Targeting cancer-associated fibroblasts: therapeutic strategies, translational challenges, and future perspectives

Yanping Jiang; Bo Ding; Han Yin; Yue Hua; Ning Ma; Pengcheng Hu; Jianbin Deng; Fu-Gen Wu; Yang Shen

doi:10.1186/s13045-026-01845-z

. 2026 Sep 29;19:78. doi: 10.1186/s13045-026-01845-z

Targeting cancer-associated fibroblasts: therapeutic strategies, translational challenges, and future perspectives

Yanping Jiang ¹, Bo Ding ¹, Han Yin ¹, Yue Hua ¹, Ning Ma ¹, Pengcheng Hu ², Jianbin Deng ², Fu-Gen Wu ^2,^✉, Yang Shen ^1,^✉

PMCID: PMC13625475 PMID: 42811335

Abstract

Cancer-associated fibroblasts (CAFs) act as key drivers of cancer progression and therapeutic resistance within the tumor microenvironment, but their diverse and context-dependent functions complicate therapeutic targeting. The limited clinical efficacy of CAF-targeting therapeutics has prompted a critical reassessment of CAF biology, therapeutic development, and emerging CAF-directed strategies. Unlike prior reviews that predominantly focused on CAF heterogeneity or isolated therapeutic modalities, this review systematically compares pharmacological and biologic CAF-targeting therapeutics, multifunctional nanomedicines, and biologically inspired and living platforms in terms of their underlying mechanisms of action and translational bottlenecks. Drawing on clinical evidence from CAF-targeting therapeutics, we identify several interconnected barriers to successful translation, including CAF heterogeneity and plasticity, patient-selection and biomarker limitations, preclinical–clinical mismatch and combination-treatment dependency, insufficient tumor penetration and systemic toxicity, and signaling redundancy and pathway compensation. Importantly, lessons from unsuccessful clinical trials are used to critically reassess whether current CAF-targeting nanomedicines and platforms adequately address these barriers. In parallel, we evaluate platform-specific translational challenges, particularly those related to biosafety and manufacturing feasibility. These analyses highlight the persistent gaps between the evolving understanding of CAF functional states, existing design rationales for nanomedicine and platforms, and real-world clinical practice. Future directions should therefore prioritize the identification of functionally defined CAF states, the optimization of clinically relevant models of advanced disease, and the development of therapeutic platforms tailored to clearly defined clinical needs. Finally, we propose an iterative translational roadmap and a hypothesis-generating sequential cancer cell/CAF dual-targeting framework to guide the development of clinically translatable CAF-directed therapies.

Keywords: Cancer-associated fibroblasts, CAF heterogeneity and plasticity, Nanomedicine, Biomimetic platforms, Clinical translation

Introduction

Cancer-associated fibroblasts (CAFs) are a heterogeneous population of non-epithelial, non-cancerous, non-endothelial, and non-immune cells derived from interstitial cells of mesenchymal lineage, located within or adjacent to tumors [1, 2]. CAFs shape the tumor microenvironment (TME) by secreting extracellular matrix (ECM) components, cytokines, and chemokines [3, 4]. CAF-derived ECM can form a dense, protective physical barrier that impedes drug penetration, whereas CAF-derived cytokines and chemokines regulate cancer cells via paracrine or juxtacrine signaling pathways [5–9]. Beyond their direct interactions with cancer cells, CAFs also communicate with neighboring CAFs, immune cells, and other stromal cells, thereby regulating tumor vascularization, innervation, metabolism, and immunity [10–13]. These interactions exhibit either tumor-promoting or tumor-suppressive properties, influencing cancer progression and drug responses [14–16]. Notably, functionally analogous mesenchymal and fibroblastic stromal populations also contribute to fibrotic stromal remodeling, cancer cell stemness, chemoresistance, and immune evasion in hematological malignancies [17–19]. These observations provide a rationale for targeting tumor-promoting CAFs or CAF-like populations, together with their crosstalk with malignant and immune cells, across both solid and hematological cancers.

CAF heterogeneity and plasticity represent major barriers to the development of effective CAF-targeting strategies. CAFs originate predominantly from local precursors, including quiescent tissue-resident fibroblasts, pancreatic stellate cells, and hepatic stellate cells [20, 21]. Other potential origins include epithelial and endothelial cells [22–25], adipocytes [26], bone marrow-derived mesenchymal stromal cells (BMSCs) [27], pericytes [28], macrophages [29, 30], and mesothelial cells [31, 32]. Lineage tracing studies have linked specific CAF states to distinct precursor populations, providing direct evidence for the cellular origins of CAF heterogeneity [33]. Notably, cellular origin alone does not determine CAF function, as fibroblasts undergo extensive phenotypic and functional reprogramming in response to microenvironmental and treatment-related stimuli [34]. This context-dependent plasticity further complicates efforts to define the relative contributions and functional significance of different CAF origins. In hematological malignancies, mesenchymal stromal cells may similarly undergo disease-associated reprogramming toward CAF-like states [18, 35, 36]. In contrast, fibroblastic reticular cells and follicular dendritic cells represent specialized stromal populations that share several functional features with solid-tumor CAFs [19, 37].

