Analysis Of Structural Variants Research Articles

Prognostic value of structural variants in early breast cancer patients

Genomic analysis of structural variants(SVs) in breast cancer (BC) patients has been conducted, but the relationship between genomic alterations and BC prognosis remains unclear. We performed RNA sequencing of 297 early BC fresh-frozen tissues. We identified SVs using three tools (STAR.Arriba, STAR.fusion, and STAR.SEQR) with the COSMIC and Mitelman databases as guide references. We found a median of five to eight fusions per sample. In BC intrinsic subtypes, normal subtype had the fewest fusions (median: 1, interquartile range [IQR]: 0, 3) followed by luminal A (median: 5.5, IQR: 2.75, 10.25), luminal B (median: 9, IQR: 6, 16.5), HER2-enriched (median: 9, IQR: 6, 16.5) and basal (median 10, IQR: 6, 15.5) subtypes (p < 0.05). Intrachromosomal fusion was more frequent observed rather than interchromosomal fusion. In location, chromosome 17 had the most fusions followed by chromosome 1 and 11. When samples were divided into high and low fusion groups based on a cut-off value of 11 fusions, five-year event-free survival (5Y-EFS) was 68.1% in the high fusion group (n = 72) and 80.1% in the low fusion group (n = 125) (p = 0.024) while 75.6% among all patients (95% confidence interval: 0.699, 0.819). Among BC subtype, TNBCs with more fusions had shorter EFS compared to those with fewer fusions (5Y-EFS rate: 65.1% vs. 85.7%; p = 0.013) but no EFS differences were observed in other BC subtypes. ESTIMATE ImmuneScore was also associated with the number of fusions in TNBC (p < 0.005) and TNBCs with high ImmuneScore had better 5Y-EFS compared to those with low ImmuneScore (p = 0.041). In conclusion, diverse fusions were observed by BC subtype, and the number of fusions was associated with BC survival outcome and immune status in TNBC.

VISTA: an integrated framework for structural variant discovery.

Structural variation (SV) refers to insertions, deletions, inversions, and duplications in human genomes. SVs are present in approximately 1.5% of the human genome. Still, this small subset of genetic variation has been implicated in the pathogenesis of psoriasis, Crohn's disease and other autoimmune disorders, autism spectrum and other neurodevelopmental disorders, and schizophrenia. Since identifying structural variants is an important problem in genetics, several specialized computational techniques have been developed to detect structural variants directly from sequencing data. With advances in whole-genome sequencing (WGS) technologies, a plethora of SV detection methods have been developed. However, dissecting SVs from WGS data remains a challenge, with the majority of SV detection methods prone to a high false-positive rate, and no existing method able to precisely detect a full range of SVs present in a sample. Previous studies have shown that none of the existing SV callers can maintain high accuracy across various SV lengths and genomic coverages. Here, we report an integrated structural variant calling framework, Variant Identification and Structural Variant Analysis (VISTA), that leverages the results of individual callers using a novel and robust filtering and merging algorithm. In contrast to existing consensus-based tools which ignore the length and coverage, VISTA overcomes this limitation by executing various combinations of top-performing callers based on variant length and genomic coverage to generate SV events with high accuracy. We evaluated the performance of VISTA on comprehensive gold-standard datasets across varying organisms and coverage. We benchmarked VISTA using the Genome-in-a-Bottle gold standard SV set, haplotype-resolved de novo assemblies from the Human Pangenome Reference Consortium, along with an in-house polymerase chain reaction (PCR)-validated mouse gold standard set. VISTA maintained the highest F1 score among top consensus-based tools measured using a comprehensive gold standard across both mouse and human genomes. VISTA also has an optimized mode, where the calls can be optimized for precision or recall. VISTA-optimized can attain 100% precision and the highest sensitivity among other variant callers. In conclusion, VISTA represents a significant advancement in structural variant calling, offering a robust and accurate framework that outperforms existing consensus-based tools and sets a new standard for SV detection in genomic research.

Genome Assembly and Structural Variation Analysis of Luffa acutangula Provide Insights on Flowering Time and Ridge Development.

