Whole Exome Sequencing Data Research Articles

Integration of transcriptomic analysis and multiple machine learning approaches identifies NAFLD progression-specific hub genes to reveal distinct genomic patterns and actionable targets

BackgroundNonalcoholic fatty liver disease (NAFLD) is a leading public health problem worldwide. Approximately one fourth of patients with nonalcoholic fatty liver (NAFL) progress to nonalcoholic steatohepatitis (NASH), an advanced stage of NAFLD. Hence, there is an urgent need to make a better understanding of NAFLD heterogeneity and facilitate personalized management of high-risk NAFLD patients who may benefit from more intensive surveillance and preventive intervene.MethodsIn this study, a series of bioinformatic methods were performed to identify NAFLD progression-specific pathways and genes, and three machine learning approaches were combined to construct a risk-stratification gene signature to quantify risk assessment. In addition, bulk RNA-seq, single-cell RNA-seq (scRNA-seq) transcriptome profiling data and whole-exome sequencing (WES) data were comprehensively analyzed to reveal the genomic alterations and altered pathways between distinct molecular subtypes.ResultsTwo distinct subtypes of NAFL were identified with the NAFLD progression-specific genes, and one subtype has a high similarity of the inflammatory pattern and fibrotic potential with NASH. The established risk-stratification gene signature could discriminate advanced samples from overall NAFLD. COL1A2, one key gene closely related to NAFLD progression, is specifically expressed in fibroblasts involved in hepatocellular carcinoma (HCC), and significantly correlated with EMT and angiogenesis in pan-cancer. Moreover, the β-catenin/COL1A2 axis might play a critical role in fibrosis severity and inflammatory response during NAFLD-HCC progression.ConclusionIn summary, our study provided evidence for the necessity of molecular classification and established a risk-stratification gene signature to quantify risk assessment of NAFLD, aiming to identify different risk subsets and to guide personalized treatment.

Prognostic mutation signature would serve as a potential prognostic predictor in patients with diffuse large B-cell lymphoma

The present study aimed to elucidate the prognostic mutation signature (PMS) associated with long-term survival in a diffuse large B-cell lymphoma (DLBCL) cohort. All data including derivation and validation cohorts were retrospectively retrieved from The Cancer Genome Atlas (TCGA) database and whole-exome sequencing (WES) data. The Lasso Cox regression analysis was used to construct the PMS based on WES data, and the PMS was determined using the area under the receiver operating curve (AUC). The predictive performance of eligible PMS was analyzed by time-dependent receiver operating curve (ROC) analyses. After the initial evaluation, a PMS composed of 94 PFS-related genes was constructed. Notably, this constructed PMS accurately predicted the 12-, 36-, and 60-month PFS, with AUC values of 0.982, 0.983, and 0.987, respectively. A higher level of PMS was closely linked to a significantly worse PFS, regardless of the molecular subtype. Further evaluation by forest plot revealed incorporation of international prognostic index or tumor mutational burden into PMS increased the prediction capability for PFS. The drug-gene interaction and pathway exploration revealed the PFS-related genes were associated with DNA damage, TP53, apoptosis, and immune cell functions. In conclusion, this study utilizing a high throughput genetic approach demonstrated that the PMS could serve as a prognostic predictor in DLBCL patients. Furthermore, the identification of the key signaling pathways for disease progression also provides information for further investigation to gain more insight into novel drug-resistant mechanisms.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD)and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES)data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

A cross-trait study of lung cancer and its related respiratory diseases based on large-scale exome sequencing population.

