Differential gene expression identifies subgroups of ovarian carcinoma

Functional Enrichment Analysis of Tumor Microenvironment-Driven Molecular Alterations That Facilitate Epithelial-to-Mesenchymal Transition and Distant Metastasis

Nowadays, hepatocellular carcinoma (HCC) is the second leading cause of cancer deaths, and identifying the effective factors in causing this disease can play an important role in its prevention and treatment. Tumors provide effective agents for invasion and metastasis to other organs by establishing appropriate communication between cancer cells and the microenvironment. Epithelial-to-mesenchymal transition (EMT) can be mentioned as one of the effective phenomena in tumor invasion and metastasis. Several factors are involved in inducing this phenomenon in the tumor microenvironment, which helps the tumor survive and migrate to other places. It can be effective to identify these factors in the use of appropriate treatment strategies and greater patient survival. This study investigated the molecular differences between tumor border cells and tumor core cells or internal tumor cells in HCC for specific EMT genes. Expression of NOTCH1, ID1, and LST1 genes showed a significant increase at the HCC tumor border. Targeting these genes can be considered as a useful therapeutic strategy to prevent distant metastasis in HCC patients.

Hypermethylation of the ADIRF promoter regulates its expression level and is involved in NNK-induced malignant transformation of lung bronchial epithelial cells

The carcinogenic mechanism of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), a well-known tobacco carcinogen, has not been fully elucidated in epigenetic studies. 5-Methylcytosine (5mC) modification plays a major role in epigenetic regulation. In this study, the 5mC level increased in both BEAS-2B human bronchial epithelium cells treated with 100 mg/L NNK for 24 h and NNK-induced malignant-transformed BEAS-2B cells (2B-NNK cells), suggesting that 5mC modification is associated with the malignant transformation mechanism of NNK. Using a combination of Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq), RNA sequencing (RNA-seq), and bioinformatics analysis of data from the Genomic Data Commons database, we found that the Adipogenesis regulatory factor (ADIRF) promoter region was abnormally hypermethylated, yielding low ADIRF mRNA expression, and that ADIRF overexpression could inhibit the proliferation, migration, and invasion of 2B-NNK cells. This finding suggests that ADIRF plays a tumor suppressor role in the NNK-induced malignant transformation of cells. Subsequently, using 5-Aza-2'-deoxycytidine (5-Aza-2'-dC) and the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Catalytically Dead Cas9 (dCas9 system), we verified that the demethylation of the ADIRF promoter region in 2B-NNK cells inhibited the proliferation, migration, and invasion ability of the cells and increased their apoptosis ability. These results suggest that abnormal 5mC modification of the ADIRF promoter plays a positive regulatory role in the pathogenesis of NNK-induced lung cancer. This study offers a new experimental basis for the epigenetic mechanism of NNK-induced lung cancer.

Radiomics and Radiogenomics of Ovarian Cancer

Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer.

Variable number tandem repeats mediate the expression of proximal genes

Variable number tandem repeats (VNTRs) account for significant genetic variation in many organisms. In humans, VNTRs have been implicated in both Mendelian and complex disorders, but are largely ignored by genomic pipelines due to the complexity of genotyping and the computational expense. We describe adVNTR-NN, a method that uses shallow neural networks to genotype a VNTR in 18 seconds on 55X whole genome data, while maintaining high accuracy. We use adVNTR-NN to genotype 10,264 VNTRs in 652 GTEx individuals. Associating VNTR length with gene expression in 46 tissues, we identify 163 "eVNTRs". Of the 22 eVNTRs in blood where independent data is available, 21 (95%) are replicated in terms of significance and direction of association. 49% of the eVNTR loci show a strong and likely causal impact on the expression of genes and 80% have maximum effect size at least 0.3. The impacted genes are involved in diseases including Alzheimer's, obesity and familial cancers, highlighting the importance of VNTRs for understanding the genetic basis of complex diseases.

Understanding the Role of the Transcription Factor Sp1 in Ovarian Cancer: from Theory to Practice

Ovarian cancer (OC) is one of the deadliest cancers among women contributing to high risk of mortality, mainly owing to delayed detection. There is no specific biomarker for its detection in early stages. However, recent findings show that over-expression of specificity protein 1 (Sp1) is involved in many OC cases. The ubiquitous transcription of Sp1 apparently mediates the maintenance of normal and cancerous biological processes such as cell growth, differentiation, angiogenesis, apoptosis, cellular reprogramming and tumorigenesis. Sp1 exerts its effects on cellular genes containing putative GC-rich Sp1-binding site in their promoters. A better understanding of the mechanisms underlying Sp1 transcription factor (TF) regulation and functions in OC tumorigenesis could help identify novel prognostic markers, to target cancer stem cells (CSCs) by following cellular reprogramming and enable the development of novel therapies for future generations. In this review, we address the structure, function, and biology of Sp1 in normal and cancer cells, underpinning the involvement of Sp1 in OC tumorigenesis. In addition, we have highlighted the influence of Sp1 TF in cellular reprogramming of iPSCs and how it plays a role in controlling CSCs. This review highlights the drugs targeting Sp1 and their action on cancer cells. In conclusion, we predict that research in this direction will be highly beneficial for OC treatment, and chemotherapeutic drugs targeting Sp1 will emerge as a promising therapy for OC.

