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  • Perspective
  • Published:

Synthetic living materials in cancer biology

Abstract

Living materials, which are made either of or by living cells, or are synthetic materials with programmable elements catered to cells, are environmentally responsive and can self-repair, allowing controlled and predictable interactions with biological systems. Such features can be achieved in purely synthetic materials using chemical approaches to create dynamic and responsive materials that can undergo programmed changes, be remodelled by cells in a predictive way, sense their microenvironment and report back, or respond to remote triggers to rearrange in physical or chemical ways. In this Perspective, we discuss synthetic approaches to design such cell-responsive and environment-responsive living materials, with a particular focus on their applications in cancer. We highlight how synthetic and systems biology approaches can be implemented in the design of synthetic living materials, and we outline key cancer-related applications, including modelling of tumour heterogeneity, the tumour microenvironment and tumour evolution in response to therapy. Finally, we emphasize the importance of inclusive designs that should be based on an understanding of how health and disease manifest in and affect humans from all racial and ethnic backgrounds, skin colours, sexes and genders.

Key points

  • Living materials can be made from living cells, can refer to composites of cells and synthetic or biological materials, or can be designed entirely synthetically based on dynamic and responsive elements that can evolve over time in a predictable manner.

  • Synthetic living materials are particularly useful for applications in cancer biology, because tumour microenvironments are highly dynamic and cell-responsive, and this can be modelled in responsive materials.

  • Synthetic and systems biology approaches can be applied to design synthetic living materials that mimic tumour heterogeneity and tumour evolution in response to therapy.

  • The engineering of synthetic living materials requires inclusive design strategies and an understanding of how cancer affects humans from all racial and ethnic backgrounds, skin colours, sexes and genders.

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Fig. 1: Applications of living materials in cancer.
Fig. 2: Synthetic design of living materials.
Fig. 3: Systems and synthetic biology in the design and analysis of living materials.
Fig. 4: Synthetic biology approaches to introduce smart cells into the tumour milieu.

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References

  1. Kase, Y., Kataoka, M. & Miyata, T. An improved method for determination of micro amounts of piperidine in living materials. Jpn. J. Pharmacol. 19, 354–362 (1969).

    Article  Google Scholar 

  2. Hernandez-Arriaga, A. M., Campano, C., Rivero-Buceta, V. & Prieto, M. A. When microbial biotechnology meets material engineering. Microb. Biotechnol. 15, 149–163 (2022).

    Article  Google Scholar 

  3. Chen, B. et al. Programmable living assembly of materials by bacterial adhesion. Nat. Chem. Biol. 18, 289–294 (2022).

    Article  Google Scholar 

  4. He, F. et al. 3D Printed biocatalytic living materials with dual-network reinforced bioinks. Small 18, e2104820 (2022).

    Article  Google Scholar 

  5. Caro-Astorga, J., Walker, K. T., Herrera, N., Lee, K. Y. & Ellis, T. Bacterial cellulose spheroids as building blocks for 3D and patterned living materials and for regeneration. Nat. Commun. 12, 5027 (2021).

    Article  Google Scholar 

  6. Manjula-Basavanna, A., Duraj-Thatte, A. M. & Joshi, N. S. Robust self-regeneratable stiff living materials fabricated from microbial cells. Adv. Funct. Mater. 31, 2010784 (2021).

    Article  Google Scholar 

  7. Gilbert, C. et al. Living materials with programmable functionalities grown from engineered microbial co-cultures. Nat. Mater. 20, 691–700 (2021).

    Article  Google Scholar 

  8. Gilbert, C. & Ellis, T. Biological engineered living materials: growing functional materials with genetically programmable properties. ACS Synth. Biol. 8, 1–15 (2019).

    Article  Google Scholar 

  9. Cai, P. C. et al. Dynamic light scattering microrheology for soft and living materials. Soft Matter 17, 1929–1939 (2021).

    Article  Google Scholar 

  10. Rivera-Tarazona, L. K., Campbell, Z. T. & Ware, T. H. Stimuli-responsive engineered living materials. Soft Matter 17, 785–809 (2021).

    Article  Google Scholar 

  11. Priks, H. et al. Physical confinement impacts cellular phenotypes within living materials. ACS Appl. Bio Mater. 3, 4273–4281 (2020).

    Article  Google Scholar 

  12. Chen, A. Y., Zhong, C. & Lu, T. K. Engineering living functional materials. ACS Synth. Biol. 4, 8–11 (2015).

    Article  Google Scholar 

  13. Nguyen, P. Q., Courchesne, N. D., Duraj-Thatte, A., Praveschotinunt, P. & Joshi, N. S. Engineered living materials: prospects and challenges for using biological systems to direct the assembly of smart materials. Adv. Mater. 30, e1704847 (2018).

    Article  Google Scholar 

  14. Smith, R. S. H. et al. Hybrid living materials: digital design and fabrication of 3D multimaterial structures with programmable biohybrid surfaces. Adv. Funct. Mater. 30, 1907401 (2020).

    Article  Google Scholar 

  15. Duraj-Thatte, A. M. et al. Programmable microbial ink for 3D printing of living materials produced from genetically engineered protein nanofibers. Nat. Commun. 12, 6600 (2021).

    Article  Google Scholar 

  16. Roumeli, E. et al. Biological matrix composites from cultured plant cells. Proc. Natl Acad. Sci. USA 119, e2119523119 (2022). This article reports a method to synthesize plant-derived biocomposites, that is, hierarchical materials from cultured plant cells, that are strong and biodegradable.

    Article  Google Scholar 

  17. Gjorevski, N. et al. Tissue geometry drives deterministic organoid patterning. Science 375, eaaw9021 (2022). Culture of stem cell-derived organoids in a fully synthetic matrix revealed that geometry and confinement of cells within the matrix has a profound impact on organoid growth, differentiation and patterning.

    Article  Google Scholar 

  18. Bretherton, R. C. et al. User-controlled 4D biomaterial degradation with substrate-selective sortase transpeptidases for single-cell biology. Adv. Mater. 35, 2209904 (2023).

    Article  Google Scholar 

  19. Shou, Y. et al. Dynamic magneto-softening of 3D hydrogel reverses malignant transformation of cancer cells and enhances drug efficacy. ACS Nano 17, 2851–2867 (2023).

    Article  Google Scholar 

  20. Adelmund, S. M., Ruskowitz, E. R., Farahani, P. E., Wolfe, J. V. & DeForest, C. A. Light-activated proteomic labeling via photocaged bioorthogonal non-canonical amino acids. ACS Chem. Biol. 13, 573–577 (2018).

    Article  Google Scholar 

  21. Ruskowitz, E. R. & DeForest, C. A. Photoresponsive biomaterials for targeted drug delivery and 4D cell culture. Nat. Rev. Mater. 3, 17087 (2018).

    Article  Google Scholar 

  22. Griffin, D. R. et al. Synthesis of photodegradable macromers for conjugation and release of bioactive molecules. Biomacromolecules 14, 1199–1207 (2013).

    Article  Google Scholar 

  23. Fisher, S. A. et al. Photo-immobilized EGF chemical gradients differentially impact breast cancer cell invasion and drug response in defined 3D hydrogels. Biomaterials 178, 751–766 (2018).

    Article  Google Scholar 

  24. Engler, A. J., Sen, S., Sweeney, H. L. & Discher, D. E. Matrix elasticity directs stem cell lineage specification. Cell 126, 677–689 (2006).

    Article  Google Scholar 

  25. Chaudhuri, O. et al. Hydrogels with tunable stress relaxation regulate stem cell fate and activity. Nat. Mater. 15, 326–334 (2016).

