Chromosome segregation errors cause aneuploidy and genomic heterogeneity, which are hallmarks of cancer in humans. A persistent high frequency of these errors (chromosomal instability (CIN)) is predicted to profoundly impact tumor evolution and therapy response. It is unknown, however, how prevalent CIN is in human tumors. Using three-dimensional live-cell imaging of patient-derived tumor organoids (tumor PDOs), we show that CIN is widespread in colorectal carcinomas regardless of background genetic alterations, including microsatellite instability. Cell-fate tracking showed that, although mitotic errors are frequently followed by cell death, some tumor PDOs are largely insensitive to mitotic errors. Single-cell karyotype sequencing confirmed heterogeneity of copy number alterations in tumor PDOs and showed that monoclonal lines evolved novel karyotypes over time in vitro. We conclude that ongoing CIN is common in colorectal cancer organoids, and propose that CIN levels and the tolerance for mitotic errors shape aneuploidy landscapes and karyotype heterogeneity.
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The accession number for the single-cell and bulk whole-genome sequencing is PRJEB27084 (ENA repository).
Greaves, M. Evolutionary determinants of cancer. Cancer Discov. 5, 806–821 (2015).
Davis, A., Gao, R. & Navin, N. Tumor evolution: linear, branching, neutral or punctuated? Biochim. Biophys. Acta Rev. Cancer 1867, 151–161 (2017).
Swanton, C. Intratumor heterogeneity: evolution through space and time. Cancer Res. 72, 4875–4882 (2012).
Landau, D. A. et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).
Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).
Navin, N. E. Tumor evolution in response to chemotherapy: phenotype versus genotype. Cell Rep. 6, 417–419 (2014).
Gerlinger, M. & Swanton, C. How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br. J. Cancer 103, 1139–1143 (2010).
Kreso, A. et al. Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science 339, 543–548 (2013).
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).
Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).
Morrissy, A. S. et al. Divergent clonal selection dominates medulloblastoma at recurrence. Nature 529, 351–357 (2016).
Roylance, R. et al. Expression of regulators of mitotic fidelity are associated with intercellular heterogeneity and chromosomal instability in primary breast cancer. Breast Cancer Res. Treat. 148, 221–229 (2014).
Jamal-Hanjani, M. et al. Tracking the evolution of non–small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Kim, T. M. et al. Subclonal genomic architectures of primary and metastatic colorectal cancer based on intratumoral genetic heterogeneity. Clin. Cancer Res. 21, 4461–4472 (2015).
Gao, R. et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48, 1119–1130 (2016).
Morrissy, A. S. et al. Spatial heterogeneity in medulloblastoma. Nat. Genet. 49, 780–788 (2017).
Navin, N. E. Delineating cancer evolution with single-cell sequencing. Sci. Transl. Med. 7, 296fs29 (2015).
Navin, N. et al. Tumor evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).
van Jaarsveld, R. H. & Kops, G. J. P. L. Difference makers: chromosomal instability versus aneuploidy in cancer. Trends Cancer 2, 561–571 (2016).
Duijf, P. H. G., Schultz, N. & Benezra, R. Cancer cells preferentially lose small chromosomes. Int. J. Cancer 132, 2316–2326 (2013).
Mitelman, F., Johansson, B. & Mertens, F. Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (National Cancer Institute, 2018); http://cgap.nci.nih.gov/Chromosomes/Mitelman
Janssen, A., van der Burg, M., Szuhai, K., Kops, G. J. P. L. & Medema, R. H. Chromosome segregation errors as a cause of DNA damage and structural chromosome aberrations. Science 333, 1895–1898 (2011).
Crasta, K. et al. DNA breaks and chromosome pulverization from errors in mitosis. Nature 482, 53–58 (2013).
Zhang, C.-Z. et al. Chromothripsis from DNA damage in micronuclei. Nature 522, 179–184 (2015).
Ly, P. et al. Selective Y centromere inactivation triggers chromosome shattering in micronuclei and repair by non-homologous end joining. Nat. Cell Biol. 19, 68–75 (2017).
Lengauer, C., Kinzler, K. W. & Vogelstein, B. Genetic instability in colorectal cancers. Nature 386, 623–627 (1997).
Shih, I. et al. Evidence that genetic instability occurs at an early stage of colorectal tumorigenesis. Cancer Res. 61, 818–822 (2001).
Cho, K. R. & Vogelstein, B. Genetic alterations in the adenoma–carcinoma sequence. Cancer 70, 1727–1731 (1992).
Burrell, R. A. et al. Replication stress links structural and numerical cancer chromosomal instability. Nature 494, 492–496 (2013).
Solomon, D. A. et al. Mutational inactivation of STAG2 causes aneuploidy in human cancer. Science. 333, 1039–1043 (2011).