Advances in single-cell sequencing have substantially refined the classification of CAFs. Inflammatory CAFs (iCAFs), myofibroblastic CAFs (myCAFs), and antigen-presenting CAFs (apCAFs) are widely recognized as three major CAF states [14, 15]. These states exhibit distinct molecular and functional properties within the TME. iCAFs are predominantly associated with inflammatory and immunomodulatory programs, whereas myCAFs display contractile and ECM remodeling features [38–41]. apCAFs express high levels of major histocompatibility complex class II (MHC II) and cluster of differentiation (CD) 74, thereby modulating T cell immunity [42–45]. Additional CAF populations associated with vascularization, metabolism, mesothelial origin, senescence, and mechanotransduction have also been identified across different cancer types [4, 15, 46]. Importantly, these states are not necessarily discrete or stable, and individual CAFs may display overlapping programs and transition between functional states [34, 47–49]. Stage-resolved and longitudinal analyses have also shown that CAF composition and functional programs evolve throughout tumor progression and treatment [33, 50–53]. Spatial transcriptomics and multi-omics have further demonstrated that CAF function depends not only on transcriptional identity but also on anatomical location and neighboring cell communities [54, 55]. CAFs located near malignant cells, blood vessels, invasive margins, or immune aggregates may perform distinct matrix remodeling, angiogenic, and immunoregulatory functions; CAFs located more distantly from tumor cells may exhibit inflammatory and secretory programs that regulate immune-cell recruitment and tumor-stromal communication [56, 57].

Collectively, CAF heterogeneity is shaped by multiple interconnected factors, including cellular origin, transcriptional state, spatial localization, tumor type, disease stage, and treatment exposure, and is therefore increasingly regarded as a spatially and temporally organized continuum of plastic functional states rather than a fixed hierarchy of marker-defined subtypes [34]. Reflecting this functional continuum, commonly established biomarkers for CAFs—including fibroblast activation protein (FAP), α-smooth muscle actin (α-SMA), S100A4, platelet-derived growth factor receptors α and β (PDGFRα/β), and vimentin (VIM)—are shared among multiple CAF states and are not fully subtype-specific [58, 59]. Accordingly, no standardized classification system has yet been established. Table 1 summarizes the major CAF states and phenotypes recently described, along with their molecular characteristics and functions.

Table 1.

CAF classifications, defining features, characteristics, biomarkers, main signaling pathways, and functions in TME

