【Biomaterials】青岛大学施春英|突破90%抑瘤率：智能多肽纳米平台精准调控三阴性乳腺癌微环境#

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：Chunying Shi, Xiao Zou, Guoxin Sun

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

文章概要#

本研究成功构建了一种名为QGS@DOX的肿瘤微环境响应型自组装多肽纳米治疗平台，实现了超越90%的抑瘤率。该平台巧妙地将主动肿瘤靶向、成纤维细胞活化蛋白-α（FAP-α）酶驱动释放、抗血管生成治疗以及阿霉素化学疗法集成于一体。在有效抑制三阴性乳腺癌生长和远端肺转移的同时，显著降低了传统化疗药引起的化学性心肌损伤，为临床治疗难治性实体瘤提供了创新的多模态协同方案。

引言#

三阴性乳腺癌由于缺乏雌激素受体、孕激素受体及人表皮生长因子受体2的表达，至今仍是临床上最具挑战性的恶性肿瘤之一。作为一线化疗药物，阿霉素虽能强效杀伤肿瘤并诱导免疫原性细胞死亡，但其应用常受限于严重的全身毒性，特别是剂量依赖性的心脏毒性。此外，三阴性乳腺癌复杂的肿瘤微环境包含密集的细胞外基质和异常的血管网络，构成了阻碍药物渗透的天然屏障。为了克服这些难题，探索能同时靶向肿瘤核心、清除基质屏障并重塑免疫微环境的智能化集成递送系统成为当前研究的迫切需求。

Scheme 1. Schematic illustration of synergistic Chemotherapy and anti-angiogenic therapy mechanisms of QGS@DOX (created by Figdraw.com). A. Self-assembly procedure of QGS@DOX nanoparticles. B. Mechanism of action: QGS@DOX exerts antitumor effects by inhibiting EMT, activating the cGAS–STING pathway and inducing ICD, thereby suppressing the proliferation, migration and invasion of tumor cells while promoting apoptosis. C. Immunoprecipitation-mass spectrometry indicated SML may interact with MSN.#

主要实验及结论#

研究团队首先对微环境中的关键靶点和多肽进行了系统筛选。在前期发现的靶向多肽中，通过细胞和活体实验对比了不同序列的归巢效率。如图1所示，体外免疫荧光与体内小动物活体成像结果共同证实，SML多肽展现出远超其他对照组的超高肿瘤靶向富集能力。与此同时，血管生成实验表明抗血管多肽QKS能高效抑制内皮细胞的小管形成及增殖。组织学检测则进一步证实，FAP-α在三阴性乳腺癌组织中呈特异性高度表达，这为其作为智能释放的精准开关奠定了坚实基础。

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.#

在明确各组件功能后，团队通过化学合成将抗血管多肽、C18烷基链、FAP-α特异性可裂解连接子以及SML靶向肽融为一体，驱动其与阿霉素自组装。如图2所示，透射电镜下展现出形态均匀且分散良好的球形纳米颗粒，阿霉素的高效包裹率达到了81.78%。体外释放动力学证实，在FAP-α酶的存在下，纳米颗粒会发生显著的结构解体并快速释放药物，而在无酶生理条件下则能保持优异的胶体稳定性，这种完美的响应性极大地规避了外周血循环中的药物渗漏风险。

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.#

细胞层面的功能验证进一步凸显了该平台的协同优势。如图3所示，流式细胞术与细胞摄取实验表明，得益于SML多肽的主动靶向与酶促释放的协同效应，QGS@DOX在肿瘤细胞内的阿霉素摄取效率达到了最高峰值。随后的细胞生物学实验证实，该药物平台能强效抑制癌细胞的存活率、克隆形成能力以及迁移与侵袭活性，且未载药的空载多肽本身也表现出了一定的内在抗转移潜能。

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. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)#

随后的活体肿瘤重塑实验将研究推向了核心。在动物模型中，团队对其体内疗效与全身安全性进行了深度追踪。如图4所示，相较于生理盐水组及游离药物组，QGS@DOX治疗组的肿瘤生长曲线几乎完全抚平，抑瘤率惊人地超过了90%，并彻底阻断了肺部转移结节的产生。更为重要的是，相比于游离阿霉素引起的心肌结构紊乱和血清损伤标志物飙升，纳米组成功将动物的心脏功能维持在正常生理范围内，完美达成了增效减毒的预设目标。

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.#

为了从病理和分子机制层面揭示其控瘤根源，团队进行了深入的转录组学和免疫学分析。如图5所示，肿瘤组织切片病理分析表明，QGS@DOX导致了最广泛的细胞坏死与凋亡，同时使微血管密度和免疫抑制性成纤维细胞显著减少。如图6所示，RNA测序及蛋白质印迹技术表明，该平台大规模重塑了肿瘤转录组，通过上调促凋亡基因并下调间质标志物，强烈抑制了上皮-间充质转化（EMT）通路及伴随的胶原纤维沉积，从根本上削弱了肿瘤的侵袭力。

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.#

此外，针对靶向机制的深度发掘有了突破性进展。如图7所示，通过免疫沉淀质谱分析、分子动力学模拟以及细胞共定位技术，研究团队首次明确鉴定出埃兹蛋白家族中的Moesin（MSN）是SML靶向肽的直接结合受体，揭示了该多肽介导精准递送的全新分子通路。在药代动力学层面，如图8和图9所示，TAMRA标记的追踪实验证实，纳米制剂显著延长了药物的血循环半衰期，实现了在免疫健全小鼠模型中的精准富集与高效控瘤。

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-231 cells (Green arrow represented pseudopodia, scale bar: 50 μm). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)#

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. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)#

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.#

最后，该纳米平台在免疫微环境重塑方面的表现尤为亮眼。如图10所示，靶向递送的阿霉素诱导了强烈的免疫原性细胞死亡，促使肿瘤释放大量损伤相关分子。转录组引导的机制研究表明，QGS@DOX强效激活了肿瘤内部的cGAS-STING信号通路，引发了下游核心节点的级联磷酸化。这一过程成功促进了树突状细胞的成熟与抗原呈递，并在全身循环中释放了高水平的系统性炎性细胞因子，最终高效募集大量效应T细胞深入肿瘤内部，成功将三阴性乳腺癌由“冷肿瘤”转化为“热肿瘤”。

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. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)#

总结及展望#

本研究成功开发了一种多功能、模块化的自组装多肽纳米治疗系统，在攻克难治性三阴性乳腺癌方面展现出极高的临床转化潜力。该平台通过精妙的结构设计，不仅跨越了基质与血管构成的物理屏障，实现了高于90%的卓越抑瘤功效，更通过激活cGAS-STING通路引发了持久的系统性抗肿瘤免疫反应。未来的研究将进一步聚焦于其在不同生理梯度下的长期稳定性评估，并结合基因敲除模型深入阐明多肽与受体相互作用的精细晶体结构，以期加速这一多模态协同治疗方案走向临床应用。