Identification of circulating biomarkers for differentiating patients with papillary thyroid cancers from benign thyroid tumors

BACKGROUND

This study aimed to identify the potential circulating biomarkers of protein, mRNAs, and long non-coding RNAs (lncRNAs) to differentiate the papillary thyroid cancers from benign thyroid tumors.

METHODS

The study population of 100 patients was classified into identification (10 patients with papillary thyroid cancers and 10 patients with benign thyroid tumors) and validation groups (45 patients with papillary thyroid cancers and 35 patients with benign thyroid tumors). The Sengenics Immunome Protein Array-combined data mining approach using the Open Targets Platform was used to identify the putative protein biomarkers, and their expression validated using the enzyme-linked immunosorbent assay. Next-generation sequencing by Illumina HiSeq was used for the detection of dysregulated mRNAs and lncRNAs. The website Timer v2.0 helped identify the putative mRNA biomarkers, which were significantly over-expressed in papillary thyroid cancers than in adjacent normal thyroid tissue. The mRNA and lncRNA biomarker expression was validated by a real-time polymerase chain reaction.

RESULTS

Although putative protein and mRNA biomarkers have been identified, their serum expression could not be confirmed in the validation cohorts. In addition, seven lncRNAs (TCONS_00516490, TCONS_00336559, TCONS_00311568, TCONS_00321917, TCONS_00336522, TCONS_00282483, and TCONS_00494326) were identified and validated as significantly downregulated in patients with papillary thyroid cancers compared to those with benign thyroid tumors. These seven lncRNAs showed moderate accuracy based on the area under the curve (AUC = 0.736) of receiver operating characteristic in predicting the occurrence of papillary thyroid cancers.

CONCLUSIONS

We identified seven downregulated circulating lncRNAs with the potential for predicting the occurrence of papillary thyroid cancers.

HIV-Associated Cancer Biomarkers: A Requirement for Early Diagnosis

Globally, HIV/AIDS and cancer are increasingly public health problems and continue to exist as comorbidities. The sub-Saharan African region has the largest number of HIV infections. Malignancies previously associated with HIV/AIDS, also known as the AIDS-defining cancers (ADCs) have been documented to decrease, while the non-AIDS defining cancer (NADCs) are on the rise. On the other hand, cancer is a highly heterogeneous disease and precision oncology as the most effective cancer therapy is gaining attraction. Among HIV-infected individuals, the increased risk for developing cancer is due to the immune system of the patient being suppressed, frequent coinfection with oncogenic viruses and an increase in risky behavior such as poor lifestyle. The core of personalised medicine for cancer depends on the discovery and the development of biomarkers. Biomarkers are specific and highly sensitive markers that reveal information that aid in leading to the diagnosis, prognosis and therapy of the disease. This review focuses mainly on the risk assessment, diagnostic, prognostic and therapeutic role of various cancer biomarkers in HIV-positive patients. A careful selection of sensitive and specific HIV-associated cancer biomarkers is required to identify patients at most risk of tumour development, thus improving the diagnosis and prognosis of the disease.

MicroRNA as a Prognostic and Diagnostic Marker in T-Cell Acute Lymphoblastic Leukemia

T cell acute lymphoblastic leukemia (T-ALL) is a biologically and genetically heterogeneous disease with a poor prognosis overall and several subtypes. The neoplastic transformation takes place through the accumulation of numerous genetic and epigenetic abnormalities. There are only a few prognostic factors in comparison to B cell precursor acute lymphoblastic leukemia, which is characterized by a lower variability and more homogeneous course. The microarray and next-generation sequencing (NGS) technologies exploring the coding and non-coding part of the genome allow us to reveal the complexity of the genomic and transcriptomic background of T-ALL. miRNAs are a class of non-coding RNAs that are involved in the regulation of cellular functions: cell proliferations, apoptosis, migrations, and many other processes. No miRNA has become a significant prognostic and diagnostic factor in T-ALL to date; therefore, this topic of investigation is extremely important, and T-ALL is the subject of intensive research among scientists. The altered expression of many genes in T-ALL might also be caused by wide miRNA dysregulation. The following review focuses on summarizing and characterizing the microRNAs of pediatric patients with T-ALL diagnosis and their potential future use as predictive factors.

The Immune Profile of Pituitary Adenomas and a Novel Immune Classification for Predicting Immunotherapy Responsiveness

CONTEXT

The tumor immune microenvironment is associated with clinical outcomes and immunotherapy responsiveness.

OBJECTIVE

To investigate the intratumoral immune profile of pituitary adenomas (PAs) and its clinical relevance and to explore a novel immune classification for predicting immunotherapy responsiveness.

DESIGN, PATIENTS, AND METHODS

The transcriptomic data from 259 PAs and 20 normal pituitaries were included for analysis. The ImmuCellAI algorithm was used to estimate the abundance of 24 types of tumor-infiltrating immune cells (TIICs) and the expression of immune checkpoint molecules (ICMs).

RESULTS

The distributions of TIICs differed between PAs and normal pituitaries and varied among PA subtypes. T cells dominated the immune microenvironment across all subtypes of PAs. The tumor size and patient age were correlated with the TIIC abundance, and the ubiquitin-specific protease 8 (USP8) mutation in corticotroph adenomas influenced the intratumoral TIIC distributions. Three immune clusters were identified across PAs based on the TIIC distributions. Each cluster of PAs showed unique features of ICM expression that were correlated with distinct pathways related to tumor development and progression. CTLA4/CD86 expression was upregulated in cluster 1, whereas programmed cell death protein 1/programmed cell death 1 ligand 2 (PD1/PD-L2) expression was upregulated in cluster 2. Clusters 1 and 2 exhibited a "hot" immune microenvironment and were predicted to exhibit higher immunotherapy responsiveness than cluster 3, which exhibited an overall "cold" immune microenvironment.

CONCLUSIONS

We summarized the immune profile of PAs and identified 3 novel immune clusters. These findings establish a foundation for further immune studies on PAs and provide new insights into immunotherapy strategies for PAs.

