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Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers

Nature Genetics volume 52, pages 891–897 (2020)Cite this article

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Abstract

Extrachromosomal DNA (ecDNA) amplification promotes intratumoral genetic heterogeneity and accelerated tumor evolution1,2,3; however, its frequency and clinical impact are unclear. Using computational analysis of whole-genome sequencing data from 3,212 cancer patients, we show that ecDNA amplification frequently occurs in most cancer types but not in blood or normal tissue. Oncogenes were highly enriched on amplified ecDNA, and the most common recurrent oncogene amplifications arose on ecDNA. EcDNA amplifications resulted in higher levels of oncogene transcription compared to copy number-matched linear DNA, coupled with enhanced chromatin accessibility, and more frequently resulted in transcript fusions. Patients whose cancers carried ecDNA had significantly shorter survival, even when controlled for tissue type, than patients whose cancers were not driven by ecDNA-based oncogene amplification. The results presented here demonstrate that ecDNA-based oncogene amplification is common in cancer, is different from chromosomal amplification and drives poor outcome for patients across many cancer types.

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Fig. 1: Frequency of circular amplification across tumor and nontumor tissues.
Fig. 2: Oncogene content and structural component of circular amplification.
Fig. 3: Gene expression and chromatin accessibility of amplicon classes.
Fig. 4: Presence of circular amplification is associated with poor outcomes.

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Data availability

Information on accessing the data from the ICGC, including raw read files, can be found at https://docs.icgc.org/pcawg/data/. All open access TCGA data are publicly available through the National Cancer Institute Genomic Data Commons (https://gdc.cancer.gov/). The datasets marked ‘Controlled’ contain potentially identifiable information and require authorization from the ICGC and TCGA Data Access Committees. In accordance with the data access policies of the ICGC and TCGA projects, most molecular, clinical and specimen data are in an open tier that does not require access approval. To access sequencing data, researchers need to apply to the TCGA Data Access Committee via the database of Genotypes and Phenotypes (https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?page=login) for access to the TCGA portion of the dataset and to the ICGC Data Access Compliance Office (http://icgc.org/daco) for the ICGC portion. All images analyzed are available from figshare at https://figshare.com/s/6c3e2edc1ab299bb2fa0 and https://figshare.com/s/ab6a214738aa43833391.

Code availability

AmpliconArchitect is available at https://github.com/virajbdeshpande/AmpliconArchitect. EcSeg is available at https://github.com/UCRajkumar/ecSeg.

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Acknowledgements

This work was supported by the Ludwig Institute for Cancer Research (P.S.M.), Defeat GBM Program of the National Brain Tumor Society (P.S.M.), NVIDIA Foundation, Compute for the Cure (P.S.M.), Ben and Catherine Ivy Foundation (P.S.M.), generous donations from the Ziering Family Foundation in memory of Sigi Ziering (P.S.M.) and Ruth L. Kirschstein National Research Service Award. This work was also supported by the following National Institutes of Health grants: NS73831 (to P.S.M.), GM114362 (to V.B.), R01 CA190121, R01 CA237208 and R21 NS114873. This work was supported by Cancer Center Support Grant P30 CA034196 (R.G.W.V), grant nos. R35CA209919 (to H.Y.C.) and RM1-HG007735 (to H.Y.C.), R35GM133600 (to C.R.B.), National Science Foundation grant nos. NSF-IIS-1318386 and NSF-DBI-1458557 (to V.B.), and grants from the Musella Foundation, B*CURED Foundation, Brain Tumour Charity and Department of Defense grant no. W81XWH1910246 (to R.G.W.V). H.Y.C. is an Investigator of the Howard Hughes Medical Institute. The results published in this paper are in whole or part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga) and the International Cancer Genome Consortium (https://icgc.org/). Analysis of the TCGA and International Cancer Genome Consortium datasets was made possible through the Cancer Genomics Cloud of the Institute for Systems Biology (ISB-CGC) and the Amazon Web Services Cloud, respectively.