Myoferlin, a Membrane Protein with Emerging Oncogenic Roles

Myoferlin (MYOF), initially identified in muscle cells, is a member of the Ferlin family involved in membrane fusion, membrane repair, and membrane trafficking. Dysfunction of this protein is associated with muscular dysfunction. Recently, a growing body of studies have identified MYOF as an oncogenic protein. It is overexpressed in a variety of human cancers and promotes tumorigenesis, tumor cell motility, proliferation, migration, epithelial to mesenchymal transition, angiogenesis as well as metastasis. Clinically, MYOF overexpression is associated with poor outcome in various cancers. It can serve as a prognostic marker of human malignant disease. MYOF drives the progression of cancer in various processes, including surface receptor transportation, endocytosis, exocytosis, intercellular communication, fit mitochondrial structure maintenance and cell metabolism. Depletion of MYOF demonstrates significant antitumor effects both in vitro and in vivo, suggesting that targeting MYOF may produce promising clinical benefits in the treatment of malignant disease. In the present article, we reviewed the physiological function of MYOF as well as its role in cancer, thus providing a general understanding for further exploration of this protein.

Radiomics: an Introductory Guide to What It May Foretell

PURPOSE OF REVIEW

To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine.

RECENT FINDINGS

Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.

Effect of chemotherapy on cancer stem cells and tumor-associated macrophages in a prospective study of preoperative chemotherapy in soft tissue sarcoma

Background

Cancer stem cells (CSC) may respond to chemotherapy differently from other tumor cells.

Methods

This study examined the expression of the putative cancer stem cell markers ALDH1, CD44, and CD133; the angiogenesis marker CD31; and the macrophage marker CD68 in soft tissue sarcomas (STS) before and after 4 cycles of chemotherapy with doxorubicin and ifosfamide in 31 patients with high-grade soft tissue sarcoma in a prospective clinical trial.

Results

None of the markers clearly identified CSCs in STS samples. Macrophages represented a prominent component in viable tumor areas in pre-treatment STS biopsies, ranging from < 5 to > 50%. Furthermore, macrophages expressed CD44 and ALDH1. Macrophage density correlated with baseline maximum standardized uptake value (SUVmax) on fluoro-deoxyglucose positron emission tomography (PET) imaging. Pre-chemotherapy CD68 staining correlated positively with the baseline SUVmax, and negatively with the percent of viable tumor cells in post-chemotherapy resection samples. In particular, cases with more CD68-positive cells at biopsy had fewer viable tumor cells at resection, suggesting a better response to chemotherapy.

Conclusions

In conclusion, ALDH1, CD44, and CD133 are not likely to be useful markers of CSCs in STS. However, our observation of infiltrating macrophages in STS specimens indicates that these immune cells may contribute significantly to STS biology and response to chemotherapy, and could provide a potential target of therapy. Future studies should investigate macrophage contribution to STS pathophysiology by cytokine signaling.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1883-6) contains supplementary material, which is available to authorized users.

Identifying gene network rewiring by combining gene expression and gene mutation data

Understanding how gene dependency networks rewire between different disease states is an important task in genomic research. Although many computational methods have been proposed to undertake this task via differential network analysis, most of them are designed for a predefined data type. With the development of the high throughput technologies, gene activity measurements can be collected from different aspects (e.g., mRNA expression and DNA mutation). Different data types might share some common characteristics and include certain unique properties. New methods are needed to explore the similarity and difference between differential networks estimated from different data types. In this study, we develop a new differential network inference model which identifies gene network rewiring by combining gene expression and gene mutation data. Similarity and difference between different data types are learned via a group bridge penalty function. Simulation studies have demonstrated that our method consistently outperforms the competing methods. We also apply our method to identify gene network rewiring associated with ovarian cancer platinum resistance. There are certain differential edges common to both data types and some differential edges unique to individual data types. Hub genes in the differential networks inferred by our method play important roles in ovarian cancer drug resistance.

