Identifying tumor origin using a gene expression-based classification map

Phillip Buckhaults, Zhen Zhang, Yu Chi Chen, Tian-Li Wang, Brad St. Croix, Saurabh Saha, Alberto Bardelli, Patrice J. Morin, Kornelia Polyak, Ralph H Hruban, Victor E Velculescu, Ie Ming Shih

Research output: Contribution to journalArticle

Abstract

Identifying the primary site in cases of metastatic carcinoma of unknown origin has profound clinical importance in managing cancer patients. Although transcriptional profiling promises molecular solutions to this clinical challenge, simpler and more reliable methods for this purpose are needed. A training set of 11 serial analysis of gene expression (SAGE) libraries was analyzed using a combination of supervised and unsupervised computational methods to select a small group of candidate genes with maximal power to discriminate carcinomas of different tissue origins. Quantitative real-time PCR was used to measure their expression levels in an independent validation set of 62 samples of ovarian, breast, colon, and pancreatic adenocarcinomas and normal ovarian surface epithelial controls. The diagnostic power of this set of genes was evaluated using unsupervised cluster analysis methods. From the training set of 21,321 unique SAGE transcript tags derived from 11 libraries, five genes were identified with expression patterns that distinguished four types of adenocarcinomas. Quantitative real-time PCR expression data obtained from the validation set clustered tumor samples in an unsupervised manner, generating a self-organized map with distinctive tumor site-specific domains. Eighty-one percent (50 of 62) of the carcinomas were correctly allocated in their corresponding diagnostic regions. Metastases clustered tightly with their corresponding primary tumors. A classification map diagnostic of tumor types was generated based on expression patterns of five genes selected from the SAGE database. This expression map analysis may provide a reliable and practical approach to determine tumor type in cases of metastatic carcinoma of clinically unknown origin.

Original languageEnglish (US)
Pages (from-to)4144-4149
Number of pages6
JournalCancer Research
Volume63
Issue number14
StatePublished - Jul 15 2003

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Gene Expression
Carcinoma
Neoplasms
Gene Library
Real-Time Polymerase Chain Reaction
Adenocarcinoma
Genes
Gene Expression Profiling
Cluster Analysis
Colon
Breast
Databases
Neoplasm Metastasis

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

Identifying tumor origin using a gene expression-based classification map. / Buckhaults, Phillip; Zhang, Zhen; Chen, Yu Chi; Wang, Tian-Li; St. Croix, Brad; Saha, Saurabh; Bardelli, Alberto; Morin, Patrice J.; Polyak, Kornelia; Hruban, Ralph H; Velculescu, Victor E; Shih, Ie Ming.

In: Cancer Research, Vol. 63, No. 14, 15.07.2003, p. 4144-4149.

Research output: Contribution to journalArticle

Buckhaults, P, Zhang, Z, Chen, YC, Wang, T-L, St. Croix, B, Saha, S, Bardelli, A, Morin, PJ, Polyak, K, Hruban, RH, Velculescu, VE & Shih, IM 2003, 'Identifying tumor origin using a gene expression-based classification map', Cancer Research, vol. 63, no. 14, pp. 4144-4149.
Buckhaults P, Zhang Z, Chen YC, Wang T-L, St. Croix B, Saha S et al. Identifying tumor origin using a gene expression-based classification map. Cancer Research. 2003 Jul 15;63(14):4144-4149.
Buckhaults, Phillip ; Zhang, Zhen ; Chen, Yu Chi ; Wang, Tian-Li ; St. Croix, Brad ; Saha, Saurabh ; Bardelli, Alberto ; Morin, Patrice J. ; Polyak, Kornelia ; Hruban, Ralph H ; Velculescu, Victor E ; Shih, Ie Ming. / Identifying tumor origin using a gene expression-based classification map. In: Cancer Research. 2003 ; Vol. 63, No. 14. pp. 4144-4149.
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