Luffa spp. is an important worldwide cultivated vegetable and medicinal plant from the Cucurbitaceae family. In this study, we report a high-quality chromosome-level genome of the high-generation inbred line SG261 of Luffa acutangula. The genomic sequence was determined by PacBio long reads, Hi-C sequencing reads, and 10× Genomics sequencing, with an assembly size of 739.82 Mb, contig N50 of 18.38 Mb, and scaffold N50 of 56.08 Mb. The genome of L. acutangula SG261 was predicted to contain 27,312 protein-coding genes and 72.56% repetitive sequences, of which long terminal repeats (LTRs) were an important form of repetitive sequences, accounting for 67.84% of the genome. Phylogenetic analysis reveals that L. acutangula evolved later than Luffa cylindrica, and Luffa is closely related to Momodica charantia. Comparing the genome of L. acutangula SG261 and L. cylindrica with PacBio data, 67,128 high-quality structural variations (SVs) and 55,978 presence-absence variations (PAVs) were identified in SG261, resulting in 2424 and 1094 genes with variation in the CDS region, respectively, and there are 287 identical genes affected by two different structural variation analyses. In addition, we found that the transcription factor FY (FLOWERING LOCUS Y) families had a large expansion in L. acutangula SG261 (flowering in the morning) compared to L. cylindrica (flowering in the afternoon), which may result in the early flowering time in L. acutangula SG261. This study provides valuable reference for the breeding of and pan-genome research into Luffa species.

Genetic mosaic of the Mediterranean fig: comprehensive genomic insights from a gene bank collection.

High-depth whole-genome resequencing of 53 diverse fig tree genotypes yielded a rich dataset of genetic variants. We successfully identified 5,501,460 single-nucleotide polymorphisms (SNPs) and 1,228,537 insertions and deletions (InDels), providing a high-density and excellent-quality genetic map of the fig tree. We also performed a detailed population structure analysis, dividing the 53 genotypes into three geographical groups and assessing their genetic diversity and divergence. Analysis of structural variants (SVs) and copy number variations (CNVs) revealed their potential functional impact, particularly in plant-pathogen interaction and secondary metabolism. Metabolomic fingerprinting of fig genotypes uncovered extensive variation in primary metabolites and polyphenolic compounds, highlighting the influence of genotype on fruit quality traits such as nutritional content and bioactive compound composition. The genome-wide association study (GWAS) identified critical SNPs associated with fruit quality and morphological features. The discovery of significant candidate genes, such as AGL62, GDSL, and COBRA-like protein 4 genes, offers promising targets for marker-assisted selection and genome editing approaches to improve fig fruit morphological and quality traits. This extensive genomic analysis of fig trees enhances our understanding of the genetic basis of important agronomic traits and provides a rich resource for future research in this economically and nutritionally significant fruit.

Thirty years of writing assessment: A bibliometric analysis of research trends and future directions

This study employs a bibliometric analysis to identify the research trends in the field of writing assessment over the last 30 years (1993–2022). Employing a dataset of 1,712 articles and 52,092 unique references, keyword co-occurrence analyses were used to identify prominent research topics, co-citation analyses were conducted to identify influential publications and journals, and a structural variation analysis was employed to identify transformative research in recent years. The results revealed the growing popularity of the writing assessment field, and the increasing diversity of research topics in the field. The research trends have become more associated with technology and cognitive and metacognitive processes. The influential publications indicate changes in research interest towards cross-disciplinary publications. The journals identified as key venues for writing assessment research also changed across the three decades. The latest transformative research points out possible future directions, including the integration of computational methods in writing assessment, and investigations into relationships between writing quality and various factors. This study contributes to our understanding of the development and future directions of writing assessment research, and has implications for researchers and practitioners.

Structural variant landscapes reveal convergent signatures of evolution in sheep and goats

BackgroundSheep and goats have undergone domestication and improvement to produce similar phenotypes, which have been greatly impacted by structural variants (SVs). Here, we report a high-quality chromosome-level reference genome of Asiatic mouflon, and implement a comprehensive analysis of SVs in 897 genomes of worldwide wild and domestic populations of sheep and goats to reveal genetic signatures underlying convergent evolution.ResultsWe characterize the SV landscapes in terms of genetic diversity, chromosomal distribution and their links with genes, QTLs and transposable elements, and examine their impacts on regulatory elements. We identify several novel SVs and annotate corresponding genes (e.g., BMPR1B, BMPR2, RALYL, COL21A1, and LRP1B) associated with important production traits such as fertility, meat and milk production, and wool/hair fineness. We detect signatures of selection involving the parallel evolution of orthologous SV-associated genes during domestication, local environmental adaptation, and improvement. In particular, we find that fecundity traits experienced convergent selection targeting the gene BMPR1B, with the DEL00067921 deletion explaining ~10.4% of the phenotypic variation observed in goats.ConclusionsOur results provide new insights into the convergent evolution of SVs and serve as a rich resource for the future improvement of sheep, goats, and related livestock.

Multiplex-genome-editing based rapid directional improvement of complex traits in rice.

