Christine J. Ye1*
Jason Chen
Henry H. Heng- 1The Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
- 2Center for Molecular Medicine and Genomics, Wayne State University School of Medicine, Detroit, MI, United States
- 3Department of Pathology, Wayne State University School of Medicine, Detroit, MI, United States
Background
Somatic genomic mosaicism occurs when somatic cells of the body display different genotypes (Table 1), it has recently received increased attention because of its implications in disease, including neurodegenerative diseases and Down syndrome (Iourov et al., 2008, 2010, 2019; Biesecker and Spinner, 2013; Hultén et al., 2013; Vijg, 2014; Campbell et al., 2015; Fernández et al., 2016). Genomic mosaicism also contributes to high levels of cellular heterogeneity in pathological conditions, which is a distinguishing feature of cancer (Heng, 2015, 2019). In fact, high heterogeneity in cancer represents an extreme example of genomic mosaicism.
Table 1. Definitions/terminologies.
The genomic basis of somatic genomic mosaicism, however, remains to be elucidated. Traditional explanations have focused on defective cellular processes, including imperfect DNA replication and repair, abnormal chromosomal machinery, and a faulty stress response to environmental challenges. As illustrated by the evolutionary mechanism of cancer (Ye et al., 2009), nearly all molecular pathways/mechanisms can contribute to variations in cellular systems. The conventional wisdom is that biosystems are not perfect and that error-generating opportunities exist. Thus, the major goals of molecular medicine have been to detect and fix these errors.
Nevertheless, bioerrors (or imperfect-biosystems) do not explain the high degree of genomic mosaicism revealed by large-scale -omics technologies (Vattathil and Scheet, 2016), and plausible mechanisms are not yet revealed (Heng et al., 2016). These novel mechanisms should address (a) both the positive and negative contributions of cellular heterogeneity in normal and disease conditions and (b) the survival strategy of cancer cells to drastically elevate the level of heterogeneity in crisis conditions. Using multiple myeloma (MM) as an example, these mechanisms will be examined in the context of bio-information, adaptive systems (Table 1), and emergent behavior during cancer evolution.
A High Degree of Somatic Genomic Mosaicism, A Necessary and Sufficient Condition for Evolution, is Common in MM
MM patients display a high level of karyotype heterogeneity. Different patient genotypes can involve poly-aneuploidy, hyperdiploidy, hypodiploidy, chromosomal translocation, chaotic genomes (such as chromothripsis) (Table 1), and/or a combination of other gene mutations and chromosomal aberrations (Garcia-Sanz et al., 1995; Avet-Loiseau et al., 2007; Klein et al., 2011; Magrangeas et al., 2011; Keats et al., 2012; Bolli et al., 2014; Lee et al., 2017; Kaur et al., 2018; Smetana et al., 2018; Ashby et al., 2019; Maura et al., 2019).
Four key realizations from the Genome Theory (Table 1) can explain why such karyotype heterogeneity is observed in MM patients:
(1) Karyotype changes lead to new genomic information packages. According to the Genome Theory, the karyotype codes “system inheritance” (the genomic blueprint), while the genes code for “parts inheritance” (Table 1) (Ye et al., 2019b). Specifically, karyotype coding ensures the order of genes and other DNA sequences along and among chromosomes for a given species.
Karyotype coding changes can replace the function of a specific gene (Rancati et al., 2008) and impact global gene interaction, leading to new genome systems (Stevens et al., 2013, 2014). In MM, unique gene expression patterns are associated with recurrent chromosomal translocation and ploidy (Zhou et al., 2009). A recent cancer genome analysis has illustrated that the profile of chromosome aberrations is much more useful than gene mutation profiles when correlated with clinical outcomes either as prognostic or predictive markers (Davoli et al., 2017; Jamal-Hanjani et al., 2017). This result was also confirmed in MM, as karyotypic events have a stronger impact on prognosis than mutations (Bolli et al., 2018). In fact, chromosomal profiles have extensively been associated with prognosis in MM, based on specific translocation, hyperdiploidy, chromosomal amplification/deletion, and chromosomal copy number abnormalities (Garcia-Sanz et al., 1995; Avet-Loiseau et al., 2007, 2009; Walker et al., 2010; Shah et al., 2018). By converting DNA sequence data into aneuploidy data, we showed that the status of aneuploidy can suggest clinical MM outcomes (Ye et al., 2019a).
