Common protein biomarkers assessed by reverse phase protein arrays show considerable intratumoral heterogeneity in breast cancer tissues

Proteins are used as prognostic and predictive biomarkers in breast cancer. However, the variability of protein expression within the same tumor is not well studied. The aim of this study was to assess intratumoral heterogeneity in protein expression levels by reverse-phase-protein-arrays (RPPA) (i) within primary breast cancers and (ii) between axillary lymph node metastases from the same patient. Protein was extracted from 106 paraffin-embedded samples from 15 large (≥3 cm) primary invasive breast cancers, including different zones within the primary tumor (peripheral, intermediate, central) as well as 2-5 axillary lymph node metastases in 8 cases. Expression of 35 proteins including 15 phosphorylated proteins representing the HER2, EGFR, and uPA/PAI-1 signaling pathways was assessed using reverse-phase-protein-arrays. All 35 proteins showed considerable intratumoral heterogeneity within primary breast cancers with a mean coefficient of variation (CV) of 31% (range 22-43%). There were no significant differences between phosphorylated (CV 32%) and non-phosphorylated proteins (CV 31%) and in the extent of intratumoral heterogeneity within a defined tumor zone (CV 28%, range 18-38%) or between different tumor zones (CV 24%, range 17-38%). Lymph node metastases from the same patient showed a similar heterogeneity in protein expression (CV 27%, range 18-34%). In comparison, the variation amongst different patients was higher in primary tumors (CV 51%, range 29-98%) and lymph node metastases (CV 65%, range 40-146%). Several proteins showed significant differential expression between different tumor stages, grades, histological subtypes and hormone receptor status. Commonly used protein biomarkers of breast cancer, including proteins from HER2, uPA/PAI-1 and EGFR signaling pathways showed higher than previously reported intratumoral heterogeneity of expression levels both within primary breast cancers and between lymph node metastases from the same patient. Assessment of proteins as diagnostic or prognostic markers may require tumor sampling in several distinct locations to avoid sampling bias.     

Ovarian Carcinoma Subtypes Are Different Diseases: Implications for Biomarker Studies

Background

Although it has long been appreciated that ovarian carcinoma subtypes (serous, clear cell, endometrioid, and mucinous) are associated with different natural histories, most ovarian carcinoma biomarker studies and current treatment protocols for women with this disease are not subtype specific. With the emergence of high-throughput molecular techniques, distinct pathogenetic pathways have been identified in these subtypes. We examined variation in biomarker expression rates between subtypes, and how this influences correlations between biomarker expression and stage at diagnosis or prognosis.

Methods and Findings

In this retrospective study we assessed the protein expression of 21 candidate tissue-based biomarkers (CA125, CRABP-II, EpCam, ER, F-Spondin, HE4, IGF2, K-Cadherin, Ki-67, KISS1, Matriptase, Mesothelin, MIF, MMP7, p21, p53, PAX8, PR, SLPI, TROP2, WT1) in a population-based cohort of 500 ovarian carcinomas that was collected over the period from 1984 to 2000. The expression of 20 of the 21 biomarkers differs significantly between subtypes, but does not vary across stage within each subtype. Survival analyses show that nine of the 21 biomarkers are prognostic indicators in the entire cohort but when analyzed by subtype only three remain prognostic indicators in the high-grade serous and none in the clear cell subtype. For example, tumor proliferation, as assessed by Ki-67 staining, varies markedly between different subtypes and is an unfavourable prognostic marker in the entire cohort (risk ratio [RR] 1.7, 95% confidence interval [CI] 1.2%–2.4%) but is not of prognostic significance within any subtype. Prognostic associations can even show an inverse correlation within the entire cohort, when compared to a specific subtype. For example, WT1 is more frequently expressed in high-grade serous carcinomas, an aggressive subtype, and is an unfavourable prognostic marker within the entire cohort of ovarian carcinomas (RR 1.7, 95% CI 1.2%–2.3%), but is a favourable prognostic marker within the high-grade serous subtype (RR 0.5, 95% CI 0.3%–0.8%).

