Classifications of ovarian cancer tissues by proteomic patterns

Proteomic landscape of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

MALDI Mass Spectrometry Imaging of Early- and Late-Stage Serous Ovarian Cancer Tissue Reveals Stage-Specific N-Glycans

Epithelial ovarian cancer is one of the most fatal gynecological malignancies in adult women. As studies on protein N-glycosylation have extensively reported aberrant patterns in the ovarian cancer tumor microenvironment, obtaining spatial information will uncover tumor-specific N-glycan alterations in ovarian cancer development and progression. matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is employed to investigate N-glycan distribution on formalin-fixed paraffin-embedded ovarian cancer tissue sections from early- and late-stage patients. Tumor-specific N-glycans are identified and structurally characterized by porous graphitized carbon-liquid chromatography-electrospray ionization-tandem mass spectrometry (PGC-LC-ESI-MS/MS), and then assigned to high-resolution images obtained from MALDI-MSI. Spatial distribution of 14 N-glycans is obtained by MALDI-MSI and 42 N-glycans (including structural and compositional isomers) identified and structurally characterized by LC-MS. The spatial distribution of oligomannose, complex neutral, bisecting, and sialylated N-glycan families are localized to the tumor regions of late-stage ovarian cancer patients relative to early-stage patients. Potential N-glycan diagnostic markers that emerge include the oligomannose structure, (Hex)₆ + (Man)₃ (GlcNAc)₂ , and the complex neutral structure, (Hex)₂ (HexNAc)₂ (Deoxyhexose)₁ + (Man)₃ (GlcNAc)₂ . The distribution of these markers is evaluated using a tissue microarray of early- and late-stage patients.

Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

Proteomics of ovarian cancer: functional insights and clinical applications

In the past decade, there has been an increasing interest in applying proteomics to assist in understanding the pathogenesis of ovarian cancer, elucidating the mechanism of drug resistance, and in the development of biomarkers for early detection of ovarian cancer. Although ovarian cancer is a spectrum of different diseases, the strategies for diagnosis and treatment with surgery and adjuvant therapy are similar across ovarian cancer types, increasing the general applicability of discoveries made through proteomics research. While proteomic experiments face many difficulties which slow the pace of clinical applications, recent advances in proteomic technology contribute significantly to the identification of aberrant proteins and networks which can serve as targets for biomarker development and individualized therapies. This review provides a summary of the literature on proteomics' contributions to ovarian cancer research and highlights the current issues, future directions, and challenges. We propose that protein-level characterization of primary lesion in ovarian cancer can decipher the mystery of this disease, improve diagnostic tools, and lead to more effective screening programs.

Annexin A4 and cancer

Annexin A4 (Anxa4) is one of the Ca(2+)-regulated and phospholipid-binding annexin superfamily proteins. Anxa4 has a potential role in diagnosis, prognosis, and treatment of certain cancers. Studies indicate that Anxa4 up-regulation promotes the progression of tumor and chemoresistance of colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC), endometrial carcinoma (EC), gastric cancer (GC), chemoresistant lung cancer (LC), malignant mesothelioma (MM), renal cell carcinoma (RCC), ovarian clear cell carcinoma (OCCC), cholangiocarcinoma, hepatocellular carcinoma (HCC), breast cancer (BC), and laryngeal cancer. Interestingly, Anxa4 also might specifically function as a tumor suppressor for prostate cancer (PCa) and have a paradoxical role for pancreatic cancer (PCC). Differential expression of Anxa4 may distinguish major salivary gland tumor (MSGT) from thyroid cancer. In addition, its differential expression was linked to Sirt1-induced cisplatin resistance of oral squamous cell carcinoma (OSCC) and miR-7-induced migration and invasion inhibition of glioma. This current review summarizes and discusses the clinical significance of Anxa4 in cancer as well as its potential mechanisms of action. It may provide new integrative understanding for future studies on the exact role of Anxa4 in cancer.

