Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Characterizing the ecological and evolutionary dynamics of cancer

Abstract

Tumor initiation and progression are somatic evolutionary processes driven by the accumulation of genetic alterations, some of which confer selective fitness advantages to the host cell. This gene-centric model has shaped the field of cancer biology and advanced understanding of cancer pathophysiology. Importantly, however, each genotype encodes diverse phenotypic traits that permit acclimation to varied microenvironmental conditions. Epigenetic and transcriptional changes also contribute to the heritable phenotypic variation required for evolution. Additionally, interactions between cancer cells and surrounding stromal and immune cells through autonomous and non-autonomous signaling can influence competition for survival. Therefore, a mechanistic understanding of tumor progression must account for evolutionary and ecological dynamics. In this Perspective, we outline technological advances and model systems to characterize tumor progression through space and time. We discuss the importance of unifying experimentation with computational modeling and opportunities to inform cancer control.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Schematic illustration of tumor progression from initiation through treatment resistance and metastasis.
Fig. 2: Correlative studies in patient tissue and plasma samples enable investigation of tumor evolution and cellular phenotypes associated with disease progression.
Fig. 3: Schematic overview of experimental cancer models and assays to study cancer gene function and cellular phenotypes.
Fig. 4: Overview of biological processes associated with cancer progression and approaches to characterize them.

References

  1. 1.

    Seth, S. et al. Pre-existing functional heterogeneity of tumorigenic compartment as the origin of chemoresistance in pancreatic tumors. Cell Rep. 26, 1518–1532.e9 (2019).

    CAS  PubMed  Google Scholar 

  2. 2.

    Bhang, H. E. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).

    CAS  PubMed  Google Scholar 

  3. 3.

    Pogrebniak, K. L. & Curtis, C. Harnessing tumor evolution to circumvent resistance. Trends Genet. 34, 639–651 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    CAS  PubMed  Google Scholar 

  6. 6.

    Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Gatenby, R. A. & Brown, J. Mutations, evolution and the central role of a self-defined fitness function in the initiation and progression of cancer. Biochim Biophys. Acta Rev. Cancer 1867, 162–166 (2017).

    CAS  PubMed  Google Scholar 

  9. 9.

    Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Boutros, P. C. et al. Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat. Genet. 47, 736–745 (2015).

    CAS  PubMed  Google Scholar 

  12. 12.

    Gao, R. et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48, 1119–1130 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016).

    CAS  PubMed  Google Scholar 

  14. 14.

    Turajlic, S. et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx Renal. Cell 173, 595–610.e11 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Shlush, L. I. et al. Tracing the origins of relapse in acute myeloid leukaemia to stem cells. Nature 547, 104–108 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

    Maley, C. C. et al. Classifying the evolutionary and ecological features of neoplasms. Nat. Rev. Cancer 17, 605–619 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Yaffe, M. B. Why geneticists stole cancer research even though cancer is primarily a signaling disease. Sci. Signal. 12, eaaw3483 (2019).

    CAS  PubMed  Google Scholar 

  18. 18.

    Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Tsao, J. L. et al. Genetic reconstruction of individual colorectal tumor histories. Proc. Natl Acad. Sci. USA 97, 1236–1241 (2000).

    CAS  PubMed  Google Scholar 

  21. 21.

    Martincorena, I. et al. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).

    CAS  PubMed  Google Scholar 

  24. 24.

    Jaiswal, S. & Ebert, B. L. Clonal hematopoiesis in human aging and disease. Science 366, eaan4673 (2019).

    CAS  PubMed  Google Scholar 

  25. 25.

    Srivastava, S., Ghosh, S., Kagan, J. & Mazurchuk, R. The PreCancer Atlas (PCA). Trends Cancer 4, 513–514 (2018).

    PubMed  Google Scholar 

  26. 26.

    Uchi, R. et al. Integrated multiregional analysis proposing a new model of colorectal cancer evolution. PLoS Genet. 12, e1005778 (2016).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Cross, W. et al. The evolutionary landscape of colorectal tumorigenesis. Nat. Ecol. Evol. 2, 1661–1672 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal. Cell 173, 581–594.e12 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Hu, Z. et al. Quantitative evidence for early metastatic seeding in colorectal cancer. Nat. Genet. 51, 1113–1122 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Hu, Z., Li, Z., Ma, Z. & Curtis, C. Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nat. Genet. 52, 701–708 (2020).

    CAS  PubMed  Google Scholar 

  31. 31.

    Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Werner, B. et al. Measuring single cell divisions in human tissues from multi-region sequencing data. Nat. Commun. 11, 1035 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat. Genet. 49, 1015–1024 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Gruber, M. et al. Growth dynamics in naturally progressing chronic lymphocytic leukaemia. Nature 570, 474–479 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Landau, D. A. et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Landau, D. A. et al. The evolutionary landscape of chronic lymphocytic leukemia treated with ibrutinib targeted therapy. Nat. Commun. 8, 2185 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Rueda, O. M. et al. Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups. Nature 567, 399–404 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Zhang, A. W. et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell 173, 1755–1769.e22 (2018).

