POSTN/TGFBI-associated stromal signature predicts poor prognosis in serous epithelial ovarian cancer

Identification of hedgehog signaling as a potential oncogenic driver in an aggressive subclass of human hepatocellular carcinoma: A reanalysis of the TCGA cohort

Hepatocellular carcinoma (HCC) is a heterogeneous disease and the second most common cause of cancer-related death worldwide. Marked developments in genomic technologies helped scientists to understand the heterogeneity of HCC and identified multiple HCC-related molecular subclasses. An integrative analysis of genomic datasets including 196 patients from The Cancer Genome Atlas (TCGA) group has recently reported a new HCC subclass, which contains three subgroups (iCluster1, iCluster2, and iCluster3). However, the transcriptional molecular characteristics underlying the iClusters have not been thoroughly investigated. Herein, we identified a more aggressive subset of HCC patients in the iCluster1, and re-clustered the TCGA samples into novel HCC subclasses referred to as aggressive (Ag), moderate-aggressive (M-Ag), and less-aggressive (L-Ag) subclasses. The Ag subclass had a greater predictive power than the TCGA iCluster1, and a higher level of alpha fetoprotein, microscopic vascular invasion, immune infiltration, isocitrate dehydrogenase 1/2 mutation status, and a worse survival than M-Ag and L-Ag subclasses. Global transcriptomic analysis showed that activation of hedgehog signaling in the Ag subclass may play key roles in tumor development of aggressive HCC. GLI1, a key transcriptional regulator of hedgehog signaling upregulated in the Ag subclass, was correlated with poor prognosis of HCC, and may be a potential prognostic biomarker and therapeutic target for Ag subclass HCC patients.

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.

Cancer-associated stroma significantly contributes to the mesenchymal subtype signature of serous ovarian cancer

OBJECTIVE

Mesenchymal (MES) subtype of high-grade serous ovarian cancer (HGSOC) is associated with worse outcomes including survival and resectability compared with other molecular subtypes. Molecular subtypes have historically been derived from 'tumor', consisting of both cancer and stromal cells. We sought to determine the origins of multiple MES subtype gene signatures in HGSOC.

METHODS

Fifteen patients with MES subtype of HGSOC diagnosed between 2010 and 2013 were identified. Formalin-fixed paraffin-embedded (FFPE) blocks from primary surgery were sectioned for immunohistochemistry (IHC) staining of relevant proteins. Eight genes (ACTA2, COL5A1, COL11A1, FAP, POSTN, VCAN, ZEB1 and p-SMAD2) were selected for IHC staining based on their differential expression in MES vs. non-MES subtypes of HGSOC. Slides were scored for intensity and localization and simple statistics were used to compare expression results in cancer vs. stroma and between primary and metastatic sites.

RESULTS

COL5A1, VCAN, FAP, and ZEB1 proteins were almost exclusively expressed by stroma as opposed to cancer cells. In addition, stromal expression was dominant for ACTA2, COL11A1, POSTN and p-SMAD2. In general there were minimal differences in expression of proteins between primary and metastatic sites, exceptions being COL5A1 (reduced in metastases) and COL11A1 (increased in metastases). Nuclear p-SMAD2 expression was more common in metastatic stroma.

CONCLUSIONS

The existing molecular classification of HGSOC MES subtype reflects a significant stromal contribution, suggesting an important role in HGSOC behavior and thus stroma may be a relevant therapeutic target. Specific patterns of expression indicate that collagens and TGF-β signaling are involved in the metastatic process.

Development of a Novel Autophagy-related Prognostic Signature for Serous Ovarian Cancer

Purpose: Considerable evidence suggests that autophagy plays a crucial role in the biological processes of ovarian cancer. The aim of this study was to develop a novel autophagy-related prognostic signature for serous ovarian cancer.