Conclusions

The association of biomarker expression with survival varies substantially between subtypes, and can easily be overlooked in whole cohort analyses. To avoid this effect, each subtype within a cohort should be analyzed discretely. Ovarian carcinoma subtypes are different diseases, and these differences should be reflected in clinical research study design and ultimately in the management of ovarian carcinoma.     

TROP2在卵巢浆液性囊腺癌中的表达及其与预后的关系

目的:探讨TROP2在不同卵巢组织中的表达及其与卵巢浆液性囊腺癌患者临床病理因素之间的关系,明确TROP2表达是否与卵巢浆液性囊腺癌患者预后有关。方法:应用免疫组化法检测TROP2在98例卵巢浆液性囊腺癌组织及50例正常卵巢组织中的表达情况,分析TROP2表达与卵巢浆液性囊腺癌患者临床病理特征的关系,采用COX比例风险回归模型分析影响卵巢浆液性囊腺癌患者预后的因素。结果:TROP2在卵巢浆液性囊腺癌、正常卵巢组织中的表达阳性率分别为55.1%及24.0%,差异有统计学意义(P0.05)。TROP2表达与卵巢浆液性囊腺癌患者年龄无显著相关(P0.05),而与患者有无淋巴结转移,组织学分级和临床分期有显著相关(P0.05);临床分期、病理组织学分级、术后残余灶大小、TROP2基因表达均为影响卵巢浆液性囊腺癌患者预后的危险因素(P0.05)。结论:TROP2可能成为卵巢浆液性囊腺癌患者治疗的新靶点。     

Serum IL-10 Predicts Worse Outcome in Cancer Patients: A Meta-Analysis

BackgroundIL–10 is an important immunosuppressive cytokine which is frequently elevated in tumor microenvironment. Some studies have reported that overexpression of serous IL–10 is correlated with worse outcome in patients with malignant tumor. Here, we conducted a meta-analysis to assess the prognostic impact of serous IL–10 expression in cancer patients.MethodsWe searched PubMed and EBSCO for studies in evaluating the association of IL–10 expression—in serum and clinical outcome in cancer patients. Overall survival (OS) was the primary prognostic indicator and disease-free survival (DFS) was the secondary indicator. Extracted data were computed into odds ratios (ORs) and 95% confidence interval (CI) or a P value for survival at 1, 3 and 5 years. Pooled data were weighted using the Mantel–Haenszel Fixed-effect model. All statistical tests were two-sided.ResultsA total of 1788 patients with cancer from 21 published studies were incorporated into this meta-analysis. High level of serum IL–10 was significantly associated with worse OS at 1-year (OR = 3.70, 95% CI = 2.81 to 4.87, P < 0.00001), 3-year (OR = 3.33, 95% CI = 2.53 to 4.39, P < 0.0001) and 5-year (OR = 2.80, 95% CI = 1.90 to 4.10, P < 0.0001) of cancer. Subgroup analysis showed that the correlation between serous IL–10 expression and outcome of patients with solid tumors and hematological malignancies are consistent. The association of IL–10 with worse DFS at 1-year (OR = 3.34, 95% CI = 1.40 to 7.94, P = 0.006) and 2-year (OR = 3.91, 95% CI = 1.79 to 8.53, P = 0.0006) was also identified.ConclusionsHigh expression of serous IL–10 leads to an adverse survival in most types of cancer. IL–10 is a valuable biomarker for prognostic prediction and targeting IL–10 treatment options for both solid tumors and hematological malignancies.     

卵巢高/低级别浆液性癌的定量蛋白质组学比较研究

目的:探讨卵巢高级别浆液性癌和低级别浆液性癌的差异表达蛋白,为阐明卵巢癌发生机制及寻找诊断和预后标志物的提供线索。方法:收集卵巢癌新鲜组织标本冻存于液氮中,经病理学确诊为高级别浆液性癌和低级别浆液性癌,两种类型各收集15例。应用i TRAQ定量蛋白质组学技术筛选及鉴定高/低级别浆液性癌的差异表达蛋白,并进行生物信息学分析。结果:卵巢高级别和低级别浆液性癌组织的定量蛋白质组学比较研究鉴定出差异表达蛋白314个,其中与低级别浆液性癌组比较,高级别浆液性癌组上调蛋白有97种,下调蛋白有217种。GO分析显示这些差异蛋白在分子功能、生物学功能、细胞成分方面均具有一定分布特点。KEGG分析显示这些差异蛋白涉及复杂的信号通路。结论:高/低级别浆液性癌之间存在差异表达蛋白,这些蛋白涉及复杂的功能和信号通路可能在两型卵巢癌发生机制及肿瘤生物学行为差异中具有重要意义。     

