Genome Sequencing Datasets Research Articles

A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.

In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes natural language processing techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach. The training dataset consisted of RNA sequencing data from 6 samples. Multiple natural language processing models were examined, differing in the type of dictionary components (word length, step, context) as well as the encoding length and number of sequences required for algorithm training. The best configuration analyses the entire sequence and uses a word length of 3 base pairs with one-word neighbor on each side. For the considered FDXR gene, the achieved mean balanced accuracy (BACC) was 98.29% and NPV was 99.25%, compared to minimap2's performance in a cross-validation scenario. The next stage focused on exploring the dictionary components and attempting to optimize it, employing statistical techniques as well as those relying on the explainability of the decisions made. Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and the NPV to 98.15%. Obtained model, validated on an external independent genome sequencing dataset, gave NPV of 99.64% for complete and 95.87% for reduced dictionary. The salmon-estimated read counts differed from the classical approach on average by 3.48% for the complete dictionary and by 5.82% for the reduced one. We conclude that for long Oxford nanopore reads, a natural language processing-based approach can reliably replace classical mapping when there is a need for fast, reliable and energy and computationally efficient targeted mapping of a pre-defined subset of transcripts. The developed model can be easily retrained to identify selected transcripts and/or work with various long-read sequencing techniques. Our results of the study clearly demonstrate the potential of applying techniques known from classical text processing to nucleotide sequences.

Linkage Disequilibrium-Informed Deep Learning Framework to Identify Genetic Loci for Alzheimer's Disease Using Whole Genome Sequencing Data.

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk. The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7,416 non-Hispanic white participants (3,150 cognitively normal older adults (CN), 4,266 AD). First, 30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1,500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog. The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving interactions between SNPs in performing the dimensionality reduction, which can potentially introduce bias or lead to information loss. The Deep-Block approach identified both known and novel genetic variation, enhancing our understanding of the genetic architecture of and demonstrating the framework's potential for application in large-scale sequencing studies.

A comprehensive atlas of nuclear sequences of mitochondrial origin (NUMT) inserted into the pig genome

BackgroundThe integration of nuclear mitochondrial DNA (mtDNA) into the mammalian genomes is an ongoing, yet rare evolutionary process that produces nuclear sequences of mitochondrial origin (NUMT). In this study, we identified and analysed NUMT inserted into the pig (Sus scrofa) genome and in the genomes of a few other Suinae species. First, we constructed a comparative distribution map of NUMT in the Sscrofa11.1 reference genome and in 22 other assembled S. scrofa genomes (from Asian and European pig breeds and populations), as well as the assembled genomes of the Visayan warty pig (Sus cebifrons) and warthog (Phacochoerus africanus). We then analysed a total of 485 whole genome sequencing datasets, from different breeds, populations, or Sus species, to discover polymorphic NUMT (inserted/deleted in the pig genome). The insertion age was inferred based on the presence or absence of orthologous NUMT in the genomes of different species, taking into account their evolutionary divergence. Additionally, the age of the NUMT was calculated based on sequence degradation compared to the authentic mtDNA sequence. We also validated a selected set of representative NUMT via PCR amplification.ResultsWe have constructed an atlas of 418 NUMT regions, 70 of which were not present in any assembled genomes. We identified ancient NUMT regions (older than 55 million years ago, Mya) and NUMT that appeared at different time points along the Suinae evolutionary lineage. We identified very recent polymorphic NUMT (private to S. scrofa, with < 1 Mya), and more ancient polymorphic NUMT (3.5–10 Mya) present in various Sus species. These latest polymorphic NUMT regions, which segregate in European and Asian pig breeds and populations, are likely the results of interspecies admixture within the Sus genus.ConclusionsThis study provided a first comprehensive analysis of NUMT present in the Sus scrofa genome, comparing them to NUMT found in other species within the order Cetartiodactyla. The NUMT-based evolutionary window that we reconstructed from NUMT integration ages could be useful to better understand the micro-evolutionary events that shaped the modern pig genome and enriched the genetic diversity of this species.

Identification of 16 novel Alzheimer's disease susceptibility loci using multi-ancestry meta-analyses of clinical Alzheimer's disease and AD-by-proxy cases from four whole genome sequencing datasets.

