Somatic Structural Variants Research Articles

Tumor Suppressor Genes with Segmental Duplications are Prone to Somatic Deletions and Structural Variations.

Segmental duplications (SDs) are blocks of genomic DNA with high sequence homology that are hotspots for chromosomal rearrangements, coinciding with copy-number and single-nucleotide variations in the population. SDs could represent unstable genomic regions that are susceptible to somatic alterations in human cancers. Here, we aimed to elucidate the genomic locations of SDs in relation to cancer-related genes and their propensity for somatic alterations in cancer. The analysis showed that tumor suppressor genes (TSGs) were less associated with SDs compared to non-cancer genes in nearly all mammalian species. TSGs with SDs were larger in size in humans but only modestly conserved among mammals. In humans, the proportion of non-cancer genes with SDs decreased as the gene age increased. However, for TSGs, a loss of association with SDs was apparent among young genes. Pan-cancer analysis revealed that TSGs with SDs were more prone to deletions and structural variations independently of gene size. Re-analysis of publicly available experimental data further revealed that genes with SDs tended to replicate late and were more likely to undergo the error-prone mitotic DNA synthesis upon replication stress. In conclusion, the loss of SDs from TSGs during mammalian evolution protects against tumor formation by reducing somatic alterations.

Abstract 4905: Mosaic chromosomal alterations overlapping hotspot and coldspot sites of somatic structural variation are associated with increased odds of hematological malignancy

Abstract BACKGROUND: Clonal hematopoiesis (CH) occurs when hematopoietic cells acquire somatic mutations and proliferate to generate clones in blood. CH can be driven by mosaic chromosomal alterations (mCAs), which are large somatic structural variants, and is associated with increased risk of blood malignancy. We conducted a genome-wide search for sites significantly more or less impacted by mCA events, termed hotspots and coldspots. We tested whether the odds of blood malignancy in participants harboring mCAs overlapping hotspots or coldspots differed from the overall association between mCAs and blood malignancy. METHODS: This study utilized two cohorts of the Canadian Partnership for Tomorrow’s Health, including the Ontario Health Study (OHS; n=7070) and CARTaGENE (n=28,639). Participants were genotyped with the UK Biobank Axiom or Infinium Global Screening Array and completed a baseline cancer questionnaire. All mCA calling was performed using MoChA software. We identified hotspot and coldspot sites of autosomal mCA accumulation using binomial tests scaled by chromosome and array type. We determined whether the number of overlapping mCAs at each query site was greater or less than would be expected under the null expectation. Then, we calculated the prevalence of cancer in participants with or without mCAs overlapping hotspots/coldspots and conducted Fisher’s exact tests to generate odds ratios. RESULTS: In OHS, participants with an mCA had a significantly greater odds of having a hematological malignancy at baseline (OR=7.29, 95% CI=3.72-13.38, p=7.84e-08). They were also more likely to have a cancer diagnosis of any type (OR=1.63, 95% CI=1.14-2.30, p=5.59e-03). Participants with an mCA overlapping a hotspot site had a further increased odds of harboring hematological malignancy (OR=9.79, 95% CI=3.94-21.29, p=5.05-e06). For participants with an mCA overlapping a coldspot site, the OR reached 20.52 (95% CI=3.68-76.85, p=7.36e-04). In CARTaGENE, participants with an mCA were also at increased odds of hematological malignancy (OR=11.31, 95% CI=6.78-18.17, p=9.77e-16), and any cancer diagnosis (OR=1.93, 95% CI=1.53-2.41, p=5.95e-08). Those carrying an mCA overlapping a hotspot site were 14.6 times more likely to have a hematological malignancy (95% CI=7.94-25.29, p=3.30e-13), and those with an mCA overlapping a coldspot site were 14.2 times more likely (95% CI=5.0-32.9, p=7.63e-06). CONCLUSIONS: These results suggest that there may be genomic locations at which somatic structural variation has a larger impact on the development of blood malignancy, relative to other regions. Further work is needed to characterize the functional consequences of somatic mutation at these key regions and explore whether these associations of hotspots, coldspots, and cancer incidence are conserved across a range of tissues. Citation Format: Jasmine Ryu Won Kang, Vanessa Bruat, Kimberly Skead, Mawusse Agbessi, June Kim, Elias Gbeha, Marie-Julie Fave, Philip Awadalla. Mosaic chromosomal alterations overlapping hotspot and coldspot sites of somatic structural variation are associated with increased odds of hematological malignancy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4905.

