Abstract 4234: Computational analysis of immune-associated genomic and transcriptomic elements differentiating papillary thyroid cancer subtypes

Abstract

Abstract Approximately 600,000 patients live with papillary thyroid cancer (PTC) in the United States, with its incidence rising from 4.8 to 14.9 cases per 100,000 people from 1975 to 2012. Despite being the fastest growing cancer, the therapeutic options for PTC has remained unchanged and are primarily limited to surgery, radioactive iodine, and chemotherapy. Considering the untapped potential of immunotherapy for thyroid cancer, this study explores the genomic and transcriptomic features of PTC subtypes in order to identify dysregulated immune-associated (IA) genes and IA pathways that may serve as therapeutic targets. Using data from The Cancer Genome Atlas (TCGA), the three most common histological subtypes of PTC, including the classical (CPTC), the follicular variant (FVPTC), and the tall cell (TCPTC), were analyzed computationally and compared to normal tissue to identify differentially expressed IA genes. These dysregulated IA genes were further filtered for prognostic significance using MACIS scores. To investigate the molecular mechanism for their dysregulation and explore the possibility of genome-based patient stratification, we profiled the correlation of genomic alterations, including mutation and copy number variation, in the various PTC subtypes to IA gene expression using the information theory-based algorithm REVEALER. Our analyses revealed that the expression profiles of CPTC and TCPTC were the most similar from an immunological perspective, with 360 IA genes similarly dysregulated in both of those subtypes. Additionally, TCPTC had the most unique immunological profile, with 300 IA genes dysregulated exclusively in TCPTC, compared to 107 in FVPTC and 22 in CPTC. Specifically, gene such as RAET1E, FGFR11, and MUC1, which are associated with innate and adaptive immune responses, cell survival and proliferation, and TP53-transcription respectively, were dysregulated only in TCPTC. We also studied gene expression regulators and determined that microRNAs had a limited role in the gene dysregulation we observed. However, the regulatory pathways and mutational features unique to certain subtypes were indicative of pathways differentiating cancer prognosis between PTC subtypes. Most notably, genes in the glycosylation pathway mediated by MUC1 and B3GNT3 are upregulated in TCPTC but are downregulated in the less aggressive FVPTC and CPTC subtypes, suggesting a possible mechanism for the aggressive progression of TCPTC. In summary, our findings indicate that subtypes of PTC have both common and unique dysregulated IA genes and pathways, which may be exploited to develop novel immunotherapy targets or biomarkers predictive of prognosis for the different subtypes. Citation Format: Jaideep Chakladar, Megan Chu, Aditi Gnanasekar, Kristen F. Rosenberg, Joseph C. Tsai, Lindsay M. Wong, Weg M. Ongkeko. Computational analysis of immune-associated genomic and transcriptomic elements differentiating papillary thyroid cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4234.