMT gene promoter and overexpression of gene VEGF.

Conclusion:

We have produced a robust equation to confidently discriminate Gb subtypes based in the normalised expression level of only four genes.

A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

New biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language1 classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politécnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents.

Gene expression profiling of meningiomas: current status after a decade of microarray-based transcriptomic studies

Purpose

This article provides a review of the transcriptomic expression profiling studies that have been performed on meningiomas so far. We discuss some future prospects and challenges ahead in the field of gene expression profiling.

Methods

We performed a systematic search in the PubMed and EMBASE databases in May 2010 using the following search terms alone or in combination: “meningioma”, “microarray analysis”, “oligonucleotide array sequence analysis”, or “gene expression profiling”. Only original research articles in English that had used RNA hybridized to high-resolution microarray chips to generate gene expression profiles were included.

Results

We identified 13 articles matching the inclusion criteria. All studies had been performed during the last decade.

Conclusions

The main results of the studies can be grouped in three categories: (1) several groups have identified meningioma-specific genes and genes associated with the three WHO grades, and the main histological subtypes of grade I meningiomas; (2) one publication has shown that the general transcription profile of samples of all WHO grades differs in vivo and in vitro; (3) one report provides evidence that microarray technology can be used in an automated fashion to classify tumors. Due to lack of consensus on how microarray data are presented, possible general trends found across the studies are difficult to extract. This could obstruct the discovery of important genes and pathways universally involved in meningioma biology.