nature.com

Characterizing the ecological and evolutionary dynamics of cancer - Nature Genetics

  • ️Curtis, Christina
  • ️Mon Jul 27 2020
  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

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

    CAS  PubMed  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  • 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 

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

    PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

  • 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  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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  PubMed Central  Google Scholar 

  • 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 

  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

  • 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 

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

    PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

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

    CAS  PubMed  Google Scholar 

  • 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 

  • 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 

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

    CAS  PubMed  Google Scholar 

  • 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 

  • 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 

  • 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  PubMed Central  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

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

    CAS  PubMed  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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 

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

    CAS  PubMed  Google Scholar 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

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

    CAS  PubMed  Google Scholar 

  • 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 

  • 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 

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

    PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  • 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 

  • 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  PubMed Central  Google Scholar 

  • 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