Classification	Definition	Representative characteristics	Representative biomarkers	Major signaling pathways	Reported functions	References
Pan-CAFs	An operational umbrella for fibroblastic stromal cells within tumors rather than a discrete functional subtype.	Lineage exclusion: EPCAM⁻, PTPRC/CD45⁻, and PECAM1/CD31⁻ Identification: Requires multiple fibroblast/ECM markers; FAP or α-SMA alone is insufficient	Core fibroblast/ECM: COL1A1, COL1A2, COL3A1, DCN, LUM, VIM Surface/receptors: THY1/CD90, PDGFRA, PDGFRB, FAP, PDPN Activation/remodeling: ACTA2/α-SMA, S100A4/FSP1, POSTN, FN1, SPARC, MMPs, LOX, COL11A1	Subtype-dependent; no single dominant pathway	ECM production and remodeling; paracrine signaling; immune regulation; effects on tumor growth and treatment response are context-dependent.	[2, 59]
Myofibroblastic CAFs (myCAFs)	A contractile, myofibroblast-like CAF state enriched for cytoskeletal and ECM programs.	Typical profile: ACTA2/α-SMA-high, TAGLN-high, and IL6-low Spatial feature: Often located near malignant epithelial cells in PDAC	Contractile: ACTA2/α-SMA, TAGLN, MYL9, TPM1/TPM2, HOPX ECM/remodeling: COL1A1, COL1A2, COL3A1, COL5A1, FN1, POSTN, CCN2/CTGF, MMP11 Surface/receptors: PDGFRB, FAP	TGF-β/SMAD; integrin–FAK–RhoA/ROCK–YAP/TAZ	Cell contraction and ECM deposition/remodeling; may promote invasion, metastasis, immune exclusion, and treatment resistance, although some myofibroblastic programs can be tumor-restraining in specific contexts.	[38, 248]
Inflammatory CAFs (iCAFs)	A secretory CAF state enriched for inflammatory cytokines, chemokines, and complement-related programs.	Typical profile: IL6-high, LIF-high, and ACTA2/α-SMA-low Spatial feature: Often spatially separated from tumor-adjacent myCAFs in PDAC	Cytokines/chemokines: IL6, IL11, LIF, CXCL1, CXCL2, CXCL12, CXCL14, CCL2 Matrix/metabolic: HAS1, HAS2, PLA2G2A Lineage/complement: PDGFRA, CFD, C3, CD34	IL-1–NF-κB–LIF/JAK–STAT; IL-6/JAK–STAT3; CXCL12/CXCR4; TGF-β can suppress iCAF differentiation in PDAC	Inflammatory signaling and immune-cell recruitment or exclusion; context-dependent promotion or restraint of tumor progression.	[1, 249, 250]
Antigen-presenting CAFs (apCAFs)	An MHC II-expressing CAF state with an antigen-processing and antigen-presentation program.	Typical profile: MHC II-high and CD74-high Co-stimulation: Expression of canonical co-stimulatory molecules is often low or variable	Antigen presentation: CD74, HLA-DRA, HLA-DRB1, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1 Transcriptional regulator: CIITA	IFN-γ–JAK/STAT1–CIITA; context-dependent regulation by IL-1 and TGF-β	Antigen processing and presentation; may promote CD4 + T-cell activation, tolerance/anergy, or Treg expansion depending on tumor context and co-stimulatory capacity.	[33, 43, 45]
Matrix CAFs	An ECM-high CAF state that overlaps with myCAF, CTHRC1⁺, COL11A1⁺, and LRRC15⁺ programs.	Typical profile: COL1A1/COL1A2-high with variable COL11A1, CTHRC1, or LRRC15 enrichment Cell assignment: Mural, epithelial, and endothelial cell identities should be excluded	Structural ECM: COL1A1, COL1A2, COL3A1, COL5A1, COL11A1, COL12A1 Matricellular/glycoproteins: FBN1, LUM, DCN, POSTN, TNC, FN1, THBS2, SPARC, VCAN, MGP Remodeling/activation: CTHRC1, MMP11, LOX/LOXL2, FAP, LRRC15, INHBA, ITGA11	TGF-β/SMAD; integrin–FAK–YAP/TAZ; LOX-dependent mechanosignaling	ECM production and deposition, tissue stiffening, stromal-barrier formation, immune modulation or exclusion, and impaired drug penetration.	[251–254]
Metabolic/glycolytic CAF states (meCAFs/glyCAFs)	CAF states enriched for glycolytic, hypoxic, lipid, or amino-acid metabolic programs; the dominant signature varies by tumor type.	Context-specific examples: CXCL16⁺ GLUT1-dependent glyCAFs in soft-tissue sarcoma; CD36⁺ lipid-metabolic CAFs in hepatocellular carcinoma	Glycolysis/hypoxia: SLC2A1/GLUT1, HK2, PKM2, PGK1, LDHA, ENO1, ENO2, CA9, HIF1A, ANGPTL4 Lipid metabolism: FASN, ACLY, CD36 Amino-acid exchange: GLS, GLUL, NTNG1 Additional context-specific feature: Low stromal CAV1	Hypoxia/HIF-1α; oxidative-stress responses; glycolysis–CXCL16/CXCR6; context-dependent TGF-β signaling	Metabolic support of tumor cells, glycolysis and lactate accumulation, immune suppression or T-cell exclusion, invasion, metastasis, and treatment resistance.	[10, 255–257]
Vascular/perivascular CAFs (vCAFs)	A vessel-associated fibroblastic state with angiogenic and mural-like transcriptional programs.	Typical profile: MCAM⁺, NOTCH3⁺, COL18A1⁺, or CD248⁺ Important caveat: RGS5, CSPG4, DES, and MYH11 are also pericyte/vascular smooth-muscle markers; careful lineage distinction is required	Perivascular/mural-like: NOTCH3, MCAM/CD146, COL18A1, CD248, RGS5, PDGFRB, CSPG4/NG2, DES, MYH11, NID2 Contractile/hypoxic: ACTA2/α-SMA, EPAS1/HIF-2α, NR2F2 Angiogenic: VEGFA	PDGF-BB/PDGFRβ; NOTCH3; VEGF-related angiogenic signaling; TGF-β and hypoxia-related signaling	Angiogenesis, vascular remodeling, perivascular ECM organization, vessel stabilization or destabilization, tumor invasion, and metastatic dissemination.	[28, 46, 205, 258]
Mesothelial-derived CAFs (mesCAFs)	CAFs derived from mesothelial cells through mesothelial-to-mesenchymal transition, retaining mesothelial-lineage features while acquiring fibroblastic programs.	Typical profile: Co-expression of MSLN, WT1, or UPK3B with fibroblast/ECM markers Context-specific feature: May acquire an MHC II antigen-presenting program in PDAC	Mesothelial lineage: MSLN, WT1, UPK1B, UPK3B, GPM6A, LRRN4, KRT8, KRT18, pan-cytokeratin, CALB2/calretinin, PDPN, EZR Fibroblastic acquisition: COL1A1, COL3A1, PDGFRA	TGF-β-driven mesothelial-to-mesenchymal transition; IL-1; IFN-γ/JAK–STAT–CIITA when an apCAF program is present	Peritoneal adhesion and tumor implantation, ECM remodeling, metastatic-niche formation, chemoresistance, and context-dependent immune regulation, including Treg expansion in PDAC.	[31, 32, 259]
Senescent CAF state (senCAFs)	A stable cell-cycle-arrest state with a senescence-associated secretory phenotype that can be acquired by multiple CAF subtypes.	Required assessment: Multiple orthogonal criteria, such as SA-β-gal activity, p16/p21 expression, reduced proliferation, and the presence of SASP Important caveat: No single marker is sufficient to establish senescence	Senescence/cell-cycle arrest: SA-β-gal/GLB1, CDKN2A/p16, CDKN1A/p21, TP53/p53, RB1/pRB, reduced MKI67, and often reduced LMNB1 CAF identity: VIM, PDPN, FAP, PDGFRA/PDGFRB, COL1A1, FN1, POSTN SASP: IL6, CXCL8/IL8, CXCL1, CXCL2, CCL2, MMPs, TGFB1, VEGFA	p53/p21; p16INK4A/RB; NF-κB/C/EBPβ-mediated SASP activation	Stable arrest can constrain cell proliferation, whereas the SASP may promote immune suppression, tumor progression, recurrence, and treatment resistance in a context-dependent manner.	[260–264]
Immunomodulatory CAF states (IM-CAFs)	A functional umbrella for CAF states that shape the tumor immune microenvironment; it overlaps with iCAF, CAF-S1, LRRC15⁺, and CD10⁺GPR77⁺ programs.	Context-specific examples: FAP⁺CXCL12⁺ CAFs; CAF-S1; LRRC15⁺ CAFs; CD10⁺GPR77⁺ CAFs	Immunoregulatory mediators: TGFB1, IL6, CCL2, CXCL1, CXCL2, CXCL12, CXCL14, CXCL16 Surface/checkpoint: FAP, PDPN, LRRC15, CD274/PD-L1, PDCD1LG2/PD-L2, NT5E/CD73, CD10/MME, GPR77/C5AR2 Matrix/contractile: PDGFRB, ACTA2/α-SMA, POSTN, COL1A1	TGF-β; IL-1/NF-κB; JAK/STAT; CXCL12/CXCR4	T-cell exclusion or dysfunction, Treg recruitment, myeloid-cell polarization, and resistance to immunotherapy; effects vary among CAF states and tumor types.	[252, 265, 266]
Steady-state-like CAFs (SSL-CAFs)	CAFs that retain homeostatic, adventitial, or basement-membrane-associated programs and relatively low activation signatures.	Typical profile: PI16⁺DPP4⁺DPT⁺CD34⁺ or COL15A1⁺states Potential behavior: May act as progenitor-like fibroblasts and transition into activated states	Adventitial-like: PI16, DPP4/CD26, DPT, CD34 Basement-membrane-associated: COL15A1 Additional: ADH1B, C7, CXCL12	No single dominant pathway established; niche-dependent homeostatic signaling	Maintenance of stromal homeostasis and tissue architecture; potential progenitor reservoir; tumor-related functions remain incompletely defined.	[1, 267–269]
Mechanoresponsive CAF states (MR-CAFs)	CAF states enriched for mechanosensitive, cytoskeletal, contractile, and ECM-remodeling programs.	Typical profile: YAP/TAZ-active, contractile, and responsive to matrix stiffness Context-specific example: CD248⁺ CAFs in non-small-cell lung cancer	Surface/adhesion: CD248, LRRC15, CDH11 Contractile/mechanosensitive: ACTA2/α-SMA, TAGLN, MYL9, CCN2/CTGF ECM: POSTN, MGP, COL1A1, COL1A2, COL3A1, COL6A1, FN1, THBS2 Remodeling: MMPs, LOX/LOXL2	Integrin–FAK/Src–RhoA/ROCK–YAP/TAZ; TGF-β; MAPK	ECM deposition and remodeling, tissue stiffening, stromal compression, invasion, metastasis, and impaired drug penetration.	[1, 267], 269– [271]

