microRNA-based Assay Research Articles

Multicentre validation of a microRNA-based assay for diagnosing indeterminate thyroid nodules utilising fine needle aspirate smears

AimsThe distinction between benign and malignant thyroid nodules has important therapeutic implications. Our objective was to develop an assay that could classify indeterminate thyroid nodules as benign or suspicious, using...

Analytical validity of a microRNA-based assay for diagnosing indeterminate thyroid FNA smears from routinely prepared cytology slides.

BACKGROUNDThe majority of thyroid nodules are diagnosed using fine‐needle aspiration (FNA) biopsies. The authors recently described the clinical validation of a molecular microRNA‐based assay, RosettaGX Reveal, which can diagnose thyroid nodules as benign or suspicious using a single stained FNA smear. This paper describes the analytical validation of the assay.METHODSMore than 800 FNA slides were tested, including slides stained with Romanowsky‐type and Papanicolaou stains. The assay was examined for the following features: intranodule concordance, effect of stain type, minimal acceptable RNA amounts, performance on low numbers of thyroid cells, effect of time since sampling, and analytical sensitivity, specificity, and reproducibility.RESULTSThe assay can be run on FNA slides for which as little as 1% of the cells are thyroid epithelial cells or from which only 5 ng of RNA have been extracted. Samples composed entirely of blood failed quality control and were not classified. Stain type did not affect performance. All slides were stored at room temperature. However, the length of time between FNA sampling and processing did not affect assay performance. There was a high level of concordance between laboratories (96%), and the concordance for slides created from the same FNA pass was 93%.CONCLUSIONSThe microRNA‐based assay was robust to various physical processing conditions and to differing sample characteristics. Given the assay's performance, robustness, and use of routinely prepared FNA slides, it has the potential to provide valuable aid for physicians in the diagnosis of thyroid nodules. Cancer Cytopathol 2016;124:711–21. © 2016 Rosetta Genomics. Cancer Cytopathology published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

Second Annual Moffitt Anatomic Pathology Symposium.

Dr Magliocco, chair of the Department of Anatomic Pathology, welcomed a near-capacity audience made up of pathologists, histotechnologists, researchers, and pathology residents and medical students at the 2nd Annual Moffitt Anatomic Pathology Symposium: Practicing Pathology in a Changing World that took place in Clearwater Beach, Florida, on September 26, 2015. Th focus of the symposium was on the updates of contemporary and emerging information and technology that impact practicing pathologists in an ever-changing world.Invited speakers included Drs Odze and Schnitt, both from Harvard Medical School in Boston, who shared their expert knowledge in gastrointestinal (GI) and breast pathology, as well as Theresa B. Burchette, Lawrence Patton Jr, John J. Shelley, and Steven Westra. Experts from the H. Lee Moffitt Cancer Center & Research Institute included Drs Magliocco, Bui, Centeno, Coppola, Hussaini, Khalil, Khazia, and Rosa, as well as Mr Hicks and Ms Balasi, Molina, and Snyder. Drs Minton and Malafa also presented topics in molecular pathology, digital pathology, and GI and breast cancers. The Ask the Expert Digital Pathology session was an interactive discussion of real breast and GI pathology cases among the experts and the audience.A total of 22 scientific abstracts were proffered by students, residents, fellows, and staff. Dr Altiok, chair of the research section of the Department of Anatomic Pathology, presented awards to the 3 best submissions.Dr Ryzhova and colleagues earned the Best Poster award for their work titled “Mutation Profiling of High-Grade Serous Ovarian Adenocarcinomas Suggests STK11 and NOTCH1 as Candidate Biomarkers.” This paper reported identification of STK11 mutations as possible biomarkers for platinum-therapy response and detailed actionable mutations in several patients with ovarian cancer.The Best Research Project prize was awarded to Dr Dettloff and colleagues for their work “Utility of a microRNA-Based Assay for Diagnosing Tumor Origin in Soft Tissue Sarcomas.” The investigators examined the recent experience at Moffitt Cancer Center with noncoding microRNA tumor profiling of tumors of uncertain origin. They listed several situations in which the technique can be particularly useful, such as the identification of germ-cell tumors. They also recognized pitfalls of the technique, such as a bias toward sarcoma, possibly representing a biological shift in recurrent or previously treated tumors. The Best Clinicopathologic Correlation award was won by Dr Liu and colleagues for their poster titled “Spindle Cell/Sclerosing Rhabdomyosarcoma Is a New and Underdiagnosed Entity: A Clinical and Tissue Microarray Immunohistochemical Study With Literature Review.” This work emphasized the utility of using tissue microarray to screen previously encountered cases with antibodies directed against emerging biomarkers.