Cystatin B as a potential diagnostic biomarker in ovarian clear cell carcinoma

Epithelial ovarian cancer (EOC) consists of four major subtypes: clear cell carcinoma (CCC), endometrioid adenocarcinoma (EA), mucinous adenocarcinoma (MA) and serous adenocarcinoma (SA). Relative to the other subtypes, the prognosis of CCC is poor due to a high recurrence rate and chemotherapy resistance, but CCC-specific biomarkers have yet to be identified. With the aim of identifying diagnostic and treatment biomarkers for CCC, we analyzed 96 cases of EOC (32 CCC, 13 EA, 19 MA, 32 SA) using liquid chromatography/mass spectrometry (LC/MS) followed by immunohistochemistry (IHC) and quantitative reverse transcription PCR (RT-qPCR). Semi-quantification of protein differences between subtypes showed upregulation of 150 proteins and downregulation of 30 proteins in CCC relative to the other subtypes. Based on hierarchical clustering that revealed a marked distinction in the expression levels of cystatin B (CYTB) and Annexin A4 (ANXA4) in CCC relative to the other subtypes, we focused the study on CYTB and ANXA4 expression in EOCs by IHC, RT-qPCR and western blot analyses using tissue specimens and cultured cells. As a result, compared to the other subtypes, CCC showed significantly high expression levels of CYTB and ANXA4 in the analyses. To examine the possibility of CYTB and ANXA4 as serum diagnostic biomarkers of CCC, we checked the protein levels in conditioned media and cell lysates using culture cells. Compared with the other subtypes, CCC cell lines showed a significantly higher level of expression of CYTB in both conditioned media and cell lysates, while ANXA4 showed a higher level of expression in cell lysates only. Our results demonstrate that CYTB and ANXA4 overexpression may be related to carcinogenesis and histopathological differentiation of CCC. CYTB may be a secreted protein, and may serve as a potential serum diagnostic biomarker of CCC, while ANXA4 may be useful as an intracellular marker.

A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients

BACKGROUND

Tumor-associated macrophages (TAMs) are classified into two major phenotypes, M1 and M2. M1 TAMs suppress cancer progression, while M2 TAMs promote it. However, little is known regarding the role of TAMs in the development of ovarian cancer. Here, we investigated the relationship between TAM distribution patterns (density, microlocalization, and differentiation) and ovarian cancer histotypes, and we explored whether altered TAM distribution patterns influence long-term outcomes in ovarian cancer patients.

METHODS

A total of 112 ovarian cancer patients were enrolled in this study, and the subjects were divided into two groups according to their survival (< 5 years vs. ≥ 5 years). Immunohistochemistry and immunofluorescence were used to determine the density, microlocalization, and differentiation status of TAMs in ovarian cancer tissues for each histotype. Kaplan-Meier survival and multivariate Cox regression analyses were used to evaluate the prognostic significance of TAM-related parameters in ovarian cancer.

RESULTS

TAMs most frequently infiltrated into the cancer tissue of the serous histotype, followed by mucinous, undifferentiated, endometrioid, and clear cell histotypes (p = 0.049). The islet/stroma ratio of total TAMs varied among the cancer histotypes, with mucinous and undifferentiated cancers displaying the lowest and highest ratios, respectively (p = 0.005). The intratumoral TAM density significantly increased with increasing cancer stage and grade (p = 0.023 and 0.006, respectively). However, the overall M1/M2 TAM ratio decreased as the cancer stage increased (p = 0.012). In addition, the intra-islet M1/M2 ratio inversely correlated with the residual site size (p = 0.004). Among the TAM-related parameters, only the increased overall and intra-islet M1/M2 TAM ratios displayed prognostic significance in both the Kaplan-Meier survival and multivariate Cox regression analyses; however, the values of these two parameters did not differ significantly among the cancer histotypes.

CONCLUSIONS

The patients with increased overall or intra-islet M1/M2 TAM ratios presented with an improved 5-year prognosis. Nevertheless, the TAM distribution patterns did not influence the overall outcomes of different ovarian cancer histotypes.