(2) Cancer often represents an evolutionary trade-off of cellular variation-mediated function. Since genomic variations are needed for cellular adaptation, and many essential bioprocesses often can generate harmful byproducts, genomic variations seem unavoidable. For example, normal B-cell development (affinity maturation in the germinal center) and antibody generation require somatic hypermutation and class-switch recombination. However, these key processes also generate DNA breaks and chromosomal translocations, which are central characteristics of MM (Manier et al., 2017). This represents an immune system trade-off: performing immune functions comes with the risk of malignant transformation [via translocation of cancer genes into immunoglobulin (Ig) loci and/or new karyotype formation] (Gonzalez et al., 2007).
(3) Even though heterogeneity has growth disadvantages (including in cancer), being highly heterogeneous is the winning strategy for most cancers. Genome chaos is essential for population survival under crises, even though it is extremely expensive due to the massive death and often slow growth of the cell population. The key is to create new survivable genomes (through macro-cellular-evolution) (Table 1), after which relatively homogenous growth will soon follow with the help of oncogenes in a stochastic fashion (through micro-cellular-evolution) (Ye et al., 2018; Heng, 2019). This principle is used to develop an MM model by synthesizing new patterns of clonal evolution as well as sequencing data (Manier et al., 2017; Maura et al., 2019; Ye et al., 2019d).
(4) The only way for a new system to emerge is to break the constraints above that system (e.g., cellular competition, tissue organization, immuno-systems, and chemo-drugs). In general, different genome systems are required to break different types of constraints (e.g., different karyotypes are involved during different stages of cancer evolution). It is also difficult for any new genome to become dominant. This high level of aberrated genomes therefore become a sufficient condition for cancer evolution.
In addition to the karyotypic level of mosaicism discussed, different types of somatic mosaicism include copy number variations (CNVs) (Walker et al., 2010, 2015; Lohr et al., 2014; Bolli et al., 2018; Aktas Samur et al., 2019), gene mutations (both driver and passenger) (Chapman et al., 2011; Egan et al., 2012; Keats et al., 2012; Bolli et al., 2014, 2018; Lohr et al., 2014; Walker et al., 2015), and non-genetic variations (e.g., epigenetic variations) (Huang, 2009; Heng, 2019). Together, the multiple levels of genetic variation represent the high degree of somatic genomic mosaicism in MM.
The Main Mechanism of Somatic Genomic Mosaicism is “Fuzzy Inheritance” Which is Coded by Living Systems to Adapt to Microenvironmental Dynamics
Cellular heterogeneity has biological significance and genomic basis. Essential cellular heterogeneity is ensured by fuzzy inheritance, a key component of the self-regulating features in bio-adaptive systems. Specifically, heterogeneity is encoded by the genome and realized by genotype-environment interaction (even though bio-errors can also contribute).
Under classical inheritance theory, the gene codes for a fixed or defined genotype, while the environment can influence the real phenotype. For complex polygenic traits, many individuals are needed to illustrate the mode of inheritance. Unfortunately, as shown by the effort of the genome-wide association studies, the multiple genes that contribute to a polygenic trait are hard to identify despite huge sample sizes used. Many loci are involved, and each only contributes to a tiny portion of the phenotype.
To solve this confusion, the new concept of fuzzy inheritance was proposed: genes and chromosomes code for a potential range or spectrum of phenotypes, and the environment serves as a selective “scanner” to “choose” a specific phenotype among the many defined by the genotype (Heng, 2015, 2019). Although the environment plays an important role in phenotypic selection, it is limited by the range established by the inherited genotype: the ultimate phenotype can only be selected from that range. Since diseases are variable phenotypes defined by the interaction between genomic information and environment (Heng et al., 2016), a normal gene can produce a disease phenotype, and disease-associated gene mutations can display a normal phenotype, depending on the environment.
