{"paper_id":"5b4f739d-a9b4-4217-8d70-f9eb322b34bc","body_text":"1 \n \nMain Manuscript for 1 \nBioengineered human bone-mimetic niche compositions regulate 2 \nbreast cancer cell quiescence and therapy response  3 \nEvrim C. Kabaka,b, Manuele G. Muraroa, Sok L. Foob, Ewelina M. Bartoszekc, Andres Garcia-4 \nGarciaa, Atharva Damlea, Florian Pouzeta,d, Mohamed Bentires-Aljb,1,*, Ivan Martina,1,* 5 \naLaboratory of Tissue Engineering , Department of Biomedicine, University Hospital Basel, 6 \nUniversity of Basel, 4031 Basel, Switzerland; bLaboratory of Tumor Heterogeneity, Metastasis and 7 \nResistance, Department of Biomedicine, University Hospital Basel, University of Basel, 4031 Basel, 8 \nSwitzerland; cMicroscopy Core Facility, Department of Biomedicine, University of Basel, 4031 9 \nBasel, Switzerland; dRESTORE Research Center, Université de Toulouse, INSERM 1301, CNRS 10 \n5070, EFS, ENVT, Toulouse, France.  11 \n1Equally contributing authors 12 \n*Ivan Martin and Mohamed Bentires-Alj 13 \nEmail: ivan.martin@usb.ch, m.bentires-alj@unibas.ch 14 \nAuthor Contributions:  E.C.K. conceived the study, designed and conducted experiments, 15 \nanalyzed and interpreted the results, and wrote the manuscript. M.G.M contributed to experimental 16 \nplanning, bioreactor experiments, data interpretation, and the manuscript  refinement. S.L.F. 17 \ncontributed to experimental planning, data interpretation,  and manuscript  refinement. E.M.B. 18 \ndeveloped and automated image quantification workflows.  A.G.-G. contributed to data 19 \ninterpretation and manuscript refinement. A.D. contributed to bioreactor experiments, histology, 20 \nand imaging. F.P. assisted with bioreactor experiments, histology, immunofluorescence staining, 21 \nand imaging. I.M. and M.B.-A. conceived and supervised the project. All authors read and provided 22 \nfeedback on the manuscript.  23 \nCompeting Interest Statement: The authors declare no competing interests.  24 \nClassification: Biological sciences. Cell biology.  25 \nKeywords: bone metastasis , bone marrow niche, tumor dormancy, perivascular niche, 3D 26 \nbioreactor culture 27 \nThis PDF file includes: 28 \nMain Text 29 \nFigures 1 to 4 30 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n2 \n \nAbstract 1 \nDisseminated tumor cells (DTCs) in the bone are widely regarded as the cellular seeds of late 2 \nmetastatic relapse in estrogen receptor-positive (ER+) breast cancer (BC). However, how distinct 3 \nbone compartments influence BC cell quiescence and  endocrine therapy response  remains 4 \nunclear. Mechanistic insight has been hindered by limited access to human bone samples and by 5 \nthe lack of relevant human models that permit controlled manipulation of stromal compartments. 6 \nHere, we developed a fully human, 3D bone-mimetic system for modular, controllable assembly of 7 \nengineered osteoblastic (eON), vascularized (eVN), and vascularized osteoblastic (eVON) niche 8 \ncompositions. These niches were generated by perfusion culture of human bone marrow-derived 9 \nmesenchymal stromal cells (hBM-MSCs) and/or human adipose tissue-derived stromal vascular 10 \nfraction (hAT-SVF) cells within porous ceramic scaffolds. The resulting tissue microenvironments 11 \nwere then used as a substrate for the culture of an ER+ BC cell line, expressing a mutant reporter 12 \nof p27 to monitor the quiescent status . We found that the eON enhanced BC cell proliferation, 13 \nwhereas the eVN was enriched in quiescent BC cells positive for NR2F1, a dormancy-associated 14 \ntranscription factor, and located near perivascular elements.  Treatment with the selective ER 15 \ndegrader fulvestrant reduced BC cell numbers in vascularized niches (eVN and eVON) but not in 16 \neON, despite comparable receptor degradation. In summary, we developed  a modular human 17 \nplatform for dissecting niche -specific regulation of BC quiescence, proliferation, and endocrine 18 \ntherapy response. The system can be further used to investigate perivascular niche-dependent 19 \nmechanisms of BC cell dormancy and to guide the development of therapeutic strategies 20 \npreventing recurrence in ER+ BC patients.  21 \n 22 \nMain Text 23 \nIntroduction  24 \nBone is the predominant site of metastatic relapse in estrogen receptor -positive (ER+) breast 25 \ncancer (BC) [1-4]. Disseminated tumor cells (DTCs) are frequently detected in bone marrow (BM) 26 \naspirates of patients lacking clinically detectable metastases. The presence of DTCs in the bone 27 \ncompartments correlates with poor prognosis, highlighting their potential as seeds for future 28 \nmetastases [5-9]. Understanding the contribution of bone niches to DTC outgrowth is therefore 29 \ncritical for developing strategies to prevent metastatic relapse.  30 \nWithin the bone, DTCs localize to specialized microenvironments, primarily the osteogenic 31 \nand perivascular niches [10, 11] . The  osteogenic niche comprises osteoblast - and osteoclast-32 \nlineage cells lining the bone surface [12]. The osteoclast-driven “vicious cycle” of advanced stage 33 \nof bone metastasis, marked by tumor-induced osteolysis and release of growth factors that promote 34 \nfurther tumor growth, is well -characterized [2, 9, 13 -16]. Once the osteolytic lesions emerge, 35 \ntreatment is largely palliative [17-21]. In contrast, the contribution of osteoblasts during early 36 \nmetastatic stages is understudied [12, 22]. Growing evidence suggests that osteoblasts promote 37 \nearly colonization, therapeutic resistance, and proliferation of ER+ BC cells [23-25]. Targeting these 38 \nearly cellular interactions, before the onset of irreversible osteolysis, may represent a more effective 39 \ntherapeutic window. 