Recurrent exon-deleting activating mutations in AHR act as drivers of urinary tract cancer.

Bladder cancer has a high recurrence rate and low survival of advanced stage patients. Few genetic drivers of bladder cancer have thus far been identified. We performed in-depth structural variant analysis on whole-genome sequencing data of 206 metastasized urinary tract cancers. In ~ 10% of the patients, we identified recurrent in-frame deletions of exons 8 and 9 in the aryl hydrocarbon receptor gene (AHRΔe8-9), which codes for a ligand-activated transcription factor. Pan-cancer analyses show that AHRΔe8-9 is highly specific to urinary tract cancer and mutually exclusive with other bladder cancer drivers. The ligand-binding domain of the AHRΔe8-9 protein is disrupted and we show that this results in ligand-independent AHR-pathway activation. In bladder organoids, AHRΔe8-9 induces a transformed phenotype that is characterized by upregulation of AHR target genes, downregulation of differentiation markers and upregulation of genes associated with stemness and urothelial cancer. Furthermore, AHRΔe8-9 expression results in anchorage independent growth of bladder organoids, indicating tumorigenic potential. DNA-binding deficient AHRΔe8-9 fails to induce transformation, suggesting a role for AHR target genes in the acquisition of the oncogenic phenotype. In conclusion, we show that AHRΔe8-9 is a novel driver of urinary tract cancer and that the AHR pathway could be an interesting therapeutic target.

Biallelic variants in POLR3GL cause endosteal hyperostosis and oligodontia.

RNA polymerase III (Pol III) is an essential 17-subunit complex responsible for the transcription of small housekeeping RNAs such as transfer RNAs and 5S ribosomal RNA. Biallelic variants in four genes (POLR3A, POLR3B, and POLR1C and POLR3K) encoding Pol III subunits have previously been found in individuals with (neuro-) developmental disorders. In this report, we describe three individuals with biallelic variants in POLR3GL, a gene encoding a Pol III subunit that has not been associated with disease before. Using whole exome sequencing in a monozygotic twin and an unrelated individual, we detected homozygous and compound heterozygous POLR3GL splice acceptor site variants. RNA sequencing confirmed the loss of full-length POLR3GL RNA transcripts in blood samples of the individuals. The phenotypes of the described individuals are mainly characterized by axial endosteal hyperostosis, oligodontia, short stature, and mild facial dysmorphisms. These features largely fit within the spectrum of phenotypes caused by previously described biallelic variants in POLR3A, POLR3B, POLR1C, and POLR3K. These findings further expand the spectrum of POLR3-related disorders and implicate that POLR3GL should be included in genetic testing if such disorders are suspected.

Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants.

BACKGROUND: Genomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients carrying de novo SVs are frequently unknown. METHODS: We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing. RESULTS: In 7 of these cases (18%), whole-genome sequencing analysis revealed disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict the effects on genes directly affected by SVs and on genes indirectly affected likely due to the changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In 8 cases, evidence was found for the involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to two cohorts containing a total of 379 patients with previously detected and classified de novo SVs and identified candidate driver genes in 189 cases (50%), including 40 cases whose SVs were previously not classified as pathogenic. Pathogenic position effects were predicted in 28% of all studied cases with balanced SVs and in 11% of the cases with copy number variants. CONCLUSIONS: These results demonstrate an integrated computational and experimental approach to predict driver genes based on analyses of WGS data with phenotype association and chromatin organization datasets. These analyses nominate new pathogenic loci and have strong potential to improve the molecular diagnosis of patients with de novo SVs.