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Eligibility NCT00522834 Melanoma - Eligibility

- 20-09-21 - 1 modulo, 2 itemgroups, 13 elementi, 2 lingue
Itemgroups: Inclusion Criteria, Exclusion Criteria
ODM derived from http://clinicaltrials.gov/show/NCT00522834

Eligibility DRKS00003621 NCT00518895 Melanoma - Eligibility

- 20-09-21 - 1 modulo, 2 itemgroups, 16 elementi, 2 lingue
Itemgroups: Inclusion Criteria, Exclusion Criteria
ODM derived from http://clinicaltrials.gov/show/NCT00518895

Eligibility NCT00395070 Metastatic Melanoma - Eligibility

- 15-04-14 - 1 modulo, 2 itemgroups, 11 elementi, 2 lingue
Itemgroups: Inclusion Criteria, Exclusion Criteria
ODM derived from http://clinicaltrials.gov/show/NCT00395070

Eligibility NCT00844064 Pancreatic Neoplasms, Melanoma, Colorectal Neoplasms - Eligibility

- 20-09-21 - 1 modulo, 2 itemgroups, 33 elementi, 2 lingue
Itemgroups: Inclusion Criteria, Exclusion Criteria
ODM derived from http://clinicaltrials.gov/show/NCT00844064

dbGaP phs000559 PAGE: Epidemiologic Architecture for Genes Linked to Environment (EAGLE) - BioVU Cancer Project - Eligibility

- 12-12-22 - 5 moduli, 1 ItemGroup, 20 elementi, 1 linguaggio
ItemGroup: IG.elig
Principal Investigator: Dana Crawford, PhD, Vanderbilt University, Nashville, TN, USA MeSH: Neoplasms,Breast Neoplasms,Colorectal Neoplasms,Endometrial Neoplasms,Lung Neoplasms,Lymphoma, Non-Hodgkin,Ovarian Neoplasms,Melanoma,Prostatic Neoplasms https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000559 As part of Population Architecture using Genomics and Epidemiology PAGE study (Phase I), the Epidemiologic Architecture using Genomics and Epidemiology (EAGLE I) project accessed both epidemiologic- and clinic-based collections. The epidemiologic-based collection of EAGLE I included the National Health and Nutritional Examination Surveys (NHANES), ascertained between 1991-1994 (NHANES III), 1999-2002, and 2007-2008. NHANES is a population-based cross-sectional survey now conducted every year in the United States to assess the health status of Americans at the time of ascertainment and to assess trends over the years of survey. Genetic NHANES consists of 19,613 DNA samples linked to thousands of variables including demographics, health and lifestyle variables, physical examination variables, laboratory variables, and exposures. NHANES is diverse with almost one-half of the samples (46.4%) coming from self-reported Mexican Americans and non-Hispanic blacks. In contrast to NHANES, BioVU is a clinic-based collection of 150,000 DNA samples from Vanderbilt University Medical Center linked to de-identified electronic medical records (EMRs). Approximately 12% of BioVU's overall DNA sample collection is from African American, Hispanic, and Asian patients. The overall goals of PAGE I and EAGLE I were broad and several-fold:- Replicate genome-wide association study (GWAS)- identified variants in European Americans; - Identify population-specific and trans-population genotype-phenotype associations; - Identify genetic and environmental modifiers of these associations. NHANES is an excellent resource for the study of quantitative traits associated with common human diseases. However, given that the age range of NHANES spans childhood to late adulthood and not all diseases are surveyed, NHANES is less useful for the study of adult-onset diseases such as major cancers. Therefore, under American Recovery and Reinvestment Act (ARRA) funding, EAGLE as part of PAGE I defined eight major cancers sites for genetic analysis in BioVU, Vanderbilt's biorepository linked to de-identified EMRs. The eight major cancers defined for this study included melanoma, breast, ovarian, prostate, colorectal, lung, endometrial, and Non-Hodgkin's lymphoma (NHL). Cancer cases were defined using a combination of ICD-9 codes and tumor registry entries. Controls include BioVU participants without cancer and encompassing the age and gender distributions of cancer cases. Targeted genotyping of GWAS-identified variants for these diseases (124 SNPs) and ancestry informative markers (128 AIMs) was performed by the Center for Human Genetics Research Vanderbilt DNA Resources Core. After quality control, a total of 116 cancer-associated SNPs and 122 AIMs were available for downstream analyses.

