Search Results - EGA European Genome-Phenome Archive

Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

Study EGAS00001004861

Metformin for Oral Cancer Prevention

The M4OC-PREVENT clinical trial addressed whether metformin has utility in the prevention of oral cancer. It is a phase IIa clinical trial of metformin ER 2,000 mg per day given for 12-14 weeks to individuals with oral premalignant lesions, either oral leukoplakia or erythroplakia (which are precursors to oral cancer). The goal was to determine if metformin decreases the size of oral leukoplakia (clinical response). Additionally, effects on biomarkers of cancer risk and metformin effect were ascertained. The clinical trial was performed at the University of California San Diego Moores Cancer Center, the University of Minnesota, and the British Columbia Cancer Agency, with monitoring and oversight provided by the University of Arizona.

Study phs002437

Etiological Investigation of Cancer Susceptibility in Inherited Bone Marrow Failure Syndromes: A Natural History Study

Background: A prospective cohort of Inherited Bone Marrow Failure Syndrome (IBMFS) will provide new information regarding cancer rates and types in these disorders. Mutations in IBMFS genes are relevant to carcinogenesis in sporadic cancers. Patients with IBMFS who develop cancer differ in their genetic and/or environmental features from patients with IBMFS who do not develop cancer. These cancer-prone families are well suited for cancer screening and prevention trials targeting those at increased genetic risk of cancer. Carriers of IBMFS gene mutations are at increased risk of cancer. The prototype disorder is Fanconi Anemia (FA); other IBMFS will also be studied. Objectives: To determine the types and incidence of specific cancers in patients with an IBMFS. To investigate the relevance of IBMFS gene mutations in the carcinogenesis pathway of the sporadic counterparts of IBMFS-associated cancers. To identify risk factors for IBMFS-related cancers in addition to the primary germline mutations. To determine the risk of cancer in IBMFS carriers. Design: Natural history study, with questionnaires, clinical evaluations, clinical and research laboratory tests, review of medical records, cancer surveillance. Primary endpoints are all cancers, solid tumors, and cancers specific to each type of IBMFS. Secondary endpoints are markers of pre-malignant conditions, such as leukoplakia, serum or tissue evidence of carcinogenic viruses, and bone marrow morphologic myelodyplastic syndrome or cytogenetic clones.

Study phs001481

OncoArray: Follow-up of Ovarian Cancer Genetic Association and Interaction Studies (FOCI)

The Follow-up of Ovarian Cancer Genetic Association and Interaction Studies (FOCI) was one of five projects funded in 2010 as part of the NCI's Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative (http://epi.grants.cancer.gov/gameon/). FOCI represents a collective effort that builds upon the strengths and history of collaboration inherent in the Ovarian Cancer Association Consortium (OCAC), a multidisciplinary group comprised of epidemiologists, genetic epidemiologists, statistical geneticists, molecular and cell biologists and clinicians that was formed in 2005. The other four funded GAME-ON projects were: the ColoRectal TransdisciplinaryStudy (CORECT), Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE), Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE), and Transdisciplinary Research in Cancer of the Lung (TRICL). As part of our aim to discover, expand, and replicate ovarian cancer susceptibility loci, the GAME-ON projects and other consortia formed the OncoArray network (http://epi.grants.cancer.gov/oncoarray/) to develop and genotype a new custom genotyping array in large numbers of cancer cases and controls (over 400,000 samples) across multiple cancer types. The FOCI data includes over 50,000 ovarian cancer cases and controls genotyped with the Oncoarray at the Center for Inherited Disease Research (CIDR). Genotype calling and quality control procedures were performed under a standardized protocol across the Oncoarray consortium, and over 490,000 SNPs passed QC and are included under this dbGaP submission.

Study phs001882

Multidimensional Proteomics analysis of intractable cancers with prospective observational cohort for precision medicine

Multi-omics project of Non small cell Lung Cancer. Exome and RNAseq datasets were generated from normal-tumor pairs of Non small cell Lung Cancer patients from the retrospective cohort.

Study EGAS50000000592

germline variants in children with hematological cancer

Trio parent-child whole exome sequencing has been used to identify cancer predisposing syndrome (CPS) in our pediatric patients with hematological cancer, and it was identified in 3.8% of the total number of patients(131).

Study EGAS00001006907

The Oral Mycobiome and Risk of Pancreatic Cancer

The purpose of our study was to assess the influence of oral microbiota on the development of pancreatic cancer. Our preliminary case-control studies showed that oral carriage of a periodontal pathogen is related to increased risk for pancreatic cancer. We hypothesize that the oral bacteria and fungi are associated with risk of pancreatic cancer. We conducted a prospective study nested in two large well-established U.S. cohorts to determine whether the oral bacterial and fungal microbiome is associated with subsequent risk of pancreatic cancer.

Study phs003994

12086 results for "cancer rna-seq"

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