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Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems

La Porta, Caterina A.M., Zapperi, Stefano·2018·Seminars in Cancer Biology
DOI 10.1016/j.semcancer.2018.07.003
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Abstract

Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity of cancer cells, highlighting the kinetics of cancer stem cell and the role of the epithelial mesenchymal transition for metastasis. It appears that the tumor micro-environment plays a crucial role in triggering phenotypic transitions, as we illustrate discussing the challenges posed by macrophages and cancer associated fibroblasts. To disentangle the complexity of environmentally induced phenotypic transitions, there is a growing need for novel advanced algorithms as those proposed in our recent work combining single cell data analysis and numerical simulations of gene regulatory networks. We conclude discussing recent developments in artificial intelligence and its applications to personalized cancer treatment.

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Research works printed but not linked: 74

  1. Clevers, 2011, The cancer stem cell: premises, promises and challenges, Nat. Med., 17, 313, 10.1038/nm.2304 · DOI
  2. Nowell, 1976, The clonal evolution of tumor cell populations, Science, 194, 23, 10.1126/science.959840 · DOI · Paper page
  3. Gupta, 2011, Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells, Cell, 146, 633, 10.1016/j.cell.2011.07.026 · DOI · Paper page
  4. Roesch, 2010, A temporarily distinct subpopulation of slow-cycling melanoma cells is required for continuous tumor growth, Cell, 141, 583, 10.1016/j.cell.2010.04.020 · DOI
  5. Chaffer, 2013, Poised chromatin at the ZEB1 promoter enables breast cancer cell plasticity and enhances tumorigenicity, Cell, 154, 61, 10.1016/j.cell.2013.06.005 · DOI
  6. Monzani, 2007, Melanoma contains CD133 and ABCG2 positive cells with enhanced tumourigenic potential, Eur. J. Cancer, 43, 935, 10.1016/j.ejca.2007.01.017 · DOI
  7. Marjanovic, 2013, Cell plasticity and heterogeneity in cancer, Clin. Chem., 59, 168, 10.1373/clinchem.2012.184655 · DOI
  8. Auffinger, 2014, Conversion of differentiated cancer cells into cancer stem-like cells in a glioblastoma model after primary chemotherapy, Cell Death Differ., 21, 1119, 10.1038/cdd.2014.31 · DOI
  9. Iliopoulos, 2011, Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion, Proc. Natl. Acad. Sci. U. S. A., 108, 1397, 10.1073/pnas.1018898108 · DOI
  10. Vlashi, 2015, Cancer stem cells, cancer cell plasticity and radiation therapy, Semin. Cancer Biol., 31, 28, 10.1016/j.semcancer.2014.07.001 · DOI
  11. Sellerio, 2015, Overshoot during phenotypic switching of cancer cell populations, Sci. Rep., 5, 15464, 10.1038/srep15464 · DOI
  12. Kim, 2017, Microenvironment-derived factors driving metastatic plasticity in melanoma, Nat. Commun., 8, 14343, 10.1038/ncomms14343 · DOI
  13. Medema, 2011, Microenvironmental regulation of stem cells in intestinal homeostasis and cancer, Nature, 474, 318, 10.1038/nature10212 · DOI
  14. Clevers, 2014, Stem cell signaling – an integral program for tissue renewal and regeneration: Wnt signaling and stem cell control, Science, 346, 1248012, 10.1126/science.1248012 · DOI
  15. Huels, 2018, Wnt ligands influence tumour initiation by controlling the number of intestinal stem cells, Nat. Commun., 9, 1132, 10.1038/s41467-018-03426-2 · DOI
