Hypoxia-Inducible Factor-2-Altered Urothelial Carcinoma: Clinical and Genomic Features

Background: Hypoxia is recognized as a key feature of cancer growth and is involved in various cellular processes, including proliferation, angiogenesis, and immune surveillance. Besides hypoxia-inducible factor 1-alpha (HIF-1α), which is the main mediator of hypoxia effects and can also be activated under normoxic conditions, little is known about its counterpart, HIF-2. This study focused on investigating the clinical and molecular landscape of HIF-2-altered urothelial carcinoma (UC). Methods: Publicly available next-generation sequencing (NGS) data from muscle-invasive UC cell lines and patient tumor samples from the MSK/TCGA 2020 cohort (n = 476) were interrogated for the level of expression (mRNA, protein) and presence of mutations, copy number variations, structural variants in the EPAS1 gene encoding HIF-2, and findings among various clinical (stage, grade, progression-free and overall survival) and molecular (tumor mutational burden, enriched gene expression) parameters were compared between altered and unaltered tumors. Results: 19% (7/37) of UC cell lines and 7% (27/380) of patients with muscle-invasive UC display high EPAS1 mRNA and protein expression or/and EPAS1 alterations. EPAS1-altered tumors are associated with higher stage, grade, and lymph node metastasis as well as with shorter PFS (14 vs. 51 months, q = 0.01) and OS (15 vs. 55 months, q = 0.01). EPAS1 mRNA expression is directly correlated with that of its target-genes, including VEGF, FLT1, KDR, DLL4, CDH5, ANGPT1 (q EPAS1-altered tumors (9.9 vs. 4.9 mut/Mb), they are enriched in and associated with genes promoting immune evasion, including ARID5B, SPINT1, AAK1, CLIC3, SORT1, SASH1, and FGFR3, respectively (q Conclusions: HIF-2-altered UC has an aggressive clinical and a distinct genomic and immunogenomic profile enriched in angiogenesis- and immune evasion-promoting genes

MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package