Pathway and network analysis of more than 2500 whole cancer genomes Journal Article


Authors: Reyna, M. A.; Haan, D.; Paczkowska, M.; Verbeke, L. P. C.; Vazquez, M.; Kahraman, A.; Pulido-Tamayo, S.; Barenboim, J.; Wadi, L.; Dhingra, P.; Shrestha, R.; Getz, G.; Lawrence, M. S.; Pedersen, J. S.; Rubin, M. A.; Wheeler, D. A.; Brunak, S.; Izarzugaza, J. M. G.; Khurana, E.; Marchal, K.; von Mering, C.; Sahinalp, S. C.; Valencia, A.; PCAWG Drivers and Functional Interpretation Working Group; Reimand, J.; Stuart, J. M.; Raphael, B. J.; & PCAWG Consortium
Contributors: Kahles, A.; Lehmann, K. V.; Liu, E. M.; Sander, C.; Abeshouse, A.; Al-Ahmadie, H.; Armenia, J.; Chen, H. W.; Davidson, N. R.; Ghossein, R.; Giri, D. D.; Gundem, G.; Heins, Z.; Huse, J.; Iacobuzio-Donahue, C. A.; King, T. A.; Kundra, R.; Levine, D. A.; Ochoa, A.; Pastore, A.; Rätsch, G.; Reis-Filho, J.; Reuter, V.; Roehrl, M. H. A.; Sanchez-Vega, F.; Schultz, N.; Senbabaoglu, Y.; Singer, S.; Socci, N. D.; Stark, S. G.; Vázquez-García, I.; Yellapantula, V. D.; Zhang, H.
Article Title: Pathway and network analysis of more than 2500 whole cancer genomes
Abstract: The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments. © 2020, The Author(s).
Keywords: mutation; gene expression; genome; cancer; network analysis
Journal Title: Nature Communications
Volume: 11
ISSN: 2041-1723
Publisher: Nature Publishing Group  
Date Published: 2020-02-05
Start Page: 729
Language: English
DOI: 10.1038/s41467-020-14367-0
PUBMED: 32024854
PROVIDER: scopus
PMCID: PMC7002574
DOI/URL:
Notes: Article -- Erratum issued, see DOI: 10.1038/s41467-022-32334-9 -- Export Date: 2 March 2020 -- Source: Scopus
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  1. Ronald A Ghossein
    482 Ghossein
  2. Dilip D Giri
    184 Giri
  3. Douglas A Levine
    380 Levine
  4. Tari King
    186 King
  5. Samuel Singer
    337 Singer
  6. Jason T Huse
    143 Huse
  7. Nicholas D Socci
    266 Socci
  8. Chris Sander
    210 Sander
  9. Victor Reuter
    1223 Reuter
  10. Nikolaus D Schultz
    486 Schultz
  11. Gunnar Ratsch
    68 Ratsch
  12. Andre Kahles
    31 Kahles
  13. Hsiao-Wei Chen
    30 Chen
  14. Kjong Van Stephan Fritz Lehmann
    22 Lehmann
  15. Stefan G Stark
    17 Stark
  16. Michael H Roehrl
    127 Roehrl
  17. Alessandro   Pastore
    55 Pastore
  18. Joshua   Armenia
    56 Armenia
  19. Zachary Joseph Heins
    22 Heins
  20. Ritika   Kundra
    88 Kundra
  21. Hongxin Zhang
    47 Zhang
  22. Angelica Ochoa
    30 Ochoa
  23. Gunes Gundem
    56 Gundem
  24. Minwei Liu
    24 Liu