  16. La Porta, 2017, Complexity in cancer stem cells and tumor evolution: toward precision medicine, Semin. Cancer Biol., 44, 3, 10.1016/j.semcancer.2017.02.007 · DOI
  17. Hong, 2016, Circulating tumor cell clusters: What we know and what we expect, Int. J. Oncol., 49, 2206, 10.3892/ijo.2016.3747 · DOI
  18. Ye, 2015, Epithelial–mesenchymal plasticity: a central regulator of cancer progression, Trends Cell Biol., 25, 675, 10.1016/j.tcb.2015.07.012 · DOI
  19. Huber, 2005, Molecular requirements for epithelial–mesenchymal transition during tumor progression, Curr. Opin. Cell Biol., 17, 548, 10.1016/j.ceb.2005.08.001 · DOI
  20. Rhim, 2012, EMT and dissemination precede pancreatic tumor formation, Cell, 148, 349, 10.1016/j.cell.2011.11.025 · DOI · Paper page
  21. Sarrió, 2008, Epithelial–mesenchymal transition in breast cancer relates to the basal-like phenotype, Cancer Res., 68, 989, 10.1158/0008-5472.CAN-07-2017 · DOI
  22. Bitterman, 1990, The significance of epithelial differentiation in mixed mesodermal tumors of the uterus: a clinicopathologic and immunohistochemical study, Am. J. Surg. Pathol., 14, 317, 10.1097/00000478-199004000-00002 · DOI
  23. Haraguchi, 1999, Pulmonary carcinosarcoma: immunohistochemical and ultrastructural studies, Pathol. Int., 49, 903, 10.1046/j.1440-1827.1999.00964.x · DOI
  24. Paniz Mondolfi, 2013, Primary cutaneous carcinosarcoma: insights into its clonal origin and mutational pattern expression analysis through next-generation sequencing, Hum. Pathol., 44, 2853, 10.1016/j.humpath.2013.07.014 · DOI
  25. Jolly, 2016, Stability of the hybrid epithelial/mesenchymal phenotype, Oncotarget, 7, 27067, 10.18632/oncotarget.8166 · DOI
  26. George, 2017, Survival outcomes in cancer patients predicted by a partial emt gene expression scoring metric, Cancer Res., 77, 6415, 10.1158/0008-5472.CAN-16-3521 · DOI
  27. Font-Clos, 2018, Topography of epithelial–mesenchymal transitions, Proc. Natl. Acad. Sci. U. S. A., 115, 5902, 10.1073/pnas.1722609115 · DOI
  28. Kauffman, 1969, Metabolic stability and epigenesis in randomly constructed genetic nets, J. Theor. Biol., 22, 437, 10.1016/0022-5193(69)90015-0 · DOI
  29. Li, 2004, The yeast cell-cycle network is robustly designed, Proc. Natl. Acad. Sci. U. S. A., 101, 4781, 10.1073/pnas.0305937101 · DOI
  30. Huang, 2007, Bifurcation dynamics in lineage-commitment in bipotent progenitor cells, Dev. Biol., 305, 695, 10.1016/j.ydbio.2007.02.036 · DOI · Paper page
  31. Wang, 2010, The potential landscape of genetic circuits imposes the arrow of time in stem cell differentiation, Biophys. J., 99, 29, 10.1016/j.bpj.2010.03.058 · DOI
  32. Wang, 2011, Quantifying the Waddington landscape and biological paths for development and differentiation, Proc. Natl. Acad. Sci. U. S. A., 108, 8257, 10.1073/pnas.1017017108 · DOI
  33. Huang, 2012, The molecular and mathematical basis of Waddington's epigenetic landscape: a framework for post-darwinian biology?, Bioessays, 34, 149, 10.1002/bies.201100031 · DOI
  34. Li, 2013, Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation, J. R. Soc. Interface, 10, 20130787, 10.1098/rsif.2013.0787 · DOI
  35. Luo, 2015, Epithelial–mesenchymal plasticity of breast cancer stem cells: implications for metastasis and therapeutic resistance, Curr. Pharm. Des., 21, 1301, 10.2174/1381612821666141211120604 · DOI
