Vineet Bafna

Extrachromosomal DNA (ecDNA) plays a key role in cancer pathology. ecDNAs mediate high oncogene amplification and expression and are associated with worse patient outcomes. Accurately determining the structure of these circular molecules is essential for understanding their function, yet reconstructing ecDNA cycles from sequencing data remains challenging. We introduce Cycle-Extractor (CE) for this task. CE takes as input a breakpoint graph derived from either short or long read sequencing data and extracts cycles of maximum length-weighted copy number. CE utilizes a mixed-integer linear program (MILP) and a separate traversal procedure, enabling fast optimization and compatibility with open-source ILP solvers. We evaluated CE against CoRAL (long-read, mixed-integer quadratically constrained programming), Decoil (long-reads), and AmpliconArchitect (AA; short reads) on both simulated data and real cancer cell lines. On simulated ecDNA, CE achieves accuracy comparable to CoRAL across three metrics and consistently outperforms AA and Decoil. On cancer cell lines, CE produces longer and heavier cycles than AA, and performs comparably to CoRAL, while being on average 40× faster. These results demonstrate that CE accurately reconstructs ecDNA from both short and long-read sequencing data, and that long-read inputs allow CE to recover more complete, higher-confidence ecDNA structures. CE improved the prediction of many ecDNA structures. On PC3 ecDNA containing MYC, CE uses ONT data to reconstruct a substantially larger and higher-copy sequence (4.2 Mbp) compared to the short-read-derived reconstruction (690 kbp). CRISPR-CATCH experiments confirm the presence of a large ecDNA molecule, validating the long-read-based CE reconstruction.

Here we present AmpliconSuite, a toolkit for focal amplification analysis from cancer whole-genome sequencing that identifies extrachromosomal DNA (ecDNA), breakage–fusion–bridges and other amplification modes. Its ecContext module categorizes ecDNA structures to understand formation mechanisms. Validation across 175 metaphase fluorescence in situ hybridization-probed genes in 72 models demonstrated accurate predictions. Analysis of 6,506 patient pan-cancer samples revealed ecDNA-mediated oncogene co-amplification preferences. To democratize data sharing, we created AmpliconRepository, a community-contributable platform, enabling investigation of results across >15,000 publicly hosted samples.

Extrachromosomal DNA (ecDNA) contributes to cancer genome instability by enabling high-copy oncogene amplification and genetic intratumoral heterogeneity. Micronuclei (MNs) are common in chromosomally unstable cancers, yet their role in ecDNA-driven tumors remains unclear. Here we investigate how ecDNA damage, mitotic segregation and MN formation are linked. We find that ecDNAs are a major source of MNs following their damage, particularly under replication stress. Replication stress promotes mitotic ecDNA clustering, asymmetric inheritance, mis-segregation into MNs and reduced transcriptional output. Single-MN sequencing reveals that multiple distinct ecDNA species coalesce within individual MNs. Cells inheriting ecDNA⁺ MNs display impaired proliferative capacity and are more likely to undergo cell death. In patients with MYCN-amplified neuroblastoma, higher frequencies of ecDNA⁺ MNs at diagnosis are associated with improved overall survival. Together, these findings identify ecDNA mis-segregation as a mechanism that limits tumor fitness, highlighting mitotic ecDNA retention as a potential therapeutic vulnerability.

Extrachromosomal DNA (ecDNA) can arise early in the development of aggressive tumors or later upon progression, but the impact of chronology on patient outcome is not known. Currently, it is not possible to determine when an ecDNA arose in the course of tumor development and progression, based on genome sequencing data. Here, we develop an analytic method, ecEvol, to infer ecDNA emergence time, selection strength, mutation rate, and clonal dominance from allele-frequency spectra in bulk whole-genome sequencing. EcEvol outperforms other methods and predicts parameters with high accuracy across a wide range of parameter values. Applying ecEvol to primary, treatment-naive tumors, we find that ecDNA typically emerges early and undergoes strong positive selection, albeit with substantial variation across cancer types. EcDNAs carrying known oncogenes emerge earlier and reach greater clonal dominance than ecDNAs lacking known oncogenes. In advanced and predominantly treated tumors, ecDNA emerges substantially later than in matched primary tumors, consistent with the generation and selection of new ecDNA during tumor progression and therapy. Longitudinal samples further support continued expansion of ecDNA-bearing lineages. Finally, earlier ecDNA emergence is associated with poorer overall survival. These results provide insight into the chronology of ecDNA formation from a single tumor biopsy taken at any point during the course of the disease, revealing that allele-frequency spectra encode the evolutionary history of ecDNA and provide a quantitative framework for reconstructing its emergence and expansion.

