The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge

By Katarzyna Tomczak, Patrycja Czerwińska, M. Wiznerowicz. Contemporary oncology, 2015

3,946 citations recorded by the source corpus.

The Cancer Genome Atlas (TCGA) is a public funded project that aims to catalogue and discover major cancer-causing genomic alterations to create a comprehensive “atlas” of cancer genomic profiles. So far, TCGA researchers have analysed large cohorts of over 30 human tumours through large-scale genome sequencing and integrated multi-dimensional analyses. Studies of individual cancer types, as well as comprehensive pan-cancer analyses have extended current knowledge of tumorigenesis. A major goal of the project was to provide publicly available datasets to help improve diagnostic methods, treatment standards, and finally to prevent cancer. This review discusses the current status of TCGA Research Network structure, purpose, and achievements.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/acd0db37bf411c34b18c751ec1e915f340bbf1e1

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Citations in the corpus

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx (2019, background): “5 petabytes of publicly available data over the past decade [8,9].”
Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021, background): “In the cancer research community, major large-scale projects, such as The Cancer Genome Atlas (TCGA, which includes 516 LGG samples and 617 GBM samples as of October 18, 2019) [18] and the International Cancer Genome Consortium [ICGC, which includes 80 adult GBM samples and 50 pediatric GBM samples ”
miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions (2017, positive): “Datasets from the GEO and TCGA with miRNA and mRNA expression profiles were incorporated into miRTarBase.”
UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses (2017, background): “…copy number, and RNA and protein expression), the Cancer Genome Atlas (TCGA) consortium has generated molecular profiles of over ten thousand samples related to multiple cancer types [4], leading to studies involving the genomic and molecular characterization of individual cancer types [5 – 24].”
The future of digital health with federated learning (2020, background): “Substantial, albeit smaller, initiatives have also made data available to the general community such as The Human Connectome [20], UK Biobank [21], The Cancer Imaging Archive (TCIA) [22], NIH CXR8 [23], NIH DeepLesion [24], The Cancer Genome Atlas (TCGA) [25], the Alzheimer’s disease neu-”
How Machine Learning will Transform Biomedicine (2020, neutral): “In biomedicine, work in the human genome project (Venter et al., 2001), efforts in cancer omics (e.g., The Cancer Genome Atlas (Tomczak et al., 2015), the International Cancer Genome Consortium (Zhang et al., 2019), and the Clinical Proteomic Tumor Analysis Consortium (Ellis et al., 2013)), and nume”
Advances and Trends in Omics Technology Development (2022, background): “The Cancer Genome Atlas (TCGA) (276) and Gene Expression Omnibus (GEO) (277), among others, have been frequently used for omics data deposit and retrieval.”
Sangerbox: A comprehensive, interaction‐friendly clinical bioinformatics analysis platform (2022, methodology): “These data are derived from The Cancer Genome Atlas (TCGA) [22], International Cancer Genome Consortium (ICGC) [23], Gene Expression Omnibus (GEO) [24], and other databases with clinical follow ‐ up information, clinical data, mutation data, and expression profile data.”
AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors (2018, methodology): “In addition, we collected TF expression from TCGA (31), EMBL-EBI Expression Atlas (32), RNA-seq data published by Li et al.”
Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types (2019, methodology): “The TCGA clinical data were used for survival analysis of network clusters (Table S4).”
Understanding Global Feature Contributions With Additive Importance Measures (2020, methodology): “For datasets, we used MNIST [19], a bike sharing demand dataset [10], the German credit quality dataset [21], the Portuguese bank marketing dataset [26], and a breast cancer (BRCA) subtype classiﬁcation dataset [4, 39].”
The Application of Deep Learning in Cancer Prognosis Prediction (2020, background): “The Cancer Genome Atlas (TCGA) database has clinical and molecular data of over 11,000 tumor patients across 33 different tumor types [17,18], including genomic (whole genome and/or exome sequencing, WGS/WES), transcriptomic (RNAseq, small RNAseq), epigenomic (HumanMethylation450 BeadChip) and prote”