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PubMed2025

Optical genome mapping improves clinical interpretation of constitutional copy-number gains and reduces their VUS burden.

PURPOSE: Genomic structure of copy-number gains is critical for their clinical interpretation but cannot be determined by chromosomal microarray (CMA) analysis, which does not provide information about chromosomal location and orientation of multiplied regions. We thus hypothesized that in CMA testing gains have higher probability than losses to be classified as variants of uncertain significance (VUS) and that structural information from optical genome mapping (OGM) may improve their interpretation. METHODS: Using a χ2 test, we assessed the association between classification of copy-number variants as VUS and their type (gains vs losses) in a cohort of 4073 CMA cases. Thirty-three VUS gains involving disease-associated genes were characterized by OGM to evaluate if OGM data enable their more conclusive clinical interpretation. RESULTS: The proportion of variants reported as VUS compared with likely pathogenic/pathogenic was significantly higher for gains than losses, confirming their increased VUS burden. OGM successfully determined genomic structure for all 33 copy-number gains, showing that 26 of 33 were tandem duplications and 7 of 33 were complex rearrangements. Structural information facilitated clinical interpretation in majority of the cases; it supported benign nature for 27 of 33 gains and was inconclusive or supported pathogenic role for 6 of 33. An estimated 20% of reported VUS gains would not have been reportable if we had OGM data. CONCLUSION: We illustrate a specific advantage of OGM compared with CMA: in addition to detecting both copy-number variants and balanced rearrangements, OGM improves clinical interpretation of copy-number gains by providing structural information and is thus expected to significantly decrease their VUS burden.

Humans↗

PubMed2025

Assessment of the variant prioritization strategy for genomic newborn screening in the Generation Study.

PURPOSE: Genomic sequencing offers the opportunity to screen for hundreds of rare genetic conditions. To minimize potential negative impact on families and clinical services, it is crucial to reduce false-positive results while prioritizing clinical utility. We present an automated variant prioritization approach in the Generation Study, a research study investigating genomic sequencing in 100,000 newborns in England. Prioritized variants will subsequently undergo manual review by a registered clinical scientist and a specialist clinician before being reported back to parents. METHODS: We assessed specificity of our automated variant prioritization approach in 34,410 samples not enriched for rare diseases and sensitivity in 546 samples from patients with diagnostic variants in genes relevant to newborn screening. We used coverage and copy-number variants callability metrics to evaluate variant detection. RESULTS: We estimated that 3% to 5% of samples will have prioritized variants that require manual review and that <1% of cases will have reportable variants requiring further confirmation of the condition. Sensitivity in genes included in the Generation Study was estimated to be approximately 80%. Gene-level specificity results led to changes in variant prioritization rules and conditions that are included. CONCLUSION: Gene-specific assessment of variant prioritization is crucial to establish analytical validity prior to inclusion in genomic newborn screening.

Humans↗

PubMed2025

De novo rare EMX2 variants lead to idiopathic hypogonadotropic hypogonadism.

PURPOSE: The genetic etiology of infertility remains unknown. To identify genes for human infertility, we applied a de novo variant analysis in 142 parent-proband trios with idiopathic hypogonadotropic hypogonadism (IHH), an infertility disorder caused by gonadotropin-releasing hormone (GnRH) deficiency. METHODS: Rare de novo copy-number and single-nucleotide variants (CNVs and SNVs) were called from exome sequencing data of the IHH trios. An association study of common EMX2 variants and disease outcomes was performed in the Massachusetts General Brigham Biobank (N = 65,253). GnRH neuronal development and migration was studied in organotypic explants with knocked down of Emx2 and in a mouse model lacking Emx2. RESULTS: We identified that the gene EMX2 harbored both rare de novo CNVs and SNVs. Rare de novo EMX2 variants led to IHH, developmental delay, and hearing loss. Common EMX2 variants were linked to infertility, Parkinson disease, and hearing loss. Knockdown of Emx2 in nasal explants resulted in attenuated GnRH cell migration and GnRH cells were confined to nasal regions of Emx2 knockout (KO) mice, consistent with IHH pathogenesis. CONCLUSION: By utilizing a de novo variant analysis and cellular assays, EMX2 was uncovered as a gene for human infertility.

Humans↗

PubMed2025

Methylation Signatures Identify Two Distinct Clusters of Uterine Leiomyosarcoma With Unique Histologic and Clinical Behaviors.

