Biology subjects

Plon, S. E.

Publications and source records attributed to Plon, S. E..

7 recordsLinked to original sources

bioRxiv2026

Distinct mutational landscapes when comparing germline and somatic cancer variants in forty tumor suppressor genes.

Germline and somatic cancer variants in tumor suppressor genes (TSGs) share loss of function mechanisms with studies of a few genes (DICER1, CEBPA) highlighting differences in variant consequence and location. To systematically assess whether TSGs display distinct mutational patterns we leveraged large public genetic databases and compared high-quality pathogenic/likely pathogenic (P/LP) germline variants in ClinVar, with oncogenic/likely oncogenic (O/LO) somatic tumor variants from cBioPortal across 40 TSGs. We obtained 32,941 P/LP germline and 12,907 O/LO somatic variants. Only 3,863 (9.2%) variants were shared. Eighteen TSGs showed significant differences in distributions of variant occurrences by consequence replicated with non-overlapping somatic data from the COSMIC database (chi-squared tests, false discovery rate=5%). DICER1, TP53, and SMAD4 displayed excess somatic missense events, while 9 TSGs (e.g., RB1, APC) contained excess somatic stop-gains throughout the coding sequence. Analysis of tumor type revealed excess stop-gains in tissues exposed to environmental mutagens with corresponding mutation signatures. Germline and somatic events also distributed unevenly across cDNA location with 103 regions of preferential clustering in 39 TSGs (78 somatic, 25 germline). Twenty somatic clusters overlapped recurring frameshifts in homopolymer runs, many in tumors with microsatellite instability. Germline clusters contain more germline-exclusive variants, some driving non-cancer phenotypes reflecting genetic pleiotropy. In some TSGs (WT1), germline variants predispose to tumors representing a minority of somatic data. Altogether, germline and somatic variants of TSGs represent unique sets with substantially different patterns shaped by selection pressures including environmental, phenotypic, and functional mechanisms. Characterizing these distinctions enables more accurate clinical interpretation of cancer variants.

genetics↗

bioRxiv2020

Ancestry-Specific Predisposing Germline Variants in Cancer

BackgroundCancer risk differs across ancestries and these differences may result from differing prevalence of inherited genetic predisposition. Yet, most germline genomic studies performed to date have focused on individuals of European ancestry. Ancestry-specific analyses of germline genomes are required to inform cancer genetic risk and prognosis for each ancestral group. Here, we investigate potentially germline pathogenic variants in cancer predisposition genes (CPG) and their somatic effects in patients across diverse ancestral backgrounds. MethodsWe performed a retrospective analysis of germline genomic data of 9,899 patients from 33 cancer types generated by The Cancer Genome Atlas (TCGA) project along with matching somatic genomic and transcriptomic data. By collapsing pathogenic and likely pathogenic variants to the gene level, we analyzed the association between variants in CPGs and cancer types within each ancestry. We also identified ancestry- specific predisposing variants and their associated somatic two-hit events and gene expression levels. ResultsRecent genetic ancestry analysis classified the cohort of 9,899 cancer cases into individuals of primarily European, (N = 8,184, 82.7%), African (N = 966, 9.8%), East Asian (N = 649, 6.6%), South Asian (N=48, 0.5%), Native/Latin American (N=41, 0.4%), and admixed (N=11, 0.1%) ancestries. In the African ancestry, we discovered a potentially novel association of BRCA2 in lung squamous cell carcinoma (OR = 41.4 [95% CI, 6.1-275.6]; FDR = 0.002) along with the previously identified association of BRCA2 in ovarian serous cystadenocarcinoma (OR=8.5 [95% CI, 1.5-47.4]; FDR=0.045). Similarly, in the East Asian ancestry, we discovered one previously known association of BRIP1 in stomach adenocarcinoma (OR=12.8 [95% CI, 1.8-90.84]; FDR=0.038). Rare variant burden analysis further identified 7 suggestive associations for cancer-gene pairs in African ancestry individuals previously well described in European ancestry including SDHB in pheochromocytoma and paraganglioma, ATM in prostate adenocarcinoma, VHL in kidney renal clear cell carcinoma, FH in kidney renal papillary cell carcinoma, and PTEN in uterine corpus endometrial carcinoma. Loss of heterozygosity was identified for 7 out of the 15 African ancestry carriers of predisposing variants. Further, tumors from the SDHB or BRCA2 carriers showed simultaneous allelic specific expression and low gene expression of their respective affected genes; and FH splice-site variant carriers showed mis-splicing of FH. ConclusionWhile several predisposing genes are shared across patients, many pathogenic variants are found to be ancestry-specific and trigger somatic effects. Analysis of larger diverse ancestries genomic cohorts are required to pinpoint ancestry- specific genetic predisposition to inform personalized diagnosis and screening strategies.