Genome-wide association studies (GWASs) explain the genetic susceptibility between diseases and common variants. Nevertheless, with the appearance of large-scale sequencing profiles, we could explore the rare coding variants in disease pathogenesis. We estimated the genetic correlation of nine respiratory diseases and lung cancer in the UK Biobank (UKB) by linkage disequilibrium score regression (LDSC). Then, we performed exome-wide association studies at single-variant level and gene-level for lung cancer and lung cancer-related respiratory diseases using the whole-exome sequencing (WES) data of 427,934 European participants. Cross-trait meta-analysis was conducted by association analysis based on subsets (ASSET) to identify the pleiotropic variants, while in-silico functional analysis was performed to explore their function. Causal mediation analysis was used to explore whether these pleiotropic variants lead to lung cancer is mediated by affecting the chronic respiratory diseases. Five respiratory diseases [emphysema, pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and fibrosis] were genetically correlated with lung cancer. We identified 102 significant independent variants at single-variant levels for lung cancer and five lung cancer-related diseases. 15:78590583:G>A (missense variant in CHRNA5) was shared in lung cancer, emphysema, and COPD. Meanwhile, 14 significant genes and 87 suggestive genes were identified in gene-based association tests, including HSD3B7 (lung cancer), SRSF2 (pneumonia), TNXB (asthma), TERT (fibrosis), MOSPD3 (emphysema). Based on the cross-trait meta-analysis, we detected 145 independent pleiotropic variants. We further identified abundant pathways with significant enrichment effects, demonstrating that these pleiotropic genes were functional. Meanwhile, the proportion of mediation effects of these variants ranged from 6 to 23 (emphysema: 23%; COPD: 20%; pneumonia: 20%; fibrosis: 7%; asthma: 6%) through these five respiratory diseases to the incidence of lung cancer. The identified shared genetic variants, genes, biological pathways, and potential intermediate causal pathways provide a basis for further exploration of the relationship between lung cancer and respiratory diseases.

Abstract IA021: Novel genomic features of POLE-mutant tumors: the utility for tumor classification and identification of new driver alleles

Abstract Alterations in the exonuclease domain of POLE drive the development of ultramutated endometrial tumors with better prognosis. A hallmark of these tumors is the accumulation of G>T mutations in AGA sequences; however, understanding of their genomic features beyond the trinucleotide motifs is limited. We applied a novel computational framework to the whole-exome sequencing data of 524 endometrial tumors reported by The Cancer Genome Atlas network to analyze the extended DNA sequence context of ultramutation. We found that the presence of POLE driver alleles is associated with an increased frequency of G>T mutations in polypurine tracts. Sequences containing three consecutive purines, including the classic AGA context, are only moderately mutable, but the mutability increased abruptly as the tract length reached six or more purines. Moreover, mutations showed a strong preference for certain positions within the tracts, creating a characteristic quantifiable pattern. Using this signature, we developed a machine learning classifier to identify tumors with hitherto unknown POLE drivers and validated two new drivers, POLE-E978G and POLE-S461L, by functional assays in yeast. Unlike previously known pathogenic variants, the E978G substitution affects the DNA polymerase rather than exonuclease domain of POLE. We further show that the extended genomic signature of POLE ultramutation is shared by tumors with POLD1 drivers. These findings uncover new mechanisms of ultramutation and highlight the limitations of the current focus on POLE exonuclease domain variants when stratifying patients. Citation Format: Daria Ostroverkhova, Kathryn Tyryshkin, Annette K. Beach, Elizabeth A. Moore, Yosef Masoudi-Sobhanzadeh, Stephanie R. Barbari, Igor B Rogozin, Konstantin V Shaitan, Anna R Panchenko, Polina V. Shcherbakova. Novel genomic features of POLE-mutant tumors: the utility for tumor classification and identification of new driver alleles [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr IA021.

Characterizing Homozygous Variants in Bardet-Biedl Syndrome-Associated Genes Within Iranian Families: Unveiling a Founder Variant in BBS2, c.471G>A.

