Transcription profiling of human normal, benign, borderline malignant and malignant ovarian tissue
ABSTRACT: The Homeobox (HOX) family of genes encodes transcription factors involved in basic developmental processes, most notably during embryogenesis. A possible HOX gene link between development and oncogenesis has recently been described. Dysregulation of HOX genes may be an early event in malignant transformation likely to induce antibody response and thus provide a potential marker for early diagnosis of cancer. Ovarian cancer is characterized by poor early detection and serves as an excellent model system to develop potential markers for early diagnosis. In this study we begin to characterize HOX gene expression in malignant tumors of the ovary and analyze the potential role of HOX genes as biomarkers for early detection of ovarian cancer. Microarray analysis of mRNA from human ovarian tissues was performed on 65 samples of normal, benign, borderline malignant and malignant ovarian tissue. These samples were analyzed using the Affymetrix Human Genome Focus GeneChip (HG-Focus) microarray to distinguish the differential pattern of mRNA expression between the four types of samples. Real-time reverse transcription PCR was utilized to confirm up-regulation of HOX genes as determined by microarray analysis. Our results demonstrate multiple HOX genes to be up-regulated in ovarian cancer. We have shown stepped increase in HOX expression comparing normal, benign neoplastic, and malignant human ovarian tissue samples. This suggests dysregulation of HOX genes may be an early event in malignant transformation and warrants additional studies to validate HOX gene products as potential markers for early detection of ovarian cancer. Experiment Overall Design: 67 samples were analyzed. 4 groupings based on pathology (normal, benign, borderline malignant, malignant). An average of normal samples was used as controls. Cell lines were used as ovarian cancer control samples.
Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as “C-signatures”) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers. Total RNA obtained from serous ovarian adenocarcinomas, adenomas and borderline tumors. Gene expression profiling using Illumina's HT12 v4 bead arrays. Application of ovarian cancer molecular subtypes and intrinsic breast cancer subtypes using nearest centroid classification. KRAS and BRAF mutation analyses in the malignant and borderline tumors.
Project description:The goal of the study was to collect ovarian cancer samples for gene expression profiling (n = 529). The data will be used in several publications, the first of which described molecular signatures associated with high-grade serous ovarian cancer subtype Overall design: Ovarian tumors (n=529) were compared to a reference pool of 106 ovarian samples. Mixed reference includes normal, benign, borderline, and malignant sample of various histolgies.
Project description:The goal of the study was to identify molecular subgroups that might predict clinical outcomes in serous epithelial ovarian cancer (EOC) patients. A second objective was to identify potential therapeutic targets for serous EOC based on improved understanding of the molecular diversity of the disease. Ovarian tissues and matched peripheral blood samples were prospectively obtained from sequential patients undergoing planned gynecologic surgery at Cedars-Sinai Medical Center between 1989 and 2005. All patients underwent surgery and received adjuvant chemotherapy with a contemporaneous standard-of-care regimen. Ovarian tissue samples (n=172) were compared to a reference pool of 106 ovarian samples. Mixed reference includes normal, benign, borderline, and malignant samples.
Project description:The goal of the study was to examine the transcriptional profile of ovarian cancer cancers in order to develop validated clinically useful prognostic signatures with the potential to guide therapy decisions. Fresh frozen samples were prospectively collected from a series of 107 consecutive women with high-grade serous ovarian, primary peritonial, or fallopian tube cancer as well as high grade clear cell and endometrioid cancer who underwent surgery by a gynecologic oncologist at Mayo Clinic between 1994 and 2005. All patients received postoperative chemotherapy with a platinum agent, and 75% received a taxane. All patients signed an Institutional Review Board approved consent for bio-banking, clinical data extraction and molecular analysis. Median follow-up time was 35 months (range, 1-202 months). Fourteen patients (8%) were included in the TCGA study. Overall design: High grade serous, clear cell and endometrioid ovarian tumors (n=107) were compared to a reference pool of 106 ovarian samples. Mixed reference includes normal, benign, borderline, and malignant sample of various histolgies.
