Expression and molecular profiles of the AlkB family in ovarian serous carcinoma.
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ABSTRACT:
SUBMITTER: Cai Y
PROVIDER: S-EPMC8064172 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Many high-grade serous carcinomas (HGSCs) of the pelvis are thought to originate in the distal portion of the fallopian tube. Serous tubal intra-epithelial carcinoma (STIC) lesions are the putative precursor to HGSC and identifiable in ~ 50% of advanced stage cases. To better understand the molecular etiology of HGSCs, we report a multi-center integrated genomic analysis of advanced stage tumors with and without STIC lesions and normal tissues. The most significant focal DNA SCNAs were shared between cases with and without STIC lesions. The RNA sequence and the miRNA data did not identify any clear separation between cases with and without STIC lesions. HGSCs had molecular profiles more similar to normal fallopian tube epithelium than ovarian surface epithelium or peritoneum. The data suggest that the molecular features of HGSCs with and without associated STIC lesions are mostly shared, indicating a common biologic origin, likely to be the distal fallopian tube among all cases.High-grade serous carcinomas (HGSCs) are associated with precursor lesions (STICs) in the fallopian epithelium in only half of the cases. Here the authors report the molecular analysis of HGSCs with and without associated STICs and show similar profiles supporting a common origin for all HGSCs.
| S-EPMC5645359 | biostudies-literature
Project description:Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes.Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%; P < 10-5) and are associated with overall survival in a meta-analysis across datasets (P < 10-5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037-47. ©2018 AACR.
| S-EPMC6207081 | biostudies-literature
Project description:PurposeOvarian cancer is typically diagnosed at late stages, and thus, patients' prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets.MethodsAn unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR.ResultsWe found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them-DSPG3 and LOX-were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival.ConclusionsWe distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.
| S-EPMC4869753 | biostudies-literature
Project description:BackgroundSmall sample sizes used in previous studies result in a lack of overlap between the reported gene signatures for prediction of chemotherapy response. Although morphologic features, especially tumor nuclear morphology, are important for cancer grading, little research has been reported on quantitatively correlating cellular morphology with chemotherapy response, especially in a large data set. In this study, we have used a large population of patients to identify molecular and morphologic signatures associated with chemotherapy response in serous ovarian carcinoma.Methodology/principal findingsA gene expression model that predicts response to chemotherapy is developed and validated using a large-scale data set consisting of 493 samples from The Cancer Genome Atlas (TCGA) and 244 samples from an Australian report. An identified 227-gene signature achieves an overall predictive accuracy of greater than 85% with a sensitivity of approximately 95% and specificity of approximately 70%. The gene signature significantly distinguishes between patients with unfavorable versus favorable prognosis, when applied to either an independent data set (P = 0.04) or an external validation set (P<0.0001). In parallel, we present the production of a tumor nuclear image profile generated from 253 sample slides by characterizing patients with nuclear features (such as size, elongation, and roundness) in incremental bins, and we identify a morphologic signature that demonstrates a strong association with chemotherapy response in serous ovarian carcinoma.ConclusionsA gene signature discovered on a large data set provides robustness in accurately predicting chemotherapy response in serous ovarian carcinoma. The combination of the molecular and morphologic signatures yields a new understanding of potential mechanisms involved in drug resistance.
