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.Find out about The Cancer Genome Atlas ( TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing.Dr. Paul Spellman talks about The Cancer Genome Atlas ( TCGA) and how this could help further the treatment of cancer. TCGA is a project working to catalog genetic mutations responsible for cancer. Clinicians are sequencing the genomes of patients with any of 20 different cancers and hope that this could target clinical trials at the specific patient sub-groups that would benefit most.Koch, Alexander; De Meyer, Tim; Jeschke, Jana; Van Criekinge, Wim2015-08-26In recent years, increasing amounts of genomic and clinical cancer data have become publically available through large-scale collaborative projects such as The Cancer Genome Atlas ( TCGA). However, as long as these datasets are difficult to access and interpret, they are essentially useless for a major part of the research community and their scientific potential will not be fully realized.
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To address these issues we developed MEXPRESS, a straightforward and easy-to-use web tool for the integration and visualization of the expression, DNA methylation and clinical TCGA data on a single-gene level ( ). In comparison to existing tools, MEXPRESS allows researchers to quickly visualize and interpret the different TCGA datasets and their relationships for a single gene, as demonstrated for GSTP1 in prostate adenocarcinoma. We also used MEXPRESS to reveal the differences in the DNA methylation status of the PAM50 marker gene MLPH between the breast cancer subtypes and how these differences were linked to the expression of MPLH. We have created a user-friendly tool for the visualization and interpretation of TCGA data, offering clinical researchers a simple way to evaluate the TCGA data for their genes or candidate biomarkers of interest.Huang, Zhenzhen; Duan, Huilong; Li, Haomin2015-01-01Large-scale human cancer genomics projects, such as TCGA, generated large genomics data for further study.
Exploring and mining these data to obtain meaningful analysis results can help researchers find potential genomics alterations that intervene the development and metastasis of tumors. We developed a web-based gene analysis platform, named TCGA4U, which used statistics methods and models to help translational investigators explore, mine and visualize human cancer genomic characteristic information from the TCGA datasets. Furthermore, through Gene Ontology (GO) annotation and clinical data integration, the genomic data were transformed into biological process, molecular function, cellular component and survival curves to help researchers identify potential driver genes. Clinical researchers without expertise in data analysis will benefit from such a user-friendly genomic analysis platform.Roy W. Tarnuzzer, Ph.D., the Biospecimen Core Resource Program Manager at the TCGA Program Office, provides an overview of the Formalin-fixed Paraffin Pilot Project, an initiative to investigate best practices for use of FFPE specimens in genomic studies.Wei, Lin; Jin, Zhilin; Yang, Shengjie; Xu, Yanxun; Zhu, Yitan; Ji, Yuan2018-05-01The Cancer Genome Atlas ( TCGA) program has produced huge amounts of cancer genomics data providing unprecedented opportunities for research.
In 2014, we developed TCGA-Assembler, a software pipeline for retrieval and processing of public TCGA data. In 2016, TCGA data were transferred from the TCGA data portal to the Genomic Data Commons (GDCs), which is supported by a different set of data storage and retrieval mechanisms. In addition, new proteomics data of TCGA samples have been generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program, which were not available for downloading through TCGA-Assembler. It is desirable to acquire and integrate data from both GDC and CPTAC.
We develop TCGA-assembler 2 (TA2) to automatically download and integrate data from GDC and CPTAC. We make substantial improvement on the functionality of TA2 to enhance user experience and software performance. TA2 together with its previous version have helped more than 2000 researchers from 64 countries to access and utilize TCGA and CPTAC data in their research.
Availability of TA2 will continue to allow existing and new users to conduct reproducible research based on TCGA and CPTAC data. TCGA-Assembler/ or TCGA-Assembler-2. Zhuyitan@gmail.com or koaeraser@gmail.com. Supplementary data are available at Bioinformatics online.Biostatistician Dr. Jill Barnholtz-Sloan strives to make a difference in the field of cancer research while inspiring her students at the same time.
