Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.
In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial. Imaging Source Site ISS Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource.
We would like to acknowledge the individuals and institutions that have provided data for this collection:. TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:.
Lucchesi, F. The Cancer Imaging Archive. TCIA maintains a list of publications which leverage our data. At this time we are not aware of any manuscripts based on this data.
Evaluate Confluence today. Space shortcuts How-to articles Troubleshooting articles. Child pages. Browse pages. A t tachments 7 Page History. Dashboard Wiki Collections. Jira links. Acknowledgements We would like to acknowledge the individuals and institutions that have provided data for this collection:.
Detailed Description Image Statistics. Data Citation Lucchesi, F. No labels.An overview of the data model, including a visual representation of its components, is provided on the GDC website.
This section provides technical details about its implementation for data users, submitters, and developers. Each entity in the GDC has a set of properties and links. The GDC Data Dictionary determines which properties and links an entity can have according to entity type. Functionally similar entity types are grouped under the same category.
When an entity is created, it is assigned a unique identifier in the form of a version 4 universally unique identifier UUID. Programs are the highest level of organization of GDC datasets.Google contacts
Each program is assigned a unique program. Datasets within a program are organized into projects, and each project is assigned a project. This property can contain any string that the submitter wishes to use to identify the entity e.
This can be used to identify a corresponding entry in the submitter's records. For more information see Data Access Processes and Tools. For more information see Data Submission Processes and Tools.Letter from ceo to clients
Properties are key-value pairs associated with an entity. Properties cannot be nested, which means that the value must be numerical, boolean, or a string, and cannot be another key-value set.
Properties can be either required or optional. The following properties are of particular importance in constructing the GDC Data Model: Type is a required property for all entities.
System properties are properties used in GDC system operation and maintenance. They cannot be modified except under special circumstances. Unique keys are properties, or combinations of properties, that can be used to uniquely identify the entity in the GDC.
See GDC Identifiers below for details. Links define relationships between entities, and the multiplicity of those relationships e. Next: Data Security Previous: Introduction.Lymphangioleiomyomatosis LAM is a rare, progressive and systemic disease that typically results in cystic lung destruction.
It predominantly affects women, especially during childbearing years. The average age of onset is the early to mid 30s.
What Is Acute Myeloid Leukemia (AML)?
Diagnosis is typically delayed 5 to 6 years. Lung destruction in LAM is a consequence of diffuse infiltration by neoplastic smooth muscle-like cells that invade all lung structures including the lymphaticsairway walls, blood vessels and interstitial spaces. The typical disease course displays progressive dyspnea on exertion, spaced by recurrent pneumothoraces and in some patients, chylous pleural effusions or ascites.
Most people have dyspnea on exertion with daily activities by 10 years after symptom onset. Many patients require supplemental oxygen over that interval. LAM cells behave, in many ways, like metastatic tumor cells. Lung remodeling may be mediated by an imbalance between matrix degrading metalloproteinases MMPs and their endogenous inhibitors TIMPs. Clinical and histopathological evidence demonstrate the lymphatic involvement in LAM. LAM can come to medical attention in several ways, most of which trigger a chest CT.
Thin-walled cystic change in the lungs may be found incidentally on CT scans of the heart, chest or abdomen on the cuts that include lung bases obtained for other purposes.
Progressive dyspnea on exertion without the exacerbations and remissions that are characteristic of asthma or COPD sometimes prompt a chest CT. If none of these clinical features are present, a biopsy may be necessary to make the diagnosis. Cytology of chylous fluids, aspirated abdominal nodes or lymphatic masses can also be diagnostic. Diagram 1 outlines a proposed algorithm for the diagnosis of LAM.
The chest radiograph may appear relatively normal, even late in the disease, or may suggest hyperinflation only. As the disease progresses, the chest radiograph often demonstrates diffuse, bilateral and symmetric reticulonodular opacities, cysts, bullae or a "honeycomb" i.