Novel microRNA-based assay demonstrates 92% agreement with diagnosis based on clinicopathologic and management data in a cohort of patients with carcinoma of unknown primary

BackgroundCancer of unknown or uncertain primary is a major diagnostic and clinical challenge, since identifying the tissue-of-origin of metastases is crucial for selecting optimal treatment. MicroRNAs are a family of non-coding, regulatory RNA molecules that are tissue-specific, with a great potential to be excellent biomarkers.MethodsIn this study we tested the performance of a microRNA-based assay in formalin-fixed paraffin-embedded samples from 84 CUP patients.ResultsThe microRNA based assay agreed with the clinical diagnosis at presentation in 70% of patients; it agreed with the clinical diagnosis obtained after patient management, taking into account response and outcome data, in 89% of patients; it agreed with the final clinical diagnosis reached with supplemental immunohistochemical stains in 92% of patients, indicating a 22% improvement in agreement from diagnosis at presentation to the final clinical diagnosis. In 18 patients the assay disagreed with the presentation diagnosis and was in agreement with the final clinical diagnosis, which may have resulted in the administration of more effective chemotherapy. In three out of four discordant cases in which supplemental IHC was performed, the IHC results validated the assay’s molecular diagnosis.ConclusionsThis novel microRNA-based assay shows high accuracy in identifying the final clinical diagnosis in a real life CUP patient cohort and could be a useful tool to facilitate administration of optimal therapy.

Abstract 802: Development and validation of a microRNA-based diagnostic assay for the classification of renal cell tumors.

Abstract 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

Development and validation of a microRNA-based diagnostic assay for classification of renal cell carcinomas

Renal cancers account for more than 3% of adult malignancies and cause 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, chromophobe and the benign oncocytoma. These histological subtypes vary in their clinical course and prognosis, and different clinical strategies have been developed for their management. In some kidney tumor cases it can be very difficult for the pathologist to distinguish between tumor types on the basis of morphology and immunohistochemistry (IHC). In this publication we present the development and validation of a microRNA-based assay for classifying primary kidney tumors. The assay, which classifies the four main kidney tumor types, was developed based on the expression of a set of 24 microRNAs. A validation set of 201 independent samples was classified using the assay and analyzed blindly. The assay produced results for 92% of the samples with an accuracy of 95%.

The Diagnostic and Prognostic Role of microRNA in Colorectal Cancer - a Comprehensive review

The discovery of microRNA, a group of regulatory short RNA fragments, has added a new dimension to the diagnosis and management of neoplastic diseases. Differential expression of microRNA in a unique pattern in a wide range of tumor types enables researches to develop a microRNA-based assay for source identification of metastatic disease of unknown origin. This is just one example of many microRNA-based cancer diagnostic and prognostic assays in various phases of clinical research.Since colorectal cancer (CRC) is a phenotypic expression of multiple molecular pathways including chromosomal instability (CIN), micro-satellite instability (MIS) and CpG islands promoter hypermethylation (CIMP), there is no one-unique pattern of microRNA expression expected in this disease and indeed, there are multiple reports published, describing different patterns of microRNA expression in CRC.The scope of this manuscript is to provide a comprehensive review of the scientific literature describing the dysregulation of and the potential role for microRNA in the management of CRC. A Pubmed search was conducted using the following MeSH terms, "microRNA" and "colorectal cancer". Of the 493 publications screened, there were 57 papers describing dysregulation of microRNA in CRC.

Classification of the Four Main Types of Lung Cancer Using a MicroRNA-Based Diagnostic Assay

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.

