Dataset Information

MicroRNA profiling for the identification of cancers with unknown primary tissue-of-origin.

ABSTRACT: Cancer of unknown primary (CUP) represents a common and important clinical problem. There is evidence that most CUPs are metastases of carcinomas whose primary site cannot be recognized. Driven by the hypothesis that the knowledge of primary cancer could improve patient's prognosis, we investigated microRNA expression profiling as a tool for identifying the tissue of origin of metastases. We assessed microRNA expression from 101 formalin-fixed, paraffin-embedded (FFPE) samples from primary cancers and metastasis samples by using a microarray platform. Forty samples representing ten different cancer types were used for defining a cancer-type-specific microRNA signature, which was used for predicting primary sites of metastatic cancers. A 47-miRNA signature was identified and used to estimate tissue-of-origin probabilities for each sample. Overall, accuracy reached 100% for primary cancers and 78% for metastases in our cohort of samples. When the signature was applied to an independent published dataset of 170 samples, accuracy remained high: correct prediction was found within the first two options in 86% of the metastasis cases (first prediction was correct in 68% of cases). This signature was also applied to predict 16 CUPs. In this group, first predictions exhibited probabilities higher than 90% in most of the cases. These results establish that FFPE samples can be used to reveal the tissue of origin of metastatic cancers by using microRNA expression profiling and suggest that the approach, if applied, could provide strong indications for CUPs, whose correct diagnosis is presently undefined.

SUBMITTER: Ferracin M

PROVIDER: S-EPMC4325368 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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MicroRNA profiling for the identification of cancers with unknown primary tissue-of-origin.

Ferracin Manuela M Pedriali Massimo M Veronese Angelo A Zagatti Barbara B Gafà Roberta R Magri Eros E Lunardi Maria M Munerato Gardenia G Querzoli Giulia G Maestri Iva I Ulazzi Linda L Nenci Italo I Croce Carlo M CM Lanza Giovanni G Querzoli Patrizia P Negrini Massimo M

The Journal of pathology 20110601 1

Cancer of unknown primary (CUP) represents a common and important clinical problem. There is evidence that most CUPs are metastases of carcinomas whose primary site cannot be recognized. Driven by the hypothesis that the knowledge of primary cancer could improve patient's prognosis, we investigated microRNA expression profiling as a tool for identifying the tissue of origin of metastases. We assessed microRNA expression from 101 formalin-fixed, paraffin-embedded (FFPE) samples from primary cance ...[more]

PMID: 21630269

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Project description:BackgroundCarcinoma of unknown primary (CUP) is a subset of metastatic cancers in which the primary tissue source of the cancer cells remains unidentified. CUP is the eighth most common malignancy worldwide, accounting for up to 5% of all malignancies. Representing an exceptionally aggressive metastatic cancer, the median survival is approximately 3 to 6 months. The tissue in which cancer arises plays a key role in our understanding of sensitivities to various forms of cell death. Thus, the lack of knowledge on the tissue of origin (TOO) makes it difficult to devise tailored and effective treatments for patients with CUP. Developing quick and clinically implementable methods to identify the TOO of the primary site is crucial in treating patients with CUP. Noncoding RNAs may hold potential for origin identification and provide a robust route to clinical implementation due to their resistance against chemical degradation.ObjectiveThis study aims to investigate the potential of microRNAs, a subset of noncoding RNAs, as highly accurate biomarkers for detecting the TOO through data-driven, machine learning approaches for metastatic cancers.MethodsWe used microRNA expression data from The Cancer Genome Atlas data set and assessed various machine learning approaches, from simple classifiers to deep learning approaches. As a test of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive. We used permutation feature importance to determine the potential microRNA biomarkers and assessed them with principal component analysis and t-distributed stochastic neighbor embedding visualizations.ResultsOur results show that it is possible to design robust classifiers to detect the TOO for metastatic samples on The Cancer Genome Atlas data set, with an accuracy of up to 97% (351/362), which may be used in situations of CUP. Our findings show that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% (226/362) with decision trees to 93.2% (337/362) with logistic regression, finally achieving 97% (351/362) accuracy using deep learning on metastatic samples. On the Sequence Read Archive validation set, a lower accuracy of 41.2% (77/188) was achieved by the decision tree, while deep learning achieved a higher accuracy of 80.4% (151/188). Notably, our feature importance analysis showed the top 3 most important features for predicting TOO to be microRNA-10b, microRNA-205, and microRNA-196b, which aligns with previous work.ConclusionsOur findings highlight the potential of using machine learning techniques to devise accurate tests for detecting TOO for CUP. Since microRNAs are carried throughout the body via extracellular vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma. Our work serves as a foundation toward developing blood-based cancer detection tests based on the presence of microRNA.

Project description:Metastasis is responsible for the majority of cancer-related deaths. Particularly, challenging is the management of metastatic cancer of unknown primary site (CUP), whose tissue of origin (TOO) remains undetermined even after extensive investigations and whose therapy is rather unspecific and poorly effective. Molecular approaches to identify the most probable TOO of CUPs can overcome some of these issues. In this study, we applied a predetermined set of 89 microRNAs (miRNAs) to infer the TOO of 53 metastatic cancers of unknown or uncertain origin. The miRNA expression was assessed with droplet digital PCR in 159 samples, including primary tumors from 17 tumor classes (reference set) and metastases of known and unknown origin (test set). We combined two different statistical models for class prediction to obtain the most probable TOOs: the nearest shrunken centroids approach of Prediction Analysis of Microarrays (PAMR) and the least absolute shrinkage and selection operator (LASSO) models. The molecular test was successful for all formalin-fixed paraffin-embedded samples and provided a TOO identification within 1 week from the biopsy procedure. The most frequently predicted origins were gastrointestinal, pancreas, breast, lung, and bile duct. The assay was applied also to multiple metastases from the same CUP, collected from different metastatic sites: The predictions showed a strong agreement, intrinsically validating our assay. The final CUPs' TOO prediction was compared with the clinicopathological hypothesis of primary site. Moreover, a panel of 13 miRNAs proved to have prognostic value and be associated with overall survival in CUP patients. Our study demonstrated that miRNA expression profiling in CUP samples could be employed as diagnostic and prognostic test. Our molecular analysis can be performed on request, concomitantly with standard diagnostic workup and in association with genetic profiling, to offer valuable indications about the possible primary site, thereby supporting treatment decisions.

Project description:Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Dataset Information

MicroRNA profiling for the identification of cancers with unknown primary tissue-of-origin.

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MicroRNA profiling for the identification of cancers with unknown primary tissue-of-origin.

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