    Article  Google Scholar 

  26. Mosiewicz, K. A., Kolb, L., van der Vlies, A. J. & Lutolf, M. P. Microscale patterning of hydrogel stiffness through light-triggered uncaging of thiols. Biomater. Sci. 2, 1640–1651 (2014).

    Article  Google Scholar 

  27. Pradhan, S., Keller, K. A., Sperduto, J. L. & Slater, J. H. Fundamentals of laser-based hydrogel degradation and applications in cell and tissue engineering. Adv. Healthc. Mater. 6, 1700681 (2017).

    Article  Google Scholar 

  28. Stowers, R. S., Allen, S. C. & Suggs, L. J. Dynamic phototuning of 3D hydrogel stiffness. Proc. Natl Acad. Sci. USA 112, 1953–1958 (2015).

    Article  Google Scholar 

  29. Rapp, T. L. & DeForest, C. A. Visible light-responsive dynamic biomaterials: going deeper and triggering more. Adv. Healthc. Mater. 9, 1901553 (2020). This review discusses how different wavelengths of light can be used to trigger hydrogel degradation, crosslinking and reactive group uncaging, allowing specific and spatial control of reactive groups and deep penetration into synthetic materials.

    Article  Google Scholar 

  30. Batalov, I., Stevens, K. R. & DeForest, C. A. Photopatterned biomolecule immobilization to guide three-dimensional cell fate in natural protein-based hydrogels. Proc. Natl Acad. Sci. USA 118, e2014194118 (2021).

    Article  Google Scholar 

  31. Xiao, W. et al. Matrix stiffness mediates pancreatic cancer chemoresistance through induction of exosome hypersecretion in a cancer associated fibroblasts-tumor organoid biomimetic model. Matrix Biol. Plus 14, 100111 (2022).

    Article  Google Scholar 

  32. Wiley, K. L., Sutherland, B. P., Ogunnaike, B. A. & Kloxin, A. M. Rational design of hydrogel networks with dynamic mechanical properties to mimic matrix remodeling. Adv. Healthc. Mater. 11, 2101947 (2022).

    Article  Google Scholar 

  33. Gilchrist, C. L. et al. TRPV4-mediated calcium signaling in mesenchymal stem cells regulates aligned collagen matrix formation and vinculin tension. Proc. Natl Acad. Sci. USA 116, 1992–1997 (2019).

    Article  Google Scholar 

  34. Lee, J. C. et al. Instructional materials that control cellular activity through synthetic Notch receptors. Biomaterials 297, 122099 (2023).

    Article  Google Scholar 

  35. Bonnans, C., Chou, J. & Werb, Z. Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 15, 786–801 (2014).

    Article  Google Scholar 

  36. Brouns, J. E. P. & Dankers, P. Y. W. Introduction of enzyme-responsivity in biomaterials to achieve dynamic reciprocity in cell–material interactions. Biomacromolecules 22, 4–23 (2021).

    Article  Google Scholar 

  37. Li, Y., Wong, I. Y. & Guo, M. Reciprocity of cell mechanics with extracellular stimuli: emerging opportunities for translational medicine. Small 18, 2107305 (2022).

    Article  Google Scholar 

  38. Veis, A., Anesey, J. & Cohen, J. The long range reorganization of gelatin to the collagen structure. Arch. Biochem. Biophys. 94, 20–31 (1961).

    Article  Google Scholar 

  39. Karamichos, D., Brown, R. A. & Mudera, V. Collagen stiffness regulates cellular contraction and matrix remodeling gene expression. J. Biomed. Mater. Res. A 83, 887–894 (2007).

    Article  Google Scholar 

  40. Maller, O. et al. Tumour-associated macrophages drive stromal cell-dependent collagen crosslinking and stiffening to promote breast cancer aggression. Nat. Mater. 20, 548–559 (2021).

    Article  Google Scholar 

  41. Tirella, A., Mattei, G., La Marca, M., Ahluwalia, A. & Tirelli, N. Functionalized enzyme-responsive biomaterials to model tissue stiffening in vitro. Front. Bioeng. Biotechnol. 8, 20 (2020).

    Article  Google Scholar 

  42. Tran, Y. H., Rasmuson, M. J., Emrick, T., Klier, J. & Peyton, S. R. Strain-stiffening gels based on latent crosslinking. Soft Matter 13, 9007–9014 (2017). One of the first demonstrations of applied external force to trigger material crosslinking (and therefore stiffening) without the need for reactive mechanophores.

    Article  Google Scholar 

  43. Loebel, C., Mauck, R. L. & Burdick, J. A. Local nascent protein deposition and remodelling guide mesenchymal stromal cell mechanosensing and fate in three-dimensional hydrogels. Nat. Mater. 18, 883–891 (2019).

    Article  Google Scholar 

  44. Brooks, E. A., Gencoglu, M. F., Corbett, D. C., Stevens, K. R. & Peyton, S. R. An omentum-inspired 3D PEG hydrogel for identifying ECM-drivers of drug resistant ovarian cancer. APL Bioeng. 3, 026106 (2019).

    Article  Google Scholar 

  45. Galarza, S., Crosby, A. J., Pak, C. & Peyton, S. R. Control of astrocyte quiescence and activation in a synthetic brain hydrogel. Adv. Healthc. Mater. 9, e1901419 (2020).

    Article  Google Scholar 

  46. Jansen, L. E. et al. A poly(ethylene glycol) three-dimensional bone marrow hydrogel. Biomaterials 280, 121270 (2022).

    Article  Google Scholar 

  47. Fritze, U. F. & von Delius, M. Dynamic disulfide metathesis induced by ultrasound. Chem. Commun. 52, 6363–6366 (2016).

    Article  Google Scholar 

  48. Deneke, N., Rencheck, M. L. & Davis, C. S. An engineer’s introduction to mechanophores. Soft Matter 16, 6230–6252 (2020).

    Article  Google Scholar 

  49. Madl, C. M. et al. Maintenance of neural progenitor cell stemness in 3D hydrogels requires matrix remodelling. Nat. Mater. 16, 1233–1242 (2017).

    Article  Google Scholar 

  50. Wang, C., Tong, X., Jiang, X. & Yang, F. Effect of matrix metalloproteinase-mediated matrix degradation on glioblastoma cell behavior in 3D PEG-based hydrogels. J. Biomed. Mater. Res. A 105, 770–778 (2017).

    Article  Google Scholar 

  51. Li, W., Tao, C., Wang, J., Le, Y. & Zhang, J. MMP-responsive in situ forming hydrogel loaded with doxorubicin-encapsulated biodegradable micelles for local chemotherapy of oral squamous cell carcinoma. RSC Adv. 9, 31264–31273 (2019).

    Article  Google Scholar 

  52. Paul, C. D., Mistriotis, P. & Konstantopoulos, K. Cancer cell motility: lessons from migration in confined spaces. Nat. Rev. Cancer 17, 131–140 (2017).

    Article  Google Scholar 

  53. Tang, S., Richardson, B. M. & Anseth, K. S. Dynamic covalent hydrogels as biomaterials to mimic the viscoelasticity of soft tissues. Prog. Mater. Sci. 120, 100738 (2021).

    Article  Google Scholar 

  54. Liu, K., Wiendels, M., Yuan, H., Ruan, C. & Kouwer, P. H. J. Cell-matrix reciprocity in 3D culture models with nonlinear elasticity. Bioact. Mater. 9, 316–331 (2022).

    Google Scholar 

  55. Rizwan, M., Baker, A. E. G. & Shoichet, M. S. Designing hydrogels for 3D cell culture using dynamic covalent crosslinking. Adv. Healthc. Mater. 10, 2100234 (2021).