A Second-Generation MicroRNA-Based Assay for Diagnosing Tumor Tissue Origin

Cancers of unknown primary origin (CUP) constitute 3%-5% (50,000 to 70,000 cases) of all newly diagnosed cancers per year in the United States. Including cancers of uncertain primary origin, the total number increases to 12%-15% (180,000 to 220,000 cases) of all newly diagnosed cancers per year in the United States. Cancers of unknown/uncertain primary origins present major diagnostic and clinical challenges because the tumor tissue of origin is crucial for selecting optimal treatment. MicroRNAs are a family of noncoding, regulatory RNA genes involved in carcinogenesis. MicroRNAs that are highly stable in clinical samples and tissue specific serve as ideal biomarkers for cancer diagnosis. Our first-generation assay identified the tumor of origin based on 48 microRNAs measured on a quantitative real-time polymerase chain reaction platform and differentiated 25 tumor types. We present here the development and validation of a second-generation assay that identifies 42 tumor types using a custom microarray. A combination of a binary decision-tree and a k-nearest-neighbor classifier was developed to identify the tumor of origin based on the expression of 64 microRNAs. Overall assay sensitivity (positive agreement), measured blindly on a validation set of 509 independent samples, was 85%. The sensitivity reached 90% for cases in which the assay reported a single answer (>80% of cases). A clinical validation study on 52 true CUP patients showed 88% concordance with the clinicopathological evaluation of the patients. The abilities of the assay to identify 42 tumor types with high accuracy and to maintain the same performance in samples from patients clinically diagnosed with CUP promise improved utility in the diagnosis of cancers of unknown/uncertain primary origins.

New microRNA-based diagnostic test for lung cancer classification.

10528 Background: Lung cancer is the leading cause of cancer deaths in the US. Treatment options are determined by tumor subtyping, for which there is lack of standardized, objective, and highly accurate techniques. In 20%-30% of cases significant limitations of tumor quantity and quality prevent full classification of the tumor using traditional diagnostic methods. Using microRNA microarray data generated from over a hundred formalin-fixed, paraffin-embedded (FFPE) primary lung cancer samples, we have identified microRNA expression profiles that differ significantly for the main lung cancer types. Based on these findings, we have developed and validated a microRNA-based qRT-PCR assay that differentiates primary lung cancers into four types: squamous cell carcinoma, non-squamous non-small cell lung cancer, carcinoid and small cell carcinoma. Methods: Over 700 primary tumor samples from different histological types of lung cancer were collected. Samples included FFPE blocks from resection or biopsies and cell blocks from cytology specimens including fine needle aspiration, bronchial brushing and bronchial washing. High-quality RNA was extracted from the samples using proprietary protocols. Expression levels of potential microRNA biomarkers were profiled using microarrays followed by a sensitive and specific qRT-PCR platform. An assay for lung tumors classification using 8 microRNAs on qRT-PCR was developed based on data from 261 samples. This assay was validated on an independent blinded set of 451 cytological and pathological samples. Results: Using the expression levels of 8 microRNAs measured in qRT-PCR, accurate classification of the primary lung tumors into the four main cancer types is obtained. The microRNA-based assay reached an accuracy of 94%. Moreover, cytological samples composed over 50% of the validation set and reached an accuracy of 95%. Conclusions: We present here a new microRNA-based assay for the classification of the four main types of lung cancer based only on the expression of 8 microRNAs. This assay displays very high levels of accuracy for both pathological and cytological samples. The assay comprises a standardized, well-tested and objective tool which can assist physicians in the diagnosis of lung cancer.

Prospective Gene Signature Study Using microRNA to Identify the Tissue of Origin in Patients with Carcinoma of Unknown Primary

Accurate identification of tissue of origin (ToO) for patients with carcinoma of unknown primary (CUP) may help customize therapy to the putative primary and thereby improve the clinical outcome. We prospectively studied the performance of a microRNA-based assay to identify the ToO in CUP patients. Formalin-fixed paraffin-embedded (FFPE) metastatic tissue from 104 patients was reviewed and 87 of these contained sufficient tumor for testing. The assay quantitates 48 microRNAs and assigns one of 25 tumor diagnoses by using a biologically motivated binary decision tree and a K-nearest neighbors (KNN). The assay predictions were compared with clinicopathologic features and, where suitable, to therapeutic response. Seventy-four of the 87 cases were processed successfully. The assay result was consistent or compatible with the clinicopathologic features in 84% of cases processed successfully (71% of all samples attempted). In 65 patients, pathology and immunohistochemistry (IHC) suggested a diagnosis or (more often) a differential diagnosis. Out of those, the assay was consistent or compatible with the clinicopathologic presentation in 55 (85%) cases. Of the 9 patients with noncontributory IHC, the assay provided a ToO prediction that was compatible with the clinical presentation in 7 cases. In this prospective study, the microRNA diagnosis was compatible with the clinicopathologic picture in the majority of cases. Comparative effectiveness research trials evaluating the added benefit of molecular profiling in appropriate CUP subsets are warranted. MicroRNA profiling may be particularly helpful in patients in whom the IHC profile of the metastasis is nondiagnostic or leaves a large differential diagnosis.