Although thousands of genes have been identified or cloned in rice (Oryza sativa) in the last two decades, the majority of them have only been separately characterized in specific varieties or single-gene modified backgrounds, thus limiting their practical application. We developed an optimized multiplex genome editing (MGE) toolbox that can efficiently assemble and stably express up to twelve sgRNA targets in a single plant expression vector. In this study, we established the MGE-based Rapid Directional Improvement (MRDI) strategy for directional improvement of complex agronomic traits in one small-scale rice transformation. This approach provides a rapid and practical procedure, encompassing sgRNA assembly, transgene-free screening and the creation of promising germplasm, by combining the precision of gene editing with phenotype-based field breeding. The MRDI strategy was used to generate the full diversity of twelve main agronomic genes in rice cultivar FXZ for the directional improvement of its growth duration and plant architecture. After applying the MRDI to FXZ, ideal plants with the desired traits of early heading date reduced plant height, and more effective panicles were generated without compromising yield, blast resistance and grain quality. Furthermore, the results of whole-genome sequencing (WGS), including the analysis of structural variations (SVs) and single nucleotide variations (SNVs) in the MGE plants, confirmed the high specificity and low frequency of unwanted mutations associated with this strategy. The MRDI breeding strategy would be a robust approach for exploring and applying crucial agronomic genes, as well as for generating novel elite germplasm in the future.

Somatic structural variants drive distinct modes of oncogenesis in melanoma.

The diversity of structural variants (SVs) in melanoma and how they impact oncogenesis are incompletely known. We performed harmonized analysis of SVs across melanoma histological and genomic subtypes, and we identified distinct global properties between subtypes. These included the frequency and size of SVs and SV classes, their relation to chromothripsis events, and the role of topologically associated domain (TAD) boundary altering SVs on cancer-related genes. Following our prior identification of double-stranded break repair deficiency in a subset of triple wild-type cutaneous melanoma, we identified MRE11 and NBN loss-of-function SVs in melanomas with this mutational signature. Experimental knockouts of MRE11 and NBN, followed by olaparib cell viability assays in melanoma cells, indicated that dysregulation of each of these genes may cause sensitivity to PARPi in cutaneous melanomas. Broadly, harmonized analysis of melanoma SVs revealed distinct global genomic properties and molecular drivers, which may have biological and therapeutic impact.

Comparative chloroplast genomes of Dactylicapnos species: insights into phylogenetic relationships

BackgroundDactylicapnos is a climbing herbaceous vine, distributed from the Himalayas to southwestern China, and some of the species have important medicinal values. However, the chloroplast genomes of Dactylicapnos have never been investigated. In this study, chloroplast genomes of seven Dactylicapnos species covering all three sections and one informal group of Dactylicapnos were sequenced and assembled, and the detailed comparative analyses of the chloroplast genome structure were provided for the first time.ResultsThe results showed that the chloroplast genomes of Dactylicapnos have a typical quadripartite structure with lengths from 172,344 bp to 176,370 bp, encoding a total of 133–140 genes, containing 88–94 protein-coding genes, 8 rRNAs and 37–39 tRNAs. 31 codons were identified as relative synonymous codon usage values greater than one in the chloroplast genome of Dactylicapnos genus based on 80 protein-coding genes. The results of the phylogenetic analysis showed that seven Dactylicapnos species can be divided into three main categories. Phylogenetic analysis revealed that seven species form three major clades which should be treated as three sections.ConclusionsThis study provides the initial report of the chloroplast genomes of Dactylicapnos, their structural variation, comparative genomic and phylogenetic analysis for the first time. The results provide important genetic information for development of medical resources, species identification, infrageneric classification and diversification of Dactylicapnos.

Solanum aculeatissimum and Solanum torvum chloroplast genome sequences: a comparative analysis with other Solanum chloroplast genomes

BackgroundSolanum aculeatissimum and Solanum torvum belong to the Solanum species, and they are essential plants known for their high resistance to diseases and adverse conditions. They are frequently used as rootstocks for grafting and are often crossbred with other Solanum species to leverage their resistance traits. However, the phylogenetic relationship between S. aculeatissimum and S. torvum within the Solanum genus remains unclear. Therefore, this paper aims to sequence the complete chloroplast genomes of S. aculeatissimum and S. torvum and analyze them in comparison with 29 other previously published chloroplast genomes of Solanum species.ResultsWe observed that the chloroplast genomes of S. aculeatissimum and S. torvum possess typical tetrameric structures, consisting of one Large Single Copy (LSC) region, two reverse-symmetric Inverted Repeats (IRs), and one Small Single Copy (SSC) region. The total length of these chloroplast genomes ranged from 154,942 to 156,004 bp, with minimal variation. The highest GC content was found in the IR region, while the lowest was in the SSC region. Regarding gene content, the total number of chloroplast genes and CDS genes remained relatively consistent, ranging from 128 to 134 and 83 to 91, respectively. Nevertheless, there was notable variability in the number of tRNA genes and rRNAs. Relative synonymous codon usage (RSCU) analysis revealed that both S. aculeatissimum and S. torvum preferred codons that utilized A and U bases. Analysis of the IR boundary regions indicated that contraction and expansion primarily occurred at the junction between SSC and IR regions. Nucleotide polymorphism analysis and structural variation analysis demonstrated that chloroplast variation in Solanum species mainly occurred in the LSC and SSC regions. Repeat sequence analysis revealed that A/T was the most frequent base pair in simple repeat sequences (SSR), while Palindromic and Forward repeats were more common in long sequence repeats (LSR), with Reverse and Complement repeats being less frequent. Phylogenetic analysis indicated that S. aculeatissimum and S. torvum belonged to the same meristem and were more closely related to Cultivated Eggplant.ConclusionThese findings enhance our comprehension of chloroplast genomes within the Solanum genus, offering valuable insights for plant classification, evolutionary studies, and potential molecular markers for species identification.