    Article  Google Scholar 

  56. Lämmermann, T. & Sixt, M. Mechanical modes of ‘amoeboid’ cell migration. Curr. Opin. Cell Biol. 21, 636–644 (2009).

    Article  Google Scholar 

  57. Wolf, K. et al. Compensation mechanism in tumor cell migration: mesenchymal-amoeboid transition after blocking of pericellular proteolysis. J. Cell Biol. 160, 267–277 (2003).

    Article  Google Scholar 

  58. Tozluoğlu, M. et al. Matrix geometry determines optimal cancer cell migration strategy and modulates response to interventions. Nat. Cell Biol. 15, 751–762 (2013).

    Article  Google Scholar 

  59. Panková, K., Rösel, D., Novotný, M. & Brábek, J. The molecular mechanisms of transition between mesenchymal and amoeboid invasiveness in tumor cells. Cell. Mol. Life Sci. 67, 63–71 (2010).

    Article  Google Scholar 

  60. Dongre, A. & Weinberg, R. A. New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat. Rev. Mol. Cell Biol. 20, 69–84 (2019).

    Article  Google Scholar 

  61. Liu, Y. J. et al. Confinement and low adhesion induce fast amoeboid migration of slow mesenchymal cells. Cell 160, 659–672 (2015).

    Article  Google Scholar 

  62. Graziani, V., Rodriguez-Hernandez, I., Maiques, O. & Sanz-Moreno, V. The amoeboid state as part of the epithelial-to-mesenchymal transition programme. Trends Cell Biol. 32, 228–242 (2022).

    Article  Google Scholar 

  63. Richardson, B. M., Wilcox, D. G., Randolph, M. A. & Anseth, K. S. Hydrazone covalent adaptable networks modulate extracellular matrix deposition for cartilage tissue engineering. Acta Biomater. 83, 71–82 (2019).

    Article  Google Scholar 

  64. Arkenberg, M. R. & Lin, C.-C. Orthogonal enzymatic reactions for rapid crosslinking and dynamic tuning of PEG–peptide hydrogels. Biomater. Sci. 5, 2231–2240 (2017).

    Article  Google Scholar 

  65. Holt, S. E. et al. Supramolecular click product interactions induce dynamic stiffening of extracellular matrix-mimetic hydrogels. Biomacromolecules 22, 3040–3048 (2021).

    Article  Google Scholar 

  66. Marozas, I. A., Anseth, K. S. & Cooper-White, J. J. Adaptable boronate ester hydrogels with tunable viscoelastic spectra to probe timescale dependent mechanotransduction. Biomaterials 223, 119430 (2019).

    Article  Google Scholar 

  67. Smithmyer, M. E. et al. Self-healing boronic acid-based hydrogels for 3D co-cultures. ACS Macro Lett. 7, 1105–1110 (2018).

    Article  Google Scholar 

  68. Tang, S. et al. Adaptable fast relaxing boronate-based hydrogels for probing cell–matrix interactions. Adv. Sci. 5, 1800638 (2018).

    Article  Google Scholar 

  69. Richardson, B. M. et al. Viscoelasticity of hydrazone crosslinked poly(ethylene glycol) hydrogels directs chondrocyte morphology during mechanical deformation. Biomater. Sci. 8, 3804–3811 (2020).

    Article  Google Scholar 

  70. Liu, F. et al. Rheological images of dynamic covalent polymer networks and mechanisms behind mechanical and self-healing properties. Macromolecules 45, 1636–1645 (2012).

    Article  Google Scholar 

  71. McKinnon, D. D., Domaille, D. W., Cha, J. N. & Anseth, K. S. Bis-aliphatic hydrazone-linked hydrogels form most rapidly at physiological pH: identifying the origin of hydrogel properties with small molecule kinetic studies. Chem. Mater. 26, 2382–2387 (2014).

    Article  Google Scholar 

  72. Borelli, A. N. et al. Stress relaxation and composition of hydrazone-crosslinked hybrid biopolymer–synthetic hydrogels determine spreading and secretory properties of MSCs. Adv. Healthc. Mater. 11, 2200393 (2022).

    Article  Google Scholar 

  73. Carberry, B. J., Hernandez, J. J., Dobson, A., Bowman, C. N. & Anseth, K. S. Kinetic analysis of degradation in thioester cross-linked hydrogels as a function of thiol concentration, pKa, and presentation. Macromolecules 55, 2123–2129 (2022).

    Article  Google Scholar 

  74. Carberry, B. J., Rao, V. V. & Anseth, K. S. Phototunable viscoelasticity in hydrogels through thioester exchange. Ann. Biomed. Eng. 48, 2053–2063 (2020).

    Article  Google Scholar 

  75. McKinnon, D. D., Domaille, D. W., Cha, J. N. & Anseth, K. S. Biophysically defined and cytocompatible covalently adaptable networks as viscoelastic 3D cell culture systems. Adv. Mater. 26, 865–872 (2014).

    Article  Google Scholar 

  76. Saunders, L. & Ma, P. X. Self-healing supramolecular hydrogels for tissue engineering applications. Macromol. Biosci. 19, 1800313 (2019).

    Article  Google Scholar 

  77. Loebel, C. et al. Tailoring supramolecular guest–host hydrogel viscoelasticity with covalent fibrinogen double networks. J. Mater. Chem. B 7, 1753–1760 (2019).

    Article  Google Scholar 

  78. Rosales, A. M. et al. Reversible control of network properties in azobenzene-containing hyaluronic acid-based hydrogels. Bioconjug. Chem. 29, 905–913 (2018).

    Article  Google Scholar 

  79. Diba, M. et al. Engineering the dynamics of cell adhesion cues in supramolecular hydrogels for facile control over cell encapsulation and behavior. Adv. Mater. 33, 2008111 (2021).

    Article  Google Scholar 

  80. Nelson, B. R. et al. Photoinduced dithiolane crosslinking for multiresponsive dynamic hydrogels. Adv. Mater. https://doi.org/10.1002/adma.202211209 (2023).

    Article  Google Scholar 

  81. Ding, H. et al. Preparation and application of pH-responsive drug delivery systems. J. Control. Release 348, 206–238 (2022).

    Article  Google Scholar 

  82. Gencoglu, M. F. et al. Comparative study of multicellular tumor spheroid formation methods and implications for drug screening. ACS Biomater. Sci. Eng. 4, 410–420 (2018).

    Article  Google Scholar 

  83. Wang, J. et al. Designer exosomes enabling tumor targeted efficient chemo/gene/photothermal therapy. Biomaterials 276, 121056 (2021).

    Article  Google Scholar 

  84. Lee, Y. B. et al. Induction of four-dimensional spatiotemporal geometric transformations in high cell density tissues via shape-changing hydrogels. Adv. Funct. Mater. 31, 2010104 (2021).

    Article  Google Scholar 

  85. Zhang, W., Torres-Rojas, C., Yue, J. & Zhu, B. M. Adipose-derived stem cells in ovarian cancer progression, metastasis, and chemoresistance. Exp. Biol. Med. 246, 1810–1815 (2021).

    Article  Google Scholar 

  86. Fraley, S. I. et al. A distinctive role for focal adhesion proteins in three-dimensional cell motility. Nat. Cell Biol. 12, 598–604 (2010).

    Article  Google Scholar 

  87. Reticker-Flynn, N. E. et al. A combinatorial extracellular matrix platform identifies cell–extracellular matrix interactions that correlate with metastasis. Nat. Commun. 3, 1122 (2012).

    Article  Google Scholar 

  88. Meyer, A. S. et al. 2D protrusion but not motility predicts growth factor-induced cancer cell migration in 3D collagen. J. Cell Biol. 197, 721–729 (2012).

