The Megacomplex protects ER-alpha from degradation by Fulvestrant in epithelial ovarian cancer.

Ovarian cancer, a significant contributor to cancer-related mortality, exhibits limited responsiveness to hormonal therapies targeting the estrogen receptor (ERα). This study aimed to elucidate the mechanisms behind ERα resistance to the therapeutic drug Fulvestrant (ICI182780 or ICI). Notably, compared to the cytoplasmic version, nuclear ERα was minimally degraded by ICI, suggesting a mechanism for drug resistance via the protective confines of the nuclear substructures. Of these substructures, we identified a 1.3MDa Megacomplex comprising transcription factors ERα, FOXA1, and PITX1 using size exclusion chromatography (SEC) in the ovarian cancer cell line, PEO4. ChIP-seq revealed these factors colocalized at 6,775 genomic positions representing sites of Megacomplex formation. Megacomplex ERα exhibited increased resistance to degradation by ICI compared to cytoplasmic and nuclear ERα. A small molecule inhibitor of active chromatin and super-enhancers, JQ1, in combination with ICI significantly enhanced ERα degradation from Megacomplex as revealed by SEC and ChIP-seq. Interestingly, this combination degraded both the cytoplasmic as well as nuclear ERa. Pathway enrichment analysis showed parallel results for RNA-seq gene sets following Estradiol, ICI, or ICI plus JQ1 treatments as those defined by Megacomplex binding identified through ChIP-seq. Furthermore, similar pathway enrichments were confirmed in mass-spec analysis of the Megacomplex macromolecule fractions after modulation by Estradiol or ICI. These findings implicate Megacomplex in ERα-driven ovarian cancer chromatin regulation. This combined treatment strategy exhibited superior inhibition of cell proliferation and viability. Therefore, by uncovering ERα's resistance within the Megacomplex, the combined ICI plus JQ1 treatment elucidates a novel drug treatment vulnerability.

Cortico-striatal differences in the epigenome in attention-deficit/ hyperactivity disorder.

While epigenetic modifications have been implicated in ADHD through studies of peripheral tissue, to date there has been no examination of the epigenome of the brain in the disorder. To address this gap, we mapped the methylome of the caudate nucleus and anterior cingulate cortex in post-mortem tissue from fifty-eight individuals with or without ADHD. While no single probe showed adjusted significance in differential methylation, several differentially methylated regions emerged. These regions implicated genes involved in developmental processes including neurogenesis and the differentiation of oligodendrocytes and glial cells. We demonstrate a significant association between differentially methylated genes in the caudate and genes implicated by GWAS not only in ADHD but also in autistic spectrum, obsessive compulsive and bipolar affective disorders through GWAS. Using transcriptomic data available on the same subjects, we found modest correlations between the methylation and expression of genes. In conclusion, this study of the cortico-striatal methylome points to gene and gene pathways involved in neurodevelopment, consistent with studies of common and rare genetic variation, as well as the post-mortem transcriptome in ADHD.

Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection.

The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.

Mapping the cortico-striatal transcriptome in attention deficit hyperactivity disorder.

Despite advances in identifying rare and common genetic variants conferring risk for ADHD, the lack of a transcriptomic understanding of cortico-striatal brain circuitry has stymied a molecular mechanistic understanding of this disorder. To address this gap, we mapped the transcriptome of the caudate nucleus and anterior cingulate cortex in post-mortem tissue from 60 individuals with and without ADHD. Significant differential expression of genes was found in the anterior cingulate cortex and, to a lesser extent, the caudate. Significant downregulation emerged of neurotransmitter gene pathways, particularly glutamatergic, in keeping with models that implicate these neurotransmitters in ADHD. Consistent with the genetic overlap between mental disorders, correlations were found between the cortico-striatal transcriptomic changes seen in ADHD and those seen in other neurodevelopmental and mood disorders. This transcriptomic evidence points to cortico-striatal neurotransmitter anomalies in the pathogenesis of ADHD, consistent with current models of the disorder.

Characterization and clustering of kinase isoform expression in metastatic melanoma.

