Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2024 Oct 30;18(45):30943–30956. doi: 10.1021/acsnano.3c12967

Tumor-Derived Extracellular Vesicles Enable Tumor Tropism Chemo-Genetherapy for Local Immune Activation in Triple-Negative Breast Cancer

Zaihui Peng †, Tingting Zhao †, Pingping Gao †, Guozhi Zhang †, Xiujuan Wu †, Hao Tian †, Man Qu †, Xuanni Tan †, Yi Zhang †,*, Xiang Zhao ‡,*, Xiaowei Qi †,*
PMCID: PMC11562804  PMID: 39474658

Abstract

graphic file with name nn3c12967_0010.jpg

Triple-negative breast cancer (TNBC) is highly heterogeneous, lacks accessible therapeutic targets, and features an immunosuppressive tumor microenvironment (TME). Anthracycline-based chemotherapy remains the primary treatment method for TNBC, while the current popular immune checkpoint inhibitors persistently encounter therapeutic resistance. Therefore, there is an urgent need to explore combined therapeutic strategies to remodel the TME and improve the treatment response. Considering the highly specific homing ability of tumor cell-derived vesicles and the key role of the signal transduction and activation of the transcription factor 3 (STAT3) pathway in TNBC, we propose a synergistic therapeutic strategy that integrates gene therapy, chemotherapy, and immunotherapy based on STAT3 short interfering RNA (siSTAT3) and doxorubicin (DOX)-functionalized tumor-derived extracellular vesicles (TEVs) (siSTAT3-DOX@TEV). The in vitro and in vivo results demonstrate that siSTAT3-DOX@TEV target tumor tissues precisely, downregulate STAT3 expression, and synergistically and efficiently induce immunogenic death, thereby reversing the immunosuppressive TME. Moreover, mass cytometry and immunohistochemistry reveal the local immune activation effect of siSTAT3-DOX@TEV, with a significant increase in M1 macrophages, CD4+ T cells, and CD8+ T cells in tumor tissues. These results provide strong hints for the development of TEV-based chemo-gene therapeutic agents for TNBC treatment at the clinical level.

Keywords: tumor-derived extracellular vesicles, doxorubicin, STAT3, synergistic therapy, TNBC

Triple-negative breast cancer (TNBC) has negative drug targets, high malignancy, recurrence, and the worst prognosis among all breast cancer subtypes.1−3 Therefore, this is a perpetual issue in breast cancer treatment. A widely used therapeutic agent, doxorubicin (DOX), has been the first-line chemotherapy option for TNBC and proven more effective when combined with immune checkpoint inhibitors. This is because DOX triggers immunogenic cell death (ICD) by releasing calmodulin (CRT), high mobility group box 1 (HMGB1), and adenosine triphosphate (ATP),4 then promotes dendritic cell (DC) maturation and potentiates cytotoxic T lymphocyte infiltration to elicit antitumor immunogenicity.5 Nevertheless, this synergistic chemoimmunotherapy encounters tumor resistance and recurrence due to treatment-responsive hyperactivation of numerous oncogenic signals in the highly heterogeneous tumor microenvironment (TME) of TNBC.6,7

A convergence point of numerous oncogenic signaling pathways, signal transduction and activation of transcription factor 3 (STAT3) is closely associated with the tumor immunosuppressive microenvironment.8,9 Specifically, activated STAT3 promotes the infiltration of immunosuppressive cells, which include regulatory T cells, M2 tumor-associated macrophages, and myeloid-derived suppressor cells, which in turn mediate tumor immune evasion and immunotherapy resistance.10 Additionally, downregulating STAT3 promotes tumor cell ICD11 and remodels the immunosuppressive microenvironment, thereby inducing a synergistic antitumor immunotherapy effect.12 Therefore, STAT3 might be a promising target to reverse the immunosuppressive microenvironment of TNBC. Although peptides and nonpeptide small-molecule inhibitors that directly target STAT3 exhibit excellent physicochemical properties in vitro, their low water solubility and cell permeability result in unsatisfactory clinical efficacy.13,14 Different from the inhibitors that disrupt the STAT3 functional domain, we chose anti-STAT3 short interfering RNA (siRNA) (siSTAT3) to silence STAT3 gene expression as an effective strategy against an “undruggable” tumor target. The approach demonstrated superb specificity and high stability, while still requiring a suitable carrier to enhance its plasma stability and tumor tropism.15−17 Therefore, it is necessary to develop a smart transport carrier with the advantages of good biocompatibility, excellent plasma stability, precise tumor tropism, and effective endonuclease/lysosome escape that can enhance the chemo-gene therapeutic effect at the clinical level.

Based on their excellent tumor tropism ability, tumor-derived extracellular vesicles (TEVs) recently emerged as promising and excellent vectors for delivering agents (therapeutic nucleic acid, proteins, and small molecules) to tumor sites.18,19 Furthermore, engineered TEVs could carry tumor antigens and deliver them to DCs and prevent autologous tumor development in a manner dependent on CD4+ and CD8+ T cells.20−22 With the advantage of specific intracellular uptake and immune activation of tumor cells as compared with other delivery vectors, TEVs are expected to greatly boost the antitumor effect. Accordingly, we employed TEVs derived from mouse TNBC cells (4T1) as the delivery vehicle and prepared siSTAT3 and DOX-functionalized TEVs (siSTAT3-DOX@TEV) for treating TNBC (Figure 1). The in vitro and in vivo results demonstrated that siSTAT3-DOX@TEV precisely targeted tumor tissues, downregulated STAT3 expression, and synergistically and efficiently induced tumor ICD, thereby reversing the immunosuppressive TME. Moreover, mass cytometry (CyTOF) and immunohistochemistry revealed the local immune activation effect of siSTAT3-DOX@TEV, with a significant increase in the levels of M1 macrophages, CD4+ T cells, and CD8+ T cells in tumor tissues. These results provided strong hints for developing TEV-based synergistic chemoimmunotherapeutic agents for TNBC at the clinical level.

Figure 1.

Figure 1

Open in a new tab

Schematic of siSTAT3-DOX@TEV preparation and TNBC treatment. siSTAT3-DOX@TEV effectively induces ICD to reverse the immunosuppressive TME, thereby mediating a strong antitumor immune response. i.v., intravenous injection.

Results

Preparation and Characterization of siSTAT3-DOX@TEV

The TEVs were harvested from the 4T1 cell culture supernatant and then characterized by transmission electron microscopy (TEM), Western blotting, and nanoparticle tracking analysis (NTA). The TEVs presented characteristic round morphologies with a double-membrane structure and narrow size distribution with approximately 95.6 ± 5.5 nm diameter (Figure 2A,B). Western blotting demonstrated that TEVs contained increased expression of the exosomal protein biomarkers TSG101, CD63, and Flotillin-1 (Figure 2C). These data aligned with the previously reported typical EV characteristics, which indicated that EV isolation was successful.23

Figure 2.