A second-generation microRNA-based assay for diagnosing tumor tissue origin.

10575 Background: Cancer of unknown primary (CUP) constitutes 3%-5% of all newly diagnosed cancer cases. It presents a major diagnostic challenge as knowing the tumor tissue of origin (ToO) of the cancer is crucial for choosing the optimal treatment. MicroRNAs are a family of non-coding, regulatory RNA genes involved in development and differentiation that were shown to be involved in carcinogenesis. MicroRNAs, highly stable in clinical samples and tissue-specific, serve as ideal biomarkers for cancer diagnosis. Our first generation assay identifies the ToO using a set of 48 microRNAs measured on a qRT-PCR platform and differentiates 25 different tumor types. We present here the development and validation of a second generation clinical assay that can identify 42 different tumor types using a custom array platform. Methods: Over 1300 primary and metastatic tumor formalin-fixed and paraffin-embedded (FFPE) tissue samples were used for the training and development of the assay. High-purity RNA was extracted using proprietary protocols. Expression levels of all known and additional Rosetta’s proprietary microRNAs were profiled using a custom array platform. A combination of a decision tree with a KNN classifier was developed to identify the ToO based on the expression of 64 microRNAs. A validation set of more than 500 independent FFPE tissue samples of primary tumors and metastases was analyzed blindly and classified using the developed assay. Results: The assay is able to identify 42 tumor types that include carcinomas, soft tissue tumors, lymphoma and other malignancies with very high accuracy. Based on the validation set, the sensitivity (positive agreement) for identifying the tumor origin is 85%. In more than 80% of the cases, a single answer is reported, and for these cases, the sensitivity is 90%. Conclusions: Previous studies have highlighted the tissue-specificity of microRNA expression, and have demonstrated their potential use for classification of human malignancies. An enlarged panel of 42 ToO that can be identified with high accuracy promises improved utility for the diagnosis of cancers of unknown/uncertain primary.

Abstract 4943: microRNAs as clinical biomarkers for lung cancer classification

Abstract Background: Lung cancers have traditionally been subdivided into two main groups: Small Cell Lung Cancer (SCLC) and Non Small Cell Lung Cancer (NSCLC). Due to limited tumor sample amounts and an abundance of poor quality samples, accurate subclassification of primary lung cancer using small pre-operative biopsies presents a diagnostic challenge using current tools (H&E and immunohistochemical stains). For patients with primary lung cancer, accurate determination of the tumor type significantly influences treatment decisions. Our previous studies have shown that microRNAs are differentially expressed in different subtypes of lung cancer; hence microRNA expression profiling holds a promising new strategy for the precise subclassification of lung cancers. Based on these findings, we present here the development of a microRNA-based qRT-PCR diagnostic assay that uses pathology and cytology samples in order to classify primary lung cancers into four subtypes: squamous, non-squamous, SCLC and carcinoid. Methods: FFPE tissue blocks and cell blocks from different histological subtypes of lung cancer, taken from primary lung cancer resection or pre-operative diagnostic procedures, were collected. High-quality RNA, including the well-preserved microRNA fraction, was extracted from the samples using proprietary protocols. Expression levels of potential microRNA biomarkers were profiled using a sensitive and specific qRT-PCR platform. Results: We find that combinations of a few microRNAs can successfully differentiate between different histological types of lung tumors. A classification algorithm was developed that reached high accuracy in the identification of the four main subtypes of lung tumors. Conclusion: We developed a highly accurate microRNA-based diagnostic assay that classifies primary lung cancer types with very high sensitivity and specificity. This assay is a simple and reliable diagnostic assay which will offer an accurate and standardized tool for primary lung cancer cytological samples, including those that presently fail pathological evaluation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4943. doi:10.1158/1538-7445.AM2011-4943

