Genomic Datasets Research Articles

Prevalence of targetable and potentially targetable alterations in metastatic/recurrent cervical cancer specimens in a large genomic dataset

Prevalence of targetable and potentially targetable alterations in metastatic/recurrent cervical cancer specimens in a large genomic dataset

A host shift as the origin of tomato bacterial canker caused by Clavibacter michiganensis.

The Actinomycetota (formerly Actinobacteria) genus Clavibacter includes phytopathogens with devasting effects in several crops. Clavibacter michiganensis, the causal agent of tomato bacterial canker, is the most notorious species of the genus. Yet, its origin and natural reservoirs remain elusive, and its populations show pathogenicity profiles with unpredictable plant disease outcomes. Here, we generate and analyse a decade-long genomic dataset of Clavibacter from wild and commercial tomato cultivars, providing evolutionary insights that directed phenotypic characterization. Our phylogeny situates the last common ancestor of C. michiganensis next to Clavibacter isolates from grasses rather than to the sole strain we could isolate from wild tomatoes. Pathogenicity profiling of C. michiganensis isolates, together with C. phaseoli and C. californiensis as sister taxa and the wild tomato strain, was found to be congruent with the proposed phylogenetic relationships. We then identified gene enrichment after the evolutionary event, leading to the appearance of the C. michiganesis clade, including known pathogenicity factors but also hitherto unnoticed genes with the ability to encode adaptive traits for a pathogenic lifestyle. The holistic perspective provided by our evolutionary analyses hints towards a host shift event as the origin of C. michiganensis as a tomato pathogen and the existence of pathogenic genes that remain to be characterized.

Progressing towards personalised medicine: the Genomic Data Infrastructure (GDI) project

Abstract Funded by the European Commission under the Digital Europe Programme and through co-funding from participating Member States, the European Genomic Data Infrastructure (GDI) aims to support the 1+Million Genome initiative for establishing an effective data infrastructure for genomic data. GDI has the potential to fill the gap between genomic research and clinical practice, promoting the implementation of data-driven precision medicine in Europe with the harmonisation of genomic and related phenotypic and clinical data across adhering countries. Launched in November 2022, the GDI project is a collaborative effort involving partners from 20 European countries, divided into 8 Work Packages across three Pillars. GDI is structured with the development of a framework (Pillar I Long-term sustainability) and the necessary tools (Pillar II 1+MG infrastructure deployment), with the implementations of use cases (Pillar III Application and innovation solution) to support research and innovation, integrate findings into the clinic and healthcare, and improve public health measures. In the first 2 years, we completed the starter kit which we are currently testing with real data. Over the subsequent years, the GDI project aims to provide a federated and sustainable infrastructure for facilitating research that use genomic data. Through authorized secure access, clinicians and researchers can leverage genomic insights for faster and more precise clinical decision-making, diagnosis, and treatments. The GDI project represents a significant opportunity to improve healthcare outcomes and public health measures by leveraging the potential of genomics. By providing secure access to extensive genomic datasets combined with phenotyped data, the infrastructure sets the foundation for international partnerships in personalized medicine. Ultimately, this initiative holds the promise of advancing medical understanding, enhancing patient care, and fostering economic growth across Europe. Key messages • GDI drives personalised medicine via international collaboration, building a sustainable infrastructure. Harmonizing data accelerates innovation, improving outcomes and fostering growth in Europe. • GDI aims to revolutionize healthcare by collecting and harmonizing genomic and clinical data in Europe. It facilitates faster and more precise decisions and treatments, improving healthcare outcomes.

Enrichment Strategies for Low-Abundant Single Nucleotide Mutations.

Over the past three decades, significant advancements have been made in mutation enrichment methods, driven by the increasing need for precise and efficient identification of rare genetic variants associated with diseases. Mutation-enrichment methods have emerged to boost sensitivity and enable easy detection of low-frequency mutations. These methods are crucial in genomics research and clinical diagnostics, allowing for the detection of low-frequency mutations within large genomic datasets. This review presents a summary of technological developments in rare mutation enrichment and emphasizes their mechanisms and applications in liquid biopsies.