Uterine leiomyosarcoma (uLMS) is a rare and deadly gynecologic malignancy. uLMS is histologically heterogeneous and presents with a wide spectrum of tumor differentiation, with a broad range of genomic DNA instability, which can make the diagnosis and prognosis of uLMS challenging. Methylation has emerged as a useful molecular tool in tumor classification and diagnosis in certain neoplasms. We initiated this study to investigate the role of global methylation in the differential diagnosis of uLMS from its mimics in correlation with pathologic characteristics and clinical outcomes. In this study, we performed array-based global methylation profiling analysis in a total of 71 uLMS and compared the methylation signatures of uLMS with several other uterine mesenchymal tumors and soft tissue leiomyosarcoma. We found that uLMS demonstrated distinct methylation patterns differing from all other tumor types. Notably, methylation profiling defines 2 distinct subgroups of uLMS with differing copy number alterations, resulting in unique histologic and clinical behaviors, further emphasized by differences in methylation pathway analysis. This study is the first to report methylation profiling as a useful diagnostic tool in differentiating uLMS from mimics and defines 2 subtypes of uLMS based on methylation signatures.

Humans↗

PubMed2026

Copy Number-low/TP53-mutated Endometrial Cancer With Wild-type p53 Immunoexpression: Implications for Risk Stratification and Management When Using Next-generation Sequencing for Molecular Classification.

Endometrial cancers with the Cancer Genome Atlas (TCGA) molecular profile of TP53-mutated, POLE-wild-type, and microsatellite-stable generally exhibit a high burden of copy number (CN) alterations and carry an increased risk for adverse outcomes, meriting maximal adjuvant therapy. In contrast, the prognosis associated with a TP53 mutation that coexists with a POLE mutation or microsatellite instability aligns with that of ultramutated or hypermutated cancers, respectively. In this study, we characterized a rare molecular subclass of endometrial cancers defined by TP53 mutation but low burden of CN alterations, wild-type p53 immunoexpression (immunohistochemistry [IHC]), and low TP53 variant allele frequency (median 13% and maximum 47%). Among 723 consecutive endometrial cancers prospectively classified using next-generation sequencing, 16 (2.2%) were CN-low/TP53-mutated/p53 wild-type IHC. Two additional cases were identified in a separate retrospective cohort of 32 recurrent low-grade early-stage endometrial cancers, bringing the total to 18 cases. They affected postmenopausal patients, exhibited low-grade endometrioid histotype, and were mostly confined to the uterus without lymphovascular space invasion. The recurrence rate was 6.25% (1/16) in the prospective cohort, and none died, placing their prognosis closer to that of CN-low than CN-high cancers. We conclude that next-generation sequencing-based TCGA classification of TP53-mutated, POLE-wild-type, microsatellite-stable endometrial cancers with TP53 variant allele frequency < 50% requires further evaluation using CN analysis and/or p53 IHC to detect this rare molecular category. IHC-based TCGA classification, such as the ProMisE protocol, will not be able to detect these cases because the p53 IHC pattern is wild-type and there are no distinguishing morphological features; this may be of relevance for analyzing ProMisE protocol-based clinical trials and outcomes studies. Long-term outcome studies are needed to refine risk stratification and treatment decisions for this unique molecular class of endometrial cancers that further contributes to the evolving understanding that the clinical significance of TP53 mutation in endometrial cancer is complex and depends on coexisting molecular alterations.

Humans↗

PubMed2026

Assessing the de novo paradigm in sporadic early-onset Alzheimer disease trios.

The genetic architecture of sporadic Early-Onset Alzheimer Disease (sEOAD, onset ≤65 years) remains largely unknown. To assess the de novo mutation (DNM) hypothesis, we performed a nationwide recruitment of 37 novel sEOAD patients-unaffected parents trios. After assessing known monogenic genes, we performed trio-based exome sequencing and jointly analyzed novel trios with 12 previously reported ones. Of these, we selected 16 trios for genome sequencing. We identified three patients with a pathogenic DNM in APP or PSEN1. Then, from the 46 remaining trios, we identified 38 non-synonymous coding DNM and 4 de novo copy number variants (CNVs) in exome data. Four DNM (2 novel, in SPHK2 and DDR1) and bi-allelic inherited variants in two genes affected Alzheimer disease-related genes. No significant burden of rare coding variants in exome/genome data from 5643 EOAD cases and 16097 controls was identified using nested windows centered on each DNM position, at the transcript level. From genome data, one non-coding DNM was predicted to affect splicing in an AD-associated gene, PINX1. Overall, 48% probands carried ≥1 inherited risk factor with odds ratio (OR) > 1.5 and GWAS-defined Genetic Risk Scores (GRS) distribution was more consistent with random distribution than enrichment in higher scores in probands. We confirm that DNMs in known monogenic genes explain sEOAD in a minority of cases, while candidate DNMs in other genes might account for a small proportion of additional cases. The majority of sEOAD patients may have a complex etiology including multiple inherited variants, however, GRS might not explain most of its genetic component.

