Identification of novel therapeutic targets in microdissected clear cell ovarian cancers

Clear cell ovarian cancer is an epithelial ovarian cancer histotype that is less responsive to chemotherapy and carries poorer prognosis than serous and endometrioid histotypes. Despite this, patients with these tumors are treated in a similar fashion as all other ovarian cancers. Previous genomic analysis has suggested that clear cell cancers represent a unique tumor subtype. Here we generated the first whole genomic expression profiling using epithelial component of clear cell ovarian cancers and normal ovarian surface specimens isolated by laser capture microdissection. All the arrays were analyzed using BRB ArrayTools and PathwayStudio software to identify the signaling pathways. Identified pathways validated using serous, clear cell cancer cell lines and RNAi technology. In vivo validations carried out using an orthotopic mouse model and liposomal encapsulated siRNA. Patient-derived clear cell and serous ovarian tumors were grafted under the renal capsule of NOD-SCID mice to evaluate the therapeutic potential of the identified pathway. We identified major activated pathways in clear cells involving in hypoxic cell growth, angiogenesis, and glucose metabolism not seen in other histotypes. Knockdown of key genes in these pathways sensitized clear cell ovarian cancer cell lines to hypoxia/glucose deprivation. In vivo experiments using patient derived tumors demonstrate that clear cell tumors are exquisitely sensitive to antiangiogenesis therapy (i.e. sunitinib) compared with serous tumors. We generated a histotype specific, gene signature associated with clear cell ovarian cancer which identifies important activated pathways critical for their clinicopathologic characteristics. These results provide a rational basis for a radically different treatment for ovarian clear cell patients.     

An Outlook on Ovarian Cancer and Borderline Ovarian Tumors: Focus on Genomic and Proteomic Findings

Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers. Ovarian tumors can be classified on the basis of the cells of origin in epithelial, stromal and germ cell tumors. Epithelial ovarian tumors display great histological heterogeneity and can be further subdivided into benign, intermediate or borderline, and invasive tumors. Several studies on ovarian tumors, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomolecular study and genomic discovery; unfortunately, validity of these should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management. Therefore, considerable interest lies in identifying molecular and protein biomarkers and indicators to guide treatment decisions and clinical follow up. In this review, the current state of knowledge about the genoproteomic and potential clinical value of gene expression profiling in ovarian cancer and ovarian borderline tumors is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian cancers and borderline tumors, identifying different genotypes of ovarian tissue and identifying proteins linked to cancer or tumor development. By these targets, authors focus on the use of novel molecules, developed on the proteomics and genomics researches, as potential protein biomarkers in the management of ovarian cancer or borderline tumor, overlooking on current state of the art and on future perspectives of researches.Key Words: Ovarian cancer, borderline ovarian tumors, markers, genomics, proteomics, oncogenes.     

MUC1 Positive,Kras and Pten Driven Mouse Gynecologic Tumors Replicate Human Tumors and Vary in Survival and Nuclear Grade Based on Anatomical Location

Activating mutations of Kras oncogene and deletions of Pten tumor suppressor gene play important roles in cancers of the female genital tract. We developed here new preclinical models for gynecologic cancers, using conditional (Cre-loxP) mice with floxed genetic alterations in Kras and Pten. The triple transgenic mice, briefly called MUC1KrasPten, express human MUC1 antigen as self and carry a silent oncogenic Kras^G12D and Pten deletion mutation. Injection of Cre-encoding adenovirus (AdCre) in the ovarian bursa, oviduct or uterus activates the floxed mutations and initiates ovarian, oviductal, and endometrial cancer, respectively. Anatomical site-specific Cre-loxP recombination throughout the genital tract of MUC1KrasPten mice leads to MUC1 positive genital tract tumors, and the development of these tumors is influenced by the anatomical environment. Endometrioid histology was consistently displayed in all tumors of the murine genital tract (ovaries, oviducts, and uterus). Tumors showed increased expression of MUC1 glycoprotein and triggered de novo antibodies in tumor bearing hosts, mimicking the immunobiology seen in patients. In contrast to the ovarian and endometrial tumors, oviductal tumors showed higher nuclear grade. Survival for oviduct tumors was significantly lower than for endometrial tumors (p = 0.0015), yet similar to survival for ovarian cancer. Oviducts seem to favor the development of high grade tumors, providing preclinical evidence in support of the postulated role of fallopian tubes as the originating site for high grade human ovarian tumors.     

