Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Accurate classification of metastatic brain tumors using a novel microRNA-based test.

ABSTRACT:

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.

SUBMITTER: Mueller WC

PROVIDER: S-EPMC3228095 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

DNA methylation-based classification of sinonasal tumors [test set]

Project description:The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, whereas tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid cystic carcinomas are highly aggressive. Our findings have the potential to dramatically improve the diagnostic classification of sinonasal tumors and will fundamentally change the current perception of SNUCs.

2022-09-25 | GSE196227 | GEO

Robust methylation based classification of brain tumors using nanopore sequencing

Project description:Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined.

2022-07-30 | GSE209865 | GEO

Accurate primary germ cell cancer diagnosis using serum based microRNA detection (ampTSmiR test).

Project description:Multiple studies, including various methods and overall limited numbers of mostly heterogeneous cases, indicate that the level of embryonic stem cell microRNAs (miRs) (e.g. 371a-3p, 372-3p, 373-3p, and 367-3p) are increased in serum at primary diagnosis of almost all testicular germ cell cancer (TGCC). Here we determine the status of three of these miRs in serum samples of 250 TGCC patients, collected at time of primary diagnosis, compared with 60 non-TGCC patients and 104 male healthy donors. The levels of miRs were measured by the robust ampTSmiR test, including magnetic bead-based miR isolation and target specific pre-amplification followed by real-time quantitative PCR (RT-qPCR) detection. Calibration is performed based on the non-human spike-in ath-miR-159a, and normalization on the endogenous control miR-30b-5p. The serum levels of miR-371a-3p, 373-3p, and 367-3p are informative to accurately detect TGCC patients, both seminomas and non-seminomas, at the time of primary diagnosis (p< 0.000). Receiver Operating Characteristic (ROC) analysis demonstrate that the Area Under the Curve (AUC) for miR-371a-3p is 0.951 (being 0.888 for miR-373-3p and 0.861 for miR-367-3p), with a sensitivity of 90%, and a specificity of 86% (positive predictive value of 94% and negative predictive value of 79%). Inclusion of miR-373-3p and 367-3p resulted in a AUC of 0.962, with a 90% sensitivity and 91% specificity. Similar results were obtained using the raw Ct data. Importantly, the results demonstrate that ampTSmiR is not suitable to detect pure teratoma as well as the precursor of TGCC, i.e., Germ Cell Neoplasia In Situ (GCNIS). The largest series evaluated so far, demonstrate that detection of the embryonic stem cell miR-371a-3p, 373-3p and 367-3p is highly informative to diagnose patients with a primary TGCC.

| S-EPMC5601631 | biostudies-literature

Epigenetic profiling for the molecular classification of metastatic brain tumors.

Project description:Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet this challenge, here we profile DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n = 96). Using supervised machine learning and integration of DNA methylomes from normal, primary, and metastatic tumor specimens (n = 1860), we unravel epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically relevant subtypes. BrainMETH predictions are supported by routine histopathologic evaluation. We further characterize and validate the most predictive genomic regions in a large cohort of brain tumors (n = 165) using quantitative-methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

| S-EPMC6219520 | biostudies-literature

Robust methylation based classification of brain tumors using nanopore sequencing

Project description:Robust methylation based classification of brain tumors using nanopore sequencing

| PRJNA862740 | ENA

Epigenetic profiling for the molecular classification of metastatic brain tumors

Project description:Genome-wide DNA methylation profiling of brain metastases from lung cancer, breast cancer, and melanoma samples. The Illumina Infinium 450K Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 450,000 methylation sites in formalin-fixed paraffin-embedded (FFPE) samples from brain metastases. Samples included 30 breast cancer brain metastases, 18 lung cancer brain metastases, 37 melanoma brain metastases, and 4 samples with brain metastases from patients with uncertain primary.

2018-08-28 | GSE108576 | GEO

DNA methylation-based classification of sinonasal tumors [test set]

Project description:DNA methylation-based classification of sinonasal tumors [test set]

| PRJNA803928 | ENA

CPO Complete, a novel test for fast, accurate phenotypic detection and classification of carbapenemases.

Project description:Carbapenemase-producing organisms (CPOs) are Gram-negative bacteria that are typically resistant to most or all antibiotics and are responsible for a global pandemic of high mortality. Rapid, accurate detection of CPOs and the classification of their carbapenemases are valuable tools for reducing the mortality of the CPO-associated infections, preventing the spread of CPOs, and optimizing use of new ?-lactamase inhibitor combinations such as ceftazidime/avibactam, meropenem/vaborbactam and imipenem/relebactam. The current study evaluated the performance of CPO Complete, a novel, manual, phenotypic carbapenemase detection and classification test. The test was evaluated for sensitivity and specificity against 262 CPO isolates of Enterobacteriaceae, Pseudomonas aeruginosa and Acinetobacter baumannii and 67 non-CPO isolates. It was also evaluated for carbapenemase classification accuracy against 205 CPOs that produced a single carbapenemase class. The test exhibited 100% sensitivity 98.5% specificity for carbapenemase detection within 90 minutes and detected 74.1% of carbapenemases within 10 minutes. In the classification evaluation, 99.0% of carbapenemases were correctly classified for isolates that produced a single carbapenemase. The test is technically simple and has potential for adaptation to automated instruments. With lyophilized kit storage at temperatures up to 38°C, the CPO Complete test has the potential to provide rapid, accurate carbapenemase detection and classification in both limited resource and technologically advanced laboratories.

| S-EPMC6905549 | biostudies-literature

Epigenetic profiling for the molecular classification of metastatic brain tumors

Project description:Epigenetic profiling for the molecular classification of metastatic brain tumors

| PRJNA427718 | ENA

Classification of developmental toxicants in a human iPSC transcriptomics-based test

Project description:There is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we used the hiPSC-based UKK-24h-test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. All compounds were analyzed at the maximal plasma concentration (Cmax) and the 20-fold Cmax for a 24 h incubation period in three independent experiments. Using the 1,000 probe sets with the highest variability and including information on cytotoxicity, a penalized logistic regression based classifier (top-1,000 classifier) reached an AUC, accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96 and 0.88, respectively to correctly classify the compounds as teratogens or non-teratogens. This top-1,000 classifier reached a higher AUC, accuracy and sensitivity than a second classifier (SPS-classifier), which used the number of significant probe sets to classify the compounds. At least, the SPS-classifier had a specificity of 1. Classification at 20-fold Cmax did not lead to better performance metrics than testing at 1-fold Cmax. Finally, inclusion of cytotoxicity information into the classifier clearly improved the performance metrics. In conclusion, although further optimization by additional readouts and inclusion of cell systems that model different developmental processes is required, the UKK-24h-test will support the early discovery-phase detection of human developmental toxicants already in its present form.

2022-06-24 | GSE187001 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data