3D genomic organization in cancers

Abstract

Background: The hierarchical three-dimensional (3D) architectures of chromatin play an important role in fundamental biological processes, such as cell differentiation, cellular senescence, and transcriptional regulation. Aberrant chromatin 3D structural alterations often present in human diseases and even cancers, but their underlying mechanisms remain unclear.

Results: 3D chromatin structures (chromatin compartment A/B, topologically associated domains, and enhancer-promoter interactions) play key roles in cancer development, metastasis, and drug resistance. Bioinformatics techniques based on machine learning and deep learning have shown great potential in the study of 3D cancer genome.

Conclusion: Current advances in the study of the 3D cancer genome have expanded our understanding of the mechanisms underlying tumorigenesis and development. It will provide new insights into precise diagnosis and personalized treatment for cancers.

Graphical abstract

Keywords

the three-dimensional (3D) genome / chromatin compartment / topologically associated domain (TAD) / loop / cancer

References

[1] Siegel, R. L., Miller, K. D., Fuchs, H. E. (2021). Cancer statistics, 2021. CA Cancer J. Clin., 71: 7–33

[2] Hanahan, D. Weinberg, R. (2000). The hallmarks of cancer. Cell, 100: 57–70

[3] Hanahan, D. Weinberg, R. (2011). Hallmarks of cancer: the next generation. Cell, 144: 646–674

[4] Hanahan, D. (2022). Hallmarks of cancer: new dimensions. Cancer Discov., 12: 31–46

[5] Ouyang, W., Cao, Z., Xiong, D., Li, G. (2020). Decoding the plant genome: from epigenome to 3D organization. J. Genet. Genomics, 47: 425–435

[6] Feng, F., Yao, Y., Wang, X. Q. D., Zhang, X. (2022). Connecting high-resolution 3D chromatin organization with epigenomics. Nat. Commun., 13: 2054

[7] Rao, S. S., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S. . (2014). A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 159: 1665–1680

[8] Fullwood, M. J., Liu, M. H., Pan, Y. F., Liu, J., Xu, H., Mohamed, Y. B., Orlov, Y. L., Velkov, S., Ho, A., Mei, P. H. . (2009). An oestrogen-receptor-alpha-bound human chromatin interactome. Nature, 462: 58–64

[9] Mumbach, M. R., Rubin, A. J., Flynn, R. A., Dai, C., Khavari, P. A., Greenleaf, W. J. Chang, H. (2016). HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods, 13: 919–922

[10] Bickmore, W. A. (2013). Genome architecture: domain organization of interphase chromosomes. Cell, 152: 1270–1284

[11] Dekker, J. (2015). Long-range chromatin interactions. Cold Spring Harb. Perspect. Biol., 7: a019356

[12] Rowley, M. J. Corces, V. (2018). Organizational principles of 3D genome architecture. Nat. Rev. Genet., 19: 789–800

[13] Cremer, T. (2001). Chromosome territories, nuclear architecture and gene regulation in mammalian cells. Nat. Rev. Genet., 2: 292–301

[14] Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O. . (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 326: 289–293

[15] Wu, P., Li, T., Li, R., Jia, L., Zhu, P., Liu, Y., Chen, Q., Tang, D., Yu, Y. (2017). 3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations. Nat. Commun., 8: 1937

[16] Zheng, H. (2019). The role of 3D genome organization in development and cell differentiation. Nat. Rev. Mol. Cell Biol., 20: 535–550

[17] Guo, Y., Xu, Q., Canzio, D., Shou, J., Li, J., Gorkin, D. U., Jung, I., Wu, H., Zhai, Y., Tang, Y. . (2015). Crispr inversion of ctcf sites alters genome topology and enhancer/promoter function. Cell, 162: 900–910

[18] Schwarzer, W., Abdennur, N., Goloborodko, A., Pekowska, A., Fudenberg, G., Loe-Mie, Y., Fonseca, N. A., Huber, W., Haering, C. H., Mirny, L. . (2017). Two independent modes of chromatin organization revealed by cohesin removal. Nature, 551: 51–56

[19] Nora, E. P., Lajoie, B. R., Schulz, E. G., Giorgetti, L., Okamoto, I., Servant, N., Piolot, T., van Berkum, N. L., Meisig, J., Sedat, J. . (2012). Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature, 485: 381–385