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apCAF: antigen-presenting CAF; CAF: cancer-associated fibroblast; C/EBP β: CCAAT/enhancer-binding protein β; CD: cluster of differentiation; CTGF: connective tissue growth factor; ECM: extracellular matrix; FAK: focal adhesion kinase; FAP: fibroblast activation protein α; FSP1: fibroblast-specific protein 1; GLUT1: glucose transporter 1; glyCAF: glycolytic CAF; HIF: hypoxia-inducible factor; HLA: human leukocyte antigen; iCAF: inflammatory CAF; IFN-γ: interferon gamma; IL: interleukin; IM-CAF: immunomodulatory CAF; JAK: Janus kinase; LIF: leukemia inhibitory factor; MAPK: mitogen-activated protein kinase; meCAF: metabolic CAF; mesCAF: mesothelial-derived CAF; MHC II: major histocompatibility complex class II; MMP: matrix metalloproteinase; MR-CAF: mechanoresponsive CAF; myCAF: myofibroblastic CAF; NF-κB: nuclear factor kappa B; NG2: neuron-glial antigen 2; PDAC: pancreatic ductal adenocarcinoma; PDGF: platelet-derived growth factor; PDGFR: platelet-derived growth factor receptor; PD-L1: programmed death-ligand 1; PD-L2: programmed death-ligand 2; RhoA: Ras homolog family member A; ROCK: Rho-associated coiled-coil-containing protein kinase; SA-β-gal: senescence-associated β-galactosidase; SASP: senescence-associated secretory phenotype; α-SMA: α-smooth muscle actin; senCAF: senescent CAF; SSL-CAF: steady-state-like CAF; STAT: signal transducer and activator of transcription; TAZ: transcriptional coactivator with PDZ-binding motif; TGF-β: transforming growth factor-β; Treg: regulatory T cell; vCAF: vascular/perivascular CAF; VEGF: vascular endothelial growth factor; YAP: Yes-associated protein

