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Extrachromosomal oncogene amplification drives tumor evolution and genetic heterogeneity

Human cells have twenty-three pairs of chromosomes but in cancer, genes can be amplified in chromosomes or in circular extrachromosomal DNA (ECDNA), whose frequency and functional significance are not understood1–4. We performed whole genome sequencing, structural modeling and cytogenetic analyses of 17 different cancer types, including 2572 metaphases, and developed ECdetect to conduct unbiased integrated ECDNA detection and analysis. ECDNA was found in nearly half of human cancers varying by tumor type, but almost never in normal cells. Driver oncogenes were amplified most commonly on ECDNA, elevating transcript level. Mathematical modeling predicted that ECDNA amplification elevates oncogene copy number and increases intratumoral heterogeneity more effectively than chromosomal amplification, which we validated by quantitative analyses of cancer samples. These results suggest that ECDNA contributes to accelerated evolution in cancer.

ECDNA was abundant in the cancer samples ( Fig. 2A), but was rarely found in normal cells. Approximately 30% of the ECDNAs were paired DMs (Source Data Table S2). ECDNA levels varied among tumor types, with substantially higher levels in patient-derived cultures (Fig. 2B). Using the conservative metric of at least 2 ECDNAs in ≥ 10% (2 of 20) metaphases, ECDNA was detected in nearly 40% of tumor cell lines and nearly 90% of patient-derived brain tumor models (  Table S2). The diverse array of treatments relative to sample size limited our ability to definitively determine the impact of specific therapies on ECDNA levels. ECDNA number varied greatly from cell to cell within a tumor culture (  19 . These data demonstrate that ECDNA is common in cancer, varies greatly from cell to cell, and is very rare in normal tissue. WGS with median coverage of 1.19× (Extended Data Fig. E4) revealed focal amplifications that were nearly identical to the amplifications found in the TCGA analyses of the same cancer types ( Fig. 3A; Source Data Table S3), including amplified oncogenes found in a pan-cancer analysis of 13 different cancer types 20 . All of the amplified oncogenes tested were found solely on ECDNA, or concurrently on ECDNA and chromosomal homogenous staining regions (HSRs) ( To determine whether extra-and intrachromosomal structures had a common origin, we developed 'AmpliconArchitect' to elucidate the finer genomic structure using sequencing data (Methods). To better understand the relationship between subnuclear location and amplicon structure, we took advantage of spontaneously occurring subclone of GBM39 cells in which high copy EGFRvIII shifted from ECDNA exclusively to HSRs. Independent replicates of GBM39 containing an ECDNA amplicon, revealed a consistent circular structure of 1.29 MB containing one copy of EGFRvIII (Extended Data Fig. E8). Remarkably, the GBM39 subclone harboring EGFRvIII exclusively on HSRs had an identical structure with tandem duplications containing multiple copies of EGFRvIII, indicating that the HSRs arose from reintegration of EGFRvIII-containing ECDNA elements (Extended Data Fig. E8) 14 . In GBM39 cells, resistance to the EGFR tyrosine kinase inhibitors is caused by reversible loss of EGFRvIII on ECDNA 21 . Structural analysis revealed a conservation of the fine structure of the EGFRvIII amplicon containing ECDNA in naïve cells, in treatment, and upon regrowth with discontinuation of therapy (Extended Data Fig. E9), indicating that ECDNA can dynamically relocate to chromosomal HSRs while maintaining key structural features 14,22 .