40 \nThe other specialized bone compartment to which DTCs mainly traffic is the perivascular 41 \nniche, which is often anatomically adjacent to or overlapping with the osteogenic niche [26]. While 42 \nthe osteogenic niche is primarily associated with promoting DTC proliferation during early 43 \nmetastatic progression, prior work has shown that  the perivascular niche can sustain DTC 44 \ndormancy [27]. Using intravital imaging on bone marrow (BM) of a BC xenograft model, DTCs were 45 \nfound predominantly localized near perisinusoidal vessels. Analyses of patient BM samples also 46 \nrevealed that non-proliferative, Ki67-negative (Ki67-) DTCs are more frequently found adjacent to 47 \nsinusoidal vessels than endosteal surface [26]. Thrombospondin 1 (TSP1) expression was strongly 48 \nassociated with BC cell dormancy in an in vitro organotypic model composed of endothelial and 49 \nstromal cells, mimicking the perivascular niche [27]. Despite their distinct features, the osteogenic 50 \nand perivascular niches are interconnected through shared mesenchymal progenitors that give rise 51 \nto both endothelial and osteoblastic lineages. Indeed, specialized type H capillaries, enriched in 52 \nCD31 and endomucin (encoded by Emcn in mice), typically reside near the endosteum and 53 \ncontribute to both angiogenesis and osteogenesis via factors such as Noggin [10, 28-30]. These 54 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n3 \n \ndevelopmental and anatomical overlaps underscore the importance of not only dissecting the 55 \nindividual effects of osteogenic and perivascular microenvironments on DTC outgrowth, but also of 56 \nmodeling transitional zones, where osteogenic and vascular cues converge. 57 \nAlthough recent in vivo  studies have advanced understanding of how discrete bone  58 \ncompartments influence DTC outgrowth, they also underscore the need for fully human ex vivo 59 \nsystems to dissect and compare niche -specific contributions to metastatic progression. A central 60 \nlimitation is the scarcity of solitary DTCs in mouse bone, which hampers spatial and functional 61 \ninterrogation of discrete bone niches during early metastatic stages. Additionally, murine models 62 \noften fail to recapitulate spontaneous tumor cell dissemination and are further constrained by the 63 \nrarity of ER+ mammary tumor cell lines compatible with immunocompetent hosts  [31-33]. 64 \nComplementing these challenges, existing in vitro and ex vivo models often lack the modularity to 65 \nindependently engineer bone-mimetic niches and the dynamic perfusion required for physiological 66 \ndelivery of oxygen, nutrients, and drugs [27, 34-40]. Together, these limitations restrict the ability 67 \nto resolve niche-specific effects on BC cell dormancy and reactivation, particularly in ER+ disease, 68 \nwhere the underlying mechanisms remain poorly understood.  69 \nWe have  previously developed a human, bioreactor -based 3D BM niche model using 70 \nhydroxyapatite scaffolds seeded with human BM-derived MSCs (hBM -MSCs), and/or adipose 71 \ntissue-derived stromal vascular fraction (hAT -SVF) cells [41-43]. These engineered constructs 72 \nmimic the mineralized architecture of trabecular bone with or without vascularization . They also 73 \nreproduce key features of mineralized and perivascular bone regions [42, 44, 45].  74 \nIn this study, we investigated the influence of distinct engineered bone-mimetic niches on 75 \nBC cell quiescence by adapting our previously established 3D perfusion-based culture systems. 76 \nThis platform enables modular engineering of osteogenic, vascular, and vascularized osteogenic 77 \nbone niches under controlled conditions. We used MCF7-tdTomato/mVenus-p27K- cells to track 78 \nBC cells in G0 cell cycle arrest by a mutant p27 reporter [46, 47] . To validate niche -specific 79 \ndormancy, we further evaluated the expression of nuclear receptor subfamily 2, group F member 80 \n1 (NR2F1), an orphan nuclear receptor used as a functional dormancy marker in metastatic mouse 81 \nmodels and a prognostic biomarker of dormant DTCs in the BM of BC patients [48-50]. Indeed, our 82 \nside-by-side comparative approach revealed that nuclear NR2F1+ MCF7 cells were enriched in 83 \neVN, underscoring its utility as a platform to study cell-extrinsic regulation of BC dormancy in bone. 84 \n 85 \nResults 86 \nEngineered osteoblastic niche promotes MCF7 cell proliferation 87 \nTo assess the effects of an osteogenic niche on BC cell quiescence and proliferation, hBM-MSCs 88 \nwere seeded on hydroxyapatite scaffolds and expanded for one week, followed by three weeks of 89 \nosteogenic differentiation  to engineer an osteoblastic niche (eON)  [41, 43, 44] . MCF7 -90 \ntdTomato/mVenus-p27K- cells were then seeded and cultured for two weeks under perfusion in the 91 \neON or in the niche-free scaffold (NFS) as control (Fig. 1A) [46, 47]. The total number of MCF7-92 \ntdTomato/mVenus-p27K- cells increased after two weeks of culture in the eON compared to NFS 93 \n(Fig. 1B). Immunofluorescence (IF) staining for tdTomato and mVenus further revealed a reduced 94 \nfraction of quiescent (mVenus+) MCF7 cells in eON (Fig. 1C). Consistently, Ki67 staining showed 95 \na higher proportion of proliferating MCF7 cells in eON compared to the control, highlighting that 96 \neON enhances MCF7 cell proliferation  (Fig. 1D). The eON was previously validated by matrix 97 \ndeposition of collagen type I alpha 1 (COL1A1) and osteocalcin (OCN) [44, 45].  98 \nCOL1A1 and OCN staining confirmed osteogenic differentiation in eON, whereas NFS lacked 99 \ndetectable levels of these markers (SI Appendix, Fig. S1). These experiments validated the 100 \nfeasibility of culturing MCF7 cells in an engineered niche as a prerequisite to evaluate how bone 101 \nniche compositions influence BC cell cycle states.  102 \neON, eVN, and eVON model distinct osteoblastic and vascular features of bone 103 \nAlthough prior studies have suggested that the perivascular niche of the bone may contribute to 104 \nBC dormancy, the specific influence of vascular  and perivascular cues on BC cell cycle state, 105 \nparticularly in the presence of osteogenic elements, has not yet been addressed [26, 27, 51]. To 106 \naddress this, we engineered two additional environments: a vascularized niche (eVN) and a 107 \nvascularized osteogenic niche (eVON).  eVN was generated by seeding hAT -SVF cells into 108 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n4 \n \nhydroxyapatite scaffolds and culturing them for two weeks in medium supplemented with fibroblast 109 \ngrowth factor 2 (FGF2). For eVON, hAT-SVF cells were seeded into a pre -osteogenic niche and 110 \ncultured in osteogenic medium supplemented with FGF2 for two weeks [42]. Perfusion bioreactors 111 \nensured uniform distribution of MCF7-tdTomato/mVenus-p27K- cells upon subsequent seeding. 