King L. Hung

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.

The discovery of sensory ion channels, such as thermosensitive transient receptor potential (TRP) channels and mechanosensitive PIEZOs, have transformed our understanding of mammalian sensory biology. However, the sensory receptor landscape remains incomplete, as many physiologically relevant sensory stimuli still lack identified molecular targets. Here, we describe a novel screening strategy utilizing FM 1-43, a fluorescent marker for activity of various cation channels, with a CRISPRa library (MPCL) targeting multi-transmembrane domain proteins. We validate this method by focusing on allyl isothiocyanate (AITC) and its putative receptor TRPA1. Specifically, we show that CRISPRa-mediated overexpression of TRPA1 is sufficient for FM 1-43 labeling when co-treated with AITC. Furthermore, we show that using FM 1-43 and AITC, we can efficiently FACS enrich TRPA1-expressing cells from a pool of MPCL-expressing cells. Collectively, this presents a novel method for rapidly screening select cation-dependent sensory stimuli.

Extrachromosomal DNA (ecDNA) drives oncogene amplification and intratumoural heterogeneity in aggressive cancers. While transposable element reactivation is common in cancer, its role on ecDNA remains unexplored. Here we map the 3D architecture of MYC -amplified ecDNA in colorectal cancer cells and identify 68 ecDNA-interacting elements—genomic loci enriched for transposable elements that are frequently integrated onto ecDNA. We focus on an L1M4a1#LINE/L1 fragment co-amplified with MYC , which functions only in the ecDNA-amplified context. Using CRISPR-CATCH, CRISPR interference and reporter assays, we confirm its presence on ecDNA, enhancer activity and essentiality for cancer cell fitness. These findings reveal that repetitive elements can be reactivated and co-opted as functional rather than inactive sequences on ecDNA, potentially driving oncogene expression and tumour evolution. Our study uncovers a mechanism by which ecDNA harnesses repetitive elements to shape cancer phenotypes, with implications for diagnosis and therapy.

Simulating multi-ecDNA dynamics in vivo.A, DNA FISH and nascent RNAscope images from the tumor of patient A5, showing the coexistence of both EGFR-ecDNA and PDGFRA-ecDNA. B, Circular structures of three distinct oncogenic ecDNAs detected in the tumor of patient A5. C, GBMs in the GB-UK and PCAWG cohorts containing more than one oncogenic species and their respective copy numbers. D, In a multiple-ecDNA application of SPECIES, the clone-initiating cell begins with k1 and k2 copies of ecDNA 1 and ecDNA 2, respectively. E, Representation of the impacts of ecDNA cosegregation and coselection on the number of inherited ecDNA copies during cell division. Cosegregation drives correlated inheritance of both ecDNA types, whereas coselection favors tumor cells carrying at least one copy of each type. F, Application of SPECIES to simulate two separate ecDNA species. Simulated tumors were initialized with a single cell, carrying k1 and k2 copies of each ecDNA species. Representative model images show examples of low and high cosegregation and coselection. G and H, Parameter inference summary for the tumor of patient A5, for which we measured both the EGFR-ecDNA and PDGFRA-ecDNA copy-number distributions using DNA FISH, using multiple-ecDNA SPECIES. Parameters sp and sm represent selection coefficients for tumor cells with 1 (pure) or 2 (mixed) ecDNA species, respectively. I, Comparison of inferred k and k1 values for all patient tumors confirmed by WGS to harbor two or more ecDNA species. J, The presence of ecDNA amplifications may be used to aid stratification, given that oncogenes on ecDNA have an inherent resistance mechanism through the ability of ecDNA to dynamically adjust copy number in response to targeted agents. Earlier monitoring and intervention are recommended for those patient tumors with ecDNA that will receive targeted therapies.