Methods: A univariate Cox proportional regression model was used to analyze mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) for the purpose of selecting autophagy-related prognostic genes. A multivariate Cox proportional regression model and the survival analysis were used to develop an eight-gene prognostic signature. The multivariate Cox and stratification analysis suggested that this signature was an independent prognostic factor for serous ovarian cancer patients. Bioinformatics functions were investigated by a principal components analysis and gene set enrichment analysis (GSEA). Finally, the correlation between the prognostic signature and gene mutation status was further analyzed in serous ovarian cancer, and especially with regard to the mutation status of BRCA1 and BRCA2 (BRCA1/2) genes.

Results: Distinctly different autophagy-related gene expression profiles were identified in normal ovarian tissues and serous ovarian cancer tissues. We profiled an autophagy-related gene set and identified eight genes with significant prognostic values for serous ovarian cancer. Subsequently, an autophagy-related ovarian cancer risk signature was constructed, and patients at a high-risk or low-risk for poor prognosis were identified based on their signature. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) times than low-risk patients. GSEA results suggested an enhanced intensity of autophagy regulation in high-risk patients when compared with low-risk patients. When studied as an independent prognostic factor for serous ovarian cancer, the significant prognostic value of this signature could be seen in the stratified cohorts. For clinical use, we developed a nomogram that included the prognostic classifier and seven clinical risk factors. Additionally, we identified the 10 most frequently mutated genes found in serous ovarian cancer patients, and analyzed them for their differences in high-risk and low-risk patients. Among 293 patients, 62 had BRCA1/2 gene mutations, and this result was significantly correlated with the autophagy-related prognostic signature.

Conclusions: Our findings suggest that the eight-gene autophagy-related signature could serve as an independent prognostic indicator for cases of serous ovarian cancer.

Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

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.

Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

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.

Establishment of a SVM classifier to predict recurrence of ovarian cancer

Gene expression data using retrieved ovarian cancer (OC) samples were used to identify genes of interest and a support vector machine (SVM) classifier was subsequently established to predict the recurrence of OC. Three datasets (GSE17260, GSE44104 and GSE51088) investigating OC gene expression were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in samples from patients with non-recurrent and recurrent OC were revealed via a homogeneity test and quality control analysis. A protein-protein interaction (PPI) network was subsequently established for the DEGs using data from Biological General Repository for Interaction Datasets, Human Protein Reference Database and Database of Interacting Proteins. Degrees of interaction and betweenness centrality (BC) scores were calculated for each node in the PPI network. The top 100 genes ranked by BC scores were selected to identify feature genes via recursive feature elimination using the GSE17260 dataset. Following this, a SVM classifier was constructed and further validated using the GSE44104 and GSE51088 datasets and independent gene expression data obtained from the Cancer Genome Atlas (TCGA). A total of 639 DEGs were identified from the three gene expression datasets, and a PPI network including 249 nodes and 354 edges was constructed. A SVM classifier consisting of 39 feature genes (including cullin 3, mouse double minute 2 homolog, aurora kinase A, WW domain containing oxidoreducatase, large tumor suppressor kinase 2, sirtuin 6, staphylococcal nuclease and tudor domain containing 1, leucine rich repeats and immunoglobulin like domains 1 and aurora kinase 1 interacting protein 1) was subsequently constructed. The prediction accuracies of the SVM classifier for GSE17260, GSE44104 and GSE51088 datasets as well as data downloaded from TCGA were revealed to be 92.7, 93.3, 96.6 and 90.4%, respectively. Furthermore, the results of the present study revealed that patients with predicted non-recurrent OC survived significantly longer compared with the patients with predicted recurrent OC (P=6.598×10⁻⁶). A SVM classifier consisting of 39 feature genes was established for predicting the recurrence and prognosis of OC. Therefore, the results of the present study suggested that the 39 feature genes may serve important roles in the development of OC and may represent therapeutic biomarkers of OC.