Alzheimer's disease (AD) is the most prevalent form of dementia. While many AD-associated genetic determinants have been previously identified, few studies have analyzed individuals of non-European ancestry. Here, we describe a multi-ancestry genome-wide association study of clinically-diagnosed AD and AD-by-proxy using whole genome sequencing data from NIAGADS, NIMH, UKB, and All of Us (AoU) consisting of 49,149 cases (12,074 clinically-diagnosed and 37,075 AD-by-proxy) and 383,225 controls. Nearly half of NIAGADS and AoU participants are of non-European ancestry. For clinically-diagnosed AD, we identified 14 new loci - five common (FBN2,/SCL27A6, AC090115.1, DYM, KCNG1/AL121785.1, TIAM1) and nine rare (VWA5B1, RNU6-755P/LMX1A, MOB1A, MORC1-AS1, LINC00989, PDE4D, RNU2-49P/CDO1, NEO1, and SLC35G3/AC022916.1). Meta-analysis of UKB and AoU AD-by-proxy cases yielded two new rare loci (RPL23/LASP1 and CEBPA/ AC008738.6) which were also nominally significant in NIAGADS. In summary, we provide evidence for 16 novel AD loci and advocate for more studies using WGS-based GWAS of diverse cohorts.

Pediatric Chordoma: A Tale of Two Genomes.

Little is known about the genomic alterations in chordoma, with the exception of loss of SMARCB1, a core member of the SWI/SNF complex, in poorly differentiated chordomas. A TBXT duplication and rs2305089 polymorphism, located at 6q27, are known genetic susceptibility loci. A comprehensive genomic analysis of the nuclear and mitochondrial genomes in pediatric chordoma has not yet been reported. In this study, we performed WES and mtDNA genome sequencing on 29 chordomas from 23 pediatric patients. Findings were compared with that from whole-genome sequencing datasets of 80 adult patients with skull base chordoma. In the pediatric chordoma cohort, 81% of the somatic mtDNA mutations were observed in NADH complex genes, which is significantly enriched compared with the rest of the mtDNA genes (P = 0.001). In adult chordomas, mtDNA mutations were also enriched in the NADH complex genes (P < 0.0001). Furthermore, a progressive increase in heteroplasmy of nonsynonymous mtDNA mutations was noted in patients with multiple tumors (P = 0.0007). In the nuclear genome, rare likely germline in-frame indels in ARID1B, a member of the SWI/SNF complex located at 6q25.3, were observed in five pediatric patients (22%) and four patients in the adult cohort (5%). The frequency of rare ARID1B indels in the pediatric cohort is significantly higher than that in the adult cohort (P = 0.0236, Fisher's exact test), but they were both significantly higher than that in the ethnicity-matched populations (P < 5.9e-07 and P < 0.0001174, respectively). Implications: germline ARID1B indels and mtDNA aberrations seem important for chordoma genesis, especially in pediatric chordoma.

Copy number variation introduced by a massive mobile element facilitates global thermal adaptation in a fungal wheat pathogen

Copy number variation (CNV) can drive rapid evolution in changing environments. In microbial pathogens, such adaptation is a key factor underpinning epidemics and colonization of new niches. However, the genomic determinants of such adaptation remain poorly understood. Here, we systematically investigate CNVs in a large genome sequencing dataset spanning a worldwide collection of 1104 genomes from the major wheat pathogen Zymoseptoria tritici. We found overall strong purifying selection acting on most CNVs. Genomic defense mechanisms likely accelerated gene loss over episodes of continental colonization. Local adaptation along climatic gradients was likely facilitated by CNVs affecting secondary metabolite production and gene loss in general. One of the strongest loci for climatic adaptation is a highly conserved gene of the NAD-dependent Sirtuin family. The Sirtuin CNV locus localizes to an ~68-kb Starship mobile element unique to the species carrying genes highly expressed during plant infection. The element has likely lost the ability to transpose, demonstrating how the ongoing domestication of cargo-carrying selfish elements can contribute to selectable variation within populations. Our work highlights how standing variation in gene copy numbers at the global scale can be a major factor driving climatic and metabolic adaptation in microbial species.

Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing datasets.