112 \nMCF7 cells were then introduced into each niche configuration and co-cultured for an additional 113 \ntwo weeks (Fig. 2A).  114 \nTo determine whether the engineered niches recapitulate key features of bone 115 \nmicroenvironments, we assessed osteogenic matrix deposition  (i.e., COL1A1 and OCN ) and 116 \nendothelial (i.e., CD31) network formation. The osteogenic compartment of the three niches was 117 \ncharacterized by IF staining and quantified using an optimized image analysis pipeline (SI Appendix 118 \nFig. S2A, S2B). The highest levels of COL1A1 deposition were observed in eVON (57.8 ± 12.4%), 119 \nfollowed by eON (35.0 ± 13.3%), and lowest in eVN (6.9 ± 1.9%) (Fig. 2B, 2C), likely due to the 120 \nhigher stromal cell content in eVON. OCN deposition was low in eVN (2.6 ± 0.8%), but higher in 121 \nboth eON (14.1 ± 6.4%) and eVON (16.0 ± 9.2%) (Fig. 2D, 2E). These results confirm that eON and 122 \neVON retain osteogenic matrix features compared to eVN.  123 \nStaining for CD31 revealed the self-organized, branched endothelial networks in both eVN 124 \nand eVON after two weeks of co -culture with MCF7 -tdTomato/mVenus-p27K- cells (Fig. 2F). 125 \nNotably, these networks formed and persisted without exogeneous angiogenic factor 126 \nsupplementation, indicating stable endothelial organization. eON lacked CD31+ endothelial 127 \nstructures, consistent with the avascular nature of mineralized osteoblastic niches (Fig. 2F). Using 128 \nthe pericyte marker  neuron-glial antigen 2 -positive (NG2+; also known as chondroitin sulfate 129 \nproteoglycan 4, CSPG4) , we measured proportion of perivascular cells associated with CD31+ 130 \nstructures using an optimized image analysis pipeline ( SI Appendix Fig. S2C). CD31+ network 131 \ndensity was comparable between eVN and eVON (Fig. 2G). However, NG2+ cells more frequently 132 \ncolocalized with CD31+ networks in eVN, reflecting a more developed perivascular architecture 133 \n(Fig. 2F, 2H). These findings indicate that endothelial network -forming capacity is maintained in 134 \nboth niches, while perivascular organization is less extensive in eVON.  135 \nNuclear NR2F1+ MCF7 cells are enriched in eVN  136 \nNext, we assessed the effect of distinct cellular compositions provided by eON, eVN, and eVON 137 \non MCF7-tdTomato/mVenus-p27K- cell quiescence and proliferation. To this end, we first quantified 138 \nthe number of MCF7 cells retrieved from each niche after two weeks of co-culture. Flow cytometry 139 \nanalysis revealed the highest number of MCF7 cells in eVON, followed by eON, with eVN yielding 140 \nthe lowest cell numbers. These findings suggest that combined  osteoblastic and vascular 141 \ncomponents correlate with enhanced MCF7 cell count (Fig. 3A).   142 \nIF staining showed that mVenus -p27K- and Ki67 expression were mutually exclusive, 143 \nconfirming the specificity of the quiescence reporter ( mVenus-p27K-) (Fig. 3B). Both quiescent 144 \n(tdTomato+/mVenus+/Ki67-) and proliferating (tdTomato+/mVenus -/Ki67+) cancer cells were 145 \ndetected across all niches. Quiescent cancer cells were also found as solitary cells or as clusters 146 \nwith proliferative cells in all niches (Fig. 3B, SI Appendix, Fig. S3). Quiescent and proliferating 147 \ncancer cells were quantified using an optimized image analysis pipeline ( SI Appendix, Fig. S4). 148 \neVN had the highest proportion of quiescent MCF7 cells relative to eON and eVON (Fig. 3C). 149 \nCoherently, it had the lowest fraction Ki67+ MCF7 cells (Fig. 3D).  150 \nGiven the higher percentage of quiescent MCF7-tdTomato/mVenus-p27K- cells in eVN, we 151 \nasked whether this phenotype was associated with a dormancy -linked transcriptional program. 152 \nPrior studies have shown that microenvironmental cues in the bone, including cytokine-mediated 153 \nactivation of p38 mitogen-activated protein kinase (MAPK) signaling, can induce dormancy through 154 \nupregulation of specific transcriptional regulators such as NR2F1 [48-50, 52]. Notably, we found 155 \nnuclear colocalization of mVenus-p27K- and NR2F1 expression (SI Appendix, Fig. S5) and more 156 \nfrequently within the eVN compared to eON and eVON (Fig. 3E, F), suggesting that eVN promotes 157 \ndormancy phenotype. Furthermore, the higher abundance of NG2+/CD31+ endothelial network in 158 \neVN correlated with increased frequencies of nuclear NR2F1+ and quiescent MCF7 cells. 159 \nFulvestrant-induced MCF7 cell number reduction is confined to eVN and eVON 160 \nTo test whether distinct niche compositions influence therapeutic response, we treated engineered 161 \nco-cultures with fulvestrant for one week. Qualitative assessment of ER α immunohistochemistry 162 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n5 \n \n(IHC) revealed a pronounced loss in nuclear ERα staining in MCF7 cells across all three conditions 163 \n(Fig. 4A). Consistent with this, the percentage of n uclear ERα+ MCF7 -tdTomato/mVenus-p27K- 164 \ncells w as markedly reduced across all three niches, confirming target engagement (Fig. 4B). 165 \nNotably, MCF7 cell numbers significantly decreased in eVN and eVON upon treatment, but 166 \nremained unchanged in eON (Fig. 4C), indicating that fulvestrant-induced reduction in cell number 167 \nwas confined to the niches incorporating endothelial networks.  168 \n 169 \nDiscussion 170 \nIn this study, we developed a fully human, modular 3D bone-mimetic model to investigate cancer-171 \nstroma interactions during BC bone metastatic progression. This platform allows the stepwise 172 \nintroduction of microenvironmental complexity and supports direct comparison of different cellular 173 \ncomponents in a modular fashion under controlled conditions. Importantly, our model allows cancer 174 \ncells to be seeded after bone -mimetic niche formation thanks to perfusion flow, thereby better 175 \nmimicking the temporal sequence of spontaneous cancer cell dissemination. This design enables 176 \nspatial analysis of niche -specific effects on cancer cell cycle states, including quiescence and 177 \nproliferation, in a physiologically relevant and experimentally tractable context. 