    Article  Google Scholar 

  89. Ford Versypt, A. N. Multiscale modeling in disease. Curr. Opin. Syst. Biol. 27, 100340 (2021).

    Article  Google Scholar 

  90. Fletcher, A. G. & Osborne, J. M. Seven challenges in the multiscale modeling of multicellular tissues. WIREs Mech. Dis. 14, e1527 (2022).

    Article  Google Scholar 

  91. Xue, K. et al. Biomaterials by design: harnessing data for future development. Mater. Today Bio 12, 100165 (2021).

    Article  Google Scholar 

  92. Tape, C. J. et al. Oncogenic KRAS regulates tumor cell signaling via stromal reciprocation. Cell 165, 910–920 (2016).

    Article  Google Scholar 

  93. Ferrall-Fairbanks, M. C., West, D. M., Douglas, S. A., Averett, R. D. & Platt, M. O. Computational predictions of cysteine cathepsin-mediated fibrinogen proteolysis. Protein Sci. 27, 714–724 (2018). This article demonstrates a new computational model to predict cathepsin degradation and its intracellular proteolysis.

    Article  Google Scholar 

  94. Shockey, W. A., Kieslich, C. A., Wilder, C. L., Watson, V. & Platt, M. O. Dynamic model of protease state and inhibitor trafficking to predict protease activity in breast cancer cells. Cell Mol. Bioeng. 12, 275–288 (2019).

    Article  Google Scholar 

  95. Jain, H. & Jackson, T. Mathematical modeling of cellular cross-talk between endothelial and tumor cells highlights counterintuitive effects of VEGF-targeted therapies. Bull. Math. Biol. 80, 971–1016 (2018).

    Article  MathSciNet  MATH  Google Scholar 

  96. Song, M., Li, D., Makaryan, S. Z. & Finley, S. D. Quantitative modeling to understand cell signaling in the tumor microenvironment. Curr. Opin. Syst. Biol. 27, 100345 (2021).

    Article  Google Scholar 

  97. Li, D. & Finley, S. D. Exploring the extracellular regulation of the tumor angiogenic interaction network using a systems biology model. Front. Physiol. 10, 823 (2019).

    Article  Google Scholar 

  98. Cess, C. G. & Finley, S. D. Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity. J. Theor. Biol. 489, 110125 (2020).

    Article  MATH  Google Scholar 

  99. Rejniak, K. A. et al. The role of tumor tissue architecture in treatment penetration and efficacy: an integrative study. Front. Oncol. 3, 111 (2013).

    Article  Google Scholar 

  100. Ramanujan, S. et al. Diffusion and convection in collagen gels implications for transport in the tumor interstitium. Biophys. J. 83, 1650–1660 (2002).

    Article  Google Scholar 

  101. Karolak, A. & Rejniak, K. A. Micropharmacology: an in silico approach for assessing drug efficacy within a tumor tissue. Bull. Math. Biol. 81, 3623–3641 (2019).

    Article  MathSciNet  MATH  Google Scholar 

  102. Heidary, Z., Haghjooy Javanmard, S., Izadi, I., Zare, N. & Ghaisari, J. Multiscale modeling of collective cell migration elucidates the mechanism underlying tumor-stromal interactions in different spatiotemporal scales. Sci. Rep. 12, 16242 (2022).

    Article  Google Scholar 

  103. Shuttleworth, R. & Trucu, D. Cell-scale degradation of peritumoural extracellular matrix fibre network and its role within tissue-scale cancer invasion. Bull. Math. Biol. 82, 65 (2020).

    Article  MathSciNet  MATH  Google Scholar 

  104. Shuttleworth, R. & Trucu, D. Multiscale dynamics of a heterotypic cancer cell population within a fibrous extracellular matrix. J. Theor. Biol. 486, 110040 (2020).

    Article  MathSciNet  MATH  Google Scholar 

  105. Suveges, S., Eftimie, R. & Trucu, D. Directionality of macrophages movement in tumour invasion: a multiscale moving-boundary approach. Bull. Math. Biol. 82, 148 (2020).

    Article  MathSciNet  MATH  Google Scholar 

  106. Nguyen Edalgo, Y. & Ford Versypt, A. Mathematical modeling of metastatic cancer migration through a remodeling extracellular matrix. Processes 6, 58 (2018).

    Article  Google Scholar 

  107. Blache, U. et al. Engineered hydrogels for mechanobiology. Nat. Rev. Methods Primers 2, 98 (2022).

    Article  Google Scholar 

  108. Deng, B. et al. Biological role of matrix stiffness in tumor growth and treatment. J. Transl. Med. 20, 540 (2022).

    Article  Google Scholar 

  109. Elosegui-Artola, A. The extracellular matrix viscoelasticity as a regulator of cell and tissue dynamics. Curr. Opin. Cell Biol. 72, 10–18 (2021).

    Article  Google Scholar 

  110. Chaudhuri, O., Cooper-White, J., Janmey, P. A., Mooney, D. J. & Shenoy, V. B. Effects of extracellular matrix viscoelasticity on cellular behaviour. Nature 584, 535–546 (2020).

    Article  Google Scholar 

  111. Yamada, K. M., Doyle, A. D. & Lu, J. Cell–3D matrix interactions: recent advances and opportunities. Trends Cell Biol. 32, 883–895 (2022).

    Article  Google Scholar 

  112. Crawford, A. J. et al. Tumor proliferation and invasion are coupled through cell-extracellular matrix friction. Preprint at bioRxiv https://doi.org/10.1101/2022.11.15.516548 (2022).

  113. Strychalski, W., Copos, C. A., Lewis, O. L. & Guy, R. D. A poroelastic immersed boundary method with applications to cell biology. J. Comput. Phys. 282, 77–97 (2015).

    Article  MathSciNet  MATH  Google Scholar 

  114. Pakshir, P. et al. Dynamic fibroblast contractions attract remote macrophages in fibrillar collagen matrix. Nat. Commun. 10, 1850 (2019). This article highlights how cell-generated forces on their surroundings can act as durotactic signals for immune cells, which is relevant for macrophage-mediated tumour homing and killing.

    Article  Google Scholar 

  115. Camacho-Gomez, D., Garcia-Aznar, J. M. & Gomez-Benito, M. J. A 3D multi-agent-based model for lumen morphogenesis: the role of the biophysical properties of the extracellular matrix. Eng. Comput. 38, 4135–4149 (2022).

    Article  Google Scholar 

  116. Ilina, O. et al. Cell–cell adhesion and 3D matrix confinement determine jamming transitions in breast cancer invasion. Nat. Cell Biol. 22, 1103–1115 (2020).

    Article  Google Scholar 

  117. Shuttleworth, R. & Trucu, D. Multiscale modelling of fibres dynamics and cell adhesion within moving boundary cancer invasion. Bull. Math. Biol. 81, 2176–2219 (2019).

    Article  MathSciNet  MATH  Google Scholar 

  118. van Oers, R. F., Rens, E. G., LaValley, D. J., Reinhart-King, C. A. & Merks, R. M. Mechanical cell-matrix feedback explains pairwise and collective endothelial cell behavior in vitro. PLoS Comput. Biol. 10, e1003774 (2014).

    Article  Google Scholar 

  119. Macklin, P., Edgerton, M. E., Thompson, A. M. & Cristini, V. Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression. J. Theor. Biol. 301, 122–140 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  120. Hirway, S. U., Lemmon, C. A. & Weinberg, S. H. Multicellular mechanochemical hybrid cellular Potts model of tissue formation during epithelial‐mesenchymal transition. Comput. Syst. Oncol. 10.002/cso2.1031 (2021).

  121. Suveges, S., Chamseddine, I., Rejniak, K. A., Eftimie, R. & Trucu, D. Collective cell migration in a fibrous environment: a hybrid multiscale modelling approach. Front. Appl. Math. Stat. 7, 680029 (2021).