EcDNA accumulation in genetically engineered in vivo and ex vivo murine models. A, Neural stem cells were propagated within the SVZ of genetically engineered mice for 4 months. B,Myc-ecDNAs were induced in adult murine neural stem cells, which were then cultured ex vivo for 5 weeks. C, DNA FISH analysis of neural stem cells within the SVZ of genetically engineered mice (i) without Myc-ecDNA (Myc+/+; P53fl/fl; Nestin-Cre, n = 2) and (ii) with Myc-ecDNA (Mycec/+; p53fl/fl; Nestin-Cre, n = 2), both on a background of homozygous Trp53 loss. D, ecDNA copy-number dynamics in murine adult neural stem cells (data from ref. 33) with corresponding simulated dynamics, assuming either neutral or positively selected ecDNA. (A and B, created using BioRender assets. https://BioRender.com/a1hpt9n.)

Selection dynamics of EGFR SVs on ecDNA. A, A subclonal EGFRvIII variant on ecDNA in the tumor of patient A23. B, Variant heteroplasmy in the core region of all samples across the GB-UK and PCAWG cohorts, which contained EGFR variant–bearing ecDNA. C, Effects of (i) pre- and (ii) postclonal expansion mutation of EGFR-ecDNA on its resulting spatial pattern in the expanded tumor. D, SPECIES was applied to simulate the emergence of an advantageous EGFRvIII mutation on ecDNA, either prior to or after the onset of clonal expansion of the tumor. The example shown is for a pre-expansion mutation, with parameters (kwt=20, kvar=1, Vvar=0). The SD of ecDNA heteroplasmy is shown by the shaded region in the lower plot. E, Model predictions for resulting variant heteroplasmy in a tumor for a range of wild type- and variant-bearing ecDNA copy numbers in the clone-initiating tumor cell (for pre-expansion EGFR mutation) and mutation times (for post-expansion EGFR mutation). The red-shaded region denotes parameter combinations inconsistent with observed patient core heteroplasmy values. Mean ± variance is derived from 1,000 simulated tumors. F, SPECIES model predictions for the probability of observing EGFRvIII ecDNA across tumor core and margin, for a pair of core and random margin regions. Mean values are derived from 1,000 simulated tumors. G, (Top) IHC slide for the tumor of patient A20, showing the presence of EGFRvIII in the tumor core and infiltrating margin (blue = hematoxylin; brown = EGFRvIII). (Bottom) Summary of EGFRvIII in core and margins of GB-UK cohort samples, determined by IHC. H, Variant allele frequencies of point mutations within EGFR in PCAWG samples harboring EGFR-ecDNA. Red lines represent the frequency expected for a mutation carried by all ecDNAs within a sample. I, Distributions of EGFRwt-ecDNA abundance in the cell that acquires the first mutation, for a range of ecDNA mutation rates. Patient tumor values are derived from their respective inferred k values, assuming that the arrival of a mutation on ecDNA initiates clonal expansion, implying that the number of EGFRwt-ecDNA in the mutating cell is thus equal to k − 1 (with the remaining one ecDNA carrying the mutated allele). J, Timeline of EGFR-ecDNA tumorigenesis. (i) Initial EGFRwt-ecDNA is generated and accumulates in precancerous cells, conferring a moderately positive selective advantage. In time, the EGFRvIII mutation occurs on one of the accumulated EGFRwt-ecDNAs, conferring a strong positive advantage. (ii) Reconstructed ecDNA structures from the tumor of GB-UK patient A5 confirm that EGFRwt and EGFRvIII ecDNAs share common breakpoints, indicating that EGFRvIII arose from the mutation of an existing EGFRwt-ecDNA. (iii) EGFRvIII-ecDNA further promotes clonal expansion, driving tumor growth.

