Pan-Cancer Analysis

Pan-Cancer Analysis aims to examine the similarities and differences among the genomic and cellular alterations found across diverse tumor types.[1][2] International efforts have performed pan-cancer analysis on exomes and on the whole genomes of cancers including its non-coding regions. The Cancer Genome Atlas (TCGA) Research Network, in 2018, used exome, transcriptome, and DNA methylome data to develop an integrated picture of commonalities, differences and emergent themes across tumor types [see http://www.nature.com/tcga/ TCGA Pan-Cancer Analysis].

In 2020, the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project published 23 papers, analysing whole cancer genomes and transcriptomic data from 38 tumor types . A comprehensive overview of the project is provided in its flagship paper.[3]

Pan-Cancer analysis of RNA-Binding Proteins[4] across Human Cancers also were constructed to explore the expression, somatic copy number alteration (SCNA), and mutation profiles of 1,542 RBPs in ∼7,000 clinical specimens across 15 cancer types. Pan-cancer analysis of RNA-Binding proteins revealed the oncogenic property of six RBPs (NSUN6, ZC3H13, BYSL, ELAC1, RBMS3, and ZGPAT) in colorectal and liver cancer cell lines by using functional experiments.

Several studies have proven that there is a causal, predictable connection between genomic alterations (intended as short nucleotide variants or large copy number variants) and gene expression across all tumor types. This pan-cancer relationship between genomic status and transcriptomic quantititative data is generally valid and it can be used as the basis for machine learning approaches, to predict the presence of a specific genomic alteration from gene expression profiles alone.[5]


Resources and Databases

All the data obtained from the TCGA efforts are available at USA's National Cancer Institute TARGET Data Matrix and the web portal ProteinPaint.[6]

Recently, Pan-Cancer resources[7] were created for the Networks Of lncRNAs, microRNAs, CeRNAs and RNA-Binding Proteins (RBPs).

The nearly 800 Terabytes of data from the ICGC/TCGA PCAWG project have been made available through various portals and repositories, including at the Ontario Institute for Cancer Research, the European Molecular Biology Laboratory's European Bioinformatics Institute, and the National Center for Biotechnology Information (key public portals are listed at http://www.nature.com/collections/PCAWG).

Pan-Cancer Studies

Pan-Cancer studies aim to locate the conductive genes precisely, as well as recurrent genomic events or aberrations between different types of tumors. For these studies it is necessary to standardize the data between multiple platforms establishing criteria between different groups of researchers to work on the data and present the results. Omics data allow the identification and quantification of thousands of molecules in a single experiment, in a short space of time. Genomics gives information about what happened, that is, the potentiality that something may occur, proteomics of what is happening and metabolomics of what has happened. The genes contain information that something could potentially occur. The proteins give information of what happens now in a tissue that is being studied, are those that exert the functions, and the metabolites arise as a consequence of the functions of the proteins. The combination of all of them gives information about biology systems.


References:

  1. Cancer Genome Atlas Research, Network; Weinstein, JN; Collisson, EA; Mills, GB; Shaw, KR; Ozenberger, BA; Ellrott, K; Shmulevich, I; Sander, C; Stuart, JM (Oct 2013). "The Cancer Genome Atlas Pan-Cancer analysis project". Nature Genetics. 45 (10): 1113–20. doi:10.1038/ng.2764. PMC 3919969. PMID 24071849.
  2. Omberg, L; Ellrott, K; Yuan, Y; Kandoth, C; Wong, C; Kellen, MR; Friend, SH; Stuart, J; Liang, H; Margolin, AA (Oct 2013). "Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas". Nature Genetics. 45 (10): 1121–6. doi:10.1038/ng.2761. PMC 3950337. PMID 24071850.
  3. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium (5 February 2020). "Pan-cancer analysis of whole genomes". Nature. 578 (7793): 82–93. doi:10.1038/s41586-020-1969-6. PMC 7025898. PMID 32025007.
  4. Wang, ZL; Li, B; Luo, YX; Lin, Q; Liu, SR; Zhang, XQ; Zhou, H; Yang, JH; Qu, LH (2 January 2018). "Comprehensive Genomic Characterization of RNA-Binding Proteins across Human Cancers". Cell Reports. 22 (1): 286–298. doi:10.1016/j.celrep.2017.12.035. PMID 29298429.
  5. Mercatelli, Daniele; Ray, Forest; Giorgi, Federico M. (2019). "Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression". Frontiers in Genetics. 10: 671. doi:10.3389/fgene.2019.00671. ISSN 1664-8021. PMC 6657420. PMID 31379928.
  6. "Exploring genomic alteration in pediatric cancer using ProteinPaint". Nature Genetics.
  7. Li, JH; Liu, S; Zhou, H; Qu, LH; Yang, JH (January 2014). "starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data". Nucleic Acids Research. 42 (Database issue): D92-7. doi:10.1093/nar/gkt1248. PMC 3964941. PMID 24297251.
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