【Biomaterials】青岛大学施春英|效率狂飙90%！肿瘤微环境响应型自组装纳米疗法整合化疗、抗血管生成和免疫刺激疗法治疗三阴性乳腺癌#

文章标题：Tumor Microenvironment-Responsive Self-Assembling Nanotherapeutics Integrating Chemo-, Anti-Angiogenic, and Immunostimulatory Therapy for Triple-Negative Breast Cancer

通讯作者：Jianye Xie, Xiao Zou, Chunying Shi

文章链接：https://doi.org/10.1016/j.biomaterials.2026.124435

文章概要#

本研究成功开发了一种肿瘤微环境响应型自组装纳米药物平台QGS@DOX，该平台巧妙地将主动靶向、多肽介导的抗血管生成、精准化疗以及免疫激活融合于一体。通过攻克三阴性乳腺癌特有的基质物理屏障，该智能纳米颗粒实现了药物在肿瘤部位的高效富集与控释。实验结果表明，该系统不仅实现了超过90%的肿瘤生长抑制率，成功阻断了肺转移，更首次揭示了靶向肽SML与膜突蛋白Moesin（MSN） 相互作用的新机制，并通过激活cGAS-STING信号通路重塑了免疫抑制微环境，为三阴性乳腺癌的临床精准多模态治疗提供了极具转化前景的新策略。

引言#

三阴性乳腺癌作为乳腺癌中最具侵袭性的亚型，因缺乏雌激素受体、孕激素受体及HER2的表达，导致临床上缺乏明确的靶向治疗位点。作为一线化疗药物的阿霉素虽然杀伤力强且能诱导免疫原性细胞死亡，但其应用长期受到严重的剂量依赖性心脏毒性以及肿瘤靶向性差的掣肘。更棘手的是，三阴性乳腺癌的肿瘤微环境如同一座坚固的堡垒，富含激活的癌相关纤维细胞和致密的细胞外基质，形成了强大的物理屏障。如何突破这道屏障，在降低毒副作用的同时实现多机制协同抗肿瘤，是当前肿瘤纳米医学领域亟待解决的重大科学问题。

主要实验及结论#

研究团队首先对多种三阴性乳腺癌靶向多肽进行了系统筛选。如图1所示，体外和体内的免疫荧光及活体成像分析证实，SML多肽展现出远超其他候选多肽的靶向效率和肿瘤归巢能力。同时，抗血管生成肽QKS显著抑制了内皮细胞的管腔形成和增殖，而成纤维细胞激活蛋白-α（FAP-α）在三阴性乳腺癌组织中的表达水平显著升高，为其作为特异性释放开关提供了坚实的理论依据。

Fig. 1. Screening and validation of TNBC-targeting peptides and characterization of FAP-α expression. A. Representative fluorescence images of the binding of Biotin-labeled peptides to MDA-MB-231 cells (Scale bar,100 μm). B. Quantitative analysis of the fluorescence intensity in (A) (n = 3). C. Ex vivo fluorescence imaging showing the distribution of the peptides in major organs of tumor-bearing mice. D. Quantitative analysis of the fluorescence intensity in tumors from (C) (n = 3). E. Representative fluorescence images of tumor tissue cryosections (Scale bar, 50 μm). F. Quantitative analysis of the fluorescence intensity in (E) (n = 3). G. Representative images of the tube formation assay in HUVECs treated with the anti-angiogenic peptide QGS (Scale bar, 200 μm). H. Quantitative analysis of the tube formation area, number of junctions, and branch length in (G) (n = 4). I. Effect of the QKS peptide on the proliferation viability of HUVECs (n = 6). J. Representative images of FAP-α IHC staining in mouse tumor tissues. K. Quantitative analysis of relative FAP-α expression by IHC (n = 3). L. Western blot analysis of FAP-α protein expression in tumor tissues and quantification (n = 15). All data were presented as mean ± SD. p < 0.05, p < 0.01, p < 0.001.#

