Melanoma is the most serious form of skin cancer, developed by the malignant evolution of melanocytes. Malignant melanoma incidence is increasing faster than most other cancers. While stage zero melanoma is highly treatable, survivability dramatically decreases in its advanced stages. Melanoma has shown to be one of the most heterogeneous cancers from RNA and exome analyses by The Cancer Genome Atlas and other groups. A better understanding of the key genomic and epigenomic events that characterize the diverse subclonal populations in melanoma may reveal key insights into what drives its progression and therapeutic resistance. In this study, we leveraged Nanopore long-read sequencing to study the evolution of the mouse B2905 melanoma cell line. Twenty-four distinct clonal sublines were derived in vitro from single cells of the cell line, and the genetically homogeneous population from each subline was sequenced using PromethION R10 flow cells. Enabled by long reads to perform haplotype phasing and accurate structural variation detection, our goal is to integrate small and structural variants to better our understanding of melanoma evolution, and build upon prior analyses of short-read sequenced sublines. We employed multiple SNV calling approaches, including DeepVariant and Clair, in order to provide highly accurate variants for phylogeny reconstruction using Trisicell. We performed structural variant calling with our cancer somatic structural variant (SV) caller Severus as well as copy-number alteration (CNA) analysis with our method Wakhan. Lastly, we placed SNVs, SVs, and CNAs on our reconstructed phylogeny to examine the progression of different types of variants during subline evolution. We identified approximately 560k unique SNVs and around 2, 400 unique SVs. The majority of SNVs (19%) are either clonal or private (73%); however, a meaningful fraction of subclonal variants were available for phylogenetic tree reconstruction. SVs are distributed across the phylogenetic tree branches similarly to SNVs. We identified loss of heterozygosity (LOH) events throughout the subline evolution as well as subclonal CNAs resulting from chromosomal translocations. We find clonal and subclonal evidence of densely clustered SNVs and SV, resembling kataegis; however, our analysis of mutational signatures did not reveal APOBEC-mediated mutations. By analyzing mutational signatures within individual branches of the phylogenetic tree, we observed relative timing of different mutational processes, such as early clonal signatures of UV damage. By incorporating structural variations, copy number changes, and small variant data in the phylogenetic reconstruction, our analysis offers a better characterization of the genetic landscape of subclonal evolution in melanoma. Anton Goretsky, Yuelin Liu, Ayse Keskus, Tanveer Ahmad, Salem Malikic, Glenn Merlino, Chi-Ping Day, Erin Molloy, S. Cenk Sahinalp, Mikhail Kolmogorov. Nanopore sequencing of single-cell derived sublines provides insights into melanoma heterogeneity and evolution [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 7497.