Preservation of lung volumes in the presence of increased interstitial markings is a radiographic hallmark of LAM that helps distinguish it from most other interstitial lung diseases, in which alveolar septal and interstitial expansion tend to increase the lung's elastic recoil properties and decreased lung volumes. The high-resolution computed tomography HRCT chest scan is better than the chest radiograph to detect cystic parenchymal disease and is almost always abnormal at the time of diagnosis, even when the chest radiograph and pulmonary function assessments are normal.
In one study ventilation-perfusion scans were abnormal in 34 of 35 LAM patients. Fat density within a renal mass is pathognomonic of AMLs.Naya nepali ketiharu sex
Pulmonary function testing in patients with LAM may be normal or may reveal obstructive, restrictive or mixed patterns. Obstructive physiology is the most common abnormality. Cardiopulmonary exercise testing in a much larger cohort of patients with LAM revealed a reduced maximal oxygen consumption VO 2 max and anaerobic threshold in patients.
In most patients, exercise was thought to be ventilation limited, owing to airflow obstruction and increased dead-space ventilation. Disease progression is usually accompanied by a progressive obstructive ventilatory defect. Decline in FEV1 is the most commonly used parameter to monitor disease progression.
Although resting pulmonary hypertension appears to be unusual in LAM, pulmonary arterial pressure often rises with low levels of exercise, related in part to hypoxemia. Grossly, LAM lungs are enlarged and diffusely cystic, with dilated air spaces as large as several centimeters in diameter. LAM lesions often contain an abundance of lymphatic channels, forming an anastomosing meshwork of slit-like spaces lined by endothelial cells.
LAM cells generally expand interstitial spaces without violating tissue planes but have been observed to invade the airways, the pulmonary artery, the diaphragm, aorta, and retroperitoneal fat, to destroy bronchial cartilage and arteriolar walls, and to occlude the lumen of pulmonary arterioles. There are two major cell morphologies in the LAM lesion: small spindle-shaped cells and cuboidal epithelioid cells.
The cuboidal cells within LAM lesions also react with a monoclonal antibody called HMB, developed against the premelanosomal protein gp, an enzyme in the melanogenesis pathway.
The Cancer Genome Atlas Program
Estrogen and progesterone receptors are also present in LAM lesions,    but not in adjacent normal lung tissue.The Cancer Genome Atlas TCGAa landmark cancer genomics program, molecularly characterized over 20, primary cancer and matched normal samples spanning 33 cancer types.
This joint effort between the National Cancer Institute and the National Human Genome Research Institute began inbringing together researchers from diverse disciplines and multiple institutions. Over the next dozen years, TCGA generated over 2. The data, which has already lead to improvements in our ability to diagnose, treat, and prevent cancer, will remain publicly available for anyone in the research community to use.
TCGA has changed our understanding of cancer, how research is conducted, how the disease is treated in the clinic, and more.Pompe funebri santarossa
A collection of cross-cancer analyses delving into overarching themes on cancer, including cell-of-origin patterns, oncogenic processes and signaling pathways. Published in at the program's close.
An overview of the 33 different cancers types TCGA selected for study and the criteria used to select them. Some of the data processing, visualization, and other computational tools developed by TCGA network researchers and collaborators. Descriptions and supporting materials for each of the sequencing platforms and other technologies used to generate the TCGA dataset.
The events leading up to TCGA's inception in and major milestones in the program's history. Menu Contact Dictionary Search. Understanding Cancer. What Is Cancer? Cancer Statistics.
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Leukemia is a cancer of the bone marrow, where new blood cells are made. As the disease progresses, leukemia cells are usually found in the bone marrow and in the blood. While there are several types of leukemia, they all start with problems in the creation of blood cells. In a healthy person, immature stem cells in the bone marrow develop through several stages, eventually forming healthy, mature blood cells. These cell types include:.