Two miRNA prognostic signatures of head and neck squamous cell carcinoma: A bioinformatic analysis based on the TCGA dataset

MicroRNAs(miRNAs) are maladjusted in multifarious malignant tumor and can be considered as both carcinogens and tumor‐inhibiting factor. In the present study, we analyzed the miRNAs expression profiles and clinical information of 481 patients with head and neck squamous cell carcinoma (HNSCC) through the TCGA dataset to identify the prognostic miRNAs signature. A total of 114 significantly differentially expressed miRNAs (SDEMs) were identified, consisting of 60 up‐adjusted and 54 down‐adjusted miRNAs. The Kaplan‐Meier survival method identified the prognostic function of 2 miRNAs (miR‐4652‐5p and miR‐99a‐3P). Univariate and multivariate Cox regression analyses indicated that the 2 miRNAs were significant prognostic elements of HNSCC. Furthermore, bioinformatic analysis was conducted by means of 4 online gene predicted toolkits to recognize the target genes, and enrichment analysis was performed on the target genes by DAVID. The outcomes depicted that target genes were correlated with calcium, as well as cell proliferation, circadian entrainment, EGFR, PI3K‐Akt‐mTOR, and P53 signaling pathways. Finally, the PPI network was conducted in view of STRING database and Cytoscape. Eight hub genes were identified by CytoHubba and MCODE app, respectively, CBL, SKP1, H2AFX, HGF, POLR2F, UBE2I, VAMP2, and GNAI2 genes. As a result, we identified 2 miRNAs signatures, 8 hub genes, and significant signaling pathways for estimating the prognosis of HNSCC. In order to further explore the molecular mechanism of HNSCC occurrence and development, more comprehensive basic and clinical studies are needed.

Functions of circular RNAs and their potential applications in gastric cancer

Introduction: In recent years, circular RNAs (circRNAs) have emerged in the field of RNA research and their biological functions are now being gradually identified. circRNAs are divided into three categories: exonic circular RNAs (ecircRNAs), exon-intron circular RNAs (EIciRNAs), and intronic circular RNAs (ciRNAs). The circular structure of circRNAs confers unique biological characteristics upon them, such as enhanced stability over linear RNAs.Areas covered: circRNAs function to competitively bind with microRNAs (miRNAs) and proteins, participate in protein coding, regulate transcription, and form pseudogenes after reverse transcription. In gastric cancer, the circRNA-miRNA-mRNA axis is the most studied mechanisms underlying gastric cancer occurrence and development. Some specific and sensitive circRNAs, such as hsa_circ_102958, hsa_circ_0000520, and hsa_circ_0001017 may have potential diagnostic potential in early-stage gastric cancer. Abnormal expression of some circRNAs, including circ-LMTK2, circ-PSMC3, and circ-DLST are associated with the development of gastric cancer. Other circRNAs, such as hsa_circ_0001368, circ-ZFR, and circ-ERBB2, may also play important roles in gastric cancer treatment.Expert opinion: Exploring the roles of circRNAs in gastric cancer occurrence and development will help us to elucidate the functions of circRNAs and develop potential tools for early diagnosis and effective treatment of gastric cancer.

Study of gene expressions' correlation structures in subgroups of Chronic Lymphocytic Leukemia Patients

In chronic lymphocytic leukemia (CLL) the interaction of leukemic cells with the microenvironment ultimately affects patient outcome. CLL cases can be divided in two subgroups with different clinical course based on the mutational status of the immunoglobulin heavy variable (IGHV) genes: mutated CLL (M-CLL) and unmutated CLL (U-CLL). Since in CLL, the differentiated relation of genes between the two subgroups is of greater importance than the individual gene behavior, this paper investigates the differences between the groups' gene interactions, by comparing their correlation structures. Fisher's test and Zou's confidence intervals are employed to detect differences of correlation coefficients. Afterwards, networks created by the genes participating in most differences are estimated with the use of structural equation models (SEM). The analysis is enhanced with graph modeling in order to visualize the between group differences in the gene structures of the two subgroups. The applied methodology revealed stronger correlations between genes in U-CLL patients, a finding in line with related biomedical literature. Using SEM for multigroup analysis, different gene structures between the two groups of patients were confirmed. The study of correlation structures can facilitate the detection of differential gene expression profiles in CLL subgroups, with potential applications in other diseases. Comparison of correlations can be very useful in understanding the complex internal structural differences which signify the variations of a disease.

Molecular characterization, expression analysis and heterologous expression of two translationally controlled tumor protein genes from Cucumis sativus

The translationally controlled tumor protein (TCTP) is a family of abundant and ubiquitous proteins involved in several important primary functions. Cucumbers harbor two TCTP genes, CsTCTP1 and CsTCTP2; however, their functional roles remain unclear. In this study, we isolated CsTCTP1 and CsTCTP2 (XP-004134215 and XP-004135602, respectively) promoters, full-length cDNA and genomic sequences from Cucumis sativus. Bioinformatics analysis, containing cis-acting elements, structural domains, phylogenetic tree and conserved motifs, suggested the conservation and divergence of CsTCTP1 and CsTCTP2, thus providing knowledge regarding their functions. Expression analysis indicated that CsTCTP1 and CsTCTP2 exhibited tissue-specific expression and were regulated by biotic or abiotic stresses in C. sativus. Furthermore, CsTCTP1 and CsTCTP2 were regulated by ABA and may be associated with the TOR (target of rapamycin) signaling pathway. In a prokaryotic expression analysis, CsTCTP1 and CsTCTP2 showed positive responses to salt and heat stresses and a negative response to drought and HgCl2 stresses. TCTP may exert multiple functions in various cellular processes.

Role of miR-139 as a surrogate marker for tumor aggression in breast cancer

MicroRNAs are non-protein coding molecules that play a key role in oncogenesis, tumor progression, and metastasis in many types of malignancies including breast cancer. In the current study, we studied the expression of microRNA-139-5p (miR-139) in invasive ductal carcinoma (IDC) of the breast and correlated its expression with tumor grade, molecular subtype, hormonal status, human epidermal growth factor receptor 2 status, proliferation index, tumor size, lymph node status, patient's age, and overall survival in 74 IDC cases. In addition, we compared and correlated miR-139 expression in 18 paired serum and tissue samples from patients with IDC to assess its value as a serum marker. Our data showed that miR-139 was down-regulated in all tumor tissue samples compared with control. More pronounced down-regulation was seen in tumors that were higher grade, estrogen receptor negative, progesterone receptor negative, more proliferative, or larger in size (P < .05). Although not statistically significant, lower miR-139 level was frequently associated with human epidermal growth factor receptor 2 overexpression. In addition, significantly lower miR-139 tissue level was seen in patients who were deceased (P = .027), although older age (>50 years) and positive local nodal disease did not adversely affect miR-139 expression. In contrast, serum miR-139 profile of the patients appeared similar to that of normal control. In conclusion, our study demonstrated that down-regulation of miR-139 was associated with aggressive tumor behavior and disease progression in breast cancer. miR-139 may serve as a risk assessment biomarker in tailoring treatment options.