Figure 2

Open in a new tab

Characterization of siSTAT3-DOX@TEV. (A) Representative TEM of TEVs. Scale bars: 200 nm. (B) Particle size distribution of TEVs detected by NTA. (C) Western blotting analysis of the expression of exosomal protein biomarkers (Flotillin-1, TSG101, and CD63) of 4T1 cell lysate and TEVs. (D) Fluorescence spectra of siSTAT3 and siSTAT3@TEV (siSTAT3-Cy5). (E) Encapsulation efficiency of siSTAT3. (F) Agarose gel electrophoresis of naked siSTAT3 and siSTAT3@TEV after incubation with RNase A. (G) UV/vis spectra of DOX with gradient concentration. (H) UV/vis spectra of DOX and siSTAT3-DOX@TEV. (I) DOX encapsulation efficiency at different concentration points.

The obtained TEVs were subsequently transfected with Cy5-labeled siSTAT3 (siSTAT3-cy5) by the commercial reagent Exo-Fect, which is highly efficient and simple and has short incubation and centrifugation steps. Figure 2D illustrates that after mixing with Exo-Fect, the fluorescence spectrum generated by TEVs loaded with siSTAT3 (siSTAT3@TEV) demonstrated the same absorption bands as pure siSTAT3-cy5, and it was evident that the siSTAT3 had been efficiently packaged into TEVs with an encapsulation efficiency of approximately 70% (Figure 2E). The siRNA integrity was examined to confirm that the TEVs would protect the loaded siRNA from rapid nuclease degradation. After 30 min of incubation with RNase A, the naked siRNA was entirely degraded, while the siSTAT3@TEV were well shielded from RNase degradation after 2 h of incubation (Figure 2F). The siSTAT3-DOX@TEV were prepared using a previously described coincubation method to load DOX on the siSTAT3@TEV.24 DOX and siSTAT3@TEV were mixed at a 1:1 volume ratio and incubated overnight at room temperature with 500 rpm rotation. The UV/vis spectrum of DOX is depicted in Figure 2G. Figure 2H illustrates that the UV/vis spectrum produced by siSTAT3-DOX@TEV demonstrated the same absorption bands as pure DOX and that DOX was efficiently loaded into siSTAT3@TEV. Figure 2I depicts the optimization of the best initial input of DOX. The encapsulation efficiency of DOX with respect to various initial DOX input concentrations (1, 2, and 4 μg/mL) was calculated and ranged from nearly 77.68% to 93.36%. Figure S1 demonstrates that both siSTAT3@TEV and siSTAT3-DOX@TEV exhibited a typical cup-shaped appearance the same as the TEV group, indicating that the TEVs maintained good integrity after siSTAT3 and DOX loading. Additionally, the siSTAT3@TEV diameter was approximately 111 nm, which was slightly increased compared to the unmodified TEV. The main peak increased to approximately 147 nm upon further DOX loading. Overall, the encapsulation of siSTAT3 and DOX may not obviously affect the integrity of TEV. We investigated the pH-dependent release behavior of DOX from siSTAT3-DOX@TEV in a physiological environment (PBS buffer, pH 7.2) and a tumor acidic microenvironment (PBS buffer, pH 5.5 and pH 6.5) at 37 °C. Figure S2 demonstrates that the siSTAT3-DOX@TEV demonstrated obviously faster release at pH 5.5 and pH 6.5, with more accumulative DOX release of up to approximately 75% and 55% at the end point, respectively. The pH-dependent DOX release behavior may be due to the increased solubility of DOX and decreased electrostatic adsorption from the lipid membrane under acidic conditions. Overall, the tumor tropism ability of the TEVs was beneficial, where DOX was controllably released into tumor cells in a pH-triggered manner. Furthermore, the siSTAT3@TEV and siSTAT3-DOX@TEV groups exhibited tiny changes in morphology and particle size distribution after 48 h incubation in a physiological environment (pH 7.2) and an acidic environment (pH 6.5) (Figure S3), which highlighted the good stability of the proposed siSTAT3-DOX@TEV.

TEV-Mediated Targeting Efficacy In Vitro and In Vivo

As a drug delivery vehicle, the ability of TEVs to efficiently target tumor cells is critical to therapeutic efficacy. Accordingly, TEVs were labeled with the fluorescent probe Dil, then coincubated with 4T1 cells. Confocal microscopy revealed that the 4T1 cells effectively internalized the TEVs (Figure 3A). Flow cytometry analysis confirmed that the TEVs efficiently delivered siRNA and DOX into the tumor cells (Figure S4). To explore the tumor tropism ability of TEVs in vivo, Dil-labeled TEVs were injected intravenously into an orthotopic 4T1 tumor-bearing mouse model. Tumors and major organs were collected at 6, 12, 24, and 48 h after treatment to measure and quantify fluorescence intensity (Figure 3B, C). The continuous and intense fluorescence signals in the tumor tissues indicated the favorable tumor tropism ability of the TEVs as a delivery vehicle for TNBC treatment.

Figure 3.

Figure 3

Open in a new tab

Specific tumor tropism behaviors of TEVs in vitro and in vivo. (A) Confocal microscopy images of 4T1 cells treated with PBS and TEVs for 4 h at 37 °C. TEVs were labeled with Dil (red) and cytoskeleton was stained with Actin-Tracker Green-488. Scale bars: 20 μm. (B) Fluorescence images of mouse major organs and tumors after PBS or TEV intravenous injection. TEVs were labeled with Dil. (C) Quantification of the fluorescence intensity of the tumor tissues. Data are the mean ± SD (n = 3).

Mechanism of the Cellular Uptake of TEVs

To explore the cellular uptake pathway of TEVs, 4T1 cells were pretreated with endocytosis inhibitors. Amiloride (Na+/H+ pump-related micropinocytosis inhibitor) and M-β-CD (clathrin endocytosis inhibitor) did not hinder uptake (Figure 4A), while the TEV uptake was significantly inhibited by dynasore (dynamin-dependent endocytosis inhibitor). Taken together, these results demonstrated that the TEVs were mainly endocytosed via the dynamin-dependent pathway, where endocytosis was previously reported as the main pathway for the EV uptake.25

Figure 4.