Mutations to the human kinome are known to play causal roles in cancer. The kinome regulates numerous cell processes including growth, proliferation, differentiation, and apoptosis. In addition to aberrant expression, aberrant alternative splicing of cancer-driver genes is receiving increased attention as it could lead to loss or gain of functional domains, altering a kinase's downstream impact. The present study quantifies changes in gene expression and isoform ratios in the kinome of metastatic melanoma cells relative to primary tumors. We contrast 538 total kinases and 3,040 known kinase isoforms between 103 primary tumor and 367 metastatic samples from The Cancer Genome Atlas (TCGA). We find strong evidence of differential expression (DE) at the gene level in 123 kinases (23%). Additionally, of the 468 kinases with alternative isoforms, 60 (13%) had significant difference in isoform ratios (DIR). Notably, DE and DIR have little correlation; for instance, although DE highlights enrichment in receptor tyrosine kinases (RTKs), DIR identifies altered splicing in non-receptor tyrosine kinases (nRTKs). Using exon junction mapping, we identify five examples of splicing events favored in metastatic samples. We demonstrate differential apoptosis and protein localization between SLK isoforms in metastatic melanoma. We cluster isoform expression data and identify subgroups that correlate with genomic subtypes and anatomic tumor locations. Notably, distinct DE and DIR patterns separate samples with BRAF hotspot mutations and (N/K/H)RAS hotspot mutations, the latter of which lacks effective kinase inhibitor treatments. DE in RAS mutants concentrates in CMGC kinases (a group including cell cycle and splicing regulators) rather than RTKs as in BRAF mutants. Furthermore, isoforms in the RAS kinase subgroup show enrichment for cancer-related processes such as angiogenesis and cell migration. Our results reveal a new approach to therapeutic target identification and demonstrate how different mutational subtypes may respond differently to treatments highlighting possible new driver events in cancer.

A novel role for nucleolin in splice site selection.

Latent 5' splice sites, not normally used, are highly abundant in human introns, but are activated under stress and in cancer, generating thousands of nonsense mRNAs. A previously proposed mechanism to suppress latent splicing was shown to be independent of NMD, with a pivotal role for initiator-tRNA independent of protein translation. To further elucidate this mechanism, we searched for nuclear proteins directly bound to initiator-tRNA. Starting with UV-crosslinking, we identified nucleolin (NCL) interacting directly and specifically with initiator-tRNA in the nucleus, but not in the cytoplasm. Next, we show the association of ini-tRNA and NCL with pre-mRNA. We further show that recovery of suppression of latent splicing by initiator-tRNA complementation is NCL dependent. Finally, upon nucleolin knockdown we show activation of latent splicing in hundreds of coding transcripts having important cellular functions. We thus propose nucleolin, a component of the endogenous spliceosome, through its direct binding to initiator-tRNA and its effect on latent splicing, as the first protein of a nuclear quality control mechanism regulating splice site selection to protect cells from latent splicing that can generate defective mRNAs.

Differential gene expression identifies a transcriptional regulatory network involving ER-alpha and PITX1 in invasive epithelial ovarian cancer.

BACKGROUND: The heterogeneous subtypes and stages of epithelial ovarian cancer (EOC) differ in their biological features, invasiveness, and response to chemotherapy, but the transcriptional regulators causing their differences remain nebulous. METHODS: In this study, we compared high-grade serous ovarian cancers (HGSOCs) to low malignant potential or serous borderline tumors (SBTs). Our aim was to discover new regulatory factors causing distinct biological properties of HGSOCs and SBTs. RESULTS: In a discovery dataset, we identified 11 differentially expressed genes (DEGs) between SBTs and HGSOCs. Their expression correctly classified 95% of 267 validation samples. Two of the DEGs, TMEM30B and TSPAN1, were significantly associated with worse overall survival in patients with HGSOC. We also identified 17 DEGs that distinguished stage II vs. III HGSOC. In these two DEG promoter sets, we identified significant enrichment of predicted transcription factor binding sites, including those of RARA, FOXF1, BHLHE41, and PITX1. Using published ChIP-seq data acquired from multiple non-ovarian cell types, we showed additional regulatory factors, including AP2-gamma/TFAP2C, FOXA1, and BHLHE40, bound at the majority of DEG promoters. Several of the factors are known to cooperate with and predict the presence of nuclear hormone receptor estrogen receptor alpha (ER-alpha). We experimentally confirmed ER-alpha and PITX1 presence at the DEGs by performing ChIP-seq analysis using the ovarian cancer cell line PEO4. Finally, RNA-seq analysis identified recurrent gene fusion events in our EOC tumor set. Some of these fusions were significantly associated with survival in HGSOC patients; however, the fusion genes are not regulated by the transcription factors identified for the DEGs. CONCLUSIONS: These data implicate an estrogen-responsive regulatory network in the differential gene expression between ovarian cancer subtypes and stages, which includes PITX1. Importantly, the transcription factors associated with our DEG promoters are known to form the MegaTrans complex in breast cancer. This is the first study to implicate the MegaTrans complex in contributing to the distinct biological trajectories of malignant and indolent ovarian cancer subtypes.