[20] Misteli, T. (2009). The emerging role of nuclear architecture in DNA repair and genome maintenance. Nat. Rev. Mol. Cell Biol., 10: 243–254

[21] Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S. (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485: 376–380

[22] Rhie, S. K., Schreiner, S., Witt, H., Armoskus, C., Lay, F. D., Camarena, A., Spitsyna, V. N., Guo, Y., Berman, B. P., Evgrafov, O. V. . (2018). Using 3D epigenomic maps of primary olfactory neuronal cells from living individuals to understand gene regulation. Sci. Adv., 4: eaav8550

[23] Schoenfelder, S. (2019). Long-range enhancer-promoter contacts in gene expression control. Nat. Rev. Genet., 20: 437–455

[24] Deng, W., Lee, J., Wang, H., Miller, J., Reik, A., Gregory, P. D., Dean, A. Blobel, G. (2012). Controlling long-range genomic interactions at a native locus by targeted tethering of a looping factor. Cell, 149: 1233–1244

[25] Zakharova, V. V., Magnitov, M. D., Del Maestro, L., Ulianov, S. V., Glentis, A., Uyanik, B., Williart, A., Karpukhina, A., Demidov, O., Joliot, V. . (2022). SETDB1 fuels the lung cancer phenotype by modulating epigenome, 3D genome organization and chromatin mechanical properties. Nucleic Acids Res., 50: 4389–4413

[26] Li, M., Huang, H., Wang, B., Jiang, S., Guo, H., Zhu, L., Wu, S., Liu, J., Wang, L., Lan, X. . (2022). Comprehensive 3D epigenomic maps define limbal stem/progenitor cell function and identity. Nat. Commun., 13: 1293

[27] Stadhouders, R., Filion, G. J. (2019). Transcription factors and 3D genome conformation in cell-fate decisions. Nature, 569: 345–354

[28] Peng, A., Peng, W., Wang, R., Zhao, H., Yu, X. (2022). Regulation of 3D organization and its role in cancer biology. Front. Cell Dev. Biol., 10: 879465

[29] ez-Olvera, S. I., Puente-Rivera, J., rez-Plasencia, C., Salinas-Vera, Y. M., Aguilar-Arnal, L. (2021). Three-dimensional genome organization in breast and gynecological cancers: how chromatin folding influences tumorigenic transcriptional programs. Cells, 11: 75

[30] Ouimette, J. F., Rougeulle, C. Veitia, R. (2019). Three-dimensional genome architecture in health and disease. Clin. Genet., 95: 189–198

[31] Anania, C. ez, D. (2020). Order and disorder: abnormal 3D chromatin organization in human disease. Brief. Funct. Genomics, 19: 128–138

[32] Adeel, M. M., Jiang, H., Arega, Y., Cao, K., Lin, D., Cao, C., Cao, G., Wu, P. (2021). Structural variations of the 3D genome architecture in cervical cancer development. Front. Cell Dev. Biol., 9: 706375

[33] Tao, H., Li, H., Xu, K., Hong, H., Jiang, S., Du, G., Wang, J., Sun, Y., Huang, X., Ding, Y. . (2021). Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles. Brief. Bioinform., 22: 1–18

[34] MacKay, K. (2020). Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data. Brief. Funct. Genomics, 19: 292–308

[35] Haddad, N., Vaillant, C. (2017). IC-Finder: inferring robustly the hierarchical organization of chromatin folding. Nucleic Acids Res., 45: e81

[36] Lyu, H., Li, L., Wu, Z., Wang, T., Zheng, J. (2020). TADBD: a sensitive and fast method for detection of typologically associated domain boundaries. Biotechniques, 69: 376–383

[37] Belokopytova, P. S., Nuriddinov, M. A., Mozheiko, E. A., Fishman, D. (2020). Quantitative prediction of enhancer-promoter interactions. Genome Res., 30: 72–84

[38] Tang, L., Hill, M. C., Wang, J., Wang, J., Martin, J. F. (2020). Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model. Genome Res., 30: 1835–1845