A tip of the iceberg: genome survey indicated a complex evolutionary history of Garuga Roxb. species

Background Garuga Roxb. is a genus endemic to southwest China and other tropical regions in Southeast Asia facing risk of extinction due to the loss of tropical forests and changes in land use. Conducting a genome survey of G. forrestii contribute to a deeper understanding and conservation of the genus.ResultsThis study utilized genome survey of G. forrestii generated approximately 54.56 GB of sequence data, with approximately 112 × coverage. K-mer analysis indicated a genome size of approximately 0.48 GB, smaller than 0.52GB estimated by flow cytometry. The heterozygosity is of about 0.54%, and a repeat rate of around 51.54%. All the shotgun data were assembled into 339,729 scaffolds, with an N50 of 17,344 bp. The average content of guanine and cytosine was approximately 35.16%. A total of 330,999 SSRs were detected, with mononucleotide repeats being the most abundant at 70.16%, followed by dinucleotide repeats at 20.40%. We conducted a preliminary ploidy assessment using Smudgeplot and observed a clear bimodal distribution in G. forrestii at 1/2 relative coverage depth and total coverage depth (2n), suggesting a potential diploid genome structure. A pseudo chromosome of G. forrestii and a gemone of Boswellia sacra were used as reference genome to perform a primer population resequencing analysis within three Garuga species. Principal component analysis (PCA) indicated three distinct groups, but genome wide phylogenetics represented conflicting both between the dataset of different reference genomes and between maternal and nuclear genome.ConclusionIn summary, the genome of G. forrestii is small, and the phylogenetic relationships within the Garuga genus are complex. The genetic data presented in this study holds significant value for comprehensive whole-genome analyses, the evaluation of population genetic diversity, investigations into adaptive evolution, the advancement of artificial breeding efforts, and the support of species conservation and restoration initiatives. Ultimately, this research contributes to reinforcing the conservation and management of natural ecosystems, promoting biodiversity conservation, and advancing sustainable development.

Towards the genomic sequence code of DNA fragility for machine learning.

Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzing the differences and commonalities across myriads of genomic breakage datasets, we extract the sequence-linked rules and patterns behind DNA fragility. We show the overall deconvolution of the sequence influence into short-, mid- and long-range effects, and the stressor-dependent differences in defining the range and compositional effects on DNA fragility. We summarize and release our feature compendium as a library that can be seamlessly incorporated into genomic machine learning procedures, where DNA fragility is of concern, and train a generalized DNA fragility model on cancer-associated breakages. Structural variants (SVs) tend to stabilize regions in which they emerge, with the effect most pronounced for pathogenic SVs. In contrast, the effects of chromothripsis are seen across regions less prone to breakages. We find that viral integration may bring genome fragility, particularly for cancer-associated viruses. Overall, this work offers novel insights into the genomic sequence basis of DNA fragility and presents a powerful machine learning resource to further enhance our understanding of genome (in)stability and evolution.

Diversity and distribution of a prevalent Microviridae group across the global oceans

Small single-stranded DNA phages of the Microviridae family are diverse and prevalent in oceans. Our understanding of Microviridae phages that infect the ecologically important marine Roseobacter is currently limited, comprising few isolates. Here, we report six roseophages that infect Roseobacter RCA strains. Genomic and phylogenetic analyses revealed that they were new members of the previously identified subfamily Occultatumvirinae. Additionally, 232 marine uncultivated virus genomes (UViGs) affiliated to Occultatumvirinae were obtained from environmental genome datasets. Phylogenomic analysis revealed that marine Occultatumvirinae phages could be further grouped into 11 subgroups. Moreover, meta-omics based read-mapping analysis showed that Occultatumvirinae phages were globally distributed, with two low G + C subgroups showing the most prevalent distribution. Furthermore, one phage in subgroup 2 was found to be extremely ubiquitous. Overall, this study expands our understanding of the diversity and ecology of the Occultatumvirinae microviruses in the ocean and highlights their ecological impacts.

The origin and maintenance of supergenes contributing to ecological adaptation in Atlantic herring

Chromosomal inversions are associated with local adaptation in many species. However, questions regarding how they are formed, maintained and impact various other evolutionary processes remain elusive. Here, using a large genomic dataset of long-read and short-read sequencing, we ask these questions in one of the most abundant vertebrates on Earth, the Atlantic herring. This species has four megabase-sized inversions associated with ecological adaptation that correlate with water temperature. The S and N inversion alleles at these four loci dominate in the southern and northern parts, respectively, of the species distribution in the North Atlantic Ocean. By determining breakpoint coordinates of the four inversions and the structural variations surrounding them, we hypothesize that these inversions are formed by ectopic recombination between duplicated sequences immediately outside of the inversions. We show that these are old inversions (>1 MY), albeit formed after the split between the Atlantic herring and its sister species, the Pacific herring. There is evidence for extensive gene flux between inversion alleles at all four loci. The large Ne of herring combined with the common occurrence of opposite homozygotes across the species distribution has allowed effective purifying selection to prevent the accumulation of genetic load and repeats within the inversions.

