Artesunate Nanoplatform Targets the Serine-MAPK Axis in Cancer-Associated Fibroblasts to Reverse Photothermal Resistance in Triple-Negative Breast Cancer

Abstract

Cancer-associated fibroblasts (CAFs) play a pivotal role in inducing photothermal therapy (PTT) resistance of triple-negative breast cancer (TNBC), but with unclear mechanism. Herein, aminoethyl anisamide-modified nano-biomimetic low-density lipoprotein (A-aLDL) is used to target deliver the PTT agent and artesunate (ARS) to both CAFs and cancer cells. Though CAFs are sensitive to PTT and notably transition to heat-resistant phenotype, the formed protective barrier is destroyed by ARS. Subsequently, the outstanding anti-tumor effects are achieved through PTT in multiple models with such kind of combination therapy. Interestingly, the mechanism is discovered that serine metabolism plays a major role in CAF resistance through spatially omics. ARS disrupts serine homeostasis, thereby attenuating the cascade activity of GTPases in MAPK pathway. Meanwhile, MAP2K7 is the most potential target for sensitizing PTT. By integrating ARS with PTT agents, the serine-MAPK axis in CAFs is successfully modulated, thereby overcoming PTT resistance in TNBC therapy.

Keywords: artesunate nanoplatform; breast cancer; cancer‐associated fibroblasts; photothermal therapy.

Scheme 1

Schematic illustration of this study outline. A) Schematic diagram of the components and structure of the artesunate nanoplatform (AI‐aLDL@A). B) The artesunate nanoplatform effectively disrupts the CAF‐mediated tissue resistance barrier (TRB), thereby significantly potentiating the in vivo photothermal therapy (PTT) efficacy. C) Spatial multi‐omics and multi‐omics sequencing assist in exploring the mechanism of the artesunate nanoplatform sensitizing PTT in TNBC. D) The artesunate nanoplatform selectively targets ECM CAF, functioning as a GTPase inhibitor through disruption of intracellular serine homeostasis. This metabolic intervention effectively suppresses MAPK cascade activity, which consequently inhibits PTT‐induced CAF to ECM CAF differentiation. By attenuating this phenotypic transition, the nanoplatform significantly reduces the formation of TRB structure, ultimately enhancing tumor sensitivity to PTT.

Figure 1

CAFs are involved in PTT resistance and characterization of the artesunate nanoplatform. A) sc‐RNA‐seq data showing the mRNA expression levels of heat shock proteins in different cell clusters within Eo771 tumors (n = 6 independent samples). B) Comparison of tumor weights between the Control group and the Fibrosis group on day 6 after PTT treatment (n = 6 independent samples, data expressed as Mean ± S.E.M, statistical comparison using t‐test). C) Immunoblotting assessment of ECM CAF marker expression levels in pooled tissue proteins from the control group and tissues treated with free ICG‐mediated PTT. D) ATF4 expression in ECM CAFs and wound healing CAFs in the control group, PD‐1 mAb group, and combination therapy group (PLGA coated with the ARS nanomedicine and PD‐1 mAb combination therapy); statistical comparisons were performed via the Wilcoxon rank‐sum test, data are presented as boxplot. E) Schematic of the structure of the dual‐targeting artesunate nanoplatform. F) FESEM images of LDL@A (left) and AI‐aLDL@A (right); scale bar: 50 nm. G) Hydrated sizes of LDL@A and AI‐aLDL@A at different time points (0, 2, 8, 12, and 24 h) (n = 3 independent experiments; data are expressed as the mean ± S.E.M. values). H) UV absorbance spectra of AI‐aLDL@A, A‐aLDL@A, and free ICG. I) The targeting of AI‐aLDL (red) to CAFs and TNBC cells (blue) was assessed via laser confocal microscopy; scale bar: 10 µm. J) Representative fluorescence images of a mouse within 48 h after intravenous injection (red circle: coimplanted CAFs and MDA‐MB‐231 cells; blue circle: MDA‐MB‐231 cells only) and quantification of the fluorescence intensities in the tumors on both sides at different time points (n = 6 independent samples; data are expressed as the mean ± S.E.M. values; statistical comparisons of the quantified data at the 48 h time point were performed via Student's t test). Some data (A, D) were reanalyzed on the basis of our previously published data. * p < 0.05, **** p < 0.0001.