Spinal muscular atrophy (SMA) is a genetic disorder that causes progressive degeneration of lower motor neurons and the subsequent loss of muscle function throughout the body. It is the second most common recessive disorder in individuals of European descent and is present in all populations. Accurate tools exist for diagnosing SMA from genome sequencing data. However, there are no publicly available tools for GRCh38-aligned data from panel or exome sequencing assays which continue to be used as first line tests for neuromuscular disorders. This deficiency creates a critical gap in our ability to diagnose SMA in large existing rare disease cohorts, as well as newly sequenced exome and panel datasets. We therefore developed and extensively validated a new tool - SMA Finder - that can diagnose SMA not only in genome, but also exome and panel sequencing samples aligned to GRCh37, GRCh38, or T2T-CHM13. It works by evaluating aligned reads that overlap the c.840 position of SMN1 and SMN2 in order to detect the most common molecular causes of SMA. We applied SMA Finder to 16,626 exomes and 3,911 genomes from heterogeneous rare disease cohorts sequenced at the Broad Institute Center for Mendelian Genomics as well as 1,157 exomes and 8,762 panel sequencing samples from Tartu University Hospital. SMA Finder correctly identified all 16 known SMA cases and reported nine novel diagnoses which have since been confirmed by clinical testing, with another four novel diagnoses undergoing validation. Notably, out of the 29 total SMA positive cases, 23 had an initial clinical diagnosis of muscular dystrophy, congenital myasthenic syndrome, or myopathy. This underscored the frequency with which SMA can be misdiagnosed as other neuromuscular disorders and confirmed the utility of using SMA Finder to reanalyze phenotypically diverse neuromuscular disease cohorts. Finally, we evaluated SMA Finder on 198,868 individuals that had both exome and genome sequencing data within the UK Biobank (UKBB) and found that SMA Finder's overall false positive rate was less than 1 / 200,000 exome samples, and its positive predictive value (PPV) was 97%. We also observed 100% concordance between UKBB exome and genome calls. This analysis showed that, even though it is located within a segmental duplication, the most common causal variant for SMA can be detected with comparable accuracy to monogenic disease variants in non-repetitive regions. Additionally, the high PPV demonstrated by SMA Finder, the existence of treatment options for SMA in which early diagnosis is imperative for therapeutic benefit, as well as widespread availability of clinical confirmatory testing for SMA, warrants the addition of SMN1 to the ACMG list of genes with reportable secondary findings after genome and exome sequencing.

The chloroplast genome sequences of Ipomoea alba and I. obscura (Convolvulaceae): genome comparison and phylogenetic analysis

Ipomoea species have diverse uses as ornamentals, food, and medicine. However, their genomic information is limited; I. alba and I. obscura were sequenced and assembled. Their chloroplast genomes were 161,353 bp and 159,691 bp, respectively. Both genomes exhibited a quadripartite structure, consisting of a pair of inverted repeat (IR) regions, which are separated by the large single-copy (LSC) and small single-copy (SSC) regions. The overall GC content was 37.5% for both genomes. A total of 104 and 93 simple sequence repeats, 50 large repeats, and 30 and 22 short tandem repeats were identified in the two chloroplast genomes, respectively. G and T were more preferred than C and A at the third base position based on the Parity Rule 2 plot analysis, and the neutrality plot revealed correlation coefficients of 0.126 and 0.105, indicating the influence of natural selection in shaping the codon usage bias in most protein-coding genes (CDS). Genome comparative analyses using 31 selected Ipomoea taxa from Thailand showed that their chloroplast genomes are rather conserved, but the presence of expansion or contraction of the IR region was identified in some of these Ipomoea taxa. A total of five highly divergent regions were identified, including the CDS genes accD, ndhA, and ndhF, as well as the intergenic spacer regions psbI-atpA and rpl32-ccsA. Phylogenetic analysis based on both the complete chloroplast genome sequence and CDS datasets of 31 Ipomoea taxa showed that I. alba is resolved as a group member for series (ser.) Quamoclit, which contains seven other taxa, including I. hederacea, I. imperati, I. indica, I. nil, I. purpurea, I. quamoclit, and I. × sloteri, while I. obscura is grouped with I. tiliifolia, both of which are under ser. Obscura, and is closely related to I. biflora of ser. Pes-tigridis. Divergence time estimation using the complete chloroplast genome sequence dataset indicated that the mean age of the divergence for Ipomoeeae, Argyreiinae, and Astripomoeinae, was approximately 29.99 Mya, 19.81 Mya, and 13.40 Mya, respectively. The node indicating the divergence of I. alba from the other members of Ipomoea was around 10.06 Mya, and the split between I. obscura and I. tiliifolia is thought to have happened around 17.13 Mya. The split between the I. obscura accessions from Thailand and Taiwan is thought to have taken place around 0.86 Mya.

