Molecular analysis of aggressive renal cell carcinoma with unclassified histology reveals distinct subsets Journal Article

Authors: Chen, Y. B.; Xu, J.; Skanderup, A. J.; Dong, Y.; Brannon, A. R.; Wang, L.; Won, H. H.; Wang, P. I.; Nanjangud, G. J.; Jungbluth, A. A.; Li, W.; Ojeda, V.; Hakimi, A. A.; Voss, M. H.; Schultz, N.; Motzer, R. J.; Russo, P.; Cheng, E. H.; Giancotti, F. G.; Lee, W.; Berger, M. F.; Tickoo, S. K.; Reuter, V. E.; Hsieh, J. J.
Article Title: Molecular analysis of aggressive renal cell carcinoma with unclassified histology reveals distinct subsets
Abstract: Renal cell carcinomas with unclassified histology (uRCC) constitute a significant portion of aggressive non-clear cell renal cell carcinomas that have no standard therapy. The oncogenic drivers in these tumours are unknown. Here we perform a molecular analysis of 62 high-grade primary uRCC, incorporating targeted cancer gene sequencing, RNA sequencing, single-nucleotide polymorphism array, fluorescence in situ hybridization, immunohistochemistry and cell-based assays. We identify recurrent somatic mutations in 29 genes, including NF2 (18%), SETD2 (18%), BAP1 (13%), KMT2C (10%) and MTOR (8%). Integrated analysis reveals a subset of 26% uRCC characterized by NF2 loss, dysregulated Hippo-YAP pathway and worse survival, whereas 21% uRCC with mutations of MTOR, TSC1, TSC2 or PTEN and hyperactive mTORC1 signalling are associated with better clinical outcome. FH deficiency (6%), chromatin/DNA damage regulator mutations (21%) and ALK translocation (2%) distinguish additional cases. Altogether, this study reveals distinct molecular subsets for 76% of our uRCC cohort, which could have diagnostic and therapeutic implications. © The Author(s) 2016.
Journal Title: Nature Communications
Volume: 7
ISSN: 2041-1723
Publisher: Nature Publishing Group  
Date Published: 2016-10-07
Start Page: 13131
Language: English
DOI: 10.1038/ncomms13131
PROVIDER: scopus
PMCID: PMC5059781
PUBMED: 27713405
Notes: Article -- Export Date: 2 November 2016 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Paul Russo
    449 Russo
  2. Robert Motzer
    762 Motzer
  3. Satish K Tickoo
    369 Tickoo
  4. Martin Henner Voss
    127 Voss
  5. Yingbei Chen
    221 Chen
  6. Lu Wang
    139 Wang
  7. Yiyu Dong
    16 Dong
  8. James J Hsieh
    114 Hsieh
  9. Achim Jungbluth
    340 Jungbluth
  10. Emily H Cheng
    55 Cheng
  11. Michael Forman Berger
    386 Berger
  12. Victor Reuter
    913 Reuter
  13. Wei Li
    6 Li
  14. Nikolaus D Schultz
    202 Schultz
  15. Helen Hyeong-Eun Won
    79 Won
  16. Angela Rose Brannon
    31 Brannon
  17. William Lee
    38 Lee
  18. Abraham Ari Hakimi
    129 Hakimi
  19. Jianing Xu
    9 Xu
  20. Patricia Ibai Wang
    12 Wang