Interestingly, fuzzy inheritance and dynamic environmental interaction will likely be responsible for the majority of phenotypic plasticity. Given the importance of the microenvironment in MM, the role of fuzzy inheritance in cancer evolution should be a top research priority.
The Importance of Somatic Genomic Mosaicism for New Emergent Genomes
Cellular heterogeneity can alter emergent properties, and cells that diverge from the average population—outliers—often define the direction of cancer evolution (Heng, 2015, 2019). However, cancer researchers have traditionally ignored the contribution of outliers and focused solely on average profiles or dominant clones. Under normal developmental or physiological conditions, this approach may work (although one must note that, even under normal conditions, the 80/20 principle where about 80% of the effects come from 20% of the causes can still play a role). However, under pathological conditions, especially under cellular crisis conditions, some outliers, such as cells with extremely different phenotypes, often become the dominant population. The general conditions for tipping the balance include new altered genomes that favor survival, environmental constraint, and status of the mosaicism. Interestingly, under the right conditions, even a slight change can trigger the tipping point. For example, when the proportion of outliers in the cellular population changes, even in the range of a few percent, an evolutionary phase transition can occur. Such tipping-point system behavior significantly increases the success of cancer evolution when high heterogeneity exists in the cellular population (Maura et al., 2019). When combined with the difference in initial conditions, cellular heterogeneity makes it very hard to predict the outcomes for most cancer cases.
Equally important, since different subpopulations can be molecularly profiled, especially after becoming dominant clones, a huge number of molecular mechanisms can be characterized. Data from recent studies illustrate diverse genetic variations in MM disease evolution (Egan et al., 2012; Keats et al., 2012; Bolli et al., 2014, 2018; Pawlyn and Morgan, 2017; Aktas Samur et al., 2019; Maura et al., 2019). A better way to understand MM is to study the evolutionary mechanism of cancer (Ye et al., 2009), rather than continue identifying individual molecular mechanisms: when there are so many, the clinical prediction of any single mechanism is low due to highly dynamic evolutionary processes.
The Clinical Implications of Genomic Somatic Mosaicism and System Constraint
First, it is important to identify the phase of evolution before initiating or changing treatment. Since different types of inheritance are directly related to micro- and macro-somatic evolution, and all cancer phase transitions are defined by macrocellular evolution, the selection of new systems is significantly different from selection on individual genes, especially since the function of any individual gene is influenced by its genomic context. The relationship between disease progression (from MGUS, smoldering MM to active MM) and evolutionary pattern (micro-and macro-somatic evolution) of MM remains to be determined. This will guide when and how to intervene at different stages of the disease in different subpopulations of patients (Table 1).
Applying somatic mosaicism in the clinic represents a new approach. On the surface, it is challenging to directly target mosaicism compared to a molecular pathway. However, this seeming disadvantage is actually an advantage when dealing with adaptive systems in which many pathways are involved (e.g., when the causative role for any pathogenic effect is difficult to elucidate and therapies can lead to toxicity and/or secondary malignancies).
In the case of MM: it is worthwhile to investigate whether asymptomatic patients at the stage of smoldering MM can be distinguished by mosaicism. Of course, it is also possible that this clinical challenge will remain even after analyzing evolutionary profiles. Only future investigations will tell.