Numerous comprehensive reviews have summarized CAF biology, classification, clinical relevance, and therapeutic advances from distinct perspectives [1, 4, 15, 34, 60–62]. Zhang et al. highlighted the role of CAFs in the TME and advances in CAF-targeting therapeutics [1]. Fan et al. summarized CAF biology, as well as emerging technologies and experimental models used in CAF research [4]. Chen et al. focused on the widely used CAF biomarkers and their clinical and therapeutic implications [15]. Pramod et al. analyzed CAF heterogeneity at three interconnected levels: spatial location within distinct tumor niches, dynamic phenotypic states and functions within the tumor ecosystem, and adaptation to changes in the broader TME over time [34]. Tang et al. reviewed how heterogeneous CAF states and tertiary lymphoid structures (TLSs) jointly reshape the spatial organization of tumor immunity [60]. Zhang et al. [61] and Chen et al. [62] reviewed the advances in CAF-targeting nanomedicines, highlighting the properties and advantages of these nanomaterials. The scope and emphasis of these previous CAF-focused reviews are compared with those of the present review in Table 2. Despite these important contributions, an integrated analysis linking pharmacological and biologic CAF-targeting therapeutics, emerging nanomedicines, and biologically inspired and living platforms, along with the barriers to their limited clinical translation, remains lacking. Accordingly, the present review extends beyond summaries of CAF biology or individual therapeutic modalities by comparatively evaluating CAF-targeting strategies across distinct therapeutic modalities. In addition to summarizing their mechanisms of action and the biological, technological, and manufacturing challenges to clinical translation, we analyze the potential reasons for clinical trial failures and identify mechanistic similarities between CAF-targeting therapeutics combined with chemotherapy and multifunctional CAF-targeting nanoparticles (NPs). Drawing on lessons from unsuccessful clinical trials of conventional therapeutics, we critically assess the translational potential of CAF-targeting nanomedicines and platforms and discuss how future nanoplatforms could be optimized for advanced, metastatic, and recurrent cancers. This cross-modality, clinically oriented perspective distinguishes the present review from previous publications and may help bridge preclinical innovation and clinical development.

Table 2.

