Cd25ec3b-24e4-4260-afb6-7e65f4fadb73 - samples
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PROVIDER: EGAD00001009790 | EGA |
REPOSITORIES: EGA
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Project description:Background There is a lack of tools for identifying the site of origin in mucinous cancer. This study aimed to evaluate the performance of a transcriptome-based classifier for identifying the site of origin in mucinous cancer. Methods Transcriptomic data of 1,878 non-mucinous and 82 mucinous cancer specimens, with 7 sites of origin, namely, the uterine cervix (CESC), colon (COAD), pancreas (PAAD), stomach (STAD), uterine endometrium (UCEC), uterine carcinosarcoma (UCS), and ovary (OV), obtained from The Cancer Genome Atlas, were used as the training and validation sets, respectively. Transcriptomic data of 14 mucinous cancer specimens from a tissue archive were used as the test set. For identifying the site of origin, a set of 100 differentially expressed genes for each site of origin was selected. After removing multiple iterations of the same gene, 626 genes were chosen, and their RNA expression profiles, at each site of origin, were used to train the deep neural network classifier. The performance of the classifier was estimated using the training, validation, and test sets. Results The accuracy of the model in the training set was 0.977, while that in the validation set was 0.939 (77/82). In the test set, the model showed an accuracy of 0.857 (12/14). t-SNE analysis revealed that samples in the test set were part of the clusters obtained for the training set. Conclusion Although limited by small sample size, we showed that a transcriptome-based classifier could correctly identify the site of origin of mucinous cancer.
2020-12-15 | GSE163126 | GEO
Project description:53 tumors used in the clinical training and validation of a CUP machine learning classifier using methylation profiling
2025-01-15 | GSE249688 | GEO
Project description:53 tumors used in the clinical training and validation of a CUP machine learning classifier using methylation profiling
2025-01-15 | GSE249711 | GEO
Project description:We aimed to investigate the diagnostic potential of tumor educated platelets in ESCC. In this study, seventy-one ESCC patients and eighty healthy individuals were enrolled and divided into training cohort (23 patients and 27 healthy individuals) and validation cohort (48 patients and 53 healthy individuals). Next-generation RNA sequencing was performed on platelets isolated from peripheral blood of all participants and a support vector machine/leave-one-out cross validation (SVM/LOOCV) approach was used for binary classification. A diagnostic signature composed of ARID1A, GTF2H2 and PRKRIR discriminated ESCC patients from healthy individuals with 91.3% sensitivity and 85.2% specificity in the training cohort and 87.5% sensitivity and 81.1% specificity in the validation cohort. The AUC was 0.924 (95% CI, 0.845–0.956) and 0.893 (95% CI, 0.821–0.966), respectively, in the training cohort and validation cohort. This 3-gene platelet RNA signature could effectively discriminate ESCC from healthy control. Our data highlighted the potential of tumor educated platelets for the noninvasive diagnosis of ESCC. Moreover, we found keratin and collagen protein families, and ECM-related pathways might be involved in tumor progression and metastasis of ESCC, which might provide insights to understand ESCC pathobiology and advance novel therapeutics.
2022-06-01 | GSE197514 | GEO
Project description:There are currently no screening methods for high grade ovarian cancer (HGOC) that guarantee effective early detection for high risk women such as germline BRCA mutation carriers. Therefore, the standard-of-care remains risk reducing salpingo-oophorectomy (RRSO) around age 40. Proximal liquid biopsy has been shown to be a promising source of biomarkers, but sensitivity has not yet qualified for clinical implementation. We report the discriminant performance of a novel proteomic classifier for detection of HGOC in high-risk population, and the safety and feasibility of simplified utero-tubal lavage (UtL) as a method for sampling proximal liquid biopsy.The training set included 93 women with high-risk for HGOC (BRCA1 and BRCA2 mutation carriers), including: 16 HGOC patients and 77 asymptomatic women, who donated UtL liquid biopsies, in 3 Israeli medical centers (Biomarkers for Early Detection of Ovarian Cancer Using Uterine Lavage (BEDOCA); ClinicalTrials.gov Identifier: NCT03150121). The proteome of the microvesicle fraction of the samples was profiled by mass spectrometry and a classifier was developed using logistic regression. An independent cohort of 104 BRCA mutation carriers was used as validation. Safety information was collected for all women who opted to UtL in a clinic setting.
2022-09-13 | PXD030390 | Pride
Project description:Background We have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites. Methods For this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain. Results The updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition. Conclusions The updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.
2024-11-20 | GSE252130 | GEO
Project description:Purpose: Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population. Method:A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 235 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance.
2014-11-13 | GSE62116 | GEO
Project description:The 2021 WHO Classification of Tumors of the Central Nervous System includes several tumor types and subtypes for which the diagnosis is at least partially reliant on utilization of whole genome methylation profiling. The current approach to array DNA methylation profiling utilizes a reference library of tumor DNA methylation data, and a machine learning-based tumor classifier. This approach was pioneered and popularized by the German Cancer Research Network (DKFZ) and University Hospital Heidelberg. This research group has kindly made their classifier for central nervous system tumors freely available as a research tool via a web-based portal. However, this classifier is not maintained in a clinical testing environment. Therefore, we validated our own DNA methylation-based classifier of central nervous system tumors. We validated our classifier using the same training and validation datasets as the DKFZ group. In addition, we performed a validation of samples tested in our own laboratory and compared the performance of both classifiers. Using the validation data set, our classifier’s performance showed high concordance (92%) and comparable accuracy (specificity 94.0% v. 84.9% for DKFZ, sensitivity 88.6% v. 94.7% for DKFZ). Receiver operator curve showed areas under the curve of 0.964 v. 0.966 for NM and DKFZ classifiers, respectively. Our classifier performed comparably well with samples tested in our own laboratory and is currently offered for clinical testing.
2022-03-19 | GSE198855 | GEO
Project description:To identify and assess exosomal miRNA signatures with potential to predict individuals with persistent organ failure (POF) at early phase of acute pancreatitis. We analyzed serum collected from 790 AP patients. In discovery cohort, we profiled exosomal miRNAs in sera sampled from AP patients with or without POF (5 vs. 5) using microarrays and identified a list of miRNAs with increased expression pattern. Of notes, 10 AP samples with/without POF are collected within 24 hours after AP onset and later did/didn’t develop POF. We further constructed a miRNA classifier (Cmi) through logistic regression and identified certain individual miRNAs (OR>2) as candidate predictive markers in the training cohort of 227 AP samples. Predictive performance of these markers were validated in three independent cohorts (255, 226 and 78 AP samples respectively).
2024-08-01 | GSE136030 | GEO
Project description:How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, we present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's cross-behavior potential, when exposed to novel biocide and antibiotic combinations, led to accelerated knowledge discovery with predictive models that are more accurate while needing 44% fewer data to train. Selecting experiments favoring broader exploration followed by fine-tuning emerged as the optimal strategy. This led to the discovery of 29 cross-protection and 4 cross-vulnerability conditions, with further validation revealing the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to automate omics data collection for training accurate predictive models, evidence-driven decision making and accelerated knowledge discovery in life sciences.
2020-07-09 | GSE144604 | GEO