Humans↗

PubMed2021

1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans.

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

Brain↗

PubMed2025

Molecular analysis of lung adenocarcinomas from the SAFIR02-Lung cohort reveals new metastasis-associated copy-number alterations including frequent mutant-specific KRAS-allelic imbalance and identifies CDKN2A homozygous deletions as an independent biomarker of poor prognosis.

BACKGROUND: Identifying molecular alterations specific to advanced lung adenocarcinomas could provide insights into tumour progression and dissemination mechanisms. METHOD: We analysed tumour samples, either from locoregional lesions or distant metastases, from patients with advanced lung adenocarcinoma from the SAFIR02-Lung trial by targeted sequencing of 45 cancer genes and comparative genomic hybridisation array and compared them to early tumours samples from The Cancer Genome Atlas. RESULTS: Differences in copy-number alterations frequencies suggest the involvement in tumour progression of LAMB3, TNN/KIAA0040/TNR, KRAS, DAB2, MYC, EPHA3 and VIPR2, and in metastatic dissemination of AREG, ZNF503, PAX8, MMP13, JAM3, and MTURN. Conversely, no meaningful difference was found in pathogenic single-nucleotide variant frequencies, reinforcing the notion that they are early events in tumorigenesis. CDKN2A homozygous deletion was linked to poor clinical outcome in patients with early tumours (overall survival hazard ratio 2.17, 95% CI: 1.43-3.28, corrected p-value = 0.01). Furthermore, we found that KRAS mutant allele specific imbalance, i.e. focal amplification of the mutant allele, is more prevalent in locoregional or distant samples of metastatic patients than in early lesions (8.4%, 13% and 2.8% respectively). This observation was replicated in three public cohorts. Tumours with KRAS mutant allele specific imbalance show specific patterns of co-occurrence and mutual exclusion with alterations in key cancer genes like CDKN2A, TP53, STK11 and NKX2-1, often in a tumour type dependent manner. CONCLUSION: Advanced LUAD tumours exhibit higher copy-number alteration burden, with distinct alterations associated with tumour progression and metastasis. CDKN2A homozygous deletions predict poor prognosis in early disease, while KRAS mutant allele-specific imbalance is enriched in advanced tumours.

Humans↗

PubMed2021

Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities.

Multiple myeloma (MM) is consistently preceded by precursor conditions recognized clinically as monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM). We interrogate the whole genome sequence (WGS) profile of 18 MGUS and compare them with those from 14 SMMs and 80 MMs. We show that cases with a non-progressing, clinically stable myeloma precursor condition (n = 15) are characterized by later initiation in the patient's life and by the absence of myeloma defining genomic events including: chromothripsis, templated insertions, mutations in driver genes, aneuploidy, and canonical APOBEC mutational activity. This data provides evidence that WGS can be used to recognize two biologically and clinically distinct myeloma precursor entities that are either progressive or stable.

DNA Copy Number Variations↗

PubMed2026

Plasticity of extrachromosomal DNA segregation during drug adaptation.

Uneven segregation during mitosis is a striking feature of extrachromosomal DNA (ecDNA). Because ecDNA lacks a centromere, it is thought to segregate stochastically, generating intratumoral heterogeneity in genomic copy number. Drug treatment can readily change ecDNA copy number, enabling cells to acquire drug resistance, yet whether these changes reflect static selection of pre-existing clones or active reconfiguration under stress remains unresolved. To address this, we develop a high-throughput framework combining single-cell DNA sequencing with cellular barcoding for clonal tracking. Single-cell cloning reveals that not all clones exhibit identical segregation modes even under drug-free conditions. Under treatment, resistant populations do not simply arise from pre-existing clones with favorable ecDNA states; instead, some clones actively reconfigure their segregation behavior to generate resistant cells. Thus, although ecDNA generally segregates stochastically, it can undergo nonrandom, actively regulated segregation under drug stress, raising the possibility of therapeutically targeting ecDNA segregation mechanisms to counteract adaptive resistance.