178 \nOur findings that the eON promotes MCF7-tdTomato/mVenus-p27K- cell proliferation are 179 \nconsistent with previous in vivo  studies [24] showing that  osteoblast-rich microenvironments 180 \nenhance early colonization and proliferation of BC cells in the bone [23, 24]. This underscores the 181 \noften-overlooked contribution of osteoblasts to the early, non -osteolytic stages of metastatic 182 \nprogression, and highlights the importance of modeling these niches independently of the 183 \nosteoclast-driven vicious cycle. 184 \nThe eVN and eVON enabled us to investigate the influence of endothelial and perivascular 185 \nelements on cancer cell quiescence in the absence or presence of an osteoblastic niche. eVN 186 \nyielded the lowest total number of MCF7 cells yet contained a higher fraction of quiescent cells 187 \nthan eON and eVO N. In contrast, eVON supported the highest MCF7 cell numbers , while the 188 \nquiescent fraction was reduced relative to eVN. This pattern suggests that osteogenic cues may 189 \nmodulate the impact of vascular elements on BC cell quiescence, potentially by interfering with 190 \nperivascular organization [10, 25, 28].  191 \nPrior studies have shown that specialized capillaries in the BM, such as type H vessels 192 \ncoordinate osteogenesis through endothelial Notch signaling [28, 29]. Disruption of these vascular-193 \nosteogenic interactions may alter the microenvironmental signaling, potentially affecting DTC 194 \noutgrowth. Importantly, the NG2+/CD31+ networks resemble the dormancy-supportive 195 \nvasculature, where DTCs adjacent to stable microvessels had elevated p27 expression and 196 \nremained quiescent, in contrast to reactivation near sprouting vessels  [27]. Perivascular cells 197 \nexpressing NG2 were more frequently associated with CD31+ endothelial networks in eVN than in 198 \neVON, suggesting a more stable engineered vasculature in the absence of osteogenic 199 \ndifferentiation. Whether this contributes to the higher proportion of quiescent cells in eVN compared 200 \nto eVON remains to be addressed.  201 \nIn line with the enrichment of the quiescent cell fraction in eVN, we also observed a 202 \nsignificantly higher proportion of nuclear NR2F1+ cells in this niche compared to eON and eVON. 203 \nBM-derived soluble factors, including TGF -β2 and BMP7, particularly enriched in perivascular 204 \nregions, can activate the p38 MAPK pathway and upregulate dormancy-associated genes such as 205 \nNR2F1 [48, 49, 52 -54]. This association suggests that structural features of the eVN niche, 206 \nincluding enriched NG2+/CD31+ networks, may potentially contribute to the activation of 207 \ndormancy-related signaling programs. Together, these results validate the pathophysiological 208 \nrelevance of the eVN  and underscore its utility for modeling dormancy -permissive 209 \nmicroenvironments. 210 \nA striking observation was the presence of quiescent cells both as solitary cells and within 211 \nclusters that also contained proliferative cells. This spatial distribution mirrors features of both 212 \ncellular dormancy and tumor mass dormancy  [55]. While tumor mass dormancy is defined by a 213 \nbalance between proliferation and apoptosis that limits net growth, we did not assess apoptosis in 214 \nthis study. Thus, we cannot determine whether a dormancy equilibrium exists in the observed 215 \nclusters. Future studies incorporating apoptotic markers  (e.g., cleaved PARP, Annexin V/PI , or 216 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n6 \n \nTUNEL) and longitudinal live -cell imaging will be necessary to distinguish between cellular 217 \ndormancy and tumor mass dormancy. 218 \nBeyond cell cycle regulation, our system also demonstrates utility for therapeutic testing. 219 \nFulvestrant treatment reduced BC cell numbers in eVN and eVON, compared to eON, despite the 220 \nconfirmed nuclear ERα degradation . One potential explanation for the limited response in eON 221 \ncould be fulvestrant-induced changes in ECM, as previous studies have shown that collagen-rich 222 \nmicroenvironments can reduce the efficacy of ER -targeted therapies [56, 57] . These data 223 \nunderscore the potential of our platform in dissecting microenvironment -dependent treatment 224 \nresponses and in refining specific therapeutic strategies.  225 \nThere are limitations specifically regarding the further validation of the dormancy model. 226 \nAlthough our data support the presence of NR2F1-associated dormancy in eVN, several important 227 \ncriteria remain unmet for full dormancy validation [58]. Most critically, dormancy is defined not only 228 \nby G0 cell cycle arrest, but also by its reversibility  [59]. Although we observed enrichment of 229 \nmVenus-p27K-+ and Ki67- proportion of MCF7 cells, additional assays are needed to demonstrate 230 \nthe capacity of these cells to re-enter the cell cycle upon niche alteration or exogenous stimulation. 231 \nFurthermore, the possibility that some quiescent cells are senescent rather than dormant 232 \npopulations must be considered, and should be addressed through assessment of senescence 233 \nmarkers such as senescence associated β-galactosidase (SA-β-Gal) or p16INK4a [55, 60]. 234 \nAnother critical gap is the lack of functional perturbation studies to establish causality 235 \nbetween niche components and dormancy induction. Although our data suggest that stable 236 \nperivascular architecture in eVN is associated with NR2F1+ quiescence, functional validation such 237 \nas selective depletion of endothelial or perivascular cell populations, or genetic manipulation of 238 \ndormancy-related signaling pathways will be essential to define causality. 239 \nDespite these limitations, our system offers a modular platform that recapitulates key 240 \nphenotypic features of the human bone metastatic microenvironment. It enables direct comparison 241 \nof distinct niche types and allows for phenotypic interrogation of cancer cell states in a 242 \npathophysiologically relevant context. Given its human origin and architectural fidelity, this platform 243 \nholds promise for in-depth studies on the cellular and molecular determinants of tumor dormancy 244 \nand for drug testing applications. In particular, it could be used to assess the efficacy of agents 245 \ntargeting dormant cells or preventing metastatic outgrowth in niche -specific contexts, offering a 246 \nvaluable preclinical model for therapeutic development.  