The Role of Mass Spectrometry in the "Omics" Era

Mass spectrometry (MS) is one of the key analytical technology on which the emerging ''-omics'' approaches are based. It may provide detection and quantization of thousands of proteins and biologically active metabolites from a tissue, body fluid or cell culture working in a ''global'' or ''targeted'' manner, down to ultra-trace levels. It can be expected that the high performance of MS technology, coupled to routine data handling, will soon bring fruit in the request for a better understanding of human diseases, leading to new molecular biomarkers, hence affecting drug targets and therapies. In this review, we focus on the main advances in the MS technologies, influencing genomics, transcriptomics, proteomics, lipidomics and metabolomics fields, up to the most recent MS applications to meta-omic studies.

Protein isoform-specific validation defines multiple chloride intracellular channel and tropomyosin isoforms as serological biomarkers of ovarian cancer

New serological biomarkers for early detection and clinical management of ovarian cancer are urgently needed, and many candidates have been reported. A major challenge frequently encountered when validating candidates in patients is establishing quantitative assays that distinguish between highly homologous proteins. The current study tested whether multiple members of two recently discovered ovarian cancer biomarker protein families, chloride intracellular channel (CLIC) proteins and tropomyosins (TPM), were detectable in ovarian cancer patient sera. A multiplexed, label-free multiple reaction monitoring (MRM) assay was established to target peptides specific to all detected CLIC and TPM family members, and their serum levels were quantitated for ovarian cancer patients and non-cancer controls. In addition to CLIC1 and TPM1, which were the proteins initially discovered in a xenograft mouse model, CLIC4, TPM2, TPM3, and TPM4 were present in ovarian cancer patient sera at significantly elevated levels compared with controls. Some of the additional biomarkers identified in this homolog-centric verification and validation approach may be superior to the previously identified biomarkers at discriminating between ovarian cancer and non-cancer patients. This demonstrates the importance of considering all potential protein homologs and using quantitative assays for cancer biomarker validation with well-defined isoform specificity.

BIOLOGICAL SIGNIFICANCE

This manuscript addresses the importance of distinguishing between protein homologs and isoforms when identifying and validating cancer biomarkers in plasma or serum. Specifically, it describes the use of targeted in-depth LC-MS/MS analysis to determine the members of two protein families, chloride intracellular channel (CLIC) and tropomyosin (TPM) proteins that are detectable in sera of ovarian cancer patients. It then establishes a multiplexed isoform- and homology-specific MRM assay to quantify all observed gene products in these two protein families as well as many of the closely related tropomyosin isoforms. Using this assay, levels of all detected CLICs and TPMs were quantified in ovarian cancer patient and control subject sera. These results demonstrate that in addition to the previously known CLIC1, multiple tropomyosins and CLIC4 are promising new ovarian cancer biomarkers. Based on these initial validation studies, these new ovarian cancer biomarkers appear to be superior to most previously known ovarian cancer biomarkers.

Ovarian cancer molecular pathology

Ovarian cancer (OVC) is the fourth leading cause of cancer mortality among women in Europe and the United States. Its early detection is difficult due to the lack of specificity of clinical symptoms. Unfortunately, late diagnosis is a major contributor to the poor survival rates for OVC, which can be attributed to the lack of specific sets of markers. Aside from patients sharing a strong family history of ovarian and breast cancer, including the BRCA1 and BRCA2 tumor suppressor genes mutations, the most used biomarker is the Cancer-antigen 125 (CA-125). CA-125 has a sensitivity of 80 % and a specificity of 97 % in epithelial cancer (stage III or IV). However, its sensitivity is 30 % in stage I cancer, as its increase is linked to several physiological phenomena and benign situations. CA-125 is particularly useful for at-risk population diagnosis and to assess response to treatment. It is clear that alone, CA-125 is inadequate as a biomarker for OVC diagnosis. There is an unmet need to identify additional biomarkers. Novel and more sensitive proteomic strategies such as MALDI mass spectrometry imaging studies are well suited to identify better markers for both diagnosis and prognosis. In the present review, we will focus on such proteomic strategies in regards to OVC signaling pathways, OVC development and escape from the immune response.