Abstract 5056: Full spectrum of somatic structural variations (SVs) detection in COLO829 with long-read sequencing

Abstract 5056: Full spectrum of somatic structural variations (SVs) detection in COLO829 with long-read sequencing

Abstract 2780: Topoisomerase IIb binding underlies frequently mutated elements in cancer genomes

Abstract Type-II topoisomerases resolve topological stress in DNA through controlled double-strand breaks. While TOP2A is a chemotherapy target in proliferating cells, the ubiquitously expressed TOP2B is a potential off-target. Here we explore roles of TOP2B in mutagenesis by generating DNA-binding maps of TOP2B, CTCF, and RAD21 in human cancer samples and analyzing these maps for driver mutations and mutational processes in 6500 whole cancer genomes. TOP2B-CTCF-RAD21 and TOP2B-RAD21 sites are enriched in somatic mutations and structural variants (SVs), especially at evolutionary conserved sites displaying high transcription and long-range chromatin interactions. TOP2B binding underlies SVs and hotspot mutations in cancer-driving genes such as TP53, MYC, FOXA1, and VHL, and many cis-regulatory elements. We show that the TOP2B-bound mutational hotspot at RMRP drives tumor initiation and growth in vivo. These data highlight TOP2B as a protector of the genome from topological challenges whose aberrant activity promotes driver and passenger mutations in cancer genomes. Citation Format: Jüri Reimand, 1 Christian A. Lee, 1 Robin H. Oh, 2 Zoe P. Klein, 1 Nina Adler, 1 Sana Akhtar Alvi, 3 Ellen Langille, 2 Elisa Pasini, 4 Kevin C. Cheng, 1 Diala Abd-Rabbo, 1 Huayun Hou, 3 Ricky Tsai, 2 Mamatha Bhat, 1 Daniel Schramek, 2 Michael D. Wilson, 3 Liis Uusküla-Reimand3. Topoisomerase IIb binding underlies frequently mutated elements in cancer genomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2780.

Abstract 2749: Single-cell 3D genome structure analysis reveals clonal evolution and heterogeneity in acute myeloid leukemia

Abstract Alterations in the 3D genome organization have been reported in different types of cancer and have been linked with gene dysregulation and cancer progression. However, majority of the studies were performed using bulk chromatin interaction assays and therefore, cannot address the heterogeneity and clone evolution in primary human tumors. In this study, we generated single cell Hi-C and single cell ATAC-Seq data in a cohort of AML patients, comprising samples at different disease stages, including diagnosis, remission, and relapse. To our knowledge, this is first large collection of single cell Hi-C data in primary tumor. In total, we profiled 153,517 cells across 13 samples, revealing distinct contact patterns and significant heterogeneity in genome-wide contact features between different samples, as well as within the same sample. We demonstrated the impact of 3D genome alterations on AML-specific gene expression programs, highlighting the role of compartmentalization in driving AML pathogenesis. Moreover, we identified hundreds of AML-specific chromatin domains with decreased heterogeneity during the transition from initial diagnosis to post-treatment relapse, suggesting the emergence and expansion of therapy-resistant subclones. By integrating a clonal evolution model, we delineated the dynamic relationship between somatic mutation, structural variation, and 3D genome architecture alterations. Finally, we discovered a chromatin loop uniquely present in a subclone of the relapse sample, which hijacked a distal enhancer to the known chemo-resistance-related oncogene and led to its upregulation. To summarize, our findings demonstrated the heterogeneity of 3D genome structure in AML and provide insights on the disease progression and relapse mechanisms. Citation Format: Yu Luan, Ye Hou, Yutong Lei, Qiushi Jin, Jie Xu, Bei Jia, Siwei Xu, Ping Wang, Alok Swaroop, Yihao Fu, Juan Wang, Qixuan Wang, Hong Zheng, Jing Zhang, Feng Yue. Single-cell 3D genome structure analysis reveals clonal evolution and heterogeneity in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2749.