Long-read sequencing and optical mapping generates near T2T assemblies that resolves a centromeric translocation

Long-read genome sequencing (lrGS) is a promising method in genetic diagnostics. Here we investigate the potential of lrGS to detect a disease-associated chromosomal translocation between 17p13 and the 19 centromere. We constructed two sets of phased and non-phased de novo assemblies; (i) based on lrGS only and (ii) hybrid assemblies combining lrGS with optical mapping using lrGS reads with a median coverage of 34X. Variant calling detected both structural variants (SVs) and small variants and the accuracy of the small variant calling was compared with those called with short-read genome sequencing (srGS). The de novo and hybrid assemblies had high quality and contiguity with N50 of 62.85 Mb, enabling a near telomere to telomere assembly with less than a 100 contigs per haplotype. Notably, we successfully identified the centromeric breakpoint of the translocation. A concordance of 92% was observed when comparing small variant calling between srGS and lrGS. In summary, our findings underscore the remarkable potential of lrGS as a comprehensive and accurate solution for the analysis of SVs and small variants. Thus, lrGS could replace a large battery of genetic tests that were used for the diagnosis of a single symptomatic translocation carrier, highlighting the potential of lrGS in the realm of digital karyotyping.

A study concept of expeditious clinical enrollment for genetic modifier studies in Charcot-Marie-Tooth neuropathy 1A.

Caused by duplications of the gene encoding peripheral myelin protein 22 (PMP22), Charcot-Marie-Tooth disease type 1A (CMT1A) is the most common hereditary neuropathy. Despite this shared genetic origin, there is considerable variability in clinical severity. It is hypothesized that genetic modifiers contribute to this heterogeneity, the identification of which may reveal novel therapeutic targets. In this study, we present a comprehensive analysis of clinical examination results from 1564 CMT1A patients sourced from a prospective natural history study conducted by the RDCRN-INC (Inherited Neuropathy Consortium). Our primary objective is to delineate extreme phenotype profiles (mild and severe) within this patient cohort, thereby enhancing our ability to detect genetic modifiers with large effects. We have conducted large-scale statistical analyses of the RDCRN-INC database to characterize CMT1A severity across multiple metrics. We defined patients below the 10th (mild) and above the 90th (severe) percentiles of age-normalized disease severity based on the CMT Examination Score V2 and foot dorsiflexion strength (MRC scale). Based on extreme phenotype categories, we defined a statistically justified recruitment strategy, which we propose to use in future modifier studies. Leveraging whole genome sequencing with base pair resolution, a future genetic modifier evaluation will include single nucleotide association, gene burden tests, and structural variant analysis. The present work not only provides insight into the severity and course of CMT1A, but also elucidates the statistical foundation and practical considerations for a cost-efficient and straightforward patient enrollment strategy that we intend to conduct on additional patients recruited globally.

Deciphering the Genomic Characterization of the GGP Gene Family and Expression Verification of CmGGP1 Modulating Ascorbic Acid Biosynthesis in Melon Plants.

Ascorbic acid (AsA), also known as vitamin C, is a well-known antioxidant found in living entities that plays an essential role in growth and development, as well as in defensive mechanisms. GDP-L-galactose phosphorylase (GGP) is a candidate gene regulating AsA biosynthesis at the translational and transcriptional levels in plants. In the current study, we conducted genome-wide bioinformatic analysis and pinpointed a single AsA synthesis rate-limiting enzyme gene in melon (CmGGP1). The protein prediction analysis depicted that the CmGGP1 protein does not have a signaling peptide or transmembrane structure and mainly functions in the chloroplast or nucleus. The constructed phylogenetic tree analysis in multispecies showed that the CmGGP1 protein has a highly conserved motif in cucurbit crops. The structural variation analysis of the CmGGP1 gene in different domesticated melon germplasms showed a single non-synonymous type-base mutation and indicated that this gene was selected by domestication during evolution. Wild-type (WT) and landrace (LDR) germplasms of melon depicted close relationships to each other, and improved-type (IMP) varieties showed modern domestication selection. The endogenous quantification of AsA content in both the young and old leaves of nine melon varieties exhibited the major differentiations for AsA synthesis and metabolism. The real-time quantitative polymerase chain reaction (qRT-PCR) analysis of gene co-expression showed that AsA biosynthesis in leaves was greater than AsA metabolic consumption, and four putative interactive genes (MELO3C025552.2, MELO3C007440.2, MELO3C023324.2, and MELO3C018576.2) associated with the CmGGP1 gene were revealed. Meanwhile, the CmGGP1 gene expression pattern was noticed to be up-regulated to varying degrees in different acclimated melons. We believe that the obtained results would provide useful insights for an in-depth genetic understanding of the AsA biosynthesis mechanism, aimed at the development of improving crop plants for melon.