Does ECDNA localization confer any particular benefit? We hypothesized ECDNA amplification may enable an oncogene to rapidly reach higher copy number because of the unequal segregation to daughter cells 15 than would be possible by intrachromosomal amplification. We used a simplified Galton-Watson branching process to model the evolution of a tumor 23 , where each cell in the current generation either replicates or dies to create the next generation. A cell with k copies of the amplicon is selected for replication with probability b k ; b k /(1-b k ) = 1 + sf m (k). We provided a positive selection bias towards cells with higher ECDNA counts by choosing s ϵ {0.5,1} along with different selection functions for f. Specifically, f m (k) increases to a maximum value f m (15) = 1, then declines in a logistic manner with f m (m) = 0.5 to reflect metabolic constraints (Methods). We allowed the amplicon copy number to grow to 1000 copies (Extended Data Fig. E10), but set b k = 0 for k ≥ 10 3 . During cell division, the 2k copies resulting from the replication of each of the k ECDNA copies segregate independently into the two daughter cells. We contrasted this with an intrachromosomal model of duplication with identical selection constraints, but with the change in copy number affected by mitotic recombination, and achieved by incrementing or decrementing k by 1, with duplication probability p d . A range of values for p d , (0.01 ≤ p d ≤ 0.1) was used, where the upper bound reflects a change in copy number once every 5 divisions. The full assumptions of the model are explained in detail in Supplementary Material Section 4. Starting with an initial population of 10 5 cells, with s = 0.5 and m = 100 and a selection function f 100 (k) (Fig. 4A), we find that an oncogene can reach much higher copy number in a tumor if it is amplified on ECDNA, rather than on a chromosome (Fig.  4B). As predicted by the model, we detected significantly higher copy number of the most frequently amplified oncogenes EGFR (including EGFRvIII) and c-MYC, when they were contained within ECDNA instead of within chromosomes (Fig. 4C). We also reasoned that if an oncogene is amplified intra-chromosomally, the heterogeneity of the tumor (in terms of the distribution of copies of the oncogene) would stabilize at a much lower level. In contrast, unequal segregation of ECDNA would be likely to rapidly enhance heterogeneity and maintain it. Our model confirmed this prediction (Fig. 4D), consistently for a wide range of simulation parameters (Supplementary Material Section 4.3). The heterogeneity of copy number change stabilizes and even decreases over time 10,24 , much as predicted in Fig. 4C-D. We also tested the validity of the model by comparing the Shannon entropy against the average number of amplicons per cell in our tumor samples. Heterogeneity of a tumor with respect to oncogene copy number would be more likely to rise relatively slowly if it is present on a chromosome, but would rise more rapidly and be maintained much longer, if that oncogene is present on ECDNA, as confirmed by a plot of Shannon entropy vs copy number (Fig. 4E). Moreover, the predicted correlation in Fig. 4E is completely recapitulated by the experimental data ( Fig. 4F), thereby validating the central tenets of the model.
There is growing evidence that genetically heterogeneous tumors are remarkably difficult to treat 10 . The data presented here identifies a mechanism by which tumors maintain cell-tocell variability in the copy number and transcriptional level of oncogenes that drive tumor progression and drug resistance. We suggest that EC oncogene amplification may enable tumors to adapt more effectively to variable environmental conditions by increasing the likelihood that a subpopulation of cells will express that oncogene at a level that maximizes its proliferation and survival 12,21,[25][26][27][28] , rendering tumors progressively more aggressive and difficult to treat over time. Even when using a selection function that only mildly depends on copy number, we detected a very large difference between intra-and extrachromosomal amplification mechanisms leading to higher copy number of amplicons and greater heterogeneity of copy number. Thus, even small increases in selection advantage conferred by oncogenes amplified on ECDNA would be expected to yield a very high fitness advantage (Supplementary Material Section 4.3). The strikingly high frequency of ECDNA in cancer, as shown here, coupled to the benefits to tumors of EC gene amplification relative to chromosomal inheritance, suggest that oncogene amplification on ECDNA may be a driving force in tumor evolution and the development of genetic heterogeneity in human cancer. Understanding the underlying molecular mechanisms of tumor evolution, including oncogene amplification in ECDNA, may help to identify more effective treatments that either prevent cancer progression or more effectively eradicate it.