Second, the stability of higher systems above cancer cells, i.e., the broader microenvironment, organ system, and immune system, can be applied to constrain cancer evolution by slowing down or stabilizing the specific phase of evolution. As all medical treatment can function as cellular stress that may alter the system's evolutionary dynamics (Kultz, 2005; Horne et al., 2014), caution is crucial when weighing the impact of treatment in the context of evolution. For example, within the stable micro-evolutionary phase, moderately treating cells is a better approach than maximal killing, as an over-killing strategy will trigger genome chaos, leading to rapid drug resistance (Heng, 2015, 2019). MM resistance is frequently associated with chromothripsis (Lee et al., 2017) and likely involves treatment-induced genome chaos. Thus, therapies using an adaptive strategy might confer better long-term benefits (Gatenby et al., 2009; Lohr et al., 2014). So far, clinical trials using adaptive strategies in MM treatment (moderate dosage and treatment schedule) have been explored and likely to yield better clinical outcomes (Ye et al., 2019c). On the other hand, instead of putting stress or therapeutic pressure directly on cancer cells, using immunotherapy to modulate the cancer microenvironment (to enhance immune cytotoxic effects and system constraint) is an attractive strategy.
Author Contributions
CY and HH drafted the manuscript. JC and GL participated in the discussion, literature search, and editing of the manuscript.
Funding
This work was partially supported by the start-up fund for CY from the University of Michigan's Department of Internal Medicine, Hematology/Oncology Division.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
We thank Julie Heng and Jessica Mercer for editing the manuscript.
References
Aktas Samur, A., Minvielle, S., Shammas, M., Fulciniti, M., Magrangeas, F., Richardson, P. G., et al. (2019). Deciphering the chronology of copy number alterations in Multiple Myeloma. Blood Cancer J. 9:39. doi: 10.1038/s41408-019-0199-3
Ashby, C., Boyle, E. M., Walker, B. A., Bauer, M. A., Ryan, K. R., Dent, J., et al. (2019). Chromoplexy and chromothripsis are important prognostically in myeloma and deregulate gene function by a range of mechanisms. Blood 134, 3767–3767. doi: 10.1182/blood-2019-130335
Avet-Loiseau, H., Attal, M., Moreau, P., Charbonnel, C., Garban, F., Hulin, C., et al. (2007). Genetic abnormalities and survival in multiple myeloma: the experience of the Intergroupe Francophone du Myélome. Blood 109, 3489–3495. doi: 10.1182/blood-2006-08-040410
Avet-Loiseau, H., Li, C., Magrangeas, F., Gouraud, W., Charbonnel, C., Harousseau, J. L., et al. (2009). Prognostic significance of copy-number alterations in multiple myeloma. J. Clin. Oncol. 27, 4585–4590. doi: 10.1200/jco.2008.20.6136
Biesecker, L. G., and Spinner, N. B. (2013). A genomic view of mosaicism and human disease. Nat. Rev. Genet. 14, 307–320. doi: 10.1038/nrg3424
Bolli, N., Avet-Loiseau, H., Wedge, D. C., Van Loo, P., Alexandrov, L. B., Martincorena, I., et al. (2014). Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 5:2997. doi: 10.1038/ncomms3997
Bolli, N., Biancon, G., Moarii, M., Gimondi, S., Li, Y., de Philippis, C., et al. (2018). Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia 32, 2604–2616. doi: 10.1038/s41375-018-0037-9