Overexpression of calcyphosine is associated with poor prognosis in esophageal squamous cell carcinoma

Calcyphosine (CAPS), a calcium-binding protein, has been identified as a potential diagnostic and prognostic biomarker in several human carcinomas. However, little is known about CAPS in esophageal squamous cell carcinoma (ESCC). The present study aimed to investigate the expression levels of CAPS in ESCC tissues and evaluate its clinicopathological significance. Reverse transcription-quantitative polymerase chain reaction and immunohistochemical staining were conducted to detect the expression of CAPS in ESCC tissues and adjacent non-cancerous tissues. ESCC samples exhibited higher levels of CAPS mRNA than paired non-cancerous samples (P=0.0015), and the mRNA level of CAPS was positively associated with histological grade (P=0.0013) and tumor invasion depth (P=0.0206). In addition, Kaplan-Meier survival analysis revealed that patients with high CAPS expression experienced significantly shorter 5-year overall survival times than those with low CAPS expression (P=0.0112). Multivariate analysis demonstrated that CAPS protein expression was an independent prognostic biomarker for patients with ESCC. In conclusion, the findings of the present study demonstrated that CAPS may represent a novel diagnostic indicator and an independent prognostic biomarker in ESCC.

C10orf116 Gene Copy Number Loss in Prostate Cancer: Clinicopathological Correlations and Prognostic Significance

Background

Prostate cancer (PCa) is the second most commonly diagnosed cancer in males worldwide. This study aimed to identify differentially expressed genes and to investigate the potential correlation between gene abnormalities and clinical features in PCa to evaluate disease progression and prognosis.

Material/Methods

A total of 4 independent microarrays of PCa patients from the Oncomine database were used to identify differences in expression of genes contributing to cancer progression. Quantitative real-time polymerase chain reaction (RT-qPCR) analysis was used to evaluate the mRNA expression of the target in human prostate cancer cells. To explore the relationship between the DNA copy number alteration and mRNA expression changes, dataset containing copy number alteration, DNA methylation, and gene expression in PCa were obtained from the cBioPortal online platform (n=273).

Results

We identified 40 genes that were significantly dysregulated in PCa from 4 independent microarrays. Among these, 3 genes showed a consistent change of over 2-fold in the 4 microarrays. The mRNA expression of C10orf116 showed consistent expression in prostate cancer cells compared with that in prostate gland cells as assessed by RT-qPCR. Moreover, C10orf116 loss was associated with poor distant relapse-free survival (DFS) by analyzing data of 273 PCa patients, but it was not identified as an independent prognostic risk factor for DFS. In addition, we found that C10orf116 loss was associated with higher pathological stage, higher clinical stage, and lymph node metastasis in PCa, and that C10orf116 copy number was highly correlated with PTEN copy number and mRNA expression.

Conclusions

As a predictive indicator, C10orf116 loss contributes to our understating of the biology of aggressive changes in PCa and also helps evaluate the prognosis of patients.

A multiplex platform for the identification of ovarian cancer biomarkers

Background

Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.

Methods

We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek^® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.

Results

Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek^® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.

Conclusions

These data demonstrate that the Proseek^® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-017-9169-6) contains supplementary material, which is available to authorized users.

Gene expression identifies heterogeneity of metastatic behavior among gastrointestinal stromal tumors

BACKGROUND

Adjuvant imatinib is useful in patients with gastrointestinal stromal tumors (GIST) at high risk of recurrence. At present, the risk of recurrence is determined based on tumor size, mitotic rate, tumor site, and tumor rupture. Previous studies using various biochemical pathways identified gene expression patterns that distinguish two subsets of aggressive fibromatosis (AF), serous ovarian carcinoma (OVCA), and clear cell renal cell carcinoma (RCC). These gene sets separated soft tissue sarcomas into two groups with different probabilities of developing metastatic disease. The present study used these gene sets to identify GIST subgroups with different probabilities of developing metastatic disease.

METHODS

We utilized these three gene sets, hierarchical clustering, and Kaplan-Meier analysis, to examine 60 primary resected GIST samples using Agilent chip expression profiling.

RESULTS

Hierarchical clustering using both the combined and individual AF-, OVCA-, and RCC- gene sets identified differences in probabilities of developing metastatic disease between the clusters defined by the first branch point of the clustering dendrograms (p = 0.029 for the combined gene set, p = 0.003 for the AF-gene set, p < 0.001 for the OVCA-gene set, and p = 0.003 for the RCC-gene set).

CONCLUSIONS

Hierarchical clustering using these gene sets identified at least two subsets of GIST with distinct clinical behavior and risk of metastatic disease. The use of gene expression analysis along with other known prognostic factors may better predict the long-term outcome following surgery, and thus restrict the use of adjuvant therapy to high-risk GIST, and reduce heterogeneity among groups in clinical trials of new drugs.