Author information

Author notes

  1. Nam-Phuong Nguyen & Kristen Turner

    Present address: Boundless Bio, La Jolla, CA, USA

  2. Viraj Deshpande

    Present address: Illumina, San Diego, CA, USA

  3. These authors contributed equally: Hoon Kim, Nam-Phuong Nguyen.

Authors and Affiliations

  1. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    Hoon Kim, Amit D. Gujar, Jihe Liu, Sandeep Namburi, Samirkumar B. Amin, Eunhee Yi, Francesca Menghi, Christine R. Beck & Roel G. W. Verhaak

  2. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA

    Nam-Phuong Nguyen, Jens Luebeck, Viraj Deshpande, Utkrisht Rajkumar & Vineet Bafna

  3. Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA

    Kristen Turner, Sihan Wu & Paul S. Mischel

  4. Bioinformatics & Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA

    Jens Luebeck

  5. Department of Pediatric Hematology and Oncology, Charité-Universitätsmedizin Berlin, Berlin, Germany

    Johannes H. Schulte & Anton G. Henssen

  6. Berlin Institute of Health, Berlin, Germany

    Johannes H. Schulte & Anton G. Henssen

  7. Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Berlin, Germany

    Anton G. Henssen

  8. Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA

    Howard Y. Chang

  9. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

    Howard Y. Chang

  10. Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, USA

    Christine R. Beck

  11. Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA

    Paul S. Mischel

  12. Department of Pathology, University of California, San Diego, San Diego, CA, USA

    Paul S. Mischel

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  1. Hoon Kim
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Contributions

H.K., N.P.N., P.S.M., V.B. and R.G.W.V. conceived the study and designed the experiments. Data analysis was led by H.K. and N.P.N. in collaboration with S.W., J. Luebeck, V.D., S.N., S.B.A., F.M., U.R., H.Y.C., E.Y. and C.R.B. Cloud data access was performed by H.K. and S.N. The FISH experiments were performed by K.T., S.W., E.Y. and A.D.G. EcSeg was performed by U.R. and J. Liu. The CIRCLE-seq data were provided by J.H.S. and A.G.H. H.K., N.P.N., P.S.M., V.B. and R.G.W.V. wrote the manuscript. E.Y. reviewed the manuscript. All coauthors discussed the results and commented on the manuscript and the supplementary information.

Corresponding authors

Correspondence to Paul S. Mischel, Vineet Bafna or Roel G. W. Verhaak.

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Competing interests

H.Y.C., P.S.M., V.B. and R.G.W.V. are scientific cofounders of Boundless Bio and serve as consultants. V.B. is a cofounder and has equity interest in Digital Proteomics, and receives income from Digital Proteomics. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. N.P.N. and K.T. are employees of Boundless Bio.

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Extended data

Extended Data Fig. 1 Amplicon classification.

a. Validation on cell line data. Validation of the classification scheme on cell line data with FISH experiments for detecting ecDNA from the Turner et al. and deCarvalho et al. studies, in addition to newly generated data. FISH probes were designed for selected oncogenes and DAPI staining was performed to determine whether the FISH probe landed on chromosomal DNA or ecDNA. For each cell (represented as an image of the cell in metaphase), the number of positive ecDNA probes were counted, and for each cell line, the average positive ecDNA per cell was reported. For each probe, we report whether it landed in an amplicon (inferred from AmpliconArchitect), and if so, what was the amplicon’s classification. The distribution for the average ecDNA per cell between the Circular and non-circular classes was statistically significantly different (p-value < 1e-9; Wilcoxon rank sum test). b–d. Whole-genome sequencing derived based Circular amplicon regions (blue) were validated with Circle-seq (red) for three neuroblastoma samples (CB2001, CB2022, and CB2050, respectively) used in the Koche et al. study.