Figure 4

Open in a new tab

Cellular uptake mechanism and cargo transferring ability of TEVs. (A) Confocal microscopy images of the cellular uptake of TEVs (Dil-TEV, red) in 4T1 cells at 4 h (TEV concentration: 30 μg/mL). The nucleus was stained with DAPI (blue). Scale bars: 20 μm. (B) Confocal microscopy images of 4T1 cells incubated with siSTAT3@TEV (Cy5, red). Cells were stained with LysoTracker (green) after further incubation at 37 °C for 4, 6, 12, and 24 h. Scale bars: 20 μm.

When siRNA enters the cell, it is easily phagocytosed by the large number of lysosomes, rendering it difficult to transport smoothly to the cytoplasm.26 Therefore, whether it can escape lysosomal phagocytosis in vivo is the key to intracellular delivery of vectors. To evaluate lysosome escape ability of TEV, we loaded siSTAT3-Cy5 into TEVs for coincubation with cells, which were then stained with a lysosome tracker. Confocal microscopy observation revealed that siSTAT3-Cy5 was completely trapped in the lysosome when it first entered the cells for 4 h, but siSTAT3-Cy5 showed reduced overlap with the lysosome over time (Figure 4B). Taken together, these results indicated that the TEVs escaped from the lysosome.

Synergistic Chemo-Genetherapy Effect In Vitro

DOX is a widely used chemotherapeutic agent in breast cancer. In this study, we introduced DOX into siSTAT3@TEV to verify its synergistic antitumor effect. The antitumor proliferative activity was assessed by Cell Counting Kit-8 (CCK-8) assay after 48 h. All groups exhibited dose-dependent cytotoxicity, with lower proliferative activity observed, particularly in the synergistic siSTAT3-DOX@TEV group (Figure 5A). Cell viability in the naked siSTAT3 and siSTAT3@TEV groups was not significantly affected compared to the control group (PBS). Cell proliferative activity was reduced in both DOX-containing groups, but the synergistic treatment group had the lowest proliferative activity, with only 4.5% residual surviving cells (Figure 5B).

Figure 5.

Figure 5

Open in a new tab

siSTAT3-DOX@TEV synergistic therapy in vitro. (A) 4T1 cell viability after 48 h treatment with DOX, siSTAT3-DOX, or siSTAT3-DOX@TEV. (B) 4T1 cell viability after 48 h treatment with Control, siSTAT3, siSTAT3@TEV, siSTAT3-DOX, DOX, or siSTAT3-DOX@TEV. (C) Cell apoptosis rate analyzed by flow cytometry with AF647/7AAD staining after 48 h treatment with control, siSTAT3, siSTAT3@TEV, siSTAT3-DOX, DOX, or siSTAT3-DOX@TEV. (D) Quantification of cell apoptosis rate. Data are the mean ± SD, **P < 0.001.

Apoptosis was assessed by staining with annexin AF647/7AAD (Figure 5C,D). The cells incubated with control, siSTAT3, DOX, siSTAT3@TEV, and siSTAT3-DOX exhibited apoptotic rates of 21.73%, 19.62%, 29%, 48.2%, and 43.2%, respectively, while the apoptosis rates in the siSTAT3-DOX@TEV group were as high as 64.9%. These results suggested that the synergistic siSTAT3-DOX@TEV group exhibited much better antitumor efficiency. The suppressive effects of siSTAT3-DOX@TEV on STAT3 expression were measured by real-time quantitative PCR and Western blotting. The STAT3 expression was obviously increased in 4T1 cells treated by DOX and siSTAT3-DOX. However, there was a strong inhibition of STAT3 mRNA and protein levels in siSTAT3@TEV and siSTAT3-DOX@TEV groups (Figure S5).

Synergistic Chemo-Genetherapy Promoted ICD In Vivo

The CRT and HMGB1 expression in tumors were detected by immunohistochemical methods to investigate the role of siSTAT3-DOX@TEV synergistic therapy in inducing tumor cell ICD in vivo. Figure 6A depicts the indicated treatment schedule. The CRT and HMGB1 expressions were lower in the control, siSTAT3, and siSTAT3@TEV groups. Both CRT translocation to the cell membrane and HMGB1 expression were significantly increased in the siSTAT3-DOX@TEV group compared with the DOX and siSTAT3-DOX groups (Figure 6B–D). The above results suggested that siSTAT3-DOX@TEV synergistic therapy enhanced the ICD of tumor cells.

Figure 6.

Figure 6

Open in a new tab

siSTAT3-DOX@TEV synergistic therapy in vivo. (A) Indicated treatment schedules, iv, intravenous injection. (B) Immunohistochemical analysis of CRT and HMGB1 expression in tumors. Scale bars: 50 μm. Immunohistochemical analysis of positivity rates of (C) CRT and (D) HMGB1 in tumor tissues performed at the end of the antitumor study. Data are the mean ± SD (n = 5). nsP > 0.05, **P < 0.001.

Synergistic Chemo-Genetherapy Inhibited TNBC Growth In Vivo

The antitumor effect of siSTAT3-DOX@TEV was evaluated with orthotopic 4T1 tumor-bearing mouse model in vivo. With reference to previous reports,27,28 an in vivo injection dose of 100 nM for the loaded siRNA and 5 mg/kg for the chemotherapeutic drug DOX was chosen to evaluate their antitumor effects. Figure 7A depicts the treatment protocol. The body weights of each group were recorded every other day during the treatment period (Figure 7B) to ensure that the mice were in good physical condition. After 1 week of tumor loading, when the tumor volume reached nearly 50 mm3, the mice were injected with control, siSTAT3, DOX, siSTAT3-DOX, siSTAT3@TEV, or siSTAT3-DOX@TEV via the tail vein. There was almost no difference in tumor volume between the control and siSTAT3 groups, and DOX and siSTAT3-DOX moderately inhibited tumor growth but not sufficiently. Notably, siSTAT3@TEV demonstrated stronger antitumor effects than the individual siSTAT3 due to the tumor tropism ability of TEVs. The siSTAT3-DOX@TEV demonstrated the best antitumor effect, indicating that DOX and siRNA had synergistic effects in vivo. These results were validated by tumor tissue size (Figure 7C–E). The siSTAT3-DOX@TEV group had a mean tumor weight of only 0.23 g (Figure 7D), which was significantly lower than those of the other groups. Similarly, distant metastasis in the siSTAT3-DOX@TEV group was significantly reduced in the mouse distant tumor metastasis model (Figure S6), which verified the synergistic immune activation effect of the proposed strategy.

Figure 7.

