Somatic Loss-of-Function PIK3R1 and Activating Non-hotspot PIK3CA Mutations Associated with Capillary Malformation with Dilated Veins (CMDV).

Common capillary malformations are red vascular skin lesions, most commonly associated with somatic activating GNAQ or GNA11 mutations. We focused on capillary malformations lacking such a mutation to identify previously unreported genetic causes. We used targeted next-generation sequencing on 82 lesions. Bioinformatic analysis allowed the identification of 9 somatic pathogenic variants in PIK3R1 and PIK3CA, encoding for the regulatory and catalytic subunits of phosphoinositide 3-kinase, respectively. Recharacterization of these lesions unraveled a common phenotype: a pale capillary malformation associated with visible dilated veins. Primary endothelial cells from 2 PIK3R1-mutated lesions were isolated, and PI3k-Akt-mTOR and RAS-RAF-MAPK signaling were assessed by western blot. This unveiled an abnormal increase in Akt phosphorylation, effectively reduced by PI3K pathway inhibitors, such as mTOR, Akt, and PIK3CA inhibitors. The effects of mutant PIK3R1 were further studied using zebrafish embryos. Endothelium-specific expression of PIK3R1 mutants resulted in abnormal development of the posterior capillary-venous plexus. In summary, capillary malformation associated with visible dilated veins emerges as a clinical entity associated with somatic pathogenic variants in PIK3R1 or PIK3CA (nonhotspot). Our findings suggest that the activated Akt signaling can be effectively reversed by PI3K pathway inhibitors. In addition, the proposed zebrafish model holds promise as a valuable tool for future drug screening aimed at developing patient-tailored treatments.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations.

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.

Non-hotspot PIK3CA mutations are more frequent in CLOVES than in common or combined lymphatic malformations.

BACKGROUND: Theragnostic management, treatment according to precise pathological molecular targets, requests to unravel patients' genotypes. We used targeted next-generation sequencing (NGS) or digital droplet polymerase chain reaction (ddPCR) to screen for somatic PIK3CA mutations on DNA extracted from resected lesional tissue or lymphatic endothelial cells (LECs) isolated from lesions. Our cohort (n = 143) was composed of unrelated patients suffering from a common lymphatic malformation (LM), a combined lymphatic malformation [lymphatico-venous malformation (LVM), capillaro-lymphatic malformation (CLM), capillaro-lymphatico-venous malformation (CLVM)], or a syndrome [CLVM with hypertrophy (Klippel-Trenaunay-Weber syndrome, KTS), congenital lipomatous overgrowth-vascular malformations-epidermal nevi -syndrome (CLOVES), unclassified PIK3CA-related overgrowth syndrome (PROS) or unclassified vascular (lymphatic) anomaly syndrome (UVA)]. RESULTS: We identified a somatic PIK3CA mutation in resected lesions of 108 out of 143 patients (75.5%). The frequency of the variant allele ranged from 0.54 to 25.33% in tissues, and up to 47% in isolated endothelial cells. We detected a statistically significant difference in the distribution of mutations between patients with common and combined LM compared to the syndromes, but not with KTS. Moreover, the variant allele frequency was higher in the syndromes. CONCLUSIONS: Most patients with an common or combined lymphatic malformation with or without overgrowth harbour a somatic PIK3CA mutation. However, in about a quarter of patients, no such mutation was detected, suggesting the existence of (an)other cause(s). We detected a hotspot mutation more frequently in common and combined LMs compared to syndromic cases (CLOVES and PROS). Diagnostic genotyping should thus not be limited to PIK3CA hotspot mutations. Moreover, the higher mutant allele frequency in syndromes suggests a wider distribution in patients' tissues, facilitating detection. Clinical trials have demonstrated efficacy of Sirolimus and Alpelisib in treating patients with an LM or PROS. Genotyping might lead to an increase in efficacy, as treatments could be more targeted, and responses could vary depending on presence and type of PIK3CA-mutation.

ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations.

A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be.