Project description:The goal of the study was to further delineate the molecular signatures associated with high-grade serous ovarian cancer in order to develop validated clinically useful prognostic signatures with the potential to guide therapy decisions. Fresh frozen samples were prospectively collected from a series of 174 consecutive women with high-grade serous ovarian, primary peritonial, or fallopian tube cancer who underwent surgery by a gynecologic oncologist at Mayo Clinic betweern 1994 and 2005. All patients received postoperative chemotherapy with a platinum agent, and 75% received a taxane. All patients signed an Institutional Review Board approved consent for bio-banking, clinical data extraction and molecular analysis. Median follow-up time was 35 months (range, 1-202 months). Fourteen patients (8%) were included in the TCGA study. Overall design: High grade serous ovarian tumors (n=174) were compared to a reference pool of 106 ovarian samples. Mixed reference includes normal, benign, borderline, and malignant sample of various histolgies.
Project description:The paper describes a model on the detection of cancer based on cancer and immune biomarkers.
Created by COPASI 4.25 (Build 207)
This model is described in the article:
Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach
Raluca Eftimie and and Esraa Hassanein
J Transl Med (2018) 16:73
Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.
Methods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.
Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times.
Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.
Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times
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Project description:Serous borderline tumours (SBOT) are a challenging group of ovarian tumours positioned between benign and malignant disease. We have profiled the DNA methylomes of 12 low grade serous carcinoma (LGSC), 19 SBOT and 16 benign serous tumours (BST) across 27,578 CpG sites to further characterise the epigenomic relationship between these subtypes of ovarian tumours. Unsupervised hierarchical clustering of DNA methylation levels showed that LGSC differ distinctly from BST, however, not from SBOT. Gene ontology analysis of genes showing differential methylation at linked CpG sites between LGSC and BST revealed significant enrichment of gene groups associated with cell adhesion, cell-cell signalling and the extracellular region consistent with a more invasive phenotype of LGSC as compared to BST. Consensus clustering highlighted differences between SBOT methylomes and returned subgroups with malignant-like or benign-like methylation profiles. Furthermore, a two loci DNA methylation signature can distinguish between these SBOT subgroups with benign-like and malignant-like methylation characteristics. Our findings indicate striking similarities between SBOT and LGSC methylomes which supports a common origin and the view that LGSC may arise from SBOT. A subgroup of SBOT can be classified into tumours with a benign-like or a malignant-like methylation profile which may help in identifying tumours more likely to progress into LGSC. Array-based methylation profiling was performed using the Infinium HumanMethylation27 BeadChip in 12 low grade serous carcinoma, 19 serous borderline tumours and 16 benign serous tumours. The reproducibility of the Infinium HumanMethylation27 BeadChips was evaluated using four biological replicates of the high grade serous ovarian cancer cell line PEO1. Differential methylation cutoff was estimated from four biological replicates by bootstrap resampling and set at Δβ ≥ 0.25 corresponding to a FDR ≤ 0.09.
Project description:Epithelial ovarian cancer (EOC) constitutes a major gynecological malignancy, with a reported incidence rate of 3-12/100 000 woman annually. As early symptoms of ovarian cancer are often clinically atypical or absent, the majority of ovarian cancer patients are diagnosed at a late stage, when the five-year survival rate is extremely low. This condition underscores the urgency of early detection of these patients and establishment of new therapeutic targets for successful intervention. Considering that the predominant biological characteristic that differentiates malignant from benign tumors is the ability to metastasize, it is necessary to identify novel metastasis-related molecules for ovarian cancer. In this study, we found that CAFs could significantly increase the metastatic potential of ovarian cancer cells compared with non-cancer associated fibroblasts(NAFs), which is associated with over-expression of CXCL14 in CAFs. We examined the impact of CAF-secreted CXCL14 on the lncRNA expression profiles in ovarian cancer during metastasis. We treated A2780s ovarian cancer cell line with recombinant CXCL14 protein and control respectively and subjected them to Arraystar Human LncRNA microarray v3.0 to profile differential lncRNAs in ovarian cancer upon treatment of CXCL14