ParthenoGenius: A User-Friendly Heuristic for Inferring Presence and Mechanism of Facultative Parthenogenesis from Genetic and Genomic Data Sets.

Facultative parthenogenesis (FP), or asexual reproduction by sexually-reproducing female animals, has been reported across several clades of vertebrates and is increasingly being recognized as a reproductive mechanism with significant implications for the genetic variation of captive and wild populations. The definitive identification of parthenogens requires molecular confirmation, with large genomic data sets necessary to accurately parse the parthenogenetic mechanism (i.e., endoduplication, gametic duplication, terminal fusion automixis, or central fusion automixis). Current methods for inferring FP from large genomic data sets are statistically intensive, require competency in R scripting for their execution, and are not designed for detection of facultative parthenogenesis or screening of large numbers of mother/offspring pairs, whereas small data sets (i.e., microsatellites) that can be evaluated visually lack the power to discriminate among FP mechanisms. Here, we present the user-friendly software program, ParthenoGenius, that uses intuitive logic to infer presence and mechanism of FP from even large genomic data sets comprising many mothers and offspring. ParthenoGenius runs relatively quickly and does not require the researcher to have knowledge of R scripting or statistics. ParthenoGenius was tested on eight empirical data sets, and in each case identified parthenogens (and parthenogenic mechanism when present) consistent with results of previous studies or corroborating evidence. ParthenoGenius will facilitate the rapid screening of large genomic data sets comprising many mothers and offspring for the presence and mechanism of parthenogenesis, improving our understanding of the frequency and phylogenetic distribution of FP across the animal kingdom.

Bioinformatic insights into sugar signaling pathways in sugarcane growth

The SnRK1, hexokinase, and TORC1 (TOR, LST8, RAPTOR) are three pivotal kinases at the core of sugar level sensing, significantly impacting plant metabolism and development. We retrieved and analyzed protein sequences of these three kinase pathways from seven sugarcane transcriptome and genome datasets, identifying protein domains, phylogenetic relationships, sequence ancestry, and in silico expression levels. Additionally, we predicted HXK subcellular localization and assessed its enzymatic activity in sugarcane leaves and culms along development in the field. We retrieved 11 TOR, 23 RAPTOR, 55 LST8, 95 SnRK1α, 98 HXK, and 14 HXK-like putative full-length sequences containing all the conserved domains. Most of these transcripts seem to share a common origin with the three ancestral species of sugarcane: Saccharum officinarum, Saccharum spontaneum, and Saccharum barberi. We accessed the expression profile of sequences from one sugarcane transcriptome. We found the highest enzymatic activity of HXK in culms in the first month, which, at this stage, provides carbon (sucrose) and nitrogen (amino acids) for initial plant development. Our approach places novel sugar sensing sequences that work as a guideline for further research into the underlying signaling mechanisms and biotechnology applications in sugarcane.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine.

Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.

Development and extensive sequencing of a broadly-consented Genome in a Bottle matched tumor-normal pair.

The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first to be explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from thirteen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, and Hi-C, and karyotyping. These data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, de novo assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic mutations. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology.

Comprehensive analysis of lactylation-related gene sets and mitochondrial functions in gastric adenocarcinoma: implications for prognosis and therapeutic strategies.

Gastric adenocarcinoma (STAD) is characterized by high heterogeneity and aggressiveness, leading to poor prognostic outcomes worldwide. This study explored the prognostic significance of lactylation-related gene sets and mitochondrial functions in STAD by integrating large-scale genomic datasets, including TCGA and several GEO datasets. We utilized Spatial transcriptomics and single-cell RNA sequencing to delineate the tumor microenvironment and assess the heterogeneity of cellular responses within the tumor. Additionally, the study identified distinct molecular subtypes within STAD that correspond with unique survival outcomes and immune profiles, enhancing the molecular classification beyond current paradigms. Prognostic models incorporating these molecular markers demonstrated superior predictive capabilities over existing models across multiple validation datasets. Furthermore, our analysis of immune landscapes revealed that variations in lactylation could influence immune cell infiltration and responsiveness, pointing towards novel avenues for tailored immunotherapy approaches. These comprehensive insights provide a foundation for targeted therapeutic strategies and underscore the potential of metabolic and immune modulation in improving STAD treatment outcomes.

