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

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 fiveclass 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. (Cancer Res 2005; 65(10): 4031-40)

Mutation of ERBB2 Provides a Novel Alternative Mechanism for the Ubiquitous Activation of RAS-MAPK in Ovarian Serous Low Malignant Potential Tumors

“…Cilia are present in normal adult ovarian surface epithelium (57) and commonly observed in benign and LMP tumor cells (58). Previous literature has suggested retention of some basic expression pattern from the cell of origin to tumor tissue and even cell lines (59,60); this may represent the gene expression pattern from a relatively well-differentiated ovarian surface epithelial cell. Although invasive serous tumors are also thought to arise from the ovarian surface epithelium, most lack expression of this Dynein cluster.…”

Validation of a microRNA-based qRT-PCR test for accurate identification of tumor tissue origin

“…38,39 Recent work suggested the use of large panels of mRNA features for identification of tumor tissue of origin. 9,10,14,15 Our test uses a significantly smaller number of tissue-specific microRNA biomarkers, set forth in a straightforward classification scheme, which provides specific tissue origins in a majority of cases. Cases with consensus classifications are accurately identified with a higher confidence.…”

“…[8][9][10][11][12] Most studies of molecular diagnosis of tissue of origin used messenger RNA (mRNA) profiling of tumor samples. 8,[13][14][15] MicroRNAs, 21-23-nucleotide-long functional RNAs, have an important function in tissue differentiation 16,17 and tumorigenesis, 18,19 and have highly tissue-specific expression patterns. [20][21][22] The unique expression profiles of microRNAs characteristic of each tissue and the malignancies arising in them can permit their accurate identification.…”

Gene expression profiling may improve diagnosis in patients with carcinoma of unknown primary

“…Recently, several independent studies have demonstrated proof of principle for the use of gene expression microarrays (GEMs) in identifying a foster primary site for CUP (Giordano et al, 2001;Ramaswamy et al, 2001;Su et al, 2001;Dennis et al, 2002;Buckhaults et al, 2003;Shedden et al, 2003;Bloom et al, 2004;Tothill et al, 2005) Data from GEM of known primary tumours have been examined 'blindly' and the correct primary site identified with up to 89% accuracy. The success of these studies demonstrates that patterns of gene expression remain consistent with tissue of origin, both in cell lines (Ross et al, 2000) and tumour samples (Khan et al, 2001).…”