Assessing ZNF154 methylation in patient plasma as a multicancer marker in liquid biopsies from colon, liver, ovarian and pancreatic cancer patients.

One epigenetic hallmark of many cancer types is differential DNA methylation occurring at multiple loci compared to normal tissue. Detection and assessment of the methylation state at a specific locus could be an effective cancer diagnostic. We assessed the effectiveness of hypermethylation at the CpG island of ZNF154, a previously reported multi-cancer specific signature for use in a blood-based cancer detection assay. To predict its effectiveness, we compared methylation levels of 3698 primary tumors encompassing 11 solid cancers, 724 controls, 2711 peripheral blood cell samples, and 350 noncancer disease tissues from publicly available methylation array datasets. We performed a single-molecule high-resolution DNA melt analysis on 71 plasma samples from cancer patients and 20 noncancer individuals to assess ZNF154 methylation as a candidate diagnostic metric in liquid biopsy and compared results to KRAS mutation frequency in the case of pancreatic carcinoma. We documented ZNF154 hypermethylation in early stage tumors, which did not increase in most noncancer disease or with respect to age or sex in peripheral blood cells, suggesting it is a promising target in liquid biopsy. ZNF154 cfDNA methylation discriminated cases from healthy donor plasma samples in minimal plasma volumes and outperformed KRAS mutation frequency in pancreatic cancer.

DNA methylation profiles unique to Kalahari KhoeSan individuals.

Genomes of KhoeSan individuals of the Kalahari Desert provide the greatest understanding of single nucleotide diversity in the human genome. Compared with individuals in industrialized environments, the KhoeSan have a unique foraging and hunting lifestyle. Given these dramatic environmental differences, and the responsiveness of the methylome to environmental exposures of many types, we hypothesized that DNA methylation patterns would differ between KhoeSan and neighbouring agropastoral and/or industrial Bantu. We analysed Illumina HumanMethylation 450 k array data generated from blood samples from 38 KhoeSan and 42 Bantu, and 6 Europeans. After removing CpG positions associated with annotated and novel polymorphisms and controlling for white blood cell composition, sex, age and technical variation we identified 816 differentially methylated CpG loci, out of which 133 had an absolute beta-value difference of at least 0.05. Notably SLC39A4/ZIP4, which plays a role in zinc transport, was one of the most differentially methylated loci. Although the chronological ages of the KhoeSan are not formally recorded, we compared historically estimated ages to methylation-based calculations. This study demonstrates that the epigenetic profile of KhoeSan individuals reveals differences from other populations, and along with extensive genetic diversity, this community brings increased accessibility and understanding to the diversity of the human genome.

Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers.

BACKGROUND: Variation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass ) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification. RESULTS: We developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-ml) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers. CONCLUSIONS: Our results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitates the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically diverse ovarian carcinomas, indicating broad utility of ZNF154 for use as a biomarker of ovarian cancer.

MethylToSNP: identifying SNPs in Illumina DNA methylation array data.

