An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

By Jianfang Liu, T. Lichtenberg, Katherine A. Hoadley, L. Poisson, A. Lazar, A. Cherniack, A. Kovatich, C. Benz, D. Levine, Adrian V. Lee and 5 more. Cell, 2018

3,211 citations recorded by the source corpus.

SUMMARY For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. 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.

Subfields: Cancer Research, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/758db88987fd8bc8811e800fabe8257dbd3a23f5

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Rodney Hull
2. F. Francies
3. Zodwa Dlamini
4. R. Marima
5. Casey S. Greene
6. G. Way
7. Matthew Meyerson
8. Gad Getz

In Radiology, Nuclear Medicine and Imaging

1. Feixiong Cheng
2. Christopher Sander
3. M. Davis

Citations in the corpus

• Human-in-the-loop machine learning: a state of the art (2022, background): “In medicine we can find big databases with clinical cases, for example, The Cancer Genome Atlas (TCGA) (Liu et al. 2018b; Tomczak et al. 2015) but when we focus on a particular type of cancer, the data available may not be sufficient to train a deep learning model.”
• Artificial intelligence (AI) and big data in cancer and precision oncology (2020, methodology): “The purpose of the TCGA project was to enhance precision oncology with accurate knowledge about the molecular landscape of these cancers, this included pathogenesis of cancers, classification of tumour subtypes based on molecular modifications and identifying therapeutic targets to drive drug develo”
• Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas (2018, positive): “When tested against PanCanAtlas survival data encompassing 28 cancer types (Liu et al., 2018), we identified associations among most DDR footprint scores and clinical outcomes after controlling for covariates such as age, cancer grade, and stage (Figure S7).”
• Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer (2020, methodology): “Clinical data used were obtained from previous reports [20], LUAD data were extracted from TCGA-CDR-Supplementary Table S1, and smoking history indicator was downloaded from the GDC data portal website [21] by selecting the “bcr biotab” option on the “Data Format” list, under the “Files” tab.”
• Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, background): “…RESOURCE SOURCE IDENTIFIER Deposited Data Public MC3 MAF Ellrott et al., 2018 https://gdc.cancer.gov/about-data/publications/mc3-2017 Clinical Data Liu et al., 2018 https://gdc.cancer.gov/about-data/publications/pancanatlas Target Drug Database - Phial Van Allen et al., 2014…”
• The novel prognostic marker, EHMT2, is involved in cell proliferation via HSPD1 regulation in breast cancer (2018, result): “This result may have been due to a limitation of the TCGA cohort, in which the sample composition was inadequate for a prognostic prognosis (23), although many samples were included in the cohort.”
• The Immune Landscape of Cancer (2018, methodology): “First, source datasets from all 33 TCGA cancer types and six molecular platforms (mRNA, microRNA, and exome sequencing; DNA methylation-, copy number-, and reversephase protein arrays) were harmonized by the PanCanAtlas consortium for uniform quality control, batch effect correction, normalization, ”