Learn more about how she uses TCGA data in her career in this TCGA in Action Researcher Profile.TCGA researchers analyzed nearly 300 cases of diffuse low- and intermediate-grade gliomas, which together comprise lower-grade gliomas. LGGs occur mainly in adults and include astrocytomas, oligodendrogliomas and oligoastrocytomas.Computational biologist Dr. Ilya Shmulevich suggests that renting cloud computing power might widen the bottleneck for analyzing genomic data. Learn more about his experience with the Cloud in this TCGA in Action Case Study.Investigators with The Cancer Genome Atlas ( TCGA) Research Network have identified molecular characteristics of a type of breast cancer, invasive lobular carcinoma (ILC), that distinguishes it from invasive ductal carcinoma (IDC), the most common invasive breast cancer subtype.Sun, Wenjie; Duan, Ting; Ye, Panmeng; Chen, Kelie; Zhang, Guanling; Lai, Maode; Zhang, Honghe2018-05-29Collaborative projects such as The Cancer Genome Atlas ( TCGA) have generated various -omics and clinical data on cancer. Many computational tools have been developed to facilitate the study of the molecular characterization of tumors using data from the TCGA.
Alternative splicing of a gene produces splicing variants, and accumulating evidence has revealed its essential role in cancer-related processes, implying the urgent need to discover tumor-specific isoforms and uncover their potential functions in tumorigenesis. We developed TSVdb, a web-based tool, to explore alternative splicing based on TCGA samples with 30 clinical variables from 33 tumors. TSVdb has an integrated and well-proportioned interface for visualization of the clinical data, gene expression, usage of exons/junctions and splicing patterns. Researchers can interpret the isoform expression variations between or across clinical subgroups and estimate the relationships between isoforms and patient prognosis.
TSVdb is available at, and the source code is available at. TSVdb will inspire oncologists and accelerate isoform-level advances in cancer research.New findings by researchers at UNC Lineberger Comprehensive Cancer Center suggest that the most common form of malignant brain cancer in adults, glioblastoma multiforme (GBM), is probably not a single disease but a set of diseases, each with a distinct underlying molecular disease process. The study was published by Cell Press in the January issue of the journal Cancer Cell and the researchers are part of the The Cancer Genome Atlas.In this lecture, Dr. David Haussler provides a historical overview of the field of genomics leading up to TCGA, including the Cancer Genomics Hub at the University of California, Santa Cruz, and the TCGA Pan-Cancer initiative.Drs.Scott Hwang and Chad Holder of Emory University discuss the development of VARSARI and The Cancer Imaging Program's TCGA Radiology Initiative. Learn more about their and Dr. Carl Jaffe's work in this TCGA In Action Case Study.Dr. David Gutman and Dr.
Lee Cooper developed The Cancer Digital Slide Archive (CDSA), a web platform for accessing pathology slide images of TCGA samples. Find out how they did it and how to use the CDSA website in this Case Study.Dr.Velazquez, E Rios; Narayan, V; Grossmann, P2015-06-15Purpose: To compare the complementary prognostic value of automated Radiomic features to that of radiologist-annotated VASARI features in TCGA-GBM MRI dataset.
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Methods: For 96 GBM patients, pre-operative MRI images were obtained from The Cancer Imaging Archive. The abnormal tumor bulks were manually defined on post-contrast T1w images. The contrast-enhancing and necrotic regions were segmented using FAST. From these sub-volumes and the total abnormal tumor bulk, a set of Radiomic features quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the post-contrast T1w images. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative Radiomic, VASARI andmore » combined Radiomic-VASARI features in 70% of the dataset (training-set). Multivariate Cox-proportional hazards models were evaluated in 30% of the dataset (validation-set) using the C-index for OS. A bootstrap procedure was used to assess significance while comparing the C-Indices of the different models.
