Robin M Hallett1, Alex B K Seong2, David R Kaplan3, Meredith S Irwin2. 1. Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada; James Birrell Laboratories, The Hospital for Sick Children, Toronto, Ontario, Canada. 2. Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada; James Birrell Laboratories, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. 3. Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada; James Birrell Laboratories, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Abstract
BACKGROUND: In the pediatric cancer neuroblastoma (NB), patients are stratified into low, intermediate or high-risk subsets based in part on MYCN amplification status. While MYCN amplification in general predicts unfavorable outcome, no clinical or genomic factors have been identified that predict outcome within these cohorts of high-risk patients. In particular, it is currently not possible at diagnosis to determine which high-risk neuroblastoma patients will ultimately fail upfront therapy. EXPERIMENTAL DESIGN: We analyzed the prognostic potential of most published gene expression signatures for NB and developed a new prognostic signature to predict outcome for patients with MYCN amplification. Network and pathway analyses identified candidate therapeutic targets for this MYCN-amplified patient subset with poor outcome. RESULTS: Most signatures have a high capacity to predict outcome of unselected NB patients. However, the majority of published signatures, as well as most randomly generated signatures, are highly confounded by MYCN amplification, and fail to predict outcome in subpopulations of high-risk patients with MYCN-amplified NB. We identify a MYCN module signature that predicts patient outcome for those with MYCN-amplified tumors, that also predicts potential tractable therapeutic signaling pathways and targets including the DNA repair enzyme Poly [ADP-ribose] polymerase 1 (PARP1). CONCLUSION: Many prognostic signatures for NB are confounded by MYCN amplification and fail to predict outcome for the subset of high-risk patients with MYCN amplification. We report a MYCN module signature that is associated with distinct patient outcomes, and predicts candidate therapeutic targets in DNA repair pathways, including PARP1 in MYCN-amplified NB.
BACKGROUND: In the pediatric cancer neuroblastoma (NB), patients are stratified into low, intermediate or high-risk subsets based in part on MYCN amplification status. While MYCN amplification in general predicts unfavorable outcome, no clinical or genomic factors have been identified that predict outcome within these cohorts of high-risk patients. In particular, it is currently not possible at diagnosis to determine which high-risk neuroblastomapatients will ultimately fail upfront therapy. EXPERIMENTAL DESIGN: We analyzed the prognostic potential of most published gene expression signatures for NB and developed a new prognostic signature to predict outcome for patients with MYCN amplification. Network and pathway analyses identified candidate therapeutic targets for this MYCN-amplified patient subset with poor outcome. RESULTS: Most signatures have a high capacity to predict outcome of unselected NB patients. However, the majority of published signatures, as well as most randomly generated signatures, are highly confounded by MYCN amplification, and fail to predict outcome in subpopulations of high-risk patients with MYCN-amplified NB. We identify a MYCN module signature that predicts patient outcome for those with MYCN-amplified tumors, that also predicts potential tractable therapeutic signaling pathways and targets including the DNA repair enzyme Poly [ADP-ribose] polymerase 1 (PARP1). CONCLUSION: Many prognostic signatures for NB are confounded by MYCN amplification and fail to predict outcome for the subset of high-risk patients with MYCN amplification. We report a MYCN module signature that is associated with distinct patient outcomes, and predicts candidate therapeutic targets in DNA repair pathways, including PARP1 in MYCN-amplified NB.