Figure 7

Open in a new tab

siSTAT3-DOX@TEV synergistic therapy in vivo. (A) Indicated treatment schedules, i.v., intravenous injection. (B) Body weights of 4T1 tumor-bearing mice following treatment. (C) Representative tumor images and (D) excised tumor weight on day 14 after treatment. (E) Tumor growth curves in 4T1 tumor-bearing mice during treatment. (F) H&E and Ki67 staining of mouse tumor tissues at the end of the antitumor study. Scale bars: 50 μm. (G) Immunohistochemical analysis of α-SMA and CCL2 expression levels in tumor tissues at the end of the study. Scale bars: 50 μm. Data are the mean ± SD (n = 5). *P < 0.01, **P < 0.001.

Hematoxylin and eosin (H&E) staining of the tumor tissues demonstrated that tumor cells in the siSTAT3-DOX@TEV group were the most sparsely arranged and interstitial compared with other groups, further illustrating the significant antitumor effects (Figure 7F). Immunohistochemical analysis demonstrated that the tumor tissues of the siSTAT3@TEV- and siSTAT3-DOX@TEV-treated mice had reduced STAT3 expression levels compared with the control, siSTAT3, DOX, and siSTAT3-DOX groups (Figure S7A). The tumor cell proliferative activity was investigated by immunohistochemical analysis of Ki67 levels. The results demonstrated that the siSTAT3-DOX@TEV group had the lowest proliferative index (Figures 7F and S7B). Moreover, the Western blotting results verified that the STAT3 expression level was significantly decreased in the siSTAT3@TEV and siSTAT3-DOX@TEV groups compared with the control, siSTAT3, DOX, and siSTAT3-DOX groups (Figure S7C,D). These data confirmed that the codelivery strategy significantly downregulated STAT3 expression levels.

The viscous and dense extracellular matrix (ECM) in the TME naturally constitutes a physical barrier around the tumor tissue and induces “immune-excluded zones”. These zones severely limit the penetration of antitumor agents into tumor sites and the ability of immune cells to infiltrate tumor tissues, affecting antitumor therapy efficacy.29 Also known as monocytic chemotactic protein 1 (MCP-1), CCL2 is abundant in the TME and promotes tumor invasion and metastasis,30 with a negative effect on prognosis in patients with tumors.31 More importantly, CCL2 also mediates stromal cell responses in the TME.32 Alpha smooth muscle actin (α-SMA) is a marker of cancer-associated fibroblasts (CAFs).33 A recent study demonstrated that the decrease of the tumor stroma-associated protein α-SMA effectively reduced tumor stroma production and improved drug penetration to the tumors.34 Immunohistochemical analysis showed that both CCL2 and α-SMA were highly expressed in the control and siSTAT3 groups, which indicated that the matrix proteins were abundant in the tumors. The α-SMA and CCL2 expression was moderately reduced in the DOX, siSTAT3-DOX, and siSTAT3@TEV groups, while their elimination rate was highest in the siSTAT3-DOX@TEV group (Figures 7G and S8). This revealed that siSTAT3-DOX@TEV reduced the ECM in the TME, thereby better facilitating drug penetration and improving the antitumor effect.

Local Immune Activation Effect of siSTAT3-DOX@TEV

We obtained a more profound understanding of TME variation by siSTAT3-DOX@TEV treatment through a CyTOF analysis of the tumor immune landscape (Figure 8A). The orthotopic model samples were analyzed, and 26 clusters were determined by using 42 immune markers (Figure 8B). The parallels among all samples were compared, and hierarchical clustering was performed using the CyTOF data. The treatment groups presented distinct immune cell profiles (Figure S9). Certain known cell types were identified from the clusters based on typically expressed markers (Figure 8C). Generally, synergistic therapy significantly altered the TNBC immune landscape as compared with the control, enhancing immune cell infiltration in tumor tissue (Figure S10). Notably, the results revealed that the synergistic group had significantly reduced M2-polarized macrophages (immunosuppressive macrophages), and greatly increased M1-polarized macrophages (antitumor associated macrophages) compared with the control and siSTAT3@TEV groups. Moreover, T cell reclustering analysis of tumor-infiltrating lymphocytes revealed that the siSTAT3-DOX@TEV group had significantly increased CD4+ T cell and CD8+ T cell numbers compared with the control and siSTAT3@TEV groups (Figure 8D,E). Additionally, the siSTAT3-DOX@TEV group decreased the expression levels of immunosuppressive markers (PD-1 and PD-L1) in the TME (Figures S11 and S12). Altogether, the immune landscape analysis demonstrated that siSTAT3-DOX@TEV altered the immune-privileged TME to an immunotherapy-favorable TME marked by reduced immune-suppressive M2-polarized macrophage numbers and increased M1-polarized macrophage, CD4+ T cell, and CD8+ T cell numbers that might activate the local antitumor immune response.

Figure 8.

Figure 8

Open in a new tab

CyTOF analysis of tumor-infiltrating immune cells after therapy. (A) Scheme depicting the CyTOF analysis. (B) Heatmap of normalized expression for markers expressed in immune cells. Left and right sides present the types and proportions of immune cells, respectively. (C) t-SNE plots color-coded expression of marker for main immune cell types. (D) t-SNE plots color-coded expression of marker for CD4+ T cells and CD8+ T cells. The main immune cell types are marked using red boxes. (E) Frequency diagram of immune cell subsets.

To further confirm the siSTAT3-DOX@TEV synergistic immune activation effect, we used immunohistochemical analysis to assess the post-treatment changes in the immune cells. Interestingly, consistent with the CyTOF results, the siSTAT3-DOX@TEV treatment group demonstrated significantly higher proportions of CD4+ T cells and CD8+ T cells than the other groups (Figure S13). These data indicated that siSTAT3-DOX@TEV effectively promoted immune cell infiltration and produced strong antitumor immune response.

Biocompatibility Assessment

Biocompatibility is a crucial factor for the codelivery system. Accordingly, we conducted a hemolysis assay to investigate the TEV hemolytic activity and the hemolysis ratio of the TEVs was <5% in tested concentrations of 1–5 mg/mL (Figure 9A,B), which was sufficient to ensure the safety of intravenous drug delivery.35 To further evaluate the biocompatibility of siSTAT3-DOX@TEV, the major organs (heart, liver, spleen, lung, and kidneys) were obtained at the end of treatments in vivo. The H&E staining results demonstrated no significant histopathological observations of these organs (Figure 9C). These results collectively suggested that the proposed siSTAT3-DOX@TEV had good biocompatibility.

Figure 9.

Figure 9

Open in a new tab

siSTAT3-DOX@TEV biocompatibility profiles. (A) Images of erythrocytes incubated with siSTAT3-DOX@TEV. (B) Hemolysis rate of erythrocytes incubated with siSTAT3-DOX@TEV. (C) H&E staining of major organs at the end of the antitumor study. Scale bars: 50 μm. Data are the mean ± SD (n = 3).