Bardet-Biedl syndrome (BBS) is a rare inherited ciliopathy disorder characterized by a broad spectrum of clinical symptoms such as retinal dystrophy, obesity, polydactyly, genitourinary and kidney anomalies, learning disability, and hypogonadism. The understanding of the variants involved in BBS-causing genes remains incomplete, highlighting the need for further research to develop a molecular diagnostic strategy for this syndrome. Singleton whole-exome sequencing (WES) was performed on sixteen patients. Our study revealed (1) nine patients carried eight homozygous pathogenic variants with four of them being novel (2) Specifically, a synonymous splicing variant (c.471G > A) in BBS2 gene in six patients with Baloch ethnicity. The identification of runs of homozygosity (ROH) calling was performed using the BCFtools/RoH software on WES data of patients harboring c.471G > A variant. The presence of shared homozygous regions containing the identified variant was confirmed in these patients. In-silico analysis predicted the effect of the c.471G > A variants on BBS2 mRNA splicing. This variant results in disrupted wild-type donor site and intron retention in the mature mRNA. (3) And a deletion of exons 14 to 17 in the BBS1 gene was identified in one patient by Copy-Number Variation (CNV) analysis using the ExomeDepth pipeline. Our results identified the founder variant c.471G > A in the BBS2 gene in the Baloch ethnicity of the Iranian population. This finding can guide the diagnostic approach of this syndrome in future studies.

Genomic features reveal potential benefit of adding anti-PD-1 immunotherapy to treat non-upper aerodigestive tract natural killer/T-cell lymphoma.

Natural killer/T-cell lymphoma (NKTCL) is a highly heterogeneous disease with a poor prognosis. However, the genomic characteristics and proper treatment strategies for non-upper aerodigestive tract NKTCL (NUAT-NKTCL), a rare subtype of NKTCL, remain largely unexplored. In this study, 1589 patients newly diagnosed with NKTCL at 14 hospitals were assessed, 196 (12.3%) of whom had NUAT-NKTCL with adverse clinical characteristics and an inferior prognosis. By using whole-genome sequencing (WGS) and whole-exome sequencing (WES) data, we found strikingly different mutation profiles between upper aerodigestive tract (UAT)- and NUAT-NKTCL patients, with the latter group exhibiting significantly higher genomic instability. In the NUAT-NKTCL cohort, 128 patients received frontline P-GEMOX chemotherapy, 37 of whom also received anti-PD-1 immunotherapy. The application of anti-PD-1 significantly improved progression-free survival (3-year PFS rate 53.9% versus 17.0%, P = 0.009) and overall survival (3-year OS rate 63.7% versus 29.2%, P = 0.01) in the matched NUAT-NKTCL cohort. WES revealed frequent mutations involving immune regulation and genomic instability in immunochemotherapy responders. Our study showed distinct clinical characteristics and mutational profiles in NUAT-NKTCL compared with UAT patients and suggested adding anti-PD-1 immunotherapy in front-line treatment of NUAT-NKTCL. Further studies are needed to validate the efficacy and related biomarkers for immunochemotherapy proposed in this study.

Machine learning-based extrachromosomal DNA identification in large-scale cohorts reveals its clinical implications in cancer.

The clinical implications of extrachromosomal DNA (ecDNA) in cancer therapy remain largely elusive. Here, we present a comprehensive analysis of ecDNA amplification spectra and their association with clinical and molecular features in multiple cohorts comprising over 13,000 pan-cancer patients. Using our developed computational framework, GCAP, and validating it with multifaceted approaches, we reveal a consistent pan-cancer pattern of mutual exclusivity between ecDNA amplification and microsatellite instability (MSI). In addition, we establish the role of ecDNA amplification as a risk factor and refine genomic subtypes in a cohort from 1015 colorectal cancer patients. Importantly, our investigation incorporates data from four clinical trials focused on anti-PD-1 immunotherapy, demonstrating the pivotal role of ecDNA amplification as a biomarker for guiding checkpoint blockade immunotherapy in gastrointestinal cancer. This finding represents clinical evidence linking ecDNA amplification to the effectiveness of immunotherapeutic interventions. Overall, our study provides a proof-of-concept of identifying ecDNA amplification from cancer whole-exome sequencing (WES) data, highlighting the potential of ecDNA amplification as a valuable biomarker for facilitating personalized cancer treatment.

Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models

Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that integrates patient-oriented brain structural magnetic resonance imaging (sMRI) scans with DNA whole-exome sequencing (WES) data. Multimodal data fusion aims to improve the detection of mood disorders by employing established deep-learning architectures for computer vision and machine-learning strategies. We analyzed brain imaging genetic data of 321 East Asian individuals, including 147 patients with MDD, 78 patients with BD, and 96 healthy controls. We developed and evaluated six fusion models by leveraging common computer vision models in image classification: Vision Transformer (ViT), Inception-V3, and ResNet50, in conjunction with advanced machine-learning techniques (XGBoost and LightGBM) known for high-dimensional data analysis. Model validation was performed using a 10-fold cross-validation. Our ViT ⊕ XGBoost fusion model with MRI scans, genomic Single Nucleotide polymorphism (SNP) data, and unweighted polygenic risk score (PRS) outperformed baseline models, achieving an incremental area under the curve (AUC) of 0.2162 (32.03% increase) and 0.0675 (+8.19%) and incremental accuracy of 0.1455 (+25.14%) and 0.0849 (+13.28%) compared to SNP-only and image-only baseline models, respectively. Our findings highlight the opportunity to refine mood disorder diagnostics by demonstrating the transformative potential of integrating diverse, yet complementary, data modalities and methodologies.

Association of epilepsy and neurological impairments with homozygous recessive missense mutations found in the genes responsible for ganglioside biosynthesis (ST3GAL5) and calcium voltage-gated channels (CACNA1H) - insights through molecular dynamic simulations

With over 2.2 million cases, the incidence rate of epilepsy in Pakistan is far higher than the rest of the world due primarily to the frequent, traditionally imposed cousin marriages. In the present study, comprehensive whole exome sequencing (WES) analyses of a three-generation family with four affected members presenting ‘unexplained’ childhood absence epilepsy (CAE), seizures and dementia, was performed in a quest to identify heritable, epilepsy-causal gene variants to better aid in carrier screening and genetic counselling. The WES data was generated, analyzed, and validated through Sanger’s sequencing, molecular dynamic simulation (MDS) analysis, and molecular mechanics with generalized Born and surface area solvation (MM/GBSA) studies. Two homozygous recessive, missense mutations in ST3GAL5 (c.311A > G, p. His104Arg) and CACNA1H (c.6230G > A, p. Arg2077His) genes, earlier regarded as benign or of uncertain significance, have been identified as a potential etiology. Comparative MDS and free binding energy calculations revealed substantial structural perturbations in mutant forms of ST3GAL5 leading to decreased binding and reduced catalytic activity of the p.His104Arg and two other functional variants (p.Val74Glu and p.Arg288Ter) when compared with wild type. Our findings reinforce that WES analyses may uncover ‘hidden’, heritable variants and together with MDS and MM/GBSA may provide plausible clues to answer the unexplained causes of epilepsy for an effective management and better patient outcome. Further, revisit of epilepsy-associated mutational landscape in population context is imperative as the variants with ‘benign’ tags may turn out to be ‘non-benign’, when exist in combination with other benign. Communicated by Ramaswamy H. Sarma