SPECIES spatial modeling of ecDNA evolution. A and B, DNA FISH staining of two representative GBM samples from the GB-UK cohort, revealing (A) EGFR- and (B) PDGFRA-ecDNA. EGFR-ecDNA seem to congregate into “hubs” (white arrows). Single-cell ecDNA copy-number distributions are derived from DNA FISH images using unbiased image analysis. C, (i) SPECIES is initiated with a single tumor cell containing k copies of ecDNA. (ii) Model of ecDNA selection. Cells carrying one or more copies of ecDNA divide at a rate s faster than cells lacking ecDNA. (iii) Cells push neighbors within a radius q on the lattice to make space required for cell division. D, Spatial computational model of ecDNA-driven tumors. ABC was used to infer optimal model parameters for the initial number of ecDNA, k; ecDNA-conferred selection, s; and cell pushing strength, q, for a given set of patient tumor measurements. E and F, Example output of the ABC model fitting algorithm using (E) 2 and (F) 3 spatial regions. (Left to right) Inferred k, s, and q and model best-fit distributions for each tumor region. The sum of the Wasserstein distance between patient and simulated distributions for each tumor region, representing closeness of fit, is denoted by σ. G, Comparison of inferred k, s, and q when analyzing 2 (core and margin) vs. 3 (core, margin, and leading edge) spatial regions. H, Summary of inferred model parameters for all sampled human GBM tumors. The highlighted color represents ecDNA-amplified oncogenes (EGFR, PDGFRA, or CDK4) in each tumor. I, Inferred model parameters for all patient tumors (excluding the tumor of patient A5, which contained separate EGFR- and PDGFRA-ecDNA species), stratified by ecDNA-amplified oncogene (EGFR or PDGFRA). (D, created using BioRender assets. https://BioRender.com/7f2pume)

Workflow of the study analysis. A, A total of 94 IDHwt GBMs were analyzed (35 from the PCAWG cohort and 59 from the GB-UK cohort). B, The core region of all patient tumors was analyzed using WGS, and GB-UK patient tumors were further subjected to multiregion DNA FISH and nascent RNAscope analysis to characterize spatial patterns of oncogenic ecDNAs. C, The ecDNA genetic landscape and fine structure were analyzed using WGS information, and mathematical modeling of single-cell copy number was employed to reconstruct evolutionary histories in GB-UK patient tumors. (B, created using BioRender assets. https://BioRender.com/kgae4q5; https://BioRender.com/7f2pume)

Supplementary Methods, providing details about methodology used throughout the study.

Diversity and high copy numbers of ecDNA in IDHwt GBMs. A, Focal copy-number amplifications across the GB-UK and PCAWG cohorts, categorized by amplicon type [ecDNA, breakage–fusion–bridge (BFB) cycle, linear]. Only tumors containing focal amplifications are shown. Oncogenes with the same colored box indicate they exist on the same ecDNA molecule. The horizontal line between patients A61 and A3 (GB-UK) and patients SP27603 and SP23622 (PCAWG) separates patient samples with (above) or without (below) ecDNA, detected by WGS, DNA FISH, or nascent RNAscope. Oncogenes tested with DNA FISH and/or nascent RNAscope, EGFR, PDGFRA, and CDK4, are separated to the left of the vertical dashed line. B, Median ecDNA copy number vs. number of ecDNA-amplified genes for GB-UK and PCAWG cohorts (Spearman correlation). C, Oncogenes contained within ecDNA in both GB-UK and PCAWG cohorts ranked by maximum copy number. The number of samples with each oncogene as ecDNA in the GB-UK cohort is described in brackets. Only copy numbers detected by WGS are shown here. D, Mean copy number of linear focal amplifications vs. ecDNA in GB-UK. E, Mean copy number of non-oncogenes compared with oncogenes amplified on ecDNA in GB-UK. F, Complexity score of linear focal amplifications vs. ecDNA in GB-UK. G, Number of uni- and multichromosomal ecDNAs in GB-UK and PCAWG cohorts. H, Distribution of ecDNA lengths in GB-UK and PCAWG cohorts. I, Median ecDNA copy number vs. length of intergenic DNA for EGFR-ecDNA in GB-UK and PCAWG cohorts (Spearman correlation).