    Article  Google Scholar 

  122. Cruz, D. A. & Kemp, M. L. Hybrid computational modeling methods for systems biology. Prog. Biomed. Eng. 4, 012002 (2021).

    Article  Google Scholar 

  123. Bull, J. A. & Byrne, H. M. Quantification of spatial and phenotypic heterogeneity in an agent-based model of tumour-macrophage interactions. PLoS Comput. Biol. 19, e1010994 (2023).

    Article  Google Scholar 

  124. Jenner, A. L. et al. Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy. iScience 25, 104395 (2022).

    Article  Google Scholar 

  125. Leighow, S. M., Landry, B., Lee, M. J., Peyton, S. R. & Pritchard, J. R. Agent-based models help interpret patterns of clinical drug resistance by contextualizing competition between distinct drug failure modes. Cell. Mol. Bioeng. 15, 521–533 (2022).

    Article  Google Scholar 

  126. Miller, A. K. et al. The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous proteasome inhibitor resistant disease. Preprint at bioRxiv https://doi.org/10.1101/2022.11.13.516335 (2022).

  127. West, J. et al. Tumor-immune metaphenotypes orchestrate an evolutionary bottleneck that promotes metabolic transformation. Preprint at bioRxiv https://doi.org/10.1101/2022.06.03.493752 (2022).

  128. Cess, C. G. & Finley, S. D. Multi-scale modeling of macrophage-T cell interactions within the tumor microenvironment. PLoS Comput. Biol. 16, e1008519 (2020).

    Article  Google Scholar 

  129. Frankenstein, Z. et al. Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression. Nat. Ecol. Evol. 4, 870–884 (2020).

    Article  Google Scholar 

  130. Chowkwale, M., Mahler, G. J., Huang, P. & Murray, B. T. A multiscale in silico model of endothelial to mesenchymal transformation in a tumor microenvironment. J. Theor. Biol. 480, 229–240 (2019).

    Article  MATH  Google Scholar 

  131. Nguyen Edalgo, Y. T., Zornes, A. L. & Ford Versypt, A. N. A hybrid discrete–continuous model of metastatic cancer cell migration through a remodeling extracellular matrix. AIChE J. 65, e16671 (2019).

    Article  Google Scholar 

  132. Roy, M. & Finley, S. D. Metabolic reprogramming dynamics in tumor spheroids: insights from a multicellular, multiscale model. PLoS Comput. Biol. 15, e1007053 (2019).

    Article  Google Scholar 

  133. Ghaffarizadeh, A., Heiland, R., Friedman, S. H., Mumenthaler, S. M. & Macklin, P. PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol. 14, e1005991 (2018).

    Article  Google Scholar 

  134. Kather, J. N. et al. In silico modeling of immunotherapy and stroma-targeting therapies in human colorectal cancer. Cancer Res. 77, 6442–6452 (2017).

    Article  Google Scholar 

  135. Kumar, S., Kapoor, A., Desai, S., Inamdar, M. M. & Sen, S. Proteolytic and non-proteolytic regulation of collective cell invasion: tuning by ECM density and organization. Sci. Rep. 6, 19905 (2016).

    Article  Google Scholar 

  136. Poleszczuk, J., Hahnfeldt, P. & Enderling, H. Evolution and phenotypic selection of cancer stem cells. PLoS Comput. Biol. 11, e1004025 (2015).

    Article  Google Scholar 

  137. Araujo, A., Cook, L. M., Lynch, C. C. & Basanta, D. An integrated computational model of the bone microenvironment in bone-metastatic prostate cancer. Cancer Res. 74, 2391–2401 (2014).

    Article  Google Scholar 

  138. Hatzikirou, H., Basanta, D., Simon, M., Schaller, K. & Deutsch, A. ‘Go or grow’: the key to the emergence of invasion in tumour progression? Math. Med. Biol. 29, 49–65 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  139. Robertson-Tessi, M., Gillies, R. J., Gatenby, R. A. & Anderson, A. R. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res. 75, 1567–1579 (2015).

    Article  Google Scholar 

  140. Wang, Y. et al. Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach. Sci. Rep. 11, 1710 (2021).

    Article  Google Scholar 

  141. Cess, C. G. & Finley, S. D. Multiscale modeling of tumor adaption and invasion following anti‐angiogenic therapy. Comput. Syst. Oncol. 2, e1032 (2022).

    Article  Google Scholar 

  142. Yu, J. S. & Bagheri, N. Agent-based models predict emergent behavior of heterogeneous cell populations in dynamic microenvironments. Front. Bioeng. Biotechnol. 8, 249 (2020).

    Article  Google Scholar 

  143. Feng, Y. et al. Bone marrow stromal cells from myeloma patients support the growth of myeloma stem cells. Stem Cell Dev. 19, 1289–1296 (2010).

    Article  Google Scholar 

  144. Feng, Y. et al. Unique biomechanical interactions between myeloma cells and bone marrow stroma cells. Prog. Biophys. Mol. Biol. 103, 148–156 (2010).

    Article  Google Scholar 

  145. Su, J. et al. Targeting the biophysical properties of the myeloma initiating cell niches: a pharmaceutical synergism analysis using multi-scale agent-based modeling. PLoS ONE 9, e85059 (2014).

    Article  Google Scholar 

  146. Desai, R. A., Gopal, S. B., Chen, S. & Chen, C. S. Contact inhibition of locomotion probabilities drive solitary versus collective cell migration. J. R. Soc. Interface 10, 20130717 (2013).

    Article  Google Scholar 

  147. Mayor, R. & Carmona-Fontaine, C. Keeping in touch with contact inhibition of locomotion. Trends Cell Biol. 20, 319–328 (2010).

    Article  Google Scholar 

  148. Peela, N. et al. A three dimensional micropatterned tumor model for breast cancer cell migration studies. Biomaterials 81, 72–83 (2016).

    Article  Google Scholar 

  149. Schwartz, A. D. et al. A biomaterial screening approach reveals microenvironmental mechanisms of drug resistance. Integr. Biol. 9, 912–924 (2017).

    Article  Google Scholar 

  150. Kolda, T. G. & Bader, B. W. Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009).

    Article  MathSciNet  MATH  Google Scholar 

  151. Martino, C. et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat. Biotechnol. 39, 165–168 (2021).

    Article  Google Scholar 

  152. Tan, Z. C., Murphy, M. C., Alpay, H. S., Taylor, S. D. & Meyer, A. S. Tensor-structured decomposition improves systems serology analysis. Mol. Syst. Biol. 17, e10243 (2021).

    Article  Google Scholar 

  153. Gross, S. M. et al. A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses. Commun. Biol. 5, 1066 (2022).

    Article  Google Scholar 

  154. Shao, X. et al. MatrisomeDB 2.0: 2023 updates to the ECM-protein knowledge database. Nucleic Acids Res. 51, D1519–D1530 (2023).

    Article  Google Scholar 

  155. SenNet Consortium. NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health. Nat. Aging 2, 1090–1100 (2022).

    Article  Google Scholar 

  156. Azer, K. et al. History and future perspectives on the discipline of quantitative systems pharmacology modeling and its applications. Front. Physiol. 12, 637999 (2021).

    Article  Google Scholar 

  157. Mejias, J. C., Nelson, M. R., Liseth, O. & Roy, K. A 96-well format microvascularized human lung-on-a-chip platform for microphysiological modeling of fibrotic diseases. Lab Chip 20, 3601–3611 (2020).

    Article  Google Scholar 

  158. McAleer, C. W. et al. On the potential of in vitro organ-chip models to define temporal pharmacokinetic–pharmacodynamic relationships. Sci. Rep. 9, 9619 (2019).