Authors: Shizhen Zhu; Jeong-Soo Lee; Feng Guo; Jimann Shin; Antonio R Perez-Atayde; Jeffery L Kutok; Scott J Rodig; Donna S Neuberg; Daniel Helman; Hui Feng; Rodney A Stewart; Wenchao Wang; Rani E George; John P Kanki; A Thomas Look Journal: Cancer Cell Date: 2012-03-20 Impact factor: 31.743
Authors: R Ladenstein; I M Ambros; U Pötschger; G Amann; C Urban; F M Fink; K Schmitt; R Jones; M Slociak; F Schilling; J Ritter; F Berthold; H Gadner; P F Ambros Journal: Med Pediatr Oncol Date: 2001-01
Authors: C Guo; P S White; M J Weiss; M D Hogarty; P M Thompson; D O Stram; R Gerbing; K K Matthay; R C Seeger; G M Brodeur; J M Maris Journal: Oncogene Date: 1999-09-02 Impact factor: 9.867
Authors: Idoia Garcia; Gemma Mayol; José Ríos; Gema Domenech; Nai-Kong V Cheung; André Oberthuer; Matthias Fischer; John M Maris; Garrett M Brodeur; Barbara Hero; Eva Rodríguez; Mariona Suñol; Patricia Galvan; Carmen de Torres; Jaume Mora; Cinzia Lavarino Journal: Clin Cancer Res Date: 2012-02-10 Impact factor: 12.531
Authors: Paolo Fardin; Andrea Cornero; Annalisa Barla; Sofia Mosci; Massimo Acquaviva; Lorenzo Rosasco; Claudio Gambini; Alessandro Verri; Luigi Varesio Journal: J Biomed Biotechnol Date: 2010-06-28
Authors: Daria Thompson; Kieuhoa T Vo; Wendy B London; Matthias Fischer; Peter F Ambros; Akira Nakagawara; Garrett M Brodeur; Katherine K Matthay; Steven G DuBois Journal: Cancer Date: 2015-12-28 Impact factor: 6.860
Authors: Ali Tofigh; Matthew Suderman; Eric R Paquet; Julie Livingstone; Nicholas Bertos; Sadiq M Saleh; Hong Zhao; Margarita Souleimanova; Sean Cory; Robert Lesurf; Solmaz Shahalizadeh; Norberto Garcia Lopez; Yasser Riazalhosseini; Atilla Omeroglu; Josie Ursini-Siegel; Morag Park; Vanessa Dumeaux; Michael Hallett Journal: Cell Rep Date: 2014-10-02 Impact factor: 9.423
Authors: Johan van Nes; Alvin Chan; Tim van Groningen; Peter van Sluis; Jan Koster; Rogier Versteeg Journal: Clin Cancer Res Date: 2013-05-06 Impact factor: 12.531
Authors: Jennifer C Kasemeier-Kulesa; Santiago Schnell; Thomas Woolley; Jennifer A Spengler; Jason A Morrison; Mary C McKinney; Irina Pushel; Lauren A Wolfe; Paul M Kulesa Journal: Biophys Chem Date: 2018-04-30 Impact factor: 2.352
Authors: Wei Zhang; Bo Liu; Wenhui Wu; Likun Li; Bradley M Broom; Spyridon P Basourakos; Dimitrios Korentzelos; Yang Luan; Jianxiang Wang; Guang Yang; Sanghee Park; Abul Kalam Azad; Xuhong Cao; Jeri Kim; Paul G Corn; Christopher J Logothetis; Ana M Aparicio; Arul M Chinnaiyan; Nora Navone; Patricia Troncoso; Timothy C Thompson Journal: Clin Cancer Res Date: 2017-11-14 Impact factor: 12.531
Authors: Ivan Petrov; Maria Suntsova; Elena Ilnitskaya; Sergey Roumiantsev; Maxim Sorokin; Andrew Garazha; Pavel Spirin; Timofey Lebedev; Nurshat Gaifullin; Sergey Larin; Olga Kovalchuk; Dmitry Konovalov; Vladimir Prassolov; Alexander Roumiantsev; Anton Buzdin Journal: Oncotarget Date: 2017-07-28
Authors: Saurabh Agarwal; Giorgio Milazzo; Kimal Rajapakshe; Ronald Bernardi; Zaowen Chen; Eveline Barbieri; Jan Koster; Giovanni Perini; Cristian Coarfa; Jason M Shohet Journal: Oncotarget Date: 2018-04-17
Authors: Eniko Papp; Dorothy Hallberg; Gottfried E Konecny; Daniel C Bruhm; Vilmos Adleff; Michaël Noë; Ioannis Kagiampakis; Doreen Palsgrove; Dylan Conklin; Yasuto Kinose; James R White; Michael F Press; Ronny Drapkin; Hariharan Easwaran; Stephen B Baylin; Dennis Slamon; Victor E Velculescu; Robert B Scharpf Journal: Cell Rep Date: 2018-11-27 Impact factor: 9.423