Oncogene amplification on extrachromosomal DNA is a common driver of tumor progression and is associated with acquired drug resistance and poor patient survival. While bulk whole genome sequencing studies have revealed the landscape of genes amplified on extrachromosomal DNA in tumors, it remains challenging to study the subclonal heterogeneity and functional (e.g., transcriptomic) consequences of extrachromosomal DNA on tumors. To address this, we introduce scAmp : a probabilistic algorithm for detecting and analyzing extrachromosomal DNA from single-cell datasets. Using well-characterized cell lines, we demonstrate that scAmp has improved specificity over bulk genome sequencing in predicting extrachromosomal DNA status and can resolve the status of chromosomal amplifications that were historically extrachromosomal. We further showcase scAmp by analyzing 73 patient tumors profiled with single-cell assay for transposase-accessible chromatin by sequencing, where we characterize the subclonal evolution of subclones with extrachromosomal DNA and identify the effect of these amplifications on the chromatin accessibility landscape of cancer cells. Finally, we provide proof-of-concept analyses that scAmp aids in the detection of extrachromosomal DNA from clinical histopathology assays. Together, we anticipate that scAmp will broadly enable further studies – both retrospective and prospective – that dissect critical questions of how extrachromosomal DNAs affect cancer cells and the tumors in which they reside.

Focal oncogene amplification is a key driver of tumor progression. Remarkably, the increased pathology depends on the context-whether the amplification is extrachromosomal (ecDNA) or intrachromosomal. EcDNA amplifications promote heterogeneity, therapy resistance, and poor prognosis. Focal intrachromosomal amplifications often arise through breakage-fusion-bridge (BFB) cycles, which produce highly rearranged but stable chromosomes. Distinguishing BFB from ecDNA remains challenging due to overlapping genomic signatures. To address this, we present BFBArchitect, a computational method leveraging long-read Oxford Nanopore data to identify BFB sequences consistent with both copy number and structural variations. We provide a novel combinatorial characterization of BFB, which naturally leads to an integer linear programming (ILP) optimization. The ILP optimization generates a BFB sequence that best explains experimentally observed copy numbers and foldback structural variants. We implement this idea in a tool called BFBArchitect, which achieves near-perfect accuracy in distinguishing BFB from non-BFB structures in extensive simulations as well as on 18 validated tumor samples. Moreover, it generates sequence-level BFB reconstructions that provide mechanistic insights into BFB formation, including repair mechanisms with template switching and other structural variants, and recapture of telomere for stabilization. BFBArchitect is available at https://github.com/AmpliconSuite/BFBArchitect.

Ecologists and conservation biologists rely on genetic diversity as a key essential biodiversity variable (EBV) used to track population health and dynamics, and utilize the population parameter θ (estimated by the average pairwise genomic distance) as a key metric of diversity. While whole-genome-sequencing (wgs) is increasingly affordable, it will be considerable time before the full diversity of life is represented by high-quality assembled genomes; even then, constant monitoring will still require repeated sampling of populations. In contrast, genome skimming (low-coverage, short-read wgs) is highly cost-effective but challenging to analyze because the coverage is too low for assembly and reliable error correction. Mature methods, such as Mash, exist for estimating pairwise genomic distances based on the Jaccard similarity of k -mer sets computed using sketching techniques. Some, such as Skmer, additionally model the impacts of low coverage. These methods have been successfully applied to assembly-free species identification and phylogenetics; however, their use in population genetics has been limited. This is because these methods implicitly treat genomes as haploid and heterozygosity confounds true estimates of genomic distance for diploid organisms. In this paper, we address this problem through a number of technical advances. First, we use coalescent theory to mathematically derive how the Jaccard index between two diploid samples changes with the scaled population size parameter ( θ ). Next, we derive an estimator that computes θ from the Jaccard index, in addition to several auxiliary variables, which we also estimate from the genome skims. The resulting method, DipSkmer, enables more accurate estimates of coverage, sequencing error, and pairwise nucleotide distance for diploid samples. Analyses of both simulated and empirical datasets show that for diploids and low distances (e.g., < 2%), Dip-Skmer produces the most accurate pairwise distance estimates, outperforming existing alignment-free methods such as Mash and Skmer, and closely approximates ANGSD, a reference and alignment-based tool. Availability: The code for DipSkmer is available at https://github.com/echarvel3/ReSkmer/tree/DipSkmer-REFACTOR . Simulation scripts and environments are available at https://github.com/echarvel3/dipskmer_scripts . Author Summary: The process of obtaining full-genome population genomic measurements for biodiversity monitoring remains expensive due to the need for high-coverage sequencing and reference assemblies. Genome skimming has been shown to be a viable, low-coverage alternative for obtaining genomic distances, and alignment- and assembly-free methods exist for analyzing nuclear data from skimming data to estimate the distance between samples. However, existing methods fail to model within-sample heterozygosity, expected for diploid organisms. Given the dominance of diploidy among species of interest to ecologists, the implications of these simplifying assumptions warrant further study. Here, we present a mathematical model of the k -mer sets sampled from two diploid genomes from a Wright-Fisher population. We use the model to develop DipSkmer, a k -mer-based, reference-free method for estimating nucleotide diversity and population divergence that, unlike its predecessors, models within-sample heterozygosity. Benchmarking shows more accurate genomic diversity estimates compared to existing reference-free, genome-skimming methods and comparable performance to the popular high-coverage, reference-based method, ANGSD. Thus, DipSkmer enables accessible, less expensive population monitoring through genetic diversity estimates.