Serum Protein Profile at Remission Can Accurately Assess Therapeutic Outcomes and Survival for Serous Ovarian Cancer

Background

Biomarkers play critical roles in early detection, diagnosis and monitoring of therapeutic outcome and recurrence of cancer. Previous biomarker research on ovarian cancer (OC) has mostly focused on the discovery and validation of diagnostic biomarkers. The primary purpose of this study is to identify serum biomarkers for prognosis and therapeutic outcomes of ovarian cancer.

Experimental Design

Forty serum proteins were analyzed in 70 serum samples from healthy controls (HC) and 101 serum samples from serous OC patients at three different disease phases: post diagnosis (PD), remission (RM) and recurrence (RC). The utility of serum proteins as OC biomarkers was evaluated using a variety of statistical methods including survival analysis.

Results

Ten serum proteins (PDGF-AB/BB, PDGF-AA, CRP, sFas, CA125, SAA, sTNFRII, sIL-6R, IGFBP6 and MDC) have individually good area-under-the-curve (AUC) values (AUC = 0.69–0.86) and more than 10 three-marker combinations have excellent AUC values (0.91–0.93) in distinguishing active cancer samples (PD & RC) from HC. The mean serum protein levels for RM samples are usually intermediate between HC and OC patients with active cancer (PD & RC). Most importantly, five proteins (sICAM1, RANTES, sgp130, sTNFR-II and sVCAM1) measured at remission can classify, individually and in combination, serous OC patients into two subsets with significantly different overall survival (best HR = 17, p<10⁻³).

Conclusion

We identified five serum proteins which, when measured at remission, can accurately predict the overall survival of serous OC patients, suggesting that they may be useful for monitoring the therapeutic outcomes for ovarian cancer.     

Activation of the PI3K/AKT pathway mediates FSH-stimulated VEGF expression in ovarian serous cystadenocarcinoma

There is evidence to suggest that follicle-stimulating hormone （FSH） can facilitate the neovascularization of ovarian cancers by increasing vascular endothelial growth factor （VEGF） expression in cancer cells, although the underlying molecular mechanism of this process is not well known. Therefore, we investigated the effect of FSH on VEGF expression in the ovarian cancer cell lines SKOV-3 and ES-2. Treatment with FSH significantly increased VEGF expression in a dose- and time-dependent manner. In addition, FSH treatment enhanced the expression of survivin and hypoxlainducible factor-1 （HIF-1α）. Knockdown of survivin or HIF-1α suppressed VEGF expression, but only knockdown of survivin inhibited FSH-stimulated VEGF expression. Pretreatment with LY294002, a phosphoinositide 3-kinase （PI3K）/AKT inhibitor, neutralized the enhanced expression of survivin induced by FSH, but treatment with U0126, a mitogen-activated protein kinase/extracellular signal-regulated kinase inhibitor, had no such effect. We further showed that ovarian serous cystadenocarcinoma samples had much higher incidence of positive AKT and phosphorylated AKT （pAKT） protein staining than did benign ovarian cystadenoma samples （p 〈 0.01）. The 5-year survival rate was only about 15% in patients with ovarian serous cystadenocarcinoma who had AKT and pAKT expression, whereas it was about 80% in those who did not have AKT or pAKT expression. Taken together, these results indicate that FSH increases the expression of VEGF by upregulating the expression of survivin, which is activated by the PI3K/AKT signaling pathway. Understanding the role of the PI3K/AKT pathway in FSH-stimulated expression of survivin and VEGF will be beneficial for evaluating the prognosis for patients with ovarian serous cystadenocarcinoma and for pursulug effective treatment against this disease.     

