MicroRNA profiling by array of human cancers to identify cancers with unknown primary tissue-of-origin
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Ferracin M
PROVIDER: S-ECPF-TABM-1135 | biostudies-other | 2011 Sep
REPOSITORIES: biostudies-other
Similar Datasets
Project description: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.
| S-EPMC4325368 | biostudies-literature
Project description:Cancer of unknown primary (CUP), in which metastatic diseases exist without an identifiable primary location, accounts for about 3-5% of all cancer diagnoses. Successful diagnosis and treatment of such patients are difficult. This study aimed to assess the expression characteristics of 90 genes as a method of identifying the primary site from CUP samples. We validated a 90-gene expression assay and explored its potential diagnostic utility in 44 patients at Jiangsu Cancer Hospital. For each specimen, the expression of 90 tumor-specific genes in malignant tumors was analyzed, and similarity scores were obtained. The types of malignant tumors predicted were compared with the reference diagnosis to calculate the accuracy. In addition, we verified the consistency of the expression profiles of the 90 genes in CUP secondary malignancies and metastatic malignancies in The Cancer Genome Atlas. We also reported a detailed description of the next-generation coding sequences for CUP patients. For each clinical medical specimen collected, the type of malignant tumor predicted and analyzed by the 90-gene expression assay was compared with its reference diagnosis, and the overall accuracy was 95.4%. In addition, the 90-gene expression profile generally accurately classified CUP into the cluster of its primary tumor. Sequencing of the exome transcriptome containing 556 high-frequency gene mutation oncogenes was not significantly related to the 90 genes analysis. Our results demonstrate that the expression characteristics of these 90 genes can be used as a powerful tool to accurately identify the primary sites of CUP. In the future, the inclusion of the 90-gene expression assay in pathological diagnosis will help oncologists use precise treatments, thereby improving the care and outcomes of CUP patients.
| S-EPMC8529103 | biostudies-literature
Project description:Carcinomas of unknown primary (CUP) are characterized by early metastatic dissemination in the absence of a detectable primary tumor. This disease accounts for about 3% of all malignant tumors. Most CUPs are poorly responsive to chemotherapy and have a rapid fatal evolution. The biological mechanisms supporting metastatic growth in various sites combined with regression or absence of growth in the primary site are still poorly understood. The aim of this project was to investigate characteristics of gene expression profile specific of CUPs with special attention to genes overexpressed or silenced in CUPs but not in classical secondary metastases. Three series of experiments were performed in 2006 and 2007. In all experiments, the mRNA used as a reference was obtained from diploid untransformed human fibroblasts (MRC5). The CUP samples were 2 xenografted CUP tumors (Capi1 and Capi3) and 4 CUP biopsies including a squamous carcinoma (Aud) and 3 adenocarcinomas (Gal, Pro, Gag). Samples representative of secondary metastases were xenografted tumors derived from metastases of nasopharyngeal carcinoma (C17), lung adenocarcinoma (IC14) and pancreatic adenocarcinoma (xenografted Capan 1 cell line) and one biopsy from a breast carcinoma (Vuc).
| S-ECPF-TABM-1058 | biostudies-other
Project description:Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.
| S-EPMC11108774 | biostudies-literature
Project description: Not available
| S-EPMC8202556 | biostudies-literature
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.
| S-EPMC8486570 | biostudies-literature
Project description:IntroductionCancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings-in addition to ambiguous clinical presentations such as recurrence versus new primary-a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8-11 months.MethodsHere, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype.ResultsWe show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype.DiscussionCombining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.
| S-EPMC10300170 | biostudies-literature
Project description:Brain metastasis is one of the most feared complications of cancer and the most common intracranial malignancy in adults. Its underlying mechanisms remain unknown. From breast cancer patients with metastatic disease we isolated cell populations that aggressively colonize the brain. Transcriptomic analysis of these cells yielded overlapping gene sets whose expression is selectively associated with brain metastasis. The expression of seventeen of these genes in primary breast tumors is associated with brain relapse in breast cancer patients. Some of these genes are also associated with metastasis to lung but not to liver, bone or lymph nodes, providing a molecular basis for the long-observed link between brain and lung metastasis. Among the functionally validated brain metastasis genes, the cyclooxigenase COX-2, the EGFR ligand HB-EGF, and the brain-specific 2-6 sialyltransferase ST6GALNAC5 mediate cancer cell passage through the blood-brain barrier. Other brain metastasis genes encode inflammatory factors and brain-specific proteolytic regulators, suggesting a multifaceted program for breast cancer colonization of the brain. Experiment Overall Design: 204 primary tumors from breast cancer patients with known site of relapse were studied, focussing on brain relapse versus other relapse. Identified genes were validated in this cohort.
| S-ECPF-GEOD-12276 | biostudies-other
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.
| S-EPMC11306940 | biostudies-literature
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.
| S-EPMC10099529 | biostudies-literature