Thompson, S. L. & Compton, D. A. Examining the link between chromosomal instability and aneuploidy in human cells. J. Cell Biol. 180, 665–672 (2008).
Bakhoum, S. F. et al. The mitotic origin of chromosomal instability. Curr. Biol. 24, R148–R149 (2014).
Thompson, S. L., Bakhoum, S. F. & Compton, D. A. Mechanisms of chromosomal instability. Curr. Biol. 20, R285–R295 (2010).
Kops, G. J. P. L., Weaver, B. A. A. & Cleveland, D. W. On the road to cancer: aneuploidy and the mitotic checkpoint. Nat. Rev. Cancer 5, 773–785 (2005).
Sachs, N. & Clevers, H. Organoid cultures for the analysis of cancer phenotypes. Curr. Opin. Genet. Dev. 24, 68–73 (2014).
Knouse, K. A., Lopez, K. E., Bachofner, M. & Amon, A. Chromosome segregation fidelity in epithelia requires tissue architecture. Cell 175, 200–211 (2018).
Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).
Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011).
van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).
Broutier, L. et al. Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat. Med. 23, 1424–1435 (2017).
Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–382 (2017).
Fujii, M. et al. A colorectal tumor organoid library demonstrates progressive loss of niche factor requirements during tumorigenesis. Cell Stem Cell 18, 827–838 (2016).
Pauli, C. et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462–477 (2017).
Schütte, M. et al. Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors. Nat. Commun. 8, 14262 (2017).
Zhang, M. et al. Aneuploid embryonic stem cells exhibit impaired differentiation and increased neoplastic potential. EMBO J. 35, 2285–2300 (2016).
Drost, J. et al. Sequential cancer mutations in cultured human intestinal stem cells. Nature 521, 43–47 (2015).
Verissimo, C. S. et al. Targeting mutant RAS in patient-derived colorectal cancer organoids by combinatorial drug screening. eLife 5, e18489 (2016).
Bakker, B. et al. Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies. Genome Biol. 17, 115 (2016).
Dewhurst, S. M. et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 4, 175–185 (2014).
Buccitelli, C. et al. Pan-cancer analysis distinguishes transcriptional changes of aneuploidy from proliferation. Genome Res. 27, 501–511 (2017).
Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689 (2018).
The Cancer Genome Atlas Network Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).
Lengauer, C., Kinzler, K. W. & Vogelstein, B. Genetic instabilities in human cancers. Nature 396, 643–649 (1998).
Lu, Y. W. et al. Colorectal cancer genetic heterogeneity delineated by multi-region sequencing. PLoS ONE 11, e0152673 (2016).
Losi, L., Baisse, B., Bouzourene, H. & Benhattar, J. Evolution of intratumoral genetic heterogeneity during colorectal cancer progression. Carcinogenesis 26, 916–922 (2005).
Mamlouk, S. et al. DNA copy number changes define spatial patterns of heterogeneity in colorectal cancer. Nat. Commun. 8, 14093 (2017).
Roerink, S. F. et al. Intra-tumour diversification in colorectal cancer at the single-cell level. Nature 556, 457–462 (2018).
Sansregret, L., Vanhaesebroeck, B. & Swanton, C. Determinants and clinical implications of chromosomal instability in cancer. Nat. Rev. Clin. Oncol. 15, 139–150 (2018).
Schukken, K. M. & Foijer, F. CIN and aneuploidy: different concepts, different consequences. BioEssays 40, 1700147 (2018).
Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).
Gregan, J., Polakova, S., Zhang, L., Tolic-Nørrelykke, I. M. & Cimini, D. Merotelic kinetochore attachment: causes and effects. Trends Cell Biol. 21, 374–381 (2011).
Santaguida, S. et al. Chromosome mis-segregation generates cell-cycle-arrested cells with complex karyotypes that are eliminated by the immune system. Dev. Cell 41, 638–651 (2017).
Soto, M. et al. p53 prohibits propagation of chromosome segregation errors that produce structural aneuploidies. Cell Rep. 19, 2423–2431 (2017).
Bakhoum, S. F., Kabeche, L., Murnane, J. P., Zaki, B. I. & Compton, D. A. DNA-damage response during mitosis induces whole-chromosome missegregation. Cancer Discov. 4, 1281–1289 (2014).
López-García, C. et al. BCL9L dysfunction impairs caspase-2 expression permitting aneuploidy tolerance in colorectal cancer. Cancer Cell 31, 79–93 (2017).
Janssen, A., Kops, G. J. P. L. & Medema, R. H. Elevating the frequency of chromosome mis-segregation as a strategy to kill tumor cells. Proc. Natl Acad. Sci. USA 106, 19108–19113 (2009).
Shaner, N. C. et al. A bright monomeric green fluorescent protein derived from Branchiostoma lanceolatum. Nat. Methods 10, 407–409 (2013).
Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016).
We thank members of the Kops, Snippert and Clevers laboratories for reagents, suggestions and discussions; Y. Bollen and E. Stelloo for help with bulk genome sequencing; S. Sonneveld and F. Ferreira for help with R; and R. Wardenaar for the modifications on the Aneufinder algorithm. We are grateful to the Hubrecht Imaging Centre, particularly to A. Graaf, the Hubrecht Flow Cytometry facility, the USEQ Utrecht sequencing facility, and the ERIBA FACS and DNA sequencing facilities. This work is part of the Oncode Institute, which is partly financed by the Dutch Cancer Society, and was funded by the gravitation program CancerGenomiCs.nl from the Netherlands Organisation for Scientific Research (NWO), by a grant from the Dutch Cancer Society (KWF/HUBR-2015-7848), by a FP7-MSCA-ITN-2013 grant (PloidyNet, 607722), an NWO TOP grant (grant 91215003 to F.F.), an ERC starting grant (to H.J.G.S.) and by an Advanced Grant (to P.M.L.).
H.C. is inventor on several patents related to organoid technology. All other authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
(a) Overview of mutational status in the most commonly mutated genes in colorectal cancer present in tumor PDOs as determined in (van de Wetering, M. et al. Cell 161, 933–945, 2015). (b) Expression of mismatch repairs genes (MLH1, PMS2, MSH2 and MSH6) analyzed by immunohistochemistry. n = 1 independent experiment. Scale bar 100 μm.
(a) Chromosome numbers distribution of cells of healthy colon PDOs and tumor PDOs, as determined by metaphase spreads. n = 1, 2 or 3 independent experiments. Centre values are the median. (b) Bulk genome-wide copy number plots of two healthy colon PDOs show euploidy. n = 1 independent experiment. (c) Genome-wide single cell copy number plots (scKaryo-seq) of one healthy colon tissue. Each row represents a cell and the copy-number state (5Mb bins) is indicated in colors. (d) Genome-wide copy number profile of six single cells of one healthy colon tissue. Example of single cells described in (C). The copy-number state (5Mb bins) is indicated in colors.
Supplementary Figure 3 Impact of lentiviral infection on mitotic fidelity and CNAs in a colon PDO line.
(a) Quantification of segregation errors observed in a colon PDO line before infection (inf 0) and after infection (inf 1, 2, 3, representing independent infections). One of the organoid lines (inf 0) was grown in medium with Wnt (used in colon PDOs) and the three other organoid lines (inf 1,2,3) in medium without Wnt (used in tumor PDOs). n= 2 or 3 independent experiments. The data is represented as a box-and-whisker plot with the location of the whiskers based on the Tukey method. The boxes represent quartiles 2 and 3. The horizontal line in the box represents the median. The whiskers represent the lowest values within 1.5 times the interquartile range of the lower quartile (Q1-1.5*(Q3-Q1)), and the highest values within 1.5 times the interquartile range of the upper quartile (Q3+1.5*(Q3-Q1)). The circles are the data points which are more than 1.5 times the interquartile range from the end of a box. (b) Genome-wide copy number plots (scKaryo-seq) of early and late passage of a non-infected colon PDO line, and three independently infected H2B-Neon expressing organoid lines i.e. same lines as in A (inf0 grown in medium with Wnt and inf 1 and inf 2 in medium without Wnt). Each row represents a cell and the copy-number state (5Mb bins) is indicated in colors.
(a) Genome-wide copy number plots of two tumor PDOs that were independently H2B-Neon infected expressing tumor PDOs (9 and 9_2). Each row represents a cell and the copy-number state (2Mb bins) is indicated in colors. (b) Quantification of segregation errors observed in independently infected H2B-Neon expressing tumor PDOs. Alternative representation of data shown in Figure 2C. n = 2 or 3 independent experiments. Centre values are the mean. (c) Correspondence of our nomenclature to the one used in (Roerink, S. F. et al. Nature 556, 457–462, 2018) (d) Expression of mismatch repairs genes (MLH1, PMS2, MSH2 and MSH6) analyzed by immunohistochemistry. n = 1 independent experiment.