Abstract 2848: Severus: A tool for detecting and characterizing complex structural variants in cancer using long-read sequencing

Abstract Most human cancers arise from somatic alterations, ranging from single nucleotide variations to structural variations (SVs) that can alter the genomic organization. Pathogenic SVs are identified in various cancer types and subtypes, and they play a crucial role in diagnosis and patient stratification. However, the studies on structural variations have been limited due to biological and computational challenges, including tumor heterogeneity, aneuploidy, and the diverse spectrum of SVs from simpler deletions and focal amplifications to catastrophic events shuffling large fragments from one or multiple chromosomes. Long-read sequencing provides the advantage of improved mappability and direct haplotype phasing. Yet, no tool currently exists to comprehensively analyze complex rearrangements within the cancer genome using long-read sequencing. Here, we present Severus, a tool for somatic SV calling and complex SV characterization using long reads. Severus first detects individual SV junctions from phased split alignments, then constructs a phased breakpoint graph to cluster junctions into complex rearrangement events. We first benchmarked the somatic SV calling performance using six tumor/normal cell line pairs (HCC1395, H1437, H2009, HCC1937, HCC1954, Hs578T). We sequenced all cell lines with Illumina, ONT, and PacBio HiFi. We then established a set of high-confidence calls supported by multiple technologies and tools. Severus consistently had the highest F1 scores compared to the HiFi, ONT, and Illumina methods against this high-confidence SV call set. We then extend our analysis to complex SVs. Severus accurately detected complex events, i.e., chromothripsis and chromoplexy, and templated insertion cycles/chains (TIC), reported for these cell lines. We then compared Severus’ performance with Jabba and Linx, two widely used tools for complex SV calling in short-read sequencing. Our comparison revealed that Severus showed higher agreement with Linx, while Jabba failed to detect most of the SV clusters identified by both Severus and Linx. Severus also outperformed the other tools in characterizing complex reciprocal translocations and TICs. Most of the junctions in complex SVs called by either of the tools but not Severus were either simple SVs with a single long-read junction or were not present in long-read sequencing. In contrast, Severus effectively resolved overlapping SVs by utilizing long-read connectivity, allowing for more accurate clustering of smaller genomic segments. We have also applied Severus to seventeen pediatric leukemia cases. Severus identified two chromoplexy and two cryptic translocations, which were missed by FISH and karyotype panels and were incomplete in Illumina SV calls, further validated by RNA-seq. This highlights the potential of the long-read whole genome sequencing approach for diagnosing complex cases driven by SVs. Citation Format: Ayse Keskus, Asher Bryant, Tanveer Ahmad, Anton Goretsky, Byunggil Yoo, Sergey Aganezov, Ataberk Donmez, Lisa A. Lansdon, Isabel Rodriguez, Jimin Park, Yuelin Liu, Xiwen Cui, Joshua Gardner, Brandy McNulty, Samuel Sacco, Jyoti Shetty, Yongmei Zhao, Bao Tran, Giuseppe Narzisi, Adrienne Helland, Daniel Cook, Pi-Chuan Chang, Alexey Kolesnikov, Andrew Carroll, Erin Molloy, Chengpeng Bi, Adam Walter, Margaret Gibson, Irina Pushel, Erin Guest, Tomi Pastinen, Kishwar Shafin, Karen Miga, Salem Malikic, Chi-Ping Day, Nicolas Robine, Cenk Sahinalp, Michael Dean, Midhat S. Farooqi, Benedict Paten, Mikhail Kolmogorov. Severus: A tool for detecting and characterizing complex structural variants in cancer using long-read sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2848.

Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing

Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing

Machine learning-predicted chromatin organization landscape across pediatric tumors.

Structural variants (SVs) are increasingly recognized as important contributors to oncogenesis through their effects on 3D genome folding. Recent advances in whole-genome sequencing have enabled large-scale profiling of SVs across diverse tumors, yet experimental characterization of their individual impact on genome folding remains infeasible. Here, we leveraged a convolutional neural network, Akita, to predict disruptions in genome folding caused by somatic SVs identified in 61 tumor types from the Children's Brain Tumor Network dataset. Our analysis reveals significant variability in SV-induced disruptions across tumor types, with the most disruptive SVs coming from lymphomas and sarcomas, metastatic tumors, and germline cell tumors. Dimensionality reduction of disruption scores identified five recurrently disrupted regions enriched for high-impact SVs across multiple tumors. Some of these regions are highly disrupted despite not being highly mutated, and harbor tumor-associated genes and transcriptional regulators. To further interpret the functional relevance of high-scoring SVs, we integrated epigenetic data and developed a modified Activity-by-Contact scoring approach to prioritize SVs with disrupted genome contacts at active enhancers. This method highlighted highly disruptive SVs near key oncogenes, as well as novel candidate loci potentially implicated in tumorigenesis. These findings highlight the utility of machine learning for identifying novel SVs, loci, and genetic mechanisms contributing to pediatric cancers. This framework provides a foundation for future studies linking SV-driven regulatory changes to cancer pathogenesis.