Abstract A038: Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data

Abstract Ductal carcinoma in situ (DCIS) is a very common non-life threatening, pre-invasive form of breast cancer constituting 25% of all new breast cancer diagnoses in the USA, and is normally treated with invasive measures. However, only 20 to 30% of DCIS cases will progress to life-threatening invasive breast cancer in their lifetime. There is a need for patient stratification to decrease treatment burden and focus resources on patients who actually require treatment. To ameliorate this issue, we aim to develop evolutionary biomarkers that predict both DCIS recurrence and its progression to invasive breast cancer. We conducted an observational longitudinal study in which two DCIS samples from each primary tumor were acquired prior to treatment—to account for intratumor heterogeneity—and followed up with (median event-free patient observation time: 106 months) to classify each patient’s outcome into its respective cohort category of [1] no DCIS recurrence, [2] DCIS recurrence, or [3] invasive ductal carcinoma (IDC) recurrence (progression). Each of the three cohort groups comprises approximately 30 patients, for which our primary data includes low-pass whole genome sequencing (lpWGS) and whole exome sequencing (WES) of the two primary DCIS samples, in addition to WES data for normal tissue control, clinical and epidemiological covariates, and time to recurrence. Preliminary estimates of copy number alterations (CNAs) generated using lpWGS data show that CNA burden, representing the proportion of altered genome, has statistically significant predictive capability for DCIS progression (Univariate Cox regression: p=0.014, HR=1.5). CNA divergence, representing the proportion of non-overlapping altered genome, has predictive capabilities of both recurrence and progression (Univariate Cox regression: Recurrence: p=0.021, HR=0.74; Progression: p=0.024, HR=0.67). In this work, our primary objective is to test if CNAs generated utilizing WES data are comparable in their predictive power to those yielded by lpWGS data for the same human DNA samples. It is not expected that the copy number alteration (CNA) estimates will be identical between the two methods, but rather that mutational burden and intratumor heterogeneity estimated using different data sources are strongly correlated and will have similar predictive capabilities. If we successfully confirm that CNAs estimated with both lpWGS and WES data have similar predictive power of DCIS progression, we will then be able to validate our evolutionary biomarkers using a different cohort for which we only possess WES data. This would also expand the applicability of our evolutionary biomarkers to a larger number of pre-existing datasets. We ultimately aim to develop strong evolutionary biomarkers that, when utilized alongside evolutionary therapies, will revolutionize the clinical management of cancer with the potential to improve both patient prognoses and quality-of-life outcomes. Citation Format: Manasa Iyer, Diego Mallo, Carlo C. Maley, Angelo Fortunato, Luis Cisneros, Lorraine M. King, Marc D. Ryser, Joseph Y. Lo, Allison Hall, Jeffrey R. Marks, Shelley Hwang. Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A038.

Whole-exome sequencing reveals genetic variants that may play a role in neurocytomas.

Neurocytomas (NCs) are rare intracranial tumors that can often be surgically resected. However, disease course is unpredictable in many patients and medical therapies are lacking. We have used whole exome sequencing to explore the molecular etiology for neurocytoma and assist in target identification to develop novel therapeutic interventions. We used whole exome sequencing (WES) to compare the molecular landscape of 21 primary & recurrent NCs to five normal cerebellar control samples. WES data was analyzed using the Qiagen Clinical Insight program, variants of interest (VOI) were interrogated using ConSurf, ScoreCons, & Ingenuity Pathway Analysis Software to predict their potential functional effects, and Copy number variations (CNVs) in the genes of interest were analyzed by Genewiz (Azenta Life Sciences). Of 40 VOI involving thirty-six genes, 7 were pathogenic, 17 likely-pathogenic, and 16 of uncertain-significance. Of seven pathogenic NC associated variants, Glucosylceramidase beta 1 [GBA1 c.703T > C (p.S235P)] was mutated in 5/21 (24%), Coagulation factor VIII [F8 c.3637dupA (p.I1213fs*28)] in 4/21 (19%), Phenylalanine hydroxylase [PAH c.975C > A (p.Y325*)] in 3/21 (14%), and Fanconi anemia complementation group C [FANCC c.1162G > T (p.G388*)], Chromodomain helicase DNA binding protein 7 [CHD7 c.2839C > T (p.R947*)], Myosin VIIA [MYO7A c.940G > T (p.E314*)] and Dynein axonemal heavy chain 11 [DNAH11 c.3544C > T (p.R1182*)] in 2/21 (9.5%) NCs respectively. CNVs were noted in 85% of these latter 7 genes. Interestingly, a Carboxy-terminal domain RNA polymerase II polypeptide A small phosphatase 2 [CTDSP2 c.472G > A (p.E158K)] of uncertain significance was also found in > 70% of NC cases. The variants of interest we identified in the NCs regulate a variety of neurological processes including cilia motility, cell metabolism, immune responses, and DNA damage repair and provide novel insights into the molecular pathogenesis of these extremely rare tumors.

