The Use of FN-Silk Networks as Extracellular Matrix-Mimicking Support for 3D In Vitro Breast Cancer Models Improved the Model's Reproducibility in Drug Sensitivity

Abstract

To identify effective drugs for breast cancer treatment, it is essential to establish physiologically relevant models. Traditional models based on 2D cultures or 3D scaffold-free spheroids lack the crucial cell–extracellular matrix (ECM) interactions, known to significantly impact drug sensitivity. A newly developed 3D culture format using a network of the recombinant spider silk protein FN-silk has demonstrated ECM-like interactions and maintenance of subtype-specific marker expression in breast cancer cells. In the current study, chemotherapy drug treatment experiments were conducted on the breast cancer cell lines MCF-7, MDA-MB-231, and SK-BR-3 cultured in 2D, spheroids, and FN-silk networks. The results suggest that FN-silk networks hold promise as a base for an in vitro 3D model assessing the effects of chemotherapeutic agents. Comparing drug responses revealed that cells cultured in FN-silk networks were generally less sensitive to the drugs tested—as compared to 2D cultures—suggesting that FN-silk-supported 3D culture could have higher clinical relevance. Moreover, the results showed improved reproducibility with FN-silk networks compared to spheroids. This study concludes that FN-silk networks have the potential to support the establishment of a valuable 3D tumor model for the development of personalized breast cancer treatments and thereby contribute to improved success rates in drug development.

Keywords

3D model / breast cancer / dose response parameters, drug sensitivity score / drug screening / FN-silk network

References

[1] H. Sung, J. Ferlay, R. L. Siegel, et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 71, no. 3 (May 2021): 209–249.

[2] K. Polyak, “Heterogeneity in Breast Cancer,” Journal of Clinical Investigation 121, no. 10 (2011): 3786–3788.

[3] Ş. Comşa, A. M. Cîmpean, and M. Raica, “The Story of MCF-7 Breast Cancer Cell Line: 40 Years of Experience in Research,” Anticancer Research 35, no. 6 (June 2015): 3147–3154.

[4] D. L. Holliday and V. Speirs, “Choosing the Right Cell Line for Breast Cancer Research,” Breast Cancer Research 13, no. 4 (August 2011): 215.

[5] K. J. Chavez, S. V. Garimella, and S. Lipkowitz, “Triple Negative Breast Cancer Cell Lines: One Tool in the Search for Better Treatment of Triple Negative Breast Cancer,” Breast Disease 32, no. 1–2 (2010): 35–48.

[6] X. Dai, T. Li, Z. Bai, et al., “Breast Cancer Intrinsic Subtype Classification, Clinical Use and Future Trends,” American Journal of Cancer Research 5, no. 10 (2015): 2929–2943.

[7] A. Gupta, P. Gautam, K. Wennerberg, and T. Aittokallio, “A Normalized Drug Response Metric Improves Accuracy and Consistency of Anticancer Drug Sensitivity Quantification in Cell-Based Screening,” Communications Biology 3, no. 1 (January 2020): 42.

[8] E. A. Brooks, S. Galarza, M. F. Gencoglu, R. C. Cornelison, J. M. Munson, and S. R. Peyton, “Applicability of Drug Response Metrics for Cancer Studies Using Biomaterials,” Philosophical Transactions of the Royal Society, B: Biological Sciences 374, no. 1779 (August 2019): 20180226.

[9] M. Hafner, M. Niepel, M. Chung, and P. K. Sorger, “Growth Rate Inhibition Metrics Correct for Confounders in Measuring Sensitivity to Cancer Drugs,” Nature Methods 13, no. 6 (2016): 521–527.

[10] B. Yadav, T. Pemovska, A. Szwajda, et al., “Quantitative Scoring of Differential Drug Sensitivity for Individually Optimized Anticancer Therapies,” Scientific Reports 4 (June 2014): 5193.

[11] S. A. Langhans, “Three-Dimensional in Vitro Cell Culture Models in Drug Discovery and Drug Repositioning,” Frontiers in Pharmacology 9 (2018): 6.

[12] M. Kapałczyńska, T. Kolenda, W. Przybyła, et al., “2D and 3D Cell Cultures—A Comparison of Different Types of Cancer Cell Cultures,” Archives of Medical Science 14, no. 4 (June 2018): 910–919.

[13] E. Frohlich, “The Variety of 3D Breast Cancer Models for the Study of Tumor Physiology and Drug Screening,” International Journal of Molecular Sciences 24, no. 8 (April 2023): 7116.

[14] K. Bialkowska, P. Komorowski, M. Bryszewska, and K. Milowska, “Spheroids as a Type of Three-Dimensional Cell Cultures-Examples of Methods of Preparation and the Most Important Application,” International Journal of Molecular Sciences 21, no. 17 (August 2020): 6225.

[15] U. Johansson, M. Widhe, N. D. Shalaly, et al., “Assembly of Functionalized Silk Together With Cells to Obtain Proliferative 3D Cultures Integrated in a Network of ECM-Like Microfibers,” Scientific Reports 9, no. 1 (April 2019): 6291.