Diagnostic and Prognostic/Therapeutic Significance of Comprehensive Analysis of Bone and Soft Tissue Tumors Using Optical Genome Mapping and Next-Generation Sequencing.

Diagnostic and Prognostic/Therapeutic Significance of Comprehensive Analysis of Bone and Soft Tissue Tumors Using Optical Genome Mapping and Next-Generation Sequencing.

Cell-cycle dependent DNA repair and replication unifies patterns of chromosome instability

Chromosomal instability (CIN) is pervasive in human tumours and often leads to structural or numerical chromosomal aberrations. Somatic structural variants (SVs) are intimately related to copy number alterations but the two types of variant are often studied independently. Additionally, despite numerous studies on detecting various SV patterns, there are still no general quantitative models of SV generation. To address this issue, we develop a computational cell-cycle model for the generation of SVs from end-joining repair and replication after double-strand break formation. Our model provides quantitative information on the relationship between breakage fusion bridge cycle, chromothripsis, seismic amplification, and extra-chromosomal circular DNA. Given whole-genome sequencing data, the model also allows us to infer important parameters in SV generation with Bayesian inference. Our quantitative framework unifies disparate genomic patterns resulted from CIN, provides a null mutational model for SV, and reveals deeper insights into the impact of genome rearrangement on tumour evolution.

Closing the gaps, and improving somatic structural variant analysis and benchmarking using CHM13-T2T.

The complexities of cancer genomes are becoming more easily interpreted due to advancements in sequencing technologies and improved bioinformatic analysis. Structural variants (SVs) represent an important subset of somatic events in tumors. While the detection of SVs has been markedly improved by the development of long-read sequencing, somatic variant identification and annotation remain challenging. We hypothesized that the use of a completed human reference genome (CHM13-T2T) would improve somatic SV calling. Our findings in a tumor-normal matched benchmark sample and three patient samples show that the CHM13-T2T improves SV detection accuracy compared to GRCh38 with a notable reduction in false-positive calls, and thus supports improved prioritization. We also overcame the lack of annotation resources for CHM13-T2T by lifting over CHM13-T2T-aligned reads to the GRCh38 genome, therefore combining both improved alignment and advanced annotations. In this process, we assessed the current SV benchmark set for COLO829/COLO829BL across four replicates sequenced at different centers with different long-read technologies. We discovered instability of this cell line across these replicates; 346 SVs (1.13%) were only discoverable in a single replicate. We identify 54 somatic SVs, which appear to be stable as they are consistently present across the four replicates. As such, we propose this consensus set as an updated benchmark for somatic SV calling and include both GRCh38 and CHM13-T2T coordinates in our benchmark. Our work demonstrates new approaches to optimize somatic SV detection in cancer with potential improvements in other genetic diseases.

Benchmarking long-read structural variant calling tools and combinations for detecting somatic variants in cancer genomes

Cancer genomes have a complicated landscape of mutations, including large-scale rearrangements known as structural variants (SVs). These SVs can disrupt genes or regulatory elements, playing a critical role in cancer development and progression. Despite their importance, accurate identification of somatic structural variants (SVs) remains a significant bottleneck in cancer genomics. Long-read sequencing technologies hold great promise in SV discovery, and there is an increasing number of efforts to develop new tools to detect them. In this study, we employ eight widely used SV callers on paired tumor and matched normal samples from both the NCI-H2009 lung cancer cell line and the COLO829 melanoma cell line, the latter of which has a well-established somatic SV truth set. Following separate variation detection in both tumor and normal DNA, the VCF merging procedure and a subtraction method were used to identify candidate somatic SVs. Additionally, we explored different combinations of the tools to enhance the accuracy of true somatic SV detection. Our analysis adopts a comprehensive approach, evaluating the performance of each SV caller across a spectrum of variant types and numbers in finding cancer-related somatic SVs. This study, by comparing eight different tools and their combinations, not only reveals the benefits and limitations of various techniques but also establishes a framework for developing more robust SV calling pipelines. Our findings highlight the strengths and weaknesses of current SV calling tools and suggest that combining multiple tools and testing different combinations can significantly enhance the validation of somatic alterations.