Stem Cell-Like Gene Expression in Ovarian Cancer Predicts Type II Subtype and Prognosis

Although ovarian cancer is often initially chemotherapy-sensitive, the vast majority of tumors eventually relapse and patients die of increasingly aggressive disease. Cancer stem cells are believed to have properties that allow them to survive therapy and may drive recurrent tumor growth. Cancer stem cells or cancer-initiating cells are a rare cell population and difficult to isolate experimentally. Genes that are expressed by stem cells may characterize a subset of less differentiated tumors and aid in prognostic classification of ovarian cancer. The purpose of this study was the genomic identification and characterization of a subtype of ovarian cancer that has stem cell-like gene expression. Using human and mouse gene signatures of embryonic, adult, or cancer stem cells, we performed an unsupervised bipartition class discovery on expression profiles from 145 serous ovarian tumors to identify a stem-like and more differentiated subgroup. Subtypes were reproducible and were further characterized in four independent, heterogeneous ovarian cancer datasets. We identified a stem-like subtype characterized by a 51-gene signature, which is significantly enriched in tumors with properties of Type II ovarian cancer; high grade, serous tumors, and poor survival. Conversely, the differentiated tumors share properties with Type I, including lower grade and mixed histological subtypes. The stem cell-like signature was prognostic within high-stage serous ovarian cancer, classifying a small subset of high-stage tumors with better prognosis, in the differentiated subtype. In multivariate models that adjusted for common clinical factors (including grade, stage, age), the subtype classification was still a significant predictor of relapse. The prognostic stem-like gene signature yields new insights into prognostic differences in ovarian cancer, provides a genomic context for defining Type I/II subtypes, and potential gene targets which following further validation may be valuable in the clinical management or treatment of ovarian cancer.     

The Met receptor tyrosine kinase and basal breast cancer

Breast cancer is a complex disease that comprises cancers of distinct biologies and responses to treatment. Clinical management relies on traditional clinicopathological parameters, involving lymph node status, histological grade, as well as expression of the estrogen receptor or human epidermal growth factor receptor 2. Molecular pathology as well as protein and gene expression profiling have divided breast tumors into molecular subtypes associated with different clinical outcomes. One of these, defined as basal breast cancer, is associated with poor prognosis. Molecular mechanisms involved in the induction of basal breast cancer are poorly understood and targeted therapies for this subtype are lacking. Recent evidence using murine models identified a role for the Met receptor tyrosine kinase in the induction of murine mammary tumors with characteristics of human basal breast cancers. Moreover, elevated Met protein and RNA is associated with human basal tumors and poor outcome. These studies identify a link between the Met receptor tyrosine kinase, epithelial mesenchymal transition, and basal breast cancer. In this review, we provide an overview of murine Met models in relation to the spectrum of mouse models of breast cancer and a role for the Met receptor in basal breast cancer tumorigenesis.     

The epigenetic basis for the aberrant expression of kallikreins in human cancers

The tissue kallikrein gene family consists of 15 genes tandemly arranged on human chromosome 19q13.4. Most kallikrein genes are characterized by aberrant expression patterns in various human cancers, a feature that makes them ideal cancer biomarkers. In the present study, we investigated the effect of the epigenetic drug compound 5-aza-2'-deoxycytidine on the expression of downregulated kallikrein genes in prostate, breast, and ovarian cancer cell lines. Reactivation of multiple kallikrein genes was observed, although some of these genes do not contain CpG islands in their genomic sequence. Epigenetic regulation provides a new mechanism for the pharmacological modulation of kallikreins in human cancers with putative therapeutic implications.     