Project description:Introduction: Mucinous tumors are the second most common form of epithelial ovarian tumor, yet the cell of origin for this histological sub-type remains undetermined. While these tumors are thought to arise through a stepwise progression from benign cystadenoma to borderline tumor to invasive carcinoma, few studies have attempted to comprehensively characterize the genetic changes specific to this subtype or its precursors. Methods: To explore the spectrum of genomic alterations common to mucinous tumors we performed high resolution genome-wide copy number analysis, mutation screening by Sanger sequencing and immunohistochemistry on a series of primary ovarian mucinous cystadenomas (n=20) and borderline tumors (n=22). Results: Integration of copy number data, targeted mutation screening of RAS/RAF pathway members and immunohistochemistry reveals that p16 loss and RAS/RAF pathway alterations are highly recurrent events that occur early during mucinous tumor development. The frequency of concurrence of these events was observed in 40% of benign cystadenomas and 68% of borderline tumors. Conclusions: This study is the largest and highest resolution analysis of mucinous benign and borderline tumors performed to date and provides strong support for these lesions being precursors of primary ovarian mucinous adenocarcinoma. The high level of uniformity in the molecular events underlying the pathogenesis of mucinous ovarian tumors provides an opportunity for treatments targeting specific mutations and pathways. Copy number data was generated for 42 mucinous ovarian tumours (20 benign, 22 borderline). Epithelial and stromal DNA from the tumours and matched-normal lymphocyte DNA were all analysed. Processed/normalized data for the germline DNA samples are not provided because they themselves are normalised to a diploid copy number, making all the probe values 2, which is not informative.
Project description:Introduction: Serous ovarian cancer is the leading cause of gynecological cancers, with a 5-year survival rate below 45% due in part to the nonspecific symptoms and lack of accurate screening for early detection. In comparison, patients diagnosed at an early stage have a five-year survival rate of 92%, demonstrating the urgent need for biomarkers for the early detection of disease. Serum from patients with serous ovarian cancer contain antibodies to tumor antigens that are potential biomarkers for early detection. The purpose of this study is to identify a panel of novel serum autoantibody (AAb) biomarkers for the early diagnosis of serous ovarian cancer. Methods: To detect AAb we probed high-density programmable protein microarrays (NAPPA) containing 10,247 antigens with sera from patients with serous ovarian cancer (n = 30 cases/ 30 healthy controls) and measured bound IgG. We identified 735 promising tumor antigens using cutoff values of 10% sensitivity at 95% specificity and K-value>0.8, as well as visual analysis and evaluated these with an independent set of serous ovarian cancer sera (n = 30 cases/ 30 benign disease controls/ 30 heathy controls). Thirty-nine potential tumor autoantigens were identified with sensitivities ranging from 3 to 39.7% sensitivity at 95% specificity and were retested using an orthogonal programmable ELISA assay. A total of 13 potential tumor antigens were identified for further validation using an independent ovarian cancer sera set (n = 44 cases/ 34 healthy controls). Sensitivities at 95% specificity were calculated and a serous ovarian cancer classifier was constructed. In addition, we evaluated a longitudinal study using blinded serous pre-diagnostic ovarian cancer sera (n = 9 cases/ 90 controls) to examine the value of three (CTAG1, CTAG2, and p53) of these AAb in comparison to CA 125. Results: We identified 11-AAbs (ICAM3, CTAG2, p53, STYXL1, PVR, POMC, NUDT11, TRIM39, UHMK1, KSR1, and NXF3) that distinguished serous ovarian cancer cases from healthy controls with a combined 45% sensitivity at 100% specificity. In our longitudinal analysis, p53- and CTAG-AAb were detected up to 9 months prior to ovarian cancer diagnosis and increased with CA 125 levels. Conclusion: These are potential circulating biomarkers for the early detection of serous ovarian cancer, and warrant confirmation in larger clinical cohorts. In addition, p53- and CTAG1/2-AAb are detected in a subset of women with ovarian cancer up to 9 months prior to clinical diagnosis. Their utility as a biomarker for early detection, beyond CA 125, warrant further investigation. Overall design: Subsequent immunoreactivity conformation was compared between 29 serous ovarian cases, 28 benign ovarian disease controls, and 29 healthy controls against 735 human proteins that were printed on microscope slides. Contrubuted by: Arizona State University