RATIONAL USE OF SERUM TUMOUR MARKERS IN DIAGNOSTICS AND TREATMENT OF SOLID TUMOURS

Optimal management of patients with solid tumors, depending on the tumour type, includes measurement of serum tumour markers levels. Serum tumour markers are heterogeneous molecules with concentrations elevated in persons with solid tumours, but could also be found in small amounts in plasma of healthy individuals. Elevated plasma concentrations are caused by cell changes, necrosis, changed expression or secretion of different molecules. In some tumour types tumour cells by themselves could stimulate other cells to secrete particular molecules. There are several serum tumour markers in the routine clinical praxis: CEA, CA 19-9, CA15-3, CA 125, CYFRA, NSE, PSA, HCG, AFP, LDH, thyreoglobulin. There are also several serum tumour markers in experimental use, waiting to be included into the routine clinical use. National Academy of Clinical Biochemistry (NACB) practice guidelines for use of tumour markers in clinical practice are designated to encourage more appropriate use of serum tumour marker tests by general medicine practitioners, surgeons, oncologists, and other health care professionals giving care to patients with solid tumours.

Non-coding RNAs: Functions and applications in endocrine-related cancer

A significant fraction of the human genome is transcribed as non-coding RNAs (ncRNAs). This non-coding transcriptome has challenged the notion of the central dogma and its involvement in transcriptional and post-transcriptional regulation of gene expression is well established. Interestingly, several ncRNAs are dysregulated in cancer and current non-coding transcriptome research aims to use our increasing knowledge of these ncRNAs for the development of cancer biomarkers and anti-cancer drugs. In endocrine-related cancers, for which survival rates can be relatively low, there is a need for such advancements. In this review, we aimed to summarize the roles and clinical implications of recently discovered ncRNAs, including long ncRNAs, PIWI-interacting RNAs, tRNA- and Y RNA-derived ncRNAs, and small nucleolar RNAs, in endocrine-related cancers affecting both sexes. We focus on recent studies highlighting discoveries in ncRNA biology and expression in cancer, and conclude with a discussion on the challenges and future directions, including clinical application. ncRNAs show great promise as diagnostic tools and therapeutic targets, but further work is necessary to realize the potential of these unconventional transcripts.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

A gene regulatory network (GRN) represents how some genes encoding regulatory molecules such as transcription factors or microRNAs regulate the expression of other genes. Researchers commonly study GRNs involved in a specific biological process with the aim of identifying a few important regulatory genes. In higher organisms such as humans, a regulatory gene regulates multiple target genes and correspondingly any gene is regulated by multiple regulatory genes. Due to such multiplicity of interactions, a GRN usually resembles a tangled hairball wherein it is difficult to identify few most influential regulatory genes. In this study, we show that network analysis algorithms such as K-core, pagerank and betweenness centrality are useful for identifying a few important or core regulatory genes in a GRN, and the K-core algorithm is also useful for organizing regulatory genes in a hierarchical layered structure where the most influential genes in a GRN are found within the innermost layer or core. These few core regulatory genes determine to a large extent the expression status of the remaining genes in the network. We illustrate a pragmatic application of this technique to GRNs reconstructed from genome-wide gene expression measurements in the MCF-7 human breast cancer cell line.

Methylation biomarkers for pleomorphic lobular breast cancer - a short report

Background

Pleomorphic invasive lobular cancer (pleomorphic ILC) is a rare variant of ILC that is characterized by a classic ILC-like growth pattern combined with an infiltrative ductal cancer (IDC)-like high nuclear atypicality. There is an ongoing discussion whether pleomorphic ILC is a dedifferentiated form of ILC or in origin an IDC with a secondary loss of cohesion. Since gene promoter hypermethylation is an early event in breast carcinogenesis and thus may provide information on tumor progression, we set out to compare the methylation patterns of pleomorphic ILC, classic ILC and IDC. In addition, we aimed at analyzing the methylation status of pleomorphic ILC.

Methods

We performed promoter methylation profiling of 24 established and putative tumor suppressor genes by methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) analysis in 20 classical ILC, 16 pleomorphic ILC and 20 IDC cases.

Results

We found that pleomorphic ILC showed relatively low TP73 and MLH1 methylation levels and relatively high RASSF1A methylation levels compared to classic ILC. Compared to IDC, pleomorphic ILC showed relatively low MLH1 and BRCA1 methylation levels. Hierarchical cluster analysis revealed a similar methylation pattern for pleomorphic ILC and IDC, while the methylation pattern of classic ILC was different.

Conclusion

This is the first report to identify TP73, RASSF1A, MLH1 and BRCA1 as possible biomarkers to distinguish pleomorphic ILC from classic ILC and IDC.

[Development of biomarkers for molecular target drugs]

In general, the molecular target drugs were superior in the effect, less toxic and realized personalized medicine, resulting in progress in the cancer therapy. The rise in development cost of the molecular target drug and a decrease of the development efficiency become the problem of the drug industry recently. As the solution, development of the excellent biomarker associated with the drug development is considered to be the most important issue. Therefore, the development of the companion diagnostic agent which can evaluate the biomarker precisely is prosperous. A search of the biomarker and the simultaneous development of the companion diagnostic agent enter the mainstream of the development of new molecular target drug. Whereas the process of the drug development is complicated than before.

The next steps in next-gen sequencing of cancer genomes

The necessary infrastructure to carry out genomics-driven oncology is now widely available and has resulted in the exponential increase in characterized cancer genomes. While a subset of genomic alterations is clinically actionable, the majority of somatic events remain classified as variants of unknown significance and will require functional characterization. A careful cataloging of the genomic alterations and their response to therapeutic intervention should allow the compilation of an "actionability atlas" and the creation of a genomic taxonomy stratified by tumor type and oncogenic pathway activation. The next phase of genomic medicine will therefore require talented bioinformaticians, genomic navigators, and multidisciplinary approaches to decode complex cancer genomes and guide potential therapy. Equally important will be the ethical and interpretable return of results to practicing oncologists. Finally, the integration of genomics into clinical trials is likely to speed the development of predictive biomarkers of response to targeted therapy as well as define pathways to acquired resistance.

A composite model for subgroup identification and prediction via bicluster analysis

Background

A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen serotypes of microorganisms, different tumor subtypes in cancer patients, or different genetic makeups of patients related to treatment response.