    Article  Google Scholar 

  159. Emami, J. In vitro–in vivo correlation: from theory to applications. J. Pharm. Pharm Sci. 9, 169–189 (2006).

    Google Scholar 

  160. Musetti, S. & Huang, L. Nanoparticle-mediated remodeling of the tumor microenvironment to enhance immunotherapy. ACS Nano 12, 11740–11755 (2018).

    Article  Google Scholar 

  161. Finley, S. D., Angelikopoulos, P., Koumoutsakos, P. & Popel, A. S. Pharmacokinetics of anti-VEGF agent aflibercept in cancer predicted by data-driven, molecular-detailed model. CPT Pharmacomet. Syst. Pharmacol. 4, 641–649 (2015).

    Article  Google Scholar 

  162. Jenner, A. L., Frascoli, F., Yun, C.-O. & Kim, P. S. Optimising hydrogel release profiles for viro-immunotherapy using oncolytic adenovirus expressing IL-12 and GM-CSF with immature dendritic cells. Appl. Sci. 10, 2872 (2020).

    Article  Google Scholar 

  163. Prybutok, A. N., Yu, J. S., Leonard, J. N. & Bagheri, N. Mapping CAR T-cell design space using agent-based models. Front. Mol. Biosci. 9, 849363 (2022).

    Article  Google Scholar 

  164. Peng, G. C. Y. et al. Multiscale modeling meets machine learning: what can we learn? Arch. Comput. Methods Eng. 28, 1017–1037 (2021).

    Article  MathSciNet  Google Scholar 

  165. Yuan, B. et al. CellBox: interpretable machine learning for perturbation biology with application to the design of cancer combination therapy. Cell Syst. 12, 128–140.e4 (2021).

    Article  Google Scholar 

  166. Jiang, R., Singh, P., Wrede, F., Hellander, A. & Petzold, L. Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods. PLoS Comput. Biol. 18, e1009830 (2022).

    Article  Google Scholar 

  167. Aref, A. R. et al. 3D microfluidic ex vivo culture of organotypic tumor spheroids to model immune checkpoint blockade. Lab Chip 18, 3129–3143 (2018).

    Article  Google Scholar 

  168. Yi, H. G. et al. A bioprinted human-glioblastoma-on-a-chip for the identification of patient-specific responses to chemoradiotherapy. Nat. Biomed. Eng. 3, 509–519 (2019).

    Article  Google Scholar 

  169. Nguyen, M. et al. Dissecting effects of anti-cancer drugs and cancer-associated fibroblasts by on-chip reconstitution of immunocompetent tumor microenvironments. Cell Rep. 25, 3884–3893 (2018).

    Article  Google Scholar 

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

    Article  Google Scholar 

  171. Fuchs, S. et al. In-line analysis of organ-on-chip systems with sensors: integration, fabrication, challenges, and potential. ACS Biomater. Sci. Eng. 7, 2926–2948 (2021).

    Article  Google Scholar 

  172. Langer, E. M. et al. Modeling tumor phenotypes in vitro with three-dimensional bioprinting. Cell Rep. 26, 608–623.e6 (2019).

    Article  Google Scholar 

  173. Heinrich, M. A. et al. 3D-Bioprinted mini-brain: a glioblastoma model to study cellular interactions and therapeutics. Adv. Mater. 31, e1806590 (2019).

    Article  Google Scholar 

  174. Estrada, M. F. et al. Modelling the tumour microenvironment in long-term microencapsulated 3D co-cultures recapitulates phenotypic features of disease progression. Biomaterials 78, 50–61 (2016).

    Article  Google Scholar 

  175. Pedron, S. & Harley, B. A. Impact of the biophysical features of a 3D gelatin microenvironment on glioblastoma malignancy. J. Biomed. Mater. Res. A 101, 3404–3415 (2013).

    Article  Google Scholar 

  176. Meng, F. et al. 3D Bioprinted in vitro metastatic models via reconstruction of tumor microenvironments. Adv. Mater. 31, e1806899 (2019).

    Article  Google Scholar 

  177. Chen, J. E. et al. Influence of hyaluronic acid transitions in tumor microenvironment on glioblastoma malignancy and invasive behavior. Front. Mater. 5, 39 (2018).

    Article  Google Scholar 

  178. Cha, J., Kang, S. G. & Kim, P. Strategies of mesenchymal invasion of patient-derived brain tumors: microenvironmental adaptation. Sci. Rep. 6, 24912 (2016).

    Article  Google Scholar 

  179. Ulrich, T. A., de Juan Pardo, E. M. & Kumar, S. The mechanical rigidity of the extracellular matrix regulates the structure, motility, and proliferation of glioma cells. Cancer Res. 69, 4167–4174 (2009).

    Article  Google Scholar 

  180. Osuna de la Pena, D. et al. Bioengineered 3D models of human pancreatic cancer recapitulate in vivo tumour biology. Nat. Commun. 12, 5623 (2021).

    Article  Google Scholar 

  181. Xiao, W. et al. Bioengineered scaffolds for 3D culture demonstrate extracellular matrix-mediated mechanisms of chemotherapy resistance in glioblastoma. Matrix Biol. 85–86, 128–146 (2020).

    Article  Google Scholar 

  182. Klistorner, A. et al. Progression of retinal ganglion cell loss in multiple sclerosis is associated with new lesions in the optic radiations. Eur. J. Neurol. 24, 1392–1398 (2017).

    Article  Google Scholar 

  183. Li, Q. et al. Scalable production of glioblastoma tumor-initiating cells in 3 dimension thermoreversible hydrogels. Sci. Rep. 6, 31915 (2016).

    Article  Google Scholar 

  184. Ondeck, M. G. et al. Dynamically stiffened matrix promotes malignant transformation of mammary epithelial cells via collective mechanical signaling. Proc. Natl Acad. Sci. USA 116, 3502–3507 (2019).

    Article  Google Scholar 

  185. Lee, J., Abdeen, A. A., Li, Y., Goonetilleke, S. & Kilian, K. A. Gradient and dynamic hydrogel materials to probe dynamics in cancer stem cell phenotypes. ACS Appl. Bio Mater. 4, 711–720 (2021).

    Article  Google Scholar 

  186. Liu, H. Y., Korc, M. & Lin, C. C. Biomimetic and enzyme-responsive dynamic hydrogels for studying cell-matrix interactions in pancreatic ductal adenocarcinoma. Biomaterials 160, 24–36 (2018).

    Article  Google Scholar 

  187. Allen, S. C., Widman, J. A., Datta, A. & Suggs, L. J. Dynamic extracellular matrix stiffening induces a phenotypic transformation and a migratory shift in epithelial cells. Integr. Biol. 12, 161–174 (2020).

    Article  Google Scholar 

  188. Chang, C. Y. & Lin, C. C. Hydrogel models with stiffness gradients for interrogating pancreatic cancer cell fate. Bioengineering 8, 37 (2021).

    Article  Google Scholar 

  189. Discher, D. E., Janmey, P. & Wang, Y. L. Tissue cells feel and respond to the stiffness of their substrate. Science 310, 1139–1143 (2005).

    Article  Google Scholar 

  190. Chaudhuri, O. et al. Extracellular matrix stiffness and composition jointly regulate the induction of malignant phenotypes in mammary epithelium. Nat. Mater. 13, 970–978 (2014).

    Article  Google Scholar 

  191. Gjorevski, N. et al. Designer matrices for intestinal stem cell and organoid culture. Nature 539, 560–564 (2016).

    Article  Google Scholar 

  192. Trichet, L. et al. Evidence of a large-scale mechanosensing mechanism for cellular adaptation to substrate stiffness. Proc. Natl Acad. Sci. USA 109, 6933–6938 (2012).