HGG-03. TRUNCATING MUTATIONS IN PPM1D COOPERATE WITH PI3K ALTERATIONS TO DRIVE PEDIATRIC DIFFUSE MIDLINE GLIOMAS

Abstract Mutations in PPM1D – coding for the phosphatase WIP1 - are present in 8 -12% of all Diffuse Midline Glioma (DMG), H3 K27-altered – a universally fatal subtype of pediatric high-grade tumors. PPM1D mutations are mutually exclusive with TP53 mutations and mainly lead to a truncation of its C-terminal regulatory domain. Truncated PPM1D (PPM1Dtr) has increased protein stability and impairs the p53-dependent DNA Damage Response (DDR) by dephosphorylating its numerous targets (e.g p53, H2A.X, CHK1) which contributes to its oncogenicity. However, because wildtype p53 is present in PPM1Dtr tumors, its activation is suppressed but not completely ablated suggesting that PPM1D mutant tumors may evolve differently from p53 mutant ones. Our analysis of whole genome sequencing datasets indeed found that PPM1D mutations uniquely co-occur with alterations in the PI3K/mTOR pathway suggesting potential cooperativity in enhancing tumorigenesis. Using mouse neural stem cells (mNSCs) as an isogenic model system, we have engineered specific combinations of oncogenic alterations to study their impact on cell proliferation, PI3K/mTOR pathway activation and response to DNA damage and replication stress. We find that PPM1Dtr cooperates with PIK3CA alterations to increase proliferation and signaling through the PI3K/mTOR pathway. Notably, in an in utero electroporation model, mutant PIK3CA accelerates the development of high-grade tumors that infiltrate deep into the brainstem. Our study also indicates a vulnerability to inhibition of the PI3K/mTOR pathway in PPM1Dtr-PI3K co-altered tumors. Future work will aim at 1) exploring the mechanism of cooperation and impact on replicative stress response and 2) developing and testing a rational therapeutic strategy for PPM1D-mutant tumors.

VSNP: a SNP pipeline for the generation of transparent SNP matrices and phylogenetic trees from whole genome sequencing data sets

BackgroundSeveral single nucleotide polymorphism (SNP) pipelines exist, each offering its own advantages. Among them and described here is vSNP that has been developed over the past decade and is specifically tailored to meet the needs of diagnostic laboratories. Laboratories that aim to provide rapid whole genome sequencing results during outbreak investigations face unique challenges. vSNP addresses these challenges by enabling users to verify and validate sequence accuracy with ease- having utility across various pathogens, being fully auditable, and presenting results that are easy to interpret and can be comprehended by individuals with diverse backgrounds.ResultsvSNP has proven effective for real-time phylogenetic analysis of disease outbreaks and eradication efforts, including bovine tuberculosis, brucellosis, virulent Newcastle disease, SARS-CoV-2, African swine fever, and highly pathogenic avian influenza. The pipeline produces easy-to-read SNP matrices, sorted for convenience, as well as corresponding phylogenetic trees, making the output easily understandable. Essential data for verifying SNPs is included in the output, and the process has been divided into two steps for ease of use and faster processing times. vSNP requires minimal computational resources to run and can be run in a wide range of environments. Several utilities have been developed to make analysis more accessible for subject matter experts who may not have computational expertise.ConclusionThe vSNP pipeline integrates seamlessly into a diagnostic workflow and meets the criteria for quality control accreditation programs, such as 17025 by the International Organization for Standardization. Its versatility and robustness make it suitable for use with a diverse range of organisms, providing detailed, reproducible, and transparent results, making it a valuable tool in various applications, including phylogenetic analysis performed in real time.