Discussion

The proposed functionalized TEV-based precise tumor treatment method has demonstrated significant therapeutic efficacy in multiple tumor models (melanoma, colorectal cancer, and breast cancer). The efficacy is mainly attributed to the excellent homing ability to TME and enriched tumor antigens in TEVs, thereby targeting tumor tissues and serving as a tumor vaccine to activate the antitumor immune response. It is particularly valuable for TNBC, which is ER-, PR-, and HER2-negative. Targeted drugs against these receptors are poorly effective in TNBC, whereas antibody-drug conjugate drugs targeting Trop-2 have demonstrated promising tumor tropism ability and efficacy in clinical trials in patients with TNBC. However, Trop-2-targeted drugs remain subject to dose-limiting toxicity as Trop-2 is also expressed in normal human tissues such as the breast, skin, stomach, and thymus. It is often challenging for such single-target antibody or inhibitor drugs to cover the heterogeneity of the entire tumor, and they are subject to resistance resulting from target mutations and other compensatory regulatory mechanisms. In contrast, the TEV tropism ability is derived from the similar membrane topology, lipid composition, and membrane protein species to the parental cells, providing more reliable homing ability, drug loading capacity, and plasma stability.36 The in vivo imaging of the 4T1 xenograft tumor model strongly supported the above results that functionalized TEVs injected via the tail vein were consistently enriched in the TME after 12 h, which also forms the basis for delivering tumor antigens contained in TEVs and persistently activating antitumor immunity.

STAT3 regulates a series of key genes closely related to tumor cell growth, proliferation, invasion, metastasis, and immune evasion. STAT3 hyperactivation is strongly associated with the poor prognosis of TNBC and is also essential for TNBC chemoresistance and the suppressive tumor immune microenvironment. Presently, therapeutic strategies targeting STAT3 are extremely attractive in multiple tumors.9 However, despite some inhibitors targeting STAT3 or its upstream signal pathway having entered clinical trials, no STAT3 inhibitors are applicable to TNBC yet. The US Food and Drug Administration has approved the use of STAT3 inhibitors for only a few tumors, including pancreatic cancer and gastric/gastroesophageal junction cancer. The primary factor for the limited approval is the toxic adverse effects caused by unavoidable distribution in normal tissues due to the lack of a suitable drug delivery vehicle targeting the TME.

Therefore, the proposed siSTAT3-DOX@TEV, which copackages siSTAT3 and DOX in TEVs, enables synergistic tumor tropism chemo-genetherapy. The targeted knockdown of intratumoral STAT3 expression attenuates the STAT3-regulated feed-forward protumor feedback and activates the antitumor immune response. Specifically, the approach augmented the DOX-induced ICD of tumor cells and reduced the expression and secretion of immunosuppressive factors (i.e., CCL2) in the TME, eliminated the peritumoral physical barrier, and recruited and activated antitumor immune cells, synergistically promoting the transformation of “cold tumors” into “hot tumors”. Furthermore, this synergistic strategy effectively addresses the double-edged sword of the individual use of TEVs (i.e., the potential of TEVs to suppress functional immune cells and promote tumor cell invasion and metastasis). Notably, CyTOF and immunohistochemistry results comprehensively demonstrate the dynamic panorama of immune cells (CD4+ and CD8+ T cells, M1 and M2 macrophages, neutrophils, natural killer [NK] cells) after siSTAT3-DOX@TEV treatment, which confirmed that the proposed synergistic strategy significantly promoted macrophage polarization toward the M1 type and effector T cell infiltration, and weakened α-SMA expression in CAFs, reversing the immunosuppressive TME, and effectively inhibiting tumor progression.

As a promising drug delivery system, EV-based drug formulations are undergoing preclinical and clinical trials, yet still face many challenges to finally move toward practical clinical applications.28 First, EVs derived from the same tumor cell line are a de facto highly heterogeneous population of differing sizes and functional molecular compositions. It is a crucial issue to utilize advanced single-vesicle analysis techniques to identify the valuable subpopulation of TEVs with high immunogenicity, satisfactory tumor tropism, and a low protumorigenic property. Second, how the above specific TEV subpopulation can be isolated and purified is another important aspect of clinical application. Combined antibody immunoaffinity capture or functional nucleic acid nanotechnology might be required for this purpose.29 Lastly, the source of sufficient TEVs available for treatment is a fairly critical issue. Considering the superiority of personalized vaccines, isolating and culturing primary tumor cells from punctured or surgical tissues using the patient-derived organoids (PDOs) technique seem to be a feasible option for obtaining sufficient TEVs, which nevertheless requires further exploration. These are only some of the challenges for TEV- or EV-based strategies toward clinical applications, and we believe that promising TEV-based biopharmaceuticals could move toward clinical practice benefiting from the current interdisciplinary research trend.30

Conclusions

In conclusion, a tumor tropism synergistic chemo-genetherapy strategy was proposed and optimized based on copackaging siSTAT3 and DOX in TEVs. The synergistic antitumor immune effect was revealed using CyTOF and immunohistochemistry. The proposed strategy downregulated intratumoral STAT3 expression in an obvious manner, augmented the DOX-induced tumor ICD, eliminated the peritumoral physical barrier, promoted sufficient immune cell infiltration, activated the antitumor immune response, and achieved effective tumor regression. It provides a useful chemo-genetherapy strategy to precisely treat highly heterogeneous TNBC and can be extended to other types of tumors.

Experimental Section

Characterization and Purification of TEVs

The TEVs were isolated from the supernatant of the cultured 4T1 cells. In brief, exosome-depleted medium was used to culture the cells, and the culture supernatant was harvested when the cells were 90% confluent. Then, the TEVs were isolated using a Total Exosome Isolation kit (cat. no. 4478359, Thermo Fisher Scientific) according to the manufacturer’s instructions. The samples were incubated at 4 °C overnight, then centrifuged at 10,000 × g at 4 °C for 1 h. The pellet was rinsed in PBS once, resuspended in sterile PBS, and then purified by passing through a 0.22 μm filter. The TEVs were aliquoted and stored at −80 °C for the subsequent experiments.

To measure the TEV size distribution, 10 μL of TEVs was diluted in 1 mL of PBS and underwent nanoparticle tracking analysis (Particle Metrix-PMX). The TEV morphology was imaged by using high-resolution TEM (JEM-2100F, JEOL). In brief, the TEVs were dripped onto a glow-discharged grid that had been coated in carbon and was absorbed for 2 min. Then, the grid was stained for 2 min with 2% phosphotungstic acid. After 15 min of drying at room temperature, the TEVs were imaged by TEM at 80 kV accelerating voltage.