Methods

This paper proposes a composite model for subgroup identification and prediction using biclusters. A biclustering technique is first used to identify a set of biclusters from the sampled data. For each bicluster, a subgroup-specific binary classifier is built to determine if a particular sample is either inside or outside the bicluster. A composite model, which consists of all binary classifiers, is constructed to classify samples into several disjoint subgroups. The proposed composite model neither depends on any specific biclustering algorithm or patterns of biclusters, nor on any classification algorithms.

Results

The composite model was shown to have an overall accuracy of 97.4% for a synthetic dataset consisting of four subgroups. The model was applied to two datasets where the sample’s subgroup memberships were known. The procedure showed 83.7% accuracy in discriminating lung cancer adenocarcinoma and squamous carcinoma subtypes, and was able to identify 5 serotypes and several subtypes with about 94% accuracy in a pathogen dataset.

Conclusion

The composite model presents a novel approach to developing a biclustering-based classification model from unlabeled sampled data. The proposed approach combines unsupervised biclustering and supervised classification techniques to classify samples into disjoint subgroups based on their associated attributes, such as genotypic factors, phenotypic outcomes, efficacy/safety measures, or responses to treatments. The procedure is useful for identification of unknown species or new biomarkers for targeted therapy.

Autoantibodies as potential biomarkers for the early detection of esophageal squamous cell carcinoma

OBJECTIVES

Esophageal squamous cell carcinoma (ESCC) is one of the most frequent causes of cancer death worldwide and effective diagnosis is needed. We assessed the diagnostic potential of an autoantibody panel that may benefit early diagnosis.

METHODS

We analyzed data for patients with ESCC and normal controls in a test cohort and a validation cohort. Autoantibody levels were measured against a panel of six tumor-associated antigens (p53, NY-ESO-1, matrix metalloproteinase-7 (MMP-7), heat shock protein 70 (Hsp70), peroxiredoxin VI (Prx VI), and BMI1 polycomb ring finger oncogene (Bmi-1)) by enzyme-linked immunosorbent assay.

RESULTS

We assessed serum autoantibodies in 513 participants: 388 with ESCC and 125 normal controls. The validation cohort comprised 371 participants: 237 with ESCC, and 134 normal controls. Autoantibodies to at least 1 of 6 antigens demonstrated a sensitivity/specificity of 57% (95% confidence interval (CI): 52-62%)/95% (95% CI: 89-98%) and 51% (95% CI: 45-57%)/96% (95% CI: 91-99%) in the test and validation cohorts, respectively. Measurement of the autoantibody panel could differentiate early-stage ESCC patients from normal controls (sensitivity 45% (95% CI: 32-59%) and specificity 95% (95% CI: 89-98%) in the test cohort; 46% (95% CI: 35-58%) and 96% (95% CI: 91-99%) in the validation cohort). In either cohort, no significant differences were seen when patients were subdivided by age, gender, smoking status, size of tumor, site of tumor, depth of tumor invasion, histological grade, lymph node status, TNM stage, or early-stage and late-stage groups.

CONCLUSIONS

Measurement of an autoantibody response to multiple tumor-associated antigens in an optimized panel assay, to help discriminate early-stage ESCC patients from normal controls, may aid in early detection of ESCC.

Diagnostic value of biochemical biomarkers in malignant and non-malignant pericardial effusion

The aim of this study was to examine the biochemical composition of pericardial effusions of different etiology and to evaluate the diagnostic utility of biochemical parameters and tumor markers to discriminate malignant from benign effusion. Pericardial and serum levels of biochemical parameters and tumor markers were compared in 105 patients who underwent pericardiocentesis and pericardioscopy with targeted epicardial biopsy. Etiologic diagnosis was based on pericardial fluid and epicardial biopsy analysis by cytology, histology, immunohistochemistry, microbiology and polymerase chain reaction. The total of 105 patients comprised 29 patients with malignant and 76 patients with non-malignant pericardial effusions (40 autoreactive, 28 viral, 5 postcardiotomy syndromes and 3 associated with systemic diseases). Malignant pericardial effusions had significantly higher pericardial fluid levels of the tumor markers CEA, CA 19-9, CA 72-4, SCC and NSE (p < 0.001, p = 0.002, p < 0.001, p = 0.004 and p < 0.001, respectively) as well as higher pericardial fluid hemoglobin (p < 0.001), pericardial fluid white blood cells (p = 0.003), pericardial fluid LDH (p < 0.001) and ratio of pericardial to serum LDH levels compared to benign effusions. None of the biochemical or cell-count parameters tested proved to be accurate enough for distinguishing malignant from benign effusions. However, measurement of pericardial CA 72-4 levels offered a high diagnostic accuracy for malignancy, particularly in bloody pericardial effusions. None of the biochemical parameters tested was useful for the discrimination of malignant from benign effusions. However, measurement of pericardial CA 72-4 levels in bloody pericardial effusions yielded a high diagnostic accuracy and thus offers the potential as a diagnostic tool to distinguish between malignant and benign effusions.

Identification of five serum protein markers for detection of ovarian cancer by antibody arrays

BACKGROUND

Protein and antibody arrays have emerged as a promising technology to study protein expression and protein function in a high-throughput manner. These arrays also represent a new opportunity to profile protein expression levels in cancer patients' samples and to identify useful biosignatures for clinical diagnosis, disease classification, prediction, drug development and patient care. We applied antibody arrays to discover a panel of proteins which may serve as biomarkers to distinguish between patients with ovarian cancer and normal controls.

METHODOLOGY/PRINCIPAL FINDINGS

Using a case-control study design of 34 ovarian cancer patients and 53 age-matched healthy controls, we profiled the expression levels of 174 proteins using antibody array technology and determined the CA125 level using ELISA. The expression levels of those proteins were analyzed using 3 discriminant methods, including artificial neural network, classification tree and split-point score analysis. A panel of 5 serum protein markers (MSP-alpha, TIMP-4, PDGF-R alpha, and OPG and CA125) was identified, which could effectively detect ovarian cancer with high specificity (95%) and high sensitivity (100%), with AUC =0.98, while CA125 alone had an AUC of 0.87.

CONCLUSIONS/SIGNIFICANCE

Our pilot study has shown the promising set of 5 serum markers for ovarian cancer detection.

