Matthew A. Myers

Extrachromosomal DNA (ecDNA) comprises megabase-sized circular DNA elements that frequently carry oncogene amplifications, driving aggressive tumour phenotypes, therapeutic resistance and poor clinical outcomes across many cancers^1,2,3,4. Although ecDNA is thought to arise from canonical double-strand break repair, the pathways that maintain it remain unclear. Here we show that inhibition of microhomology-mediated end joining, but not non-homologous end joining or homologous recombination, selectively depletes ecDNA, induces ecDNA-specific damage and promotes its sequestration into micronuclei, compromising the fitness of cancer cells that depend on ecDNA-driven oncogene amplification. Mechanistically, TA-rich loci on ecDNA are hotspots for DNA damage and breakage^5,6. The DNA translocase FANCM suppresses break formation at these sites, while breaks that escape FANCM surveillance are cleaved by ERCC1–ERCC4 and channelled into microhomology-mediated end joining for repair. Single-cell whole-genome sequencing shows that disrupting FANCM or polymerase θ (Polθ) in COLO320DM cells causes structural instability characterized by deletions and small duplications, with breakpoints enriched at TA-rich regions. This fragility is recapitulated in human tumours, in which ecDNA rearrangements are enriched at TA repeats. Collectively, our findings reveal TA repeat fragility as an intrinsic vulnerability of circular DNA and identify Polθ inhibition as a promising strategy to potentially destabilize ecDNA and sensitize ecDNA-driven tumours to therapeutic intervention.

The binary perfect phylogeny, in which each mutation arises exactly once on an evolutionary tree and is never lost, is a well-studied idealized phylogenetic model. When observed data has errors, a common approach to phylogeny inference is to seek a tree that minimizes the number of error corrections (“flips”) needed to fit a perfect phylogeny. These objectives draw on an intuitive justification: minimizing implied errors should prefer the true tree in expectation. We test this assumption using a generative model with independent errors and prove that error-correction objectives are statistically inconsistent for all positive error rates: in expectation, the minimum-cost tree need not be the true tree. Our proof is constructive and yields counterexamples involving any tree topology and any positive error rates, demonstrating the ubiquity of the phenomenon. The core problem is that tree topologies can explain observed mutation patterns with fewer errors than actually occurred, and differ in their ability to do so, introducing systematic bias. This mechanism is distinct from previously identified sources of inconsistency such as homoplasy or incomplete lineage sorting, since the error-free setting is trivially consistent for perfect phylogenies. We investigate how often this failure may occur in practice. Simulations calibrated to error rates from single-cell sequencing data show that an incorrect tree is preferred over the true tree in a substantial fraction of cases (over 50% in some settings) with rates increasing with tree size. Moreover, winning trees are not random but share specific topological features. Notably, at error rates typical of single-cell sequencing data, trees with deeper, more imbalanced topologies are consistently favored over more balanced ones. These results demonstrate that inconsistency is not a theoretical edge case, and that understanding when and how it arises is important when interpreting results in practice. Supplementary Material: Source code : https://github.com/shahcompbio/phylo_inconsistency Funding: This research was supported in part by NCI SPORE (P50 CA247749-01)

Correctly inferring copy-number aberrations from single-cell DNA sequencing data requires estimating cellular DNA content, which is unidentifiable from read counts alone. In tagmentation-based sequencing, each fragment represents a distinct DNA molecule, thus fragment overlaps provide an orthogonal signal for copy number. We present a theoretical model of fragment overlaps as a function of copy number and coverage and introduce scPlOver, a method that uses this model to infer DNA content. scPlOver outperforms existing approaches on simulated and experimental datasets and identifies thousands of ovarian cancer cells with higher DNA content than previously estimated across a cohort of 41 patients.

High-level copy-number (CN) amplification (HLAMP) is a major mechanism of oncogene activation in human cancer. Despite progress in therapeutically targeting HLAMPs, the processes underlying HLAMP evolution are incompletely understood, leaving critical knowledge gaps in their etiology and mechanisms of therapeutic response. To address this, we analyzed using novel computational approaches, the evolutionary trajectories of HLAMP in single-cell whole-genome sequencing of 86,239 cancer cells from 94 patients and 8 experimental systems. We found that two common etiologies of oncogene amplification - intrachromosomal amplification (ICamp) and extrachromosomal circular DNA (ecDNA) - lead to distinct CN distributions across cells in amplitude, variance and clone specificity. Notably, ICamp events exhibited widespread subclonal specificity, indicating their dynamic evolution through numeric or structural modulation associated with clonal expansions. In contrast, ecDNAs exhibited higher cell-to-cell CN variation linked to structural rearrangements, whereby complex ecDNA architectures were resolved at single-nucleotide resolution in individual cell genomes. Through joint analysis of ecDNA structure and their cellular abundance, we observed both divergent and convergent modes of ecDNA evolution shaped by DNA damage, selection, and chromosomal re-integration. Finally, modeling single-cell distributions of ecDNAs substantially improved bulk genome-graph based predictions, revealing that ecDNA prevalence is tissue-type specific, rather than broadly shared across cancer types, with implications for ecDNA-directed therapy.