BTN3A2 expression in epithelial ovarian cancer is associated with higher tumor infiltrating T cells and a better prognosis

BTN3A2/BT3.2 butyrophilin mRNA expression by tumoral cells was previously identified as a prognostic factor in a small cohort of high grade serous epithelial ovarian cancer (HG-EOC). Here, we evaluated the prognostic value of BT3.2 at the protein level in specimen from 199 HG-EOC patients. As the only known role of butyrophilin proteins is in immune regulation, we evaluated the association between BT3.2 expression and intratumoral infiltration of immune cells by immunohistochemistry with specific antibodies against BT3.2, CD3, CD4, CD8, CD20, CD68 and CD206. Epithelial BT3.2 expression was significantly associated with longer overall survival and lower risk of disease progression (HR?=?0.651, p?=?0.006 and HR?=?0.642, p?=?0.002, respectively) and significantly associated with a higher density of infiltrating T cells, particularly CD4+ cells (0.272, p<0.001). We also observed a strong association between the relative density of CD206+ cells, as evaluated by the ratio of intratumoral CD206+/CD68+ expression, and risk of disease progression (HR?=?1.355 p?=?0.044, respectively). In conclusion, BT3.2 protein is a potential prognostic biomarker for the identification of HG-EOC patients with better outcome. In contrast, high CD206+/CD68+ expression is associated with high risk of disease progression. While the role of BT3.2 is still unknown, our result suggest that BT3.2 expression by epithelial cells may modulates the intratumoral infiltration of immune cells.     

The Prognostic Value of Forkhead Box P3 Expression in Operable Breast Cancer: A Large-Scale Meta-Analysis

Background

Recent studies have shown that the forkhead box P3 (FOXP3) protein has a prognostic role in breast cancer. However, these results are controversial. Therefore, the aim of this meta-analysis was to clarify the prognostic role of FOXP3 expression in operable breast cancer cases.

Methods

Eligible studies describing the use of FOXP3 as a prognostic factor for operable breast cancer cases were identified. Clinicopathological features, disease-free survival (DFS), and overall survival (OS) data were collected from these studies and were analyzed using Stata software.

Results

A total of 16 articles containing data from 13,217 breast cancer patients met the inclusion criteria established for this study. The subsequent meta-analysis that was performed showed that high levels of FOXP3 are not significantly associated with DFS and OS with significant heterogeneity. An additional subgroup analysis demonstrated that intratumoral FOXP3+ regulatory T cells (Tregs) were positively correlated with adverse clinicopathological parameters, yet they did not show an association with DFS or OS. For tumor cells, the pooled results revealed that FOXP3 is significantly associated with DFS (HR: 2.55, 95% CI: 1.23–5.30) but is not associated with clinicopathological parameters or OS. We also observed a significant correlation between FOXP3 expression and survival in the estrogen receptor-positive (ER)+ subgroup (HR: 1.83, 95% CI: 1.36–2.47 for DFS, HR: 1.87, 95% CI 1.28–2.73 for OS), in the Asian region (HR: 1.98, 95% CI: 1.56–2.50 for DFS, HR: 1.93, 95% CI: 1.12–3.35 for OS) and using the median as the FOXP3-positive cut-off value (HR: 1.94, 95% CI: 1.57–2.39 for DFS, HR: 2.06; 95% CI: 1.36–3.11 for OS).

Conclusion

This meta-analysis indicates that a prognostic role for FOXP3 expression in operable breast cancer cases depends on the FOXP3-positive region, ER status, geographic region and the FOXP3-positive cut-off value.     