247 \n 248 \nMaterials and Methods 249 \nhBM-MSC isolation and culture  250 \nHuman bone marrow derived mesenchymal stromal cells (hBM-MSCs) were isolated as previously 251 \ndescribed [41, 44, 45]. Cells were cultured in complete medium (CM) composed of a α-minimum 252 \nessential medium (αMEM) (Gibco; cat# 22571 -020), supplemented with 10% fetal bovine serum 253 \n(FBS, Invitrogen; cat# 548-62-9), 1% HEPES (1M, Gibco; cat# 15630 -056), 1% sodium pyruvate 254 \n(100 mM), 1% GlutaMAX (100X, Gibco; cat# 35050 -061), and 1% Penicillin –Streptomycin (PS, 255 \nGibco; cat# 15140 -122). Nucleated cells were plated at a density of 5  x 103 cells/cm2 and 256 \nmaintained at 37°C in a water-jacketed incubator with 5% CO2. CM was supplemented with 5 ng/ml 257 \nof fibroblast growth factor -2 (FGF -2). Medium was changed twice weekly. hBM -MSCs were 258 \nselected on adherence and proliferation after one week. 259 \nhAT-SVF cell isolation and culture 260 \nAdipose tissue was obtained from three healthy, female donors via liposuction or excision at the 261 \nUniversity Hospital Basel. Isolation of human adipose tissue derived stromal vascular fraction (hAT-262 \nSVF) cells was conducted by enzymatic digestion with collagenase type II (Worthington; cat# 263 \nLS004176), and followed by several centrifugation and purification steps as described [42, 43, 45]. 264 \nCell counting was performed using Acridine Orange/Propidium Iodide Stain (Logos biosystems; 265 \ncat# F23001) at 1:100 dilution in the cell suspension using Luna -FX7™ Automated Cell Counter 266 \n(Logos biosystems).  267 \nEthics statement 268 \nHuman BM aspirates and adipose tissue were collected with informed consent from healthy donors 269 \nat the University Hospital Basel. The study was approved by the local ethics committee 270 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n7 \n \n(Ethikkommission Nordwest- und Zentralschweiz, ref. 78/07) and conducted in accordance with EU 271 \nethical guidelines.  272 \nCancer cell culture  273 \nMCF7 cells (ATCC) were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) high glucose 274 \n(Sigma; cat# D6429), supplemented with 10% FBS, 1% PS, and 1µg/mL insulin at 37°C with 5% 275 \nCO2. Cell line identity was confirmed by short tandem repeat (STR) sequencing, and routinely 276 \ntested for mycoplasma contamination.  277 \nGeneration of lentivirus and transduced MCF7 cells  278 \nLentiviral particles were generated by co -transfecting HEK293T cells with 2µg each of VSVG 279 \nenvelope plasmid, 2 µg each of third-generation packaging plasmids, and 4µg plasmid DN (pFU-280 \nLuc2-tdTomato or pCDH-EF1-mVenus-p27K−), using FuGENE HD (Promega) at a 3:1 (reagent:μg 281 \nDNA) ratio. Viral supernatants were collected at 48- and 72-hours post-transfection, pooled, filtered, 282 \nand concentrated using Lenti-X Concentrator (Takara). MCF7 cells were sequentially transduced; 283 \nfirst with pFU-Luc2-tdTomato lentivirus and Fluorescence-activated Cell Sorting (FACS)-sorted for 284 \ntdTomato+ expression (BD Aria), followed by transduction with pCDH -EF1-mVenus-p27K− and 285 \npuromycin selection 0.75 μg/mL.  286 \nGeneration of engineered niches 287 \neON was generated by using hBM-MSC as previously described [41, 44]. Briefly, 0.75x106 hBM-288 \nMSCs were seeded into hydroxyapatite scaffolds (Engipore®, Finceramica-Faenza; 4 mm x 8 mm) 289 \nembedded in perfusion bioreactors. Cells were perfused at 3 mL/min superficial velocity for 24 290 \nhours (seeding phase), followed by 0.3 mL/min (culture phase). Constructs were cultured in 291 \nproliferative medium (PM) consisting of CM supplemented with 100 nM dexamethasone (Sigma; 292 \ncat# D4902), 0.1 mM ascorbic acid -2-phosphate (Sigma; cat# A92902) and 5 ng/mL FGF -2, 293 \nfollowed by three weeks in osteogenic medium (OM) consisting in CM supplemented with 10 nM 294 \ndexamethasone, 10 mM β-glycerophosphate, and 0.1 mM ascorbic acid-2-phosphate. The medium 295 \nwas changed twice weekly. 296 \neVN was generated by seeding 1x10 5 hAT-SVF cells into the hydroxyapatite scaffolds 297 \nembedded in perfusion bioreactors. Cells were exposed to 3 mL/min superficial velocity over 24 298 \nhours. Superficial velocity was reduced to 0.3 mL/min after 24 hours of cell seeding phase. Cells 299 \nwere then cultured for two weeks in CM supplemented with 5 ng/mL FGF2. The culture medium 300 \nwas changed twice per week. 301 \neVON was generated by seeding hAT-SVF cells at a ratio of 1:1 into pre-engineered eON 302 \nafter one week of osteogenic differentiation [42, 45]. After 24 hours of cell seeding phase, the 303 \nsuperficial velocity was reduced to 0.3 mL/min for perfusion co-culture for additional two weeks in 304 \nOM supplemented with 5 ng/mL FGF2. The culture medium was changed twice per week.   305 \nCo-culture with MCF7 cells and fulvestrant treatment 306 \nMCF7-tdTomato/mVenus-p27K- cells were seeded at 1:10 ratio onto eON, eVN, and eVON 307 \nconstructs under the perfusion bioreactor at 3 mL/min for 24 hours. Cells were co-cultured with the 308 \nrespective niches in CM at 0.3 mL/mmin for two weeks. Medium was refreshed twice weekly. 309 \nFulvestrant (100 nM) was added onto the respective niches after a week of co -culture, and 310 \nrefreshed twice weekly over a week.  311 \nCell isolation from the engineered niches (eON, eVN, and eVON) 312 \nThe constructs were washed with phosphate-buffered saline (PBS) (Gibco; cat# 20012-027), and 313 \nperfused at 3 mL/min superficial velocity with 0.3% collagenase type II (Worthington; cat# 314 \nLS004176) in PBS for an hour at 37°C. Supernatants were collected and filtered through a 100 μm 315 \nnylon mesh strainer (Corning; cat# 352360) and centrifuged at 500g for three minutes. Cells were 316 \nthen resuspended in FACS buffer (2% FBS (Gibco; cat# 10500 -064), 2 mM 317 \nethylenediaminetetraacetic acid (EDTA) (Sigma; cat# E7889), PBS). Additional digestion with 318 \n0.05% trypsin-EDTA (Gibco; cat# 25300-054) under perfusion at 3 mL/min was performed for six 319 \nminutes at 37°C. All fractions were pooled and, centrifuged at 500g for three minutes, and 320 \nresuspended in FACS buffer.  