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Published May 2008 | Supplemental Material + Accepted Version
Journal Article Open

An embryonic stem cell–like gene expression signature in poorly differentiated aggressive human tumors

Abstract

Cancer cells possess traits reminiscent of those ascribed to normal stem cells. It is unclear, however, whether these phenotypic similarities reflect the activity of common molecular pathways. Here, we analyze the enrichment patterns of gene sets associated with embryonic stem (ES) cell identity in the expression profiles of various human tumor types. We find that histologically poorly differentiated tumors show preferential overexpression of genes normally enriched in ES cells, combined with preferential repression of Polycomb-regulated genes. Moreover, activation targets of Nanog, Oct4, Sox2 and c-Myc are more frequently overexpressed in poorly differentiated tumors than in well-differentiated tumors. In breast cancers, this ES-like signature is associated with high-grade estrogen receptor (ER)-negative tumors, often of the basal-like subtype, and with poor clinical outcome. The ES signature is also present in poorly differentiated glioblastomas and bladder carcinomas. We identify a subset of ES cell-associated transcription regulators that are highly expressed in poorly differentiated tumors. Our results reveal a previously unknown link between genes associated with ES cell identity and the histopathological traits of tumors and support the possibility that these genes contribute to stem cell–like phenotypes shown by many tumors.

Additional Information

© 2008 Nature Publishing Group. We thank C. Fan and C. Perou for assistance with the intrinsic subtype classification and the proliferation cluster, J. Foekens for tumor data, K. Gurdziel and J. Rodriguez for bioinformatics assistance, and Y. Dor, T. Brummelkamp, W. Guo, H. Cedar, N. Friedman and E. Pikarsky for reviewing of the manuscript and helpful discussions. I.B.-P. is a Leukemia and Lymphoma Special Fellow; V.J.C. was supported in part by NIH P41 HG 004059 and in part by the Whitehead Institute Bioinformatics Department; A.R. is supported by the Burroughs Wellcome Career Award at the Scientific Interface; R.A.W. is supported by US National Institutes of Health/National Cancer Institute grant R01 CA078461, the Breast Cancer Research Foundation and the Ludwig Cancer Center for Molecular Oncology at the Massachusetts Institute of Technology. Author Contributions: I.B.-P. conceived the study, collected and processed datasets, performed gene set expression analyses and wrote the manuscript. M.W.T. contributed to the design of the study, collected and processed many of the datasets and performed multiple analyses including gene set expression and nearest neighbor. V.J.C. supervised the statistical aspects of the study and performed the patient survival analyses. R.G. and G.W.B. processed datasets and performed various analyses, including transcription factor clustering and gene function assignment; G.W.B. set up the study website. A.R. designed the study together with I.B.-P., provided the analytical framework, supervised the analyses and reviewed the manuscript. R.A.W. contributed to the conception of the study, provided guidance and supervision of study design and goals and assisted in the writing of the manuscript.

Attached Files

Accepted Version - nihms214217.pdf

Supplemental Material - ng.127-S1.pdf

Supplemental Material - ng.127-S2.xls

Supplemental Material - ng.127-S3.xls

Supplemental Material - ng.127-S4.xls

Supplemental Material - ng.127-S5.xls

Supplemental Material - ng.127-S6.xls

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Created:
August 19, 2023
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