Cytogenetics
Metaphase cells were obtained by treating cells with Karyomax (Gibco) at a final concentration of 0.01 µg/ml for 1-3 hours. Cells were collected, washed in PBS, and resuspended in 0.075 M KCl for 15-30 minutes. Carnoy's fixative (3:1 methanol/glacial acetic acid) was added dropwise to stop the reaction. Cells were washed an additional 3 times with Carnoy's fixative, before being dropped onto humidified glass sides for metaphase cell preparations. For ECdetect analyses, DAPI was added to the slides. Images in the main figures were captured with an Olympus FV1000 confocal microscope. All other images were captured at a magnification of 1000 with an Olympus BX43 microscope equipped with a QiClick cooled camera. FISH was performed by adding the appropriate DNA FISH probe onto the fixed metaphase spreads. A coverslip was added and sealed with rubber cement. DNA denaturation was carried out at 75 °C for 3-5 minutes and the slides were allowed to hybridize overnight at 37 °C in a humidified chamber. Slides were subsequently washed in 0.4× SSC at 50 °C for 2 minutes, followed by a final wash in 2× SSC/0.05% Tween-20. Metaphase cells and interphase nuclei were counterstained with DAPI, a coverslip was applied, and images were captured.

Cell culture
The NCI-60 cell line panel (gift from Andrew Shiau-obtained from NCI) was grown in RPMI-1640 with 10% FBS under standard culture conditions. Cell lines were not authenticated, as they were obtained from the NCI. The PDX cell lines were cultured in DMEM/F-12 media supplemented with Glutamax, B27, EGF, FGF, and Heparin. Lymphoblastoid cells (gifts from Bing Ren) were grown in RPMI-1640, supplemented with 2 mM glutamine and 15% FBS. IMR90 and ALS6-Kin4 (gift from John Ravits and Don Cleveland) cells were grown in DMEM/F-12 supplemented with 20% FBS. Normal human astrocytes (NHA) and normal human dermal fibroblasts (NHDF) were obtained from Lonza and cultured according to Lonza-specific recommendation. Cell lines were not tested for mycoplasma contamination.

Tissue samples
Tissues were obtained from the Moores Cancer Center Biorepository Tissue Shared Resource with IRB approval (#090401). All samples were de-identified and patient consent was obtained. Additional tissue samples that were obtained were approved by the UCSD IRB (#120920).

DNA library preparation
DNA was sonicated to produce 300-500bp fragments. DNA end repair was performed using End-it (Epicentre), DNA library adapters (Illumina) were ligated, and the DNA libraries were amplified. Paired-end next generation sequencing was performed and samples were run on the Illumina Hi-Seq using 100 cycles.

DNA extraction
Cells were collected and washed with 1× cold PBS. Cell pellets were resuspended in Buffer

DNase treatment
Metaphase cells were dropped onto slides and visualized via DAPI. Coverslips were removed and slides washed in 2× SSC, and subsequently treated with 2.5% trypsin, and incubated at 25 °C for 3 minutes. Slides were then washed in 2× SSC, DNase solution (1 mg/ml) was applied to the slide, and cells were incubated at 37 °C for 3 hours. Slides were washed in 2× SSC and DAPI was again applied to the slide to visualize DNA.

ECDNA count statistics
In Figures 2A and 2B, the violin plots represent the distribution of ECDNA counts in different sample types. In order to compare the ECDNA counts between the different samples, we use a one-sided Wilcoxon rank sum test, where the null hypothesis assumes the mean ECDNA count ranks of the compared sample types equal.

Estimation of frequency of samples containing ECDNA
There is a wide variation in the number of ECDNA across different samples and within metaphases of the same sample. We want to estimate and compare the frequency of samples containing ECDNA for each sample type. We label a sample as being ECDNA-positive by using the pathology standard: a sample is deemed to be ECDNA-positive if we observe ≥ 2 ECDNA in ≥ 2 images out of 20 metaphase images. Therefore, we ensure that every sample contains at least 20 metaphases.
We define indicator variable X ij = 1 if metaphase image j in sample i has ≥ 2 ECDNA; X ij = 0 otherwise. Let n i be the number of metaphase images acquired from sample i. We assume that X ij is the outcome of the j th Bernoulli trial, where the probability of success p i is drawn at random from a beta distribution with parameters determined by ∑ j X ij . Formally, We model the likelihood of observing k successes in n = 20 trials using the binomial density function as: Finally, the predictive distribution p(k), is computed using the product of the Binomial likelihood and Beta prior, modeled as a "beta-binomial distribution" 29 .
We model the probability for sample i being ECDNA-positive with the random variable Y i such that: The expected value of Y i is: Let T be the set of samples belonging to a certain sample type t, e.g. immortalized samples.

We define
We estimate the frequency of samples under sample t containing ECDNA (bar heights on

Comparison of ECDNA presence between different sample types
We construct binary ECDNA presence distributions, based on the ECDNA counts, such that an image with ≥ 2 ECDNA is represented as a 1, and 0 otherwise. In order to compare the ECDNA presence between the different samples, we use a one-sided Wilcoxon rank sum test using the binary ECDNA presence distributions, where the null hypothesis assumes the mean ranks of the compared sample types equal.