321 \nFlow cytometry  322 \nPhenotypic analysis of total MCF7 cells and mVenus-p27K-+ subpopulations was performed using 323 \nCytoFLEX flow cytometer (Beckman Coulter) and BD FACSAria cell sorter (BD Biosciences). Cell 324 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n8 \n \nsuspensions were collected from the niche -free scaffold and the respective engineered niches. 325 \nThey were filtered through 30-µm mesh cell strainer tubes to obtain single cells. Debris exclusion 326 \nand doublet discrimination were achieved by gating based on forward and side-scatter profiles and 327 \npulse width, respectively. Dead cells were identified and excluded using 4′,6 -diamidino-2-328 \nphenylindole (DAPI) staining (DAPI+ cells). Cancer cells were determined by the positive 329 \nexpression of tdTomato. Single-stained and fluorescence minus one (FMO) controls were used to 330 \nset gating parameters. 331 \nImmunofluorescence staining 332 \nMedium was removed from the bioreactors, and the scaffolds with engineered niches were taken 333 \nout of the bioreactors using tweezers. Scaffolds were rinsed with PBS, and fixed in 4% 334 \nparaformaldehyde (PFA, Thermoscientific; cat# 28908) for 24 hours at 4°C. After the fixation, 335 \nscaffolds were rinsed in PBS, and cut in half using a scalpel. One half of the scaffolds were used 336 \nfor whole -mount staining, and the other hal f were decalcified in 15% EDTA (0.5 M, pH: 7.2), 337 \nrenewed every other day for a week at 37°C with agitation. They were then embedded in paraffin, 338 \nand sections were cut in 6µm thickness using Microtome (Thermo Scientific; cat# HM 340E). Tissue 339 \nsections were hydrated in Ultraclear™ (J. T. Baker; cat# 3905.500PE), and a graded alcohol series. 340 \nSlides were then subjected to heat -induced epitope retrieval (HIER) (pH: 6, Citrate Buffer, 1x, 341 \nQuartett, AR-001-0120) for 15 min at 96°C. Sections were permeabilized and blocked with 3% 342 \nbovine serum albumin (BSA) in PBST (0.2% Triton X -100 (Sigma; cat# 9002 -93-1)) for an hour. 343 \nPrimary antibodies (SI Appendix, Table S1) were diluted in 0.5% BSA in PBST and incubated 344 \novernight at 4°C. Secondary antibodies (SI Appendix, Table S2) diluted in 0.5% BSA in PBST was 345 \napplied for 30 minutes at room temperature. Slides were counterstained for a minute with DAPI 346 \n(BD Biosciences; cat# 564907) solution. Slides were then mounted with Fluoromount ™ aqueous 347 \nmounting medium (Sigma; cat# F4680), and images were obtained using Nikon Ti2 microscope 348 \n(NIS version 5.30.07) equipped with X-Light V3 confocal unit, photometrics Kinetix (29.4mm, back-349 \nilluminated sCMOS) camera and Apo Plan lambda 20x, NA0.75 objective. Images were stored and 350 \nfigures were prepared in OMERO [61] and image analyses were conducted using QuPath software 351 \n(v0.5.1) [62].  352 \nWhole-mount IF staining 353 \nHalf of the fixed scaffolds were processed for whole -mount IF staining. Samples were 354 \npermeabilized and blocked in 3% BSA (Sigma; cat# A9647), 0.2% Triton X-100 (Sigma; cat# 9002-355 \n93-1) in PBS for 6 hours at room temperature, followed by 72 hours of primary antibody incubation 356 \nat 4°C with agitation. Antibodies were diluted in 0.5% BSA, 0.2% Triton X-100 in PBS and incubated 357 \novernight at 4°C. The following human primary antibodies were used: Anti -CD31 (Abcam; cat# 358 \nab9498), anti-NG2 (Abcam; cat# ab255811). After serial washes with PBST (0.2% Triton X-100 in 359 \nPBS) and PBS, samples were incubated with secondary antibodies (Invitrogen) overnight at 4°C, 360 \nwashed again, and counterstained with DAPI. Samples were stored in PBS at 4°C until imaging. 361 \nConfocal imaging was performed on a Nikon X-Light V3 spinning disk microscope using 0.9 µm z-362 \nstep across ~150 µm total depth (~165 optical sections per scaffold). Image stacks were converted 363 \ninto maximum intensity projections using NIS-Elements software and further processed in OMERO 364 \nsoftware.  365 \nImmunohistochemistry (IHC) staining of ERα  366 \nFormalin-fixed paraffin-embedded (FFPE) sections (6 µm) were stained for ERα (Thermo Scientific; 367 \ncat# MA5-14501) using the Ventana Discovery Ultra (RocheDiagnostics) automated slide strainer. 368 \nBriefly, tissue sections were deparaffinized and rehydrated, and were subjected to HIER, followed 369 \nby incubation with the primary antibody (manually applied; 1 hour at 37°C). After washing, sections 370 \nwere incubated with the secondary antibody for 1 hour at 37°C. Detection was performed using the 371 \nVentana DISCOVERY ChromoMAP 3,3’ -Diaminobenzidine (DAB) ( Ventana, cat# 760 -159) 372 \ndetection kit. Slides were then counterstained with hematoxylin II, followed by a bluing reagent 373 \n(Ventana; cat# 790-2208, cat# 760 -2037). The sections were dehydrated, cleared and mounted 374 \nwith permanent mounting medium. Slides were digitized using a Hamamatsu NanoZoomer S60 375 \nslide scanner equipped with a 40x objective (NA 0.95). Nuclear ER α+ MCF7 cell abundance 376 \n(DAB+) was quantified by manual evaluation. 377 \n 378 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n9 \n \nQuantification of CD31+ endothelial network and NG2+ cell abundance 379 \nMaximum intensity projection images were generated using NIS-Elements software, subjected to 380 \nball-correction filtering to minimize signal to noise ratio. Files were then converted into 381 \npyramidal.ome.tiff format using ImageJ [63] software with Kheops plugin [64], and uploaded into 382 \nQuPath. Full image annotation w as created for each field of view (FOV). A single ANN pixel 383 \nclassifier to detect CD31+ (GFP channel) endothelial networks across each FOV was trained with 384 \nexamples of positive and negative signal with gaussian filter and sigma 2.0. Once the CD31+ 385 \nendothelial network annotation was generated, a second pixel classifier was applied to detect 386 \nNG2+ (Cy5 channel) structures within or adjacent to the CD31+ networks. The ratio of 387 \nNG2+/CD31+ area per FOV was then calculated based on the workflow. The script containing the 388 \nworkflow was then run for all images, and the ratio of CD31+ endothelial network per FOV, and 389 \nNG2+/CD31+ area per FOV was calculated based on the obtained measurements.  