Abstract 2840: Discovery and independent validation of prognostic protein-coding and non-coding genomic meta-features in rhabdomyosarcoma

Abstract

Abstract INTRODUCTION: Pediatric rhabdomyosarcoma (RMS) has varying outcomes, particularly in intermediate-risk disease (IR-RMS) due to the limited ability of clinical staging to accurately risk-stratify a large proportion of patients. This study aimed to identify prognostic signatures in IR-RMS, the clinical subgroup with the most heterogeneous outcomes, which can potentially improve risk stratification compared with routine clinicopathologic metrics. Signature performance was validated on an independent set of RMS patients. METHODS: Prospectively-obtained primary tumor specimens from 80 IR-RMS patients on Children's Oncology Group clinical trial protocols formed the training set. Tumors from 54 RMS patients across all clinical risk groups formed the validation set. Whole transcriptome profiling was performed using oligonucleotide microarrays employing nearly 1.4 million probe selection regions (PSRs) and used to derive weighted meta-features. Accuracies of protein-coding and non-coding meta-features to predict survival were compared using areas under receiver operating characteristic curves. Associated biological processes were analyzed using curated pathway analysis tools. RESULTS: Histologic subtype (p = 0.94) and PAX-FKHR fusion status (p = 0.66) were unable to predict survival in the training set. Tumor site was the only clinical predictor of outcome in this set (p = 0.041). Cox regression on over 17,000 coding genes identified a prognostic 30-gene meta-feature (gMF, p = 0.001). Analysis of non-coding transcripts identified a 39-PSR meta-feature (ncMF) that also predicted survival (p<0.001). Multiple PSRs interrogating the same genomic locus were replaced by a single PSR resulting in an abbreviated 34-PSR non-coding meta-feature (ancMF), which remained prognostic (p<0.001). Predictive accuracy of ncMF was higher than gMF (96% vs. 71%, p<0.001). However, predictive accuracy of the former was comparable to the ancMF (97%, p = 0.54). When applied to the validation set, gMF, ncMF and ancMF were able to predict outcomes (p = 0.022, 0.006, 0.012, respectively). Analysis of biological processes using gMF showed enrichment for functions associated with musculoskeletal development and signaling pathways. Similar analysis of non-coding meta-features revealed enrichment for cellular assembly, cell cycle, apoptosis and cancer-associated functions. CONCLUSIONS: A 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 meta-features were independently validated in IR and non-IR RMS. This suggests that non-coding transcripts can regulate and determine RMS biology and aggressiveness, and be used as novel prognostic indicators. Citation Format: Anirban P. Mitra, Sheetal A. Mitra, Jonathan D. Buckley, James R. Anderson, Stephen X. Skapek, Douglas S. Hawkins, Timothy J. Triche. Discovery and independent validation of prognostic protein-coding and non-coding genomic meta-features in rhabdomyosarcoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2840. doi:10.1158/1538-7445.AM2015-2840