Multi-omic characterization of acquired resistance to immune checkpoint inhibitors in patients with metastatic renal cell carcinoma.

459 Background: While immune checkpoint inhibitors (ICI) have improved outcomes in patients (pts) with metastatic renal cell carcinoma (mRCC), acquired resistance (ARX) is commonly encountered. We aimed to characterize the genomic and transcriptomic correlates underlying ARX in ICI-treated mRCC patients. Methods: We identified pts with mRCC at Dana-Farber Cancer Institute and Fox Chase Cancer Center, treated with ICI-based regimens who had an initial response to therapy followed by disease progression. Tissue samples from the primary tumors or metastases were collected before ICI treatment and after ARX from a treatment-emergent escape lesion. Whole exome sequencing (WES) and bulk RNA-sequencing (RNA-seq) were performed. Somatic mutations were called using the Cancer Genome Analysis pipeline. Differential gene expression analysis (DGE) was run using DESeq2, followed by gene set enrichment analysis (GSEA). Immune cell fractions were estimated using CIBERSORTx. Cell fraction and tertiary lymphoid structures (TLS) scores were compared between post and pre-ARX samples using the Wilcoxon test. Results: A total of 41 samples (n=23 pre-ARX and n=18 post-ARX) were collected from 14 unique patients. After quality control, WES and RNA-seq data were available for 27 samples (14 pre-ARX and 13 post-ARX) and 9 samples (3 pre-ARX and 6 post-ARX), respectively. No discernable enrichment of gene mutations was detected in the post-ARX setting compared to the pre-ARX. DGE identified 61 downregulated immunoglobulin genes in the post-ARX setting. GSEA revealed significant depletion of pathways associated with B-cell and T-cell function in post-ARX samples (all adjusted p<0.05). Similarly, a trend towards decreased naïve B cell and CD8+ T cell fractions was observed in the post-ARX setting (p=0.17 for both). Three previously reported TLS-associated expression signatures were depleted in the post-ARX samples compared to pre-ARX (p=0.015, p=0.065 and p=0.065, respectively). Immunohistochemistry assessment of 3 samples from one pt using ant-CD3 and CD20 antibodies confirmed the presence of TLS in the pre-ARX sample and their absence in two different post-ARX samples. Conclusions: In our cohort of pts with mRCC, no gene mutations were implicated as drivers of ARX. However, ARX was associated with downregulation of B-cell signaling and humoral mediated immunity, as well as absence of TLS. Ongoing efforts are in progress to include more samples.

Oligogenic inheritance in severe adult obesity.

The genetic architecture of extreme non-syndromic obesity in adults remains to be elucidated. A range of genes are known to cause monogenic obesity, but even when pathogenic mutations are present, there may be variable penetrance. Whole-exome sequencing (WES) was carried out on a 15-year-old male proband of Pakistani ancestry who had severe obesity. This was followed by family segregation analysis, using Sanger sequencing. We also undertook re-analysis of WES data from 91 unrelated adults with severe obesity (86% white European ancestry) from the Personalised Medicine for Morbid Obesity (PMMO) cohort, recruited from the UK National Health Service. We identified an oligogenic mode of inheritance of obesity in the proband's family-this provided the impetus to reanalyze existing sequence data in a separate dataset. Analysis of PMMO participant data revealed two further patients who carried more than one rare, predicted-deleterious mutation in a known monogenic obesity gene. In all three cases, the genes involved had known autosomal dominant inheritance, with incomplete penetrance. Oligogenic inheritance may explain some of the variable penetrance in Mendelian forms of obesity. We caution clinicians and researchers to avoid confining sequence analysis to individual genes and, in particular, not to stop looking when the first potentially-causative mutation is found.

Intragenic homozygous duplication in HEPACAM is associated with megalencephalic leukoencephalopathy with subcortical cysts type 2A.