Transcriptomic Characterization of Endometrioid, Clear Cell, and High-Grade Serous Epithelial Ovarian Carcinoma

Background: Endometrioid carcinoma (EC) and clear cell carcinoma (CC) histotypes of epithelial ovarian cancer are understudied compared with the more common high-grade serous carcinomas (HGSC). We therefore sought to characterize EC and CC transcriptomes in relation to HGSC.Methods: Following bioinformatics processing and gene abundance normalization, differential expression analysis of RNA sequence data collected on fresh-frozen tumors was completed with nonparametric statistical analysis methods (55 ECs, 19 CCs, 112 HGSCs). Association of gene expression with progression-free survival (PFS) was completed with Cox proportional hazards models. Eight additional multi-histotype expression array datasets (N = 852 patients) were used for replication.Results: In the discovery set, tumors generally clustered together by histotype. Thirty-two protein-coding genes were differentially expressed across histotype (P < 1 × 10^-10) and showed similar associations in replication datasets, including MAP2K6, KIAA1324, CDH1, ENTPD5, LAMB1, and DRAM1 Nine genes associated with PFS (P < 0.0001) showed similar associations in replication datasets. In particular, we observed shorter PFS time for CC and EC patients with high gene expression for CCNB2, CORO2A, CSNK1G1, FRMD8, LIN54, LINC00664, PDK1, and PEX6, whereas, the converse was observed for HGSC patients.Conclusions: The results suggest important histotype differences that may aid in the development of treatment options, particularly those for patients with EC or CC.Impact: We present replicated findings on transcriptomic differences and how they relate to clinical outcome for two of the rarer ovarian cancer histotypes of EC and CC, along with comparison with the common histotype of HGSC. Cancer Epidemiol Biomarkers Prev; 27(9); 1101-9. ©2018 AACR.

Heterogeneous Periostin Expression in Different Histological Variants of Papillary Thyroid Carcinoma

Background

Periostin (PN) epithelial and stromal overexpression in tumor pathology has been studied according to tumor growth, angiogenesis, invasiveness, and metastasis, but a limited number of studies address PN in thyroid tumors.

Aim

Our study aimed to analyze PN expression in different histological variants of PTC and to correlate its expression with the clinicopathological prognostic factors.

Material and Methods

PN expression has been immunohistochemically assessed in 50 cases of PTC (conventional, follicular, oncocytic, macrofollicular, and tall cell variants), in tumor epithelial cells and intratumoral stroma. The association between PN expression and clinicopathological characteristics has been evaluated.

Results

Our results show that PTC presented different patterns of PN immunoreaction, stromal PN being significantly associated with advanced tumor stage and extrathyroidal extension. No correlations were found between PN overexpression in tumor epithelial cells and clinicopathological features, except for specific histological variants, the highest risk of poor outcome being registered for the conventional subtype in comparison to the oncocytic type.

Conclusions

Our study demonstrates differences in PN expression in histological subtypes of PTC. Our results plead in favor of a dominant protumorigenic role of stromal PN, while the action of epithelial PN is less noticeable.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

PURPOSE OF REVIEW

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

RECENT FINDINGS

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

SUMMARY

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

Purpose of review

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

Recent findings

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

Summary

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

Integrated Analysis Reveals Tubal- and Ovarian-Originated Serous Ovarian Cancer and Predicts Differential Therapeutic Responses

Purpose: The relative importance of fallopian tube (FT) compared with ovarian surface epithelium (OSE) in the genesis of serous type of ovarian cancer (SOC) is still unsettled. Here, we followed an integrated approach to study the tissue origin of SOC, as well as its association with clinical outcome and response to therapeutic drugs.Experimental Design: A collection of transcriptome data of 80 FTs, 89 OSEs, and 2,668 SOCs was systematically analyzed to determine the characteristic of FT-like and OSE-like tumors. A molecular signature was developed for identifying tissue origin of SOC and then was used to reevaluate the prognostic genes and therapeutic biomarkers of SOC of different tissue origins. IHC staining of tissue array and functional experiments on a panel of ovarian cancer cell lines were used to further validate the key findings.Results: The expression patterns of tissue-specific genes, prognostic genes, and molecular markers all support a dualistic tissue origin of SOC, from either FT or OSE. A molecular signature was established to identify the tissue identity of SOCs. Surprisingly, the signature showed a strong association with overall survival (OSE-like vs. FT-like, HR = 4.16; 95% CI, 2.67-6.48; P < 10^-9). The pharmacogenomic approach revealed AXL to be a therapeutic target of the aggressive OSE-derived SOC.Conclusions: SOC has two subtypes originated from either FT or OSE, which show different clinical and pathologic features. Clin Cancer Res; 23(23); 7400-11. ©2017 AACR.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

PURPOSE OF REVIEW

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

RECENT FINDINGS

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

SUMMARY

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.

