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An Expression-Based Site of Origin Diagnostic Method Designed for Clinical Application to Cancer of Unknown Origin

¹ Ian Potter Centre for Cancer Genomics and Predictive Medicine, Departments of ² Haematology and Medical Oncology, and ³ Pathology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, Victoria, Australia; ⁴ Diagnostics and Pathology, Genentech Inc., South San Francisco, California; ⁵ Department of Medical Oncology, St. Vincent's Hospital, Darlinghurst and School of Medicine, University of New South Wales, Sydney, Australia; ⁶ Cancer Research Program, Garvan Institute of Medical Research, St. Vincent's Hospital, Darlinghurst, Australia; ⁷ Department of Thoracic Medicine, Prince Charles Hospital, Chermside, Australia; and ⁸ Department of Pathology, Stanford University, Stanford, California

Requests for reprints: David Bowtell, Ian Potter Centre for Cancer Genomics and Predictive Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett Street, Melbourne, Victoria 8006, Australia. Phone: 61-3-96561356; Fax: 61-3-96561414; E-mail: d.bowtell{at}petermac.org.

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.

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