TEV Biodistribution In Vivo

The in vivo TEV biodistribution was investigated by establishing a 4T1 tumor model in female BALB/c mice. 4T1 cells (1 × 106) were administered subcutaneously into the right mammary fat pad of the mice. BABL/c mouse tail veins were injected with TEVs labeled with near-infrared Dil dye (Remegen Biosciences, Dil-membrane EVs labeling & purification kit) for imaging. Briefly, the mice (n = 3) were injected intravenously with 30 μg of Dil-labeled TEVs, and then the major organs (heart, liver, spleen, lung, and kidney) and tumors of the mice were imaged with the Multimode in vivo Animal Imaging System (AniView100). Living Image software was used to analyze the fluorescence intensity.

Mechanism of the Cellular Uptake

To examine the TEV endocytosis mechanism, 4T1 cells were pretreated with inhibitors of different endocytosis pathways.31 After 1 h of incubation with 50 μM amiloride, 80 μM dynasore, or 5 mM methyl-β-cyclodextrin (M-β-CD), the cells were washed with PBS once and incubated for 4 h with Dil-labeled TEVs. Next, the cells were washed three times with PBS, fixed in 4% paraformaldehyde, and stained with 4′,6-diamidino-2-phenylindole (DAPI, Beyotime). Then, the cells were visualized by confocal microscopy.

In the assay of lysosome colocalization, the TEVs were loaded with siSTAT3-Cy5. After incubation with siSTAT3@TEV for 2 h, the cells were washed with PBS. Then, 150 nM LysoTracker Green DND-26 (Yeasen) was used to treat the cells for 4, 6, 12, and 24 h and stained with DAPI for 30 min. After being washed, the cells were observed under a confocal microscope.

Antitumor Activity of siSTAT3-DOX@TEV in an Animal Model

To investigate the antitumor effects of codelivery of siSTAT3 and DOX by TEVs, the antitumor efficacy was examined using the orthotopic 4T1 tumor-bearing BALB/c mouse model. 4T1 cells (1 × 106) were injected into the right mammary fat pad of the mice. After 1 week, when the tumor volume was approximately 50 mm3, the mice were separated randomly into six groups (n = 5) and were treated with PBS, siSTAT3, DOX, siSTAT3-DOX, siSTAT3@TEV, or siSTAT3-DOX@TEV. The treatments were administered through the tail vein at days 1, 3, 5, 7, 9, and 11. A Vernier caliper was used to monitor the tumor sizes, and tumor volume was calculated as follows: 0.5 × length × width2 (mm3). On day 14, the mice were killed and the tumors and major organs (liver, heart, lung, spleen, and kidney) were collected. The tumor and organ samples were fixed in 4% paraformaldehyde and paraffin-embedded for histological examination. The tumor tissues underwent H&E and Ki67 staining. The STAT3 protein expression levels in tumor tissues were examined using anti-STAT3 primary antibody.

Immune Cell Analysis via CyTOF

Tumor tissue was disassociated into single cells with DNase, collagenase IV, and hyaluronidase (Sigma-Aldrich). Immune cells were enriched with Percoll density gradient medium (Sigma-Aldrich), and erythrocytes were removed with ACK Lysing Buffer (Sigma-Aldrich). The samples were stained and blocked for 30 min using a mixed surface antibody panel developed in-house (Table S1), then fixed overnight. Next, the cells were permeabilized, incubated with an intracellular antibody mixture, and washed, and the signals were detected with a CyTOF system (Helios, Fluidigm). The immune cell types were determined with nonlinear dimensionality reduction t-distributed stochastic neighbor embedding (t-SNE), then underwent density clustering. CyTOF analysis of the tumor tissues was performed by Zhejiang PuLuoTing Health Technology Co., Ltd.

Statistical Analyses

The data are reported as the mean ± standard deviation (SD). Significant differences between groups were determined using an unpaired t-test (for two groups) and one-way analysis of variance (for >2 groups) in Origin 8.5. Statistical significance was set at P < 0.01 and P < 0.001.

Glossary

Abbreviations

STAT3

signal transduction and activation of transcription factor 3

TNBC

triple-negative breast cancer

TME

tumor microenvironment

siSTAT3

STAT3 short interfering RNA

DOX

doxorubicin

TEVs

tumor-derived extracellular vesicles

siSTAT3@TEV

TEVs loaded with siSTAT3

siSTAT3-DOX@TEV

TEVs loaded with siSTAT3 and DOX

ICD

immunogenic cell death

CRT

calmodulin

HMGB1

high mobility group box 1

ATP

adenosine triphosphate

DC

dendritic cell

CyTOF

mass cytometry

siSTAT3-Cy5

Cy5-labeled siSTAT3

CCK-8

cell counting kit-8

MCP-1

monocytic chemotactic protein 1

α-SMA

alpha smooth muscle actin

CAFs

cancer-associated fibroblasts

M-β-CD

methyl-β-cyclodextrin

DAPI

4′,6-diamidino-2-phenylindole

H&E

hematoxylin and eosin

t-SNE

t-distributed stochastic neighbor embedding

cDC

conventional dendritic cell

SD

standard deviation

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsnano.3c12967.

  • Morphology and particle size of the TEVs, siSTAT3@TEV, and siSTAT3-DOX@TEV; standard curve of gradient concentration and released percentage of DOX; morphology and particle size of TEVs, siSTAT3@TEV, and siSTAT3-DOX@TEV in a physiological environment (PBS, pH 7.2) and acidic condition (PBS, pH 6.5) after 48 h; representative flow cytometry analysis of cellular uptake; Western blot and fluorescent quantitative PCR analysis of STAT3 expression in 4T1 cell groups; siSTAT3-DOX@TEV significantly reduces distant metastasis in vivo; immunohistochemistry and Western blot analysis of STAT3 expression of tumor tissues; analysis of immunohistochemical positivity rates of α-SMA and CCL2 in tumor tissues performed at the end of the antitumor study; immune landscapes of immune cells in the different groups; representative flow cytometry plots of CD45+ immune cells in tumor cells; CyTOF analysis of PD-1 in CD4+ T cells, CD8+ T cells, and PD-L1 expression in conventional DCs; immunohistochemistry analysis of CD4+ T cells and CD8+ T cells in tumor tissues; antibodies used for CyTOF staining; and primer sequences for qRT-PCR (PDF)

Author Contributions

§ Z.H.P., T.T.Z., and P.P.G. contributed equally to this work. Z.H.P. conducted the experiments and data analysis and wrote the paper. T.T.Z. and P.P.G. assisted and carried out experiments. Y.Z., X.Z., and X.W.Q. were responsible for the study design and project supervision. Z.Z.G., X.J.W., and H.T. drew the figures and assisted in the preparation. M.Q. and X.N.T. characterized the nanocomposite. All authors read and approved the final manuscript.