MitoSort: Robust Demultiplexing of Pooled Single-cell Genomics Data Using Endogenous Mitochondrial Variants.

Multiplexing across donors has emerged as a popular strategy to increase throughput, reduce costs, overcome technical batch effects, and improve doublet detection in single-cell genomic studies. To eliminate additional experimental steps, endogenous nuclear genome variants are used for demultiplexing pooled single-cell RNA sequencing (scRNA-seq) data by several computational tools. However, these tools have limitations when applied to single-cell sequencing methods that do not cover nuclear genomic regions well, such as single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq). Here, we demonstrate that mitochondrial germline variants are an alternative, robust, and computationally efficient endogenous barcode for sample demultiplexing. We propose MitoSort, a tool that uses mitochondrial germline variants to assign cells to their donor of origin and identify cross-genotype doublets in single-cell genomics datasets. We evaluate its performance by using in silico pooled mitochondrial scATAC-seq (mtscATAC-seq) libraries and experimentally multiplexed data with cell hashtags. MitoSort achieves high accuracy and efficiency in genotype clustering and doublet detection for mtscATAC-seq data, addressing the limitations of current computational techniques tailored for scRNA-seq data. Moreover, MitoSort exhibits versatility and can be applied to various single-cell sequencing approaches beyond mtscATAC-seq, provided the mitochondrial variants are reliably detected. Furthermore, we demonstrate the application of MitoSort in a case study where B cells from eight donors were pooled and assayed by single-cell multi-omics sequencing. Altogether, our results demonstrate the accuracy and efficiency of MitoSort, which enables reliable sample demultiplexing in various single-cell genomic applications. MitoSort is available at https://github.com/tangzhj/MitoSort.

Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method.

Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Streptococcus thermophilus (S. thermophilus) are commonly used starters in milk fermentation. Fermentation experiments revealed that L. bulgaricus-S. thermophilus interactions (LbStI) substantially impact dairy product quality and production. Traditional biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations, an artificial intelligence-based method for screening interactive starter combinations is necessary. However, in the current research on artificial intelligence based interaction prediction in the field of bioinformatics, most successful models adopt supervised learning methods, and there is a lack of research on interaction prediction with only a small number of labeled samples. Hence, this study aimed to develop a semi-supervised learning framework for predicting LbStI using genomic data from 362 isolates (181 per species). The framework consisted of a two-part model: a co-clustering prediction model (based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset) and a Laplacian regularized least squares prediction model (based on K-mer analysis and gene composition of all isolates datasets). To enhance accuracy, we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate LbStI prediction results, which were verified through milk fermentation experiments. Validation through milk fermentation experiments confirmed a high precision rate of 85% (17/20; validated with 20 randomly selected combinations of expected interacting isolates). Our data suggest that the biosynthetic pathways of cysteine, riboflavin, teichoic acid, and exopolysaccharides, as well as the ATP-binding cassette transport systems, contribute to the mutualistic relationship between these starter bacteria during milk fermentation. However, this finding requires further experimental verification. The presented model and data are valuable resources for academics and industry professionals interested in screening dairy starter cultures and understanding their interactions.

Dynamic barriers modulate cohesin positioning and genome folding at fixed occupancy.

In mammalian interphase cells, genomes are folded by cohesin loop extrusion limited by directional CTCF barriers. This interplay leads to the enrichment of cohesin at barriers, isolation between neighboring topologically associating domains, and elevated contact frequency between convergent CTCF barriers across the genome. However, recent in vivo measurements present a puzzle: reported residence times for CTCF on chromatin are in the range of a few minutes, while lifetimes for cohesin are much longer. Can the observed features of genome folding result from the action of relatively transient barriers? To address this question, we developed a dynamic barrier model, where CTCF sites switch between bound and unbound states with rates that can be directly compared with biophysical measurements. Using this model, we investigated how barrier dynamics would impact observables for a range of experimental genomic and imaging datasets, including ChIP-seq, Hi-C, and microscopy. We found the interplay of CTCF and cohesin binding timescales influence the strength of each of these features, leaving a signature of barrier dynamics even in the population-averaged snapshots offered by genomic datasets. First, in addition to barrier occupancy, barrier bound times are crucial for instructing features of genome folding. Second, the ratio of boundary to extruder lifetime greatly alters simulated ChIP-seq and simulated Hi-C. Third, large-scale changes in chromosome morphology observed experimentally after increasing extruder lifetime require dynamic barriers. By integrating multiple sources of experimental data, our biophysical model argues that CTCF barrier bound times effectively approach those of cohesin extruder lifetimes. Together, we demonstrate how models that are informed by biophysically measured protein dynamics broaden our understanding of genome folding.