Extrachromosomal DNA↗

PubMed2026

Determinants of functional burden pleiotropy and gene dosage responses across human traits.

Pleiotropic and monotonic effects of gene dosage are central to understanding comorbidities in developmental pediatric and psychiatric disorders, yet the underlying biological processes are not well characterized. Here we develop a functional burden analysis to investigate the association of all protein-coding copy-number variants, genome-wide, with 43 complex traits in approximately 500,000 UK Biobank participants. We test variant associations disrupting 172 tissue or cell-type gene sets, finding associations for all traits, which we replicate in the All of Us cohort. Functional burden pleiotropy, defined as the number of traits significantly associated with a gene set, correlates with genetic constraint and is higher for brain than non-brain functions, even after normalizing for genetic constraint. Levels of pleiotropy, measured by burden correlation, are similar in deletions and loss-of-function single-nucleotide variants, and higher than in common variants and duplications. Most gene dosage responses are non-monotonic, with deletions and duplications showing same-direction effects, and monotonic responses decrease with genetic constraint. We observe associations between functional gene sets and traits for either deletions or duplications, but rarely both, with negatively correlated effect sizes. Together, these results link genetic constraint and brain-specific mechanisms to the whole-body multimorbidity of neurodevelopmental and psychiatric conditions.

Humans↗

PubMed2026

Aneuploidy selects for the acquisition of driver genes in breast cancer.

Chromosome instability is highly prevalent in cancer and drives large-scale chromosomal imbalances, known as aneuploidies1-4. How aneuploidy contributes to tumorigenesis remains difficult to study due to the vast numbers of genes affected. Here we established a CRISPR knockout- and activation-linked assay (CRISPR-KOALA), enabling high-throughput bidirectional genetic screens in immunocompetent mouse models of cancer. We developed a compendium of the ten most frequent human chromosome-arm-level alterations in basal-like breast cancer (BLBC), a disease type that is driven by large copy-number alterations (CNAs)5-8. Using CRISPR-KOALA, we screened the mouse orthologues of 3,752 genes on these arms and identified 90 cancer driver genes, the function of the vast majority of which is unknown. These genes drive distinct signalling pathways including MAPK, HIPPO and WNT, reflecting the high degree of BLBC heterogeneity. Manipulating the identified cancer driver genes overcomes the need for CNAs in Trp53-mutant BLBC mouse models. Mechanistically, we identify that PLGRKT is a potent oncogene that lies on chromosome 9p and show that its tumour-promoting activity is associated with highly stress-resistant mitochondria and an increased ability to detoxify reactive oxygen species. Together, our findings reveal that arm-level CNAs can function to select specific driver genes to promote heterogeneous biological processes.

Animals↗

PubMed2024

Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes.

Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The Multiple Myeloma Research Foundation's Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study ( NCT01454297 ) is a longitudinal, observational clinical study of newly diagnosed patients with multiple myeloma (n = 1,143) where tumor samples are characterized using whole-genome sequencing, whole-exome sequencing and RNA sequencing at diagnosis and progression, and clinical data are collected every 3 months. Analyses of the baseline cohort identified genes that are the target of recurrent gain-of-function and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high-risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed that 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression of immunotherapy targets in this subtype, suggesting a potential therapeutic option.

Humans↗

PubMed2025

MelanoDB: A dataset of clinical and molecular features of patients with advanced melanoma treated with MAPK inhibitors.

MAPK inhibitors (MAPKi) have revolutionized the treatment of patients with advanced melanoma. However, primary and acquired resistance mechanisms limit their efficacy. Predicting MAPKi response from the tumor baseline features remains challenging due to the limited size of patient cohorts. Therefore, we collected data from nine different patient cohorts (total n = 417 patients with advanced melanoma treated with MAPKi) to identify clinical and molecular features. Our curated dataset, named MelanoDB, includes whole or partial exome sequencing data for 191 patients, copy number alteration information for 66 patients, and gene expression data for 132 patients. We provide a web application to explore the integrated dataset and data distribution across the collected studies, and we share this dataset with the scientific community according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.