[Tumor markers used in the diagnosis, monitoring, treatment, and prognosis head and neck cancer]

Cancer is currently the second to heart disease and vascular cause of death and observed tends to increase the number of cases. The cause of high mortality from cancer it is too late malignant diagnosis. In the case of head and neck cancers at diagnosis in about 40% of patients found to have metastatic lymph nodes within. Therefore, an important issue for modem oncology is the early diagnosis of the disease cancer. Currently high hopes for the early detection and diagnosis of treatmen cancer is put in simple, accessible and low-cost testing to determine the biochemical tumor markers. THE AIM OF THE STUDY was to examine the latest reports on biochemical markers useful in cancer diagnosis, disease staging, prognosis and monitoring the treatment of the most common cancers of the head and neck.

MATERIALS AND METHODS

The material consisted of references of the last 17 years. Criterion search accounted for password: biochemical tumor markers, diagnostics, monitoring treatment of cancer. 10 of 90 works were selected to examine.

CONCLUSIONS

Usefulness of biochemical tumor markers in monitoring course of the disease and evaluation of treatment effectiveness was demonstrated. In combination with other diagnostic methods as they apply to screening, as well as in the detection of cancer in the study population.

Identificationof potential lung cancer biomarkers by liquid chromatography tandem mass spectrometry-based proteomics analysis of secretomes of two lung cancer cell lines

A label-free nano-liquid chromatography tandem mass spectrometry proteomics analysis on the conditioned media (CM) of two lung cancer cell lines of different histological backgrounds to identify secreted or membrane-bound proteins as novel lung cancer biomarkers was performed. Five hundred and seventy seven proteins were identified and 38% of them were classified as extracellular or membrane-bound. For the search of potential biomarkers of lung cancer a series of selection criteria were proposed. We detected known or putative lung cancer markers. In addition, 40 novel proteins were identified, whose role as biomarkers of lung cancer should be explored further.

miRandola: extracellular circulating microRNAs database

MicroRNAs are small noncoding RNAs that play an important role in the regulation of various biological processes through their interaction with cellular messenger RNAs. They are frequently dysregulated in cancer and have shown great potential as tissue-based markers for cancer classification and prognostication. microRNAs are also present in extracellular human body fluids such as serum, plasma, saliva, and urine. Most of circulating microRNAs are present in human plasma and serum cofractionate with the Argonaute2 (Ago2) protein. However, circulating microRNAs have been also found in membrane-bound vesicles such as exosomes. Since microRNAs circulate in the bloodstream in a highly stable, extracellular form, they may be used as blood-based biomarkers for cancer and other diseases. A knowledge base of extracellular circulating miRNAs is a fundamental tool for biomedical research. In this work, we present miRandola, a comprehensive manually curated classification of extracellular circulating miRNAs. miRandola is connected to miRò, the miRNA knowledge base, allowing users to infer the potential biological functions of circulating miRNAs and their connections with phenotypes. The miRandola database contains 2132 entries, with 581 unique mature miRNAs and 21 types of samples. miRNAs are classified into four categories, based on their extracellular form: miRNA-Ago2 (173 entries), miRNA-exosome (856 entries), miRNA-HDL (20 entries) and miRNA-circulating (1083 entries). miRandola is available online at: http://atlas.dmi.unict.it/mirandola/index.html.

Development of IGF signaling antibody arrays for the identification of hepatocellular carcinoma biomarkers

Purpose

Our objective was to develop a system to simultaneously and quantitatively measure the expression levels of the insulin-like growth factor (IGF) family proteins in numerous samples and to apply this approach to profile the IGF family proteins levels in cancer and adjacent tissues from patients with hepatocellular carcinoma (HCC).

Experimental Design

Antibodies against ten IGF family proteins (IGF-1, IGF-1R, IGF-2, IGF-2R, IGFBP-1, IGFBP-2, IGFBP-3, IGFBP-4, IGFBP-6, and Insulin) were immobilized on the surface of a glass slide in an array format to create an IGF signaling antibody array. Tissue lysates prepared from patient's liver cancer tissues and adjacent tissues were then applied to the arrays. The proteins captured by antibodies on the arrays were then incubated with a cocktail of biotinylated detection antibodies and visualized with a fluorescence detection system. By comparison with standard protein amount, the exact protein concentrations in the samples can be determined. The expression levels of the ten IGF family proteins in 25 pairs of HCC and adjacent tissues were quantitatively measured using this novel antibody array technology. The differential expression levels between cancer tissues and adjacent tissues were statistically analyzed.

Results

A novel IGF signaling antibody array was developed which allows the researcher to simultaneously detect ten proteins involved in IGF signal pathway with high sensitivity and specificity. Using this approach, we found that the levels of IGF-2R and IGFBP-2 in HCC tissues were higher than those in adjacent tissues.

Conclusion

Our IGF signaling antibody array which can detect the expression of ten IGF family members with high sensitivity and specificity will undoubtedly prove a powerful tool for drug and biomarker discovery.

omniBiomarker: A Web-Based Application for Knowledge-Driven Biomarker Identification

We have developed omniBiomarker, a web-based application that uses knowledge from the NCI Cancer Gene Index to guide the selection of biologically relevant algorithms for identifying biomarkers. Biomarker identification from high-throughput genomic expression data is difficult because of data properties (i.e., small-sample size compared to large-feature size) as well as the large number of available feature selection algorithms. Thus, it is unclear which algorithm should be used for a particular dataset. These factors lead to instability in biomarker identification and affect the reproducibility of results. We introduce a method for computing the biological relevance of feature selection algorithms using an externally validated knowledge base of manually curated cancer biomarkers. Results suggest that knowledge-driven biomarker identification can improve microarray-based clinical prediction performance. omniBiomarker can be accessed at http://omnibiomarker.bme.gatech.edu/.

Pheochromocytomas and paragangliomas: assessment of malignant potential

Pheochromocytomas and paragangliomas (PPGLs) are rare catecholamine-secreting tumors which arise from the adrenal glands or sympathetic neuronal tissue. Malignant transformation of these tumors occurs in a significant proportion and may therefore lower overall survival rates. In patients with PPGLs it is impossible to identify malignant disease without the presence of metastatic disease, something which can occur as long as 20 years after initial surgery. Early identification of malignant disease would necessitate a more aggressive treatment approach, something which may result in better disease outcome. We have therefore reviewed possible predictors of malignancy and current developments in order to help clinicians to swiftly assess malignant potential in patients with PPGLs. Currently, there is no absolute marker which can objectively reflect malignant potential. Tumor size is the most reliable predictor and should therefore be used as the baseline characteristic. The combination of various clinical markers (extra-adrenal disease and post-operative hypertension), biochemical markers (high dopamine, high norepinephrine and epinephrine to total catecholamine ratio) and/or histological markers (SNAIL, microRNAs and/or microarray results) can raise or lower the suspicion of malignancy. Furthermore, we discuss how clinical markers may affect biochemical results linked to malignancy, how biochemical results may distinguish hereditary syndromes, the role of imaging in determining malignant potential and tumor detection, and recent results of proposed histological markers.