[Grading of gynecological tumors : Current aspects]

Histopathological assessment of the tumor grade and cell type is central to the management and prognosis of various gynecological malignancies. Conventional grading systems for squamous carcinomas and adenocarcinomas of the vulva, vagina and cervix are poorly defined. For endometrioid tumors of the female genital tract as well as for mucinous endometrial, ovarian and seromucinous ovarian carcinomas, the 3‑tiered FIGO grading system is recommended. For uterine neuroendocrine tumors the grading system of the gastrointestinal counterparts has been adopted. Uterine leiomyosarcomas are not graded. Endometrial stromal sarcomas are divided into low and high grades, based on cellular morphology, immunohistochemical and molecular findings. A chemotherapy response score was established for chemotherapeutically treated high-grade serous pelvic cancer. For non-epithelial ovarian malignancies, only Sertoli-Leydig cell tumors and immature teratomas are graded. At this time molecular profiling has no impact on the grading of tumors of the female genital tract.

Pooled Clustering of High-Grade Serous Ovarian Cancer Gene Expression Leads to Novel Consensus Subtypes Associated with Survival and Surgical Outcomes

Purpose: Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC).Experimental Design: Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information.Results: Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had downregulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (P < 0.001) and with rate of optimal surgical debulking (≤1 cm, P = 1.9E-4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (P = 0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors compared with >28% in other subtypes; P = 0.02).Conclusions: HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC. Clin Cancer Res; 23(15); 4077-85. ©2017 AACR.

Microarray-based detection and expression analysis of new genes associated with drug resistance in ovarian cancer cell lines

PURPOSE

The present study is to discover a new genes associated with drug resistance development in ovarian cancer.

METHODS

We used microarray analysis to determine alterations in the level of expression of genes in cisplatin- (CisPt), doxorubicin- (Dox), topotecan- (Top), and paclitaxel- (Pac) resistant variants of W1 and A2780 ovarian cancer cell lines. Immunohistochemistry assay was used to determine protein expression in ovarian cancer patients.

RESULTS

We observed alterations in the expression of 22 genes that were common to all three cell lines that were resistant to the same cytostatic drug. The level of expression of 13 genes was upregulated and that of nine genes was downregulated. In the CisPt-resistant cell line, we observed downregulated expression of ABCC6, BST2, ERAP2 and MCTP1; in the Pac-resistant cell line, we observe upregulated expression of ABCB1, EPHA7 and RUNDC3B and downregulated expression of LIPG, MCTP1, NSBP1, PCDH9, PTPRK and SEMA3A. The expression levels of three genes, ABCB1, ABCB4 and IFI16, were upregulated in the Dox-resistant cell lines. In the Top-resistant cell lines, we observed increased expression levels of ABCG2, HERC5, IFIH1, MYOT, S100A3, SAMD4A, SPP1 and TGFBI and decreased expression levels of MCTP1 and PTPRK. The expression of EPHA7, IFI16, SPP1 and TGFBI was confirmed at protein level in analyzed ovarian cancer patients..

CONCLUSIONS

The expression profiles of the investigated cell lines indicated that new candidate genes are related to the development of resistance to the cytostatic drugs that are used in first- and second-line chemotherapy of ovarian cancer.

Pooled Clustering of High-Grade Serous Ovarian Cancer Gene Expression Leads to Novel Consensus Subtypes Associated with Survival and Surgical Outcomes

Purpose: Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC).Experimental Design: Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information.Results: Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had downregulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (P < 0.001) and with rate of optimal surgical debulking (≤1 cm, P = 1.9E-4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (P = 0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors compared with >28% in other subtypes; P = 0.02).Conclusions: HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC. Clin Cancer Res; 23(15); 4077-85. ©2017 AACR.