Classification of cancer cell lines using an automated two-dimensional liquid mapping method with hierarchical clustering techniques

A two-dimensional liquid mapping method was used to map the protein expression of eight ovarian serous carcinoma cell lines and three immortalized ovarian surface epithelial cell lines. Maps were produced using pI as the separation parameter in the first dimension and hydrophobicity based upon reversed-phase HPLC separation in the second dimension. The method can be reproducibly used to produce protein expression maps over a pH range from 4.0 to 8.5. A dynamic programming method was used to correct for minor shifts in peaks during the HPLC gradient between sample runs. The resulting corrected maps can then be compared using hierarchical clustering to produce dendrograms indicating the relationship between different cell lines. It was found that several of the ovarian surface epithelial cell lines clustered together, whereas specific groups of serous carcinoma cell lines clustered with each other. Although there is limited information on the current biology of these cell lines, it was shown that the protein expression of certain cell lines is closely related to each other. Other cell lines, including one ovarian clear cell carcinoma cell line, two endometrioid carcinoma cell lines, and three breast epithelial cell lines, were also mapped for comparison to show that their protein profiles cluster differently than the serous samples and to study how they cluster relative to each other. In addition, comparisons can be made between proteins differentially expressed between cell lines that may serve as markers of ovarian serous carcinomas. The automation of the method allows reproducible comparison of many samples, and the use of differential analysis limits the number of proteins that might require further analysis by mass spectrometry techniques.     

Comparison of cellular phenotypes in tumor cyst and ascitic fluid from ovarian serous carcinoma.

Density gradient centrifugation was applied to isolate cell subsets from tumor cyst and ascitic fluid in eight patients with ovarian serous carcinoma. A comparison of cellular composition and immunologic reactivity of cells from the cysts and from ascitic fluid in each patient was performed. Some differences in density profiles were found, but in each case the consistency of morphologic cell forms in the primary tumor and ascites was documented. Immunophenotypic analyses of isolated cellular fractions using polyclonal and monoclonal antibodies against ovarian carcinoma-associated antigens showed significant immunologic intratumoral heterogeneity. However, there was a similarity of antigen expression in cells from the primary tumors and ascitic fluids. Our study indicated that morphologic and antigenic characterization of a given tumor could be determined in a single representative sample of ascitic fluid.     

Therapeutic targeting of the thrombospondin-1 receptor CD47 to treat liver cancer

CD47 is a signaling receptor for the matricellular protein thrombospondin-1 and a counter-receptor for signal regulatory protein-α (SIRPα) on macrophages. Following its initial discovery in 1992 as a cell surface protein that is over-expressed by ovarian carcinoma, elevated CD47 expression has emerged as a negative prognostic factor for a variety of cancers. CD47 is also a potential therapeutic target based on the ability of CD47 blockade to cause regression of tumors in mice, and a humanized CD47 antibody has recently entered phase I clinical trials. CD47 blockade may control tumor growth by inhibiting thrombospondin-1 signaling or by preventing inhibitory SIRPα signaling in tumor-associated macrophages. A recent publication by Lee et al. (Hepatology 60:179–191, 2014) provides evidence that blocking CD47 signaling specifically depletes tumor-initiating stem cells in hepatocellular carcinoma and implicates cathepsin-S/protease-activated receptor-2 signaling in mediating this therapeutic response.     

A Unique Four-Hub Protein Cluster Associates to Glioblastoma Progression

Gliomas are the most frequent brain tumors. Among them, glioblastomas are malignant and largely resistant to available treatments. Histopathology is the gold standard for classification and grading of brain tumors. However, brain tumor heterogeneity is remarkable and histopathology procedures for glioma classification remain unsatisfactory for predicting disease course as well as response to treatment. Proteins that tightly associate with cancer differentiation and progression, can bear important prognostic information. Here, we describe the identification of protein clusters differentially expressed in high-grade versus low-grade gliomas. Tissue samples from 25 high-grade tumors, 10 low-grade tumors and 5 normal brain cortices were analyzed by 2D-PAGE and proteomic profiling by mass spectrometry. This led to identify 48 differentially expressed protein markers between tumors and normal samples. Protein clustering by multivariate analyses (PCA and PLS-DA) provided discrimination between pathological samples to an unprecedented extent, and revealed a unique network of deranged proteins. We discovered a novel glioblastoma control module centered on four major network hubs: Huntingtin, HNF4α, c-Myc and 14-3-3ζ. Immunohistochemistry, western blotting and unbiased proteome-wide meta-analysis revealed altered expression of this glioblastoma control module in human glioma samples as compared with normal controls. Moreover, the four-hub network was found to cross-talk with both p53 and EGFR pathways. In summary, the findings of this study indicate the existence of a unifying signaling module controlling glioblastoma pathogenesis and malignant progression, and suggest novel targets for development of diagnostic and therapeutic procedures.     