Comparison of the scope and emphasis of previous CAF-focused reviews and the present review

Review	Year	Primary focus	Clinical-trial coverage	Distinctive contribution
Present review	2026	Cross-modality comparison of conventional therapeutics, nanomedicines, and biologically inspired and living platforms	Comparative analysis of representative clinical trials, including efficacy, safety, and potential reasons for failure	Using lessons from the clinical development of conventional CAF-targeting therapeutics to critically evaluate the translational potential of emerging platforms
Pramod et al. [34]	2026	CAF heterogeneity, plasticity, and therapeutic implications of dynamic CAF states	Limited; clinical implications discussed without systematic trial comparison	Integrating single-cell and spatial evidence to characterize CAF heterogeneity and plasticity across tumor contexts
Tang et al. [60]	2026	CAF–TLS interactions, spatial tumor immunity, and immune-oriented therapeutic strategies	Limited; mainly discussed in relation to immunotherapy and patient stratification	Establishing a spatial framework linking CAF states and TLS organization to antitumor immunity
Chen et al. [15]	2021	Clinical relevance of CAFs, biomarkers, and conventional CAF-targeting strategies	Selected clinical studies and therapeutic failures discussed	Highlighting context-dependent CAF functions and the risks and limitations of nonspecific CAF targeting
Zhang et al. [1]	2023	CAF biology, immunoregulation, and CAF-targeting therapeutic strategies	Broad overview of CAF-related clinical trials across multiple targets and tumor types	Integrating CAF biology, immunotherapy, therapeutic strategies, and the clinical-trial landscape
Fan et al. [4]	2025	CAF origin, classification, tumorigenicity, therapeutic targeting, and emerging technologies	Summarizes ongoing and completed CAF-related clinical trials	Integrating CAF biology and therapeutic targeting with emerging experimental technologies and models
Zhang et al. [61]	2025	Nanotechnology-based CAF targeting and combination nanotherapies	Predominantly preclinical; clinical trials are not a major focus	Providing a nanotechnology-centered overview of CAF-targeted drug delivery and associated translational challenges
Chen et al. [62]	2026	CAF-targeting nanomedicines across major material classes and therapeutic mechanisms	Primarily preclinical; selected conventional clinical failures provide translational context	Systematically evaluating CAF-targeting nanomedicine design and major barriers to clinical translation

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CAF: cancer-associated fibroblast; NPs: nanoparticles; TME: tumor microenvironment; TLS: tertiary lymphoid structure

Conceptual frameworks of CAF-targeting strategies

Broadly, CAF-targeting strategies may be categorized into three conceptual frameworks: (1) CAF depletion or stromal disruption, (2) CAF functional modulation or reprogramming, and (3) biologically inspired or living platforms for CAF-directed delivery and intervention [15, 61, 62].

Studies have demonstrated that CAFs secrete cytokines, chemokines, and ECM components that establish a tumor-promoting microenvironment and facilitate cancer progression. These observations have motivated CAF-depleting strategies aimed at eliminating activated fibroblasts or removing CAF-derived matrix components, thereby attenuating tumor-promoting signaling and improving drug penetration. However, these approaches frequently lacked selectivity and, in some cases, unexpectedly exacerbated cancer progression by indiscriminately eliminating CAF populations with tumor-restraining functions [63, 64].

Studies have also revealed that CAFs can exhibit tumor-promoting or tumor-suppressive effects in a context-dependent manner, with these divergent functions being partly attributed to distinct and interconvertible CAF states [1, 15]. These findings underscore therapeutic approaches that extend beyond indiscriminate CAF depletion to encompass functional modulation and reprogramming. Signaling- and cytokine-directed therapies represent an important form of functional modulation because they selectively suppress CAF functions without necessarily eliminating CAFs or permanently altering their phenotypes. In contrast, CAF reprogramming seeks to convert activated, tumor-promoting CAFs into quiescent or tumor-restraining states while preserving stromal integrity [15, 34]. CAF reprogramming has been extensively investigated as an adjuvant strategy, including the use of NPs carrying CAF-targeting inhibitors or gene-editing therapeutics, and has shown promising results in preclinical studies [65, 66]. Although reprogramming may reduce the risks associated with broad CAF depletion, its clinical implementation remains constrained by CAF plasticity, incomplete or reversible phenotypic conversion, and lack of validated biomarkers for monitoring durable reprogramming.

Beyond directly depleting or functionally modulating CAFs, biologically inspired and living platforms provide a complementary framework for CAF-directed therapy by exploiting biological recognition, tumor tropism, or tumor–stroma interactions to improve therapeutic localization and delivery [61, 67]. These approaches include cell-derived membrane-based systems [68], engineered bacterial platforms or cell therapies [69–71], and CAF-based/CAF-related biomimetic strategies [72, 73]. Each offers platform-specific advantages in targeting, cargo delivery, or spatial control while also introducing distinct risks related to biosafety, biological variability, and translational complexity.