Figure 2

The artesunate nanoplatform alleviates stress after PTT in multiple TNBC models. A) Schematic of the in vivo experiments with the nanomedicine. B,C) Tumor response curves in MDA‐MB‐231 and MDA‐MB‐468 xenograft models after treatment with the nanomedicine (n = 6 independent samples; statistical comparisons of tumor volume on the 18^th day were performed via Student's t test). D) Expression of HSP70, JNK, RhoA, and Ras in the tumor tissues of the mice in the I‐LDL+L and I‐LDL@A+L groups. E) Immunohistochemical images of mouse tumor tissues expressing JNK; red dashed lines indicate the interface between the ablation area and tumor tissue; scale bars: 200 µm (low magnification) and 50 µm (high magnification). F) Tumor response curves from the Eo771 mouse tumor model after treatment with the nanomedicine (n = 6 independent samples; statistical comparisons of tumor volume on the 18^th day were performed via Student's t test). G) Expression of stress‐related proteins within mouse tumor tissue in different groups (A: control, B: A‐aLDL@A, and C: I‐LDL@A+L). H) Representative immunofluorescence images of the TNC (green) and MYLK (red) expression distribution in tumor tissues (blue); scale bar: 100 µm. I) Immunoblotting of ATF4 expression in pooled tissue proteins from all the mice in the control group and the I‐LDL+L group. J) Concentrations of inflammatory cytokines in the serum of mice in different groups after treatment (n = 6 independent samples). K) The immunofluorescence intensity quantification results of TNC for each Eo771 tumor in different groups (control n = 6, I‐LDL+L n = 6, I‐LDL@A+L n = 4, AI‐aLDL+L n = 4, AI‐aLDL@A+L n = 3), statistical comparisons were performed via Student's t test. Groups that received laser irradiation treatment are indicated by +L. The data in (B,C,F,K) are expressed as the mean ± S.E.M. values.* p < 0.05, ** p < 0.01, **** p < 0.0001, ns no significance.

Figure 3

Spatial transcriptomics confirmed that the tissue resistance barrier composed of ECM CAFs participated in PTT resistance. A) Gene expression at each dot was used to predict the spatial distribution of different cell types and perform space mapping (n = 1/ group). B) Interaction strength between different CAF subtypes and macrophages in the SPP1 signaling pathway. C) Interaction strength between E (ECM CAFs), W (wound healing CAFs) and various TAM subtypes; clusters of interest are marked in red. D) H&E staining images of tumor tissues from different groups and spatial distribution prediction of ECM CAFs and plasma‐like macrophages in tumor tissue on the basis of the expression of the top gene at each site; the yellow line indicates fibrous H&E‐stained tissue. E) Multi‐immunofluorescence evaluation of the SPP1 (red) and TNC (green) distribution in tumor tissue from I‐LDL+Laser group; scale bars: 500 µm (low magnification) and 200 µm (high magnification). F) Dot plot of TNC and SPP1 expression in different macrophage subtypes (A: mononuclear MØ; C: ECM‐associated MØ; D: development MØ; E: myeloid MØ; F: M1 MØ; G: lumen‐associated MØ; and H: CD206⁺ MØ) and CAF subtypes (2: matrix CAFs 1; 3: development CAFs; 4: antigen‐presenting CAFs 1; 5: antigen‐presenting CAFs 2; 7: vascular CAFs; 8: inflammatory CAFs; and 9: matrix CAFs 2). G) Relationships between the expression levels of the ATF4 genes and the distance from the selected tumor vascular region, with distances ranging from 0 to 40 for the main comparison area. H) Strengths of the interactions between ECM CAFs, wound healing CAFs, and various TAM subtypes in the IL‐6 signaling pathway; the clusters of interest are marked in red. I) The immunofluorescence intensity quantification results of TNC for each MDA‐MB‐231 tumor in different groups (n = 6 independent samples per group, AI‐aLDL@A+L n = 5), statistical comparisons were performed via Student's t test. Groups that received laser irradiation treatment (D,I) are indicated by +L.* p < 0.05, ** p < 0.01, ns no significance.

Figure 4

ECM CAFs are highly correlated with serine metabolism. A) Prediction of the expression levels of various CAF subtypes in different metabolic pathways on the basis of gene expression from scRNA‐seq; the cluster and pathways of interest are marked in red. B) Spatial mapping of representative metabolite (nucleotide) expression in tumor tissue from the control and A‐aLDL@A groups. C) Comparison of the levels of uridine 5′‐monophosphate (pyrimidine nucleotide) and choline (one‐carbon unit) expression in ECM CAF regions and other regions, data are presented as boxplot. D) Enrichment of metabolic pathways on the basis of metabolite expression in ECM CAF regions and other regions; the pathways of interest are marked in red. E) Pseudotemporal analysis of the metabolite intensity distribution in ECM CAFs and plasma‐like macrophages. F) Comparison of the expression levels of choline, thymidine, and metabolites in ECM CAFs and plasma‐like macrophages. Data are presented as boxplot. Statistical comparisons were performed via Student's t‐test. G) Top 10 enriched metabolic pathways on the basis of the expression of all metabolites in the control and A‐aLDL@A groups; the pathways of interest are marked in red. **** p < 0.0001.

