journal article·2025-11Open access

Tumor‐Tissue Boundaries as Instructive Interfaces in Breast Cancer Cell Invasion

Cornelia Clemens, Thomas Zerjatke, Andrine Frank, Hannah Trampert, Nataliia Kotsiuba, Ingmar Glauche, Tilo Pompe

Advanced Science· Volume 12· Issue 44

Abstract

Abstract The ability of metastatic cancer cells to invade distant tissues requires them to cross a variety of tissue boundaries, each posing distinct structural and biochemical challenges. In particular, the boundary between dense, extracellular matrix (ECM)‐rich tumor tissue and surrounding stromal tissue is associated with phenotypic changes in MDA‐MB‐231 breast cancer cells following transmigration, including increased invasiveness and aggressiveness. It remains unclear whether this transition arises from selective, permissive filtering of pre‐existing subpopulations, such as cancer stem cells, or an instructive response of the entire cell population. Here, by combining single‐cell migration analysis, heterogeneity analysis of cell proliferation, and computational modeling, it is demonstrated that tumor‐tissue boundaries act as instructive interfaces. Using an established 3D fibrillar collagen I matrix model of interfaces, it is shown that all cells can transmigrate the interface with no evidence of selective filtering of subpopulations. Proliferation heterogeneity remains unchanged between transmigrated and non‐transmigrated cells, further supporting an instructive mechanism. Simulations confirm that the interface instructively modulates cell behavior. These results indicate that tissue boundaries can reprogram cancer cell phenotypes, representing a potentially targetable mechanism in metastatic progression.
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Research context

OpenAlex topics: Cancer Cells and Metastasis · Cellular Mechanics and Interactions · 3D Printing in Biomedical Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Biochemistry Leipzig University 04103 Leipzig Germany

Institute for Medical Informatics and Biometry TU Dresden 01307 Dresden Germany

Funding

Deutsche Forschungsgemeinschaft · Deutsche Krebshilfe · Freistaat Sachsen

References (50)

Show source-provided references
1. 10.1038/nrc109810.1038/nrc1098 ↗
2. 10.1002/mco2.10010.1002/mco2.100 ↗
3. 10.1126/science.95984010.1126/science.959840 ↗
4. 10.1126/science.aaf278410.1126/science.aaf2784 ↗
5. 10.1158/2159-8290.CD-21-105910.1158/2159-8290.CD-21-1059 ↗
6. 10.1172/JCI8772410.1172/JCI87724 ↗
7. 10.1016/j.ccell.2014.12.00110.1016/j.ccell.2014.12.001 ↗
8. 10.1038/nrclinonc.2015.7310.1038/nrclinonc.2015.73 ↗
9. 10.1038/s43018-021-00229-110.1038/s43018-021-00229-1 ↗
10. 10.3390/cancers1703038210.3390/cancers17030382 ↗
11. 10.1093/carcin/bgx07110.1093/carcin/bgx071 ↗
12. 10.1126/science.125693010.1126/science.1256930 ↗
13. 10.1039/C8BM00303C10.1039/C8BM00303C ↗
14. 10.1007/s12094-024-03697-w10.1007/s12094-024-03697-w ↗
15. 10.1002/adhm.20160029510.1002/adhm.201600295 ↗
16. 10.1242/jcs.07290010.1242/jcs.072900 ↗
17. 10.1088/1478-3975/10/6/06500410.1088/1478-3975/10/6/065004 ↗
18. 10.1021/acsami.3c0109910.1021/acsami.3c01099 ↗
19. 10.1039/D4BM01589D10.1039/D4BM01589D ↗
20. 10.1038/s41556-018-0131-210.1038/s41556-018-0131-2 ↗
21. 10.1152/ajpcell.00283.201310.1152/ajpcell.00283.2013 ↗
22. 10.1016/j.tcb.2022.03.00210.1016/j.tcb.2022.03.002 ↗
23. 10.1016/j.biomaterials.2015.02.02210.1016/j.biomaterials.2015.02.022 ↗
24. 10.1152/ajpcell.00225.201410.1152/ajpcell.00225.2014 ↗
25. 10.1016/j.biomaterials.2018.12.01010.1016/j.biomaterials.2018.12.010 ↗
26. 10.1016/j.bioadv.2024.21396110.1016/j.bioadv.2024.213961 ↗
27. 10.1186/1741-7015-6-1110.1186/1741-7015-6-11 ↗
28. C.Romano A.Borri M. D.Di Benedetto in2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) IEEE Piscataway NJ2024 pp.2079–2084.10.1109/CASE59546.2024.10711752 ↗
29. 10.1039/c5ib00040h10.1039/c5ib00040h ↗
30. 10.1038/s41392-024-01851-y10.1038/s41392-024-01851-y ↗
31. 10.15252/embr.20143924610.15252/embr.201439246 ↗
32. Hiraga T. · Oncol. Rep. · 2011 · 25 · 289
33. 10.1186/s12885-019-5883-y10.1186/s12885-019-5883-y ↗
34. 10.1016/j.actbio.2014.02.03610.1016/j.actbio.2014.02.036 ↗
35. 10.1093/intbio/zyz01110.1093/intbio/zyz011 ↗
36. 10.3389/fcell.2023.115588210.3389/fcell.2023.1155882 ↗
37. 10.18632/oncotarget.1193110.18632/oncotarget.11931 ↗
38. 10.21873/anticanres.1157210.21873/anticanres.11572 ↗
39. 10.1158/0008-5472.CAN-20-179910.1158/0008-5472.CAN-20-1799 ↗
40. 10.1038/ncb315710.1038/ncb3157 ↗
41. 10.1073/pnas.181420411610.1073/pnas.1814204116 ↗
42. 10.1007/s12079-020-00601-310.1007/s12079-020-00601-3 ↗
43. 10.1016/j.bbamcr.2020.11885110.1016/j.bbamcr.2020.118851 ↗
44. 10.1073/pnas.053029110010.1073/pnas.0530291100 ↗
45. 10.1038/nmeth.208910.1038/nmeth.2089 ↗
46. 10.1016/B978-0-12-391857-4.00009-410.1016/B978-0-12-391857-4.00009-4 ↗
47. 10.1038/s41586-020-2649-210.1038/s41586-020-2649-2 ↗
48. 10.1109/MCSE.2007.5510.1109/MCSE.2007.55 ↗
49. The pandas development team pandas‐dev/pandas: Pandas Zenodo2024.
50. Seabold S. · 2010 · 92