Campbell, I. M., Shaw, C. A., Stankiewicz, P., and Lupski, J. R. (2015). Somatic mosaicism: implications for disease and transmission genetics. Trends Genet. 31, 382–392. doi: 10.1016/j.tig.2015.03.013
Chapman, M. A., Lawrence, M. S., Keats, J. J., Cibulskis, K., Sougnez, C., Schinzel, A. C., et al. (2011). Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467–472. doi: 10.1038/nature09837
Davoli, T., Uno, H., Wooten, E. C., and Elledge, S. J. (2017). Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science. 355:eaaf8399. doi: 10.1126/science.aaf8399
Egan, J. B., Shi, C. X., Tembe, W., Christoforides, A., Kurdoglu, A., Sinari, S., et al. (2012). Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides. Blood 120, 1060–1066. doi: 10.1182/blood-2012-01-405977
Fernández, L. C., Torres, M., and Real, F. X. (2016). Somatic mosaicism: on the road to cancer. Nat. Rev. Cancer 16, 43–55. doi: 10.1038/nrc.2015.1
Garcia-Sanz, R., Orfao, A., Gonzalez, M., Moro, M. J., Hernandez, J. M., Ortega, F., et al. (1995). Prognostic implications of DNA aneuploidy in 156 untreated multiple myeloma patients. Castelano-Leones (Spain) Cooperative Group for the Study of Monoclonal Gammopathies. Br. J. Haematol. 90, 106–112. doi: 10.1111/j.1365-2141.1995.tb03387.x
Gatenby, R. A., Silva, A. S., Gillies, R. J., and Frieden, B. R. (2009). Adaptive therapy. Cancer Res. 69, 4894–4903. doi: 10.1158/0008-5472.CAN-08-3658
Gonzalez, D., van der Burg, M., Garcia-Sanz, R., Fenton, J. A., Langerak, A. W., Gonzalez, M., et al. (2007). Immunoglobulin gene rearrangements and the pathogenesis of multiple myeloma. Blood 110, 3112–3121. doi: 10.1182/blood-2007-02-069625
Gorelick, R., and Heng, H. H. (2011). Sex reduces genetic variation: a multidisciplinary review. Evolution 65, 1088–1098. doi: 10.1111/j.1558-5646.2010.01173.x
Heng, H., Regan, S., and Ye, C. (2016). Genotype, environment, and evolutionary mechanism of diseases. Environ. Dis. 1, 14–23. Available online at: http://www.environmentmed.org/text.asp?2016/1/1/14/180332
Heng, H. H. (2015). Debating Cancer: The Paradox in Cancer Research. Singapore: World Scientific Publishing Co. Pte Ltd. Available online at: http://www.worldscientific.com/worldscibooks/10.1142/8879#t=toc
Heng, H. H. (2019). Genome Chaos: Rethinking Genetics, Evolution, and Molecular Medicine. San Diego, CA: Academic Press. doi: 10.1016/C2016-0-05291-9
Heng, H. H., Stevens, J. B., Liu, G., Bremer, S. W., Ye, K. J., Reddy, P. V., et al. (2006). Stochastic cancer progression driven by non-clonal chromosome aberrations. J. Cell. Physiol. 208, 461–472. doi: 10.1002/jcp.20685
Horne, S. D., Chowdhury, S. K., and Heng, H. H. (2014). Stress, genomic adaptation, and the evolutionary trade-off. Front. Genet. 5:92. doi: 10.3389/fgene.2014.00092
Huang, S. (2009). Non-genetic heterogeneity of cells in development: more than just noise. Development 136, 3853–3862. doi: 10.1242/dev.035139
Hultén, M. A., Jonasson, J., Iwarsson, E., Uppal, P., Vorsanova, S. G., Yurov, Y. B., et al. (2013). Trisomy 21 mosaicism: we may all have a touch of Down syndrome. Cytogenet. Genome Res. 139, 189–192. doi: 10.1159/000346028
Iourov, I. Y., Vorsanova, S. G., and Yurov, Y. B. (2008). Chromosomal mosaicism goes global. Mol. Cytogenet. 1:26. doi: 10.1186/1755-8166-1-26
Iourov, I. Y., Vorsanova, S. G., and Yurov, Y. B. (2010). Somatic genome variations in health and disease. Curr. Genomics 11, 387–396. doi: 10.2174/138920210793176065