Abnormal expression of calcyphosine is associated with poor prognosis and cell biology function in colorectal cancer

The aim of this study was to investigate the calcyphosine (CAPS) expression in human colorectal cancer (CRC) and to explore its clinical and prognostic significances. CAPS expression was measured by Western blot, real-time polymerase chain reaction analysis, and immunohistochemistry. The relationships between the CAPS expression levels and the clinicopathological factors were investigated. The Kaplan–Meier method and log-rank test were used to investigate the overall survival of the patients. Moreover, the effects of CAPS on biological roles of CRC cells were also evaluated by MTT assay, colony formation assay, and transwell assay. CAPS was significantly overexpressed in cancerous tissue and CRC cell lines compared with adjacent nontumor tissue and a normal human intestinal epithelial cell line. Overexpression of CAPS was significantly associated with histological grade (P=0.004), invasive depth (P<0.001), lymph node metastasis (P=0.003), tumor node metastasis stage (P=0.017), and distant metastasis (P=0.042). Furthermore, silencing of CAPS expression in CRC cells inhibited their proliferation, colony formation, migration, and invasion. Kaplan–Meier survival analysis showed that high CAPS expression might demonstrate poor prognosis in CRC patients. Cox regression analysis revealed that CAPS expression was an independent prognostic factor of CRC. Our data suggested that the upregulation of CAPS might play a role in the carcinogenesis and progression of CRC. CAPS could be used as a potential diagnostic factor and be an independent good prognostic indicator for CRC patients.

Proteomics profiling identify CAPS as a potential predictive marker of tamoxifen resistance in estrogen receptor positive breast cancer

Background

Despite the success of tamoxifen since its introduction, about one-third of patients with estrogen (ER) and/or progesterone receptor (PgR) - positive breast cancer (BC) do not benefit from therapy. Here, we aim to identify molecular mechanisms and protein biomarkers involved in tamoxifen resistance.

Results

Using iTRAQ and Immobilized pH gradient-isoelectric focusing (IPG-IEF) mass spectrometry based proteomics we compared tumors from 12 patients with early relapses (<2 years) and 12 responsive to therapy (relapse-free > 7 years). A panel of 13 proteins (TCEAL4, AZGP1, S100A10, ALDH6A1, AHNAK, FBP1, S100A4, HSP90AB1, PDXK, GFPT1, RAB21, MX1, CAPS) from the 3101 identified proteins, potentially separate relapse from non-relapse BC patients. The proteins in the panel are involved in processes such as calcium (Ca²⁺) signaling, metabolism, epithelial mesenchymal transition (EMT), metastasis and invasion. Validation of the highest expressed proteins in the relapse group identify high tumor levels of CAPS as predictive of tamoxifen response in a patient cohort receiving tamoxifen as only adjuvant therapy.

Conclusions

This data implicate CAPS in tamoxifen resistance and as a potential predictive marker.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9080-y) contains supplementary material, which is available to authorized users.

Genome-wide modulation of gene transcription in ovarian carcinoma cells by a new mithramycin analogue

Ovarian cancer has a poor prognosis due to intrinsic or acquired resistance to some cytotoxic drugs, raising the interest in new DNA-binding agents such as mithramycin analogues as potential chemotherapeutic agents in gynecological cancer. Using a genome-wide approach, we have analyzed gene expression in A2780 human ovarian carcinoma cells treated with the novel mithramycin analogue DIG-MSK (demycarosyl-3D-β-D-digitoxosyl-mithramycin SK) that binds to C+G-rich DNA sequences. Nanomolar concentrations of DIG-MSK abrogated the expression of genes involved in a variety of cell processes including transcription regulation and tumor development, which resulted in cell death. Some of those genes have been associated with cell proliferation and poor prognosis in ovarian cancer. Sp1 transcription factor regulated most of the genes that were down-regulated by the drug, as well as the up-regulation of other genes mainly involved in response to cell stress. The effect of DIG-MSK in the control of gene expression by other transcription factors was also explored. Some of them, such as CREB, E2F and EGR1, also recognize C/G-rich regions in gene promoters, which encompass potential DIG-MSK binding sites. DIG-MSK affected several biological processes and molecular functions related to transcription and its cellular regulation in A2780 cells, including transcription factor activity. This new compound might be a promising drug for the treatment of ovarian cancer.

Gene expression identifies heterogeneity of metastatic behavior among high-grade non-translocation associated soft tissue sarcomas

Background

The biologic heterogeneity of soft tissue sarcomas (STS), even within histological subtypes, complicates treatment. In earlier studies, gene expression patterns that distinguish two subsets of clear cell renal carcinoma (RCC), serous ovarian carcinoma (OVCA), and aggressive fibromatosis (AF) were used to separate 73 STS into two or four groups with different probabilities of developing metastatic disease (PrMet). This study was designed to confirm our earlier observations in a larger independent data set.