ECdetect: Software for detection of extrachromosomal DNA from DAPI staining metaphase images
The software applies an initial coarse adaptive thresholding 30, 31 on the DAPI images to detect the major components in the image with a window size of 150×150 pixels, and T = 10% Components breaching 3000 pixels and 80% of solidity are masked, and small components discarded. Weakly connected components (CC) of the remaining binary image are computed to find the separate chromosomal regions. CC breaching a cumulative pixel count of 5000 are considered as candidate search regions, and their convex hull with a dilation of 100 pixels are added into the ECDNA search region. Following the manual masking and verification of the ECDNA search region, a second finer adaptive thresholding with a window size of 20×20 pixels and T = 7% is performed. Components that are greater than 75 pixels are designated as non-ECDNA structures and their 15 pixel neighborhood is removed from the ECDNA search region. Any component detected with a size less than or equal to 75 and greater than or equal to 3 pixels inside the search region is detected as ECDNA. For more detail, please see Supplementary Material Section 2.

Bioinformatic datasets
We sequenced 117 tumor samples including 63 cell lines, 19 neurospheres and 35 cancer tissues with coverage ranging from 0.6× to 3.89× and an additional 8 normal tissues as controls. See Extended Data Figure E4 for the coverage distribution across samples. We mapped the sequencing reads from each sample to hg19 (GRCh37) human reference genome 32 from UCSC genome browser 33 using BWA software version 0.7.9a 34 . We inferred an initial set of copy number variants from these mapped sequence samples using the ReadDepth CNV software 35 version 0.9.8.4 with parameters FDR = 0.05 and overDispersion = 1.
We downloaded copy number variation calls (CNV) for 11079 tumor-normal samples covering 33 different tumor types from TCGA. We applied similar filtering criteria to ReadDepth output and TCGA calls to eliminate false CN amplification calls from repetitive genomic regions and hotspots for mapping artefacts.
We used the filtered set of CNV calls from ReadDepth as input probes for AmpliconArchitect which revealed the final set of amplified intervals and the architectures of the amplicons. See Supplementary Material Section 3 for more details.

Reconstruction using AmpliconArchitect
We developed a novel tool AmpliconArchitect (AA), to automatically identify connected amplified genomic regions and reconstruct plausible amplicon architectures. For each sample, AA takes as input an initial list of amplified intervals and whole genome sequencing (WGS) paired-end reads aligned to the human reference. It implements the following steps to reconstruct the one or more architectures for each amplicon present in the sample: (a) Use discordant read-pair alignments and coverage information to iteratively visit and extend connected genomic regions with high copy numbers.

Comparison of CNV gains between the sequencing sample set and TCGA
We compared our sample set against TCGA samples to test the assumption that the genomic intervals amplified in our sample set are broadly representative of a pan-cancer dataset, by comparing against TCGA samples. Here, we deal with an abstract notation to represent different datasets and describe a generic procedure to compare amplified regions. Consider a set of K samples. For any k ∈ [1, …, K], let S k denote the set of amplified intervals in sample k.
Let c be the cancer subtype for sample k. We compare S k against TCGA samples with subtype c. Let T denote the set of all genomic regions which are amplified in at least 1% of TCGA samples of subtype c. For each interval t ∈ T, let f t denote its frequency in TCGA samples of subtype c. We define a match score The cumulative match score for all samples is defined as: To compute the significance of statistic D, we do a permutation test. We generate N random permutations of the TCGA intervals for subtype c and estimate distribution of match scores of our sample set against the random permutations. We choose a random assignment of locations of all intervals in T, while retaining their frequencies. For the j th permuted set T j , we computed the cumulative match score D j relative to our sample set. Thus the significance of overlap between our sample set and the TCGA amplified intervals is estimated by the fraction of random permutations with D j > D. Computing 1 million random permutations generated exactly one permutation breaching the TCGA score D, implying a p-value ≤ 10 −6 .

