Stormorken syndrome is a rare autosomal dominant disorder characterized by a phenotype that includes miosis, thrombocytopenia/thrombocytopathy with bleeding time diathesis, intellectual disability, mild hypocalcemia, muscle fatigue, asplenia, and ichthyosis. Using targeted sequencing and whole-exome sequencing, we identified the c.910C > T transition in a STIM1 allele (p.R304W) only in patients and not in their unaffected family members. STIM1 encodes stromal interaction molecule 1 protein (STIM1), which is a finely tuned endoplasmic reticulum Ca 2+ sensor. The effect of the mutation on the structure of STIM1 was investigated by molecular modeling, and its effect on function was explored by calcium imaging experiments. Results obtained from calcium imaging experiments using transfected cells together with fibroblasts from one patient are in agreement with impairment of calcium homeostasis. We show that the STIM1 p.R304W variant may affect the conformation of the inhibitory helix and unlock the inhibitory state of STIM1. The p.R304W mutation causes a gain of function effect associated with an increase in both resting Ca 2+ levels and store-operated calcium entry. Our study provides evidence that Stormorken syndrome may result from a single-gene defect, which is consistent with Mendelian-dominant inheritance.
Nephronophthisis-related ciliopathies (NPHP-RC) are recessive diseases characterized by renal dysplasia or degeneration. We here identify mutations of DCDC2 as causing a renal-hepatic ciliopathy. DCDC2 localizes to the ciliary axoneme and to mitotic spindle fibers in a cell-cycle-dependent manner. Knockdown of Dcdc2 in IMCD3 cells disrupts ciliogenesis, which is rescued by wild-type (WT) human DCDC2, but not by constructs that reflect human mutations. We show that DCDC2 interacts with DVL and DCDC2 overexpression inhibits β-catenin-dependent Wnt signaling in an effect additive to Wnt inhibitors. Mutations detected in human NPHP-RC lack these effects. A Wnt inhibitor likewise restores ciliogenesis in 3D IMCD3 cultures, emphasizing the importance of Wnt signaling for renal tubulogenesis. Knockdown of dcdc2 in zebrafish recapitulates NPHP-RC phenotypes, including renal cysts and hydrocephalus, which is rescued by a Wnt inhibitor and by WT, but not by mutant, DCDC2. We thus demonstrate a central role of Wnt signaling in the pathogenesis of NPHP-RC, suggesting an avenue for potential treatment of NPHP-RC.
Background: Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first clinically approved targeted therapy in bladder cancer. However, it requires previous molecular testing of each patient, which is costly and not ubiquitously available. Objective: To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer. Design, setting, and participants: We trained a deep learning network to detect FGFR3 mutations on digitized slides of muscle-invasive bladder cancers stained with hematoxylin and eosin from the Cancer Genome Atlas (TCGA) cohort (n = 327) and validated the algorithm on the "Aachen" cohort (n = 182; n = 121 pT2-4, n = 34 stroma-invasive pT1, and n = 27 noninvasive pTa tumors). Outcome measurements and statistical analysis: The primary endpoint was the area under the receiver operating curve (AUROC) for mutation detection. Performance of the deep learning system was compared with visual scoring by an uropathologist. Results and limitations: In the TCGA cohort, FGFR3 mutations were detected with an AUROC of 0.701 (p < 0.0001). In the Aachen cohort, FGFR3 mutants were found with an AUROC of 0.725 (p < 0.0001). When trained on TCGA, the network generalized to the Aachen cohort, and detected FGFR3 mutants with an AUROC of 0.625 (p = 0.0112). A subgroup analysis and histological evaluation found highest accuracy in papillary growth, luminal gene expression subtypes, females, and American Joint Committee on Cancer (AJCC) stage II tumors. In a head-to-head comparison, the deep learning system outperformed the uropathologist in detecting FGFR3 mutants. Conclusions: Our computer-based artificial intelligence system was able to detect genetic alterations of the FGFR3 gene of bladder cancer patients directly from histological slides. In the future, this system could be used to preselect patients for further molecular testing. However, analyses of larger, multicenter, muscle-invasive bladder cancer cohorts are now needed in order to validate and extend our findings.
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