[39] Tang, L., Zhong, Z., Lin, Y., Yang, Y., Wang, J., Martin, J. F. (2022). EPIXplorer: a web server for prediction, analysis and visualization of enhancer-promoter interactions. Nucleic Acids Res., 50: W290–W297

[40] Fang, K., Wang, J., Liu, L. Jin, V. (2022). Mapping nucleosome and chromatin architectures: a survey of computational methods. Comput. Struct. Biotechnol. J., 20: 3955–3962

[41] Barutcu, A. R., Lajoie, B. R., McCord, R. P., Tye, C. E., Hong, D., Messier, T. L., Browne, G., van Wijnen, A. J., Lian, J. B., Stein, J. L. . (2015). Chromatin interaction analysis reveals changes in small chromosome and telomere clustering between epithelial and breast cancer cells. Genome Biol., 16: 214

[42] Johnstone, S. E., Reyes, A., Qi, Y., Adriaens, C., Hegazi, E., Pelka, K., Chen, J. H., Zou, L. S., Drier, Y., Hecht, V. . (2020). Large-scale topological changes restrain malignant progression in colorectal cancer. Cell, 182: 1474–1489.e23

[43] Vilarrasa-Blasi, R., Soler-Vila, P., Verdaguer-Dot, N., ol, N., Di Stefano, M., Chapaprieta, V., Clot, G., Farabella, I., Kulis, M. . (2021). Dynamics of genome architecture and chromatin function during human B cell differentiation and neoplastic transformation. Nat. Commun., 12: 651

[44] Rada-Iglesias, A., Grosveld, F. G. (2018). Forces driving the three-dimensional folding of eukaryotic genomes. Mol. Syst. Biol., 14: e8214

[45] Gong, Y., Lazaris, C., Sakellaropoulos, T., Lozano, A., Kambadur, P., Ntziachristos, P., Aifantis, I. (2018). Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries. Nat. Commun., 9: 542

[46] Flavahan, W. A., Drier, Y., Liau, B. B., Gillespie, S. M., Venteicher, A. S., Stemmer-Rachamimov, A. O., Bernstein, B. (2016). Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature, 529: 110–114

[47] Kloetgen, A., Thandapani, P., Ntziachristos, P., Ghebrechristos, Y., Nomikou, S., Lazaris, C., Chen, X., Hu, H., Bakogianni, S., Wang, J. . (2020). Three-dimensional chromatin landscapes in T cell acute lymphoblastic leukemia. Nat. Genet., 52: 388–400

[48] Grabher, C., von Boehmer, H. Look, A. (2006). Notch 1 activation in the molecular pathogenesis of T-cell acute lymphoblastic leukaemia. Nat. Rev. Cancer, 6: 347–359

[49] Sanchez-Martin, M. (2017). The NOTCH1-MYC highway toward T-cell acute lymphoblastic leukemia. Blood, 129: 1124–1133

[50] Dietlein, F., Wang, A. B., Fagre, C., Tang, A., Besselink, N. J. M., Cuppen, E., Li, C., Sunyaev, S. R., Neal, J. T. Van Allen, E. (2022). Genome-wide analysis of somatic noncoding mutation patterns in cancer. Science, 376: eabg5601

[51] Akdemir, K. C., Le, V. T., Kim, J. M., Killcoyne, S., King, D. A., Lin, Y. P., Tian, Y., Inoue, A., Amin, S. B., Robinson, F. S. . (2020). Somatic mutation distributions in cancer genomes vary with three-dimensional chromatin structure. Nat. Genet., 52: 1178–1188

[52] Du, Y., Gu, Z., Li, Z., Yuan, Z., Zhao, Y., Zheng, X., Bo, X., Chen, H. (2022). Dynamic interplay between structural variations and 3D genome organization in pancreatic cancer. Adv. Sci. (Weinh.), 9: e2200818

[53] Phillips-Cremins, J. E., Sauria, M. E., Sanyal, A., Gerasimova, T. I., Lajoie, B. R., Bell, J. S., Ong, C. T., Hookway, T. A., Guo, C., Sun, Y. . (2013). Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell, 153: 1281–1295

[54] Schuetzmann, D., Walter, C., van Riel, B., Kruse, S., nig, T., Erdmann, T., nges, A., Bindels, E., Weilemann, A., Gebhard, C. . (2018). Temporal autoregulation during human PU. 1 locus SubTAD formation. Blood, 132: 2643–2655