#3020 A novel copy number analysis identifies human patients with NPHP1 whole gene deletions in previously genetically unsolved cases

Abstract Background and Aims Genome sequencing is rapidly becoming a routine approach to investigating inherited human diseases in clinical practice. Nevertheless, data generated from genome sequencing requires extensive bioinformatic analysis and filtering of variants to determine pathogenicity and remains challenging. Despite well-established workflows and software in place to process raw data, the analysis of genome sequencing data is still complex. Copy Number Variations (CNVs) are increasingly recognised to be pathogenic, especially where this leads to a whole gene being deleted. Software tools to decipher CNVs across genome sequencing datasets are being utilised but are not completely accurate. Method Using a bespoke CNV analysis of Genomics England 100,000 Genomes Project (100 kGP) WGS data from 11754 parent-child trios, we searched for homozygous deletions overlapping with annotated genes, across the 22 autosomes. Results Homozygous ∼150 kb whole gene deletion of NPHP1-MALL on chromosome 1, which aligned with a known region containing segmental duplications (SD), was the most frequent observation among all autosomal recessive (AR) OMIM-morbid genes. Clinical phenotypes were consistent with this genotype including nephronophthisis and retinal dystrophy. We extended our analysis to look for NPHP1 whole gene deletions in 18877 additional singletons within the 100 kGP. This reverse genetics approach identified additional patients, up to this point genetically unsolved, with whole gene deletions and matching phenotypes. The frequency of heterozygous NPHP1 whole gene deletion was assessed in a control cohort of 15440 cancer patients within 100 kGP and was found to be 0.35%. Conclusion In conclusion, NPHP1 was the most frequently observed AR OMIM-morbid gene knockout in our analysis. Homozygous whole gene deletions in NPHP1 may be easily missed using standard WGS pipelines which don't assess fully for CNVs and a targeted read-depth approach to identify CNVs in parent-patient trios and singletons is valuable in identifying these deletions.

A GGC-repeat expansion in ZFHX3 encoding polyglycine causes spinocerebellar ataxia type 4 and impairs autophagy.

Despite linkage to chromosome 16q in 1996, the mutation causing spinocerebellar ataxia type 4 (SCA4), a late-onset sensory and cerebellar ataxia, remained unknown. Here, using long-read single-strand whole-genome sequencing (LR-GS), we identified a heterozygous GGC-repeat expansion in a large Utah pedigree encoding polyglycine (polyG) in zinc finger homeobox protein 3 (ZFHX3), also known as AT-binding transcription factor 1 (ATBF1). We queried 6,495 genome sequencing datasets and identified the repeat expansion in seven additional pedigrees. Ultrarare DNA variants near the repeat expansion indicate a common distant founder event in Sweden. Intranuclear ZFHX3-p62-ubiquitin aggregates were abundant in SCA4 basis pontis neurons. In fibroblasts and induced pluripotent stem cells, the GGC expansion led to increased ZFHX3 protein levels and abnormal autophagy, which were normalized with small interfering RNA-mediated ZFHX3 knockdown in both cell types. Improving autophagy points to a therapeutic avenue for this novel polyG disease. The coding GGC-repeat expansion in an extremely G+C-rich region was not detectable by short-read whole-exome sequencing, which demonstrates the power of LR-GS for variant discovery.

Detection of rare variants among nuclei populating the arbuscular mycorrhizal fungal model species Rhizophagus irregularis DAOM197198.

Identifying genuine polymorphic variants is a significant challenge in sequence data analysis, although detecting low-frequency variants in sequence data is essential for estimating demographic parameters and investigating genetic processes, such as selection, within populations. Arbuscular mycorrhizal (AM) fungi are multinucleate organisms, in which individual nuclei collectively operate as a population, and the extent of genetic variation across nuclei has long been an area of scientific interest. In this study, we investigated the patterns of polymorphism discovery and the alternate allele frequency distribution by comparing polymorphism discovery in 2 distinct genomic sequence datasets of the AM fungus model species, Rhizophagus irregularis strain DAOM197198. The 2 datasets used in this study are publicly available and were generated either from pooled spores and hyphae or amplified single nuclei from a single spore. We also estimated the intraorganismal variation within the DAOM197198 strain. Our results showed that the 2 datasets exhibited different frequency patterns for discovered variants. The whole-organism dataset showed a distribution spanning low-, intermediate-, and high-frequency variants, whereas the single-nucleus dataset predominantly featured low-frequency variants with smaller proportions in intermediate and high frequencies. Furthermore, single nucleotide polymorphism density estimates within both the whole organism and individual nuclei confirmed the low intraorganismal variation of the DAOM197198 strain and that most variants are rare. Our study highlights the methodological challenges associated with detecting low-frequency variants in AM fungal whole-genome sequence data and demonstrates that alternate alleles can be reliably identified in single nuclei of AM fungi.