[16] M. Kalli, M. D. Poskus, T. Stylianopoulos, and I. K. Zervantonakis, “Beyond Matrix Stiffness: Targeting Force-Induced Cancer Drug Resistance,” Trends in Cancer 9, no. 11 (November 2023): 937–954.

[17] K. J. L. Burg, W. D. Holder, C. R. Culberson, et al., “Comparative Study of Seeding Methods for Three-Dimensional Polymeric Scaffolds,” Journal of Biomedical Materials Research 51, no. 4 (September 2000): 642–649.

[18] C. Vitale, M. Marzagalli, S. Scaglione, A. Dondero, C. Bottino, and R. Castriconi, “Tumor Microenvironment and Hydrogel-Based 3D Cancer Models for In Vitro Testing Immunotherapies,” Cancers 14, no. 4 (February 2022): 1013.

[19] E. V. Denisov, N. A. Skryabin, T. S. Gerashchenko, et al., “Clinically Relevant Morphological Structures in Breast Cancer Represent Transcriptionally Distinct Tumor Cell Populations With Varied Degrees of Epithelial-Mesenchymal Transition and CD44(+)CD24(−) Stemness,” Oncotarget 8, no. 37 (September 2017): 61163–61180.

[20] M. Stark, S. Grip, A. Rising, et al., “Macroscopic Fibers Self-Assembled From Recombinant Miniature Spider Silk Proteins,” Biomacromolecules 8, no. 5 (May 2007): 1695–1701.

[21] M. Hedhammar, H. Bramfeldt, T. Baris, et al., “Sterilized Recombinant Spider Silk Fibers of Low Pyrogenicity,” Biomacromolecules 11, no. 4 (April 2010): 953–959.

[22] M. Widhe, N. D. Shalaly, and M. Hedhammar, “A Fibronectin Mimetic Motif Improves Integrin Mediated Cell Biding to Recombinant Spider Silk Matrices,” Biomaterials 74 (January 2016): 256–266.

[23] L. Nilebäck, S. Arola, M. Kvick, A. Paananen, M. B. Linder, and M. Hedhammar, “Interfacial Behavior of Recombinant Spider Silk Protein Parts Reveals Cues on the Silk Assembly Mechanism,” Langmuir 34, no. 39 (October 2018): 11795–11805.

[24] A. Källén, N. Taebnia, M. Widhe, V. M. Lauschke, and M. Hedhammar, “3D Culture in Functionalized FN-Silk Networks Facilitate Proliferation, Differentiation and Phenotypic Stability of Mature Human Primary Cells and Stem Cells,” Biotechnology and Bioengineering 122, no. 9 (2025): 2522–2534.

[25] C. Collodet, K. Blust, S. Gkouma, et al., “Development and Characterization of a Recombinant Silk Network for 3D Culture of Immortalized and Fresh Tumor-Derived Breast Cancer Cells,” Bioengineering & Translational Medicine 8, no. 5 (September 2023): e10537.

[26] D. Cuvelier, M. Théry, Y. S. Chu, et al., “The Universal Dynamics of Cell Spreading,” Current Biology 17, no. 8 (April 2007): 694–699.

[27] N. A. Clark, M. Hafner, M. Kouril, et al., “Grcalculator: An Online Tool for Calculating and Mining Dose-Response Data,” BMC Cancer 17, no. 1 (October 2017): 698.

[28] J. Hughes, S. Rees, S. Kalindjian, and K. Philpott, “Principles of Early Drug Discovery,” British Journal of Pharmacology 162, no. 6 (March 2011): 1239–1249.

[29] R. Edmondson, J. J. Broglie, A. F. Adcock, and L. Yang, “Three-Dimensional Cell Culture Systems and Their Applications in Drug Discovery and Cell-Based Biosensors,” ASSAY and Drug Development Technologies 12, no. 4 (May 2014): 207–218.

[30] S. Breslin and L. O'Driscoll, “The Relevance of Using 3D Cell Cultures, in Addition to 2D Monolayer Cultures, When Evaluating Breast Cancer Drug Sensitivity and Resistance,” Oncotarget 7, no. 29 (July 2016): 45745–45756.

[31] F. G. Giancotti, “Integrin Signaling: Specificity and Control of Cell Survival and Cell Cycle Progression,” Current Opinion in Cell Biology 9, no. 5 (October 1997): 691–700.

[32] S. Potdar, A. Ianevski, J. P. Mpindi, et al., “Breeze: An Integrated Quality Control and Data Analysis Application for High-Throughput Drug Screening,” Bioinformatics 36, no. 11 (June 2020): 3602–3604.

[33] S. Potdar, F. Ianevski, A. Ianevski, et al., “Breeze 2.0: An Interactive Web-Tool for Visual Analysis and Comparison of Drug Response Data,” Nucleic Acids Research 51, no. W1 (2023): W57–W61.

[34] J.-H. Zhang, T. D. Y. Chung, and K. R. Oldenburg, “A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays,” SLAS Discovery 4, no. 2 (1999): 67–73.

RIGHTS & PERMISSIONS

2026 The Author(s). MedComm - Biomaterials and Applications published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).