Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

By Katherine A. Hoadley, C. Yau, T. Hinoue, D. Wolf, A. Lazar, E. Drill, Ronglai Shen, Alison M. Taylor, A. Cherniack, Vesteinn Thorsson and 13 more. Cell, 2018

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SUMMARY We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/14921768c53cafca0036cd8c1ac6e3e4ab28e326

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Citations in the corpus

15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms (2018, background): “For example, we may incorporate a subset of missense mutations from 18 additional cancer types in the TCGA PanCancer Atlas (22).”
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The frequency of Ras mutations in cancer (2020, methodology): “The Pan-Cancer Atlas used TCGA data to define the molecular subtypes of cancer and to generate deep understanding of the genetic programmes associated with driving different cancer types (45).”
Machine Learning for Precision Medicine. (2020, background): “…1900s revolutionized blood Page 3 of 32 © The Author(s) or their Institution(s) Genome Draft 4 1 transfusions, allowing for matching of donor and recipient blood types, and avoiding 2 complications associated with mismatched donor and recipient blood (Dance, 2016; 3 Giangrande, 2000; Hodson, 2016).”
The chromatin accessibility landscape of primary human cancers (2018, methodology): “We found strong concordance between this ATAC-seq–based clustering and the published multiomic iCluster scheme using TCGA mRNA-seq, microRNA (miRNA)–seq, DNAmethylation, reverse-phase protein array (RPPA), and DNA copy number data (28) (Fig.”
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023, background): “To date, it remains cost-prohibitive for most researchers to perform high-dimensional -omics studies of large patient cohorts, but there are numerous large-scale disease-relevant bulk RNA-seq datasets containing thousands of samples from consortia such as the Encyclopedia of DNA Elements (ENCODE),(6”
A Consensus Molecular Classification of Muscle-invasive Bladder Cancer (2019, result): “We compared the consensus classes with the TCGA PanCancer Atlas integrative classification [23] (Supplementary Fig.”