Abstract LB228: Evolutionary trajectories of lung cancer in never smokers

Abstract Purpose of the study Elucidating evolutionary trajectories of cancers allows us to understand the key events, and the order in which they occur, throughout their development. This can help us to find important associations with tumor progression and prognosis. Our aim was to perform de novo identification of the evolutionary trajectories within Sherlock-lung, with a dataset containing the largest collection of lung cancer in never smokers (LCINS) samples ever analyzed. Experimental procedures Our Plackett-Luce ordering model utilized copy number data from Battenberg and mutation cancer cell fraction (CCF) data from DPClust. Frequently-occurring copy number events and driver mutations are ordered within each sample using their copy number states and CCFs. An aggregate ordering is then calculated for a sample set. Mixture model analysis identifies subsets of samples displaying distinct orders of events, uncovering diverse evolutionary trajectories within a tumor set. Dataset The Sherlock-lung whole genome sequencing dataset (n=1217) was filtered to the samples that allowed us to identify subclonal expansions. Samples required at least 10 reads per chromosome copy and a minimum cellularity of 30%. This provided 458 LCINS samples of various histologies. 155 smoker samples were also analyzed for comparison. Results We identified two subsets of LCINS tumors following distinct evolutionary trajectories. The “loss-based” subset commonly saw whole genome duplication (WGD) combined with copy number losses occurring earlier, and at higher prevalence, than gains. Contrastingly, in the “gain-based” subset, WGD was relatively rare but ploidy increased via copy number gains, which were more prevalent than losses. Interestingly, these different trajectories converged on similar overall copy number states. The loss-based subset had a higher mutational burden and a higher proportion of the genome altered, and followed a more smoker-like trajectory than the gain-based subset. Considering these differences alongside the convergence in copy number states, it is intriguing that survival times were similar between the two subsets. Copy number events defined the difference between the two trajectories. However, driver mutations also played important roles in tumor evolution in LCINS. TP53 and EGFR mutations were associated with greater genomic instability. Conversely, KRAS mutations were associated with more stable genomes. Samples with early clonal mutations in TP53, ERBB2, and PIK3CA, as well as those with a copy number gain of ERBB2, exhibited shorter survival times. Conclusions Two distinct evolutionary trajectories of LCINS were identified by de novo Plackett-Luce event ordering analysis. The contrast between the subgroups was defined by different paths of copy number activity, but they ultimately converged on similar overall copy number states and outcomes. Key early driver mutations influenced genomic instability and survival times. Citation Format: Christopher Wirth, Tongwu Zhang, Wei Zhao, Phuc Hoang, Jian Sang, Nathaniel Rothman, Marcos Díaz-Gay, Ruxandra Teslo, Naser Ansari-Pour, Máire Ní Leathlobhair, Iliana Peneva, William Eagles, Lixing Yang, Ludmil Alexandrov, David C. Wedge, Maria Teresa Landi. Evolutionary trajectories of lung cancer in never smokers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB228.

Abstract 3501: Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis

Abstract Chordoma, a rare bone cancer characterized by limited actionable clinical targets, exhibits a pivotal hallmark in the activation of the developmental transcription factor gene TBXT (Brachyury) through its association with highly active enhancers. TBXT amplification emerges as a major causative factor in familial chordomas, yet the amplification mechanism and its consequential impact on genomic instability remain inadequately elucidated. In this study, we conducted a comprehensive analysis of multiple genomic and epigenomic sequencing datasets, including WGS-seq, H3K27ac ChIP-seq, and ATAC-seq in six chordoma cell lines, and HiC data in two of them, to unravel the mechanisms of structural variations and their biological implications. Notably, our focus extends to JHC7 cells, which exhibit extrachromosomal amplification of a 2.4M region encompassing TBXT and the associated enhancers. The existence of such extrachromosomal DNA (ecDNA) in JHC7 was further validated through DNA FISH and Sanger sequencing. Comparing JHC7 with CH22, a chordoma cell line lacking TBXT amplification, revealed heightened chromatin relaxation and increased DNA rearrangements in JHC7. Interestingly, FISH results showed significant genomic ploidy heterogeneity within JHC7 cells, a finding demonstrated by nucleus FACS sorting. Single-cell multiomic analysis, combining snRNA-seq and snATAC-seq, demonstrated heterogeneous gene expression and distinct copy number variations among JHC7 cells. However, the amplification of the TBXT locus was observed in nearly all cells examined, indicating the rapid genomic evolution in the cultured JHC7 cells. These results collectively show that TBXT is extensively amplified through the formation of ecDNA, providing a fitness advantage to chordoma cells and concurrently exacerbating genomic instability. The findings in this study provide an insight for understanding the pathogenesis of chordoma and may inform the development of targeted therapeutic strategies. Citation Format: Bo Zhao, Martin Barrio Ines, Lingqun Ye, Xizi Wu, Alice Soragni, Andrew Futreal, Kadir Akdemir. Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3501.

