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Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer.

ABSTRACT: Epithelial ovarian cancer (EOC) is hallmarked by a high degree of heterogeneity. To address this heterogeneity, a classification scheme was developed based on gene expression patterns of 1538 tumours. Five, biologically distinct subgroups - Epi-A, Epi-B, Mes, Stem-A and Stem-B - exhibited significantly distinct clinicopathological characteristics, deregulated pathways and patient prognoses, and were validated using independent datasets. To identify subtype-specific molecular targets, ovarian cancer cell lines representing these molecular subtypes were screened against a genome-wide shRNA library. Focusing on the poor-prognosis Stem-A subtype, we found that two genes involved in tubulin processing, TUBGCP4 and NAT10, were essential for cell growth, an observation supported by a pathway analysis that also predicted involvement of microtubule-related processes. Furthermore, we observed that Stem-A cell lines were indeed more sensitive to inhibitors of tubulin polymerization, vincristine and vinorelbine, than the other subtypes. This subtyping offers new insights into the development of novel diagnostic and personalized treatment for EOC patients.

SUBMITTER: Tan TZ

PROVIDER: S-EPMC3721473 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer.

Tan Tuan Zea TZ Miow Qing Hao QH Huang Ruby Yun-Ju RY Wong Meng Kang MK Ye Jieru J Ye Jieru J Lau Jieying Amelia JA Wu Meng Chu MC Bin Abdul Hadi Luqman Hakim LH Soong Richie R Choolani Mahesh M Davidson Ben B Nesland Jahn M JM Wang Ling-Zhi LZ Matsumura Noriomi N Mandai Masaki M Konishi Ikuo I Goh Boon-Cher BC Chang Jeffrey T JT Thiery Jean Paul JP Mori Seiichi S

EMBO molecular medicine 20130513 7

Epithelial ovarian cancer (EOC) is hallmarked by a high degree of heterogeneity. To address this heterogeneity, a classification scheme was developed based on gene expression patterns of 1538 tumours. Five, biologically distinct subgroups - Epi-A, Epi-B, Mes, Stem-A and Stem-B - exhibited significantly distinct clinicopathological characteristics, deregulated pathways and patient prognoses, and were validated using independent datasets. To identify subtype-specific molecular targets, ovarian can ...[more]

PMID: 23666744

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Project description:BACKGROUND:Medulloblastoma is the most common malignant brain tumor in children. Despite recent improvements in cure rates, prediction of disease outcome remains a major challenge and survivors suffer from serious therapy-related side-effects. Recent data showed that patients with WNT-activated tumors have a favorable prognosis, suggesting that these patients could be treated less intensively, thereby reducing the side-effects. This illustrates the potential benefits of a robust classification of medulloblastoma patients and a detailed knowledge of associated biological mechanisms. METHODS AND FINDINGS:To get a better insight into the molecular biology of medulloblastoma we established mRNA expression profiles of 62 medulloblastomas and analyzed 52 of them also by comparative genomic hybridization (CGH) arrays. Five molecular subtypes were identified, characterized by WNT signaling (A; 9 cases), SHH signaling (B; 15 cases), expression of neuronal differentiation genes (C and D; 16 and 11 cases, respectively) or photoreceptor genes (D and E; both 11 cases). Mutations in beta-catenin were identified in all 9 type A tumors, but not in any other tumor. PTCH1 mutations were exclusively identified in type B tumors. CGH analysis identified several fully or partly subtype-specific chromosomal aberrations. Monosomy of chromosome 6 occurred only in type A tumors, loss of 9q mostly occurred in type B tumors, whereas chromosome 17 aberrations, most common in medulloblastoma, were strongly associated with type C or D tumors. Loss of the inactivated X-chromosome was highly specific for female cases of type C, D and E tumors. Gene expression levels faithfully reflected the chromosomal copy number changes. Clinicopathological features significantly different between the 5 subtypes included metastatic disease and age at diagnosis and histology. Metastatic disease at diagnosis was significantly associated with subtypes C and D and most strongly with subtype E. Patients below 3 yrs of age had type B, D, or E tumors. Type B included most desmoplastic cases. We validated and confirmed the molecular subtypes and their associated clinicopathological features with expression data from a second independent series of 46 medulloblastomas. CONCLUSIONS:The new medulloblastoma classification presented in this study will greatly enhance the understanding of this heterogeneous disease. It will enable a better selection and evaluation of patients in clinical trials, and it will support the development of new molecular targeted therapies. Ultimately, our results may lead to more individualized therapies with improved cure rates and a better quality of life.

Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Project description:Recent genomic approaches have suggested the existence of multiple distinct subtypes of medulloblastoma. We studied a large cohort of medulloblastomas to determine how many subgroups of the disease exist, how they differ, and the extent of overlap between subgroups. We determined gene expression profiles and DNA copy number aberrations for 103 primary medulloblastomas. Bioinformatic tools were used for class discovery of medulloblastoma subgroups based on the most informative genes in the dataset. Immunohistochemistry for subgroup-specific ‘signature’ genes was used to determine subgroup affiliation for 294 non-overlapping medulloblastomas on two independent tissue microarrays (TMAs). Multiple unsupervised analyses of transcriptional profiles identified four distinct, non-overlapping molecular variants: WNT, SHH, Group C, and Group D. Supervised analysis of these four subgroups revealed significant subgroup-specific demographics, histology, metastatic status, and DNA copy number aberrations. Immunohistochemistry for DKK1 (WNT), SFRP1 (SHH), NPR3 (Group C), and KCNA1 (Group D) could reliably and uniquely classify formalin fixed medulloblastomas in ~98% of cases. Group C patients (NPR3 +ve tumors) exhibited a significantly diminished progression free and overall survival irrespective of their metastatic status. Our integrative genomics approach to a large cohort of medulloblastomas has identified four disparate subgroups with distinct demographics, clinical presentation, transcriptional profiles, genetic abnormalities, and clinical outcome. Medulloblastomas can be reliably assigned to subgroups through immunohistochemistry, thereby making medulloblastoma sub-classification widely available. Future research on medulloblastoma and the development of clinical trials should take into consideration these four distinct types of medulloblastoma. A total of 103 primary medulloblastoma specimens were profiled by Affymetrix exon array and gene-level analysis was performed.

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Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer.

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Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer.

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