Tuan Zea Tan1, Qing Hao Miow1,2, Ruby Yun‐Ju Huang1,3, M. K. Wong1, Jieru Ye1, Jieying Amelia Lau1, Meng Wu1, Luqman Hakim Bin Abdul Hadi1, Richie Soong1, Mahesh Choolani3, Ben Davidson4,5, Jahn M. Nesland4,5, Lingzhi Wang1,6, Noriomi Matsumura7, Masaki Mandai7, Ikuo Konishi7, Boon Cher Goh1,8,6, Jeffrey T. Chang9, Jean Paul Thiery1,10,11, Seiichi Mori1,10,12,13
1Cancer Science Institute of Singapore, National University of Singapore, Singapore
2NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
3Department of Obstetrics and Gynecology, National University Health System, Singapore
4Division of Pathology, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
5Faculty of Medicine University of Oslo Institute of Clinical Medicine Oslo Norway
6Department of Pharmacology, National University of Singapore, Singapore
7Department of Obstetrics and Gynecology, Kyoto University, Kyoto, Japan
8Department of Hematology and Oncology, National University Health System, Singapore
9Department of Integrative Biology and Pharmacology University of Texas Health Science Center at Houston TX USA
10Department of Biochemistry, National University of Singapore, Singapore
11Institute of Molecular and Cell Biology, A*STAR (Agency for Science, Technology and Research), Singapore
12Division of Cancer Genomics Cancer Institute of Japanese Foundation for Cancer Research 3‐8‐31 Ariake Koto‐ku, Tokyo Japan
13Present Address: Division of Cancer Genomics Cancer Institute of Japanese Foundation for Cancer Research Koto‐ku Tokyo Japan
Tóm tắt
AbstractEpithelial 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.