Abstract 2328: Next-generation sequencing-based regression algorithm to determine homologous recombination deficiency scores in a pan-cancer cohort

Abstract Background: Homologous recombination deficiency (HRD) inhibits double strand breaks from being repaired in DNA, leading to cancer cells failing to recover themselves and cell death. Previous studies found that HRD-positive ovarian cancer patients had more significant clinical benefits from poly ADP-ribose polymerase inhibitors (PARPi) treatment. For accurate detection of HRD, next-generation sequencing (whole exome, whole genome) was used to identify large-scale genomic aberrations including telomeric allelic imbalance (HRD-TAI score), loss of heterozygosity profiles (HRD-LOH score), and large-scale state transitions (HRD-LST score). So far, many studies (HRDetect, SigMA, CHORD and shallowHRD) have been conducted to accurately determine HRD in a pan-cancer cohort. As the study progressed, it has been revealed that PARPi is effective when applied to patients with HRD in not only ovarian cancer but also breast cancer, prostate cancer, and pancreatic cancer. Purpose: To develop an algorithm that accurately predict HRD score in patients with ovarian cancer, breast cancer, prostate cancer, and pancreatic cancer utilizing a machine learning algorithm. Methods: We used whole-genome sequencing (WGS) data of 710 samples from 309 patients, whole-exome sequencing (WES) data of 4,650 samples from 2,193 patients from the pan-cancer cohort of the TCGA (TCGA-OV, TCGA-BRCA, TCGA-PRAD, and TCGA-PAAD). The HRD-TAI/HRD-LST/HRD-LOH scores were calculated through structural variation analyses. After data cleaning processes, machine-learning models were trained and tested on 228 out of 2,502 samples, and validation was performed on 1,248 out of 2,502 samples from the TCGA dataset. Results: To assess the performance of the machine-learning regressor, the concordance between predictions and annotations was quantified by calculating the R squared (𝑅;;;^2). As a result of training using machine learning algorithm, we achieved a high 𝑅;;;^2 (0.904) with a RMSE (root mean squared error) score (7.649) for a pan-cancer cohort. Conclusions: Our regressor was robust and accurate when applied to 4 cancer types. Using our systematic pan-cancer analysis, we found novel insights into the mechanisms of HRD across cancer types with potential contribution to clinical practice. Citation Format: Boram Choi, Youn Jin Choi. Next-generation sequencing-based regression algorithm to determine homologous recombination deficiency scores in a pan-cancer cohort [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 2328.

Abstract 2946: Characterizing the genomic landscapes of breast and lung tumors using cost-effective whole genome sequencing

Abstract Whole Genome Sequencing (WGS) has emerged as a pivotal tool for unraveling the intricate genomic alterations underlying cancer, enabling the detection of a broad spectrum of somatic changes in cancer genomes, including single nucleotide variants, insertions, deletions, copy number variations, and structural rearrangements. Characterizing somatic variants across the entire genome significantly benefits from novel cost-efficient sequencing platforms, such as UG100. In the current study, we aim to demonstrate the utility of UG100 WGS in characterizing the landscape of somatic events in breast and lung cancer. Over 35 tumor-normal cancer sample pairs were profiled using UG100 WGS (mean tumor coverage: &amp;gt;100x; normal: &amp;gt;50x). Variant calling was done using a somatic deep-variant algorithm, optimized to UG100 data. Results were compared to Illumina sequencing and high concordance was observed, with over 97% of SNVs and over 93% of indels detected by Illumina identified also by UG100. UG100 sequencing was deeper and allowed for identification of a larger number of genomic variations, including known driver events in cancer-related genes. The somatic mutational signatures by UG100 were concordant with the Illumina platform, with a cosine similarity higher than 99%. Copy number variation and structural variation analysis was performed on all sample pairs, revealing a wide range of genomic aberrations. The integration of genetic sequencing into routine clinical oncology practices has proven instrumental in identifying actionable mutations, predicting treatment responses, and monitoring minimal residual disease. Moving from targeted sequencing to WGS provides a more comprehensive and unbiased assessment of the entire genome, uncovering rare or unexpected mutations that might be missed by targeted approaches. WGS allows for the simultaneous evaluation of both coding and non-coding regions of the genome, providing a deeper understanding of the regulatory elements influencing gene expression. This broader scope enhances our understanding of cancer biology and contributes to a more accurate assessment of the tumor's functional landscape. Citation Format: Hila Benjamin, Ilya Soifer, Doron Shem-Tov, Maya Levy, Islam Oguz Tuncay, Baek-Lok Oh, Jong-Yeon Shin, Young Seok Ju, Sangmoon Lee, Omer Barad, Doron Lipson. Characterizing the genomic landscapes of breast and lung tumors using cost-effective whole genome sequencing [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 2946.