Oncogene Enrichment
We compared the rank correlation of the most frequent oncogenes in our sample set with the top oncogenes as reported by TCGA pan-cancer analysis by Zack et al 20 . We identified 14 oncogenes occurring in 2 or more samples of our sample set and compared these with the top 10 oncogenes from the TCGA pan-cancer analysis. We found that 7 out of the top 10 oncogenes were represented in our list of 14 oncogenes. Considering 490 oncogenes in the COSMIC database, the significance of observing 7 or more oncogenes in common in the two datasets is given by the hypergeometric probability

Amplicon structure similarity
We found high similarity between amplicon structures of biological replicates (e.g. Extended Data Figure E8). We estimate probability of common origin between two samples by measuring the pairwise similarity between amplicon structures. In reconstructing the structures (Supplementary Material Section 3), we identify a set of locations representing change in copy number and we use the locations of change in copy number to estimate the similarity in amplicon structures.
Let L be the total length of amplified intervals. These intervals are binned into windows of size r, resulting in N b = L/r bins. We use a segmentation algorithm that determines if there is a change in copy number in any bin, within a resolution of r = 10,000 bp. (See Meanshift in coverage: Supplementary Materials Section 3.2.) Note that this is an over-estimate, since with split-reads and high density sequencing data, we can often get the resolution down to a few base pairs. Let S 1 and S 2 represent the set of bins with copy number changes in the two samples, respectively. S 1 and S 2 are selected from a candidate set of locations N b . Under the null hypothesis that S 2 is random with respect to S 1 , we expect I = S1 ∩ S2 to be small. Let m = min{|S1|, |S2|}, and M = max{|S1|, |S2|}. A p-value is computed as follows: In looking at GBM39 replicates (Extended Data Figure E8), we find that all replicates displaying EGFR ECDNA are similar to each other. Comparing replicates in row 1 and row 2 among |N b | = 129 bins (1.29 Mbp), |S1| = 5 corresponding to row 1 (EC sample), |S2| = 6 corresponding to row 2 (EC sample) and intersection set size |I| = 5, we compute the p-value for observing such structural similarity by random chance is 2.18 × 10 −8 which is the highest p-value among all EC replicate pairs. In addition, we compare the replicates displaying EGFR on ECDNA with the culture displaying EGFR on HSR. Among |N b | = 129 bins, |S1| = 6 corresponding to row 2 (EC), |S2| = 4 corresponding to row 4 (HSR), the intersection set has size |I| = 4 intervals giving a p-value of 1.98 × 10 −5 which gives the highest p-value among the 3 ECDNA replicates compared to the HSR culture, suggesting a common origin.

A branching process model for oncogene amplification
Consider an initial population of N 0 cells, of which N α cells contain a single extra copy of an oncogene. We model the population using a discrete generation Galton-Watson branching process 23 . In this simplified model, each cell in the current generation containing k amplicons (amplifying an oncogene) either replicates with probability b k to create the next generation, or dies with probability 1 -b k to create the next generation. We set the selective advantage (1) In other words, cells with k copies of the amplicon stop dividing after reaching a limit of M α amplicons. Otherwise, they have a selective advantage for 0 < k ≤ M α , where the strength of selection is described by f m (k), as follows: Here, s denotes the selection-coefficient, and parameters m and α are the 'mid-point', and 'steepness' parameters of the logistic function, respectively. Initially, f m (k) grows linearly, reaching a peak value of f m (k) = 1 for k = M s . As the viability of cells with large number of amplicons is limited by available nutrition 36  The copy number change is affected by different mechanisms for extrachromosomal (EC) and intrachromosomal (HSR) models. In the EC model, the available k amplicons are on EC elements which replicate and segregate independently. We assume complete replication of EC elements so that there are 2k copies which are partitioned into the two daughter cells via independent segregation. Formally, the daughter cells end up with k 1 and k 2 amplicons respectively, where In contrast, in the intrachromosomal model, the change in copy number happens via mitotic recombination, and the daughter cell of a cell with k amplicons will acquire either k + 1 amplicons or k − 1 amplicons, each with probability p d . With probability 1 -2p d , the daughter cell retains k amplicons. See Supplementary Material Section 4 for more details.  Figure E1.

Extended Data
Full metaphase spreads corresponding to the partial metaphase spreads shown in Figure 1. a, Images corresponding to Fig. 1B, b, images corresponding to Fig. 1C, c, images corresponding to Fig. 1D. Full metaphase spreads corresponding to the partial metaphase spreads shown in Figure 3C.  . Erlotinib removed replicates show additional rearrangements and heterogeneity as compared to naive samples. c., Cytogenetic and sequencing progression suggests the EGFRvIII ECDNAs in naive cells get reintegrated into HSRs after drug application and the copies in the HSRs break off from the chromosomes again to form ECDNAs with copy count similar to naive cells. Drug removed samples also show additional heterogeneity in structure.