Abstract B37: Small nucleolar RNAs – New players in breast cancer prognosis
Authors: Preethi Krishnan, Sunita Ghosh, Bo Wang, Dongping Li, Richard Berendt, John R. Mackey, Olga Kovalchuk, Sambasivarao Damaraju
Journal: Cancer Research (2016)

Abstract Introduction: Small nucleolar RNAs (snoRNAs) are small non-coding RNAs that are predominantly involved in biogenesis of rRNAs and in modifications (methylation and pseudouridylation) of other RNAs such as rRNAs and tRNAs. Although snoRNAs were initially considered as housekeeping genes, recent literature indicated relative variations in their expression in tumors compared to normal tissues. snoRNAs have also been observed to play a role in cellular differentiation, proliferation, apoptosis, splicing mechanisms, and in some instances, in regulation of gene expression, suggesting that dysregulation of this class of small RNAs may contribute to tumorigenesis. snoRNAs have shown promise as diagnostic and/or prognostic markers for cancers such as lung and leukemia. Elevated snoRNAs and the genes involved in their biogenesis have also been reported to be important for Breast Cancer (BC). However, their role as prognostic markers in BC has not been addressed. Objectives: (i) To identify differentially expressed (DE) snoRNAs in BC and (ii) To identify snoRNAs as prognostic markers for BC. Methods: Small RNA libraries from 104 BC cases and 11 normal breast tissues (reduction mammoplasty) were generated for next generation sequencing (NGS), and bioinformatic analysis was carried out using Partek Genomics Suite 6.6. snoRNAs were annotated using Ensembl database. RPKM normalized data was adjusted for potential batch effects and was filtered for snoRNAs with ≥ 10 read counts in at least 90% of the samples. snoRNAs exhibiting a fold change &amp;gt;2.0 and a false discovery rate &amp;lt; 0.05 were considered as DE. Our study design included two approaches to identify prognostic markers: case-control (CC) and case-only (CO). While the CC approach tests only the DE set of snoRNAs for association with outcomes (Overall Survival, OS and Recurrence Free Survival, RFS), CO approach is an unbiased approach independent of the control tissues used, and interrogates all the snoRNAs retained after filtering. Since individual markers are not adequate to capture the complex interactions involved in conferring a phenotype, risk scores were constructed using snoRNAs significant in Univariate Cox proportional hazards regression model. Estimated risk scores were subjected to receiver operating characteristic curves to dichotomize patients into low and high-risk groups, followed by a multivariate analysis to adjust for potential confounders (SAS v9.3 and R statistical program). P&amp;lt;0.05 was considered to be statistically significant for all tests. Results: In the CC approach, 768 snoRNAs were profiled and 88 were retained after filtering, of which 40 were DE (31 down regulated and 9 up regulated); of these, five and four snoRNAs were significant for OS and RFS, respectively. In the CO approach, 763 snoRNAs were profiled, of which 95 were retained after filtering; of these, twelve and ten snoRNAs were significant for OS and RFS, respectively and includes the snoRNAs identified by CC approach. In both the approaches, patients belonging to high-risk group were associated with poor prognosis and the risk score was significant after adjusting for confounders. Platform concordance of the results will be assessed by qRT-PCR for representative snoRNAs. Validations of these findings in independent datasets are in progress. Summary: This is the first study to comprehensively analyse the role of snoRNAs as prognostic markers for BC using NGS. The combined risk score from the signatures was identified as potential independent prognostic factor for BC. Conclusions: Dysregulation of snoRNAs in breast tumors relative to normal were identified, indicating that these small RNAs could potentially contribute to breast tumorigenesis. As expected, CO approach identified higher numbers of markers with prognostic significance, highlighting the importance of adopting an unbiased approach in a biomarker study. Citation Format: Preethi Krishnan, Sunita Ghosh, Bo Wang, Dongping Li, Richard Berendt, John R. Mackey, Olga Kovalchuk, Sambasivarao Damaraju. Small nucleolar RNAs – New players in breast cancer prognosis. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr B37.