Melanoma, a highly heterogeneous cancer, evolves through a complex interplay of genetic alterations, including both single nucleotide variants (SNVs) and structural variants (SVs). To study the evolutionary trajectory of melanoma, we established a model system composed of 24 single-cell-derived clonal sublines (C1-C24) from the M4 melanoma model, developed in a genetically engineered hepatocyte growth factor (HGF)-transgenic mouse. While SNVs have been extensively used to construct phylogenetic trees using Trisicell (Triple-toolkit for single-cell intratumor heterogeneity inference), a tool that analyzes intratumor heterogeneity and single-cell RNA mutations, the role and timing of SVs in melanoma evolution remain less well understood. This study integrates SV data with an SNV-driven phylogeny to investigate whether SV patterns align with SNV-based evolutionary trajectories in the mouse melanoma model, providing insights into the functional impact of SVs during tumor progression. We performed long-read sequencing on the 24 clonal sublines and detected SVs using Severus, a tool optimized for phasing in long-read sequencing. The SVs were mapped to the SNV-driven phylogeny using R and classified as either concordant (aligning with the SNV-based tree) or discordant (deviating from the SNV phylogeny). Gene ontology enrichment analysis revealed that concordant SVs were significantly enriched in genes associated with the hepatocyte growth factor receptor signaling pathway and the negative regulation of peptidyl-threonine phosphorylation, both of which represent core drivers of tumor progression. In contrast, discordant SVs were associated with a broader range of functional pathways, including the positive regulation of antigen receptor-mediated signaling and the regulation of natural killer cell-mediated cytotoxicity, though the exact mechanisms underlying these associations remain unclear. By integrating these SVs with an established SNV-driven phylogeny, this study highlights the distinct and critical roles SVs play in melanoma evolution. Concordant SVs appear to drive core oncogenic processes, while discordant SVs may contribute to other aspects of tumor evolution. These findings underscore the importance of considering SVs alongside SNVs to fully capture the complexity of melanoma evolution. Ongoing investigations will continue to explore the functional implications of these SVs and how the gene disruption patterns they cause shape the evolutionary trajectory of melanoma, offering potential targets for future therapeutic strategies. Xiwen Cui, Ayse G. Keskus, Salem Malikic, Yuelin Liu, Anton Goretsky, Chi-Ping Day, Farid R. Mehrabadi, Mikhail Kolmogorov, Glenn Merlino, S. Cenk Sahinalp. Integrating structural variants and single nucleotide variants to uncover evolutionary trajectories in melanoma [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 3898.

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. 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.

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein–Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Melanoma is characterized by significant intratumoral heterogeneity and complex evolutionary dynamics. This diversity in genomic alterations leads to the emergence of various subclonal populations within a single tumor. In our research, we established a new model system composed of 24 single-cell-derived clonal sublines (C1-C24), originating from the M4 melanoma model. This model was developed using a genetically engineered hepatocyte growth factor (HGF)-transgenic mouse. We employed Trisicell (Triple-toolkit for single-cell intratumor heterogeneity inference), a cutting-edge computational tool for scalable analysis of intratumor heterogeneity and evaluation based on single-cell RNA mutations. This enabled us to construct a phylogeny tree, revealing melanoma’s intricate branching evolutionary patterns. These patterns show ancestral clones evolving into genetically distinct subclones, which demonstrate varied phenotypic traits such as drug sensitivity or resistance, cellular plasticity, and immunogenicity. In our study, we conducted long-read sequencing on these clonal sublines in the phylogeny and identified structural variants (SVs) using Severus, a tool optimized for phasing in long-read sequencing. The types of SVs we discovered include deletions, insertions, amplifications, translocations, and inversions. We explored their roles in subclonal evolution, particularly focusing on how they disrupt genes and accumulate during melanoma progression. Our initial data from eleven sublines indicated a higher prevalence of ancestral SVs, shared by all sublines, compared to subline-specific SVs, representing later events. Notably, the individual sublines showed a higher rate of gene disruption by SVs, hinting at potential functional selection. Our analysis further revealed that SVs common to all sublines are linked with genes in key cell growth pathways, such as Rap1, Hippo, and calcium signaling pathways. In contrast, subline-specific SVs primarily affect genes involved in neurophysiological pathways, such as glutamatergic synapse and morphine addiction pathways. These findings suggest that different genes, associated with various pathways, are disrupted at distinct stages of melanoma progression, providing insights into the genetic factors that may predispose individual melanocytes to melanomagenesis. This methodology presents a comprehensive tool for characterizing tumor genomes and understanding their relationship with disease progression and therapy resistance. We are continuing our analysis to map SVs across the entire mutation-based phylogeny of all sublines, which will further our understanding of melanoma’s genomic landscape. Citation Format: Xiwen Cui, Ayse G. Keskus, Farid R. Mehrabadi, Salem Malikic, Mikhail Kolmogorov, Chi-Ping Day, Glenn Merlino, S. Cenk Sahinalp. Structural variant dynamics in melanoma: Unraveling tumor heterogeneity and evolution [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 6924.

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.