390 \nQuantification of COL1A1, OCN deposition on IF staining images 391 \nIF staining images were converted into pyramidal.ome.tiff format using ImageJ software for 392 \ncompatibility with QuPath (v.0.5.1). Tissue boundaries were detected by the autofluorescence 393 \nsignal captured in the GFP channel (SI Appendix, Fig S2B) using a thresholder with sigma 5 and 394 \nthreshold 150. For COL1A1 quantification, a pixel classifier was created using a manual 395 \nthresholding method on the Cy5 channel, where COL1A1 was visualized. Classifier resolution was 396 \nset to full (0.56µm/px), with a Gaussian pre -filter applied and smoothing set to 0. The intensity 397 \nthreshold for COL1A1 and OCN positivity was empirically defined based on the signal from 398 \nsamples. Pixels above this threshold were classified as COL1A1+ ECM  or OCN+ ECM, 399 \nrespectively. All classifiers were stored and batch-applied across full-section datasets. 400 \nQuantification of tdTomato, mVenus, Ki67, and NR2F1 expression  401 \nIF staining images were first converted into pyramidal.ome.tiff format using ImageJ software, then 402 \nuploaded into QuPath. Full image annotations were created to define FOVs. Nuclei were 403 \nsegmented using Watershed Cell Detection based on DAPI staining. Next, annotation for MCF7 404 \ncells (tdTomato+) were generated by creating a single measurement classifier based on the mean 405 \ntdTomato signal intensity in the cytoplasm of MCF7 cells in the Cy3 channel. Thresholds were 406 \nempirically defined through visual inspection. Following td Tomato+ cell annotation, cancer cells 407 \nwere segmented based on their nuclear expression of mVenus (GFP channel), Ki67 (Cy5 channel) 408 \nand NR2F1 (Cy5 channel). Single measurement object classifier was created for each marker by 409 \napplying channel-specific filters, and manually thresholding mean nuclear signal intensity to classify 410 \nthe positive cells. Cells co -expressing tdTomato with each nuclear marker were classified using 411 \ncomposite object classifiers e.g., tdTomato+/Ki67+. Scripts for each marker panel were exported 412 \nfrom QuPath and applied across all images.  Manual curation was performed to remove staining 413 \nartifacts and exclude false positives.  414 \nStatistics 415 \nData are presented as means ± standard deviation of the mean, and were analyzed by using 416 \nGraphPad Prism software (v10.1.1). Unless otherwise stated/indicated, multiple (pairwise) 417 \ncomparisons were performed using Tukey’s test. Statistical significance was determined by one -418 \nway ANOVA followed by Tukey's multiple comparison test. Statistically significant differences were 419 \ndefined as: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. 420 \n 421 \nAcknowledgments 422 \nWe thank past and present members of the Martin and Bentires-Alj laboratories for feedback and 423 \ndiscussions. We also thank the Department of Biomedicine (DBM) Flow Cytometry Core Facility, 424 \nspecifically, Morgane Hilpert, Mihaela Barbu-Stevanovic, Jelena Markovic Djuric, Stella Stefanova 425 \nfor assistance with FACS. We are grateful to the DBM Microscopy Core Facility, particularly Loïc 426 \nSauteur, for assistance with imaging and image processing. We thank the DBM Histology Core 427 \nFacility, particularly Diego Calabrese for performing the immunohistochemical staining. We thank 428 \nT. Oki and T. Kitamura for providing the pMXs-IRES-puro/mVenus-p27K- vector, and A. Bottos and 429 \nN. E. Hynes for the pFU -Luc2-tdTomato vector. We also thank for past and present members of 430 \nthe Scherberich and Barbero laboratories for their helpful feedback and discussions. We are 431 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n10 \n \ngrateful to Noemi Torriero, Gangyu Zhang, and Benedetta Guagnini for their technical assistance, 432 \nregular scientific exchange, and support. This work was supported by the European Commission 433 \nunder the Horizon Europea Marie Skłodowska-Curie Actions (MSCA) program (Grant No. 860715; 434 \nSINERGIA), by the Freiwillige Akademische Gesellschaft Basel (FAG Basel), and by the Stiftung 435 \nzur Förderung von chirurgischer Forschung und Spitalmanagement.  436 \n 437 \nReferences 438 \n1. Wang, H., et al., Bone Tropism in Cancer Metastases.  Cold Spring Harb Perspect Med, 439 \n2020. 10(10). 440 \n2. Suva, L.J., et al., Bone metastasis: mechanisms and therapeutic opportunities.  Nat Rev 441 \nEndocrinol, 2011. 7(4): p. 208-18. 442 \n3. Nuckhir, M., et al., State of the Art Modelling of the Breast Cancer Metastatic 443 \nMicroenvironment: Where Are We? J Mammary Gland Biol Neoplasia, 2024. 29(1): p. 14. 444 \n4. Quayle, L., P.D. Ottewell, and I. Holen, Bone Metastasis: Molecular Mechanisms Implicated 445 \nin Tumour Cell Dormancy in Breast and Prostate Cancer. Curr Cancer Drug Targets, 2015. 446 \n15(6): p. 469-80. 447 \n5. 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Pienta, Dormant cancer cells: programmed 569 \nquiescence, senescence, or both? Cancer Metastasis Rev, 2023. 42(1): p. 37-47. 570 \n61. Allan, C., et al., OMERO: flexible, model-driven data management for experimental biology. 571 \nNat Methods, 2012. 9(3): p. 245-53. 572 \n62. Bankhead, P., et al., QuPath: Open source software for digital pathology image analysis.  573 \nSci Rep, 2017. 7(1): p. 16878. 574 \n63. Schindelin, J., et al., Fiji: an open -source platform for biological -image analysis.  Nat 575 \nMethods, 2012. 9(7): p. 676-82. 576 \n64. BIOP. ijp-kheops: Fiji plugin for image quantification 2025  05.09.2025]; Available from: 577 \nhttps://github.com/BIOP/ijp-kheops. 