Abstract 1761: Comprehensive profiling of the FAT3-associated gene signature in deciphering immunophenotypes of gastrointestinal cancers: Analysis of institutional cohort and TCGA dataset

Abstract Gastrointestinal (GI) cancers are among the most prevalent cancers affecting the US population. Anatomically, GI cancer includes cancers of the organs in the digestive tract, from the esophagus to the rectum. Advances in molecular oncology have started to transform the therapeutic landscape and offer tremendous promise for patients across diverse lineages. However, the equitable benefit of this approach has been hampered due to tumor heterogeneity and the lack of accurate, tumor-agnostic biomarkers with prognosis and predictive utility. Thus, there is a need to identify novel biomarkers that affect gastrointestinal malignancies to improve the management of cancer patients. This study analyzed a single institution's whole genome sequencing dataset to explore the genomic variation in GI cancers. Between 2020 and 2022, 302 AU patients underwent molecular profiling, including whole-exome sequencing (WES) profiling at Caris Life Sciences. WES is an NGS assay that analyzes the DNA sequences of all protein-coding exons in the genome, representing approximately 1-2% of the human genome with coverage of over 22,000 genes. We analyzed the TCGA dataset for comparative analysis, which included cancer data from &amp;gt;1800 GI cancer patients. In addition, survival and network analyses were performed to identify a 6-gene FAT3-related signature for GI cancers. In our cohort, we identified FAT3 (Fat atypical cadherin 3) as the most frequently mutated gene after TP53, APC, and KRAS. FAT3 encodes a cadherin protein involved in cell-cell interactions and adhesion. In the institutional cohort, FAT3 was found to be mutated in 16% of all GI cases. Further analysis of TCGA datasets comprising 1808 GI cancer patients revealed FAT3 mutations in 12% of the cases. To identify additional prognostic biomarkers associated with FAT3, we performed network analysis and identified a 6-gene FAT3-related signature (FAT3, RYK, FAT2, EGFLAM, NTRK3, IGSF9, and HMGA2) that significantly stratified GI cancer patients based on overall survival, progression-free survival, and disease-specific survival. The perturbation profile of the 6-gene signature was associated with 509 patients or 28% of total GI patients. Further, immune deconvolution analysis of stratified patients revealed increased infiltration of immune cells with immunosuppressive phenotypic properties in GI cancer patients with higher expression of the FAT3-related gene signature. In summary, this analysis reveals the distribution of FAT3 mutational profiles in GI patients and identifies a gene signature that stratifies patients based on survival outcomes. This analysis can provide new tools for patient stratification and therapy implementation, leading to better outcomes for cancer patients. Citation Format: Pankaj Kumar Ahluwalia, Tiffanie Leeman, Ashis Mondal, Ashutosh Vashisht, Harmanpreet Singh, Ravindra Kolhe. Comprehensive profiling of the FAT3-associated gene signature in deciphering immunophenotypes of gastrointestinal cancers: Analysis of institutional cohort and TCGA dataset [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1761.

Untranslated regions (UTRs) are a potential novel source of neoantigens for personalised immunotherapy.