Molecular classification of gastric cancer: a new paradigm

PURPOSE

Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis.

EXPERIMENTAL DESIGN

Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (National Cancer Institute, NCI #5917) underwent endoscopic biopsy for fresh tumor procurement. Four to 6 targeted biopsies of the primary tumor were obtained. Macrodissection was carried out to ensure more than 80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package.

RESULTS

Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the 3 gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross-validation error was 0.14, suggesting that more than 85% of samples were classified correctly. Gene set analysis with the false discovery rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach.

CONCLUSIONS

Subtypes of gastric cancer that have epidemiologic and histologic distinctions are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype.

New and emerging syndromes due to neuroendocrine tumors

Neuroendocrine tumors (NETs) are rare, slow-growing neoplasms characterized by their ability to store and secrete different peptides and neuroamines. Some of these substances cause specific clinical syndromes whereas others are not associated with specific syndromes or symptom complexes. NETs usually have episodic expression that makes diagnosis difficult, erroneous, and often late. For these reasons a high index of suspicion is needed, and it is important to understand the pathophysiology of each tumor to decide which biochemical markers are more useful and when they should be used.

Breast cancer and the stromal factor. The "prometastatic healing process" hypothesis

The correlation between axillary status and several histological features of breast carcinomas has been well established, however stromal changes have rarely been analyzed. Detailed clinicopathological review of 1803 patients with infiltrating breast carcinoma was performed. Stromal myxoid changes (SMC), size (T2-T3: > 2 cm, T1c: 1-2 cm, T1 a-b: < 1cm), fibrotic focus, age, lymphovascular embolizations, tumor infiltrating lymphocytes (TIL), multifocality, histological grade (G), estrogen receptors (ER), progesterone receptors (PR) and HER2 were semi-quantitated in two or three grades and correlated to axillary status. SMC3 followed by T2-T3, G3, fibrotic focus, T1c, embolizations, SMC2, TIL2, G2 and multifocality were strongly associated with positive axillary nodes; an inverse association was found with ER+++ and PR+++. Our findings support a critical role of the peritumoral stroma in the development of metastases. These stromal alterations should be remarked in routine pathology reports as they can be easily assessed and provide important information about tumor biology and aggressiveness. They could also become, in a future, the target of novel therapeutics.

Molecular classification and novel targets in hepatocellular carcinoma: recent advancements

Hepatocellular carcinoma (HCC) is one of most lethal cancers worldwide. Strategic decisions for the advancement of molecular therapies in this neoplasm require a clear understanding of its molecular classification. Studies indicate aberrant activation of signaling pathways involved in cellular proliferation (e.g., epidermal growth factor and RAS/mitogen-activated protein kinase pathways), survival (e.g., Akt/mechanistic target of rapamycin pathway), differentiation (e.g., Wnt and Hedgehog pathways), and angiogenesis (e.g., vascular endothelial growth factor and platelet-derived growth factor), which is heterogeneously presented in each tumor. Integrative analysis of accumulated genomic datasets has revealed a global scheme of molecular classification of HCC tumors observed across diverse etiologic factors and geographic locations. Such a framework will allow systematic understanding of the frequently co-occurring molecular aberrations to design treatment strategy for each specific subclass of tumors. Accompanied by a growing number of clinical trials of molecular targeted drugs, diagnostic and prognostic biomarker development will be facilitated with special attention on study design and with new assay technologies specialized for archived fixed tissues. A new class of genomic information, microRNA dysregulation and epigenetic alterations, will provide insight for more precise understanding of disease mechanism and expand the opportunity of biomarker/therapeutic target discovery. These efforts will eventually enable personalized management of HCC.

Master regulators used as breast cancer metastasis classifier

Computational identification of prognostic biomarkers capable of withstanding follow-up validation efforts is still an open challenge in cancer research. For instance, several gene expression profiles analysis methods have been developed to identify gene signatures that can classify cancer sub-phenotypes associated with poor prognosis. However, signatures originating from independent studies show only minimal overlap and perform poorly when classifying datasets other than the ones they were generated from. In this paper, we propose a computational systems biology approach that can infer robust prognostic markers by identifying upstream Master Regulators, causally related to the presentation of the phenotype of interest. Such a strategy effectively extends and complements other existing methods and may help further elucidate the molecular mechanisms of the observed pathophysiological phenotype. Results show that inferred regulators substantially outperform canonical gene signatures both on the original dataset and across distinct datasets.

Inferring pathway activity toward precise disease classification

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.

A cautionary note on the evaluation of biomarkers of subtypes of a single disease

Heterogeneity in the molecular characteristics of a disease presents a challenge to investigators attempting to identify biomarkers of the disease. Preceding the biomarker discovery effort with stratification within a heterogeneous disease group, which amounts to grouping disease cases into more homogeneous subtypes, seems to be a natural strategy for discovering subtype-specific biomarkers. This is because biologically more homogeneous subgroups are presumably easier to distinguish from the nondiseased than the entire heterogeneous disease group. The misleading benefits of this two-step approach are illustrated using an example from a protein biomarker discovery project for breast cancer. A potential analytical pitfall in this framework is explained using a conditional probability argument.

Novel biomarker candidates for gastric cancer

Gastric cancer continues to be a major threat to human health. Molecular descriptions on the diverse phases of this disease will be valuable for a better diagnosis and development of therapeutic targets. Previously, a 92-gene classifier that distinguishes tumor from non-tumor gastric tissues was proposed. To corroborate this finding, independent approaches of gene selection and class prediction algorithm were applied to the dataset of 86 tissues profiled on 17K cDNA microarrays. As a result, 22 genes were selected, of which 18 were in common with 92 genes previously shown. The differential expression patterns of Chromogranin A (CHGA) and Thy-1 cell surface antigen (THY1) were further validated with immunohistostaining on gastric tissue microarrays. The differential expression patterns of several of the proposed genes have been proven to be critical for tumor progression in other cancer models and will likely function as novel biomarkers for gastric cancer as well.

