Abstract 5581: A non-coding RNA panel is prognostically superior to standard pathologic criteria and coding transcripts in rhabdomyosarcoma

Abstract

Abstract INTRODUCTION: Rhabdomyosarcoma (RMS), the most common pediatric soft-tissue sarcoma, is stratified by the Children's Oncology Group (COG) into low/intermediate/high risk based on clinical outcomes. Most RMS patients, however, are categorized as intermediate-risk where survival is highly heterogeneous, suggesting an inherent inability to accurately stratify a large proportion of patients. This study profiled intermediate-risk RMSs for expressions of coding and non-coding transcripts with the aim of constructing prognostic signatures. The goal was to identify RNA panels that reflect underlying tumor biology and provide better risk stratification than routine clinicopathologic parameters. METHODS: RNAs extracted from 79 prospectively-obtained primary tumors from intermediate-risk RMS patients under COG clinical trial protocols were profiled on Affymetrix Human Exon 1.0 ST microarrays. Expressions of 1,400,033 probe set regions (PSRs) representing annotated and unannotated transcripts were analyzed using the Genetrix suite of microarray analysis tools. Cox regression and leave-n-out cross validation were used to derive and finalize the expression signatures. Individual prognostic potentials of the coding and non-coding signatures, and that of a signature that combined both features were compared against each other. RESULTS: Standard pathologic prognosticators such as histologic subtype classification (alveolar versus embryonal) and PAX-FKHR fusion gene status were unable to predict outcome in this cohort (p=0.40 and 0.45, respectively). Cox regression analysis on 17,049 coding transcripts created a 42-gene meta-feature that was able to predict survival (p=0.00024). Leave-n-out cross validation of this meta-feature upheld its prognostic ability (p=0.00030). Analysis of PSRs corresponding to unannotated transcripts identified a 32-PSR meta-feature that also predicted survival with greater significance than PSRs corresponding to coding transcripts (p<0.00001). To eliminate feature redundancy, multiple PSRs interrogating the same unannotated genomic locus were replaced by a representative PSR that reduced the meta-feature size to 24 PSRs, which was still able to predict survival better than the coding gene meta-feature (p<0.00001). A meta-feature that combined coding and non-coding RNA features retained its ability to predict outcome (p=0.00002), with non-coding RNA features contributing towards the bulk of its prognostic potential. CONCLUSIONS: A more concise non-coding RNA meta-feature was able to better predict outcome in intermediate-risk RMS than a larger coding gene meta-feature, where standard pathologic prognosticators failed. 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: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5581. doi:1538-7445.AM2012-5581