Emergence of drug resistance is the main cause of therapeutic failure in patients with high-grade serous ovarian cancer (HGSOC)¹. To study drug resistance in patients, we developed CloneSeq-SV, which combines single-cell whole-genome sequencing² with targeted deep sequencing of clone-specific genomic structural variants in time-series cell-free DNA. CloneSeq-SV exploits tumour clone-specific structural variants as highly sensitive endogenous cell-free DNA markers, enabling the relative abundance measurements and evolutionary analysis of co-existing clonal populations over the therapeutic time course. Here, using this approach, we studied 18 patients with HGSOC over a multi-year period from diagnosis to recurrence and showed that drug resistance typically arose from selective expansion of a single or small subset of clones present at diagnosis. Drug-resistant clones frequently showed interpretable and distinctive genomic features, including chromothripsis, whole-genome doubling, and high-level amplifications of oncogenes such as CCNE1, RAB25, MYC and NOTCH3. Phenotypic analysis of matched single-cell RNA sequencing data³ indicated pre-existing and clone-specific transcriptional states such as upregulation of epithelial-to-mesenchymal transition and VEGF pathways, linked to drug resistance. In one notable case, clone-specific ERBB2 amplification affected the efficacy of a secondary targeted therapy with a positive patient outcome. Together, our findings indicate that drug-resistant states in HGSOC pre-exist at diagnosis, leading to positive selection and reduced clonal complexity at relapse. We suggest these findings motivate investigation of evolution-informed adaptive treatment regimens to ablate drug resistance in future HGSOC studies.

Whole-genome doubling (WGD) is a common feature of human cancers and is linked to tumour progression, drug resistance, and metastasis^1,2,3,4,5,6. Here we examine the impact of WGD on somatic evolution and immune evasion at single-cell resolution in patient tumours. Using single-cell whole-genome sequencing, we analysed 70 high-grade serous ovarian cancer samples from 41 patients (30,260 tumour genomes) and observed near-ubiquitous evidence that WGD is an ongoing mutational process. WGD was associated with increased cell–cell diversity and higher rates of chromosomal missegregation and consequent micronucleation. We developed a mutation-based WGD timing method called doubleTime to delineate specific modes by which WGD can drive tumour evolution, including early fixation followed by considerable diversification, multiple parallel WGD events on a pre-existing background of copy-number diversity, and evolutionarily late WGD in small clones and individual cells. Furthermore, using matched single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signalling and cGAS-STING pathway activation result from ongoing chromosomal instability, but this is restricted to predominantly diploid tumours (WGD-low). By contrast, predominantly WGD tumours (WGD-high), despite increased missegregation, exhibited cell-cycle dysregulation, STING1 repression, and immunosuppressive phenotypic states. Together, these findings establish WGD as an ongoing mutational process that promotes evolvability and dysregulated immunity in high-grade serous ovarian cancer.

Ongoing mutagenesis in cancer drives genetic diversity throughout the natural history of cancers. As the activities of mutational processes are dynamic throughout evolution, distinguishing the mutational signatures of ‘active’ and ‘historical’ processes has important implications for studying how tumors evolve. This can aid in understanding mutagenic states at the time of presentation, and in associating active mutational process with therapeutic resistance. As bulk sequencing primarily captures historical mutational processes, we studied whether ultra-low-coverage single-cell whole-genome sequencing (scWGS), which measures the distribution of mutations across hundreds or thousands of individual cells, could enable the distinction between historical and active mutational processes. While technical challenges and data sparsity have limited mutation analysis in scWGS, we show that these data contain valuable information about dynamic mutational processes. To robustly interpret single nucleotide variants (SNVs) in scWGS, we introduce ArtiCull, a method to identify and remove SNV artifacts by leveraging evolutionary constraints, enabling reliable detection of mutations for signature analysis. Applying this approach to scWGS data from pancreatic ductal adenocarcinoma (PDAC), triple-negative breast cancer (TNBC), and high-grade serous ovarian cancer (HGSOC), we uncover temporal and spatial patterns in mutational processes. In PDAC, we observe a temporal increase in mismatch repair deficiency (MMRd). In cisplatin-treated TNBC patient-derived xenografts, we identify therapy-induced mutagenesis and inactivation of APOBEC3 activity. In HGSOC, we show distinct patterns of APOBEC3 mutagenesis, including late tumor-wide activation in one case and clade-specific enrichment in another. Additionally, we detect a clone-specific increase in SBS17 activity, in a clone previously linked to recurrence. Our findings establish ultra-low-coverage scWGS as a powerful approach for studying active mutational processes that may influence ongoing clonal evolution and therapeutic resistance.

The genomic features of pancreatic ductal adenocarcinoma (PDAC) have been well described, yet the evolutionary contexts within which these features occur remain unexplored. We studied genome landscapes, phylogenies, and clonal compositions of 91 PDACs in relation to clinicopathologic features. There was no difference in the number of driver mutations or evolutionary timing when each mutation occurred. High truncal density, a metric of the accumulation of somatic mutations in the lineage that gave rise to each PDAC, was significantly associated with worse overall survival. Polyclonal, monoclonal, or mixed polyclonal/monoclonal metastases were identified across the cohort, highlighting multiple forms of intertumoral heterogeneity. Advanced stage and treated PDACs had higher odds of being polyclonal, whereas oligometastatic PDACs had fewer driver alterations, a lower fractional allelic loss, and increased likelihood of being monoclonal. In sum, our findings reveal novel insights into the dynamic nature of the PDAC genome beyond established genetic paradigms. Significance: Although the pancreatic cancer genome has been described, it has not been explored with respect to stages of diagnosis or treatment bottlenecks. We now describe and quantify the genomic features of PDAC in the context of evolutionary metrics and in doing so have identified a novel prognostic biomarker.

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type–specific features. Using organ-matched healthy tissues, we identified the “nearest healthy” cell types in diverse cancers, demonstrating that the chromatin signature of basal-like–subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily ‘active’ mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.