Ductal carcinoma in situ is a non-obligate precursor lesion of breast cancer. Often detected by mammography, most cases are managed through surgical and/or radiotherapy approaches. Today, it is not possible to predict which patients will progress to invasive disease. Here, we evaluate high-depth whole-genome sequenced ductal carcinoma in situ, enriched for high-grade clinical lesions, to understand whether deep WGS could reveal biological insights and/or personalized therapeutic vulnerabilities that may be targetable. We find genomic locations that are likely susceptible to producing the initiating lesion for structural variations prone to subsequent evolution, termed SHOREs. We additionally highlight individualized therapeutic potential that would otherwise not be appreciable without whole genome sequencing. We posit that holistic whole genome sequencing profiling could offer a more precise stratification approach, discerning higher-risk cases for prospective clinical studies on personalized therapies, from truly low-risk cases suitable for active monitoring.

Oncogene amplification is a key driver of cancer pathogenesis and is often mediated by extrachromosomal DNA (ecDNA). EcDNA amplifications are associated with increased pathogenicity of cancer and poorer outcomes for patients. EcDNA can be detected accurately using fluorescence in situ hybridization (FISH) when cells are arrested in metaphase. However, the majority of cancer cells are non-mitotic and must be analyzed in interphase, where it is difficult to discern extrachromosomal amplifications from chromosomal amplifications. Thus, there is a need for methods that accurately predict oncogene amplification status from interphase cells. We present interSeg, a deep learning-based tool to cytogenetically classify oncogene amplification status as extrachromosomally amplified (EC-amp), intrachromosomally amplified (HSR-amp), or not amplified, from interphase FISH images. We trained and validated interSeg on 652 images (40,446 nuclei). Tests on 215 cultured cell and tissue model images (9,733 nuclei) showed 89% and 97% accuracy at the nuclear and sample levels, respectively. The neuroblastoma patient tissue hold-out set (67 samples and 1,937 nuclei) also revealed 97% accuracy at the sample level in detecting the presence of focal amplification. In experimentally and computationally mixed images, interSeg accurately predicted the level of heterogeneity. The results showcase interSeg as an important method for analyzing oncogene amplifications.

Uterine corpus endometrial carcinoma (UCEC) is a common gynecologic malignancy with rising mortality, yet its genome-wide mutational architecture remains incompletely understood. Here we analyze deep whole-genome sequencing data from 440 UCEC tumors from The Cancer Genome Atlas, integrated with transcriptomic and clinical data, to define subtype-specific mutational processes and genomic architectures. We uncover pronounced molecular subtype-specific differences in endogenous mutational mechanisms, retrotransposition activity, structural variation, and chromosomal instability. LINE-1 retrotransposition emerges as a key contributor to genome instability in UCEC, acting as a prominent source of structural variation in copy-number stable tumors and associating with chromothripsis and ecDNA-mediated oncogene amplification in copy-number high tumors. Mutational signature SBS28 contributes significantly to mutations in POLE -deficient tumors and is strongly correlated with SBS10a and SBS10b. Mismatch repair-deficient tumors exhibit a previously unrecognized doublet base substitution signature and a defining imbalance between ID2 and ID1 indel processes, reflecting pervasive DNA template-strand replication slippage and associated with increased tumor immunogenicity. Copy-number low tumors follow a distinct low-mutagenesis evolutionary trajectory characterized by reduced replication stress, low proliferative activity, genomic stability and enrichment of SBS18 associated with oxidative damage. Notably, we identify a consistent inverse association between body mass index and tumor mutational burden in UCEC, suggesting that host metabolic state may influence fundamental cellular processes governing mutation accumulation, promoting tumor development through non-mutagenic mechanisms rather than elevated genomic instability. Together, these findings establish a mutagenesis-centric framework for UCEC that links endogenous mutational mechanisms to tumor architecture and host metabolic context, uncovering previously unrecognized subtype-specific genomic features with important implications for refined risk stratification and therapeutic strategies.