Abstract 3753: An integrated approach to identify functional signaling modules in retinoblastoma cancer progression

This study illustrates a multi-omics approach combining transcriptomics and metabolomics datasets to study molecular events leading to progression of retinoblastoma. Retinoblastoma is a pediatric ocular cancer affecting children usually less than five years of age. It is a complex disease predisposed primarily by biallelic inactivating mutations in the RB1 gene. This gene has essential roles in cell cycle, differentiation, chromosome stability and is implicated in other functions. From a cohort of 9 patients undergoing enucleation of the affected eyes, we obtained tumor, aqueous humor, vitreous humor and tear samples. We obtained retina, aqueous humor and vitreous humor from enucleated eyes of 2 deceased pediatric controls, whose cause of death is not due to any eye related disease. The patients comprise of both high and low clinical and pathological risks. In the first stage of the study, we performed mRNA and miRNA gene expression using microarrays followed by pathway analysis to identify gene enrichment that would enable functional characterization of tumors. Differential expression analysis was carried out using moderated t-test with Benjamini Hochberg multiple testing correction. 108 (p≤0.05, fold change≥10) genes were found to be unique to patients possessing high risk of metastasis. Pathway analysis revealed key pathways that are known to be involved in the progression of retinoblastoma including cell cycle and Rap1 signaling pathway. Many of these responders were verified by RT-PCR and corroborated by immuno histochemistry studies. The study also revealed 18 novel miRNAs which had not been previously implicated in the disease, with a significant overlap of the miRNA target list with our mRNA expression data set. Metabolomics studies were performed using monophasic solvent extraction of aqueous, vitreous and tear samples. The extracted metabolites were analyzed on Accurate Mass QTOF mass spectrometer in positive and negative mode on a reverse phase C18 and HILIC columns. Data dependent MS/MS analysis was performed to confirm the compounds. Pathway analysis of the differentially expressed metabolites revealed enrichment of several pathways, including nucleotide metabolism and amino acids among others. Combined pathway analysis of metabolomics and transcriptomics data was performed in order to gain an understanding of the interrelationship between changes in gene expression and metabolomic profile. The results show overlap of key cellular pathways which can be mechanistically linked to disease progression. The study provide new biological insights that are made accessible by combining data from different biological and biochemical domains with a comprehensive integrated method. The information is useful not only to correlate expression markers with disease mechanism but also to better predict appropriate chemotherapy regimens and identify new mechanisms to treat even advanced stages of retinoblastoma. Citation Format: Nilanjan Guha, Deepak SA, Syed Lateef, Seetaraman Gundimeda, Arunkumar Padmanabhan, Carolina B Livi, Nigel Skinner, Arkasubhra Ghosh, Ashwin Mallipatna, Vishnu Suresh Babu, Arun Sreekumar. An integrated approach to identify functional signaling modules in retinoblastoma cancer progression. [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 3753. doi:10.1158/1538-7445.AM2015-3753