Results: Overall, the Radiomic features showed a moderate correlation with the radiologist-annotated VASARI features (r = −0.37 – 0.49); however that correlation was stronger for the Tumor Diameter and Proportion of Necrosis VASARI features (r = −0.71 – 0.69). After MRMR feature selection, the best-performing Radiomic, VASARI, and Radiomic-VASARI Cox-PH models showed a validation C-index of 0.56 (p = NS), 0.58 (p = NS) and 0.65 (p = 0.01), respectively. The combined Radiomic-VASARI model C-index was significantly higher than that obtained from either the Radiomic or VASARI model alone (p = C (per allele odds ratio OR = 3.14; P = 6.48 × 10−11), a germline rare single-nucleotide polymorphism (SNP) in TP53, via imputation of a genome-wide association study of glioma (1,856 cases and 4,955 controls). We subsequently performed integrative analyses on the Cancer Genome Atlas ( TCGA) data for GBM (glioblastoma multiforme) and LUAD (lung adenocarcinoma). Based on SNP data, we imputed genotypes for rs78378222 and selected individuals carrying rare risk allele (C). Using RNA sequencing data, we observed aberrant transcripts with 3 kb longer than normal for those individuals.
Using exome sequencing data, we further showed that loss of haplotype carrying common protective allele (A) occurred somatically in GBM but not in LUAD. Our bioinformatic analysis suggests rare risk allele (C) disrupts mRNA termination, and an allelic loss of a genomic region harboring common protective allele (A) occurs during tumor initiation or progression for glioma. PMID:25907361.Rios Velazquez, E; Meier, R; Dunn, WPurpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features.
Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showedmore » high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively).
The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation.
Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research.« less.Chen, Lin; Zhu, Zhe; Gao, Wei; Jiang, Qixin; Yu, Jiangming; Fu, Chuangang2017-09-05Insulin-like growth factor 1 receptor (IGF-1R) is proved to contribute the development of many types of cancers. But, little is known about its roles in radio-resistance of colorectal cancer (CRC). Here, we demonstrated that low IGF-1R expression value was associated with the better radiotherapy sensitivity of CRC.
Besides, through Quantitative Real-time PCR (qRT-PCR), the elevated expression value of epidermal growth factor receptor (EGFR) was observed in CRC cell lines (HT29, RKO) with high radio-sensitivity compared with those with low sensitivity (SW480, LOVO). The irradiation induced apoptosis rates of wild type and EGFR agonist (EGF) or IGF-1R inhibitor (NVP-ADW742) treated HT29 and SW480 cells were quantified by flow cytometry.
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As a result, the apoptosis rate of EGF and NVP-ADW742 treated HT29 cells was significantly higher than that of those wild type ones, which indicated that high EGFR and low IGF-1R expression level in CRC was associated with the high sensitivity to radiotherapy. We next conducted systemic bioinformatics analysis of genome-wide expression profiles of CRC samples from the Cancer Genome Atlas ( TCGA). Differential expression analysis between IGF-1R and EGFR abnormal CRC samples, i.e. CRC samples with higher IGF-1R and lower EGFR expression levels based on their median expression values, and the rest of CRC samples identified potential genes contribute to radiotherapy sensitivity.
Functional enrichment of analysis of those differential expression genes (DEGs) in the Database for Annotation, Visualization and Integrated Discovery (DAVID) indicated PPAR signaling pathway as an important pathway for the radio-resistance of CRC. Our study identified the potential biomarkers for the rational selection of radiotherapy for CRC patients. Copyright © 2017 Elsevier B.V. All rights reserved.Walters, Lynne Masel; Green, Martha R.; Goldsby, Dianne; Walters, Timothy N.; Wang, Liangyan2016-01-01This mixed methods study examines whether engaging in a problem-solving project to create Math-eos (digital videos) increases pre-service teachers' understanding of the relationship between visual, auditory, and verbal representation and critical thinking in mathematics. Additionally, the study looks at what aspects of a digital problem solving.Breneman, A.; Cattell, C.; Wygant, J.; Kersten, K.; Wilson, L, B., III; Dai, L.; Colpitts, C.; Kellogg, P. J.; Goetz, K.; Paradise, A.2012-01-01Recently Breneman et al.
Reported observations of large amplitude lightning and transmitter whistler mode waves from two STEREO passes through the inner radiation belt (L.