| S-EPMC3348145 | biostudies-literature
Project description:High grade serous ovarian carcinoma (HGSC) without identifiable serous tubal intraepithelial carcinoma (STIC) within the fallopian tube (FT) occurs in approximately 50% of patients. The objective of this study was to use a multisite tumor sampling approach to study HGSC with and without STIC. RNAseq analysis of HGSC samples collected from multiple sites e.g. ovary, FT and peritoneum, revealed moderate levels of intrapatient heterogeneity in gene expression that could influence molecular profiles. Mixed-model ANOVA analysis of gene expression in tumor samples from patients with multiple tumor sites (n = 13) and patients with a single site tumor sample (n = 11) to compare HGSC-STIC to HGSC-NOSTIC identified neurotensin (NTS) as significantly higher (> two-fold change, False Discovery Rate (FDR) < 0.10) in HGSC-STIC. This data was validated using publicly available RNA-Seq datasets. Concordance between higher NTS gene expression and NTS peptide levels in HGSC-STIC samples was demonstrated by immunohistochemistry. To determine the role of NTS in HGSC, five ovarian cancer (OvCa) cell lines were screened for expression of NTS and its receptors, NTSR1 and NTSR3. Increased expression of NTS and NSTR1 was observed in several of the OvCa cells, whereas the NTSR3 receptor was lower in all OvCa cells, compared to immortalized FT epithelial cells. Treatment with NTSR1 inhibitor (SR48692) decreased cell proliferation, but increased cell migration in OvCa cells. The effects of SR48692 were receptor mediated, since transient RNAi knockdown of NTSR1 mimicked the migratory effects and knockdown of NTSR3 mimicked the anti-proliferative effects. Further, knockdown of NTSR1 or NTSR3 was associated with acquisition of distinct morphological phenotypes, epithelial or mesenchymal, respectively. Taken together, our results reveal a difference in a biologically active pathway between HGSC with and without STIC. Furthermore, we identify neurotensin signaling as an important pathway involved in cell proliferation and epithelial-mesenchymal transition in HGSC-STIC which warrants further study as a potential therapeutic target. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
| S-EPMC6619390 | biostudies-literature
Project description:Hormone receptor expression is a characteristic of low-grade serous ovarian carcinoma (LGSOC). Studies investigating estrogen receptor (ER) and progesterone receptor (PR) expression levels suggest its prognostic and predictive significance, although their associations with key molecular aberrations are not well understood. As such, we sought to describe the specific genomic profiles associated with different ER/PR expression patterns and survival outcomes in a cohort of patients with advanced disease. The study comprised fifty-five advanced-staged (III/IV) LGSOCs from the Canadian Ovarian Experimental Unified Resource (COEUR) for which targeted mutation sequencing, copy-number aberration, clinical and follow-up data were available. ER, PR, and p16 expression were assessed by immunohistochemistry. Tumors were divided into low and high ER/PR expression groups based on Allred scoring. Copy number analysis revealed that PR-low tumors (Allred score <2) had a higher fraction of the genome altered by copy number changes compared to PR-high tumors (p = 0.001), with cancer genes affected within specific loci linked to altered peptidyl-tyrosine kinase, MAP-kinase, and PI3-kinase signaling. Cox regression analysis showed that ER-high (p = 0.02), PR-high (p = 0.03), stage III disease (p = 0.02), low residual disease burden (p = 0.01) and normal p16 expression (p<0.001) were all significantly associated with improved overall survival. This study provides evidence that genomic aberrations are linked to ER/PR expression in primary LGSOC.
| S-EPMC9242985 | biostudies-literature
Project description:Ovarian and endometrial high-grade serous carcinomas (HGSCs) have similar clinical and pathological characteristics; however, exhaustive protein expression profiling of these cancers has yet to be reported.We performed protein expression profiling on 14 cases of HGSCs (7 ovarian and 7 endometrial) and 18 endometrioid carcinomas (9 ovarian and 9 endometrial) using iTRAQ-based exhaustive and quantitative protein analysis.We identified 828 tumour-expressed proteins and evaluated the statistical similarity of protein expression profiles between ovarian and endometrial HGSCs using unsupervised hierarchical cluster analysis (P<0.01). Using 45 statistically highly expressed proteins in HGSCs, protein ontology analysis detected two enriched terms and proteins composing each term: IMP2 and MCM2. Immunohistochemical analyses confirmed the higher expression of IMP2 and MCM2 in ovarian and endometrial HGSCs as well as in tubal and peritoneal HGSCs than in endometrioid carcinomas (P<0.01). The knockdown of either IMP2 or MCM2 by siRNA interference significantly decreased the proliferation rate of ovarian HGSC cell line (P<0.01).We demonstrated the statistical similarity of the protein expression profiles of ovarian and endometrial HGSC beyond the organs. We suggest that increased IMP2 and MCM2 expression may underlie some of the rapid HGSC growth observed clinically.
| S-EPMC4782211 | biostudies-literature
Project description:While high-grade serous ovarian carcinoma (HGSOC) is the most common histologic subtype of ovarian cancer, significant tumor heterogeneity exists. In addition, chemotherapy induces changes in gene expression and alters the mutational profile. To evaluate the notion that patients with HGSOC could be better classified for optimal treatment based on gene expression, we compared genetic variants [by DNA next-generation sequencing (NGS) using a 50 gene Ion Torrent panel] and gene expression (using the NanoString PanCancer 770 gene Panel) in the tumor from 20 patients with HGSOC before and after neoadjuvant chemotherapy (NACT). NGS was performed on plasma cell free DNA (cfDNA) on a select group of patients (n = 14) to assess the utility of using cfDNA to monitor these changes. A total of 86 genes had significant changes in RNA expression after NACT. Thirty-eight genetic variants (including SNPs) from 6 genes were identified in tumors pre-NACT, while 59 variants from 19 genes were detected in the cfDNA. The number of DNA variants were similar after NACT. Of the 59 variants in the plasma pre-NACT, only 6 persisted, whereas 33 of 38 specific variants in the tumor DNA remained unchanged. Pathway analysis showed the most significant alterations in the cell cycle and DNA damage pathways.Implications: Gene expression profiles at the time of interval debulking provide additional genetic information that could help impact treatment decisions after NACT; although, continued collection and analysis of matched tumor and cfDNA from multiple time points are needed to determine the role of cfDNA in the management of HGSOC. Mol Cancer Res; 16(5); 813-24. ©2018 AACR.
| S-EPMC6497146 | biostudies-literature
Project description:In the early 2000s a two-tier grading system was introduced for serous ovarian cancer. Since then, we have increasingly come to accept that low-grade serous ovarian carcinoma (LGSOC) is a separate entity with a unique mutational landscape and clinical behaviour. As less than 10% of serous carcinomas of the ovary are low-grade, they are present in only a small number of patients in clinical trials for ovarian cancer. Therefore the current treatment of LGSOC is based on smaller trials, retrospective series, and subgroup analysis of large clinical trials on ovarian cancer. Surgery plays a major role in the treatment of patients with LGSOC. In the systemic treatment of LGSOC, hormonal treatment and targeted therapies seem to play an important role.
| S-EPMC6195427 | biostudies-literature
Project description:Patients with high-grade serous ovarian cancer (HGSC) who have no visible residual disease (R0) after primary surgery have the best clinical outcomes, followed by patients who undergo neoadjuvant chemotherapy (NACT) and have a response enabling interval cytoreductive surgery. Clinically useful biomarkers for predicting these outcomes are still lacking. Extracellular vesicles (EVs) have been recognized as liquid biopsy-based biomarkers for early cancer detection and disease surveillance in other disease settings. In this study, we performed extensive molecular characterization of serum-derived EVs and correlated the findings with therapeutic outcomes in patients with HGSC. Using EV-DNA whole-genome sequencing and EV-RNA sequencing, we identified distinct somatic EV-DNA alterations in cancer-hallmark genes and in ovarian cancer genes, as well as significantly altered oncogenic pathways between the R0 group and NACT groups. We also found significantly altered EV-RNA transcriptomic variations and enriched pathways between the groups. Taken together, our data suggest that the molecular characteristics of EVs could enable prediction of patients with HGSC who could undergo R0 surgery or respond to chemotherapy.
| S-EPMC9330879 | biostudies-literature