基于上述发现，研究人员设计并合成了多功能自组装肽纳米载体QGS。如图2所示，Blank纳米颗粒呈现出均一的球形结构，并在包裹阿霉素后其流体力学粒径增至81.57纳米，药物包裹率高达81.78%。圆二色谱和透射电镜分析进一步证实，在特异性FAP-α酶的作用下，QGS@DOX纳米颗粒会发生显著的结构拆解与碎片化，从而实现肿瘤微环境响应型的选择性药物释放，且展现出优异的血液相容性。

Fig. 2. Preparation and physicochemical characterization of self-assembled nanoparticles. A. Schematic illustration of the chemical structure of QAS and its self-assembly with DOX into nanoparticles. B. Representative TEM image (scale bar: 100 nm) and size distribution histogram of the nanoparticles. C. Zeta potential of the nanoparticles. D. UV-vis absorption spectra of the nanoparticles. E. Circular dichroism (CD) spectral changes of the nanoparticles after incubation with FAP-α. F. EE of DOX in QGS. G. Cumulative release profile of DOX from the nanoparticles in the presence or absence of FAP-α (n = 4). H. TEM images of nanoparticles before and after cleavage by FAP-α enzyme (scale bar: 100 nm). I. Hemocompatibility analysis of the nanoparticles (n = 6). All data were presented as mean ± SD.#

在体外细胞功能实验中，QGS@DOX展现出了卓越的抗肿瘤增殖与迁移效果。如图3所示，活死细胞染色和流 cytometry 分析表明，得益于SML多肽介导的主动靶向与FAP-α敏感控释的协同效应，QGS@DOX在肿瘤细胞内的阿霉素摄取效率达到了最高峰。无论是细胞生长曲线、划痕愈合还是侵袭实验，该多功能纳米系统均展现出了比单药或非响应性对照组更强的抑制效能，甚至空白载体自身也表现出一定的抗转移潜能。

Fig. 3. In vitro antitumor activity and cellular uptake of DOX-loaded nanoparticles. A. Representative Calcein-AM/PI staining images of MDA-MB-231 cells in different treatment groups (scale bar: 100 μm) and B) the corresponding relative fluorescence intensity (n = 3, green/red indicated live/dead cells). C. Flow cytometry histograms of DOX fluorescence intensity in single cells from each group, and the quantitative analysis of DOX-positive cells (n = 3). D. Effect of various treatment groups on the proliferation of MDA-MB-231 cells in the presence of FAP-α (n = 6). E. Representative images of the wound healing assay in MDA-MB-231 cells (scale bar: 100 μm) and F) the corresponding statistics of the wound healing rate (n = 3). G. Diagram of cell migration and invasion. H. Representative images of the transwell assay (scale bar: 50 μm). I. Representative images of colony formation assay. J. Quantitative analysis of the number of migrated cells (n = 3). K. The number of invaded cells (n = 3). L. Statistical analysis of clonal efficiency (n = 3). All data were presented as mean ± SD. *p < 0.05, ** p < 0.01.#

紧接着，研究团队在动物体内深入验证了该平台的治疗潜能和生物安全性。如图4所示，在人三阴性乳腺癌小鼠模型中，QGS@DOX治疗组的肿瘤生长几乎完全陷入停滞，肿瘤抑制率突破90%，并且彻底清除了肺部的转移结节。与自由阿霉素引起的小鼠体重骤降和心肌结构损伤不同，纳米制剂组小鼠的体重平稳，射血分数等各项心脏功能指标完全正常，血清中反映心肌受损的生物标志物也维持在健康基线，充分证实了该系统极高的安全耐受性。