A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification

This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+ bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications.

Deep proteome profiling of sera from never-smoked lung cancer patients

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.

Optimal search-based gene subset selection for gene array cancer classification

High dimensionality has been a major problem for gene array-based cancer classification. It is critical to identify marker genes for cancer diagnoses. We developed a framework of gene selection methods based on previous studies. This paper focuses on optimal search-based subset selection methods because they evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this paper is the first to introduce tabu search (TS) to gene selection from high-dimensional gene array data. Our comparative study of gene selection methods demonstrated the effectiveness of optimal search-based gene subset selection to identify cancer marker genes. TS was shown to be a promising tool for gene subset selection.

Artificial neural networks and decision tree model analysis of liver cancer proteomes

Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers' discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and decision tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers.

Visualization-based cancer microarray data classification analysis

MOTIVATION

Methods for analyzing cancer microarray data often face two distinct challenges: the models they infer need to perform well when classifying new tissue samples while at the same time providing an insight into the patterns and gene interactions hidden in the data. State-of-the-art supervised data mining methods often cover well only one of these aspects, motivating the development of methods where predictive models with a solid classification performance would be easily communicated to the domain expert.

RESULTS

Data visualization may provide for an excellent approach to knowledge discovery and analysis of class-labeled data. We have previously developed an approach called VizRank that can score and rank point-based visualizations according to degree of separation of data instances of different class. We here extend VizRank with techniques to uncover outliers, score features (genes) and perform classification, as well as to demonstrate that the proposed approach is well suited for cancer microarray analysis. Using VizRank and radviz visualization on a set of previously published cancer microarray data sets, we were able to find simple, interpretable data projections that include only a small subset of genes yet do clearly differentiate among different cancer types. We also report that our approach to classification through visualization achieves performance that is comparable to state-of-the-art supervised data mining techniques.

AVAILABILITY

VizRank and radviz are implemented as part of the Orange data mining suite (http://www.ailab.si/orange).

SUPPLEMENTARY INFORMATION

Supplementary data are available from http://www.ailab.si/supp/bi-cancer.

Feature selection and molecular classification of cancer using genetic programming

Despite important advances in microarray-based molecular classification of tumors, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discriminative feature genes, many of which are known to be disease associated. GP classifiers often comprise five or less genes and successfully predict cancer types and subtypes. More importantly, GP classifiers generated in one study are able to predict samples from an independent study, which may have used different microarray platforms. In addition, GP yielded classification accuracy better than or similar to conventional classification methods. Furthermore, the mathematical expression of GP classifiers provides insights into relationships between classifier genes. Taken together, our results demonstrate that GP may be valuable for generating effective classifiers containing a practical set of genes for diagnostic/prognostic cancer classification.

Comparing adaptive and non-adaptive algorithms for cancer early detection with novel biomarkers

It may be possible to reduce cancer mortality by monitoring the concentrations of serum biomarkers over time in men and women to detect their cancer early, when it is most curable. The simplest approach to using a biomarker for screening is to sequentially use fixed thresholds as a means to determine an abnormal test (e.g., PSA exceeding 4 mg/ml, CA 125 exceeding 30 U/ml). Alternatives to the simplest single threshold (ST) rules include more sophisticated algorithms that make use of screening history that accumulates over time and determines abnormal tests using individualized reference ranges. Although in principle longitudinal algorithms should out perform fixed threshold rules, the actual benefit gained will depend on behavior of the biomarker, the screening algorithm, and the screening frequency. Little information has been available to help predict when conditions should compel the adoption of the more sophisticated algorithms and when conditions suggest the simpler algorithms should suffice, or indeed be preferred. In this manuscript we evaluate the conditions under which one should expect great benefit, and when one should not expect benefit, by comparing the ability of simple and complex algorithms to detect cancer early under a variety of biomarker behaviors and screening frequencies.

Epitomics: serum screening for the early detection of cancer on microarrays using complex panels of tumor antigens

Efforts toward the development of early detection assays for cancers have traditionally depended on single biomarker molecules. Current technologies have been disappointing and have not resulted in diagnostic tests suitable for clinical practice. Using a high-throughput cloning method, a panel of epitopes/antigens that react with autoantibodies to tumor proteins in the serum of patients with ovarian cancer have been isolated. Discovery of biomarker panels was directed in an unbiased fashion by cloning a large panel of epitopes or tumor antigens, rather than individual biomarkers without a previous notion of their function. The binding properties of these serum antitumor antibodies on microarrays and advanced bioinformatics tools led to a panel of diagnostic antigens. The sequences that were identified using this new technology will lead to the discovery of novel disease-related proteins that have diagnostic value for the presymptomatic detection of cancer. It has been demonstrated that this approach can detect these autoantibodies in the sera of Stage I ovarian cancer patients. There are numerous advantages of employing serum antibodies as the analytes, not the least of which is the ability to rapidly adapt these assays to standard clinical platforms. This technology of global epitope/antigen profiling is referred to as 'epitomics'.

Molecular classification and biomarker discovery in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with an incidence of approximately 22,000 cases in 2004 in the USA. Incidence is increasing, with a global estimate of half a million new cases this year. PTC is found in a variety of morphologic variants, usually grows slowly and is clinically indolent, although rare, aggressive forms with local invasion or distant metastases can occur. In recent years, thyroid cancer has been at the forefront of molecular pathology as a result of the consequences of the Chernobyl disaster and the recognition of the role of Ret/PTC rearrangements in PTC. Nonetheless, the molecular pathogenesis of this disease remains poorly characterized. In the clinical setting, benign thyroid nodules are far more frequent, and distinguishing between them and malignant nodules is a common diagnostic problem. It is estimated that 5-10% of people will develop a clinically significant thyroid nodule during their lifetime. Although the introduction of fine-needle aspiration has made PTC identification more reliable, clinicians often have to make decisions regarding patient care on the basis of equivocal information. Thus, the existing diagnostic tools available to distinguish benign from malignant neoplasms are not always reliable. This article will critically evaluate recently described putative biomarkers and their potential future role for diagnostic purposes in fine-needle aspiration cytology samples. It will highlight the evolution of our understanding of the molecular biology of PTC, from a narrow focus on specific molecular lesions such as Ret/PTC rearrangements to a pan-genomic approach.