Pharmacological and biologic CAF-targeting therapeutics

Pharmacological and biologic CAF-targeting therapeutics constitute the extensively investigated class of CAF-directed interventions in clinical development. Based on their mechanisms of action, these agents can be broadly classified into five categories (Fig. 1) [14, 74]: (1) ECM-targeting agents, including hyaluronan-degrading enzymes, such as pegvorhyaluronidase alfa (PEGPH20); ECM-remodeling enzymes, such as lysyl oxidase-like 2 (LOXL2) and matrix metalloproteinase 9 (MMP-9); (2) Inhibitors of CAF-activation receptors and intracellular signaling, targeting receptors or kinases such as C-X-C chemokine receptor type 4 (CXCR4), Janus kinase (JAK), transforming growth factor-β receptor (TGF-βR), components of the Hedgehog (Hh) pathway, mechanotransduction pathways, such as focal adhesion kinase (FAK); (3) neutralizing agents targeting CAF-related soluble mediators, such as those against leukemia inhibitory factor (LIF), C-X-C motif chemokine ligand 12 (CXCL12), and connective tissue growth factor (CTGF), which contribute to the establishment of a tumor-promoting TME and drug resistance; (4) CAF reprogramming agents, including all-trans retinoic acid and a vitamin D receptor agonist, which promote the normalization of activated stellate cells; (5) FAP-targeting bispecific and immunomodulatory biologics, such as the FAP-targeting 4-1BB (CD137) agonist and the FAP–CD40 agonist. Although these therapeutic classes have yielded promising results in preclinical studies, their clinical outcomes have been variable. This section therefore evaluates their mechanisms of action, performance in recent clinical trials, and major translational barriers, as summarized in Table 3.

Fig. 1 — CAF-targeting strategies and mechanisms underlying therapeutic failures. The schematic illustrates representatives of potential cellular origins of CAFs, their context-dependent functions, and the current strategies directed against CAFs (upper panel) and the major biological, therapeutic, and translational mechanisms that may contribute to clinical failures of CAF-targeting therapeutics (lower panel). apCAF: antigen-presenting CAF; CAFs: cancer-associated fibroblasts; CTGF: connective tissue growth factor; ECM: extracellular matrix; FGF1: fibroblast growth factor 1; FGFR: fibroblast growth factor receptor; HGF: hepatocyte growth factor; IL-6: interleukin-6; IL-6R: interleukin-6 receptor; IL-8: interleukin-8; mesCAF: mesothelial-derived CAF; meCAF: metabolic CAF; MSC: mesenchymal stromal cell; myCAF: myofibroblastic CAF; NRG1: neuregulin-1; PAI-1: plasminogen activator inhibitor 1; SFRP2: secreted frizzled-related protein 2; TGF-β: transforming growth factor-β; vCAF: vascular CAF

Table 3.

Representative CAF-targeting therapeutics in clinical trials: mechanisms, clinical outcomes, and translational challenges