genomics↗

bioRxiv2019

Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors

Meningiomas account for one-third of all primary brain tumors. Although typically benign, about 20% of meningiomas are aggressive, and despite the rigor of the current histopathological classification system, there remains considerable uncertainty in predicting tumor behavior. Here, we analyzed 160 tumors from all three WHO grades (I-III) using clinical, gene expression and sequencing data. Unsupervised clustering analysis identified three molecular types (A, B, and C) that reliably predicted recurrence. These groups did not directly correlate with the WHO grading system, which would classify more than half of the tumors in the most aggressive molecular type as benign. Transcriptional and biochemical analyses revealed that aggressive meningiomas involve loss of the repressor function of the DREAM complex, which results in cell cycle activation; only tumors in this category tend to recur after full resection. These findings should improve our ability to predict recurrence and develop targeted treatments for these clinically challenging tumors.\n\nSignificance StatementMeningiomas are the most common primary brain tumors. Although most of these tumors are benign, one-fifth will recur despite apparently complete resection. Several studies have demonstrated that genomic approaches can yield important insights into the biology of these tumors. We performed RNA sequencing and whole-exome sequencing of 160 tumors from 140 patients, which identified three distinct groups of meningioma that correlate with recurrence better than the current WHO grading system. Our analysis also revealed that the most aggressive type was characterized by loss of the repressive DREAM complex. These findings should improve prognostication for patients and lead to viable therapeutic targets.

cancer biology↗

bioRxiv2018

The Clinical Imperative for Inclusivity: Race, Ethnicity, and Ancestry (REA) in Genomics

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: 1) acquisition of REA data via clinical laboratory requisition forms, and 2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites as determined by variants in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about variants at clinically relevant sites in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with \"pathogenic\" and \"likely pathogenic\" expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed and adopted across clinical genomics.

genomics↗

bioRxiv2017

Landscape of human miRNA variation and conservation using Annotative Database of miRNA Elements, ADmiRE

MicroRNAs (miRNAs) are the most abundant class of non-coding RNAs that regulate expression of >60% genes and are frequently deregulated in many human diseases. Sequence variants in miRNAs are expected to have a high impact on miRNA function. However, the lack of miRNA variant annotation and prioritization guidelines has hampered this analysis from whole genome/exome sequencing (WGS/WES) studies. Through the development of an Annotative Database of miRNA Elements, ADmiRE workflow, we re-annotated the publicly available population dataset of gnomAD 15,596 WGS and 123,136 WES and describe 26,094 precursor-miRNA variants. AdmiRE annotates twice the miRNA variants predicted by existing tools which prioritize variation relative to protein coding regions. We provide the allele frequency distribution of miRNA variation which is comparable to variation in exonic regions. This distribution is similar for miRNAs located in the intragenic and intergenic genomic context. Moreover, high confidence miRNAs (designated by miRBase) harbor less variation (the majority contributed by rare variants) compared with the remaining miRNAs. We identify 279 miRNAs highly constrained with little or no variation in gnomAD. We further describe the evolutionary conservation of miRNAs across 100 vertebrates and identify 434 highly conserved miRNAs. We demonstrate that these constraint and conservation metrics (now incorporated into the ADmiRE workflow) characterize miRNAs previously implicated in human diseases. In conclusion, through the development of ADmiRE, we comprehensively analyze the landscape of miRNA sequence variation in large human population datasets and provide miRNA vertebrate conservation scores to aid future studies of miRNA variation in human diseases.

genomics↗

bioRxiv2017

Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

BackgroundThe American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.\n\nResultsUsing 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation where in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables like disease mechanism, gene constraint and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011-2017).\n\nConclusionsOur analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.

bioinformatics↗

bioRxiv2017

Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the Clinical Genome Resource

With advances in genomic sequencing technology, the number of reported gene-disease relationships has rapidly expanded. However, the evidence supporting these claims varies widely, confounding accurate evaluation of genomic variation in a clinical setting. Despite the critical need to differentiate clinically valid relationships from less well-substantiated relationships, standard guidelines for such evaluation do not currently exist. The NIH-funded Clinical Genome Resource (ClinGen) has developed a framework to define and evaluate the clinical validity of gene-disease pairs across a variety of Mendelian disorders. In this manuscript we describe a proposed framework to evaluate relevant genetic and experimental evidence supporting or contradicting a gene-disease relationship, and the subsequent validation of this framework using a set of representative gene-disease pairs. The framework provides a semi-quantitative measurement for the strength of evidence of a gene-disease relationship which correlates to a qualitative classification: \"Definitive\", \"Strong\", \"Moderate\", \"Limited\", \"No Reported Evidence\" or \"Conflicting Evidence.\" Within the ClinGen structure, classifications derived using this framework are reviewed and confirmed or adjusted based on clinical expertise of appropriate disease experts. Detailed guidance for utilizing this framework and access to the curation interface is available on our website. This evidence-based, systematic method to assess the strength of gene-disease relationships will facilitate more knowledgeable utilization of genomic variants in clinical and research settings.

genetics↗