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Background. Identification of the tissue of origin of a brain metastatic tumor is vital to its management. Carcinoma of unknown primary (CUP) is common in oncology, representing 3%–5% of all invasive malignancies. We aimed to validate a recently developed microRNA-based quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) test for identifying the tumor tissue of origin, first in a consecutive cohort of metastatic tumors of known origin and then in a cohort of CUP cases resected from the central nervous system (CNS). Patients and Methods. One hundred two resected CNS metastatic tumors with known origin, previously classified based on the patient's clinical history and pathological data, as well as a second cohort of resected CNS tumors from 57 patients originally diagnosed as CUP were studied. A qRT-PCR diagnostic assay that measures the expression level of 48 microRNAs was used to classify the tissue of origin of these metastatic tumors. Results. In this blinded study, the test predictions correctly identified the reference diagnosis of the samples of known origin, excluding samples from prostate origin, in 84% of cases. In the second CUP patient cohort, the test prediction was in agreement with the diagnosis that was later confirmed clinically or with pathological evaluation in 80% of cases. Conclusion. In a cohort of brain and spinal metastases, a previously developed test based on the expression of 48 microRNAs allowed accurate identification of the tumor tissue of origin in the majority of cases. The high accuracy of this test in identifying the tissue of origin of metastases of unknown primary is demonstrated for the first time and may have broad clinical application.

For patients with primary lung cancer, accurate determination of the tumor type significantly influences treatment decisions. However, techniques and methods for lung cancer typing lack standardization. In particular, owing to limited tumor sample amounts and the poor quality of some samples, the classification of primary lung cancers using small preoperative biopsy specimens presents a diagnostic challenge using current tools. We previously described a microRNA-based assay (miRview squamous; Rosetta Genomics Ltd., Rehovot, Israel) that accurately differentiates between squamous and nonsquamous non–small cell lung cancer. Herein, we describe the development and validation of an assay that differentiates between the four main types of lung cancer: squamous cell carcinoma, nonsquamous non–small cell lung cancer, carcinoid, and small cell carcinoma. The assay, miRview lung (Rosetta Genomics Ltd.), is based on the expression levels of eight microRNAs, measured using a sensitive quantitative RT-PCR platform. It was validated on an independent set of 451 samples, more than half of which were preoperative cytologic samples (fine-needle aspiration and bronchial brushing and washing). The assay returned a result for more than 90% of the samples with overall accuracy of 94% (95% CI, 91% to 96%), with similar performance observed in pathologic and cytologic samples. Thus, miRview lung is a simple and reliable diagnostic assay that offers an accurate and standardized classification tool for primary lung cancer using pathologic and cytologic samples.

Background: Renal cancers account for more than 3% of adult malignancies and result in more than 13,000 deaths per year in the US alone. The four most common types of kidney tumors include the malignant renal cell carcinomas: clear cell, papillary and chromophobe, as well as the benign oncocytoma. These histological subtypes vary in their clinical course and their prognosis, and different clinical strategies have been developed for their management. The differential diagnosis between the subtypes of kidney tumors based on morphology alone can be challenging, and is subjected to inter- and intra-observer variability. Even when utilizing immunohistochemistry (IHC) markers, the ability to differentiate between sub-types can be difficult, especially in the setting of uncommon morphology and biopsy sample with small amounts of tumor tissue. We present the development and validation of a microRNA-based test for classifying primary kidney tumors. Methods: 181 Formalin Fixed Paraffin Embedded (FFPE) samples from primary kidney tumors were collected and reviewed by pathologists from different institutes according to morphology and available IHC labeling data. High-quality total RNA, including the well-preserved microRNA fraction, was extracted from the FFPE samples using a proprietary protocol. Expression levels of hundreds of microRNAs were profiled using a custom microarray platform. Technical validation of the array results was performed using qRT-PCR. A diagnostic assay was developed using a K-nearest neighbor algorithm that searches for the 5 samples in the training database (181 samples used for assay development) that are most similar to the tested sample. The result for the tested sample is defined by a majority vote of the pre-determined subtypes of these 5 closest neighbors. A validation set of 201 independent samples was classified using the assay and analyzed blindly. Results: A set of 24 differentially expressed microRNAs were found to separate the four kidney tumor subtypes and were chosen as classifiers in the KNN algorithm. Clinical validation was performed using an independent, blinded sample set. The test was able to produce results for 92% of the validation set of 201 samples with accuracy of 95%. Conclusions: Expression levels of 24 microRNAs measured on a microarray platform were found to accurately differentiate the four main types of primary kidney tumors. These findings were the basis for the development and validation of a standardized diagnostic assay for the classification of renal cell tumors in FFPE samples from resections or biopsies. This assay can serve as a reliable diagnostic tool to aid physicians with the growing unmet need for kidney tumor classification. Citation Format: Robert Wassman, Brianna St. Cyr, Eddie Fridman, Iris Barshack, Yajue Huang, Sofia Zilber, Mats Sanden, Hila Benjamin, Noga Yerushalmi, Yael Spector. Development and validation of a microRNA-based diagnostic assay for the classification of renal cell tumors. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 802. doi:10.1158/1538-7445.AM2013-802