Histology image analysis for carcinoma detection and grading

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.

Proteomic characterization of ovarian cancers identifying annexin-A4, phosphoserine aminotransferase, cellular retinoic acid-binding protein 2, and serpin B5 as histology-specific biomarkers

Numerous studies have suggested that the different histological subtypes of ovarian carcinoma (i.e. clear cell, endometrioid, mucinous, and serous) have distinct clinical histories and characteristics; however, most studies that have aimed to determine biomarker have not performed comprehensive analyses based on subtype specificity. In the present study, we performed two-dimensional gel electrophoresis-based differential proteomic analysis of the different histological subtypes of ovarian carcinoma using tissue specimens from 39 patients. Seventy-seven protein spots (55 unique proteins) were found to be up- or downregulated in a subtype-specific manner. The most significant difference was observed for: (i) annexin-A4 (ANXA4) and phosphoserine aminotransferase (PSAT1), which are expressed strongly in clear cell carcinoma; (ii) cellular retinoic acid-binding protein 2 (CRABP2), which is expressed specifically in serous carcinoma; and (iii) serpin B5 (SPB5), which is upregulated in mucinous carcinoma. Validation of these candidates by western blotting using a 34 additional test sample set resulted in an expression pattern that was consistent with the screening and revealed that differential expression was independent of cancer stage or tumor grade within each subtype. Thus, the present study reinforces the notion that ovarian cancer subtypes can be clearly delineated on a molecular basis into four histopathological groups, and we propose that ANXA4, PSAT1, CRABP2, and SPB5 are candidate subtype-specific biomarkers that can help define the basis of tumor histology at a molecular level.

Proteomic analysis reflects different histologic subtypes of epithelial ovarian cancer

Ovarian carcinoma is a significant cause of cancer mortality in women worldwide. As a heterogeneous disease, distinct clinical and molecular characteristics exist among different histologic subtypes. With the developments in proteomics, surfaced-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is sensitive enough to detect minute quantity of proteins from serum or microdissected cryostat sections. Herein we hypothesized that differentially expressed protein profiles exist in ovarian carcinomas with distinct histologic subtypes. Compared with endometrioid carcinoma, two peaks were significantly higher in serous carcinoma with the m/z of 3622 Da and 4778 Da, reinforcing the need to treat different histologic subtypes of ovarian cancer as different disease entities. Better understanding of these potential diagnostic and therapeutic biomarkers followed with proof-of-target effect will finally contribute to rational combinations of novel therapy and improve individual ovarian cancer patient outcome.

Identification of glycoproteins associated with different histological subtypes of ovarian tumors using quantitative glycoproteomics

Ovarian cancer is the most lethal gynecologic malignancy in adult women. The origin of epithelial ovarian tumors is both morphologically and biologically heterogeneous, and different subtypes of ovarian tumors have different clinical outcomes. In spite of the heterogeneous nature of ovarian carcinoma, the current biomarkers and treatments for this disease are not subtype-specific. To discover the molecular basis of the ovarian tumor subtypes, we analyzed extracellular glycoproteins of seven common subtypes and normal ovary tissues using quantitative glycoproteomic analysis. Glycoproteins for different ovarian tumor subtypes were identified by liquid chromatography-tandem mass spectrometry and quantitated by spectral counting and then verified by iTRAQ labeling and Western blotting. Glycoproteins uniquely expressed in different subtypes of ovarian tumors or commonly expressed in most subtypes were identified. Using Western blots, we verified that mesothelin was overexpressed in serous carcinoma and transitional-cell carcinoma, CEA5 and CEA6 were overexpressed only in mucinous carcinoma, while versican and periostin were overexpressed in most subtypes of ovarian tumors. This study presents the first proteomic characterization of different ovarian tumor subtypes. The identified glycoproteins for histological subtypes of ovarian tumors will facilitate the understanding of the molecular basis, diagnosis of ovarian tumor subtypes, and predictions for treatment responses to therapeutic agents.