Abstract 4895: Optical genome mapping analysis across multiple solid tumor types in Bionano VIATM software

Abstract Solid tumors can show highly complex somatic structural and copy number variants (CNVs) of multiple classes. These variants can reveal mechanisms behind carcinogenesis, including the amplification of oncogenes, deletion or inactivation of tumor suppressor genes, and fusions to create new oncogenes. High degrees of heterogeneity within solid tumors can complicate analysis. Optical Genome Mapping (OGM) is a comprehensive technology which combines the resolution of molecular-level data with the scale of cytogenomic analysis. ≈5-15mg sections of freshly frozen tumor are homogenized in buffer to release ultra-high molecular weight DNA. Long fragments are then labeled at a regular 6mer, then linearized and imaged with native long-range structure (≥150kbp) preserved. In silico molecules are assembled into maps and aligned to a reference genome to detect structural variants with sensitivity to ≥5% allele fraction. Bionano VIATM software provides tools for visualization and interpretation of structural variants detected with OGM. VIA also provides CNV calling and detection of Absence of Heterozygosity (AOH) and allelic imbalance using SNP-FASST3, a segmentation algorithm for detecting mosaic events in OGM and other types of data. VIA calculates a B-Allele frequency (BAF) distribution from OGM data and adds a genome-wide BAF track to the applications suite, enabling calling of allelic imbalance events such as AOH. Visualized together, the overlay of structural variant information on copy number data provides context for chromosome fusions underlying copy number change. Additionally, balanced fusions are revealed consistently in cases where the impact is copy neutral. To demonstrate the utility of integrative analysis of structural, copy number, and allelic imbalance variants in solid tumor datasets, we processed a pilot set of six diverse solid tumors through Bionano software. Brain, breast, kidney, lung, ovarian, and prostate tumor data which had undergone previous analysis in Bionano Solve 3.7 were freshly analyzed in Solve 3.8 and VIA 7.0. In the lung tumor dataset there are clear allelic imbalance segments spanning most of chromosome 4 with no concomitant copy number change. In VIA, these segments can be merged on examination, converted to AOH calls, and further specified to reflect mosaic copy neutral loss of heterozygosity (CN-LOH). In the complex kidney tumor dataset, it is difficult to resolve ploidy state based on copy number data alone. This ambiguity is resolvable with VIA, by leveraging the copy number and BAF-based allelic imbalance distributions and recentering probes to a manually defined diploid region. The comprehensive OGM data approach facilitated by VIA can streamline analysis in challenging solid tumor datasets. Structural variant visualization, CNV detection and AOH detection are available in VIA 7.0. Citation Format: Samer I. Al-Saffar, Benjamin Clifford, Aliz Raksi, Alex Hastie, Neil Miller, Alka Chaubey. Optical genome mapping analysis across multiple solid tumor types in Bionano VIATM software [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 4895.

Abstract 7407: Melanoma clonal subline analysis reveals genetic factors driving intra-tumor heterogeneity