Methods

We utilized these gene sets, hierarchical clustering (HC), and Kaplan-Meier analysis, to examine 309 STS, using Affymetrix chip expression profiling.

Results

HC using the combined AF-, RCC-, and OVCA-gene sets identified subsets of the STS samples. Analysis revealed differences in PrMet between the clusters defined by the first branch point of the clustering dendrogram (p = 0.048), and also among the four different clusters defined by the second branch points (p < 0.0001). Analysis also revealed differences in PrMet between the leiomyosarcomas (LMS), dedifferentiated liposarcomas (LipoD), and undifferentiated pleomorphic sarcomas (UPS) (p = 0.0004). HC of both the LipoD and UPS sample sets divided the samples into two groups with different PrMet (p = 0.0128, and 0.0002, respectively). HC of the UPS samples also showed four groups with different PrMet (p = 0.0007). HC found no subgroups of the LMS samples.

Conclusions

These data confirm our earlier studies, and suggest that this approach may allow the identification of more than two subsets of STS, each with distinct clinical behavior, and may be useful to stratify STS in clinical trials and in patient management.

iTRAQ-based quantitative proteomics reveals myoferlin as a novel prognostic predictor in pancreatic adenocarcinoma

Histological differentiation is a major pathological parameter associated with poor prognosis in patients with pancreatic adenocarcinoma (PAC) and the molecular signature underlying PAC differentiation may involve key proteins potentially affecting the malignant characters of PAC. We aimed to identify the proteins which could be implicated in PAC prognosis. We used isobaric tags for relative and absolute quantitation (iTRAQ) coupled with two-dimensional liquid chromatography-tandem mass spectrometry to compare protein expression in PAC tissues with different degrees of histological differentiation. A total of 1623 proteins were repeatedly identified by performing the iTRAQ-based experiments twice. Of these, 15 proteins were differentially expressed according to our defined criteria. Myoferlin (MYOF) was selected to validate the proteomic results by western blotting. Immunohistochemistry in a further 154 PAC cases revealed that myoferlin significantly correlated with the degree of histological differentiation (P=0.004), and univariate and multivariate analyses indicated that MYOF is an independent prognostic factor for survival (hazard ratio, 1.540; 95% confidence interval, 1.061-2.234; P=0.023) of patients with PAC after curative surgery. RNA interference-mediated knockdown of MYOF alleviated malignant phenotypes of both primary and metastatic PAC cell lines in vitro and in vivo. Thus, ITRAQ-based quantitative proteomics revealed the prognostic value of MYOF in PAC.

BIOLOGICAL SIGNIFICANCE

Our results provide the possibility of novel strategies for pancreatic adenocarcinoma management.

Discovery analysis of TCGA data reveals association between germline genotype and survival in ovarian cancer patients

BACKGROUND

Ovarian cancer remains a significant public health burden, with the highest mortality rate of all the gynecological cancers. This is attributable to the late stage at which the majority of ovarian cancers are diagnosed, coupled with the low and variable response of advanced tumors to standard chemotherapies. To date, clinically useful predictors of treatment response remain lacking. Identifying the genetic determinants of ovarian cancer survival and treatment response is crucial to the development of prognostic biomarkers and personalized therapies that may improve outcomes for the late-stage patients who comprise the majority of cases.

METHODS

To identify constitutional genetic variations contributing to ovarian cancer mortality, we systematically investigated associations between germline polymorphisms and ovarian cancer survival using data from The Cancer Genome Atlas Project (TCGA). Using stage-stratified Cox proportional hazards regression, we examined >650,000 SNP loci for association with survival. We additionally examined whether the association of significant SNPs with survival was modified by somatic alterations.

RESULTS

Germline polymorphisms at rs4934282 (AGAP11/C10orf116) and rs1857623 (DNAH14) were associated with stage-adjusted survival (p= 1.12e-07 and 1.80e-07, FDR q= 1.2e-04 and 2.4e-04, respectively). A third SNP, rs4869 (C10orf116), was additionally identified as significant in the exome sequencing data; it is in near-perfect LD with rs4934282. The associations with survival remained significant when somatic alterations.

CONCLUSIONS

Discovery analysis of TCGA data reveals germline genetic variations that may play a role in ovarian cancer survival even among late-stage cases. The significant loci are located near genes previously reported as having a possible relationship to platinum and taxol response. Because the variant alleles at the significant loci are common (frequencies for rs4934282 A/C alleles = 0.54/0.46, respectively; rs1857623 A/G alleles = 0.55/0.45, respectively) and germline variants can be assayed noninvasively, our findings provide potential targets for further exploration as prognostic biomarkers and individualized therapies.