Abstract 1148: Development and validation of a second generation microRNA-based assay for diagnosing tumor tissue origin

Abstract Background: Cancer of unknown primary (CUP) constitutes 3%-5% of all newly diagnosed cancer cases. It presents a major diagnostic challenge due to the therapeutic implications of the tissue origin of the cancer. MicroRNAs are a family of non-coding, regulatory RNA genes that are involved in development, differentiation and cell cycle and were shown to be involved in oncogenesis. MicroRNAs are highly tissue-specific and therefore ideal candidates for CUP diagnosis. We have previously presented a biologically-motivated classification assay to identify the tissue of origin using a set of 48 microRNAs measured on a qRT-PCR platform. This assay can differentiate 25 different tumor types from 17 origins. We present here the development and validation of a second generation assay that can identify 42 different tumor types using a custom array platform. Methods: Over 1200 primary and metastatic tumor FFPE samples were used for the training and development of the assay. High-quality RNA, including the well-preserved microRNA fraction, was extracted from the FFPE blocks using proprietary protocols. Expression levels of all known and additional Rosetta's proprietary microRNAs were profiled using a custom array platform. A combination of a decision tree classifier with a KNN classifier was developed to identify the tissue of origin based on the microRNA expression patterns. A validation set of more than 500 independent FFPE samples of primary tumors and metastases was analyzed blindly and classified using the developed assay. Results: The assay is able to identify 42 tumor types based on the expression of only several dozens microRNAs with very high accuracy. Based on the validation set, the sensitivity (positive agreement) for identifying the tumor origin is 85%. In more than 80% of the cases, a single answer was reported, and for these cases, the sensitivity is 90%. Conclusions: Previous studies have highlighted the tissue-specificity of microRNA expression, and have demonstrated their potential use for classification of human malignancies. An assay utilizing microRNA expression in paraffin-embedded tissue for identification of the tissue of origin was previously developed. Here we present the development and the performance of a second generation assay, designed to identify 42 tumor types. This assay will provide an important new tool for diagnosing tumor tissue origin. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1148. doi:10.1158/1538-7445.AM2011-1148

A Diagnostic Assay Based on MicroRNA Expression Accurately Identifies Malignant Pleural Mesothelioma

The definitive identification of malignant pleural mesothelioma (MPM) has significant clinical implications, yet other malignancies often involve the lung pleura, confounding the diagnosis of MPM. In the absence of accurate markers, MPM can be difficult to distinguish from peripheral lung adenocarcinoma and metastatic epithelial cancers. MicroRNA expression is tissue-specific and highly informative for identifying tumor origin. We identified microRNA biomarkers for the differential diagnosis of MPM and developed a standardized microRNA-based assay. Formalin-fixed, paraffin-embedded samples of 33 MPM and 210 carcinomas were used for assay development. Using microarrays, we identified microRNAs differentially expressed between MPM and various carcinomas. Hsa-miR-193-3p was overexpressed in MPM, while hsa-miR-200c and hsa-miR-192 were overexpressed in peripheral lung adenocarcinoma and carcinomas that frequently metastasize to lung pleura. We developed a standardized diagnostic assay based on the expression of these microRNAs. The assay reached a sensitivity of 100% and a specificity of 94% in a blinded validation set of 68 samples from the lung and pleura. This diagnostic assay can provide a useful tool in the differential diagnosis of MPM from other malignancies in the pleura.