Extended Data Fig. 2 Circular vs amplified non-circular amplification comparisons.

a. 24 recurrently amplified oncogenes significantly overlap circular regions (z-score 37.8), especially compared to amplified non-circular regions (z-scores of 30.4, 29.5, 28.0 for Linear, Heavily-rearranged, and BFB). b. For all oncogenes on amplicons with copy number >= 4 and present in at least 5 samples across the cohort, we show the class distribution of that oncogene. The oncogenes are ordered by proportion on circular amplification. c. For the 24 recurrent oncogenes known to be activated via amplification (Zack et al. Nat Gen. 2013), we report the average copy number for the oncogenes for circular amplification versus amplified-noncircular amplification. d. Breakpoint location across all samples for each recurrently amplified oncogene. We identified all breakpoints from each sample containing the recurrent oncogene on ecDNA and report the total number of breakpoints across this region in 1kb binned windows. e. Distribution of breakpoint locations across all circular samples for each recurrently amplified oncogene. We identified all breakpoints from each sample containing the recurrent oncogene on ecDNA. Shown is the distribution of the number of breakpoints in each bin, which closely follows a Poisson distribution, suggesting that the breakpoints are mostly randomly distributed across the region.

Extended Data Fig. 3 Genome instability vs amplicon classes.

a. Chromosome arm aneuploidy scores showing no or marginal difference in chromosomal arm level events between circular and non-circular amplification classes. b. Genome doubling events by amplification class. c. Distribution for total DNA loss segments by amplification class. WGS-inferred CNV data was used to count the total number of DNA losses within a sample. A DNA loss was defined as a segment with CN < 2. d. Distribution for total DNA gain segments by amplification class. WGS-inferred CNV data was used to count the total number of DNA gains within a sample. A DNA gain was defined as a segment with CN > 2. Circular samples contain statistically significantly more DNA gains than BFB, Heavily-rearranged, Linear, and No-fSCNA (p-value <0.03, <0.03, <1e-20, and <1e-111, respectively; Wilcox Rank Sum Test). e. Breakpoint homology by amplification class. f. Comparison of amplicon versus locus-level chromothripsis (Pearson’s Chi-squared test data: X-squared = 4674.7, df = 3, p-value < 2.2e-16). g. Comparison of sample category versus sample-level chromothripsis (Pearson’s Chi-squared test data: X-squared = 21.58, df = 3, p-value 8e-05 (excludes ‘No fSCNA detected’ category)). h. Comparison of sample category versus sample-level tandem duplication (Pearson’s Chi-squared test data: X-squared = 7.39, df = 3, p-value 0.06 (excludes ‘No fSCNA detected’ category)).

Extended Data Fig. 4 Gene expression of amplicon classes.

Copy number of the oncogene versus its fold-change in FPKM for all oncogenes with a copy count greater than 4, for each oncogene on each amplicon. The fold-change in FPKM is computed as the oncogene’s (FPKM-UQ+1) divided by the average of (FPKM-UQ+1) for the same oncogene in all other tumor samples from the same cohort for which the oncogene is not on any amplicon (that is, not amplified). Linear regression lines, using fold change = m*CNV+b where m and b are selected to minimize error of the fit, are shown for each class. Tukey’s range test shows oncogenes on circular structures are significantly different to oncogenes on non-circular structures (p-value < 1e-7).

Extended Data Fig. 5 Lymph node stage vs amplicon classes.

Lymph node stage for primary tumors showing samples with amplification are more likely to have spread to the lymph node at time of diagnosis (Chi-square test; df=4; p-value<1e−05).

Extended Data Fig. 6 Cell cycle and immune infiltrate gene expression signatures vs amplicon classes.

a. Cell Cycle gene expression signature single sample GSEA (ssGSEA) scores by amplification category. b. Immune infiltrate gene expression signature single sample GSEA (ssGSEA) scores by amplification category.

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Kim, H., Nguyen, NP., Turner, K. et al. Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers. Nat Genet 52, 891–897 (2020). https://doi.org/10.1038/s41588-020-0678-2

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