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

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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.

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Modeling Survival Data: Extending the Cox Model

Terry M. Therneau, Patricia M. Grambsch (2000)

This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.

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Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

Andrea Bild, Guang Yao, Jeffrey Chang … (2006)

The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

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Global estimates of cancer prevalence for 27 sites in the adult population in 2008.

Freddie Bray, Jian-Song Ren, Eric Masuyer … (2013)

Recent estimates of global cancer incidence and survival were used to update previous figures of limited duration prevalence to the year 2008. The number of patients with cancer diagnosed between 2004 and 2008 who were still alive at the end of 2008 in the adult population is described by world region, country and the human development index. The 5-year global cancer prevalence is estimated to be 28.8 million in 2008. Close to half of the prevalence burden is in areas of very high human development that comprise only one-sixth of the world's population. Breast cancer continues to be the most prevalent cancer in the vast majority of countries globally; cervix cancer is the most prevalent cancer in much of Sub-Saharan Africa and Southern Asia and prostate cancer dominates in North America, Oceania and Northern and Western Europe. Stomach cancer is the most prevalent cancer in Eastern Asia (including China); oral cancer ranks as the most prevalent cancer in Indian men and Kaposi sarcoma has the highest 5-year prevalence among men in 11 countries in Sub-Saharan Africa. The methods used to estimate point prevalence appears to give reasonable results at the global level. The figures highlight the need for long-term care targeted at managing patients with certain very frequently diagnosed cancer forms. To be of greater relevance to cancer planning, the estimation of other time-based measures of global prevalence is warranted. Copyright © 2012 UICC.

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Author and article information

Journal

Journal ID (nlm-ta): EMBO Mol Med

Journal ID (iso-abbrev): EMBO Mol Med

Journal ID (publisher-id): emmm

Title: EMBO Molecular Medicine

Publisher: Blackwell Publishing Ltd

ISSN (Print): 1757-4676

ISSN (Electronic): 1757-4684

Publication date (Print): July 2013

Publication date (Electronic): 13 May 2013

Volume: 5

Issue: 7

Pages: 983-998

Affiliations

[1 ]Cancer Science Institute of Singapore, National University of Singapore Singapore

[2 ]NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore Singapore

[3 ]Department of Obstetrics and Gynecology, National University Health System Singapore

[4 ]Division of Pathology, Norwegian Radium Hospital Oslo University Hospital Oslo, Norway

[5 ]Faculty of Medicine, University of Oslo, Institute of Clinical Medicine Oslo, Norway

[6 ]Department of Pharmacology, National University of Singapore Singapore

[7 ]Department of Obstetrics and Gynecology, Kyoto University Kyoto, Japan

[8 ]Department of Hematology and Oncology, National University Health System Singapore

[9 ]Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston TX, USA

[10 ]Institute of Molecular and Cell Biology, A*STAR (Agency for Science Technology and Research), Singapore

[11 ]Department of Biochemistry, National University of Singapore Singapore

[12 ]Division of Cancer Genomics, Cancer Institute of Japanese Foundation for Cancer Research 3-8-31 Ariake, Koto-ku, Tokyo, Japan

[13 ]Present Address: Division of Cancer Genomics, Cancer Institute of Japanese Foundation for Cancer Research Koto-ku, Tokyo, Japan

Author notes

* Corresponding author: Tel: +65 6516 3241/42; +65 91472036; Fax: +65 6779 1453; E-mail: jpthiery@ 123456imcb.a-star.edu.sg

** Corresponding author: Tel: +81 3 3570 0450; Fax: +81 3 3570 0454; E-mail: seiichi.mori@ 123456jfcr.or.jp

[†]

These authors contributing equally to this work.

[‡]

Present Address: Division of Cancer Genomics, Cancer Institute of Japanese Foundation for Cancer Research, Koto-ku, Tokyo, Japan

Article

DOI: 10.1002/emmm.201201823

PMC ID: 3721473

PubMed ID: 23666744

SO-VID: c2d47080-3c9d-4a5d-aab0-63fc1f32aab8

License:

Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

History

Date received : 06 August 2012

Date revision received : 03 April 2013

Date accepted : 09 April 2013

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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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Most cited references 58

Modeling Survival Data: Extending the Cox Model

Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

Global estimates of cancer prevalence for 27 sites in the adult population in 2008.

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