A Deep Intronic PKHD1 Variant Identified by SpliceAI in a Deceased Neonate With Autosomal Recessive Polycystic Kidney Disease.

The etiologies of newborn deaths in neonatal intensive care units usually remain unknown, even after genetic testing. Whole-genome sequencing, combined with artificial intelligence-based methods for predicting the effects of non-coding variants, provide an avenue for resolving these deaths. Using one such method, SpliceAI, we identified a maternally inherited deep intronic PKHD1 splice variant (chr6:52030169T>C), in trans with a pathogenic missense variant (p.Thr36Met), in a newborn who died of autosomal recessive polycystic kidney disease at age 2 days. We validated the deep intronic variant's impact in maternal urine-derived cells expressing PKHD1. Reverse transcription polymerase chain reaction followed by Sanger sequencing showed that the variant causes inclusion of 147bp of the canonical intron between exons 29 and 30 of PKHD1 into the mRNA, including a premature stop codon. Allele-specific expression analysis at a heterozygous site in the mother showed that the mutant allele completely suppresses canonical splicing. In an unrelated healthy control, there was no evidence of transcripts including the novel splice junction. We returned a diagnostic report to the parents, who underwent in vitro embryo selection.

Bone Marrow Surveillance of Pediatric Cancer Survivors Identifies Clones that Predict Therapy-Related Leukemia.

PURPOSE: Therapy-related myelodysplastic syndrome and acute leukemias (t-MDS/AL) are a major cause of nonrelapse mortality among pediatric cancer survivors. Although the presence of clonal hematopoiesis (CH) in adult patients at cancer diagnosis has been implicated in t-MDS/AL, there is limited published literature describing t-MDS/AL development in children. EXPERIMENTAL DESIGN: We performed molecular characterization of 199 serial bone marrow samples from 52 patients treated for high-risk neuroblastoma, including 17 with t-MDS/AL (transformation), 14 with transient cytogenetic abnormalities (transient), and 21 without t-MDS/AL or cytogenetic alterations (neuroblastoma-treated control). We also evaluated for CH in a cohort of 657 pediatric patients with solid tumor. RESULTS: We detected at least one disease-defining alteration in all cases at t-MDS/AL diagnosis, most commonly TP53 mutations and KMT2A rearrangements, including involving two novel partner genes (PRDM10 and DDX6). Backtracking studies identified at least one t-MDS/AL-associated mutation in 13 of 17 patients at a median of 15 months before t-MDS/AL diagnosis (range, 1.3-32.4). In comparison, acquired mutations were infrequent in the transient and control groups (4/14 and 1/21, respectively). The relative risk for development of t-MDS/AL in the presence of an oncogenic mutation was 8.8 for transformation patients compared with transient. Unlike CH in adult oncology patients, TP53 mutations were only detectable after initiation of cancer therapy. Last, only 1% of pediatric patients with solid tumor evaluated had CH involving myeloid genes. CONCLUSIONS: These findings demonstrate the clinical relevance of identifying molecular abnormalities in predicting development of t-MDS/AL and should guide the formation of intervention protocols to prevent this complication in high-risk pediatric patients.

A systems-level analysis of drug-target-disease associations for drug repositioning.

Drug repositioning is the process of finding new therapeutic uses for existing, approved drugs-a process thathas value when considering the exorbitant costs of novel drug development. Several computational strategies exist as a way to predict these alternative applications. In this study, we used datasets on: (1) human biological drug targets and (2) disease-associated genes and, based on a direct functional interaction between them, searched for potential opportunities for drug repositioning. From the set of 1125 unique drug targets and their 88 490 interactions with disease-associated genes, 30 drug targets were analyzed and (3) discussed in detail for the purpose of this article. The current indications of the drugs thattarget them were validated through the interactions, and new opportunities for repositioning were predicted. Among the set of drugs for potential repositioning werebenzodiazepines for the treatment of autism spectrum disorders; nortriptyline for the treatment of melanoma, glioma and other cancers; and vitamin B6 in prevention of spontaneous abortions and cleft palate birth defects. Special emphasis was also placed on those new potential indications that pertained to orphan diseases-these are diseases whose rarity means that development of novel treatment is not financially viable. This computational drug repositioning approach uses existing information on drugs and drug targets, and insights into the genetic basis of disease, as a means to systematically generate the most probable new uses for the drugs on offer, and in this way harness their true therapeutic power.