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pht003615.v1.p1

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pht003616.v1.p1

1 ItemGroup 58 elementi

pht003617.v1.p1

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dbGaP phs001287 CPTAC 3 Study - Eligibility

- 13-03-23 - 3 moduli, 1 ItemGroup, 9 elementi, 1 linguaggio
ItemGroup: IG.elig
Principal Investigator: MeSH: Neoplasms https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001287 Recently, significant progress has been made in characterizing and sequencing the genomic alterations in statistically robust numbers of samples from several types of cancer. For example, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and other similar efforts are identifying genomic alterations associated with specific cancers (e.g., copy number aberrations, rearrangements, point mutations, epigenomic changes, etc.) The availability of these multi-dimensional data to the scientific community sets the stage for the development of new molecularly targeted cancer interventions. Understanding the comprehensive functional changes in cancer proteomes arising from genomic alterations and other factors is the next logical step in the development of high-value candidate protein biomarkers. Hence, proteomics can greatly advance the understanding of molecular mechanisms of disease pathology via the analysis of changes in protein expression, their modifications and variations, as well as protein=protein interaction, signaling pathways and networks responsible for cellular functions such as apoptosis and oncogenesis. Realizing this great potential, the NCI launched the third phase of the CPTC initiative in September 2016. As the Clinical Proteomic Tumor Analysis Consortium, CPTAC continues to define cancer proteomes on genomically-characterized biospecimens. The purpose of this integrative approach was to provide the broad scientific community with knowledge that links genotype to proteotype and ultimately phenotype. In this third phase of CPTAC, the program aims to expand on CPTAC II and genomically and proteomically characterize over 2000 samples from 10 cancer types (Lung Adenocarcinoma, Pancreatic Ductal Adenocarcinoma, Glioblastoma Multiforme, Acute Myeloid Leukemia, Clear cell renal Carcinoma, Head and Neck Squamous Cell Carcinoma, Cutaneous Melanoma, Sarcoma, Lung Squamous Cell Carcinoma, Uterine Corpus Endometrial Carcinoma) .Germline DNA is obtained from blood and Normal control samples for proteomics varied by organ site. All cancer samples were derived from primary and untreated tumor.

pht006104.v9.p5

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dbGaP phs000942 Use of WGS for Diagnosis and Discovery in the Cancer Genetics Clinic - Eligibility

- 27-11-24 - 5 moduli, 1 ItemGroup, 1 elemento, 1 linguaggio
ItemGroup: IG.elig
Principal Investigator: Theodora S. Ross, MD, PhD, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA, and Department of Cancer Genetics, UT Southwestern Medical Center, Dallas, TX, USA MeSH: Neoplasms,Breast Neoplasms,Ovarian Neoplasms,Peritoneal Neoplasms,Skin Neoplasms,Esophageal Neoplasms,Thyroid Neoplasms,Urinary Bladder Neoplasms,Endometrial Neoplasms,Fallopian Tube Neoplasms,Melanoma,Testicular Neoplasms,Bile Duct Neoplasms,Lung Neoplasms,Colonic Neoplasms,Adrenocortical Carcinoma,Carcinoma, Renal Cell,Colonic Polyps,Adenomatous Polyposis Coli,Lymphoma, Large B-Cell, Diffuse,Pheochromocytoma,Paraganglioma,Leiomyoma,Hemangioblastoma,Hyperparathyroidism,Pancreatic Neoplasms,Vulvar Neoplasms,Brain Neoplasms,Liver Neoplasms,Kidney Neoplasms,Prostatic Neoplasms,Glioblastoma,Oncocytoma, renal https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000942 Despite the potential of whole-genome sequencing (WGS) to improve patient diagnosis and care, the empirical value of WGS in the cancer genetics clinic is unknown. We performed WGS on members of two cohorts of cancer genetics patients: those with BRCA1/2 mutations (n = 176) and those without (n = 82). Initial analysis of potentially pathogenic variants (PPVs, defined as nonsynonymous variants with allele frequency 1% in ESP6500) in 163 clinically-relevant genes suggested that WGS will provide useful clinical results. This is despite the fact that a majority of PPVs were novel missense variants likely to be classified as variants of unknown significance (VUS). Furthermore, previously reported pathogenic missense variants did not always associate with their predicted diseases in our patients. This suggests that the clinical use of WGS will require large-scale efforts to consolidate WGS and patient data to improve accuracy of interpretation of rare variants. While loss-of-function (LoF) variants represented only a small fraction of PPVs, WGS identified additional cancer risk LoF PPVs in patients with known BRCA1/2 mutations and led to cancer risk diagnoses in 21% of non-BRCA cancer genetics patients after expanding our analysis to 3209 ClinVar genes. These data illustrate how WGS can be used to improve our ability to discover patients' cancer genetic risks. "Reprinted from doi:10.1016/j.ebiom.2014.12.003, with permission from EBioMedicine."

