Discovery And Validation Of Novel Prognostic Genomic Signatures In Rhabdomyosarcoma

Introduction: Pediatric rhabdomyosarcoma (RMS) has varying outcomes, especially in patients with intermediate-risk disease (IR-RMS), due to the inherent inability of clinical staging to accurately risk-stratify a large proportion of patients. This study aimed to identify prognostic signatures in IR-RMS patients, the clinical subgroup with the most heterogeneous outcomes, which reflect underlying tumor biology and provide better risk stratification than routine clinicopathologic parameters. Signature performance was further validated on an independent set of RMS patients. Methods: Prospectively-obtained primary tumors from 80 IR-RMS patients on Children9s Oncology Group clinical trial protocols formed the training set. Tumors from 19, 15 and 20 patients with low-risk, high-risk and IR-RMS formed the validation set. All patients underwent whole transcriptome expression profiling using Affymetrix Human Exon microarrays. Expressions of nearly 1.4 million probe selection regions (PSRs) representing annotated and unannotated transcripts were analyzed. Cox regression and leave-n-out cross validation were used to derive and finalize the weighted signatures. Potentials of the coding and non-coding signatures to predict overall survival were compared using areas under receiver operating characteristic curves that provided a measure of predictive accuracy. Associated biological processes were analyzed using curated pathway analysis tools. Results: Standard pathologic prognosticators such as histologic subtype and PAX-FKHR fusion status were unable to predict survival in the subset of IR-RMS that comprised the training set (p=0.94 and 0.66, respectively). Tumor site was the only clinical predictor of outcome in the training set (p=0.041). Iterative Cox regression on over 17,000 coding transcripts identified a 30-gene meta-feature (30gMF) that was able to predict survival in the training set (p=0.001). Analysis of PSRs corresponding to unannotated transcripts identified a 39-PSR meta-feature (39ncMF) that also predicted survival in the training set (p Conclusions: A concise non-coding RNA meta-feature was able to better predict outcome in IR-RMS than a coding gene meta-feature, where most standard clinical prognosticators failed. The prognostic value of these meta-features was independently validated in patients with IR and non-IR RMS. These observations point to the possible role of non-coding transcripts in regulating and determining RMS biology and aggressiveness, and their potential to serve as novel prognostic indicators. Citation Format: Anirban P. Mitra, Sheetal A. Mitra, Jonathan D. Buckley, Philipp Kapranov, James R. Anderson, Stephen X. Skapek, Douglas S. Hawkins, Timothy J. Triche. Discovery and validation of novel prognostic genomic signatures in rhabdomyosarcoma. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr A16.