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Spurdle, A. B.

Publications and source records attributed to Spurdle, A. B..

6 recordsLinked to original sources

Characteristics predicting reduced penetrance variants in the high-risk cancer predisposition gene TP53

Disease-causing variants with penetrance that is lower than the average expected for a given gene complicate classification, even when using gene-specific guidelines. For TP53, a gene associated with some of the highest cancer risks, even reduced penetrance disease-predisposition variants remain clinically actionable. We conducted a review of ClinVar submissions to identify TP53 variants flagged as having reduced penetrance by genetic testing laboratories and analyzed functional, bioinformatic, frequency, and clinical features of these variants compared to standard pathogenic and benign variants. Our findings show that reported reduced penetrance TP53 variants are more likely to exhibit intermediate functional activity in multiple assays and are predicted as deleterious with bioinformatic tools, though with lower scores than pathogenic variants. These variants also have a higher population frequency than pathogenic variants, and heterozygotes tend to manifest disease later in life, suggesting a need for refined clinical criteria to better capture attenuated Li-Fraumeni syndrome phenotypes. Finally, by applying a random forest prediction model to all TP53 uncertain or conflicting variants in ClinVar, we identified additional variants with potential reduced penetrance.

genetics↗

TP53 minigene analysis of 161 sequence changes provides evidence for role of spatial constraint and regulatory elements on variant-induced splicing impact

Germline TP53 genetic variants that disrupt splicing are implicated in hereditary cancer predisposition, while somatic variants contribute to tumorigenesis. We investigated the role of TP53 splicing regulatory elements (SREs), including G-runs that act as intronic splicing enhancers, using exons 3 and 6 and their downstream introns as models. Minigene microdeletion assays revealed four SRE-rich intervals: c.573_598, c.618_641, c.653_669 and c.672+14_672+36. A diagnostically reported deletion c.655_670del, overlapping an SRE-rich interval, induced an in-frame transcript {Delta}(E6q21) from new donor site usage. Within intron 6, deletion of at least four G-runs led to 100% aberrant transcript expression. Additionally, assay results suggested a donor-to-branchpoint distance cutoff of <50 nt for complete splicing aberration due to spatial constraint, and >75 nt for low risk of splicing abnormality. Overall, splicing data for 134 single nucleotide variants (SNVs) and 27 deletions in TP53 demonstrated that SRE-disrupting SNVs have weak splicing impact (up to 26% exon skipping), while deletions spanning multiple SREs can have profound splicing effects. Results also provide more data to inform splicing impact prediction for intronic deletions that shorten intron size.

genetics↗

SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion

SummarySpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to predict: the splicing aberration type including pseudoexonization, intron retention, partial exon deletion, and (multi)exon skipping using a 10 kb analysis window; the size of inserted or deleted sequence; the effect on reading frame; and the altered amino acid sequence. SAI-10k-calc has 95% sensitivity and 96% specificity for predicting variants that impact splicing, computed from a control dataset of 1,212 single nucleotide variants (SNVs) with curated splicing assay results. Notably, it has high performance ([&ge;]84% accuracy) for predicting pseudoexon and partial intron retention. The automated amino acid sequence prediction allows for efficient identification of variants that are expected to result in mRNA nonsense-mediated decay or translation of truncated proteins. Availability and implementationSAI-10k-calc is implemented in R (https://github.com/adavi4/SAI-10k-calc) and also available as a Microsoft Excel spreadsheet. Users can adjust the default thresholds to suit their target performance values. Supplementary informationSupplementary data are available online.

bioinformatics↗

Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort

More than 40% of the germline variants in ClinVar today are variants of uncertain significance (VUS). These variants remain unclassified in part because the patient-level data needed for their interpretation is siloed. Federated analysis can overcome this problem by "bringing the code to the data": analyzing the sensitive patient-level data computationally within its secure home institution and providing researchers with valuable insights from data that would not otherwise be accessible. We tested this principle with a federated analysis of breast cancer clinical data at RIKEN, derived from the BioBank Japan repository. We were able to analyze these data within RIKENs secure computational framework, without the need to transfer the data, gathering evidence for the interpretation of several variants. This exercise represents an approach to help realize the core charter of the Global Alliance for Genomics and Health (GA4GH): to responsibly share genomic data for the benefit of human health.

bioinformatics↗

Analysis of promoter-associated chromatin interactions reveals biologically relevant candidate target genes at endometrial cancer risk loci

The identification of target genes at genome-wide association study (GWAS) loci is a major obstacle for GWAS follow-up. To identify candidate target genes at the 16 known endometrial cancer GWAS risk loci, we performed HiChIP chromatin looping analysis of endometrial cell lines. To enrich for enhancer-promoter interactions, a mechanism through which GWAS variation may target genes, we captured loops associated with H3K27Ac histone, characteristic of promoters and enhancers. Analysis of HiChIP loops contacting promoters revealed enrichment for endometrial cancer GWAS heritability and intersection with endometrial cancer risk variation identified 103 HiChIP target genes at 13 risk loci. Expression of four HiChIP target genes (SNX11, SRP14, HOXB2 and BCL11A) was associated with risk variation, providing further evidence for their regulation. Network analysis functionally prioritized a set of proteins that interact with those encoded by HiChIP target genes, and this set was enriched for pan-cancer and endometrial cancer drivers. Lastly, HiChIP target genes and prioritized interacting proteins were over-represented in pathways related to endometrial cancer development. In summary, we have generated the first global chromatin looping data from endometrial cells, enabling analysis of all known endometrial cancer risk loci and identifying biologically relevant candidate target genes.

genomics↗

Assessment of Polygenic Architecture and Risk Prediction based on Common Variants Across Fourteen Cancers

We analyzed summary-level data from genome-wide association studies (GWAS) of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) contributing to risk, as well as the distribution of their associated effect sizes. All cancers evaluated showed polygenicity, involving at a minimum thousands of independent susceptibility variants. For some malignancies, particularly chronic lymphoid leukemia (CLL) and testicular cancer, there are a larger proportion of variants with larger effect sizes than those for other cancers. In contrast, most variants for lung and breast cancers have very small associated effect sizes. For different cancer sites, we estimate a wide range of GWAS sample sizes, required to explain 80% of GWAS heritability, varying from 60,000 cases for CLL to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores, compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that polygenic risk scores have substantial potential for risk stratification for relatively common cancers such as breast, prostate and colon, but limited potential for other cancer sites because of modest heritability and lower disease incidence.

genetics↗