    Article  Google Scholar 

  193. Mathivanan, S., Ji, H. & Simpson, R. J. Exosomes: extracellular organelles important in intercellular communication. J. Proteom. 73, 1907–1920 (2010).

    Article  Google Scholar 

  194. Patwardhan, S., Mahadik, P., Shetty, O. & Sen, S. ECM stiffness-tuned exosomes drive breast cancer motility through thrombospondin-1. Biomaterials 279, 121185 (2021).

    Article  Google Scholar 

  195. Ruskowitz, E. R., Comerford, M. P., Badeau, B. A. & DeForest, C. A. Logical stimuli-triggered delivery of small molecules from hydrogel biomaterials. Biomater. Sci. 7, 542–546 (2019).

    Article  Google Scholar 

  196. Gong, F. et al. Tumor microenvironment-responsive intelligent nanoplatforms for cancer theranostics. Nano Today 32, 100851 (2020).

    Article  Google Scholar 

  197. Dutta, P. K. et al. Programmable multivalent DNA-origami tension probes for reporting cellular traction forces. Nano Lett. 18, 4803–4811 (2018).

    Article  Google Scholar 

  198. Robby, A. I. et al. Tumor microenvironment-responsive touch sensor-based pH-triggered controllable conductive hydrogel. Appl. Mater. Today 25, 101259 (2021).

    Article  Google Scholar 

  199. Zhao, M. et al. NIR-II pH sensor with a FRET adjustable transition point for in situ dynamic tumor microenvironment visualization. Angew. Chem. Int. Ed. 60, 5091–5095 (2021).

    Article  Google Scholar 

  200. Boykoff, N., Freage, L., Lenn, J. & Mallikaratchy, P. Bispecific aptamer sensor toward T-cell leukemia detection in the tumor microenvironment. ACS Omega 6, 32563–32570 (2021).

    Article  Google Scholar 

  201. Fluegen, G. et al. Phenotypic heterogeneity of disseminated tumour cells is preset by primary tumour hypoxic microenvironments. Nat. Cell Biol. 19, 120–132 (2017).

    Article  Google Scholar 

  202. Tang, R. et al. A versatile system to record cell-cell interactions. eLife 9, e61080 (2020).

    Article  Google Scholar 

  203. Simeonov, K. P. et al. Single-cell lineage tracing of metastatic cancer reveals selection of hybrid EMT states. Cancer Cell 39, 1150–1162.e9 (2021).

    Article  Google Scholar 

  204. Tang, T.-C. et al. Materials design by synthetic biology. Nat. Rev. Mater. 6, 332–350 (2021).

    Article  Google Scholar 

  205. Strobl, M. A. R. et al. Turnover modulates the need for a cost of resistance in adaptive therapy. Cancer Res. 81, 1135–1147 (2021).

    Article  Google Scholar 

  206. 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).

    Article  Google Scholar 

  207. Cunningham, J. et al. Optimal control to reach eco-evolutionary stability in metastatic castrate-resistant prostate cancer. PLoS ONE 15, e0243386 (2020).

    Article  Google Scholar 

  208. Din, M. O. et al. Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536, 81–85 (2016). This article shows that engineering of the circadian clock in bacteria enables the precise, timed lysis of a colony, leading to the coordinated release of tumour-killing toxins.

    Article  Google Scholar 

  209. Chowdhury, S. et al. Programmable bacteria induce durable tumor regression and systemic antitumor immunity. Nat. Med. 25, 1057–1063 (2019).

    Article  Google Scholar 

  210. Canale, F. P. et al. Metabolic modulation of tumours with engineered bacteria for immunotherapy. Nature 598, 662–666 (2021).

    Article  Google Scholar 

  211. Caruana, I. et al. Heparanase promotes tumor infiltration and antitumor activity of CAR-redirected T lymphocytes. Nat. Med. 21, 524–529 (2015).

    Article  Google Scholar 

  212. Zhao, Y. et al. Bioorthogonal equipping CAR-T cells with hyaluronidase and checkpoint blocking antibody for enhanced solid tumor immunotherapy. ACS Cent. Sci. 8, 603–614 (2022).

    Article  MathSciNet  Google Scholar 

  213. Han, X. et al. Masked chimeric antigen receptor for tumor-specific activation. Mol. Ther. 25, 274–284 (2017).

    Article  Google Scholar 

  214. Dannenfelser, R. et al. Discriminatory power of combinatorial antigen recognition in cancer T cell therapies. Cell Syst. 11, 215–228.e5 (2020).

    Article  Google Scholar 

  215. Juillerat, A. et al. An oxygen sensitive self-decision making engineered CAR T-cell. Sci. Rep. 7, 39833 (2017).

    Article  Google Scholar 

  216. Kosti, P. et al. Hypoxia-sensing CAR T cells provide safety and efficacy in treating solid tumors. Cell Rep. Med. 2, 100227 (2021). This article reports engineered CAR T cells that contain a dual oxygen-sensing switch, which allows T cell activation at hypoxic tumour sites.

    Article  Google Scholar 

  217. Nissim, L. et al. Synthetic RNA-based immunomodulatory gene circuits for cancer immunotherapy. Cell 171, 1138–1150.e15 (2017).

    Article  Google Scholar 

  218. Kojima, R., Scheller, L. & Fussenegger, M. Nonimmune cells equipped with T-cell-receptor-like signaling for cancer cell ablation. Nat. Chem. Biol. 14, 42–49 (2018).

    Article  Google Scholar 

  219. Capes-Davis, A. & Neve, R. M. Authentication: a standard problem or a problem of standards. PLoS Biol. 14, e1002477 (2016).

    Article  Google Scholar 

  220. Freedman, L. P. et al. Reproducibility: changing the policies and culture of cell line authentication. Nat. Methods 12, 493–497 (2015).

    Article  Google Scholar 

  221. Zhang, J. et al. Recurrent BRCA1 and BRCA2 mutations in breast cancer patients of African ancestry. Breast Cancer Res. Treat. 134, 889–894 (2012).

    Article  Google Scholar 

  222. Rayford, W. et al. Comparative analysis of 1152 African-American and European-American men with prostate cancer identifies distinct genomic and immunological differences. Commun. Biol. 4, 670 (2021).

    Article  Google Scholar 

  223. Lehrberg, A. et al. Outcome of African-American compared to White-American patients with early-stage breast cancer, stratified by phenotype. Breast J. 27, 573–580 (2021).

    Article  Google Scholar 

  224. Jatoi, I., Sung, H. & Jemal, A. The emergence of the racial disparity in U.S. breast-cancer mortality. N. Engl. J. Med. 386, 2349–2352 (2022).

    Article  Google Scholar 

  225. Taylor, T. R. et al. Racial discrimination and breast cancer incidence in US black women: the black women’s health study. Am. J. Epidemiol. 166, 46–54 (2007).

    Article  Google Scholar 

  226. Basourakos, S. P. et al. Harm-to-benefit of three decades of prostate cancer screening in black men. NEJM Evid. 1, evidoa2200031 (2022).

    Article  Google Scholar 

  227. Welch, H. G. & Adamson, A. S. Should recommendations for cancer screening differentiate on race. NEJM Evid. 1, EVIDe2200070 (2022).

    Article  Google Scholar 

  228. Stevens, K. R. et al. Fund black scientists. Cell 184, 561–565 (2021). A nationwide network of biomedical engineering women faculty (BME UNITE) highlights the racial funding disparity in the National Institutes of Health (NIH), which provides a barrier to the success of Black faculty across the biomedical sciences and limits innovation in human health research.

    Article  Google Scholar 

  229. Wylie, R. G. et al. Spatially controlled simultaneous patterning of multiple growth factors in three-dimensional hydrogels. Nat. Mater. 10, 799–806 (2011).