MicroRNAs as clinical biomarkers for lung cancer classification

Abstract Background: Lung cancers have been traditionally subdivided into two main groups: neuroendocrine and non-small cell lung cancer (NSCLC). In 20-30% of lung cancer cases it is difficult to make a definitive diagnosis of the tumor subtype based on the cytological specimen (e.g., fine-needle aspirates). In general, the classification of lung cancers poses a diagnostic challenge and there is lack of standardized techniques for cancer sub-typing. For patients with lung carcinoma, accurate determination of tumor type significantly influences treatment decision. MicroRNA expression profiling is a promising new strategy for the precise subclassification of lung cancers. We have previously described a microRNA-based assay which accurately differentiates between squamous and non-squamous NSCLC in FFPE derived from resections as well as from cytological samples. We have also used microRNA microarray data generated from over a hundred formalin-fixed, paraffin-embedded (FFPE) samples of primary lung cancer, to identify microRNA expression profiles that differ significantly for various sub-types of lung cancer samples. Based on these findings, we present here the development of a microRNA-based assay that is able to differentiate primary lung cancers into neuroendocrine vs. NSCLC and then further sub-classify NSCLC into squamous vs. non-squamous and neuroendocrine into SCLC vs. carcinoid using pathology and cytology samples. Methods: FFPE of resections and cell blocks from different histological subtypes of lung cancer, taken from primary lung cancer resection or pre-operative diagnostic procedures, were collected. High-quality RNA, including the well-preserved microRNA fraction, was extracted from the samples using proprietary protocols. Expression levels of potential microRNA biomarkers were profiled using a sensitive and specific qRT-PCR platform. Results: We find that combinations of small numbers of microRNAs can successfully differentiate between the four different histological types of lung cancers, with very high sensitivity and specificity. These microRNAs are able to subclassify lung cancer FFPE samples derived from pathological and cytological samples. Conclusion: We identified microRNAs that represent excellent biomarkers for the classification of lung primary cancers. These findings are the basis for the development of a simple and reliable qRT-PCR based diagnostic assay which will offer an accurate and standardized tool for primary lung cancer pathological and cytological samples, including those that presently fail pathological evaluation.

A second-generation microRNA-based assay for diagnosing tumor tissue origin

Abstract Background: Cancer of unknown primary (CUP) constitutes 3%~5% of all newly diagnosed cancer cases. It presents a major diagnostic challenge due to the therapeutic implications of the tissue origin of the cancer. Accurate tumor of origin (ToO) assignment could help guide use of specific therapy which may impact survival. MicroRNAs are a family of noncoding, regulatory RNA genes that are involved in development, differentiation and cell cycle and were shown to be involved in oncogenesis. MicroRNAs are highly tissue specific and therefore ideal candidates for CUP diagnosis. We have previously presented a biologically-motivated classification assay to identify the tissue of origin using a set of 48 microRNAs measured on a qRT-PCR platform. This assay can differentiate 25 different tumor types from 17 origins. We present here the development and validation of a second generation assay that can identify 43 different tumor types using a custom array platform. The assay is based on the normalized expression of less than 70 microRNAs. We will present performance data including inter-laboratory concordance from the validation of the assay. Methods: Over 1200 primary and metastatic tumor FFPE samples were used for the training and development of the next generation assay. High-quality RNA, including the well-preserved microRNA fraction, was extracted from the FFPE blocks using proprietary protocols previously described. Expression levels of all known and additional Rosetta's proprietary microRNAs were profiled using a custom array platform. MicroRNAs containing information about the ToO were identified and two classifiers were developed to utilize the information of the microRNA expression patterns for the diagnosis of ToO. The assay reports a ToO based on these two classifications, where in most cases the assay result is a single ToO reported. Results: The developed assay is able to accurately identify more than 40 tumor types based on the expression of only several dozens microRNAs. The results of a comprehensive validation study using hundreds of independent tumor and metastases FFPE samples will be presented, including inter-laboratory concordance data. Conclusions: Previous studies have highlighted the tissue-specificity of microRNA expression, and have demonstrated their potential use for classification of human malignancies. An assay utilizing microRNA expression in a biopsy for identifying the tissue of origin was developed. Here we present the development and the performance of a second generation assay, designed to identify over 40 tumor types. This assay will provide an important new tool for diagnosing tumor tissue origin.