BACKGROUND: Current array-based methods for the measurement of DNA methylation rely on the process of sodium bisulfite conversion to differentiate between methylated and unmethylated cytosine bases in DNA. In the absence of genotype data this process can lead to ambiguity in data interpretation when a sample has polymorphisms at a methylation probe site. A common way to minimize this problem is to exclude such potentially problematic sites, with some methods removing as much as 60% of array probes from consideration before data analysis. RESULTS: Here, we present an algorithm implemented in an R Bioconductor package, MethylToSNP, which detects a characteristic data pattern to infer sites likely to be confounded by polymorphisms. Additionally, the tool provides a stringent reliability score to allow thresholding on SNP predictions. We calibrated parameters and thresholds used by the algorithm on simulated and real methylation data sets. We illustrate findings using methylation data from YRI (Yoruba in Ibadan, Nigeria), CEPH (European descent) and KhoeSan (southern African) populations. Our polymorphism predictions made using MethylToSNP have been validated through SNP databases and bisulfite and genomic sequencing. CONCLUSIONS: The benefits of this method are threefold. First, it prevents extensive data loss by considering only SNPs specific to the individuals in the study. Second, it offers the possibility to identify new polymorphisms in samples for which there is little known about the genetic landscape. Third, it identifies variants as they exist in functional regions of a genome, such as in CTCF (transcriptional repressor) sites and enhancers, that may be common alleles or personal mutations with potential to deleteriously affect genomic regulatory activities. We demonstrate that MethylToSNP is applicable to the Illumina 450K and Illumina 850K EPIC array data and is also backwards compatible to the 27K methylation arrays. Going forward, this kind of nuanced approach can increase the amount of information derived from precious data sets by considering samples of the project individually to enable more informed decisions about data cleaning.

Aberrant DNA methylation defines isoform usage in cancer, with functional implications.

Alternative transcript isoforms are common in tumors and act as potential drivers of cancer. Mechanisms determining altered isoform expression include somatic mutations in splice regulatory sites or altered splicing factors. However, since DNA methylation is known to regulate transcriptional isoform activity in normal cells, we predicted the highly dysregulated patterns of DNA methylation present in cancer also affect isoform activity. We analyzed DNA methylation and RNA-seq isoform data from 18 human cancer types and found frequent correlations specifically within 11 cancer types. Examining the top 25% of variable methylation sites revealed that the location of the methylated CpG site in a gene determined which isoform was used. In addition, the correlated methylation-isoform patterns classified tumors into known subtypes and predicted distinct protein functions between tumor subtypes. Finally, methylation-correlated isoforms were enriched for oncogenes, tumor suppressors, and cancer-related pathways. These findings provide new insights into the functional impact of dysregulated DNA methylation in cancer and highlight the relationship between the epigenome and transcriptome.

CAGI experiments: Modeling sequence variant impact on gene splicing using predictions from computational tools.

Improving predictions of phenotypic consequences for genomic variants is part of ongoing efforts in the scientific community to gain meaningful insights into genomic function. Within the framework of the critical assessment of genome interpretation experiments, we participated in the Vex-seq challenge, which required predicting the change in the percent spliced in measure (ΔΨ) for 58 exons caused by more than 1,000 genomic variants. Experimentally determined through the Vex-seq assay, the Ψ quantifies the fraction of reads that include an exon of interest. Predicting the change in Ψ associated with specific genomic variants implies determining the sequence changes relevant for splicing regulators, such as splicing enhancers and silencers. Here we took advantage of two computational tools, SplicePort and SPANR, that incorporate relevant sequence features in their models of splice sites and exon-inclusion level, respectively. Specifically, we used the SplicePort and SPANR outputs to build mathematical models of the experimental data obtained for the variants in the training set, which we then used to predict the ΔΨ associated with the mutations in the test set. We show that the sequence changes captured by these computational tools provide a reasonable foundation for modeling the impact on splicing associated with genomic variants.

Identification of human silencers by correlating cross-tissue epigenetic profiles and gene expression.

Compared to enhancers, silencers are notably difficult to identify and validate experimentally. In search for human silencers, we utilized H3K27me3-DNase I hypersensitive site (DHS) peaks with tissue specificity negatively correlated with the expression of nearby genes across 25 diverse cell lines. These regions are predicted to be silencers since they are physically linked, using Hi-C loops, or associated, using expression quantitative trait loci (eQTL) results, with a decrease in gene expression much more frequently than general H3K27me3-DHSs. Also, these regions are enriched for the binding sites of transcriptional repressors (such as CTCF, MECOM, SMAD4, and SNAI3) and depleted of the binding sites of transcriptional activators. Using sequence signatures of these regions, we constructed a computational model and predicted approximately 10,000 additional silencers per cell line and demonstrated that the majority of genes linked to these silencers are expressed at a decreased level. Furthermore, single nucleotide polymorphisms (SNPs) in predicted silencers are significantly associated with disease phenotypes. Finally, our results show that silencers commonly interact with enhancers to affect the transcriptional dynamics of tissue-specific genes and to facilitate fine-tuning of transcription in the human genome.