Humans↗

PubMed2025

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integrated gene expression, somatic mutation, and copy number aberration (CNA) data with drug response profiles using multi-omics integration (MI). We used the Genomics of Drug Sensitivity in Cancer (GDSC) data for training and incorporated drugs with same pathways into the training models. We then evaluated drug response predictions on independent in-vivo PDX Encyclopedia (PDX) and ex-vivo the Cancer Genome Atlas (TCGA) datasets. In addition, we conducted pathway enrichment analyses to elucidate the mechanisms underlying drug resistance for paclitaxel, 5-fluorouracil (5-FU), gemcitabine, and cetuximab. We also applied Fisher's exact test (FET) to assess potential associations between drug resistance and the presence of mutations or CNAs. Our pan-drug models outperformed other methods based on the area under the precision-recall curve (AUCPR). Our pathway enrichment analyses revealed LDHB-mediated pyruvate metabolism and FYN-mediated focal adhesion might have pivotal roles in paclitaxel resistance, while PINK1-mediated mitophagy might be critical in 5-FU resistance. In addition to transcriptional activation, FET suggested that CNAs in LDHB and PINK1 may also be associated with resistance to paclitaxel and 5-FU, respectively. Furthermore, enrichment results for paclitaxel and cetuximab indicated shared resistance mechanisms between the two drugs. Importantly, our findings are consistent with prior experimental studies, providing literature-based validation of our results. Overall, our DNN-based TL approach achieved strong predictive performance across PDX & TCGA datasets and enrichment analyses provided valuable biological insights into drug resistance mechanisms.

Humans↗

PubMed2026

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Machine Learning↗

PubMed2026

Identification of rare maternal copy number variants by genome-wide analysis of noninvasive prenatal screening data in 113,017 pregnant women.

OBJECTIVES: Knowledge of copy number variants (CNVs) is relevant to maternal and fetal health and can be obtained from noninvasive prenatal screening (NIPS) of pregnancy. However, genome-wide analysis of maternal CNVs using NIPS data has not been conducted in large populations. METHODS: For CNV analysis, the human genome was segmented into 10 kilobase pairs (Kb) bins, and the relative sequencing depth of each bin was calculated. The circular binary segmentation algorithm was used to estimate CNVs. Detected CNVs from two pregnancies of the same participant were compared to validate the reproducibility. All CNVs were merged into CNV regions (CNVRs) to evaluate their frequency, distributions, and relationship with disease-related genes and regions. RESULTS: In this study, 113,017 pregnant women were recruited. A total of 363,886 CNVs larger than 50 Kb were detected in 101,779 individuals and merged into 43,005 CNVRs. For evaluating the reproducibility of CNVs, 90.18% of deletions and 88.07% of duplications were consistent. In general, 78.13% of individuals carried CNVRs that overlapped protein-coding genes, while 14.76% overlapped OMIM genes. We detected 246 novel CNVRs, 134 (54.47%) involving protein-coding genes. For the perspective of maternal-fetal health, we identified 4,984 (4.41%) individuals as carriers of 5,243 CNVs containing known pathogenic or likely pathogenic regions, including 22q11.2 region and DMD gene.. CONCLUSIONS: NIPS sequencing data is a reliable source for maternal CNV detection. These CNVs constitute an integrate component in maternal-fetal health management.

Humans↗

PubMed2025

CCRR: a user-friendly platform for analyzing complex chromosomal rearrangements in tumors.

SUMMARY: Complex chromosomal rearrangements in tumors involve intricate genomic alterations that significantly affect gene function and contribute to cancer development. Identifying these events is crucial for cancer research but is often challenging due to the complexity and limitations of existing tools. We developed the Complex Chromosomal Rearrangements Resolver (CCRR), a comprehensive, reproducible, and user-friendly platform for analyzing complex rearrangements in tumors. CCRR integrates multiple SV and CNV detection tools within a Docker container environment, simplifying installation and configuration. It can be easily deployed, automating the execution and merging of results, providing high-confidence consensus SV and CNV calls, allowing researchers to efficiently analyze complex chromosomal rearrangements in tumors without extensive bioinformatics expertise. CCRR also includes a web server for one-click analysis and customized visualization. AVAILABILITY AND IMPLEMENTATION: The CCRR platform is freely available at https://www.ccrr.life. Source code and executables can be accessed at https://github.com/laslk/CCRR. An archived version is available at Zenodo: https://doi.org/10.5281/zenodo.15386513.

Software↗