Fig. 4. In vivo antitumor efficacy and biosafety evaluation in immunodeficient mice. A. Timeline of BALB/c tumor model establishment and different treatments. B. Representative tumor images of different groups after treatment (n = 4). C. Tumor weights measured at the end of the treatment (n = 4). D. Tumor inhibition rate. E. Tumor growth curves monitored during the treatment period (n = 4). F. Body weight changes of nude mice throughout the treatment course (n = 4). G. Representative gross anatomy of lungs (low magnification). H. Statistical analysis of the number of lung metastatic nodules (n = 4). I. Representative images of H&E-stained lung tissue sections (Scale bar, 50 μm). These histological sections provide microscopic validation and complement the macroscopic lung images shown in panel (G). J. Representative H&E-stained images of major organs (heart, liver, spleen, and kidney) (Scale bar, 50 μm). K. Representative echocardiographic images. L. Left ventricular ejection fraction (EF%) and fractional shortening (FS%) (n = 3). M. Detection of myocardial injury markers CKMB and cTnI in mouse serum (n = 4). All data were presented as mean ± SD. *p < 0.05, ** p < 0.01.#

为了揭示其深层分子机制，团队进行了微观组织病理学和转录组学研究。如图5所示，显微分析证实QGS@DOX引发了最严重的肿瘤细胞坏死与凋亡，Ki-67增殖指数降至最低，同时微血管密度显著骤减。如图6所示，RNA测序表明该平台诱导了极其广泛的转录组重编程，显著上调了p53和Bax等促凋亡基因，同时强烈抑制了表皮-间充质转化（EMT）通路及相关的纤维化进程，Masson染色也直观地印证了肿瘤组织内胶原沉积的减少。

Fig. 5. Tumor histological analysis and key biological phenotype characterization. A. Representative H&E staining images (scale bar, 50 μm). B. Immunofluorescence staining of the proliferation marker Ki-67 in tumor tissues (scale bar, 50 μm). C. Detection of tumor cell apoptosis (scale bar, 50 μm). D. Immunofluorescence staining of α-SMA (scale bar, 50 μm). E. Immunofluorescence staining of CD31 (scale bar, 50 μm). F. Quantification of Ki-67-positive cells. G. Quantification of TUNEL-positive cells. H. Statistical Analysis of α-SMA Mean Fluorescence Intensity (MFI). I. Statistics of CD31 MFI. Data in (F-I) are from multi-field analysis (n=6), presented as mean ± SD. *p < 0.05, ** p < 0.01.#

针对SML多肽的靶向靶点谜题，研究人员利用免疫沉淀质谱和分子模拟进行了破译。如图7所示，多肽拉下实验与体内外荧光共定位分析共同将MSN蛋白锁定为SML的核心结合靶点。SML多肽正是通过干扰MSN的功能，破坏了微丝骨架的稳定性并减少了细胞伪足的形成，从而在源头上削弱了乳腺癌细胞的运动与侵袭能力。

Fig. 7. Identification of MSN as the specific binding target of the SML targeting peptide. A. Schematic diagram of the workflow for identifying target proteins. B. Results of Coomassie Blue staining (red arrows indicate differential protein bands). C. Three candidate target proteins were identified by mass spectrometry analysis. D. Molecular docking of SML with the candidate proteins (WeMol). E. Representative fluorescence co-localization images of Vimentin (VIM) and SML (scale bar: 25 μm). F. Quantitative analysis of the co-localization between Vim and SML. G. Representative fluorescence co-localization images of Moesin (MSN) and SML (scale bar: 25 μm). H. Quantitative analysis of the co-localization between MSN and SML. I. VIM and MSN proteins were detected by Western blot after the pull-down assay. J. Phalloidin staining of SML-treated MDA-MB-231cells (Green arrow represented pseudopodia, scale bar: 50 μm).#

Fig. 8. Pharmacokinetics, biodistribution, and tumor penetration of TAMRA-labeled nanocarriers. A. Serum pharmacokinetic curves (n = 6). B. Tumor tissue pharmacokinetic curves (n = 6). C. In vivo biodistribution. D. Ex vivo fluorescence imaging. E. Quantification of ex vivo fluorescence. F. Fluorescence microscopic images of tumor frozen sections (Red is TAMRA, and green is CD31, scale bar: 50 μm). G. Quantitative analysis of TAMRA fluorescence intensity in tumor sections (n = 4). Data are presented as mean ± SD. *p < 0.05, ** p < 0.01.#