Gene expression patterns in ovarian carcinomas

We used DNA microarrays to characterize the global gene expression patterns in surface epithelial cancers of the ovary. We identified groups of genes that distinguished the clear cell subtype from other ovarian carcinomas, grade I and II from grade III serous papillary carcinomas, and ovarian from breast carcinomas. Six clear cell carcinomas were distinguished from 36 other ovarian carcinomas (predominantly serous papillary) based on their gene expression patterns. The differences may yield insights into the worse prognosis and therapeutic resistance associated with clear cell carcinomas. A comparison of the gene expression patterns in the ovarian cancers to published data of gene expression in breast cancers revealed a large number of differentially expressed genes. We identified a group of 62 genes that correctly classified all 125 breast and ovarian cancer specimens. Among the best discriminators more highly expressed in the ovarian carcinomas were PAX8 (paired box gene 8), mesothelin, and ephrin-B1 (EFNB1). Although estrogen receptor was expressed in both the ovarian and breast cancers, genes that are coregulated with the estrogen receptor in breast cancers, including GATA-3, LIV-1, and X-box binding protein 1, did not show a similar pattern of coexpression in the ovarian cancers.     

Transcriptional profiling develops molecular signatures for ovarian tumors.

Of the cancers unique to women, ovarian cancer has the highest mortality rate. Over 26,000 women are diagnosed with this disease in the U.S. annually, and 60% of those diagnosed will die of the disease. One of the greatest problems with this disease is the lack of strong early warning signs or symptoms resulting in advanced stage at presentation in most women, followed by the outgrowth of chemotherapy-resistant disease in the majority of patients. The 5-year survival for patients with early stage disease ranges from 50-90%, but it is less than 25% for advanced-stage disease. In collaboration with researchers at Millennium Predictive Medicine (Cambridge, MA), the Ovarian Cancer Program of the Mayo Clinic Cancer Center analyzed gene expression in over 50 primary ovarian tumors, as compared with normal ovarian epithelial cells. The technologies utilized included microarray analysis with nitrocellulose filters containing 25,000 arrayed human cDNAs, as well as the construction of subtraction suppression hybridization cDNA libraries and their subsequent sequencing. Our specific focus has been on genes that are underexpressed during the development of ovarian cancer, although this analysis has revealed a large number of consistently up- and down-regulated genes. There were more down-regulated genes in ovarian tumors than up-regulated genes. In addition, the number of genes that had altered expression levels was quite large. For example, we found 409 genes down-regulated at least 5-fold, and 72 genes up-regulated at least 5-fold in 33% of the tumors analyzed. We also observed that most of the expression alterations observed in later stage (Stages III/IV) tumors were also observed in early-stage tumors (Stages I/II). This was corroborated using comparative genomic hybridization analysis on the same tumors that were expression profiled. This analysis revealed that the late-stage tumors had more gene amplification than early-stage tumors, but most regions of change (either increases or decreases) were in common between different stage tumors. We also have verified the altered expression levels of several of these genes using several complementary strategies. Finally, we are taking top candidate genes that are consistently under-expressed in ovarian tumors and attempting to determine their functional role in the development of ovarian cancer.     

Expression signatures that correlated with Gleason score and relapse in prostate cancer

Predicting prognosis in prostate carcinoma remains a challenge when using clinical and pathologic criteria only. We used an array-based DASL assay to identify molecular signatures for predicting prostate cancer relapse in formalin-fixed, paraffin-embedded (FFPE) prostate cancers, through gene expression profiling of 512 prioritized genes. Of the 71 patients that we analyzed, all but 3 had no evidence of residual tumor (defined as negative surgical margins) following radical prostatectomy and no patient received adjuvant therapy following surgery. All of the 71 patients had an undetectable serum PSA following radical prostatectomy. Follow-up period was 44+/-15 months. Highly reproducible gene expression patterns were obtained with these samples (average R(2)=0.99). We identified a panel of 11 genes that correlated positively and 5 genes that correlated negatively with Gleason grade. A gene expression score (GEX) was derived from the expression levels of the 16 genes. We assessed the prognostic value of these genes and found the GEX significantly correlated with disease relapse (p=0.007). These results suggest that the approach we used is effective for expression profiling in heterogeneous FFPE tissues for cancer diagnosis/prognosis biomarker discovery and validation.     