[55] Desprez, P. Y., Krtolica, A. (2010). The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu. Rev. Pathol., 5: 99–118

[56] Zampetidis, C. P., Galanos, P., Angelopoulou, A., Zhu, Y., Polyzou, A., Karamitros, T., Kotsinas, A., Lagopati, N., Mourkioti, I., Mirzazadeh, R. . (2021). A recurrent chromosomal inversion suffices for driving escape from oncogene-induced senescence via subTAD reorganization. Mol. Cell, 81: 4907–4923.e8

[57] Yang, L., Chen, F., Zhu, H., Chen, Y., Dong, B., Shi, M., Wang, W., Jiang, Q., Zhang, L., Huang, X. . (2021). 3D genome alterations associated with dysregulated HOXA13 expression in high-risk T-lineage acute lymphoblastic leukemia. Nat. Commun., 12: 3708

[58] Montefiori, L. E., Bendig, S., Gu, Z., Chen, X., nen, P., Ma, X., Murison, A., Zeng, A., Garcia-Prat, L., Dickerson, K. . (2021). Enhancer hijacking drives oncogenic bcl11b expression in lineage-ambiguous stem cell leukemia. Cancer Discov., 11: 2846–2867

[59] Rhie, S. K., Perez, A. A., Lay, F. D., Schreiner, S., Shi, J., Polin, J. Farnham, P. (2019). A high-resolution 3D epigenomic map reveals insights into the creation of the prostate cancer transcriptome. Nat. Commun., 10: 4154

[60] Rheinbay, E., Nielsen, M. M., Abascal, F., Wala, J. A., Shapira, O., Tiao, G., Hess, J. M., Juul, R. I., Lin, Z. . (2020). Analyses of non-coding somatic drivers in 2658 cancer whole genomes. Nature, 578: 102–111

[61] Zhang, Y., Chen, F., Fonseca, N. A., He, Y., Fujita, M., Nakagawa, H., Zhang, Z., Brazma, A., Creighton, C. J., Group, P. S. V. W. . (2020). High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations. Nat. Commun., 11: 736

[62] Ahn, J. H., Davis, E. S., Daugird, T. A., Zhao, S., Quiroga, I. Y., Uryu, H., Li, J., Storey, A. J., Tsai, Y. H., Keeley, D. P. . (2021). Phase separation drives aberrant chromatin looping and cancer development. Nature, 595: 591–595

[63] Chu, Z., Gu, L., Hu, Y., Zhang, X., Li, M., Chen, J., Teng, D., Huang, M., Shen, C. H., Cai, L. . (2022). STAG2 regulates interferon signaling in melanoma via enhancer loop reprogramming. Nat. Commun., 13: 1859

[64] Durand, N. C., Robinson, J. T., Shamim, M. S., Machol, I., Mesirov, J. P., Lander, E. S. Aiden, E. (2016). Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst., 3: 99–101

[65] Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H. Glass, C. (2010). Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell, 38: 576–589

[66] Kruse, K., Hug, C. B. Vaquerizas, J. (2020). FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data. Genome Biol., 21: 303

[67] Zheng, X. (2018). CscoreTool: fast Hi-C compartment analysis at high resolution. Bioinformatics, 34: 1568–1570

[68] Xiong, K. (2019). Revealing Hi-C subcompartments by imputing inter-chromosomal chromatin interactions. Nat. Commun., 10: 5069

[69] Liu, Y., Nanni, L., Sungalee, S., Zufferey, M., Tavernari, D., Mina, M., Ceri, S., Oricchio, E. (2021). Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes. Nat. Commun., 12: 2439

[70] Magnitov, M. D., Garaev, A. K., Tyakht, A. V., Ulianov, S. V. Razin, S. (2022). Pentad: a tool for distance-dependent analysis of Hi-C interactions within and between chromatin compartments. BMC Bioinformatics, 23: 116

[71] Chen, F., Li, G., Zhang, M. Q. (2018). HiCDB: a sensitive and robust method for detecting contact domain boundaries. Nucleic Acids Res., 46: 11239–11250

