Abstract B61: Evaluation of tumor heterogeneity through analysis of polysomal RNA
Authors: Fernanda Sulla Lupinacci, Hermano Bellato, Martin Roffé, Hellen Kuasne, Tiago Santos, Victor Piana de Andrade, Paulo Sanematsu, Vilma Regina Martins, Silvia Rogatto, Glaucia Hajj
Journal: Clinical Cancer Research (2018)

Abstract Glioblastoma is the most common primary brain malignancy in adults and one of the most aggressive cancers. The overall treatment outcome of this tumor type remains unsatisfactory even though advanced multimodal treatments including surgery, chemotherapy, and radiotherapy have been available for decades. The median survival of patients is typically less than 2 years. Insights into the genetic landscape of glioblatomas have been achieved by high-throughput studies and patterns of gene expression have been able to identify molecular subgroups with putative prognostic or predictive significance. However, this approach provides little information about protein expression levels, since the expression of mRNAs does not necessarily reflect the levels of proteins. In addition, the establishment of molecular subgroups is compounded by the endemic problem of tumor heterogeneity, since subtype classifiers are variably expressed across individual cells within a tumor and the impact of sampling bias has not been addressed. More importantly, the relationships between different sources of intratumoral heterogeneity---genetic, transcriptional and functional--–remain obscure. Thus, to evaluate such intratumoral heterogeneity is fundamental to demonstrate potential therapeutic targets, source of tumor recurrences, and potential prognostic implications. We evaluated a specific transcriptomic and translatomic signature of the glioblastoma heterogeneity at the single-patient level, with 3 different patients. We performed a sampling of 2 pieces from each glioblastoma tumor. The samples could be histologically classified as high or low grade and total and polysomal mRNA was isolated and identified by the microarray HTA (Human Transcriptome Array). By comparing histologically high- vs. low-grade tumors we were able to identify 106 differentially transcribed and 53 differentially translated genes in one of the tumors, 170 differentially transcribed and 235 differentially translated genes in the second tumor, and 1500 differentially transcribed and 942 differentially translated genes in the third sample. Thus, our results demonstrate that the isolation of mRNA engaged in translation can be used to identify biomarkers of tumor progression, leading to new therapeutic approaches. In addition, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy. Citation Format: Fernanda Sulla Lupinacci, Hermano Bellato, Martin Roffé, Hellen Kuasne, Tiago Santos, Victor Piana de Andrade, Paulo Sanematsu, Vilma Regina Martins, Silvia Rogatto, Glaucia Hajj. Evaluation of tumor heterogeneity through analysis of polysomal RNA [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr B61.