Figure 5

Mechanism of serine regulation of the MAPK pathway in CAFs. A) Schematic representation of the regulatory mechanism of the artesunate nanoplatform in CAFs. B) Signature score analysis in pathway (MAPK cascade) on the basis of gene expression in different CAF subtypes (2: matrix CAFs 1; 3: development CAFs; 4: antigen‐presenting CAFs 1; 5: antigen‐presenting CAFs 2; 6: wound healing CAFs; 7: vascular CAFs; 8: inflammatory CAFs; and 9: matrix CAFs 2); the subtypes of interest are marked in red, and the red dashed line indicates the median value of ECM CAFs, which was used as a reference; statistical comparisons were performed via the Wilcoxon rank‐sum test. Data are presented as boxplot. C) Pathway (Top10) enrichment based on differential proteins (A‐aLDL@A Vs Control, Top100 in down‐regulated proteins) through Metascape, focus pathway marked in red. D) Expression of MAPK pathway‐related proteins in CAFs after serine depletion for different durations. E) Transcriptional expression of MAPK pathway‐related genes in CAFs after serine depletion for different durations (n = 3 independent samples; data are expressed as the means ± S.E.M. values). F) Ultrahigh‐resolution laser confocal microscopy evaluation of changes in mitochondrial morphology (TOM20: green; and HSP60: red) in CAFs after 48 h of serine depletion and quantitative analysis; scale bar: 10 µm (n = 8 independent samples; data are expressed as the mean ± S.E.M. values; statistical comparisons were performed via Student's t‐test). G) GO pathway enrichment of the differential proteins (significant differences) with ¹³C‐labeled serine in CAFs between 12 and 48 h; the pathways of interest are indicated in the red boxes.* p < 0.05, *** p < 0.001, **** p < 0.0001, ns no significance.

Figure 6

HSP70 plays a role in serine regulation of the MAPK pathway. A) Differential proteins (48 h vs 12 h) with ¹³C‐labeled serine that have protein folding and heat response functions are shown; proteins related to HSP70 are marked in red boxes, and key highlighted proteins are marked in red. B) Ultrahigh‐resolution confocal microscopy assessment of the effects of serine depletion on the distribution of HSP70 (red), TOM20 (blue), and ATP5a1 (green) in CAFs (top: HSP70 is distributed along the cell membrane; bottom: white lines indicating cell edges; scale bar: 10 µm). C) Mass spectrometry analysis of proteins after immunoprecipitation was performed to evaluate changes in expression of the proteins that interact with HSP70 in CAFs between the control and serine depletion groups after 48 h. The proteins with the most significant changes in expression and those most related to energy metabolism are noted, with key highlighted proteins shown in red (n = 6 independent samples). D) Immunoblot assessment of the inhibitory effects of different serine sources on MAPK pathway‐related proteins in CAFs. E) Immunoblotting and qPCR (n = 3 independent samples) results were validated in L929 fibroblast cell line with serine depletion for 48 h which affecting on MAPK‐related gene. F) Laser confocal microscopy results were validated in L929 fibroblast cell line with serine depletion for 48 h which affecting on HSP70 distribution, scale bar:5 µm. G) GO enriched pathways of the proteins with all the serine sites marked with ¹³C among the identified peptides; the pathways of interest are marked in red boxes. H) The expression of MAP2K7 in Eo771 tumor from the PTT and control groups (n = 4 independent samples).

Figure 7

Heat‐resistant CAFs were used for validation. A) Crystal violet staining was used to evaluate the killing effects of PTT alone and the MKK7 inhibitor (DTP3) combined with PTT in CAFs; the red dotted line indicates the laser area, and the PTT group was used as a control. B) CCK‐8 assays were used to evaluate the killing effects of PTT alone and the MKK7 inhibitor (DTP3) combined with PTT in CAFs; the PTT group was used as a control (n = 4 independent samples; statistical comparisons were performed via Student's t test). C) Schematic of the in vitro induction of CAFs ^HR . D) Cellular morphological characteristics of CAFs and CAFs ^HR under an optical microscope; scale bar: 100 µm. E) Crystal violet staining of CAFs 96 h after PTT; the laser irradiation area is marked by a red dashed line. F) Comparison of the migration abilities of CAFs and CAFs ^HR over 96 h (n = 8 independent samples; statistical comparisons were performed via Student's t test). G) Growth curves of CAFs and CAFs ^HR over 6 days (n = 4 independent samples). H) Heatmap of TNC and MYLK expression in CAFs and CAFs ^HR . I) Volcano plot showing differences in protein expression between CAFs and CAFs ^HR (n = 3 independent samples). J) GSEA was performed on the basis of the protein profiles of CAFs and CAFs ^HR . K) Immunoblotting was used to assess the expression of ATF4 in CAFs and CAFs ^HR . The data (B,F,G) are expressed as the mean ± S.E.M. values. **** p < 0.0001.