Disease-causing variants in HEPACAM are associated with megalencephalic leukoencephalopathy with subcortical cysts 2A (MLC2A, MIM# 613,925, autosomal recessive), and megalencephalic leukoencephalopathy with subcortical cysts 2B, remitting, with or without impaired intellectual development (MLC2B, MIM# 613,926, autosomal dominant). These disorders are characterised by macrocephaly, seizures, motor delay, cognitive impairment, ataxia, and spasticity. Brain magnetic resonance imaging (MRI) in these individuals shows swollen cerebral hemispheric white matter and subcortical cysts, mainly in the frontal and temporal regions. To date, 45 individuals from 39 families are reported with biallelic and heterozygous variants in HEPACAM, causing MLC2A and MLC2B, respectively. A 9-year-old male presented with developmental delay, gait abnormalities, seizures, macrocephaly, dysarthria, spasticity, and hyperreflexia. MRI revealed subcortical cysts with diffuse cerebral white matter involvement. Whole-exome sequencing (WES) in the proband did not reveal any clinically relevant single nucleotide variants. However, copy number variation analysis from the WES data of the proband revealed a copy number of 4 for exons 3 and 4 of HEPACAM. Validation and segregation were done by quantitative PCR which confirmed the homozygous duplication of these exons in the proband and carrier status in both parents. To the best of our knowledge, this is the first report of an intragenic duplication in HEPACAM causing MLC2A.

Human whole-exome genotype data for Alzheimer’s disease

The heterogeneity of the whole-exome sequencing (WES) data generation methods present a challenge to a joint analysis. Here we present a bioinformatics strategy for joint-calling 20,504 WES samples collected across nine studies and sequenced using ten capture kits in fourteen sequencing centers in the Alzheimer’s Disease Sequencing Project. The joint-genotype called variant-called format (VCF) file contains only positions within the union of capture kits. The VCF was then processed specifically to account for the batch effects arising from the use of different capture kits from different studies. We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 1.8% of the variants are with CADD > 30, indicating they are of high predicted pathogenicity. Here we show our new strategy can generate high-quality data from processing these diversely generated WES samples. The improved ability to combine data sequenced in different batches benefits the whole genomics research community.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction.

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

Genomic Landscape of Lynch Syndrome Colorectal Neoplasia Identifies Shared Mutated Neoantigens for Immunoprevention

Background & AimsLynch syndrome (LS) carriers develop mismatch repair deficient (MMRd) neoplasia with high neoantigen (neoAg) rates. No detailed information on targetable neoAgs from LS pre-cancers exists, which is crucial for vaccine development and immune-interception strategies. We report a focused somatic mutation and frameshift-neoAg landscape of microsatellite loci from colorectal polyps without malignant potential (PWOMPs), pre-cancers and early-stage cancers in LS carriers. MethodsWe generated paired whole-exome and transcriptomic sequencing data from 8 colorectal PWOMPs, 41 pre-cancers, 8 advanced pre-cancers, and 12 early-stage cancers of 43 LS carriers. A computational pipeline was developed to predict, rank, and prioritize the top 100 detected mutated neoAgs that were validated in vitro using ELISpot and tetramer assays. ResultsMutation calling revealed >10 mut/Mb in 83% of cancers, 63% of advanced pre-cancers, and 20% of pre-cancers. Cancers displayed an average of 616 MHC-I neoAgs/sample, 294 in advanced pre-cancers, and 107 in pre-cancers. No neoAgs were detected in PWOMPs. A total of 65% of our top 100 predicted neoAgs were immunogenic in vitro, and were present in 92% of cancers, 50% of advanced pre-cancers, and 29% of pre-cancers. We observed increased levels of naïve CD8+ and memory CD4+ T-cells in MMRd cancers and pre-cancers via transcriptomics analysis. ConclusionsShared frameshift-neoAgs are generated within unstable microsatellite loci at initial stages of LS carcinogenesis and can induce T-cell responses, generating opportunities for vaccine development, targeting LS pre-cancers and early-stage cancers.