When a person develops leukemia, one of the stem cells becomes abnormal or cancerous at some point in its development. It then multiplies uncontrollably. This leaves the patient prone to infection, at increased risk for bleeding, weak and sometimes short of breath.
Most leukemia cases fall into one of two categories, chronic and acute. Chronic leukemia involves mature or partially mature cells and is slow growing.
Acute leukemia impacts immature cells and is more aggressive. The disease is further broken down by the type of blood stem cell involved: myeloid or lymphoid. Of the four primary types of leukemia, acute lymphoblastic leukemia ALL is the least common.
About 6, new cases are diagnosed in the United States each year. While people of all ages develop ALL, a majority of new diagnoses are in people under age ALL impacts lymphoid stem cells. In healthy bone marrow, lymphoid stem cells, or lymphoblasts, form mature lymphocytes, a type of disease-fighting white blood cell.
About Acute Myeloid Leukemia (AML)
In ALL, lymphoid stem cells make diseased lymphoblasts, which rapidly grow. They eventually take over the bone marrow and cause high counts in the blood. ALL cells are immature and grow rapidly. They are poor at fighting infection and crowd out healthy cells.Cancer starts when cells in a part of the body begin to grow out of control. There are many kinds of cancer. Cells in nearly any part of the body can become cancer.
To learn more about cancer and how it starts and grows, see What Is Cancer? Leukemias are cancers that start in cells that would normally develop into different types of blood cells. Most often, leukemia starts in early forms of white blood cells, but some leukemias start in other blood cell types. There are several types of leukemia, which are divided based mainly on whether the leukemia is acute fast growing or chronic slower growingand whether it starts in myeloid cells or lymphoid cells.
Acute myeloid leukemia AML starts in the bone marrow the soft inner part of certain bones, where new blood cells are madebut most often it quickly moves into the blood, as well. It can sometimes spread to other parts of the body including the lymph nodes, liver, spleen, central nervous system brain and spinal cordand testicles.
Most often, AML develops from cells that would turn into white blood cells other than lymphocytesbut sometimes AML develops in other types of blood-forming cells.
Acute myeloid leukemia AML has many other names, including acute myelocytic leukemia, acute myelogenous leukemia, acute granulocytic leukemia, and acute non-lymphocytic leukemia. Bone marrow is the soft inner part of certain bones. It is made up of blood-forming cells, fat cells, and supporting tissues. A small fraction of the blood-forming cells are blood stem cells. Inside the bone marrow, blood stem cells develop into new blood cells. During this process, the cells become either lymphocytes a kind of white blood cell or other blood-forming cells, which are types of myeloid cells.
Myeloid cells can develop into red blood cells, white blood cells other than lymphocytesor platelets. These myeloid cells are the ones that are abnormal in AML.For a decade, The Cancer Genome Atlas TCGA program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11, human tumors across 33 different cancer types.
TCGA clinical data contain key features representing the democratized nature of the data collection process.
In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
In Brief Analysis of clinicopathologic annotations for over 11, cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types.
The purpose of The Cancer Genome Atlas TCGA project was to establish a coordinated team science effort to comprehensively characterize the molecular events in primary cancers and to provide these data to the public for use by researchers around the world. TCGA started in with a 3-year pilot project focusing on glioblastoma multiforme GBMlung squamous cell carcinoma LUSCand ovarian serious cystadenocarcinoma OVfollowed by the execution of the full project from to By the end of this year project, TCGA network investigators had characterized the molecular landscape of tumors from 11, patients across 33 cancer types and defined their many molecular subtypes.
The quantity and quality of TCGA molecular data have been lauded by a large number of scientists, and these data have resulted in studies that have significantly advanced our understanding of cancer biology, as documented in dozens of highly cited published TCGA marker and companion papers, including those for GBM, OV, and breast, lung, prostate, bladder, and other individual cancers Cancer Genome Atlas Network,; The Cancer Genome Atlas Research Network, ; Cancer Genome Atlas Research Network et al.