This work was supported by the National Natural Science Foundation of China (grant no. 82002250), the Natural Science Foundation of Chongqing (grant no. CSTB2022NSCQ-MSX1500), the Chongqing Talents Project (grant no. 414Z393), and the Chongqing Key Project of Technology Innovation and Application Development (grant no. CSTB2023NSCQ-JQX0012).

The authors declare no competing financial interest.

Supplementary Material

nn3c12967_si_001.pdf (1.9MB, pdf)

References

  1. Siegel R. L.; Miller K. D.; Fuchs H. E.; Jemal A. Cancer statistics, 2022. Ca-Cancer J. Clin. 2022, 72 (1), 7–33. 10.3322/caac.21708. [DOI] [PubMed] [Google Scholar]
  2. Xia C.; Dong X.; Li H.; Cao M.; Sun D.; He S.; Yang F.; Yan X.; Zhang S.; Li N.; Chen W. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin. Med. J. 2022, 135, 584–590. 10.1097/cm9.0000000000002108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Wang Y.; Gao D.; Jin L.; Ren X.; Ouyang Y.; Zhou Y.; He X.; Jia L.; Tian Z.; Wu D.; Yang Z. NADPH Selective Depletion Nanomedicine-Mediated Radio-Immunometabolism Regulation for Strengthening Anti-PDL1 Therapy against TNBC. Adv. Sci. 2023, 10 (3), e2203788 10.1002/advs.202203788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Fu L.; Ma X.; Liu Y.; Xu Z.; Sun Z. Applying nanotechnology to boost cancer immunotherapy by promoting immunogenic cell death. Chin. Chem. Lett. 2022, 33 (4), 1718–1728. 10.1016/j.cclet.2021.10.074. [DOI] [Google Scholar]
  5. Jin C.; Wang Y.; Li Y.; Li J.; Zhou S.; Yu J.; Wang Z.; Yu Y.; Zhang H.; Wang D.; He Z.; Wang Y. Doxorubicin-Near infrared dye conjugate induces immunogenic cell death to enhance cancer immunotherapy. Int. J. Pharm. 2021, 607, 121027. 10.1016/j.ijpharm.2021.121027. [DOI] [PubMed] [Google Scholar]
  6. Adams S.; Loi S.; Toppmeyer D.; Cescon D. W.; De Laurentiis M.; Nanda R.; Winer E. P.; Mukai H.; Tamura K.; Armstrong A.; Liu M. C.; Iwata H.; Ryvo L.; Wimberger P.; Rugo H. S.; Tan A. R.; Jia L.; Ding Y.; Karantza V.; Schmid P. Pembrolizumab monotherapy for previously untreated, PD-L1-positive, metastatic triple-negative breast cancer: cohort B of the phase II KEYNOTE-086 study. Ann. Oncol. 2019, 30 (3), 405–411. 10.1093/annonc/mdy518. [DOI] [PubMed] [Google Scholar]
  7. Altevogt P.; Sammar M.; Hüser L.; Kristiansen G. Novel insights into the function of CD24: A driving force in cancer. Int. J. Cancer 2021, 148 (3), 546–559. 10.1002/ijc.33249. [DOI] [PubMed] [Google Scholar]
  8. Li Y.; Song Z.; Han Q.; Zhao H.; Pan Z.; Lei Z.; Zhang J. Targeted inhibition of STAT3 induces immunogenic cell death of hepatocellular carcinoma cells via glycolysis. Mol. Oncol. 2022, 16 (15), 2861–2880. 10.1002/1878-0261.13263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Huynh J.; Chand A.; Gough D.; Ernst M. Therapeutically exploiting STAT3 activity in cancer - using tissue repair as a road map. Nat. Rev. Cancer 2019, 19 (2), 82–96. 10.1038/s41568-018-0090-8. [DOI] [PubMed] [Google Scholar]
  10. Zou S.; Tong Q.; Liu B.; Huang W.; Tian Y.; Fu X. Targeting STAT3 in Cancer Immunotherapy. Mol. Cancer 2020, 19 (1), 145. 10.1186/s12943-020-01258-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Rébé C.; Ghiringhelli F. STAT3, a Master Regulator of Anti-Tumor Immune Response. Cancers 2019, 11 (9), 1280. 10.3390/cancers11091280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Ngamcherdtrakul W.; Reda M.; Nelson M. A.; Wang R.; Zaidan H. Y.; Bejan D. S.; Hoang N. H.; Lane R. S.; Luoh S. W.; Leachman S. A.; Mills G. B.; Gray J. W.; Lund A. W.; Yantasee W. In Situ Tumor Vaccination with Nanoparticle Co-Delivering CpG and STAT3 siRNA to Effectively Induce Whole-Body Antitumor Immune Response. Adv. Mater. 2021, 33 (31), e2100628 10.1002/adma.202100628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gelain A.; Mori M.; Meneghetti F.; Villa S. Signal Transducer and Activator of Transcription Protein 3 (STAT3): An Update on its Direct Inhibitors as Promising Anticancer Agents. Curr. Med. Chem. 2019, 26 (27), 5165–5206. 10.2174/0929867325666180719122729. [DOI] [PubMed] [Google Scholar]
  14. Beebe J. D.; Liu J. Y.; Zhang J. T. Two decades of research in discovery of anticancer drugs targeting STAT3, how close are we?. Pharmacol. Ther. 2018, 191, 74–91. 10.1016/j.pharmthera.2018.06.006. [DOI] [PubMed] [Google Scholar]
  15. Song J.; Wang J.; Tian S.; Li H. Discovery of STAT3 Inhibitors: Recent Advances and Future Perspectives. Curr. Med. Chem. 2023, 30 (16), 1824–1847. 10.2174/0929867329666220819093117. [DOI] [PubMed] [Google Scholar]
  16. Zhang C.; Zhang B. RNA therapeutics: updates and future potential. Sci. China: Life Sci. 2023, 66 (1), 12–30. 10.1007/s11427-022-2171-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kara G.; Calin G. A.; Ozpolat B. RNAi-based therapeutics and tumor targeted delivery in cancer. Adv. Drug Deliv. Rev. 2022, 182, 114113. 10.1016/j.addr.2022.114113. [DOI] [PubMed] [Google Scholar]