PD-L1 expression with QR1 and E1L3N antibodies according to histological ovarian cancer subtype: A series of 232 cases

Therapeutic strategies for epithelial ovarian cancers are evolving with the advent of immunotherapy, such as PD-L1 inhibitors, with encouraging results. However, little data are available on PDL-1 expression in ovarian cancers. Thus, we set out to determine the PD-L1 expression according to histological subtype. We evaluated the expression of two PD-L1 clones – QR1 and E1L3N – with two scores, one based on the percentage of labeled tumor cells (tumor proportion score, TPS) and the other on labeled immune cells (combined proportion score, CPS) in a consecutive retrospective series of 232 ovarian cancers. PD-L1 expression was more frequent in high grade serous carcinoma (27.5% with E1L3N clone and 41.5% with QR1 clone), grade 3 endometrioid carcinoma (25% with E1L3N clone and 50% with QR1 clone), and clear-cell carcinomas (27.3% with E1L3N clone and 29.6% with QR1 clone) than other histological subtypes with CPS score. Using the CPS score, 17% of cases were labeled with E1L3N vs 28% with QR1. Using the TPS score, 14% of cases were positive to E1L3N vs 17% for QR1. For TPS and CPS, respectively, 77% and 78% of the QR1 cases were concordant with E1L3N for the thresholds of 1%. Overall and progression-free survival between PD-L1 positive and PD-L1 negative patients were not different across all histological types, and each subtype in particular for serous carcinomas expressing PD-L1. Expression of PD-L1 is relatively uncommon in epithelium ovarian tumors. When positive, usually <10% of tumor cells are labeled. QR1 clone and CPS appear the best tools to evaluate PD-L1 expression.Key words: Ovarian cancer, PD-L1 antibody, immunochemistry, histological subtype     

Gene Expression Profile for Predicting Survival in Advanced-Stage Serous Ovarian Cancer Across Two Independent Datasets

Background

Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer.

Methodology/Principal Findings

Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008).

Conclusions/Significance

The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.     

Intratumoral variations in DNA ploidy and s-phase fraction in human breast cancer.

To study intratumoral DNA ploidy heterogeneity and S-phase fraction (SPF) variability, we prospectively collected five different samples from 48 breast carcinomas and each sample was analysed separately by flow cytometry. Aneuploidy rate was 89.6% after analysis of four or five samples. DNA ploidy heterogeneity, i.e., different samples classified as either DNA euploid or DNA aneuploid in the same tumor was seen in 17%, and DNA index heterogeneity, i.e., tumor populations with different DNA indices (DIs) seen in different samples was 44%. A statistical model defining SPF heterogeneity is proposed. SPF heterogeneity as defined by us was 71%, and as expected the SPF heterogeneity rate increased significantly with increasing number of analysed samples. Four or more samples are needed to detect the most deviant (highest) SPF values. An unrecognized intratumor heterogeneity of DNA ploidy and SPF may partly explain the conflicting results reported in the literature on the above prognostic indicators.     

Classifications of ovarian cancer tissues by proteomic patterns

Ovarian cancer is a morphologically and biologically heterogeneous disease. The identification of type-specific protein markers for ovarian cancer would provide the basis for more tailored treatments, as well as clues for understanding the molecular mechanisms governing cancer progression. In the present study, we used a novel approach to classify 24 ovarian cancer tissue samples based on the proteomic pattern of each sample. The method involved fractionation according to pI using chromatofocusing with analytical columns in the first dimension followed by separation of the proteins in each pI fraction using nonporous RP HPLC, which was coupled to an ESI-TOF mass analyzer for molecular weight (MW) analysis. A 2-D mass map of the protein content of each type of ovarian cancer tissue samples based upon pI versus intact protein MW was generated. Using this method, the clear cell and serous ovarian carcinoma samples were histologically distinguished by principal component analysis and clustering analysis based on their protein expression profiles and subtype-specific biomarker candidates of ovarian cancers were identified, which could be further investigated for future clinical study.