Gene expression identifies heterogeneity of metastatic propensity in high-grade soft tissue sarcomas

BACKGROUND

Metastatic propensity of soft tissue sarcoma (STS) is heterogeneous and may be determined by gene expression patterns that do not correlate well with morphology. The authors have reported gene expression patterns that distinguish 2 broad classes of clear cell renal carcinoma (ccRCC-gene set), and other patterns that can distinguish heterogeneity of serous ovarian carcinoma (OVCA-gene set) and aggressive fibromatosis (AF-gene set); however, clinical follow-up data were not available for these samples.

METHODS

In the current study, gene expression patterns in 73 samples of high-grade STS were examined using spotted cDNA microarray slides that contained ∼16,000 unique UniGene clusters. Approximately 50% of the genes present in the ccRCC-, OVCA-, and AF-gene sets were also represented in the data from this chip set, and these were combined to form a composite gene set of 278 probes.

RESULTS

Hierarchical clustering using this composite gene set suggested the existence of subsets of the STS samples. Analysis revealed differences in the time to development of metastatic disease between the clusters defined by the first branch point of the clustering dendrogram (P = .005), and also among the 4 different clusters defined by the second branch points (P = .001).

CONCLUSIONS

This approach suggests the existence of >2 subsets of high-grade pleomorphic STS, each with distinct clinical behavior. A composite gene set such as that described here may be useful to stratify STS in clinical trials, and may be of practical utility in patient management.

Mechanistic modeling of the effects of myoferlin on tumor cell invasion

Myoferlin (MYOF) is a member of the evolutionarily conserved ferlin family of proteins, noted for their role in a variety of membrane processes, including endocytosis, repair, and vesicular transport. Notably, ferlins are implicated in Caenorhabditis elegans sperm motility (Fer-1), mammalian skeletal muscle development and repair (MYOF and dysferlin), and presynaptic transmission in the auditory system (otoferlin). In this paper, we demonstrate that MYOF plays a previously unrecognized role in cancer cell invasion, using a combination of mathematical modeling and in vitro experiments. Using a real-time impedance-based invasion assay (xCELLigence), we have shown that lentiviral-based knockdown of MYOF significantly reduced invasion of MDA-MB-231 breast cancer cells in Matrigel bioassays. Based on these experimental data, we developed a partial differential equation model of MYOF effects on cancer cell invasion, which we used to generate mechanistic hypotheses. The mathematical model predictions revealed that matrix metalloproteinases (MMPs) may play a key role in modulating this invasive property, which was supported by experimental data using quantitative RT-PCR screens. These results suggest that MYOF may be a promising target for biomarkers or drug target for metastatic cancer diagnosis and therapy, perhaps mediated through MMPs.

Leucine-rich alpha-2-glycoprotein-1 is upregulated in sera and tumors of ovarian cancer patients

BACKGROUND

New biomarkers that replace or are used in conjunction with the current ovarian cancer diagnostic antigen, CA125, are needed for detection of ovarian cancer in the presurgical setting, as well as for detection of disease recurrence. We previously demonstrated the upregulation of leucine-rich alpha-2-glycoprotein-1 (LRG1) in the sera of ovarian cancer patients compared to healthy women using quantitative mass spectrometry.

METHODS

LRG1 was quantified by ELISA in serum from two relatively large cohorts of women with ovarian cancer and benign gynecological disease. The expression of LRG1 in ovarian cancer tissues and cell lines was examined by gene microarray, reverse-transcriptase polymerase chain reaction (RT-PCR), Western blot, immunocytochemistry and mass spectrometry.

RESULTS

Mean serum LRG1 was higher in 58 ovarian cancer patients than in 56 healthy women (89.33 ± 77.90 vs. 42.99 ± 9.88 ug/ml; p = 0.0008) and was highest among stage III/IV patients. In a separate set of 193 pre-surgical samples, LRG1 was higher in patients with serous or clear cell ovarian cancer (145.82 ± 65.99 ug/ml) compared to patients with benign gynecological diseases (82.53 ± 76.67 ug/ml, p < 0.0001). CA125 and LRG1 levels were moderately correlated (r = 0.47, p < 0.0001). LRG1 mRNA levels were higher in ovarian cancer tissues and cell lines compared to their normal counterparts when analyzed by gene microarray and RT-PCR. LRG1 protein was detected in ovarian cancer tissue samples and cell lines by immunocytochemistry and Western blotting. Multiple iosforms of LRG1 were observed by Western blot and were shown to represent different glycosylation states by digestion with glycosidase. LRG1 protein was also detected in the conditioned media of ovarian cancer cell culture by ELISA, Western blotting, and mass spectrometry.