    Article  Google Scholar 

  230. Shih, H. & Lin, C.-C. Tuning stiffness of cell-laden hydrogel via host–guest interactions. J. Mater. Chem. B 4, 4969–4974 (2016).

    Article  Google Scholar 

  231. Rosales, A. M., Vega, S. L., DelRio, F. W., Burdick, J. A. & Anseth, K. S. Hydrogels with reversible mechanics to probe dynamic cell microenvironments. Angew. Chem. Int. Ed. 56, 12132–12136 (2017).

    Article  Google Scholar 

  232. Pearson, S., Feng, J. & del Campo, A. Lighting the path: light delivery strategies to activate photoresponsive biomaterials in vivo. Adv. Funct. Mater. 31, 2105989 (2021).

    Article  Google Scholar 

  233. Owen, S. C., Fisher, S. A., Tam, R. Y., Nimmo, C. M. & Shoichet, M. S. Hyaluronic acid click hydrogels emulate the extracellular matrix. Langmuir 29, 7393–7400 (2013).

    Article  Google Scholar 

  234. Lunzer, M. et al. A disulfide-based linker for thiol–norbornene conjugation: formation and cleavage of hydrogels by the use of light. Polym. Chem. 13, 1158–1168 (2022).

    Article  Google Scholar 

  235. Han, R. L., Shi, J. H., Liu, Z. J., Hou, Y. F. & Wang, Y. Near-infrared light-triggered hydrophobic-to-hydrophilic switch nanovalve for on-demand cancer therapy. ACS Biomater. Sci. Eng. 4, 3478–3486 (2018).

    Article  Google Scholar 

  236. Bian, Q., Wang, W., Wang, S. & Wang, G. Light-triggered specific cancer cell release from cyclodextrin/azobenzene and aptamer-modified substrate. ACS Appl. Mater. Interfaces 8, 27360–27367 (2016).

    Article  Google Scholar 

  237. Tam, R. Y., Cooke, M. J. & Shoichet, M. S. A covalently modified hydrogel blend of hyaluronan–methyl cellulose with peptides and growth factors influences neural stem/progenitor cell fate. J. Mater. Chem. 22, 19402–19411 (2012).

    Article  Google Scholar 

  238. Gupta, D., Tator, C. H. & Shoichet, M. S. Fast-gelling injectable blend of hyaluronan and methylcellulose for intrathecal, localized delivery to the injured spinal cord. Biomaterials 27, 2370–2379 (2006).

    Article  Google Scholar 

  239. Rodell, C. B., Kaminski, A. L. & Burdick, J. A. Rational design of network properties in guest–host assembled and shear-thinning hyaluronic acid hydrogels. Biomacromolecules 14, 4125–4134 (2013).

    Article  Google Scholar 

  240. Rezk, A. I., Obiweluozor, F. O., Choukrani, G., Park, C. H. & Kim, C. S. Drug release and kinetic models of anticancer drug (BTZ) from a pH-responsive alginate polydopamine hydrogel: towards cancer chemotherapy. Int. J. Biol. Macromol. 141, 388–400 (2019).

    Article  Google Scholar 

  241. Liao, W.-C. et al. pH- and ligand-induced release of loads from DNA–acrylamide hydrogel microcapsules. Chem. Sci. 8, 3362–3373 (2017).

    Article  Google Scholar 

  242. Ding, A. et al. Jammed micro-flake hydrogel for four-dimensional living cell bioprinting. Adv. Mater. 34, 2109394 (2022).

    Article  Google Scholar 

  243. Lutolf, M. P. et al. Synthetic matrix metalloproteinase-sensitive hydrogels for the conduction of tissue regeneration: engineering cell-invasion characteristics. Proc. Natl Acad. Sci. USA 100, 5413–5418 (2003).

    Article  Google Scholar 

  244. Isaacson, K. J., Martin Jensen, M., Subrahmanyam, N. B. & Ghandehari, H. Matrix-metalloproteinases as targets for controlled delivery in cancer: an analysis of upregulation and expression. J. Control. Release 259, 62–75 (2017).

    Article  Google Scholar 

  245. Chiappini, C. et al. Mapping local cytosolic enzymatic activity in human esophageal mucosa with porous silicon nanoneedles. Adv. Mater. 27, 5147–5152 (2015).

    Article  Google Scholar 

  246. Acar, H. et al. Cathepsin-mediated cleavage of peptides from peptide amphiphiles leads to enhanced intracellular peptide accumulation. Bioconjug. Chem. 28, 2316–2326 (2017).

    Article  Google Scholar 

  247. Arkenberg, M. R., Moore, D. M. & Lin, C. C. Dynamic control of hydrogel crosslinking via sortase-mediated reversible transpeptidation. Acta Biomater. 83, 83–95 (2019).

    Article  Google Scholar 

  248. Huang, C.-W., Wang, J., Wang, Z., Ayarza, J. & Esser-Kahn, A. P. Enhancing the piezoelectric voltage output in a gel composite through the tuning of the matrix dielectric constant. ACS Appl. Eng. Mater. 1, 175–183 (2023).

    Article  Google Scholar 

  249. Su, L. et al. Detection of cancer biomarkers by piezoelectric biosensor using PZT ceramic resonator as the transducer. Biosens. Bioelectron. 46, 155–161 (2013).

    Article  Google Scholar 

  250. Robby, A. I., Lee, G., Lee, K. D., Jang, Y. C. & Park, S. Y. GSH-responsive self-healable conductive hydrogel of highly sensitive strain-pressure sensor for cancer cell detection. Nano Today 39, 101178 (2021).

    Article  Google Scholar 

  251. Muzzalupo, R., Tavano, L., Rossi, C. O., Picci, N. & Ranieri, G. A. Novel pH sensitive ferrogels as new approach in cancer treatment: effect of the magnetic field on swelling and drug delivery. Colloids Surf. B 134, 273–278 (2015).

    Article  Google Scholar 

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Acknowledgements

The authors thank the programme officers at the US National Science Foundation (NSF) and National Cancer Institute (NCI): G. Iannacchione, N. Zahir, S. Mamaghani and K. Sunderic, who nucleated this discussion by inviting the co-authors and all participants to the NCI–NSF Square-Table Meeting on Living Materials in 2021. The authors also thank J. Shwarz at Syracuse University for discussions. The authors acknowledge funding support from the following grants: NCI U01-CA215709 to A.S.M.; NCI R01-CA241137 to S.M.M.; NCI U01-CA232137 to S.M.M. and S.D.F.; NCI R01CA241927, Cancer Prevention and Research Institute of Texas (CPRIT) RR210042, NCATS UG3TR003148 and American Cancer Society RSG-19-167-01 to S.K.S.; NCI U01CA215848 to M.L.K.; NSF 1648035 and The Billie and Bernie Marcus Foundation to K.R.; The Jane Koskina Ted Giovannis Foundation and NCI U01CA265709 to S.R.P.; NIGMS R35GM133763 to A.N.F.V.; NSERC Discovery Grant RGPIN-2021-03488 to A.P.McG.

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All authors contributed to outlining, writing and editing of the full manuscript. S.R.P., K.R. and S.M.M. coordinated the writing efforts and were co-leaders of the workshop that generated the ideas within the manuscript.

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Correspondence to Shelly R. Peyton, Krishnendu Roy or Shannon M. Mumenthaler.

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M.L.K. is a scientific advisory board member (with equity) for Parthenon Therapeutics. The other authors declare no competing interests.

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Nature Reviews Bioengineering thanks Kristopher Kilian, Aranzazu del Campo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Peyton, S.R., Chow, L.W., Finley, S.D. et al. Synthetic living materials in cancer biology. Nat Rev Bioeng 1, 972–988 (2023). https://doi.org/10.1038/s44222-023-00105-w

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