WOMEN IN CANCER THEMATIC REVIEW: Ovarian cancer-peritoneal cell interactions promote extracellular matrix processing

Ovarian cancer has a distinct tendency for metastasising via shedding of cancerous cells into the peritoneal cavity and implanting onto the peritoneum that lines the pelvic organs. Once ovarian cancer cells adhere to the peritoneal cells, they migrate through the peritoneal layer and invade the local organs. Alterations in the extracellular environment are critical for tumour initiation, progression and intra-peritoneal dissemination. To increase our understanding of the molecular mechanisms involved in ovarian cancer metastasis and to identify novel therapeutic targets, we recently studied the interaction of ovarian cancer and peritoneal cells using a proteomic approach. We identified several extracellular matrix (ECM) proteins including, fibronectin, TGFBI, periostin, annexin A2 and PAI-1 that were processed as a result of the ovarian cancer-peritoneal cell interaction. This review focuses on the functional role of these proteins in ovarian cancer metastasis. Our findings together with published literature support the notion that ECM processing via the plasminogen-plasmin pathway promotes the colonisation and attachment of ovarian cancer cells to the peritoneum and actively contributes to the early steps of ovarian cancer metastasis.

Expression signature distinguishing two tumour transcriptome classes associated with progression-free survival among rare histological types of epithelial ovarian cancer

BACKGROUND

The mechanisms of recurrence have been under-studied in rare histologies of invasive epithelial ovarian cancer (EOC) (endometrioid, clear cell, mucinous, and low-grade serous). We hypothesised the existence of an expression signature predictive of outcome in the rarer histologies.

METHODS

In split discovery and validation analysis of 131 Mayo Clinic EOC cases, we used clustering to determine clinically relevant transcriptome classes using microarray gene expression measurements. The signature was validated in 967 EOC tumours (91 rare histological subtypes) with recurrence information.

RESULTS

We found two validated transcriptome classes associated with progression-free survival (PFS) in the Mayo Clinic EOC cases (P=8.24 × 10(-3)). This signature was further validated in the public expression data sets involving the rare EOC histologies, where these two classes were also predictive of PFS (P=1.43 × 10(-3)). In contrast, the signatures were not predictive of PFS in the high-grade serous EOC cases. Moreover, genes upregulated in Class-1 (with better outcome) were showed enrichment in steroid hormone biosynthesis (false discovery rate, FDR=0.005%) and WNT signalling pathway (FDR=1.46%); genes upregulated in Class-2 were enriched in cell cycle (FDR=0.86%) and toll-like receptor pathways (FDR=2.37%).

CONCLUSIONS

These findings provide important biological insights into the rarer EOC histologies that may aid in the development of targeted treatment options for the rarer histologies.

Unsupervised analysis reveals two molecular subgroups of serous ovarian cancer with distinct gene expression profiles and survival

Purpose

Ovarian 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.

Methods

An 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.

Results

We 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.

Conclusions

We 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.

Electronic supplementary material

The online version of this article (doi:10.1007/s00432-016-2147-y) contains supplementary material, which is available to authorized users.

Wilms tumor protein 1 (WT1)-- not only a diagnostic but also a prognostic marker in high-grade serous ovarian carcinoma

AIMS

Wilms tumor protein 1 (WT1) expression is used in gynecological pathology as a diagnostic marker of serous differentiation, and is frequently co-expressed with ER-α. Early phase studies on WT1 vaccine in gynecological cancers are ongoing. In this study we aimed to determine the prognostic value of WT1 in high-grade serous ovarian carcinoma.

METHODS

WT1 protein expression was determined by immunohistochemistry in a cohort of 207 primary high-grade serous ovarian carcinomas. WT1 mRNA expression was evaluated in a cohort of 1137 ovarian carcinomas from publically available gene expression datasets.