Identification of tumor tissue origin by a microRNA-based molecular assay

11036 Background: Hundreds of thousands of patients are diagnosed each year with metastatic cancer. For ∼10% of these, the tumor primary site is never identified, and they are defined as Cancer of Unknown Primary (CUP). Identification of tumor tissue-of- origin has significant therapeutic implications and presents a major diagnostic challenge. In previous work we showed that by combining expression profiles of tissue-specific microRNAs with a biologically-motivated classification scheme, tumor tissue-of-origin can be identified with high accuracy. Here we describe the development of this approach into a practical diagnostic assay. Methods: We developed protocols for extraction of high-quality RNA that retain the microRNA fraction from FFPE archival tissue samples. Proprietary, highly specific qRT-PCR was used to profile microRNA expression levels in hundreds of samples. Results: A training set of nearly 400 primary and metastatic tumors samples with known primary sites, representing 25 different tumor types, was used to define a standardized diagnostics assay (miRview mets). The assay uses a qRT-PCR protocol to measure a panel of 48 microRNA biomarkers. The assay was validated on a test set of nearly 200 primary and metastatic tumors whose primary sites were blinded. The classification protocol identifies either a single, high-confidence origin or two possible low-confidence predictions. Overall, correct primary site was identified for 83% of the tumors. For 70% of the cases a single high-confidence prediction was made; these cases had a higher accuracy: 90% of the primary sites predicted with high confidence were accurately identified. Conclusions: Previous studies highlighted the tissue-specificity of microRNA expression. We developed this potential into a diagnostic assay that identifies tumor origins with high accuracy. This assay provides an important new tool for diagnosing tumor tissue origin. [Table: see text]

MicroRNA-based assay for differential diagnosis of mesothelioma

e22079 Background: Malignant mesothelioma is an aggressive pleural neoplasm, strongly linked to environmental exposures such as asbestos. Mesothelioma can be difficult to differentiate from other tumors in the lung or pleura such as primary lung adenocarcinoma presenting with pleural effusion or metastatic adenocarcinoma from extrathoracic sites. We addressed the increasing need for accurate differential diagnosis of these tumors by developing a diagnostic assay based on expression levels of microRNAs, a family of small, non-coding RNAs whose tissue-specificity has proven applicability for identification of cancer tissue type and histology. Methods: We developed protocols for extraction of high-quality RNA that retain the microRNA fraction from FFPE tissue samples. Microarrays were used for initial profiling. qRT-PCR was used to validate results and to develop a diagnostic assay. Results: We identified microRNAs that are differentially expressed between mesothelioma, lung adenocarcinoma, and other confounding tumor types. A diagnostic assay (miRview™ meso) was developed, that utilizes qRT-PCR measurement of a small set of microRNAs to differentiate between mesothelioma and non-mesothelioma samples. After establishing this profile in more than 30 mesotheliomas and 200 samples of confounding tumors, the microRNA biomarkers were measured using a standardized protocol on a blinded test set. The assay had accuracy greater than 90% in differentiating mesothelioma from other confounding tumor types. More than ¾ of samples were classified with high confidence, and these samples were all correctly identified. Conclusions: MicroRNAs are emerging as effective cancer biomarkers. A robust and simple assay based on the expression level of a few microRNA biomarkers can accurately differentiate mesothelioma from other possible tumors in the lung and pleura. This assay provides an important new tool for diagnosing mesothelioma. [Table: see text]

MicroRNA-based assay for differential diagnosis of squamous from non-squamous non-small cell lung carcinoma

11069 Background: Recent advances in biologically directed therapies for non-small cell lung carcinoma (NSCLC) emphasize the need for more accurate sub-classification of NSCLC, as treatment may be dictated by histologic subtype. In particular, squamous histology can be a counter-indication for treatment by VEGF inhibitors. MicroRNAs are highly tissue-specific biomarkers with potential clinical applicability for defining cancer type and origin. MicroRNAs are well preserved in formalin fixed tissue, making them ideal candidates for molecular markers for use in routinely processed material. Here we report on the development and performance of a microRNA-based assay for the differential diagnosis of squamous from non-squamous NSCLC. Methods: We developed protocols for extraction of high-quality RNA that retain the microRNA fraction from FFPE archival tissue samples. MicroRNA microarrays were used to profile more than a hundred NSCLC samples. Specific microRNA qRT-PCR was used to validate results, and to develop a diagnostic assay. Results: We identified a microRNA biomarker that is strongly overexpressed in squamous cell NSCLC. A diagnostic assay (miRview squamous) was developed, that utilizes qRT-PCR measurement of this microRNA, normalized by an additional microRNA and a small nuclear RNA. This assay was validated on a blinded test set of 64 tumor samples, and had sensitivity of 97% and specificity of 91%. More than ¾ of the samples were classified with high confidence, and these classifications were accurate in 96% of the cases. Conclusions: MicroRNAs are becoming an important tool for classification of cancers. A diagnostic assay based on the specificity of a single microRNA accurately identifies squamous from non-squamous NSCLC. This assay provides an important new tool for the classification of NSCLC. [Table: see text]