  36. Brooks, 2015, Tumor twitter: cellular communication in the breast cancer stem cell niche, Cancer Discov., 5, 469, 10.1158/2159-8290.CD-15-0327 · DOI
  37. Bos, 2009, Genes that mediate breast cancer metastasis to the brain, Nature, 459, 1005, 10.1038/nature08021 · DOI · Paper page
  38. Lu, 2011, VCAM-1 promotes osteolytic expansion of indolent bone micrometastasis of breast cancer by engaging α4β1-positive osteoclast progenitors, Cancer Cell, 20, 701, 10.1016/j.ccr.2011.11.002 · DOI
  39. Zhang, 2009, Latent bone metastasis in breast cancer tied to SRC-dependent survival signals, Cancer Cell, 16, 67, 10.1016/j.ccr.2009.05.017 · DOI
  40. Padua, 2008, TGF beta primes breast tumors for lung metastasis seeding through angiopoietin-like 4, Cell, 133, 66, 10.1016/j.cell.2008.01.046 · DOI
  41. Nguyen, 2009, Metastasis: from dissemination to organ-specific colonization, Nat. Rev. Cancer, 9, 274, 10.1038/nrc2622 · DOI
  42. Braun, 2005, Circulating and disseminated tumor cells, J. Clin. Oncol., 23, 1623, 10.1200/JCO.2005.10.073 · DOI
  43. Ross, 2009, Circulating and disseminated tumor cells in the management of breast cancer, Am. J. Clin. Pathol., 132, 237, 10.1309/AJCPJI7DEOLKCS6F · DOI
  44. Páez, 2012, Cancer dormancy: a model of early dissemination and late cancer recurrence, Clin. Cancer Res., 18, 645, 10.1158/1078-0432.CCR-11-2186 · DOI
  45. Gay, 2017, The sleeping ugly: tumour microenvironment's act to make or break the spell of dormancy, Biochim. Biophys. Acta, 1868, 231
  46. Gao, 2017, Cancer cell dormancy: mechanisms and implications of cancer recurrence and metastasis, Onco Targets Ther., 10, 5219, 10.2147/OTT.S140854 · DOI
  47. Dasgupta, 2017, Circulating and disseminated tumor cells: harbingers or initiators of metastasis?, Mol. Oncol., 11, 40, 10.1002/1878-0261.12022 · DOI
  48. Patel, 2012, Cancer stem cells, tumor dormancy, and metastasis, Front. Endocrinol. (Lausanne), 3, 125, 10.3389/fendo.2012.00125 · DOI
  49. Liu, 2016, Characteristics and significance of the pre-metastatic niche, Cancer Cell, 30, 668, 10.1016/j.ccell.2016.09.011 · DOI
  50. Qian, 2015, FLT1 signaling in metastasis-associated macrophages activates an inflammatory signature that promotes breast cancer metastasis, J. Exp. Med., 212, 1433, 10.1084/jem.20141555 · DOI
  51. Kaplan, 2005, VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche, Nature, 438, 820, 10.1038/nature04186 · DOI · Paper page
  52. Gao, 2012, Myeloid progenitor cells in the premetastatic lung promote metastases by inducing mesenchymal to epithelial transition, Cancer Res., 72, 1384, 10.1158/0008-5472.CAN-11-2905 · DOI
  53. Chen, 2017, CYP4A in tumor-associated macrophages promotes pre-metastatic niche formation and metastasis, Oncogene, 36, 5045, 10.1038/onc.2017.118 · DOI · Paper page
  54. Malanchi, 2011, Interactions between cancer stem cells and their niche govern metastatic colonization, Nature, 481, 85, 10.1038/nature10694 · DOI · Paper page
  55. Shapouri-Moghaddam, 2018, Macrophage plasticity, polarization, and function in health and disease, J. Cell. Physiol., 233, 6425, 10.1002/jcp.26429 · DOI · Paper page
  56. Mantovani, 2004, The chemokine system in diverse forms of macrophage activation and polarization, Trends Immunol., 25, 677, 10.1016/j.it.2004.09.015 · DOI · Paper page