TCGA data also make possible studies that compare and contrast multiple cancer types with the goal of identifying common themes that transcend the tissue of origin and may inform precision oncology Hoadley et al.
In addition, numerous independent investigators have used TCGA as a resource to support their own studies and to help interpret molecular testing of individual patients in a clinical setting Huo et al. However, obtaining comprehensive clinical annotation was neither a primary program objective nor a practical possibility, given the worldwide scope and severe time constraints for sample accrual goals determined at the time of TCGA program initiation and funding. The incomplete annotation of patient outcome and treatment data associated with each TCGA-acquired sample, with its relatively short-term clinical follow-up interval, has been noted by the research community Hoadley et al.
GDC Data Model
The limitations of the existing clinical dataset, associated with an otherwise rich body of genomic and molecular analyses available across all TCGA tumor types, compels thorough and systematic curation and evaluation of those clinical endpoints and other clinical features associated with each TCGA tumor so that the scientific community can optimize the translational relevance of the tumor-specific genomic and pathway conclusions drawn from the TCGA program and its pan-cancer analyses.
It is also important to demonstrate that the conclusions drawn from this newly curated TCGA pan-cancer clinical data resource have translational validity with respect to both patient prognosis and outcome parameters. In clinical studies, 5-year or year benchmark survival rates are often calculated to convey prognostic information or to compare treatment effects.
These survival rates may be based on progression or mortality events with or without disease specificity. For each endpoint, it is very important to have a sufficiently long follow-up time to capture the events of interest, and the minimum follow-up time needed depends on both the aggressiveness of the disease and the type of endpoint Tai et al.
Overall survival OS is an important endpoint, with the advantage that there is minimal ambiguity in defining an OS event Hudis et al. However, using OS as an endpoint may weaken a clinical study as deaths because of non-cancer causes do not necessarily reflect tumor biology, aggressiveness, or responsiveness to therapy.
The minimum follow-up time for these endpoints is shorter because patients generally develop disease recurrence or progression before dying of their disease.
Selection of a specific survival endpoint also depends on the study goal. With specific regard to the analysis of available TCGA clinical data, it is important to realize that short-term clinical follow-up intervals favor outcome analyses in more aggressive cancer types, which are likely to observe events within a couple of years. Studies with less aggressive cancer types, in which patients relapse only after many years or even decades, may not observe enough events during their follow-up intervals to support reliable outcome determinations.
The intent of this analysis is to examine the relative strengths and weaknesses of the TCGA pan-cancer clinical outcome measures to guide future analyses and avoid pitfalls such as insufficient follow-up intervals.
To our knowledge, there has been no systematic attempt to analyze the TCGA clinical data and derive acceptable outcome endpoints across all 33 TCGA cancer types involving 11, patients or to assess the adequacy of the clinical follow-up interval for each survival endpoint test. Here we present curated and filtered clinical and survival outcome data as a newly integrated resource for the entire scientific community, describe how problems encountered while analyzing these data were resolved, and what pitfalls researchers should be aware of when using these data for future correlative and survival studies.
Based on our comprehensive clinical review, we also provide scoring recommendations for appropriate future use and tumor-specific endpoint selection.
The same TCGA barcode structure is used for both clinical data and molecular data, enabling integrated analysis of patient-based clinical data and sample-based molecular data. Figure 1A shows a flowchart of the methods for clinical data integration and analysis as well as derivation and evaluation of 4 major clinical outcome endpoints.
We processed 33 initial enrollment data files and 97 follow-up data files for 11, patients across 33 cancer types. Table 1 shows the basic characteristics of each TCGA cohort. Primary tumor samples, not metastatic, were typically selected in each cohort for molecular characterization, with the exception of the skin cutaneous melanoma SKCM study, which allowed both.
A very limited number of metastatic tumors with matching primary tumors was also studied for other cancer types.
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