Multisite long-read sequencing reveals the early contributions of somatic structural variations to HBV-related hepatocellular carcinoma tumorigenesis.

Somatic structural variations (SVs) represent a critical category of genomic mutations in hepatocellular carcinoma (HCC). However, the accurate identification of somatic SVs using short-read high-throughput sequencing is challenging. Here, we applied long-read nanopore sequencing and multisite sampling in a cohort of 42 samples from five patients. We found that adjacent nontumor tissue is not entirely normal, as significant somatic SV alterations were detected in these nontumor genomes. The adjacent nontumor tissue is highly similar to tumor tissue in terms of somatic SVs but differs in somatic single-nucleotide variants and copy number variations. The types of SVs in adjacent nontumor and tumor tissue are markedly different, with somatic insertions and deletions identified as early genomic events associated with HCC. Notably, hepatitis B virus (HBV) DNA integration frequently results in the generation of somatic SVs, particularly inducing interchromosomal translocations (TRAs). Although HBV DNA integration into the liver genome occurs randomly, multisite shared HBV-induced SVs are early driving events in the pathogenesis of HCC. Long-read RNA sequencing reveals that some HBV-induced SVs impact cancer-associated genes, with TRAs being capable of inducing the formation of fusion genes. These findings enhance our understanding of somatic SVs in HCC and their role in early tumorigenesis.

A sequence context-based approach for classifying tumor structural variants without paired normal samples.

A sequence context-based approach for classifying tumor structural variants without paired normal samples.

Identification of a gene signature and prediction of overall survival of patients with stage IV colorectal cancer using a novel machine learning approach.

Identification of a gene signature and prediction of overall survival of patients with stage IV colorectal cancer using a novel machine learning approach.

Transcription and DNA replication collisions lead to large tandem duplications and expose targetable therapeutic vulnerabilities in cancer.

Despite the abundance of somatic structural variations (SVs) in cancer, the underlying molecular mechanisms of their formation remain unclear. In the present study, we used 6,193 whole-genome sequenced tumors to study the contributions of transcription and DNA replication collisions to genome instability. After deconvoluting robust SV signatures in three independent pan-cancer cohorts, we detected transcription-dependent, replicated-strand bias, the expected footprint of transcription-replication collision (TRC), in large tandem duplications (TDs). Large TDs are abundant in female-enriched, upper gastrointestinal tract and prostate cancers. They are associated with poor patient survival and mutations in TP53, CDK12 and SPOP. Upon inactivating CDK12, cells display significantly more TRCs, R-loops and large TDs. Inhibition of WEE1, CHK1 and ATR selectively inhibits the growth of cells deficient in CDK12. Our data suggest that large TDs in cancer form as a result of TRCs and their presence can be used as a biomarker for prognosis and treatment.

Genomes and epigenomes of matched normal and tumor breast tissue reveal diverse evolutionary trajectories and tumor-host interactions

Genomes and epigenomes of matched normal and tumor breast tissue reveal diverse evolutionary trajectories and tumor-host interactions

Performance of somatic structural variant calling in lung cancer using Oxford Nanopore sequencing technology

BackgroundLung cancer is a heterogeneous disease and the primary cause of cancer-related mortality worldwide. Somatic mutations, including large structural variants, are important biomarkers in lung cancer for selecting targeted therapy. Genomic studies in lung cancer have been conducted using short-read sequencing. Emerging long-read sequencing technologies are a promising alternative to study somatic structural variants, however there is no current consensus on how to process data and call somatic events. In this study, we preformed whole genome sequencing of lung cancer and matched non-tumour samples using long and short read sequencing to comprehensively benchmark three sequence aligners and seven structural variant callers comprised of generic callers (SVIM, Sniffles2, DELLY in generic mode and cuteSV) and somatic callers (Severus, SAVANA, nanomonsv and DELLY in somatic modes).ResultsDifferent combinations of aligners and variant callers influenced somatic structural variant detection. The choice of caller had a significant influence on somatic structural variant detection in terms of variant type, size, sensitivity, and accuracy. The performance of each variant caller was assessed by comparing to somatic structural variants identified by short-read sequencing. When compared to somatic structural variants detected with short-read sequencing, more events were detected with long-read sequencing. The mean recall of somatic variant events identified by long-read sequencing was higher for the somatic callers (72%) than generic callers (53%). Among the somatic callers when using the minimap2 aligner, SAVANA and Severus achieved the highest recall at 79.5% and 79.25% respectively, followed by nanomonsv with a recall of 72.5%.ConclusionLong-read sequencing can identify somatic structural variants in clincal samples. The longer reads have the potential to improve our understanding of cancer development and inform personalized cancer treatment.