Iourov, I. Y., Vorsanova, S. G., Yurov, Y. B., and Kutsev, S. I. (2019). Ontogenetic and pathogenetic views on somatic chromosomal mosaicism. Genes 10:379. doi: 10.3390/genes10050379
Jamal-Hanjani, M., Wilson, G. A., McGranahan, N., Birkbak, N. J., Watkins, T. B. K., Veeriah, S., et al. (2017). Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121. doi: 10.1056/NEJMoa1616288
Kaur, G., Gupta, R., Mathur, N., Rani, L., Kumar, L., Sharma, A., et al. (2018). Clinical impact of chromothriptic complex chromosomal rearrangements in newly diagnosed multiple myeloma. Leuk. Res. 76, 58–64. doi: 10.1016/j.leukres.2018.12.005
Keats, J. J., Chesi, M., Egan, J. B., Garbitt, V. M., Palmer, S. E., Braggio, E., et al. (2012). Clonal competition with alternating dominance in multiple myeloma. Blood 120, 1067–1076. doi: 10.1182/blood-2012-01-405985
Klein, B., Seckinger, A., Moehler, T., and Hose, D. (2011). Molecular pathogenesis of multiple myeloma: chromosomal aberrations, changes in gene expression, cytokine networks, and the bone marrow microenvironment. Recent Results Cancer Res. 183, 39–86. doi: 10.1007/978-3-540-85772-3_3
Kultz, D. (2005). Molecular and evolutionary basis of the cellular stress response. Annu. Rev. Physiol. 67, 225–257. doi: 10.1146/annurev.physiol.67.040403.103635
Lee, K. J., Lee, K. H., Yoon, K. A., Sohn, J. Y., Lee, E., Lee, H., et al. (2017). Chromothripsis in treatment resistance in multiple myeloma. Genomics Inform. 15, 87–97. doi: 10.5808/GI.2017.15.3.87
Lohr, J. G., Stojanov, P., Carter, S. L., Cruz-Gordillo, P., Lawrence, M. S., Auclair, D., et al. (2014). Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91–101. doi: 10.1016/j.ccr.2013.12.015
Magrangeas, F., Avet-Loiseau, H., Munshi, N. C., and Minvielle, S. (2011). Chromothripsis identifies a rare and aggressive entity among newly diagnosed multiple myeloma patients. Blood 118, 675–678. doi: 10.1182/blood-2011-03-344069
Manier, S., Salem, K. Z., Park, J., Landau, D. A., Getz, G., and Ghobrial, I. M. (2017). Genomic complexity of multiple myeloma and its clinical implications. Nat. Rev. Clin. Oncol. 14, 100–113. doi: 10.1038/nrclinonc.2016.122
Maura, F., Bolli, N., Angelopoulos, N., Dawson, K. J., Leongamornlert, D., Martincorena, I., et al. (2019). Genomic landscape and chronological reconstruction of driver events in multiple myeloma. Nat. Commun. 10:3835. doi: 10.1038/s41467-019-11680-1
Pawlyn, C., and Morgan, G. J. (2017). Evolutionary biology of high-risk multiple myeloma. Nat. Rev. Cancer 17, 543–556. doi: 10.1038/nrc.2017.63
Rancati, G., Pavelka, N., Fleharty, B., Noll, A., Trimble, R., Walton, K., et al. (2008). Aneuploidy underlies rapid adaptive evolution of yeast cells deprived of a conserved cytokinesis motor. Cell 135, 879–893. doi: 10.1016/j.cell.2008.09.039
Shah, V., Sherborne, A. L., Walker, B. A., Johnson, D. C., Boyle, E. M., Ellis, S., et al. (2018). Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients. Leukemia 32, 102–110. doi: 10.1038/leu.2017.179
Shapiro, J. A. (2017). Living organisms author their read-write genomes in evolution. Biology 6:E42. doi: 10.3390/biology6040042
Smetana, J., Oppelt, J., Štork, M., Pour, L., and Kuglík, P. (2018). Chromothripsis 18 in multiple myeloma patient with rapid extramedullary relapse. Mol. Cytogenet. 11:7. doi: 10.1186/s13039-018-0357-5
Stevens, J. B., Horne, S. D., Abdallah, B. Y., Ye, C. J., and Heng, H. H. (2013). Chromosomal instability and transcriptome dynamics in cancer. Cancer Metastasis Rev. 32, 391–402. doi: 10.1007/s10555-013-9428-6