  57. Sica, 2012, Macrophage plasticity and polarization: in vivo veritas, J. Clin. Invest., 122, 787, 10.1172/JCI59643 · DOI · Paper page
  58. Belgiovine, 2016, Tumor-associated macrophages and anti-tumor therapies: complex links, Cell. Mol. Life Sci., 73, 2411, 10.1007/s00018-016-2166-5 · DOI
  59. Chittezhath, 2014, Molecular profiling reveals a tumor-promoting phenotype of monocytes and macrophages in human cancer progression, Immunity, 41, 815, 10.1016/j.immuni.2014.09.014 · DOI
  60. Huang, 2017, M2-polarized tumour-associated macrophages in stroma correlate with poor prognosis and Epstein–Barr viral infection in nasopharyngeal carcinoma, Acta Otolaryngol., 137, 888, 10.1080/00016489.2017.1296585 · DOI
  61. Fan, 2014, Forkhead factor foxq1 promotes TGF-β1 expression and induces epithelial–mesenchymal transition, Mol. Cell. Biochem., 397, 179, 10.1007/s11010-014-2185-1 · DOI
  62. Jinushi, 2012, Regulation of cancer stem cell activities by tumor-associated macrophages, Am. J. Cancer Res., 2, 529
  63. Yi, 2011, Glioma-initiating cells: a predominant role in microglia/macrophages tropism to glioma, J. Neuroimmunol., 232, 75, 10.1016/j.jneuroim.2010.10.011 · DOI
  64. Xing, 2010, Cancer associated fibroblasts (cafs) in tumor microenvironment, Front. Biosci. (Landmark Ed.), 15, 166, 10.2741/3613 · DOI · Paper page
  65. Kalluri, 2009, The basics of epithelial–mesenchymal transition, J. Clin. Invest., 119, 1420, 10.1172/JCI39104 · DOI · Paper page
  66. Miles, 2014, Insidious changes in stromal matrix fuel cancer progression, Mol. Cancer Res., 12, 297, 10.1158/1541-7786.MCR-13-0535 · DOI
  67. Baluk, 2005, Cellular abnormalities of blood vessels as targets in cancer, Curr. Opin. Genet. Dev., 15, 102, 10.1016/j.gde.2004.12.005 · DOI
  68. Knuchel, 2015, Fibroblast surface-associated FGF-2 promotes contact-dependent colorectal cancer cell migration and invasion through FGFR-SRC signaling and integrin αvβ5-mediated adhesion, Oncotarget, 6, 14300, 10.18632/oncotarget.3883 · DOI
  69. Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 · DOI · Paper page
  70. Drier, 2011, Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?, PLoS One, 6, e17795, 10.1371/journal.pone.0017795 · DOI
  71. Marx, 2013, The big challenges of big data, Nature, 498, 255, 10.1038/498255a · DOI
  72. Font-Clos, 2017, Integrative analysis of pathway deregulation in obesity, NPJ Syst. Biol. Appl., 3, 18, 10.1038/s41540-017-0018-z · DOI
  73. Font-Clos, 2018, Gene expression signature of obesity in monozygotic twins, Physiol. Meas., 39, 044008, 10.1088/1361-6579/aab85a · DOI
  74. Garrison, 2007, Using real-world data for coverage and payment decisions: the ispor real-world data task force report, Value Health, 10, 326, 10.1111/j.1524-4733.2007.00186.x · DOI

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Metadata

Authors
La Porta, C. A. M., & Zapperi, S.
Venue
Seminars in Cancer Biology
Publisher
Elsevier BV
Published
2018-12
Volume
53
Pages
42-47
ISSN
1044-579X
DOI
10.1016/j.semcancer.2018.07.003
Cited by
27
References
7
Language
en
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems. https://app.cochat.ai/papers/10.1016/j.semcancer.2018.07.003
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La Porta, C. A. M., & Zapperi, S. (2018). Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems. Seminars in Cancer Biology. https://doi.org/10.1016/j.semcancer.2018.07.003