Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Tomato Bushy Stunt Virus Nanoparticles as a Platform for Drug Delivery to Shh-Dependent Medulloblastoma.

Medulloblastoma (MB) is a primary central nervous system tumor affecting mainly young children. New strategies of drug delivery are urgent to treat MB and, in particular, the SHH-dependent subtype-the most common in infants-in whom radiotherapy is precluded due to the severe neurological side effects. Plant virus nanoparticles (NPs) represent an innovative solution for this challenge. Tomato bushy stunt virus (TBSV) was functionally characterized as a carrier for drug targeted delivery to a murine model of Shh-MB. The TBSV NPs surface was genetically engineered with peptides for brain cancer cell targeting, and the modified particles were produced on a large scale using Nicotiana benthamiana plants. Tests on primary cultures of Shh-MB cells allowed us to define the most efficient peptides able to induce specific uptake of TBSV. Immunofluorescence and molecular dynamics simulations supported the hypothesis that the specific targeting of the NPs was mediated by the interaction of the peptides with their natural partners and reinforced by the presentation in association with the virus. In vitro experiments demonstrated that the delivery of Doxorubicin through the chimeric TBSV allowed reducing the dose of the chemotherapeutic agent necessary to induce a significant decrease in tumor cells viability. Moreover, the systemic administration of TBSV NPs in MB symptomatic mice, independently of sex, confirmed the ability of the virus to reach the tumor in a specific manner. A significant advantage in the recognition of the target appeared when TBSV NPs were functionalized with the CooP peptide. Overall, these results open new perspectives for the use of TBSV as a vehicle for the targeted delivery of chemotherapeutics to MB in order to reduce early and late toxicity.

PARP1 negatively regulates MAPK signaling by impairing BRAF-X1 translation.

In human cells BRAF oncogene is invariably expressed as a mix of two coding transcripts: BRAF-ref and BRAF-X1. These two mRNA isoforms, remarkably different in the sequence and length of their 3'UTRs, are potentially involved in distinct post-transcriptional regulatory circuits. Herein, we identify PARP1 among the mRNA Binding Proteins that specifically target the X1 3'UTR in melanoma cells. Mechanistically, PARP1 Zinc Finger domain down-regulates BRAF expression at the translational level. As a consequence, it exerts a negative impact on MAPK pathway, and sensitizes melanoma cells to BRAF and MEK inhibitors, both in vitro and in vivo. In summary, our study unveils PARP1 as a negative regulator of the highly oncogenic MAPK pathway in melanoma, through the modulation of BRAF-X1 expression.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency. Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.