Abstract Melanoma is the most invasive skin cancer caused by the malignant melanocytes. The use of immune checkpoint blockade (ICB) improved the survival rate in advanced melanoma. Yet, the response rate to ICB varies across patients due to the highly heterogeneous nature of melanoma. Recent studies reported genomic and epigenetic factors contributing to the therapeutic response. Identifying these factors involved in clonal evolution in melanoma is a key to better understanding the tumor progression and divergence in the therapy response. To study melanoma heterogeneity, we generated twenty-four clonal sublines, each derived from a single cell isolated from a parental cell line derived from the M4 mouse melanoma model. DNA prepared from each subline and a spleen from a healthy mouse were subjected to long-read sequencing. Importantly, long-read sequencing allows direct detection of methylation states, small variants, and structural variants in the same run. The results thus provide excellent means to study genetic and epigenetic factors in clonal evolution. Each of these sublines was also implanted in distinct mice for survival and tumor growth analysis and further functional evaluation. We developed Severus, a structural variation (SV) caller for long reads, which works with single (germline), paired (tumor-normal), and multiple samples (e.g., multi-site or time series). Severus takes advantage of improved phasing in long-read sequencing, attributes somatic variants to germline haplotype, and builds a haplotype-specific breakpoint graph that is used to cluster multi-break rearrangements and represent the derived chromosomal structure. We benchmarked Severus using multiplatform validated COLO829 truthset and CHM1/CHM13 haploid genomes, and Severus outperformed all other methods in terms of recall and precision. The structural variation (SV) analysis using Severus with all the sublines showed enrichment of possibly clonal SVs in chr4, chr11, and mainly in chr13. Further investigation revealed SVs in chr13 were not distributed across the chromosome but clustered in certain regions. Analysis of somatic SNVs in chr13 corroborates the localized hypermutation profile, which indicates a possible kataegis. Interestingly, a loss of the chr13 copy affected by kataegis in one of the sublines coincides with a better survival rate, as compared to other sublines, in an allograft melanoma mouse model, which further supports the association between the kataegis and tumor progression. We also identified other clonal and subclonal events, including the homozygous deletion of Cdkn2a in most of the sublines, associated with their aggressiveness in vivo. Our analysis allowed us to identify clonal and subclonal genetic and epigenetic factors involved in tumor progression and evolution in a melanoma model, which can potentially translate to human disease. Citation Format: Ayse Keskus, Anton Goretsky, Yuelin Liu, Xiwen Cui, Tanveer Ahmad, Eva Perez Guijarro, Asher Bryant, Erin Malloy, Salem Malikic, Glenn Merlino, Chi-Ping Day, Cenk Sahinalp, Mikhail Kolmogorov. Melanoma clonal subline analysis reveals genetic factors driving intra-tumor heterogeneity [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 7407.

Abstract 315: Cost-effective, comprehensive detection of gene fusions from DNA and RNA using the UG100 sequencer

Abstract Background: Gene fusions, resulting from chromosomal rearrangements, play a critical role in the oncogenesis of various cancers, often acting as diagnostic markers and therapeutic targets. Their detection is important for understanding tumor biology and guiding personalized treatment strategies. With the evolution of economical sequencing technologies, hypothesis-free gene fusion detection in cancer genomics is becoming more accessible. This study demonstrates the use of the cost-effective Ultima Genomics UG100 platform for a comprehensive analysis of gene fusions, uniquely integrating Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS). This integration capitalizes on the strengths of both methods: WGS, which enables a broad analysis of structural variations including extragenic events, and WTS for more functional characterization of expressed gene fusions and abnormal isoforms, offering a synergistic approach that enhances the detection and characterization of genomic rearrangements. Methods: Five cancer cell lines (K562, THP-1, NCI-H660, Kasumi-1, MV4-11) with 29 previously detected gene fusions were sequenced, using both WTS and WGS, in addition to a standard fusion reference sample (Seraseq Fusion RNA Mix, with 16 RNA fusions). The libraries were sequenced on a UG100 sequencer using single ended reads of length ~250bp and analyzed with publicly available software: STAR-Fusion, combined with FusionInspector were used to detect RNA fusions; Manta and GridSS were used to detect DNA structural variations, including translocations. Results: 93% of the documented fusion events were detected in DNA and in RNA, in addition to the 16 evaluated just in RNA (100% detected). Sequence data from both WTS and WGS on the UG100 platform successfully identified a spectrum of gene fusions in both DNA and RNA, thereby confirming the platform's detection capabilities in both types of assays. The structural variation events detected in the WGS were comprised mainly of translocations, but also deletions and duplications. Conclusions: The Ultima Genomics UG100 sequencer has proven to be a cost-effective and efficient tool for the genome-wide discovery and validation of gene fusions, which can be impactful in clinical oncology settings. This study underscores the potential of using an integrated WTS and WGS approach on a single platform to advance our understanding of the genomic complexity of cancer, paving the way for more effective diagnostic and therapeutic strategies. Citation Format: Gila Lithwick-Yanai, Sarah Pollock, Danit Lebanony, Keren Ben Simhon, Sima Benjamin, David Bogumil, Nika Iremadze, Matt Sexton, Peleg Winer, Maya Levy, Shlomit Gilad, Josh Haimes, Giulia Corbet, Jennifer Pavlica, Doron Lipson. Cost-effective, comprehensive detection of gene fusions from DNA and RNA using the UG100 sequencer [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 315.