Immunohistochemical classification of de novo, transformed, and relapsed diffuse large B-cell lymphoma into germinal center B-cell and nongerminal center B-cell subtypes correlates with gene expression profile and patient survival

CONTEXT

Diffuse large B-cell lymphoma (DLBCL) can be assigned to prognostic subgroups, including germinal center B-cell (GCB) and activated B-cell subgroups, by using gene expression profiling and, reportedly, immunohistochemistry for CD10, Bcl-6, and multiple myeloma-1/interferon regulatory factor-4 (MUM1/IRF4).

OBJECTIVE

To compare 2 commercial MUM1/IRF4 antibody formulations for accuracy in subtyping DLBCL against gene expression profiling, compare subtyping to patient survival, and evaluate the usefulness of GCB and non-GCB subtyping in relapsed and transformed DLBCL.

DESIGN

Evaluation of 2 commercial MUM1/IRF4 antibodies, ICSTAT/M17 and Mum-1p, by using 40 cases of de novo, relapsed, and transformed DLBCL; and comparison of the results obtained with gene expression profiling and survival.

RESULTS

Immunohistochemistry predicted the gene expression profiling subtype 71.8% and 69.2% of the time overall with use of the Mum-1p and ICSTAT/M17 antibodies, respectively, and 100% and 91.7% of the time when MUM1/IRF4 expression determined subtype. Gene expression profiling and immunohistochemistry revealed nearly identical 5-year overall survival rates for the GCB vs non-GCB subtypes (68.0% for GCB vs 24.7% for non-GCB with use of gene expression profiling [P = .03] and 70.2% vs 18.4%, respectively, with use of immunohistochemistry [P < .001]). When de novo, transformed, and relapsed cases were analyzed separately, 5-year overall survival rates were also significantly different.

CONCLUSIONS

Immunohistochemistry can be used to subclassify DLBCL, including a very small series of transformed and relapsed cases, into GCB and non-GCB subtypes and predict survival rates similar to those predicted by use of gene expression profiling. The 2 MUM1/IRF4 antibodies performed similarly.

A database application for pre-processing, storage and comparison of mass spectra derived from patients and controls

Background

Statistical comparison of peptide profiles in biomarker discovery requires fast, user-friendly software for high throughput data analysis. Important features are flexibility in changing input variables and statistical analysis of peptides that are differentially expressed between patient and control groups. In addition, integration the mass spectrometry data with the results of other experiments, such as microarray analysis, and information from other databases requires a central storage of the profile matrix, where protein id's can be added to peptide masses of interest.

Results

A new database application is presented, to detect and identify significantly differentially expressed peptides in peptide profiles obtained from body fluids of patient and control groups. The presented modular software is capable of central storage of mass spectra and results in fast analysis. The software architecture consists of 4 pillars, 1) a Graphical User Interface written in Java, 2) a MySQL database, which contains all metadata, such as experiment numbers and sample codes, 3) a FTP (File Transport Protocol) server to store all raw mass spectrometry files and processed data, and 4) the software package R, which is used for modular statistical calculations, such as the Wilcoxon-Mann-Whitney rank sum test. Statistic analysis by the Wilcoxon-Mann-Whitney test in R demonstrates that peptide-profiles of two patient groups 1) breast cancer patients with leptomeningeal metastases and 2) prostate cancer patients in end stage disease can be distinguished from those of control groups.

Conclusion

The database application is capable to distinguish patient Matrix Assisted Laser Desorption Ionization (MALDI-TOF) peptide profiles from control groups using large size datasets. The modular architecture of the application makes it possible to adapt the application to handle also large sized data from MS/MS- and Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry experiments. It is expected that the higher resolution and mass accuracy of the FT-ICR mass spectrometry prevents the clustering of peaks of different peptides and allows the identification of differentially expressed proteins from the peptide profiles.

Global topological features of cancer proteins in the human interactome

MOTIVATION

The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods.

RESULTS

We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer. In particular, cancer proteins show an increase in the number of proteins they interact with. They also appear to participate in central hubs rather than peripheral ones, mirroring their greater centrality and participation in networks that form the backbone of the proteome. Moreover, we show that cancer proteins contain a high ratio of highly promiscuous structural domains, i.e., domains with a high propensity for mediating protein interactions. These observations indicate an underlying evolutionary distinction between the two groups of proteins, reflecting the central roles of proteins, whose mutations lead to cancer.

CONTACT

paul.bates@cancer.org.uk

SUPPLEMENTARY INFORMATION

The interactome data are available though the PIP (Potential Interactions of Proteins) web server at http://bmm.cancerresearchuk.org/servers/pip. Further additional material is available at http://bmm.cancerresearchuk.org/servers/pip/bioinformatics/

Recent advances in biomarkers for cancer diagnosis and treatment

With the availability of new technologies and the increased interest of medical practitioners to use molecular biomarkers in early detection and diagnosis, and in the prediction of therapeutic treatment efficacy and clinical outcomes, the academic and research institutions, as well as the pharmaceutical industry, have increased their efforts to develop novel molecular biomarkers for several human diseases, including cancer. The identification of molecular biomarkers also enables the development of a new generation of diagnostic products and to integrate diagnostics and therapeutics. This integrated approach will aid in 'individualizing' the medical practice. Here, we address issues related to the development of biomarkers, novel technological platforms used for drug development, future technologies and strategies for validating biomarkers for their clinical utility.

Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes

MOTIVATION

Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better.

RESULTS

We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.

Lymphoma of the female genital tract: current status

SUMMARY

: Primary lymphomas affecting the female reproductive system are uncommon but often pose a diagnostic challenge if their existence is not suspected. This article reviews the pathological and clinical features of lymphomas occurring in various sites in the female genital tract including the vulva, vagina, cervix, endometrium, fallopian tubes, and ovaries. Using the recent World Health Organization classification, the various types of lymphomas are identified as separate diseases and not as morphological variations of the same disease. The immunophenotypic and cytogenetics features of the major lymphomas are summarized. The incidence, presenting symptoms, gross and microscopic features, major differential diagnostic considerations, response to therapy, and expected outcome are discussed. Using published data on patient outcome, the International Federation of Obstetricians and Gynecologists and Ann Arbor staging systems are compared for their predictive value, and the difficulty in assigning primary and secondary status in extranodal lymphomas is emphasized. The observed differences in the behavior of some lymphomas in gynecological sites compared with their usual nodal location are presented. Finally, the possible etiology of these conditions is discussed in light of the emerging paradigm of mucosa-associated lymphoid tissue lymphomas.

Improving molecular cancer class discovery through sparse non-negative matrix factorization

MOTIVATION

Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.

RESULTS

We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.