    CAS  PubMed  Google Scholar 

  45. 45.

    Failmezger, H. et al. Topological tumor graphs: a graph-based spatial model to infer stromal recruitment for immunosuppression in melanoma histology. Cancer Res. 80, 1199–1209 (2020).

    CAS  PubMed  Google Scholar 

  46. 46.

    Lloyd, M. C. et al. Darwinian dynamics of intratumoral heterogeneity: not solely random mutations but also variable environmental selection forces. Cancer Res. 76, 3136–3144 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Carmona-Fontaine, C. et al. Metabolic origins of spatial organization in the tumor microenvironment. Proc. Natl Acad. Sci. USA 114, 2934–2939 (2017).

    CAS  PubMed  Google Scholar 

  48. 48.

    Northcott, J. M., Dean, I. S., Mouw, J. K. & Weaver, V. M. Feeling stress: the mechanics of cancer progression and aggression. Front. Cell Dev. Biol. 6, 17 (2018).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Cassereau, L., Miroshnikova, Y. A., Ou, G., Lakins, J. & Weaver, V. M. A 3D tension bioreactor platform to study the interplay between ECM stiffness and tumor phenotype. J. Biotechnol. 193, 66–69 (2015).

    CAS  PubMed  Google Scholar 

  50. 50.

    Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Goltsev, Y. et al. Deep profiling of mouse splenic architecture with codex multiplexed imaging. Cell 174, 968–981.e15 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).

    CAS  PubMed  Google Scholar 

  58. 58.

    Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  Google Scholar 

  59. 59.

    Chevrier, S. et al. An immune atlas of clear cell renal cell carcinoma. Cell 169, 736–749.e18 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Decalf, J., Albert, M. L. & Ziai, J. New tools for pathology: a user’s review of a highly multiplexed method for in situ analysis of protein and RNA expression in tissue. J. Pathol. 247, 650–661 (2019).

    PubMed  Google Scholar 

  61. 61.

    Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).

    CAS  PubMed  Google Scholar 

  62. 62.

    Marusyk, A. et al. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature 514, 54–58 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).

    CAS  PubMed  Google Scholar 

  64. 64.

    Moffitt, J. R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl Acad. Sci. USA 113, 14456–14461 (2016).

    CAS  PubMed  Google Scholar 

  65. 65.

    Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    PubMed  Google Scholar 

  66. 66.

    Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    CAS  PubMed  Google Scholar 

  69. 69.

    Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205–217.e12 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Zahn, H. et al. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14, 167–173 (2017).

    CAS  PubMed  Google Scholar 

  72. 72.

    Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221.e22 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Jackson, E. L. & Lu, H. Three-dimensional models for studying development and disease: moving on from organisms to organs-on-a-chip and organoids. Integr. Biol. (Camb.) 8, 672–683 (2016).

    CAS  Google Scholar 

  77. 77.

    Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Bolan, P. O. et al. Genotype-fitness maps of EGFR-mutant lung adenocarcinoma chart the evolutionary landscape of resistance for combination therapy optimization. Cell Syst. 10, 52–65.e7 (2020).

    CAS  PubMed  Google Scholar 

  80. 80.

    Stowers, R. S. et al. Matrix stiffness induces a tumorigenic phenotype in mammary epithelium through changes in chromatin accessibility. Nat. Biomed. Eng. 3, 1009–1019 (2019).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Bissell, M. J. & Radisky, D. Putting tumours in context. Nat. Rev. Cancer 1, 46–54 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Han, K. et al. CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities. Nature 580, 136–141 (2020).

    CAS  PubMed  Google Scholar 

  83. 83.

    Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    CAS  PubMed  Google Scholar 

  84. 84.

    Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386.e10 (2018).

    CAS  PubMed  Google Scholar 

  85. 85.

    van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Clevers, H. & Tuveson, D. A. Organoid models for cancer research. Annu. Rev. Cancer Biol. 3, 223–234 (2019).

    Google Scholar 

  88. 88.

    Albritton, J. L. & Miller, J. S. 3D bioprinting: improving in vitro models of metastasis with heterogeneous tumor microenvironments. Dis. Model. Mech. 10, 3–14 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Hu, M. et al. Facile engineering of long-term culturable ex vivo vascularized tissues using biologically derived matrices. Adv. Healthc. Mater. 7, e1800845 (2018).

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Katt, M. E., Placone, A. L., Wong, A. D., Xu, Z. S. & Searson, P. C. In vitro tumor models: advantages, disadvantages, variables, and selecting the right platform. Front. Bioeng. Biotechnol. 4, 12 (2016).