A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer

Proteomics discovery of novel cancer serum biomarkers is hindered by the great complexity of serum, patient-to-patient variability, and triggering by the tumor of an acute-phase inflammatory reaction. This host response alters many serum protein levels in cancer patients, but these changes have low specificity as they can be triggered by diverse causes. We addressed these hurdles by utilizing a xenograft mouse model coupled with an in-depth 4-D protein profiling method to identify human proteins in the mouse serum. This strategy ensures that identified putative biomarkers are shed by the tumor, and detection of low-abundance proteins shed by the tumor is enhanced because the mouse blood volume is more than a thousand times smaller than that of a human. Using TOV-112D ovarian tumors, more than 200 human proteins were identified in the mouse serum, including novel candidate biomarkers and proteins previously reported to be elevated in either ovarian tumors or the blood of ovarian cancer patients. Subsequent quantitation of selected putative biomarkers in human sera using label-free multiple reaction monitoring (MRM) mass spectrometry (MS) showed that chloride intracellular channel 1, the mature form of cathepsin D, and peroxiredoxin 6 were elevated significantly in sera from ovarian carcinoma patients.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

MALDI Imaging Mass Spectrometry (MALDI-IMS)-application of spatial proteomics for ovarian cancer classification and diagnosis

MALDI imaging mass spectrometry (MALDI-IMS) allows acquisition of mass data for metabolites, lipids, peptides and proteins directly from tissue sections. IMS is typically performed either as a multiple spot profiling experiment to generate tissue specific mass profiles, or a high resolution imaging experiment where relative spatial abundance for potentially hundreds of analytes across virtually any tissue section can be measured. Crucially, imaging can be achieved without prior knowledge of tissue composition and without the use of antibodies. In effect MALDI-IMS allows generation of molecular data which complement and expand upon the information provided by histology including immuno-histochemistry, making its application valuable to both cancer biomarker research and diagnostics. The current state of MALDI-IMS, key biological applications to ovarian cancer research and practical considerations for analysis of peptides and proteins on ovarian tissue are presented in this review.

Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies

The recent explosion in procurement and availability of high-dimensional gene- and protein-expression profile datasets for cancer diagnostics has necessitated the development of sophisticated machine learning tools with which to analyze them. A major limitation in the ability to accurate classify these high-dimensional datasets stems from the 'curse of dimensionality', occurring in situations where the number of genes or peptides significantly exceeds the total number of patient samples. Previous attempts at dealing with this issue have mostly centered on the use of a dimensionality reduction (DR) scheme, Principal Component Analysis (PCA), to obtain a low-dimensional projection of the high-dimensional data. However, linear PCA and other linear DR methods, which rely on Euclidean distances to estimate object similarity, do not account for the inherent underlying nonlinear structure associated with most biomedical data. The motivation behind this work is to identify the appropriate DR methods for analysis of high-dimensional gene- and protein-expression studies. Towards this end, we empirically and rigorously compare three nonlinear (Isomap, Locally Linear Embedding, Laplacian Eigenmaps) and three linear DR schemes (PCA, Linear Discriminant Analysis, Multidimensional Scaling) with the intent of determining a reduced subspace representation in which the individual object classes are more easily discriminable.

Comparative proteomic analysis of low stage and high stage endometrioid ovarian adenocarcinomas

Ovarian cancer, the second most common gynecological malignancy, accounts for 3% of all cancers among women in the United States, and has a high mortality rate, largely because existing therapies for widespread disease are rarely curative. Ovarian endometrioid adenocarcinoma (OEA) accounts for about 20% of the overall incidence of all ovarian cancer. We have used proteomics profiling to characterize low stage (FIGO stage 1 or 2) versus high stage (FIGO stage 3 or 4) human OEAs. In general, the low stage tumors lacked p53 mutations and had frequent CTNNB1, PTEN, and/or PIK3CA mutations. The high stage tumors had mutant p53, were usually high grade, and lacked mutations predicted to deregulate Wnt/β-catenin and PI3K/Pten/Akt signaling. We utilized 2-D liquid-based separation/mass mapping techniques to elucidate molecular weight and pI measurements of the differentially expressed intact proteins. We generated 2-D protein mass maps to facilitate the analysis of protein expression between both the low stage and high stage tumors. These mass maps (over a pI range of 5.6-4.6) revealed that the low stage OEAs demonstrated protein over-expression at the lower pI ranges (pI 4.8-4.6) in comparison to the high stage tumors, which demonstrated protein over-expression in the higher pI ranges (pI 5.4-5.2). These data suggest that both low and high stage OEAs have characteristic pI signatures of abundant protein expression probably reflecting, at least in part, the different signaling pathway defects that characterize each group. In this study, the low stage OEAs were distinguishable from high stage tumors based upon the proteomic profiles. Interestingly, when only high-grade (grade 2 or 3) OEAs were included in the analysis, the tumors still tended to cluster according to stage, suggesting that the altered protein expression was not solely dependent upon tumor cell differentiation. Further, these protein profiles clearly distinguish OEA from other types of ovarian cancer at the protein level.

Alteration of cell-cycle regulation in epithelial ovarian cancer

In spite of the clinical importance of epithelial ovarian cancer (EOC), little is known about the pathobiology of its precursor lesions and progression. Regulatory mechanisms of the cell cycle are mainly composed of cyclins, cyclin-dependent kinases (CDK), and CDK inhibitors. Alteration of these mechanisms results in uncontrolled cell proliferation, which is a distinctive feature of human cancers. This review describes the current state of knowledge about the alterations of cell-cycle regulations in the context of p16-cyclin D1-CDK4/6-pRb pathway, p21-p27-cyclin E-CDK2 pathway, p14-MDM2-p53 pathway, and ATM-Chk2-CDC25 pathway, respectively. Recent evidence suggests that ovarian cancer is a heterogenous group of neoplasms with several different histologic types, each with its own underlying molecular genetic mechanism. Therefore, expression of cell cycle regulatory proteins should be tested separately according to each histologic type. In serous ovarian carcinoma, high expression of p16, p53, and p27 and low expression of p21 and cyclin E were shown. In addition, this review focuses on the prognostic significance of cell cycle-regulating proteins in EOC. However, it is difficult to compare the results from different groups due to diverse methodologies and interpretations. Accordingly, researchers should establish standardized criteria for the interpretation of immunohistochemical results.

Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

Background

In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs).

Methods

In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs.

Results

The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study – although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours – were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models.

Conclusion

We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology.

Plasma-based testing as a new paradigm for clinical testing in hematologic diseases

Recent advances in molecular biology have paved the way for the detection of minute quantities of cellular components with robust assays that are amenable for use in clinical laboratories. This review discusses the recently developed series of plasma-based assays that are changing the testing paradigm for hematologic diseases. These tests are based on the concept that a high turnover of neoplastic hematologic cells, relative to normal cells, enriches plasma with tumor-specific DNA, RNA and protein. Plasma-based testing promises to reduce the need for bone marrow biopsy, allow for more frequent and accurate monitoring of changes in bone marrow, allow the detection of more aggressive subclones of the malignant cells and provide a more quantitative means to measure the load of the malignant clone. We present data demonstrating that plasma, in some situations, allows even more sensitive detection than bone marrow cells. Moreover, the lessened impact of dilution by normal cells in plasma permits a distinction between homozygous and hemizygous abnormalities. Unlike solid tumors, currently available data suggest that in hematologic diseases, plasma is superior to cells in detecting molecular abnormalities.