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.
Suppressor of cytokine signaling 1 (SOCS1) is frequently mutated in primary mediastinal and diffuse large B-cell lymphomas (DLBCL). Currently, the prognostic relevance of these mutations in DLBCL is unknown. To evaluate the value of the SOCS1 mutation status as a prognostic biomarker in DLBCL patients, we performed full-length SOCS1 sequencing in tumors of 154 comprehensively characterized DLBCL patients. We identified 90 SOCS1 mutations in 16% of lymphomas. With respect to molecular consequences of mutations, we defined two distinct subtypes: those with truncating (major) and those with non-truncating mutations (minor), respectively. The SOCS1 mutated subgroup or the minor/major subtypes cannot be predicted on clinical grounds; however, assignment of four established gene-expression profile-based classifiers revealed significant associations of SOCS1 major cases with germinal center and specific pathway activation pattern signatures. Above all, SOCS1 major cases have an excellent overall survival, even better than the GCB-like subgroup. SOCS1 minor cases had a dismal survival, even worse than the ABC gene signature group. The SOCS1 mutation subsets retained prognostic significance in uni- and multivariate analyses. Together our data indicate that assessment of the SOCS1 mutation status is a single gene prognostic biomarker in DLBCL.
The potential role of the cell-of-origin in determining the tumor phenotype has been raised, but not adequately examined. We hypothesized that distinct cells-of-origin may play a role in determining ovarian tumor phenotype and outcome. Here we describe a new cell culture medium for in vitro culture of paired normal human ovarian (OV) and fallopian tube (FT) epithelial cells from donors without cancer. While these cells have been cultured individually for short periods of time, to our knowledge this is the first long-term culture of both cell types from the same donors. Through analysis of the gene expression profiles of the cultured OV/FT cells we identified a normal cell-of-origin gene signature that classified primary ovarian cancers into OV-like and FT-like subgroups; this classification correlated with significant differences in clinical outcomes. The identification of a prognostically significant gene expression signature derived solely from normal untransformed cells is consistent with the hypothesis that the normal cell-of-origin may be a source of ovarian tumor heterogeneity and the associated differences in tumor outcome.
Gene expression profiling has recently enabled the reclassification of aggressive non-Hodgkin lymphomas (aNHL) into distinct subgroups. In Burkitt lymphoma (BL) aberrant c-Myc activity results from IG-MYC translocations. However, MYC aberrations are not limited to BLs and then have a negative prognostic impact. In this study, we investigated to which extent aberrant c-Myc activity plays a functional role in other aNHL and whether it is independent from MYC translocations. Based on a combined microarray analysis of human germinal center (GC) B cells transfected with c-Myc and 220 aNHLs cases, we developed a "c-Myc index." This index measures the extent to which lymphomas express c-Myc responsive genes. It comprises genes that are affected in a variety of tumors compared to normal tissue. This supports the view that aberrant c-Myc expression in GC B cells triggers a tumor-like expression pattern. As expected, the "c-Myc index" is very high in molecular Burkitt lymphoma (mBL), but more importantly also high within other aNHL. It constitutes a negative prognostic marker independent of established risk factors and of the presence of a MYC translocation. Our data provide new insights into the role of c-Myc activity in different lymphomas and raises the question of treatment changes for those patients under risk.
GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human malignancies, including breast, ovarian, gastric, and pancreatic cancers. Suppression of GIPC1 in human pancreatic cancer cells inhibits in vivo tumor growth in immunodeficient mice. To better understand GIPC1 function, we suppressed its expression in human breast and colorectal cancer cell lines and human mammary epithelial cells (HMECs) and assayed both gene expression and cellular phenotype. Suppression of GIPC1 promotes apoptosis in MCF-7, MDA-MD231, SKBR-3, SW480, and SW620 cells and impairs anchorage-independent colony formation of HMECs. These observations indicate GIPC1 plays an essential role in oncogenic transformation, and its expression is necessary for the survival of human breast and colorectal cancer cells. Additionally, a GIPC1 knock-down gene signature was used to interrogate publically available breast and ovarian cancer microarray datasets. This GIPC1 signature statistically correlates with a number of breast and ovarian cancer phenotypes and clinical outcomes, including patient survival. Taken together, these data indicate that GIPC1 inhibition may represent a new target for therapeutic development for the treatment of human cancers.
Lymphomas are assumed to originate at different stages of lymphocyte development through chromosomal aberrations. Thus, different lymphomas resemble lymphocytes at distinct differentiation stages and show characteristic morphologic, genetic, and transcriptional features. Here, we have performed a microarray-based DNA methylation profiling of 83 mature aggressive B-cell non-Hodgkin lymphomas (maB-NHLs) characterized for their morphologic, genetic, and transcriptional features, including molecular Burkitt lymphomas and diffuse large B-cell lymphomas. Hierarchic clustering indicated that methylation patterns in maB-NHLs were not strictly associated with morphologic, genetic, or transcriptional features. By supervised analyses, we identified 56 genes de novo methylated in all lymphoma subtypes studied and 22 methylated in a lymphoma subtype-specific manner. Remarkably, the group of genes de novo methylated in all lymphoma subtypes was significantly enriched for polycomb targets in embryonic stem cells. De novo methylated genes in all maB-NHLs studied were expressed at low levels in lymphomas and normal hematopoietic tissues but not in nonhematopoietic tissues. These findings, especially the enrichment for polycomb targets in stem cells, indicate that maB-NHLs with different morphologic, genetic, and transcriptional background share a similar stem cell-like epigenetic pattern. This suggests that maB-NHLs originate from cells with stem cell features or that stemness was acquired during lymphomagenesis by epigenetic remodeling.
Follicular lymphoma (FL) is characterized by a large number of chromosomal aberrations. However, their exact genomic extension and involved target genes remain to be determined. For this purpose, we used array-based intermediate-high resolution genomic profiling in combination with Affymetrix gene expression analysis. Tumor specimens from 128 FL patients were analyzed for the presence of genomic aberrations and the results were correlated to clinical data sets and mRNA expression levels. In 114 (89%) of the 128 analyzed cases, a total of 688 genomic aberrations (384 gains/amplifications and 304 losses) were detected. Frequent genomic aberrations were: -1p36 (18%), +2p15 (24%), -3q (14%), -6q (25%), +7p (19%), +7q (23%), +8q (14%), -9p (16%), -11q (15%), +12q (20%), -13q (11%), -17p (16%), +18p (18%), and +18q (28%). Critical segments of these imbalances were delineated to genomic fragments with a minimum size down to 0.2 Mb. By comparison of these with mRNA gene expression data, putative candidate genes were identified. Moreover, we found that deletions affecting the tumor suppressor gene CDKN2A/B on 9p21 were detected in nontransformed FL grade I-II. For this aberration as well as for -6q25 and -6q26, an association with inferior survival was observed.
Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods.
Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.
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