Neoantigens, mutated tumour-specific antigens, are key targets of anti-tumour immunity during checkpoint inhibitor (CPI) treatment. Their identification is fundamental to designing neoantigen-directed therapy. Non-canonical neoantigens arising from the untranslated regions (UTR) of the genome are an overlooked source of immunogenic neoantigens. Here, we describe the landscape of UTR-derived neoantigens and release a computational tool, PrimeCUTR, to predict UTR neoantigens generated by start-gain and stop-loss mutations. We applied PrimeCUTR to a whole genome sequencing dataset of pre-treatment tumour samples from CPI-treated patients (n = 341). Cancer immunopeptidomic datasets were interrogated to identify MHC class I presentation of UTR neoantigens. Start-gain neoantigens were predicted in 72.7% of patients, while stop-loss mutations were found in 19.3% of patients. While UTR neoantigens only accounted 2.6% of total predicted neoantigen burden, they contributed 12.4% of neoantigens with high dissimilarity to self-proteome. More start-gain neoantigens were found in CPI responders, but this relationship was not significant when correcting for tumour mutational burden. While most UTR neoantigens are private, we identified two recurrent start-gain mutations in melanoma. Using immunopeptidomic datasets, we identify two distinct MHC class I-presented UTR neoantigens: one from a recurrent start-gain mutation in melanoma, and one private to Jurkat cells. PrimeCUTR is a novel tool which complements existing neoantigen discovery approaches and has potential to increase the detection yield of neoantigens in personalised therapeutics, particularly for neoantigens with high dissimilarity to self. Further studies are warranted to confirm the expression and immunogenicity of UTR neoantigens.

A New Cloud-Native Tool for Pharmacogenetic Analysis.

The advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions. Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC). The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38. Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements.

Impact of dietary fiber on gut microbiota composition, function and gut-brain-modules in healthy adults - a systematic review protocol.

The gut microbiota has been extensively implicated in health and disease. The functional outputs of the gut microbiota, such as microbial metabolites, are considered particularly important in this regard. Significant associations exist between alterations in the relative abundance of specific microbial taxa and mental health disorders. Dietary fiber has the potential to alter gut microbiota composition and function, modifying bacterial enzymatic function and the production of metabolites. As many taxa of microorganisms have enzymes capable of producing or degrading neurochemicals i.e. neuroactive gut brain modules, new predictive tools can be applied to existing datasets such as those harvested from dietary fiber interventions. We endeavor to perform a systematic review in order to identify studies reporting compositional gut microbiota alterations after interventions with dietary fiber in healthy individuals. We aim to also extract from the selected studies publicly available microbial genomic sequence datasets for reanalysis with a consistent bioinformatics pipeline, with the ultimate intention of identifying altered gut brain modules following dietary fiber interventions. Interventional trials and randomized controlled studies that are originally published, including cross-over and non-crossover design and involving healthy adult humans will be included. A systematic search of PubMed/MEDLINE and EMBASE, two electronic databases, will be completed. Various types of dietary fiber have an impact on the gut microbiota composition, with some promoting the growth of particular taxa while others are reduced in relative abundance. Our search focuses on the impact of this food component on the microbiota of healthy individuals. Compositional gut microbial changes have been reported and our review will compile and update these observations after reanalysis of their datasets with a consistent bioinformatic pipeline. From this it may be possible to predict more detailed functional consequences in terms of neuroactive gut brain modules, of the compositional alterations in gut microbial taxa.

Whole Genome Sequence Dataset of Mycobacterium tuberculosis Strains from Patients of Campania Region

Tuberculosis (TB) is one of the deadliest infectious disorders in the world. To effectively TB manage, an essential step is to gain insight into the lineage of Mycobacterium tuberculosis (MTB) and the distribution of drug resistance. Although the Campania region is declared a cluster area for the infection, to contribute to the effort to understand TB evolution and transmission, still poorly known, we have generated a dataset of 159 genomes of MTB strains, from Campania region collected during 2018–2021, obtained from the analysis of whole genome sequence. The results show that the most frequent MTB lineage is the 4 according for 129 strains (81.11%). Regarding drug resistance, 139 strains (87.4%) were classified as multi susceptible, while the remaining 20 (12.58%) showed drug resistance. Among the drug-resistance strains, 8 were isoniazid-resistant MTB, 4 multidrug-resistant MTB, while only one was classified as pre-extensively drug-resistant MTB. This dataset expands the existing available knowledge on drug resistance and evolution of MTB, contributing to further TB-related genomics studies to improve the management of this disease.