A community effort to optimize sequence-based deep learning models of gene regulation.

A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.

Candidate genes involved in biosynthesis and degradation of the main extracellular matrix polysaccharides of brown algae and their probable evolutionary history

BackgroundBrown algae belong to the Stramenopiles phylum and are phylogenetically distant from plants and other multicellular organisms. This independent evolutionary history has shaped brown algae with numerous metabolic characteristics specific to this group, including the synthesis of peculiar polysaccharides contained in their extracellular matrix (ECM). Alginates and fucose-containing sulphated polysaccharides (FCSPs), the latter including fucans, are the main components of ECMs. However, the metabolic pathways of these polysaccharides remain poorly described due to a lack of genomic data.ResultsAn extensive genomic dataset has been recently released for brown algae and their close sister species, for which we previously performed an expert annotation of key genes involved in ECM-carbohydrate metabolisms. Here we provide a deeper analysis of this set of genes using comparative genomics, phylogenetics analyses, and protein modelling. Two key gene families involved in both the synthesis and degradation of alginate were suggested to have been acquired by the common ancestor of brown algae and their closest sister species Schizocladia ischiensis. Our analysis indicates that this assumption can be extended to additional metabolic steps, and thus to the whole alginate metabolic pathway. The pathway for the biosynthesis of fucans still remains biochemically unresolved and we also investigate putative fucosyltransferase genes that may harbour a fucan synthase activity in brown algae.ConclusionsOur analysis is the first extensive survey of carbohydrate-related enzymes in brown algae, and provides a valuable resource for future research into the glycome and ECM of brown algae. The expansion of specific families related to alginate metabolism may have represented an important prerequisite for the evolution of developmental complexity in brown algae. Our analysis questions the possible occurrence of FCSPs outside brown algae, notably within their closest sister taxon and in other Stramenopiles such as diatoms. Filling this knowledge gap in the future will help determine the origin and evolutionary history of fucan synthesis in eukaryotes.

Proof of Concept for Genome Profiling of the Neurofibroma/Sarcoma Sequence in Neurofibromatosis Type 1.

Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disorder characterized by the predisposition to develop tumors such as malignant peripheral nerve sheath tumors (MPNSTs) which represents the primary cause of death for NF1-affected patients. Regardless of the high incidence and mortality, the molecular mechanisms underneath MPNST growth and metastatic progression remain poorly understood. In this proof-of-concept study, we performed somatic whole-exome sequencing (WES) to profile the genomic alterations in four samples from a patient with NF1-associated MPNST, consisting of a benign plexiform neurofibroma, a primary MPNST, and metastases from lung and skin tissues. By comparing genomic patterns, we identified a high level of variability across samples with distinctive genetic changes which allow for the definition of profiles of the early phase with respect to the late metastatic stages. Pathogenic and likely pathogenic variants were abundant in the primary tumor, whereas the metastatic samples exhibited a high level of copy-number variations (CNVs), highlighting a possible genomic instability in the late phases. The most known MPNST-related genes, such as TP53 and SUZ12, were identified in CNVs observed within the primary tumor. Pathway analysis of altered early genes in MPNST pointed to a potential role in cell motility, division and metabolism. Moreover, we employed survival analysis with the TCGA sarcoma genomic dataset on 262 affected patients, in order to corroborate the predictive significance of the identified early and metastatic MPNST driver genes. Specifically, the expression changes related to the mutated genes, such as in RBMX, PNPLA6 and AGAP2, were associated with reduced patient survival, distinguishing them as potential prognostic biomarkers. This study underlines the relevance of integrating genomic results with clinical information for early diagnosis and prognostic understanding of tumor aggressiveness.