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pht004836.v1.p1

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pht004837.v1.p1

1 ItemGroup 5 elementi

Cancer (clinical) DSS Metadata Online Registry (METeOR)

- 03-08-16 - 1 modulo, 16 itemgroups, 65 elementi, 1 linguaggio
Itemgroups: Chemotherapy for cancer cluster, Hormone therapy for cancer cluster, Immunotherapy for cancer cluster, Radiotherapy for cancer cluster, Surgery for cancer cluster, Systemic therapy procedure for cancer cluster, Cancer staging, Cancer treatment, Date, Establishment, Healthcare provider, Patient, Person (address), Person (name), Person with cancer, Person
Health sector data set specifications from METeOR, Australia's repository for national metadata standards, developed by the Australian Institute of Health and Welfare (http://meteor.aihw.gov.au/content/index.phtml/itemId/345165) Cancer (clinical) DSS The purpose of the Cancer (clinical) data set specification (C(C)DSS) is to define data standards for the national collection of clinical cancer data so that data collected is consistent and reliable. Collection of this data set specification is not mandated but it is recommended as best practice if clinical cancer data are to be collected. It will facilitate more consistent data collection while enabling individual treatment centres or health service areas to develop data extraction and collection processes and policies that are appropriate for their service settings. Mandatory reporting regulations have enabled population-based cancer registries in Australia to collect standard information on all incident cases of cancer apart from non-melanoma skin cancers, from which incidence, mortality and overall survival have been determined and trends monitored. The Cancer (clinical) data set specification provides a framework for the collection of more detailed and comprehensive clinical data such as stage of cancer at diagnosis, other prognostic characteristics, cancer treatment and patient outcomes. The Cancer (clinical) data set specification will support prospective data collection from the time a person with cancer symptoms is referred or first presents to a hospital or specialist through the entire duration of their illness. The majority of data items in the Cancer (clinical) data set specification are applicable to most solid tumours while many are also relevant to the haematopoietic malignancies such as leukaemia and lymphoma. Data set specifications for specialist tumour streams are also under development and these will contain supplementary data elements that will capture the special features of specific cancer types. The definitions used in this data set specification are designed to capture the provision of cancer care on a day-to-day level. They relate to the cancer care pathway and the need to optimise care by correctly diagnosing, evaluating and managing patients with cancer. In addition, end-points and patterns of care can be monitored to understand both the appropriateness and effectiveness of cancer care. The data elements specified provide a framework for: • promoting the delivery of evidence-based care to patients with cancer • facilitating the ongoing improvement in the quality and safety of cancer management in treatment settings • improving the epidemiological and public health understanding of cancer • informing treatment guidelines and professional education • guiding resource planning and the evaluation of cancer control activities They will facilitate the aggregation of data across different treatment centres. The underlying long-term goal is to provide data support to improve outcomes for patients by increasing the quality and length of life. For example, a comparison of the actual management of patients with best practice guidelines may identify shortfalls in treatment and limitations in access to treatment modalities for some patients. The working group formed under the stewardship of Cancer Australia was diverse and included representation from the following organisations: Cancer Australia, University of Sydney-Department of Gynaecological Oncology, Westmead Institute for Cancer Research, Cancer Council Victoria, Royal Brisbane & Women’s Hospital, National Breast and Ovarian Cancer Centre, The Royal Women's Hospital, Queensland Health, Ministry of Health, NSW Health, TROG Cancer Research, and the Cancer Institute NSW. To ensure the broad acceptance of the data set specification, the proposed list of data items was circulated to members of Cancer Australia’s National Cancer Data Strategy Advisory Group, a multidisciplinary group with a broad spectrum of epidemiological knowledge and expertise, and the inter-governmental Strategic Forum, comprising clinicians and senior health department officials from the Australian Government and from each state and territory government, and with strong community representation. The working group also sought consultation from cancer registry data managers, clinical leaders, pathologists, medical oncologists and radiation oncologists to achieve consensus when required. The Cancer (clinical) data set specification is intended to only describe data collected in relation to the initial course of cancer treatment. The initial course of treatment includes all treatments administered to the patient from diagnosis and before disease progression or recurrence. © Australian Institute of Health and Welfare 2015 Metadata and Classifications Unit Australian Institute of Health and Welfare GPO Box 570 Canberra ACT 2601