578 \n 579 \n  580 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n13 \n \nFigures and Tables 581 \n 582 \n 583 \n 584 \n 585 \n 586 \n 587 \n 588 \n 589 \n 590 \n 591 \n 592 \n 593 \n 594 \n 595 \n 596 \n 597 \n 598 \n 599 \n 600 \n 601 \n 602 \n 603 \n 604 \n 605 \nFigure 1. Engineered osteoblastic niche (eON) promotes MCF7 cell proliferation. (A) 606 \nExperimental scheme to generate eON co -cultured with MCF7-tdTomato/mVenus-p27K⁻ for two 607 \nweeks in perfusion bioreactors. (B) Flow cytometry quantification of MCF7 cells after two weeks of 608 \nculture in niche-free scaffold (NFS) or eON. (C, D) Percentage of (C) quiescent (mVenus-p27K⁻) 609 \nMCF7 cells, and (D) proliferating (Ki67+) MCF7 cells relative to total tdTomato+ MCF7 cells per 610 \nfield of view (FOV) in NFS or eON. Data presented as mean ± SD, n=3. Statistical significance was 611 \ndetermined by two-tailed, unpaired, parametric t test. *p<0.05, **p<0.01. (C, D) 5 FOV analyzed 612 \nper sample. 613 \n  614 \nNFSeON\n0\n1×105\n2×105\n3×105\n4×105\n5×105\nTotal number of MCF7 cells\n✱✱\nB C D\nNFSeON\n0\n20\n40\n60\n80\nmVenus-p27K+cells(%ofMCF7cells)mVenus-p27K-+ cells (% of tdTomato+ MCF7 cells) \n✱\nNFSeON\n0\n20\n40\n60\nKi67+ MCF7 cells (% of MCF7 cells per FOV)\nKi67+MCF7cells (% of MCF7 cells)\n✱✱\nA\nQuiescent\nMCF7MCF7Luc2 tdT omatoUbi\nEF1mVenus p27K-\neON\nOsteogenic differentiationExpansion\n1w 3w\nhBM-MSCs\nCeramic scaffold\n2w\n4w\nNFS\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n14 \n \n 615 \nFigure 2. eON, eVN and eVON recapitulate osteoblastic, vascularized, and vascularized 616 \nosteoblastic bone microenvironments, respectively . (A) Experimental scheme illustrating 617 \ngeneration of eVN and eVON co-cultured with MCF7-tdTomato/mVenus-p27K⁻ cells for two weeks. 618 \n(B) Representative IF staining and (C) quantification of COL1A1 deposition (percentage per area) 619 \nacross the niches. (D) Representative IF staining and (E) quantification of OCN deposition 620 \n(percentage per area) across the niches. Magnification: 20x; scale bar: 50 µm. (F) Representative 621 \nwhole-mount immunofluorescence (IF) staining of CD31 (magenta) and NG2 (cyan) in eON, eVN, 622 \nand eVON. Magnification: 20x; scale bar: 50 µm. (G, H) Quantification of (G) CD31+ endothelial 623 \nnetwork density  (percentage per FOV) and (H) NG2+ perivascular cell density on CD31+ 624 \nendothelial network (H) (percentage per FOV) in eON, eVN, and eVON. (C, E, G, H) Data presented 625 \nas mean ± SD, n=3 -5 (two independent experiments). (G, H) 5 FOV analyzed per sample. 626 \nStatistical significance was determined by one -way ANOVA followed by Tukey's multiple 627 \ncomparison test. *p<0.05, **p<0.01. Multiple comparisons were performed exclusively between 628 \neVN and eVON for vascular analysis. 629 \n  630 \nNG2CD31 DAPI MERGE\neVON eVN eON\nF\neONeVNeVON\n0\n5\n10\n15\n20\nNG2+/CD31+ endothelial network (% per FOV)\n✱✱\neONeVNeVON\n0\n5\n10\n15\nCD31+ endothelial network (% per FOV) HG\nCOL1A1 DAPI MERGE\neVNeVON eON\nB\nDAPIOCN MERGE\neONeVNeVON\nDC\neONeVNeVON\n0\n20\n40\n60\n80\nCOL1A1 deposition(% per area)\n✱ ✱✱ E\neONeVNeVON\n0\n10\n20\n30\n40\nOCN deposition(% per area)\n✱ ✱\neVON\n2w1w 1w\nhBM-MSCs\nDifferentiation\nDifferentiationExpansion\n2w2w\neVN\nhAT-SVF cells\nCeramic scaffold\nVasculogenic induction\nVasculogenic induction\nMCF7 cells\n2w\nA\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n15 \n \n 631 \nFigure 3. eVN enriches for quiescent, non -proliferative MCF7 cells with elevated nuclear 632 \nNR2F1. (A) Flow cytometry quantification of MCF7-tdTomato/mVenus-p27K- cells after two weeks 633 \nof co -culture with eON, eVN and eVON. (B) Representative IF staining showing quiescent 634 \n(mVenus-p27K⁻+; green) MCF7 cells appearing as solitary and clustered with proliferating (Ki67+; 635 \ncyan) MCF7 cells (tdTomato+; red) in eON, eVN and eVON after two weeks of co -culture. (C, D) 636 \nPercentage of (C) quiescent (mVenus-p27K-+) MCF7 cells, and  (D) proliferating (Ki67+) MCF7 637 \ncells relative to total MCF7 cells per FOV. (E) Representative IF staining of NR2F1 (cyan) and for 638 \ntdTomato (red) after two weeks of co -culture in eON, eVN, and eVON. Magnification: 20x; scale 639 \nbar: 50 µm. (F) Percentage of nuclear NR2F1+ MCF7 cells relative to total MCF7 cells per FOV in 640 \nthe respective niches. (A, C, D, F)  Data presented as mean ± SD, n=3 (three independent 641 \nexperiments; each data point represents the mean of three technical replicates per donor). 642 \nStatistical significance was determined by one-way ANOVA with Tukey’s multiple comparison test. 643 \n*p<0.05, **p<0.01, ***p<0.001. (C, D, F) 5 FOV analyzed per sample. 644 \n  645 \ntdTomato    DAPImVenus-p27K⁻ Ki67 MERGE\neONeVNeVON\nB\nDAPItdTomato MERGENR2F1\neONeVNeVON\nE\neONeVNeVON\n0\n20\n40\n60\n80\nNuclear NR2F1+ MCF7 cells (% of MCF7 cells per FOV)\n✱✱ ✱✱\nF\neONeVNeVON0102030Ki67+ MCF7 cells(% of MCF7 cells per FOV)\n✱✱✱✱\nD\neONeVNeVON\n0\n20\n40\n60\n80\nmVenus-p27K-+ MCF7 cells (% of MCF7 cells per FOV)\n✱ ✱C\neONeVNeVON\n0\n5×105\n1×106\n2×106\nTotal number of MCF7 cells\n✱\n✱✱\n✱✱✱A\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint \n\n \n \n16 \n \n 646 \nFigure 4. Fulvestrant reduces MCF7 cell numbers only in vascularized niches,  despite 647 \ncomparable ERα degradation across all conditions. (A) Representative IHC staining images of 648 \nERα and (B) percentage of nuclear ERα+ MCF7 -tdTomato/mVenus-p27K- cells relative to total 649 \nMCF7 cells per FOV upon one week of treatment in eON, eVN and eVON. (C) Flow cytometry 650 \nquantification of MCF7 cells after one week of treatment in eON, eVN and eVON. Data presented 651 \nas mean ± SD, n=3-4 (two independent experiments). Magnification: 40x; scale bar: 50 µm. (A, B) 652 \nStatistical significance was determined by two-way ANOVA with Tukey’s multiple comparison test. 653 \n*p<0.05, ****p<0.0001. 654 \n 655 \nFulvestrantVehicle\neONeVNeVON\nA\nC\neONeVNeVON02×1054×1056×1058×1051×106Total number of MCF7 cells\n✱✱\n20406080Nuclear ERα+MCF7 cells(% of MCF7 cells per FOV)\nVehicleFulvestrant✱✱✱✱✱✱✱✱✱✱✱✱\nB\neVNeONeVON020406080Nuclear ERα+MCF7 cells(% of MCF7 cells per FOV)\nVehicleFulvestrant✱✱✱✱✱✱✱✱✱✱✱✱\neVNeONeVON020406080Nuclear ERα+MCF7 cells(% of MCF7 cells per FOV)\nVehicleFulvestrant✱✱✱✱✱✱✱✱✱✱✱✱\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted December 22, 2025. ; https://doi.org/10.64898/2025.12.18.695089doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}