CONCLUSIONS

Serum LRG1 was significantly elevated in women with ovarian cancer compared to healthy women and women with benign gynecological disease, and was only moderately correlated with CA125. Ovarian cancer cells secrete LRG1 and may contribute directly to the elevated levels of LRG1 observed in the serum of ovarian cancer patients. Future studies will determine whether LRG1 may serve as a biomarker for presurgical diagnosis, disease recurrence, and/or as a target for therapy.

Identification of novel epithelial ovarian cancer biomarkers by cross-laboratory microarray analysis

The purpose of this study was to pool information in epithelial ovarian cancer by combining studies using Affymetrix expression microarray datasets made at different laboratories to identify novel biomarkers. Epithelial microarray expression information across laboratories was screened and combined after preprocessing raw microarray data, then ANOVA and unpaired T test statistical analysis was performed for identifying differentially expressed genes (DEGs), followed by clustering and pathway analysis for these DEGs. In this work, we performed a combination analysis on microarrays from three different laboratories using gene expression data on ovarian cancer and obtained a list of differential expression profiles identified as potential candidate in aggressiveness of ovarian cancer. The clustering and pathway analysis explored the different molecular basis of different ovarian cancer stages and potential important regulatory pathways in ovarian cancer development. Our results showed that combination of microarray data from different laboratories in the same platforms may overcome biases derived from probe design and technical features, thereby accelerating the identification of trustworthy DEGs, and demonstrating the advantage of integrative analysis in gene expression studies on epithelial ovarian cancer research.

Expression of p53 protein phosphorylated at serine 20 and serine 392 in malignant and benign ovarian neoplasms: correlation with clinicopathological parameters of tumors

INTRODUCTION

The modification of p53 protein by phosphorylation plays an important role in its stabilization and the regulation of its biological properties. The study investigated the expression of p53 protein phosphorylated at serine 20 (Ser20) and Ser392 and the association between clinicopathological parameters of ovarian neoplasms with respect to p53 protein overexpression.

METHODS

p53 protein expression was evaluated on tissues from malignant and benign ovarian tumors. Protein expression was measured in a subset of the specimens using immunohistochemistry.

RESULTS

The correlation between p53 protein overexpression and p53-Ser392 phosphorylation was found in ovarian carcinomas (P = 0.001, r = +0.27). In the total group of ovarian carcinomas, significant differences were observed in p53 protein overexpression between well (G1) and poor (G3) tumor grades (P = 0.005) and between serous and endometrioid types of tumor (P = 0.04), whereas p53-Ser20 phosphorylation was associated with advanced International Federation of Gynecology and Obstetrics stage (P = 0.004) and high tumor grade (P = 0.02). In p53-positive ovarian carcinomas, p53-Ser392 phosphorylation was associated with advanced tumor stage (P = 0.02) and high tumor grade (P = 0.049). p53-Ser20 phosphorylation was associated with low tumor grade of p53-positive ovarian carcinomas (P = 0.02) and with high tumor grade of p53-negative ovarian carcinomas (P = 0.02).

CONCLUSIONS

These results revealed that p53 phosphorylation at Ser20 and Ser392 is an early event in ovarian tumor development. The authors suggest that the expression of p53 protein phosphorylated at Ser20 and Ser392 in ovarian carcinomas determines their individual clinical features depending on p53 protein status and may be useful biological biomarkers characterizing their behavior.

S100A1 expression in ovarian and endometrial endometrioid carcinomas is a prognostic indicator of relapse-free survival

We sought to investigate the expression levels of S100A1 in ovarian cancer cell lines and tissues to correlate S100A1 with subtype, stage, grade, and relapse-free survival. S100A1 messenger RNA and protein were up-regulated in ovarian cancer cell lines and tumors compared with normal ovarian cell lines and tissues by gene microarray analysis, reverse transcriptase-polymerase chain reaction, quantitative reverse transcriptase-polymerase chain reaction, and Western immunoblotting. In the study, 63.7% of serous, 21.2% of clear cell, 11.2% of endometrioid, and 3% of mucinous ovarian (1/31) cancers were S100A1+ by immunohistochemical staining of tissue microarrays (n = 500). S100A1 expression increased with increasing Silverberg grade but not stage in serous tumors. Endometrial tissue microarrays (n = 127) were 9.4% S100A1+; no correlation with stage or grade and S100A1 was found. In the endometrioid subtype of ovarian and endometrial cancers, relapse-free survival was decreased for patients with S100A1+ tumors. These data suggest that S100A1 is a marker for poor prognosis of endometrioid subtypes of cancer.

Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome

PURPOSE

The study aim to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features.

EXPERIMENTAL DESIGN

Microarray gene expression profiling was done on 285 serous and endometrioid tumors of the ovary, peritoneum, and fallopian tube. K-means clustering was applied to identify robust molecular subtypes. Statistical analysis identified differentially expressed genes, pathways, and gene ontologies. Laser capture microdissection, pathology review, and immunohistochemistry validated the array-based findings. Patient survival within k-means groups was evaluated using Cox proportional hazards models. Class prediction validated k-means groups in an independent dataset. A semisupervised survival analysis of the array data was used to compare against unsupervised clustering results.

RESULTS

Optimal clustering of array data identified six molecular subtypes. Two subtypes represented predominantly serous low malignant potential and low-grade endometrioid subtypes, respectively. The remaining four subtypes represented higher grade and advanced stage cancers of serous and endometrioid morphology. A novel subtype of high-grade serous cancers reflected a mesenchymal cell type, characterized by overexpression of N-cadherin and P-cadherin and low expression of differentiation markers, including CA125 and MUC1. A poor prognosis subtype was defined by a reactive stroma gene expression signature, correlating with extensive desmoplasia in such samples. A similar poor prognosis signature could be found using a semisupervised analysis. Each subtype displayed distinct levels and patterns of immune cell infiltration. Class prediction identified similar subtypes in an independent ovarian dataset with similar prognostic trends.

CONCLUSION

Gene expression profiling identified molecular subtypes of ovarian cancer of biological and clinical importance.

Identification of heterogeneity among soft tissue sarcomas by gene expression profiles from different tumors

The heterogeneity that soft tissue sarcomas (STS) exhibit in their clinical behavior, even within histological subtypes, complicates patient care. Histological appearance is determined by gene expression. Morphologic features are generally good predictors of biologic behavior, however, metastatic propensity, tumor growth, and response to chemotherapy may be determined by gene expression patterns that do not correlate well with morphology. One approach to identify heterogeneity is to search for genetic markers that correlate with differences in tumor behavior. Alternatively, subsets may be identified based on gene expression patterns alone, independent of knowledge of clinical outcome. We have reported gene expression patterns that distinguish two subgroups of clear cell renal carcinoma (ccRCC), and other gene expression patterns that distinguish heterogeneity of serous ovarian carcinoma (OVCA) and aggressive fibromatosis (AF). In this study, gene expression in 53 samples of STS and AF [including 16 malignant fibrous histiocytoma (MFH), 9 leiomyosarcoma, 12 liposarcoma, 4 synovial sarcoma, and 12 samples of AF] was determined at Gene Logic Inc. (Gaithersburg, MD) using Affymetrix GeneChip^® U_133 arrays containing approximately 40,000 genes/ESTs. Gene expression analysis was performed with the Gene Logic Genesis Enterprise System^® Software and Expressionist software. Hierarchical clustering of the STS using our three previously reported gene sets, each generated subgroups within the STS that for some subtypes correlated with histology, and also suggested the existence of subsets of MFH. All three gene sets also recognized the same two subsets of the fibromatosis samples that we had found in our earlier study of AF. These results suggest that these subgroups may have biological significance, and that these gene sets may be useful for sub-classification of STS. In addition, several genes that are targets of some anti-tumor drugs were found to be differentially expressed in particular subsets of STS.

Unique molecular characteristics of radiation-induced glioblastoma

Radiation-induced glioblastomas (RIGs) represent a significant proportion of glioblastomas (GBMs) seen in children and young adults and manifest poor prognosis. Little is known about their underlying biology, although limited studies have suggested no unique histologic or cytogenetic characteristics to distinguish them from de novo GBMs. In this study, we confirmed that a series of 5 RIGs showed no unique histologic or cytogenetic features compared with de novo pediatric GBMs, prompting us to further investigate RIGs using gene expression microarray profiling and Western blot analysis. Despite the inability of histologic and molecular genetic studies to identify distinguishing features between RIGs and pediatric GBMs, gene microarrays suggested significant differences between these 2 tumor types, at least those occurring in pediatric patients. Pediatric RIGs show greater homogeneity of gene expression than do de novo pediatric GBMs. Greater overlap was detected in gene expression patterns between RIGs and pilocytic astrocytomas than between RIGs and GBMs, medulloblastomas, ependymomas, atypical teratoid rhabdoid tumors, or rhabdomyosarcomas, suggesting a common precursor cell for RIG and pilocytic astrocytoma. Western blot analyses confirmed that ErbB3, Sox10, and platelet-derived growth factor receptor-alpha proteins were consistently expressed in RIGs but rarely in pediatric GBMs.