  18. Clancy J. W.; D’Souza-Schorey C. Tumor-Derived Extracellular Vesicles: Multifunctional Entities in the Tumor Microenvironment. Ann. Rev. Pathol. 2023, 18, 205–229. 10.1146/annurev-pathmechdis-031521-022116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Sun M.; Shi W.; Wu Y.; He Z.; Sun J.; Cai S.; Luo Q. Immunogenic Nanovesicle-Tandem-Augmented Chemoimmunotherapy via Efficient Cancer-Homing Delivery and Optimized Ordinal-Interval Regime. Adv. Sci. 2022, 10 (1), e2205247 10.1002/advs.202205247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Whiteside T. L.; Diergaarde B.; Hong C. S. Tumor-Derived Exosomes (TEX) and Their Role in Immuno-Oncology. Int. J. Mol. Sci. 2021, 22 (12), 6234. 10.3390/ijms22126234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Prakash A.; Gates T.; Zhao X.; Wangmo D.; Subramanian S. Tumor-derived extracellular vesicles in the colorectal cancer immune environment and immunotherapy. Pharmacol. Ther. 2023, 241, 108332. 10.1016/j.pharmthera.2022.108332. [DOI] [PubMed] [Google Scholar]
  22. Jiang Y.; Krishnan N.; Zhou J.; Chekuri S.; Wei X.; Kroll A. V.; Yu C. L.; Duan Y.; Gao W.; Fang R. H.; Zhang L. Engineered Cell-Membrane-Coated Nanoparticles Directly Present Tumor Antigens to Promote Anticancer Immunity. Adv. Mater. 2020, 32 (30), e2001808 10.1002/adma.202001808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ying W.; Riopel M.; Bandyopadhyay G.; Dong Y.; Birmingham A.; Seo J. B.; Ofrecio J. M.; Wollam J.; Hernandez-Carretero A.; Fu W.; Li P.; Olefsky J. M. Adipose Tissue Macrophage-Derived Exosomal miRNAs Can Modulate In Vivo and In Vitro Insulin Sensitivity. Cell 2017, 171 (2), 372–384.e12. 10.1016/j.cell.2017.08.035. [DOI] [PubMed] [Google Scholar]
  24. Norouzi M.; Yathindranath V.; Thliveris J. A.; Kopec B. M.; Siahaan T. J.; Miller D. W. Doxorubicin-loaded iron oxide nanoparticles for glioblastoma therapy: a combinational approach for enhanced delivery of nanoparticles. Sci. Rep. 2020, 10 (1), 11292. 10.1038/s41598-020-68017-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Joshi B. S.; de Beer M. A.; Giepmans B. N. G.; Zuhorn I. S. Endocytosis of Extracellular Vesicles and Release of Their Cargo from Endosomes. ACS Nano 2020, 14 (4), 4444–4455. 10.1021/acsnano.9b10033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Odaka C.; Mizuochi T. Role of macrophage lysosomal enzymes in the degradation of nucleosomes of apoptotic cells. J. Immunol. 1999, 163 (10), 5346–5352. 10.4049/jimmunol.163.10.5346. [DOI] [PubMed] [Google Scholar]
  27. Zhao L.; Gu C.; Gan Y.; Shao L.; Chen H.; Zhu H. Exosome-mediated siRNA delivery to suppress postoperative breast cancer metastasis. J. Controlled Release 2020, 318, 1–15. 10.1016/j.jconrel.2019.12.005. [DOI] [PubMed] [Google Scholar]
  28. Tan M.; Chen Y.; Guo Y.; Yang C.; Liu M.; Guo D.; Wang Z.; Cao Y.; Ran H. A low-intensity focused ultrasound-assisted nanocomposite for advanced triple cancer therapy: local chemotherapy, therapeutic extracellular vesicles and combined immunotherapy. Biomater. Sci. 2020, 8 (23), 6703–6717. 10.1039/D0BM00804D. [DOI] [PubMed] [Google Scholar]
  29. Najafi M.; Farhood B.; Mortezaee K. Extracellular matrix (ECM) stiffness and degradation as cancer drivers. J. Cell. Biochem. 2019, 120 (3), 2782–2790. 10.1002/jcb.27681. [DOI] [PubMed] [Google Scholar]
  30. Shen Q.; Reedijk M. Notch Signaling and the Breast Cancer Microenvironment. Adv. Exp. Med. Biol. 2021, 1287, 183–200. 10.1007/978-3-030-55031-8_12. [DOI] [PubMed] [Google Scholar]
  31. Jin J.; Lin J.; Xu A.; Lou J.; Qian C.; Li X.; Wang Y.; Yu W.; Tao H. CCL2: An Important Mediator Between Tumor Cells and Host Cells in Tumor Microenvironment. Front. Oncol. 2021, 11, 722916. 10.3389/fonc.2021.722916. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Siddiqui J. A.; Partridge N. C. CCL2/Monocyte Chemoattractant Protein 1 and Parathyroid Hormone Action on Bone. Front. Endocrinol. 2017, 8, 49. 10.3389/fendo.2017.00049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Chen X.; Song E. Turning foes to friends: targeting cancer-associated fibroblasts. Nat. Rev. Drug Discovery 2019, 18 (2), 99–115. 10.1038/s41573-018-0004-1. [DOI] [PubMed] [Google Scholar]
  34. Chen X.; Jia F.; Li Y.; Deng Y.; Huang Y.; Liu W.; Jin Q.; Ji J. Nitric oxide-induced stromal depletion for improved nanoparticle penetration in pancreatic cancer treatment. Biomaterials 2020, 246, 119999. 10.1016/j.biomaterials.2020.119999. [DOI] [PubMed] [Google Scholar]
  35. Kloypan C.; Suwannasom N.; Chaiwaree S.; Prapan A.; Smuda K.; Baisaeng N.; Pruß A.; Georgieva R.; Bäumler H. In-vitro haemocompatibility of dextran-protein submicron particles. Artif. Cells, Nanomed., Biotechnol. 2019, 47 (1), 241–249. 10.1080/21691401.2018.1548476. [DOI] [PubMed] [Google Scholar]
  36. Gong C.; Zhang X.; Shi M.; Li F.; Wang S.; Wang Y.; Wang Y.; Wei W.; Ma G. Tumor Exosomes Reprogrammed by Low pH Are Efficient Targeting Vehicles for Smart Drug Delivery and Personalized Therapy against their Homologous Tumor. Adv. Sci. 2021, 8 (10), 2002787. 10.1002/advs.202002787. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

nn3c12967_si_001.pdf (1.9MB, pdf)

Articles from ACS Nano are provided here courtesy of American Chemical Society

RESOURCES