最后，在具有健全免疫功能的荷瘤小鼠体内，研究人员探究了该纳米平台对免疫微环境的重塑效应。如图8所示，体内药代动力学分析印证了TAMRA标记的纳米颗粒具备持久的血液循环寿命以及优异的肿瘤渗透扩散能力。如图9所示，在小鼠乳腺癌模型中，QGS@DOX再次复现了极其强大的抑瘤与抗转移疗效。更为关键的是如图10所示，该多功能平台高效触发了肿瘤细胞的免疫原性细胞死亡，使HMGB1和钙网蛋白大量外排，成功诱导了肿瘤实质和脾脏内树突状细胞的成熟与全身炎性细胞因子的释放。机制分析进一步表明，这主要依赖于高效激活了胞质DNA传感器cGAS及其下游STING-TBK1-IRF3信号级联放大反应，最终成功逆转了免疫抑制状态，召集了密集的CD4和CD8阳性T淋巴细胞向肿瘤内部浸润。

Fig. 9. Antitumor efficacy in immunocompetent 4T1 tumor-bearing mice. A. Schematic diagram of the subcutaneous tumor model establishment and therapeutic regimen. B. Representative images of tumors at endpoint. (n = 4). C. Statistical analysis of tumor mass from each group (n = 4). D. Tumor inhibition rate. E. Dynamic changes in tumor volume during the treatment (n = 4). F. Body weight changes of mice during the treatment (n = 4). G. Macroscopic lung images (circled areas indicated metastases). H. Statistical analysis of the number of surface metastatic nodules on the lungs (n = 4). I. Representative H&E-stained images of lung tissues (scale bar: 50 μm). J. H&E staining of tumor tissues (scale bar: 50 μm). All data are presented as mean ± SD. *p < 0.05, ** p < 0.01.#

Fig. 10. QGS@DOX induces ICD and activates anti-tumor immunity via the cGAS–STING pathway. A-B. Expression of ICD markers. (A) Representative immunofluorescence images and quantification of HMGB1 (red) and (B) CRT (red) in tumor tissues (n = 6). Nuclei were counterstained with DAPI (blue). Scale bars: 50 μm. C-D. Intratumoral T lymphocyte infiltration. (C) Representative IHC staining images and quantification of CD4+ and (D) CD8+ T cells in tumor tissues (n = 6). Scale bars: 50 μm. E. Dendritic cell (DC) infiltration in tumors. Representative IHC images of CD11c+ DCs in tumor tissues. Scale bar: 100 μm. F, G. Analysis of DC maturation status. Representative flow cytometry plots (left) and frequency quantification (right) of CD11c+ CD86+ CD80+ mature DCs in (F) tumor tissues and (G) spleens (n = 3). H. Release of systemic inflammatory cytokines. ELISA measurement of TNF-α, IFN-γ, and IL-6 levels in mouse serum (n = 6). (I, J) Activation of the cGAS-STING signaling pathway. I. Representative Western blot images of cGAS, p-STING/STING, p-TBK1/TBK1, and p-IRF3/IRF3 in tumor lysates. J. Densitometric quantification of the indicated protein levels (n = 4). All data are presented as mean ± SD. p < 0.05, p < 0.01, p < 0.01.#

总结及展望#

综上所述，本研究不仅成功构建了一个集主动靶向、微环境响应、化疗、血管阻断及免疫激活于一体的高效自组装多肽纳米药箱，而且在机理上首次确立了SML多肽与MSN相互作用抑制转移的新视角。该平台不仅在两类肿瘤模型中均展现出超过90%的抑瘤率和零转移的惊人表现，更大幅度地减轻了临床阿霉素治疗中最具威胁的心脏毒性副作用。这种模块化的设计理念和多靶点协同作战的策略，为临床突破难治性实体瘤的物理屏障和免疫耐受提供了全新的范式与广阔的转化应用前景。