Abstract 3786: Genomic and transcriptomic characterization of peritoneal, lung and liver metastases of colorectal carcinoma reveals site-specific differences

Abstract Background: Metastasis in colorectal cancer (CRC) is a critical factor in patient outcomes, with 20% of patients affected at the time of diagnosis and up to 50% developing metachronous metastatic disease later on. Despite the growing importance of metastatic sites as independent predictors of overall survival (OS) and the ongoing development of new treatments, there has been limited effort to personalize systemic treatment based on specific metastatic locations. Aim: This study aims to compare the genomic landscape of different metastatic sites in CRC to detect novel diagnostic and therapeutic targets. Methods: DNA whole-genome-sequencing and RNA gene expression data from metastatic CRC samples were retrieved from the Hartwig Medical Foundation (HMF). Single nucleotide variants (SNVs), somatic copy number aberrations (SCNAs) and structural variants (SVs) were reported by HMF with their pipelines. In addition, focal amplifications and deletions were identified using GISTIC2 (v6.15.30). We included 464 microsatellite stable samples derived from liver metastases (n=369), peritoneal metastases (n=58) and lung metastases (n=37). We compared DNA mutation frequencies between tumors from different metastatic sites (two-sided Mann-Whitney tests) and performed differential gene expression and pathway enrichment analysis. Results: Liver metastases had the highest number of focal SCNAs per sample (median 33 vs 17 in peritoneum (p &amp;lt; 0.001); vs 18 in lung (p &amp;lt; 0.001)). Furthermore, we observed 39 site-specific SCNAs in liver, 9 in peritoneal and 15 in lung metastases. Of these, the most significant focal SCNAs were an amplification within 20q13.12 (79%) and deletion within 1p35.3 (64%) in peritoneal metastases and a deletion within 18q21.1 (84%) in lung metastases. Furthermore, peritoneal metastases displayed less genomic instability, featuring lower ploidy (median 2.65 vs 2.94 in liver (p=0.025); vs 3.15 in lung (p=0.025)) and lower aneuploidy scores (median 19 vs 26 in liver (p=0.016); vs 30 in lung (p=0.016)) while having no significant differences in tumor mutational burden or individual SNVs. Analysis of SVs revealed that peritoneal metastases exhibited fewer deletions (≥10kb; 1.3x fold change increase in liver and lung) and fewer duplications (≥1kb; 1.4x fold change increase in liver). Differential gene expression analysis uncovered higher expression of gene sets involved in the apical junction and the TGF-beta-signaling pathway in peritoneal metastases. Conclusion: Specific focal SCNA and differences in genomic instability exhibit an association with preferential organotropism in CRC metastasis and could offer potential targets for the development of new diagnostic and therapeutic approaches. Citation Format: Nerma Crnovrsanin, Soufyan Lakbir, Renske d. Wit, Steven L. Ketelaars, Niels F. Kok, Petur Snaebjornsson, Gerrit A. Meijer, Sanne Abeln, Remond J. Fijneman. Genomic and transcriptomic characterization of peritoneal, lung and liver metastases of colorectal carcinoma reveals site-specific differences [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 3786.

Abstract 6257: Genome wide, high-throughput, high-resolution structural variation detection at low variant allele fraction for oncology samples

Abstract Structural variation detection remains an elusive undertaking. For decades, G-banded karyotyping has remained the standard practice for nearly all hematological malignancy samples. Cancers are diseases of the genome and structural variants (SVs) are a hallmark of nearly all cancers, are often drivers of oncogenicity, and can sometimes be targeted for treatment, as highlighted by the discovery of the Philadelphia chromosome and later the development of targeted kinase inhibitors. One of the most important limitations of karyotyping, its requirement for cells to divide in culture, has prevented its application for most solid tumors and therefore, there is considerably less understanding of the impact of SVs in solid tumors. Optical genome mapping is a technique that is revolutionizing cytogenomics and unlocking the understanding of SVs in the genome at a level never seen before. It not only can detect nearly all variants that a karyotype can, but it can provide 10,000x higher resolution and does not require cell culture. Because of this, it is being adopted for oncology studies across hematological malignancies and solid tumors. This study demonstrates the performance of a new high throughput Stratys instrument for generation of optical genome map data at extraordinarily high coverage depth. With this instrument, 12 samples can be loaded at once, each loaded onto its own cartridge and the instrument can collect 400x genome coverage for each of these samples in 24 hours. The instrument has random access to the cartridges so that completed samples can be swapped out to facilitate optimal instrument run efficiency. To date, this system has been demonstrated to run 60 samples in a week, each at 400x coverage. Data from the instrument can be processed by Bionano Access™ and VIA™ software to call SVs, copy number variants (CNVs), regions of loss of heterozygosity (LOH), on a genome wide basis, and to classify those variants automatically according to guidelines such as the National Comprehensive Cancer Network (NCCN), World Health Organization (WHO), and National Health Services (NHS, United Kingdom). This new high throughput system with advanced bioinformatic data analysis has the potential to further transform structural variation analysis in cancer research. Citation Format: Alex R. Hastie, Pat Lynch. Genome wide, high-throughput, high-resolution structural variation detection at low variant allele fraction for oncology samples [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 6257.