Cisplatin Resistant Spheroids Model Clinically Relevant Survival Mechanisms in Ovarian Tumors

The majority of ovarian tumors eventually recur in a drug resistant form. Using cisplatin sensitive and resistant cell lines assembled into 3D spheroids we profiled gene expression and identified candidate mechanisms and biological pathways associated with cisplatin resistance. OVCAR-8 human ovarian carcinoma cells were exposed to sub-lethal concentrations of cisplatin to create a matched cisplatin-resistant cell line, OVCAR-8R. Genome-wide gene expression profiling of sensitive and resistant ovarian cancer spheroids identified 3,331 significantly differentially expressed probesets coding for 3,139 distinct protein-coding genes (Fc >2, FDR < 0.05) (S2 Table). Despite significant expression changes in some transporters including MDR1, cisplatin resistance was not associated with differences in intracellular cisplatin concentration. Cisplatin resistant cells were significantly enriched for a mesenchymal gene expression signature. OVCAR-8R resistance derived gene sets were significantly more biased to patients with shorter survival. From the most differentially expressed genes, we derived a 17-gene expression signature that identifies ovarian cancer patients with shorter overall survival in three independent datasets. We propose that the use of cisplatin resistant cell lines in 3D spheroid models is a viable approach to gain insight into resistance mechanisms relevant to ovarian tumors in patients. Our data support the emerging concept that ovarian cancers can acquire drug resistance through an epithelial-to-mesenchymal transition.     

Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis

Background

The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.

Methods and Findings

We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10⁻⁴, hazard ratio = 1.47, 95% CI 1.17–1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26–2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.

Conclusions

To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.     

Gene Expression Profiling Reveals Epithelial Mesenchymal Transition (EMT) Genes Can Selectively Differentiate Eribulin Sensitive Breast Cancer Cells

Objectives

Eribulin mesylate is a synthetic macrocyclic ketone analog of the marine sponge natural product halichondrin B. Eribulin is a mechanistically unique inhibitor of microtubule dynamics. In this study, we investigated whether selective signal pathways were associated with eribulin activity compared to paclitaxel, which stabilizes microtubules, based on gene expression profiling of cell line panels of breast, endometrial, and ovarian cancer in vitro.

Results

We determined the sets of genes that were differentially altered between eribulin and paclitaxel treatment in breast, endometrial, and ovarian cancer cell line panels. Our unsupervised clustering analyses revealed that expression profiles of gene sets altered with treatments were correlated with the in vitro antiproliferative activities of the drugs. Several tubulin isotypes had significantly lower expression in cell lines treated with eribulin compared to paclitaxel. Pathway enrichment analyses of gene sets revealed that the common pathways altered between treatments in the 3 cancer panels were related to cytoskeleton remodeling and cell cycle regulation. The epithelial-mesenchymal transition (EMT) pathway was enriched in genes with significantly altered expression between the two drugs for breast and endometrial cancers, but not for ovarian cancer. Expression of genes from the EMT pathway correlated with eribulin sensitivity in breast cancer and with paclitaxel sensitivity in endometrial cancer. Alteration of expression profiles of EMT genes between sensitive and resistant cell lines allowed us to predict drug sensitivity for breast and endometrial cancers.

Conclusion

Gene expression analysis showed that gene sets that were altered between eribulin and paclitaxel correlated with drug in vitro antiproliferative activities in breast and endometrial cancer cell line panels. Among the panels, breast cancer provided the strongest differentiation between eribulin and paclitaxel sensitivities based on gene expression. In addition, EMT genes were predictive of eribulin sensitivity in the breast and endometrial cancer panels.     

High-content analysis of cancer genome DNA alterations

New technologies as well as concerted brute-force approaches have increased the content (number of genes) that can be characterized for genomic DNA alterations. Recent advances include the detection of activating point mutations in key kinase genes (BRAF, EGFR, and PIK3CA) in multiple cancer types: preliminary insight into the entire repertoire of genes that can be mutated in cancer; the discovery of new oncogenes by high-resolution profiling of DNA copy number alterations; and the bioinformatic-driven discovery of oncogenic gene fusions. High-content promoter methylation detection systems have been used to discover additional methylated genes and have provided evidence for a stem cell origin for certain tumors. Some of these advances have had significant impact on the development and clinical testing of new therapeutics.