dbGaP phs000495 The Gene Partnership (TGP) - eMERGE Data - Eligibility

- 13-12-22 - 5 moduli, 1 ItemGroup, 7 elementi, 1 linguaggio
ItemGroup: IG.elig
Principal Investigator: Isaac S. Kohane, MD, PhD, Boston Children's Hospital, Boston, MA, USA MeSH: Autistic Disorder,Heart Defects, Congenital,Asthma,Attention Deficit Disorders,Diabetes Mellitus, Type 1,Diabetes Mellitus, Type 2,Epilepsy,Gastrointestinal Diseases,Hypersensitivity,Autoimmune Diseases,Hematologic Diseases,Neoplasms,Arrhythmias, Cardiac,Chromosome Aberrations,Congenital Abnormalities,Dermatology,Developmental Disabilities,Endocrine System,Otolaryngology,Syndrome,Urogenital System,Hearing Loss,Immune System Diseases,Musculoskeletal Abnormalities,Nervous System Diseases,Neuromuscular Diseases,Metabolic Diseases,Nutrition Disorders,Vision Disorders,Mouth Diseases,Mental Disorders,Kidney Diseases,Respiration Disorders,Thyroid Diseases,Vascular Diseases https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000495 The Gene Partnership (TGP) is a prospective longitudinal registry at Boston Children's Hospital (BCH) to study the genetic and environmental contributions to childhood health and disease, collect genetic information on a large number of children who have been phenotyped, and implement the Informed Cohort and the Informed Cohort Oversight Board (ICOB). The term "*The Gene Partnership*" reflects a partnership between researchers and participants. Children seen at BCH are offered enrollment, as are their parents and siblings. DNA is collected on all enrollees. BCH has a comprehensive EMR system, and virtually all inpatient and outpatient data are captured electronically. Clinical data in the BCH EMR is loaded in the i2b2 data warehouse which is available to investigators. Cases, phenotypes, and covariates are ascertained using the i2b2 database. Participants at BCH in TGP have consented to receive any research result and/or incidental finding that arises from studies using TGP that is approved by the Informed Cohort Oversight Board (ICOB) and is in accordance with the participants' preferences; results are returned through the Personally Controlled Health Record (PCHR). BCH and Cincinnati Children's Hospital Medical Center (CCHMC) have partnered as the *P*ediatric *A*lliance for *G*enomic and *E*lectronic Medical Record (EMR) *R*esearch (*PAGER*) site for the eMERGE Phase II network for pediatric institutions, and the cohort for eMERGE at BCH is TGP.

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pht002867.v1.p1

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