Combining a prioritization strategy and functional studies nominates 5’UTR variants underlying inherited retinal disease

Background5’ untranslated regions (5’UTRs) are essential modulators of protein translation. Predicting the impact of 5’UTR variants is challenging and rarely performed in routine diagnostics. Here, we present a combined approach of a comprehensive prioritization strategy and functional assays to evaluate 5’UTR variation in two large cohorts of patients with inherited retinal diseases (IRDs).MethodsWe performed an isoform-level re-analysis of retinal RNA-seq data to identify the protein-coding transcripts of 378 IRD genes with highest expression in retina. We evaluated the coverage of their 5’UTRs by different whole exome sequencing (WES) kits. The selected 5’UTRs were analyzed in whole genome sequencing (WGS) and WES data from IRD sub-cohorts from the 100,000 Genomes Project (n = 2397 WGS) and an in-house database (n = 1682 WES), respectively. Identified variants were annotated for 5’UTR-relevant features and classified into seven categories based on their predicted functional consequence. We developed a variant prioritization strategy by integrating population frequency, specific criteria for each category, and family and phenotypic data. A selection of candidate variants underwent functional validation using diverse approaches.ResultsIsoform-level re-quantification of retinal gene expression revealed 76 IRD genes with a non-canonical retina-enriched isoform, of which 20 display a fully distinct 5’UTR compared to that of their canonical isoform. Depending on the probe design, 3–20% of IRD genes have 5’UTRs fully captured by WES. After analyzing these regions in both cohorts, we prioritized 11 (likely) pathogenic variants in 10 genes (ARL3, MERTK, NDP, NMNAT1, NPHP4, PAX6, PRPF31, PRPF4, RDH12, RD3), of which 7 were novel. Functional analyses further supported the pathogenicity of three variants. Mis-splicing was demonstrated for the PRPF31:c.-9+1G>T variant. The MERTK:c.-125G>A variant, overlapping a transcriptional start site, was shown to significantly reduce both luciferase mRNA levels and activity. The RDH12:c.-123C>T variant was found in cis with the hypomorphic RDH12:c.701G>A (p.Arg234His) variant in 11 patients. This 5’UTR variant, predicted to introduce an upstream open reading frame, was shown to result in reduced RDH12 protein but unaltered mRNA levels.ConclusionsThis study demonstrates the importance of 5’UTR variants implicated in IRDs and provides a systematic approach for 5’UTR annotation and validation that is applicable to other inherited diseases.

Early Diagnosis of Syndromic Congenital Cataracts in a Large Cohort of Congenital Cataracts

PurposeTo explore the factors related to the diagnosis yield of syndromic congenital cataracts and describe the phenotype-genotype correlation in congenital cataract patients. DesignProspective cohort study. MethodsSetting: The participants from underwent clinical examinations between 2021 and 2022. Facial and anterior eye segment photographs, pre-and post-operative ocular parameters, and medical and family histories were recorded. Bioinformatics analysis was performed using whole-exome sequencing data. Statistical and correlation analyses were performed using the basic characteristics, deep phenotype, and genotype data. Participants: 115 unrelated congenital cataract patients. Interventions: performing clinical examinations, whole-exome sequencing (WES) and bioinformatics analysis for all participants. Main outcomes and measures: Factors related to genetic diagnosis yield of syndromic congenital cataracts. ResultsBilaterally asymmetrical cataracts were identified associating with syndromic congenital cataracts. The overall genetic diagnostic yield in the cohort was 72.2%. 34.8% of the probands were early diagnosed as various syndromes with the help of genetic information. Phenotype-genotype correlation was detected for some genes and deep phenotypes. ConclusionsWe highlight the importance of screening syndromic diseases in the patients with asymmetrical congenital cataracts. Application of WES help provide early diagnosis and treatment for the patients with syndromic congenital cataracts. This study also achieved a high genetic diagnostic yield, expanded the genotypic spectrum, and found phenotype-genotype correlations. A comprehensive analysis of cataract symmetricity, family history, and deep phenotypes makes the genotype prediction of some congenital cataract patients possible.