RESULTS

High WT1 expression was a significant positive prognostic factor in primary high-grade serous ovarian carcinoma regarding overall survival (OS, p=0.008) and progression free survival (PFS, p=0.015), which was independent of age, stage, and residual tumor (OS: p=0.024, PFS: p=0.047). The prognostic significance of immunohistochemical WT1 expression could be reproduced in an independent cohort of 72 patients. On the mRNA level the prognostic significance was validated in silico in publically available gene expression datasets including TCGA data (OS: p=0.002, PFS: p=0.011). WT1 expression was significantly linked to ER-α expression (p=0.001), and tumors that co-expressed both markers (WT1+/ER-α+) had a longer survival time than tumors of all other marker combinations (OS: p=0.002, PFS: p=0.013).

CONCLUSION

We present WT1 as a robust prognostic marker in high-grade serous ovarian carcinoma, which adds prognostic information to ER-α. This should be kept in mind when WT1 is used as a biomarker in the context of WT1-targeting therapies.

ADAM12 is a prognostic factor associated with an aggressive molecular subtype of high-grade serous ovarian carcinoma

ADAM metallopeptidase domain 12 (ADAM12) is a promising biomarker because of its low expression in normal tissues and high expression in a variety of human cancers. However, ADAM12 levels in ovarian cancer have not been well characterized. We previously identified ADAM12 as one of the signature genes associated with poor survival in high-grade serous ovarian carcinoma (HGSOC). Here, we sought to determine if high levels of the ADAM12 protein and/or messenger RNA (mRNA) are associated with clinical variables in HGSOC. We show that high protein levels of ADAM12 in banked preoperative sera are associated with shorter progression-free and overall survival. Tumor levels of ADAM12 mRNA were also associated with shorter progression-free and overall survival as well as with lymphatic and vascular invasion, and residual tumor volume following cytoreductive surgery. The majority of genes co-expressed with ADAM12 in HGSOC were transforming growth factor (TGF)β signaling targets that function in collagen remodeling and cell-matrix adhesion. In tumor sections, the ADAM12 protein and mRNA were expressed in epithelial cancer cells and surrounding stromal cells. In vitro data showed that ADAM12 mRNA levels can be increased by TGFβ signaling and direct contact between epithelial and stromal cells. High tumor levels of ADAM12 mRNA were characteristic of the mesenchymal/desmoplastic molecular subtype of HGSOC, which is known to have the poorest prognosis. Thus, ADAM12 may be a useful biomarker of aggressive ovarian cancer for which standard treatment is not effective.

Upregulation of Periostin and Reactive Stroma Is Associated with Primary Chemoresistance and Predicts Clinical Outcomes in Epithelial Ovarian Cancer

PURPOSE

Up to one third of ovarian cancer patients are intrinsically resistant to platinum-based treatment. However, predictive and therapeutic strategies are lacking due to a poor understanding of the underlying molecular mechanisms. This study aimed to identify key molecular characteristics that are associated with primary chemoresistance in epithelial ovarian cancers.

EXPERIMENTAL DESIGN

Gene expression profiling was performed on a discovery set of 85 ovarian tumors with clinically well-defined response to chemotherapies as well as on an independent validation dataset containing 138 ovarian patients from the chemotreatment arm of the ICON7 trial.

RESULTS

We identified a distinct "reactive stroma" gene signature that is specifically associated with primary chemoresistant tumors and was further upregulated in posttreatment recurrent tumors. Immunohistochemistry (IHC) and RNA in situ hybridization (RNA ISH) analyses on three of the highest-ranked signature genes (POSTN, LOX, and FAP) confirmed that modulation of the reactive stroma signature genes within the peritumoral stromal compartments was specifically associated with the clinical chemoresistance. Consistent with these findings, chemosensitive ovarian cells grown in the presence of recombinant POSTN promoted resistance to carboplatin and paclitaxel treatment in vitro. Finally, we validated the reactive stroma signature in an independent dataset and demonstrated that a high POSTN expression level predicts shorter progression-free survival following first-line chemotherapy.

CONCLUSIONS

Our findings highlight the important interplay between cancer and the tumor microenvironment in ovarian cancer biology and treatment. The identified reactive stromal components in this study provide a molecular basis to the further development of novel diagnostic and therapeutic strategies for overcoming chemoresistance in ovarian cancer.