The hypothesis of ultraconserved enhancer dispensability overturned.

Two recent studies explore how redundant enhancers in mice really are.

Discovering Gene Regulatory Elements Using Coverage-Based Heuristics.

Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent.

Significant associations between driver gene mutations and DNA methylation alterations across many cancer types.

Recent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found that a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. In addition, we identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to underlying molecular characteristics, which could improve treatment efficacy.

SigSeeker: a peak-calling ensemble approach for constructing epigenetic signatures.

MOTIVATION: Epigenetic data are invaluable when determining the regulatory programs governing a cell. Based on use of next-generation sequencing data for characterizing epigenetic marks and transcription factor binding, numerous peak-calling approaches have been developed to determine sites of genomic significance in these data. Such analyses can produce a large number of false positive predictions, suggesting that sites supported by multiple algorithms provide a stronger foundation for inferring and characterizing regulatory programs associated with the epigenetic data. Few methodologies integrate epigenetic based predictions of multiple approaches when combining profiles generated by different tools. RESULTS: The SigSeeker peak-calling ensemble uses multiple tools to identify peaks, and with user-defined thresholds for peak overlap and signal strength it retains only those peaks that are concordant across multiple tools. Peaks predicted to be co-localized by only a very small number of tools, discovered to be only marginally overlapping, or found to represent significant outliers to the approximation model are removed from the results, providing concise and high quality epigenetic datasets. SigSeeker has been validated using established benchmarks for transcription factor binding and histone modification ChIP-Seq data. These comparisons indicate that the quality of our ensemble technique exceeds that of single tool approaches, enhances existing peak-calling ensembles, and results in epigenetic profiles of higher confidence. AVAILABILITY AND IMPLEMENTATION: http://sigseeker.org. CONTACT: lichtenbergj@mail.nih.gov. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CpG island methylator phenotype in adenocarcinomas from the digestive tract: Methods, conclusions, and controversies.

Over the last two decades, cancer-related alterations in DNA methylation that regulate transcription have been reported for a variety of tumors of the gastrointestinal tract. Due to its relevance for translational research, great emphasis has been placed on the analysis and molecular characterization of the CpG island methylator phenotype (CIMP), defined as widespread hypermethylation of CpG islands in clinically distinct subsets of cancer patients. Here, we present an overview of previous work in this field and also explore some open questions using cross-platform data for esophageal, gastric, and colorectal adenocarcinomas from The Cancer Genome Atlas. We provide a data-driven, pan-gastrointestinal stratification of individual samples based on CIMP status and we investigate correlations with oncogenic alterations, including somatic mutations and epigenetic silencing of tumor suppressor genes. Besides known events in CIMP such as BRAF V600E mutation, CDKN2A silencing or MLH1 inactivation, we discuss the potential role of emerging actors such as Wnt pathway deregulation through truncating mutations in RNF43 and epigenetic silencing of WIF1. Our results highlight the existence of molecular similarities that are superimposed over a larger backbone of tissue-specific features and can be exploited to reduce heterogeneity of response in clinical trials.

A Case of IL-7R Deficiency Caused by a Novel Synonymous Mutation and Implications for Mutation Screening in SCID Diagnosis.

Reported synonymous substitutions are generally non-pathogenic, and rare pathogenic synonymous variants may be disregarded unless there is a high index of suspicion. In a case of IL7 receptor deficiency severe combined immunodeficiency (SCID), the relevance of a non-reported synonymous variant was only suspected through the use of additional in silico computational tools, which focused on the impact of mutations on gene splicing. The pathogenic nature of the variant was confirmed using experimental validation of the effect on mRNA splicing and IL7 pathway function. This case reinforces the need to use additional experimental methods to establish the functional impact of specific mutations, in particular for cases such as SCID where prompt diagnosis can greatly impact on diagnosis, treatment, and survival.