👤 Piotr Tymoszuk

FGFR inhibitors are a new therapeutic option for urothelial carcinoma (UC) with FGFR2/3 alterations. In this study, we analyzed genetic alterations, co-regulation, and differential expression for 45 genes encoding FGF, FGFR, or FGF-binding proteins (FGFBPs) in five published UC cohorts (n = 3939 MIBC) and 39 UC cell lines (DepMap portal). Network analyses identified FGFR1/3 genes as critical oncogenic hubs, co-regulated with their ligands and co-receptors, and abundantly expressed at protein level in the HPA immunohistochemistry data set. Machine learning with 38 FGFR-, FGF-, and FGFBP-coding transcripts reproduced consensus molecular subtypes with high accuracy of 0.72-0.84 (Cohen's κ 0.59-0.77). FGFR3 mutations in the transmembrane/hinge region, which were enriched in luminal papillary tumors, trigger ligand-independent signaling. Conversely, overexpression of FGFR1 and its ligands and accessory protein transcripts indicates ligand-dependent FGFR1 signaling in stroma-rich and basal/squamous subtypes. The sensitivity of most DepMap UC cell lines to pan-FGFR inhibitors in the GDSC and PRISM drug screens was independent of FGFR3 alterations. In vitro, erdafitinib reduced proliferation in FGFR wild-type UC cell lines in a similar manner to FGFR3-mutated cell lines. Our findings highlight FGFR1 and FGFR3 as pivotal signaling pathways with distinct, molecular subtype-specific activation mechanisms. The results suggest that FGFR inhibitors may have therapeutic applications beyond UC tumors with FGFR2/3 alterations. Show less

Gonadotropin dysregulation seems to play a potential role in the carcinogenesis of testicular germ cell tumor (TGCT). The aim of this study was to explore the expression of specific genes related to sex hormone regulation, synthesis, and metabolism in TGCT and to define specific hormonal clusters. Two publicly available databases were used for this analysis (TCGA and GSE99420). By means of hard-threshold regularized KMEANS clustering, we assigned TGCT samples into four clusters defined in respect to different expression of the sex hormone-related genes. We analysed clinical data, protein and gene expression, signaling regarding hormonal clusters. Based on whole-transcriptome gene expression, prediction of anti-cancer drug response was made by RIDGE models. Cluster #1 (12-16%) consisted primarily of non-seminomatous germ cell tumor (NSGCT), characterized by high expression of PRL, GNRH1, HSD17B2 and SRD5A1. Cluster #2 (42-50%) included predominantly seminomas with high expression of SRD5A3, being highly infiltrated by T and B cells. Cluster #3 (8.3-18%) comprised of NSGCT with high expression of CGA, CYP19A1, HSD17B12, HSD17B1, SHBG. Cluster #4 (23-30%), which consisted primarily of NSGCT with a small fraction of seminomas, was outlined by increased expression of STAR, POMC, CYP11A1, CYP17A1, HSD3B2 and HSD17B3. Elevated fibroblast levels and increased extracellular matrix- and growth factor signaling-related gene signature scores were described in cluster #1 and #3. In the combined model of progression-free survival, S2/S3 tumor marker status, hormonal cluster #1 or #3 and teratoma histology, were independently associated with 25-30% increase of progression risk. Based on the increased receptor tyrosine kinase and growth factor signaling, cluster #1, #3 and #4 were predicted to be sensitive to tyrosine kinase inhibitors, FGFR inhibitors or EGFR/ERBB inhibitors. Cluster #2 and #4 were responsive to compounds interfering with DNA synthesis, cytoskeleton, cell cycle and epigenetics. Response to apoptosis modulators was predicted only for cluster #2. Hormonal cluster #1 or #3 is an independent prognostic factor regarding poor progression-free survival. Hormonal cluster assignment also affects the predicted drug response with cluster-dependent susceptibility to specific novel therapeutic compounds. Show less