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Kersten, K., de Visser, K. E., van Miltenburg, M. H. & Jonkers, J. Genetically engineered mouse models in oncology research and cancer medicine. EMBO Mol. Med. 9, 137–153 (2017).

    CAS  PubMed  Google Scholar 

  92. 92.

    Winters, I. P., Murray, C. W. & Winslow, M. M. Towards quantitative and multiplexed in vivo functional cancer genomics. Nat. Rev. Genet. 19, 741–755 (2018).

    CAS  PubMed  Google Scholar 

  93. 93.

    Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e22 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Echeverria, G. V. et al. High-resolution clonal mapping of multi-organ metastasis in triple negative breast cancer. Nat. Commun. 9, 5079 (2018).

    PubMed  PubMed Central  Google Scholar 

  95. 95.

    Sánchez-Rivera, F. J. et al. Rapid modelling of cooperating genetic events in cancer through somatic genome editing. Nature 516, 428–431 (2014).

    PubMed  PubMed Central  Google Scholar 

  96. 96.

    Walther, V. et al. Can oncology recapitulate paleontology? Lessons from species extinctions. Nat. Rev. Clin. Oncol. 12, 273–285 (2015).

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Rogers, Z. N. et al. Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice. Nat. Genet. 50, 483–486 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    McPherson, A. W., Chan, F. C. & Shah, S. P. Observing clonal dynamics across spatiotemporal axes: a prelude to quantitative fitness models for cancer. Cold Spring Harb. Perspect. Med. 8, a029603 (2018).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Russo, M. et al. Adaptive mutability of colorectal cancers in response to targeted therapies. Science 366, 1473–1480 (2019).

    CAS  PubMed  Google Scholar 

  100. 100.

    Gatenby, R. A., Silva, A. S., Gillies, R. J. & Frieden, B. R. Adaptive therapy. Cancer Res. 69, 4894–4903 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Enriquez-Navas, P. M. et al. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Sci. Transl. Med. 8, 327ra24 (2016).

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    West, J. et al. Towards multidrug adaptive therapy. Cancer Res. 80, 1578–1589 (2020).

    PubMed  Google Scholar 

  103. 103.

    Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 8, 1816 (2017).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Zhang, J., Fishman, M. N., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer (mCRPC): updated analysis of the adaptive abiraterone (abi) study (NCT02415621). J. Clin. Oncol. 37, 5041 (2019).

    Google Scholar 

  105. 105.

    Zhao, B. et al. Exploiting temporal collateral sensitivity in tumor clonal evolution. Cell 165, 234–246 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106.

    Lin, K. H. et al. Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer. Nat. Genet. 52, 408–417 (2020).

    CAS  PubMed  Google Scholar 

  107. 107.

    Anderson, A. R. & Quaranta, V. Integrative mathematical oncology. Nat. Rev. Cancer 8, 227–234 (2008).

    CAS  PubMed  Google Scholar 

  108. 108.

    Sharp, J. A. et al. Designing combination therapies using multiple optimal controls. J. Theor. Biol. 497, 110277 (2020).

    PubMed  Google Scholar 

  109. 109.

    Gluzman, M., Scott, J. G. & Vladimirsky, A. Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory. Proc. Biol. Sci. 287, 20192454 (2020).

    PubMed  Google Scholar 

  110. 110.

    Lind, P. A., Libby, E., Herzog, J. & Rainey, P. B. Predicting mutational routes to new adaptive phenotypes. eLife 8, e38822 (2019).

    PubMed  PubMed Central  Google Scholar 

  111. 111.

    Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    CAS  PubMed  Google Scholar 

  112. 112.

    Metzcar, J., Wang, Y., Heiland, R. & Macklin, P. A review of cell-based computational modeling in cancer biology. JCO Clin. Cancer Inform. 3, 1–13 (2019).

    PubMed  Google Scholar 

Download references

Acknowledgements

This Perspective is based on discussions from a workshop supported by the NCI’s PS-ON. A list of workshop participants and their affiliations is provided in the Supplementary Note.

Author information

Affiliations

Authors

Contributions

N.Z. recorded and synthesized notes from the PS-ON workshop. R.S. drafted an outline of concepts discussed at the workshop. C.C. wrote the manuscript and drafted the figures. N.Z., R.S., D.G. and R.A.G. provided revisions on the manuscript. All authors approved the final manuscript.

Corresponding author

Correspondence to Christina Curtis.

Ethics declarations

Competing interests

C.C. is a scientific advisor to GRAIL and reports stock options, as well as consulting for GRAIL and Genentech. N.Z., R.S., D.G. and R.A.G. have no conflicts of interest to report.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Note

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zahir, N., Sun, R., Gallahan, D. et al. Characterizing the ecological and evolutionary dynamics of cancer. Nat Genet 52, 759–767 (2020). https://doi.org/10.1038/s41588-020-0668-4

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing