Sex, Genotype, and Liver Volume Progression as Risk of Hospitalization Determinants in Autosomal Dominant Polycystic Liver Disease.

BACKGROUND & AIMS: Autosomal dominant polycystic liver disease is a rare condition with a female preponderance, based mainly on pathogenic variants in 2 genes, PRKCSH and SEC63. Clinically, autosomal dominant polycystic liver disease is characterized by vast heterogeneity, ranging from asymptomatic to highly symptomatic hepatomegaly. To date, little is known about the prediction of disease progression at early stages, hindering clinical management, genetic counseling, and the design of randomized controlled trials. To improve disease prognostication, we built a consortium of European and US centers to recruit the largest cohort of patients with PRKCSH and SEC63 liver disease. METHODS: We analyzed an international multicenter cohort of 265 patients with autosomal dominant polycystic liver disease harboring pathogenic variants in PRKCSH or SEC63 for genotype-phenotype correlations, including normalized age-adjusted total liver volumes and polycystic liver disease-related hospitalization (liver event) as primary clinical end points. RESULTS: Classifying individual total liver volumes into predefined progression groups yielded predictive risk discrimination for future liver events independent of sex and underlying genetic defects. In addition, disease severity, defined by age at first liver event, was considerably more pronounced in female patients and patients with PRKCSH variants than in those with SEC63 variants. A newly developed sex-gene score was effective in distinguishing mild, moderate, and severe disease, in addition to imaging-based prognostication. CONCLUSIONS: Both imaging and clinical genetic scoring have the potential to inform patients about the risk of developing symptomatic disease throughout their lives. The combination of female sex, germline PRKCSH alteration, and rapid total liver volume progression is associated with the greatest odds of polycystic liver disease-related hospitalization.

KidneyNetwork: using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease.

Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It uses expression patterns and established gene-phenotype associations to predict which genes could be related to what (disease) phenotypes in an unbiased manner. We applied KidneyNetwork to rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis to prioritize candidate genes. KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. The intersection of prioritized genes with genes carrying rare variants in a patient with kidney and liver cysts identified ALG6 as plausible candidate gene. We strengthen this plausibility by identifying ALG6 variants in several cystic kidney and liver disease cases without alternative genetic explanation. We present KidneyNetwork, a publicly available kidney-specific co-expression network with optimized gene-phenotype predictions for kidney disease phenotypes. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research. TRANSLATIONAL STATEMENT: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.

Modelling polycystic liver disease progression using age-adjusted liver volumes and targeted mutational analysis.

Background & Aims: Polycystic liver disease (PLD) manifests as numerous fluid-filled cysts scattered throughout the liver parenchyma. PLD most commonly develops in females, either as an extra-renal manifestation of autosomal-dominant polycystic kidney disease (ADPKD) or as isolated autosomal-dominant polycystic liver disease (ADPLD). Despite known genetic causes, clinical variability challenges patient counselling and timely risk prediction is hampered by a lack of genotype-phenotype correlations and prognostic imaging classifications. Methods: We performed targeted next-generation sequencing and multiplex ligation-dependent probe amplification to identify the underlying genetic defect in a cohort of 80 deeply characterized patients with PLD. Identified genotypes were correlated with total liver and kidney volume (assessed by CT or MRI), organ function, co-morbidities, and clinical endpoints. Results: Monoallelic diagnostic variants were identified in 60 (75%) patients, 38 (48%) of which pertained to ADPKD-gene variants (PKD1, PKD2, GANAB) and 22 (27%) to ADPLD-gene variants (PRKCSH, SEC63). Disease severity defined by age at waitlisting for liver transplantation and first PLD-related hospitalization was significantly more pronounced in mutation carriers compared to patients without genetic diagnoses. While current imaging classifications proved unable to differentiate between severe and moderate courses, grouping by estimated age-adjusted total liver volume progression yielded significant risk discrimination. Conclusion: This study underlines the predictive value of providing a molecular diagnosis for patients with PLD. In addition, we propose a novel risk-classification model based on age- and height-adjusted total liver volume that could improve individual prognostication and personalized clinical management. Lay summary: Polycystic liver disease (PLD) is a highly variable condition that can be asymptomatic or severe. However, it is currently difficult to predict clinical outcomes such as hospitalization, symptom burden, and need for transplantation in individual patients. In the current study, we aimed to investigate the clinical value of genetic confirmation and an age-adjusted total liver volume classification for individual disease prediction. While genetic confirmation generally pointed to more severe disease, estimated age-adjusted increases in liver volume could be useful for predicting clinical outcomes.