Epigenomic, transcriptomic and proteomic characterizations of reference samples.

A variety of newly developed next-generation sequencing technologies are making their way rapidly into the research and clinical applications, for which accuracy and cross-lab reproducibility are critical, and reference standards are much needed. Our previous multicenter studies under the SEQC-2 umbrella using a breast cancer cell line with paired B-cell line have produced a large amount of different genomic data including whole genome sequencing (Illumina, PacBio, Nanopore), HiC, and scRNA-seq with detailed analyses on somatic mutations, single-nucleotide variations (SNVs), and structural variations (SVs). However, there is still a lack of well-characterized reference materials which include epigenomic and proteomic data. Here we further performed ATAC-seq, Methyl-seq, RNA-seq, and proteomic analyses and provided a comprehensive catalog of the epigenomic landscape, which overlapped with the transcriptomes and proteomes for the two cell lines. We identified >7,700 peptide isoforms, where the majority (95%) of the genes had a single peptide isoform. Protein expression of the transcripts overlapping CGIs were much higher than the protein expression of the non-CGI transcripts in both cell lines. We further demonstrated the evidence that certain SNVs were incorporated into mutated peptides. We observed that open chromatin regions had low methylation which were largely regulated by CG density, where CG-rich regions had more accessible chromatin, low methylation, and higher gene and protein expression. The CG-poor regions had higher repressive epigenetic regulations (higher DNA methylation) and less open chromatin, resulting in a cell line specific methylation and gene expression patterns. Our studies provide well-defined reference materials consisting of two cell lines with genomic, epigenomic, transcriptomic, scRNA-seq and proteomic characterizations which can serve as standards for validating and benchmarking not only on various omics assays, but also on bioinformatics methods. It will be a valuable resource for both research and clinical communities.

Abstract A083: Genomic Analysis of Structural Variations in Pancreatic Cancer Using Long-Read Sequencing

Abstract Objective: Structural variations (SVs) constitute the greatest source of genomic alterations leading to the oncogenesis of many cancers, including the deadliest pancreatic cancer (PC). However, SVs in PC remain largely undefined due to technological limitations of the traditional short-read sequencing. This study aims to establish a comprehensive signature of genomic SVs in PC. Methods: PacBio whole genome long-read sequencing was employed to investigate the genomic landscape of SVs in eight tissues from four pancreatic cancer patients. A variety of SVs callers, including pbsv, Sniffles, cuteSV, and svim, were comprehensively adopted to improve the accuracy of SVs detection. Many undefined characteristics of germline and somatic SVs were revealed by comparing with multiple previously published databases. Besides, the impacts of structural variations on 3D chromatin organization were analyzed by integrating multi-omics data including Hi-C, RNA seq and ChIP seq. Results: A total of 26,844 and 27,028 non-redundant SVs were identified in human pancreatic cancer and matched para-cancerous tissues, respectively. Notably, among these SVs, 23.97% were novel and had not been previously reported. Germline SVs were further characterized and found enriched in 25 well-known susceptible genes, such as TP63, PVT1, and ABO. Additionally, we proposed a panel of germline SVs with predicted highest pathogenicity, involving STOX1, DSPP, and WRN. Totally, 616 somatic SVs were identified and presented high burdens in patients with lymph node metastasis. We observed that somatic DELs and INSs were significantly more prevalent in repetitive regions than in non-repetitive regions. Interestingly, DELs showed a decrease in the proportion occurring in simple repeat regions but an increase in the proportion occurring in LINE and SINE regions compared to INSs. Moreover, it was observed that deletions within topologically associated domains were associated with local enhanced interactions and disrupted chromatin loops, which might influence H3K27ac modification of histone contributing to enhancer or silencer hijacking. Conclusion: This study provides insights into the genomic signature of SVs in human pancreatic cancer. These findings might shed new light on the complexity of SVs and their pathogenesis by interplaying with chromatin organization. Citation Format: Yongxing Du, Xiaohao Zheng, Yunjie Duan, Chengfeng Wang. Genomic Analysis of Structural Variations in Pancreatic Cancer Using Long-Read Sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A083.