[72] Zufferey, M., Tavernari, D., Oricchio, E. (2018). Comparison of computational methods for the identification of topologically associating domains. Genome Biol., 19: 217

[73] An, L., Yang, T., Yang, J., Nuebler, J., Xiang, G., Hardison, R. C., Li, Q. (2019). OnTAD: hierarchical domain structure reveals the divergence of activity among TADs and boundaries. Genome Biol., 20: 282

[74] Du, G., Li, H., Ding, Y., Jiang, S., Hong, H., Gan, J., Wang, L., Yang, Y., Li, Y., Huang, X. . (2021). The hierarchical folding dynamics of topologically associating domains are closely related to transcriptional abnormalities in cancers. Comput. Struct. Biotechnol. J., 19: 1684–1693

[75] Guo, D., Xie, Q., Jiang, S., Xie, T., Li, Y., Huang, X., Li, F., Wang, T., Sun, J., Wang, A. . (2021). Synergistic alterations in the multilevel chromatin structure anchor dysregulated genes in small cell lung cancer. Comput. Struct. Biotechnol. J., 19: 5946–5959

[76] Jiang, S., Li, H., Hong, H., Du, G., Huang, X., Sun, Y., Wang, J., Tao, H., Xu, K., Li, C. . (2021). Spatial density of open chromatin: an effective metric for the functional characterization of topologically associated domains. Brief. Bioinform., 22: 1–11

[77] Wang, X., Xu, J., Zhang, B., Hou, Y., Song, F., Lyu, H. (2021). Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes. Nat. Methods, 18: 661–668

[78] Iyyanki, T., Zhang, B., Wang, Q., Hou, Y., Jin, Q., Xu, J., Yang, H., Liu, T., Wang, X., Song, F. . (2021). Subtype-associated epigenomic landscape and 3D genome structure in bladder cancer. Genome Biol., 22: 105

[79] Wang, J., Huang, T. Y., Hou, Y., Bartom, E., Lu, X., Shilatifard, A., Yue, F. (2021). Epigenomic landscape and 3D genome structure in pediatric high-grade glioma. Sci. Adv., 7: eabg4126

[80] Cameron, C. J., Dostie, J. (2020). HIFI: estimating DNA-DNA interaction frequency from Hi-C data at restriction-fragment resolution. Genome Biol., 21: 11

[81] Ay, F., Bailey, T. L. Noble, W. (2014). Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res., 24: 999–1011

[82] Kaul, A., Bhattacharyya, S. (2020). Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2. Nat. Protoc., 15: 991–1012

[83] Zhang, Y., An, L., Xu, J., Zhang, B., Zheng, W. J., Hu, M., Tang, J. (2018). Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus. Nat. Commun., 9: 750

[84] Liu, T. (2019). HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data. Bioinformatics, 35: 4222–4228

[85] Liu, T. (2019). Hicnn2: enhancing the resolution of hi-c data using an ensemble of convolutional neural networks. Genes (Basel), 10: 862

[86] Liu, Q., Lv, H. (2019). hicGAN infers super resolution Hi-C data with generative adversarial networks. Bioinformatics, 35: i99–i107

[87] Hong, H., Jiang, S., Li, H., Du, G., Sun, Y., Tao, H., Quan, C., Zhao, C., Li, R., Li, W. . (2020). DeepHiC: a generative adversarial network for enhancing Hi-C data resolution. PLOS Comput. Biol., 16: e1007287

[88] Marusyk, A. (2010). Tumor heterogeneity: causes and consequences. Biochim. Biophys. Acta, 1805: 105–117

[89] Maston, G. A., Evans, S. K. Green, M. (2006). Transcriptional regulatory elements in the human genome. Annu. Rev. Genomics Hum. Genet., 7: 29–59

[90] Feng, Y., Liu, X. (2021). 3D chromatin architecture and epigenetic regulation in cancer stem cells. Protein Cell, 12: 440–454

[91] Nagano, T., Lubling, Y., Stevens, T. J., Schoenfelder, S., Yaffe, E., Dean, W., Laue, E. D., Tanay, A. (2013). Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature, 502: 59–64

[92] Mi, J. Li, A. Zhou, L. (2020). Review study of interpretation methods for future interpretable machine learning. IEEE Access, 8: 191969–191985

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