Stevens, J. B., Liu, G., Abdallah, B. Y., Horne, S. D., Ye, K. J., Bremer, S. W., et al. (2014). Unstable genomes elevate transcriptome dynamics. Int. J. Cancer 134, 2074–2087. doi: 10.1002/ijc.28531
Vattathil, S., and Scheet, P. (2016). Extensive hidden genomic mosaicism revealed in normal tissue. Am. J. Hum. Genet. 98, 571–578. doi: 10.1016/j.ajhg.2016.02.003
Vijg, J. (2014). Somatic mutations, genome mosaicism, cancer and aging. Curr. Opin. Genet. Dev. 26, 141–149. doi: 10.1016/j.gde.2014.04.002
Walker, B. A., Boyle, E. M., Wardell, C. P., Murison, A., Begum, D. B., Dahir, N. M., et al. (2015). Mutational spectrum, copy number changes, and outcome: results of a sequencing study of patients with newly diagnosed myeloma. J. Clin. Oncol. 33, 3911–3920. doi: 10.1200/jco.2014.59.1503
Walker, B. A., Leone, P. E., Chiecchio, L., Dickens, N. J., Jenner, M. W., Boyd, K. D., et al. (2010). A compendium of myeloma-associated chromosomal copy number abnormalities and their prognostic value. Blood 116, e56–e65. doi: 10.1182/blood-2010-04-279596
Ye, C. J., Regan, S., Liu, G., Alemara, S., and Heng, H. H. (2018). Understanding aneuploidy in cancer through the lens of system inheritance, fuzzy inheritance and emergence of new genome systems. Mol. Cytogenet. 11:31. doi: 10.1186/s13039-018-0376-2
Ye, C. J., Sharpe, Z., Alemara, S., Mackenzie, S., Liu, G., Abdallah, B., et al. (2019a). Micronuclei and genome chaos: changing the system inheritance. Genes 10:366. doi: 10.3390/genes10050366
Ye, C. J., Stevens, J. B., Liu, G., Bremer, S. W., Jaiswal, A. S., Ye, K. J., et al. (2009). Genome based cell population heterogeneity promotes tumorigenicity: the evolutionary mechanism of cancer. J. Cell. Physiol. 219, 288–300. doi: 10.1002/jcp.21663
Ye, C. J., Stilgenbauer, L., Moy, A., Liu, G., and Heng, H. H. (2019b). What is karyotype coding and why is genomic topology important for cancer and evolution? Front. Genet. 10:1082. doi: 10.3389/fgene.2019.01082
Ye, J. C., Boonstra, P. S., Boyer, D. F., Anderson, L. D., Lipe, B. C., Kin, A., et al. (2019c). A phase 2 study with Minimal Residual Disease (MRD) driven adaptive strategy in treatment for newly diagnosed multiple myeloma with upfront daratumumab-based therapy. Blood 134, 3191–3191. doi: 10.1182/blood-2019-132204
Ye, J. C., Chen, L., Chen, J., Parkin, B., Polk, A., Kandarpa, M., et al. (2019d). Aneuploidy is associated with inferior survival in relapsed refractory multiple myeloma patients. Blood 134, 4360–4360. doi: 10.1182/blood-2019-124135
Keywords: cellular heterogeneity, fuzzy inheritance, genome chaos, genome theory, macro-cellular evolution, two phases of cancer evolution, karyotype coding, system inheritance
Citation: Ye CJ, Chen J, Liu G and Heng HH (2020) Somatic Genomic Mosaicism in Multiple Myeloma. Front. Genet. 11:388. doi: 10.3389/fgene.2020.00388
Received: 11 February 2020; Accepted: 27 March 2020;
Published: 22 April 2020.
Edited by:
Obul Reddy Bandapalli, Hopp Children's Cancer Center Heidelberg (KiTZ), GermanyReviewed by:
Ezhilarasi Chendamarai, Washington University in St. Louis, United StatesHamid Mahmoudpour, Johannes Gutenberg University Mainz, Germany
Copyright © 2020 Ye, Chen, Liu and Heng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Christine J. Ye, amNocmlzeWUmI3gwMDA0MDttZWQudW1pY2guZWR1; Henry H. Heng, aGhlbmcmI3gwMDA0MDttZWQud2F5bmUuZWR1