The role of genetic testing in the diagnostic workflow of pediatric patients with kidney diseases: the experience of a single institution

Abstract

Purpose: Inherited kidney diseases are among the leading causes of kidney failure in children, resulting in increased mortality, high healthcare costs and need for organ transplantation. Next-generation sequencing technologies can help in the diagnosis of rare monogenic conditions, allowing for optimized medical management and therapeutic choices.

Methods: Clinical exome sequencing (CES) was performed on a cohort of 191 pediatric patients from a single institution, followed by Sanger sequencing to confirm identified variants and for family segregation studies.

Results: All patients had a clinical diagnosis of kidney disease: the main disease categories were glomerular diseases (32.5%), ciliopathies (20.4%), CAKUT (17.8%), nephrolithiasis (11.5%) and tubular disease (10.5%). 7.3% of patients presented with other conditions. A conclusive genetic test, based on CES and Sanger validation, was obtained in 37.1% of patients. The highest detection rate was obtained for ciliopathies (74.4%), followed by nephrolithiasis (45.5%), tubular diseases (45%), while most glomerular diseases and CAKUT remained undiagnosed.

Conclusions: Results indicate that genetic testing consistently used in the diagnostic workflow of children with chronic kidney disease can (i) confirm clinical diagnosis, (ii) provide early diagnosis in the case of inherited conditions, (iii) find the genetic cause of previously unrecognized diseases and (iv) tailor transplantation programs.

Keywords: Clinical exome sequencing; Genetic testing; Kidney diseases; Next-generation sequencing; Pediatric cohort.

Fig. 1

Genetic data analysis pipeline and criteria for variant inclusion. Schematic representation of the analytical pipeline adopted for variant identification and prioritization, including all the filtering-in and filtering-out criteria. The resulting variants were included in the final genetic report. Whenever possible, variant(s) validation and family segregation studies were performed. Genetic reports were classified as conclusive, uncertain, or inconclusive based on the indicated criteria. Based on this classification the diagnostic rate of our next-generation sequencing (NGS) workflow was calculated. 1 KG: 1000 genomes database; Alt fr: altered frequency; C3: variant of unknown significance; C4: likely pathogenic variant; C5: pathogenic variant; AR: autosomal recessive

Fig. 2

Main features of the pediatric cohort of the study. Distribution of patients according to clinical suspicion and age at recruitment. Data are shown as a violin plot: purple dots represent female subjects while blue diamonds represent male patients. Median and quartiles are shown (a). Distribution of patients according to clinical suspicion and ethnicity. The majority of patients are of Caucasian origin, with only a minority of patients being of African origin, Asian, Latin-American or of mixed origin (b). Distribution of patients divided on the basis of the main disease macro-categories, according to gender (inner circle) and family history (outer circle) (c). Detailed description of the primary disease affecting the pediatric cohort analyzed. Inner circle represents the main macro-categories while the outer circle refers to primary diseases within each macro-category. Numbers in brackets indicate the number of patients (d) CAK: congenital abnormalities of kidney and urinary tract (CAKUT); CIL: ciliopathies; GLO: glomerulopathies; NEP: nephrolithiasis; TUB: tubulopathies; OTH: others; HUS: Hemolytic uremic syndrome; FSGS: focal segmental glomerulosclerosis.

Fig. 3

Representation of the cohort and workflow leading to the identification of causative variants. Sankey diagram summarizing the distribution of the cohort and the results of the analytical workflow. Nodes represent (i) the main disease macro-categories, (ii) variants identification or not by NGS, and (iii) classification of the genetic reports. Arrows width is proportional to the number of patients. The Sankey Matic tool was used to obtain the Sankey diagram

Fig. 4

Summary of curated variants in disease-causative genes. The curated and reported variants are listed showing the gene involved and the type of variant (colored squares). Each raw represents a gene (mode of inheritance is reported in bracket; XLD: X-linked dominant; AD: autosomal dominant; AR: autosomal recessive; XLR: X-linked recessive; NA: not available) and each column a patient (Pts). Some patients presented more than one variant. Disease macro-categories (orange square) and family history (blue square for positive and yellow square for negative) for each patient are indicated. The graph of the top showed the age at recruitment for each diagnosed patient, while the histogram plot on the left showed the number of variants mapping within each gene (a). Number of variants and their ACMG classification identified for each disease macro-category (b). Distribution of heterozygous, homozygous, compound heterozygous, hemizygous and copy number variant (CNV) within the three different categories (C3-C4-C5) of variants (c). Histogram plot showing the distribution of the identified variants according to whether they are already or not published (d). CAK: CAKUT; CIL: ciliopathies; GLO: glomerulopathies; NEP: nephrolithiasis; TUB: tubulopathies; OTH: Others