PURPOSE: To improve the risk stratification of patients with rhabdomyosarcoma (RMS) through the use of clinical and molecular biologic data. PATIENTS AND METHODS: Two independent data sets of gene-expression profiling for 124 and 101 patients with RMS were used to derive prognostic gene signatures by using a meta-analysis. These and a previously published metagene signature were evaluated by using cross validation analyses. A combined clinical and molecular risk-stratification scheme that incorporated the PAX3/FOXO1 fusion gene status was derived from 287 patients with RMS and evaluated. RESULTS: We showed that our prognostic gene-expression signature and the one previously published performed well with reproducible and significant effects. However, their effect was reduced when cross validated or tested in independent data and did not add new prognostic information over the fusion gene status, which is simpler to assay. Among nonmetastatic patients, patients who were PAX3/FOXO1 positive had a significantly poorer outcome compared with both alveolar-negative and PAX7/FOXO1-positive patients. Furthermore, a new clinicomolecular risk score that incorporated fusion gene status (negative and PAX3/FOXO1 and PAX7/FOXO1 positive), Intergroup Rhabdomyosarcoma Study TNM stage, and age showed a significant increase in performance over the current risk-stratification scheme. CONCLUSION: Gene signatures can improve current stratification of patients with RMS but will require complex assays to be developed and extensive validation before clinical application. A significant majority of their prognostic value was encapsulated by the fusion gene status. A continuous risk score derived from the combination of clinical parameters with the presence or absence of PAX3/FOXO1 represents a robust approach to improving current risk-adapted therapy for RMS.
PURPOSE: To improve the risk stratification of patients with rhabdomyosarcoma (RMS) through the use of clinical and molecular biologic data. PATIENTS AND METHODS: Two independent data sets of gene-expression profiling for 124 and 101 patients with RMS were used to derive prognostic gene signatures by using a meta-analysis. These and a previously published metagene signature were evaluated by using cross validation analyses. A combined clinical and molecular risk-stratification scheme that incorporated the PAX3/FOXO1 fusion gene status was derived from 287 patients with RMS and evaluated. RESULTS: We showed that our prognostic gene-expression signature and the one previously published performed well with reproducible and significant effects. However, their effect was reduced when cross validated or tested in independent data and did not add new prognostic information over the fusion gene status, which is simpler to assay. Among nonmetastatic patients, patients who were PAX3/FOXO1 positive had a significantly poorer outcome compared with both alveolar-negative and PAX7/FOXO1-positive patients. Furthermore, a new clinicomolecular risk score that incorporated fusion gene status (negative and PAX3/FOXO1 and PAX7/FOXO1 positive), Intergroup Rhabdomyosarcoma Study TNM stage, and age showed a significant increase in performance over the current risk-stratification scheme. CONCLUSION: Gene signatures can improve current stratification of patients with RMS but will require complex assays to be developed and extensive validation before clinical application. A significant majority of their prognostic value was encapsulated by the fusion gene status. A continuous risk score derived from the combination of clinical parameters with the presence or absence of PAX3/FOXO1 represents a robust approach to improving current risk-adapted therapy for RMS.
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Authors: Lama Jurdy; Johanus H M Merks; Bradly R Pieters; Maarten P Mourits; Roel J H M Kloos; Simone D Strackee; Peerooz Saeed Journal: Saudi J Ophthalmol Date: 2013-07
Authors: Jack F Shern; Li Chen; Juliann Chmielecki; Jun S Wei; Rajesh Patidar; Mara Rosenberg; Lauren Ambrogio; Daniel Auclair; Jianjun Wang; Young K Song; Catherine Tolman; Laura Hurd; Hongling Liao; Shile Zhang; Dominik Bogen; Andrew S Brohl; Sivasish Sindiri; Daniel Catchpoole; Thomas Badgett; Gad Getz; Jaume Mora; James R Anderson; Stephen X Skapek; Frederic G Barr; Matthew Meyerson; Douglas S Hawkins; Javed Khan Journal: Cancer Discov Date: 2014-01-23 Impact factor: 39.397
Authors: Narendra Bharathy; Noah E Berlow; Eric Wang; Jinu Abraham; Teagan P Settelmeyer; Jody E Hooper; Matthew N Svalina; Yoshihiro Ishikawa; Keith Zientek; Zia Bajwa; Martin W Goros; Brian S Hernandez; Johannes E Wolff; Michelle A Rudek; Linping Xu; Nicole M Anders; Ranadip Pal; Alexandria P Harrold; Angela M Davies; Arya Ashok; Darnell Bushby; Maria Mancini; Christopher Noakes; Neal C Goodwin; Peter Ordentlich; James Keck; Douglas S Hawkins; Erin R Rudzinski; Bishwanath Chatterjee; Hans Peter Bächinger; Frederic G Barr; Jennifer Liddle; Benjamin A Garcia; Atiya Mansoor; Theodore J Perkins; Christopher R Vakoc; Joel E Michalek; Charles Keller Journal: Sci Signal Date: 2018-11-20 Impact factor: 8.192
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