An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin

Tothill, RW, Kowalczyk, A, Rischin, D, Bousioutas, A, Haviv, I, van Laar, RK, Waring, PM, Zalcberg, J, Ward, R, Biankin, AV, Sutherland, RL, Henshall, SM, Fong, K, Pollack, JR, Bowtell, DDL and Holloway, AJ (2005) An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin. Cancer Research, 65 10: 4031-4040. doi:10.1158/0008-5472.CAN-04-3617


Author Tothill, RW
Kowalczyk, A
Rischin, D
Bousioutas, A
Haviv, I
van Laar, RK
Waring, PM
Zalcberg, J
Ward, R
Biankin, AV
Sutherland, RL
Henshall, SM
Fong, K
Pollack, JR
Bowtell, DDL
Holloway, AJ
Title An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin
Journal name Cancer Research   Check publisher's open access policy
ISSN 0008-5472
Publication date 2005-05-15
Sub-type Article (original research)
DOI 10.1158/0008-5472.CAN-04-3617
Volume 65
Issue 10
Start page 4031
End page 4040
Total pages 10
Language eng
Subject 1306 Cancer Research
2730 Oncology
Abstract Gene expression profiling offers a promising new technique for the diagnosis and prognosis of cancer. We have applied this technology to build a clinically robust site of origin classifier with the ultimate aim of applying it to determine the origin of cancer of unknown primary (CUP). A single cDNA microarray platform was used to profile 229 primary and metastatic tumors representing 14 tumor types and multiple histologic subtypes. This data set was subsequently used for training and validation of a support vector machine (SVM) classifier, demonstrating 89% accuracy using a 13-class model. Further, we show the translation of a five-class classifier to a quantitative PCR-based platform. Selecting 79 optimal gene markers, we generated a quantitative-PCR low-density array, allowing the assay of both fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissue. Data generated using both quantitative PCR and microarray were subsequently used to train and validate a cross-platform SVM model with high prediction accuracy. Finally, we applied our SVM classifiers to 13 cases of CUP. We show that the microarray SVM classifier was capable of making high confidence predictions in 11 of 13 cases. These predictions were supported by comprehensive review of the patients' clinical histories.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: Scopus Import
 
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Scopus Citation Count Cited 152 times in Scopus Article | Citations
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Created: Wed, 11 Feb 2015, 11:15:57 EST by Ms Kate Rowe