(a) Adjustment to the AneuFinder pipeline for calculating CNA heterogeneity. Transitions in copy number states can be shifted for several technical reasons (e.g. binning, limitations of the model, etc.). The positions of the transitions that are shared across different cells can therefore differ by one or more bins. This difference in position contributes to the overall heterogeneity of the sample. In order to avoid an overestimation of the heterogeneity we implemented a computation procedure (scripted in R) that shifts transitions to one shared position. For this purpose, the copy number transitions across different cells were clustered together when the transitions were within a fixed maximum distance from each other (5Mb). The transition in copy number was shifted to the middle position of all transitions within a cluster. (b) When a cell contained more than one transition within one cluster, the middle of the transitions was taken as the transition point of this cell. The states on both sides of this transition point were subsequently changed to the states of the flanking bins. (c) Segmentation and calculation of heterogeneity score. Consecutive bins with the same states in all of the cells were collapsed into segments. The heterogeneity score of each segment was calculated as the proportion of all pairwise combinations for which both cells have different copy number states. (d) The genome-wide heterogeneity score was calculated as a weighted average of the contributions of all the segments. The size of the segments was used as weight. (e) Genome-wide copy number plots of colon PDOs and tumor PDOs as generated using the Aneufinder algorithm. Each row represents a cell and the copy-number state of each whole chromosome (based on copy number state of the majority of bins) is indicated in colors. (f) Correlation plots of heterogeneity versus chromosomal instability (CIN) values per colon PDOs and tumor PDOs. Heterogeneity scores obtained in Figure S5E. n = 1 independent experiment. Mean CIN levels are derived from Figure 1C. Centre values are the mean and errors bars are SEM. n = 2 or 3 independent experiments.
(a) Quantification of single cell fate tracking per type of error and per tumor PDOs. Events were classified as cell division, cell death or survival without division. Graph shows the mean percentage of segregation errors observed in all cells per independent replicate. Graph is different representation of the data of Figure 5D. n = 2 or 3 independent experiments. (b) Quantification of single cell fate tracking. Daughter cells originating from the same mother cell were grouped. Mitotic events were classified as correct or erroneous mitoses and plots represent the respective frequencies of subsequent fate (cell division, cell death or survival without division). Graph shows the mean percentage of events observed in all cells per independent replicate. Different representation of data shown in Figure 5D. n = 2 or 3 independent experiments. (c) Multiple regression analysis of heterogeneity or aneuploidy versus the percentage of division after error (DAE, %) per tumor PDOs. Heterogeneity and aneuploidy scores obtained in 2Mb bins generated using a modified version of the Aneufinder algorithm from Figure 4. Mean CIN levels are derived from Figure 1C. Death and Growth values derived from Figure 5A. Different representation of data shown in Figure 5F. n = 1 (heterogeneity), 2 (DAE), 2 or 3 (CIN) or 3 (Growth and Death) independent experiments. Shapiro Wilk test to determine if sample is normal distributed. If p-value < α (0.05), H0: Y=b0 is rejected. Alternative hypothesis H1: Y=b0+b1X1+...+bpXp. We used right tail to check if the regression formula and parameters are statistically significant.
Supplementary Figure 7 Frequency and types of segregation errors of clones of tumor PDO 16T are stable over time.
(a) Quantification of segregation errors per organoid observed in tumor PDO 16T clones at three time points (3, 15, 24 weeks after generation of the monoclonal line). n= 1 independent experiment. The data is represented as a box-and-whisker plot with the location of the whiskers based on the Tukey method. The boxes represent quartiles 2 and 3. The horizontal line in the box represents the median. The whiskers represent the lowest values within 1.5 times the interquartile range of the lower quartile (Q1-1.5*(Q3-Q1)), and the highest values within 1.5 times the interquartile range of the upper quartile (Q3+1.5*(Q3-Q1)). The circles are the data points which are more than 1.5 times the interquartile range from the end of a box. (b) Quantification of types of segregation errors. Graph shows the mean segregation error per organoid. (c) Heterogeneity and aneuploidy scores in tumor PDO 16T clones at two time points (weeks 3 and 24) as determined by scKaryo-Seq.
Supplementary Figs. 1–7
Example of normal cell division. n = 2 or 3 independent experiments with similar results.
Example of erroneous division with a lagging chromosome. n = 2 or 3 independent experiments with similar results.
Example of multipolar division. n = 2 or 3 independent experiments with similar results.
Example of erroneous division with an anaphase bridge. n = 2 or 3 independent experiments with similar results.
Example of erroneous cell division with multiple laggings chromosomes. n = 2 or 3 independent experiments with similar results.
Example of erroneous cell division with one lagging chromosome and one anaphase bridge. n = 2 or 3 independent experiments with similar results.
Example of cell fate after cell division: correct mitosis followed by division. n = 2 or 3 independent experiments with similar results.
Example of cell fate after cell division: erroneous mitosis followed by division. n = 2 or 3 independent experiments with similar results.
Example of cell fate after cell division: erroneous mitosis followed by cell death. n = 2 or 3 independent experiments with similar results.
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Bolhaqueiro, A.C.F., Ponsioen, B., Bakker, B. et al. Ongoing chromosomal instability and karyotype evolution in human colorectal cancer organoids. Nat Genet 51, 824–834 (2019). https://doi.org/10.1038/s41588-019-0399-6
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