Category	Agent and mechanism	Cancers	Clinical trial	Clinical outcomes	Advantages, limitations and major translational barriers	References
ECM-targeting agents	PEGPH20 (a hyaluronan-degrading recombinant human hyaluronidase)	HA-high, metastatic PDAC	Phase III (NCT02715804), PEGPH20/AG (n = 327) vs. placebo/AG (n = 165)	OS: 11.2 vs. 11.5 months (HR, 1.00; 95% CI, 0.80 to 1.27; p = 0.97) PFS: 7.1 vs. 7.1 months (HR, 0.97; 95% CI, 0.75 to 1.26) ORR: 47% vs. 36% (ORR ratio, 1.29, 95% CI, 1.03 to 1.63).	1. Advantages: direct targeting of ECM component, facilitation of drug penetration 2. Limitations: Lack of selectivity, targeting a single ECM component, potential excessive ECM degradation 3. Major translation barriers: systemic toxicities, insufficient intratumoral exposure, lack of effective biomarkers, spatial ECM heterogeneity, inadequate matching between selected patients and CAF-directed agents, as well as dependence on combination regimens	[86]
		HA-high, pretreated metastatic PDAC	Phase II (NCT03634332), single-arm PEGPH20/pembrolizumab (n = 8)	OS: 7.2 months PFS: 1.5 months Two of seven patients (29%) in SD (best response); not effective outcomes, closed to accrual early		[83]
	Simtuzumab (an antibody against LOXL2)	Metastatic KRAS-mutant CRC	Phase II (NCT01479465), FOLFIRI/simtuzumab 700 mg (n = 84), FOLFIRI/simtuzumab 200 mg (n = 85), and FOLFIRI/placebo (n = 80)	PFS: 5.5, 5.4, and 5.8 months OS: 11.4, 10.5, and 16.3 months ORR: 11.9%, 5.9%, and 10%		[89]
	Simtuzumab (an antibody against LOXL2)	Metastatic PDAC	Phase II (NCT01472198), gemcitabine/simtuzumab 700 mg (n = 80), gemcitabine/simtuzumab 200 mg (n = 79), gemcitabine/placebo (n = 81)	PFS: 3.7, 3.5, and 3.7 months OS: 7.6, 5.9, and 5.7 months ORR:13.9%, 14.5%, and 23.5%		[90]
	Andecaliximab (an antibody against MMP-9)	Advanced GC or GEJ Adenocarcinoma	Phase II (NCT02864381), andecaliximab/nivolumab (n = 72) vs. nivolumab (n = 72)	PFS: exact number was not given (HR, 1.33, 95% CI, 0.94 to 1.88, p = 0.10) OS: 7.1 vs. 5.9 months (HR,1.24, 95% CI, 0.49 to 1.26, p = 0.23) ORR: 10% vs. 7% (OR,1.5, 95% CI, 0.4 to 6.1; p = 0.8)		[94]
	Andecaliximab (an antibody against MMP-9)	Advanced GC or GEJ Adenocarcinoma	Phase III (NCT02545504), mFOLFOX6/ andecaliximab (n = 218) vs. mFOLFOX6/placebo (n = 214)	ORR: 51% vs. 41% (OR, 1.47, 95% CI, 1.0 to 2.2, p = 0.049) OS: 12.5 vs. 11.8 months (HR, 0.93, 95% CI 0.74 to 1.18) PFS: 7.5 vs. 7.1 months (HR, 0.84, 95%CI: 0.67 to 1.04, p = 0.10)		[93]
Inhibitors of CAF-activation receptors and intracellular signaling	Galunisertib (a TGF-βR1 inhibitor)	Persistent or recurrent OC or UC	Phase Ib (NCT03206177), galunisertib/ CT (n = 24)	PFS: 6.6 months OS: 18.9 months	1. Advantages: Targeting pharmacologically traceable pathways 2. Limitations: limited CAF specificity and targeting a single CAF-related pathway 3. Major translation barriers: pathway redundancy, context-dependent heterogeneity, and dependence on combination regimens	[100]
	Galunisertib (a TGF-βR1 inhibitor)	Refractory solid tumors and NSCLC	phase Ib/II (NCT02423343), galunisertib/nivolumab (NSCLC, n = 25)	PFS: 5.26 months OS: 11.99 months		[101]
	Balixafortide (a CXCR4 inhibitor)	HER2-, locally recurrent or metastatic BC	Phase III (NCT03786094), balixafortide/eribulin (n = 219) vs. eribulin (n = 213)	PFS: 3.5 vs. 4.0 months (HR, 1.10, 96%CI, 0.85 to 1.41) OS: 11.0 vs. 11.2 months (HR, 1.07, 96%CI, 0.81 to 1.41)		[102]
	Ruxolitinib (a JAK1/JAK2 inhibitor)	Advanced/metastatic PDAC	Phase III, ruxolitinib/capecitabine vs. capecitabine/placebo (JANUS1 (NCT02117479): n = 161 vs. n = 160; JANUS2 (NCT02119663): n = 43 vs. n = 43)	JANUS 1: Deaths (%): 70.2% vs. 77.5% (HR, 0.969, 95% CI, 0.747 to 1.256) Earlier event of disease progression or death (%): 81.4% vs. 82.5% (HR, 1.056; 95% CI, 0.827 to 1.348) JANUS 2: Deaths (%): 67.4% vs. 53.5% (HR, 1.584; 95% CI, 0.886 to 2.830) Earlier event of disease progression or death (%): 69.8% vs. 69.8% (HR, 1.166; 95% CI, 0.687 to 1.978)		[103]
	Erdafitinib (an FGFR inhibitor)	Metastatic urothelial carcinoma	Phase II (NCT03473743), erdafitinib (n = 43) vs. erdafitinib/cetrelimab (n = 44)	ORR: 44.2% vs. 54.5% OS: 16.2 vs. 20.8 months Duration of response: 9.7 vs. 11.1 months (demonstrated antitumor activity)		[104]
		NMIBC with FGFR3/2 alteration	Phase II (NCT04172675), erdafitinib vs. intravesical chemotherapy	Cohort 1 (high-risk NMIBC, n = 74): PFS: not reached vs. 11.6 months (HR,0.28, 95%CI, 0.13 to 0.61; p = 0.0007) Cohort 2 (carcinoma in situ ± papillary, n = 16): CR rate:94% (95% CI, 0.7 to 1) at 8 weeks, and 81% (95% CI, 0.54 to 0.96) at 32 weeks Duration of response: 23.3 months (95% CI, 0.1 to not estimable) Cohort 3 (intermediate-risk NMIBC, n = 18): CR rate: 89% (95% CI, 0.65 to 0.99) Duration of response: not reached (95% CI, 0.13 to not estimable).		[107]
		FGFR-altered advanced or metastatic CCA	Phase II (RAGNAR (NCT04083976) and LUC2001 (NCT02699606), erdafitinib (n = 78)	ORR: 55% Duration of response: 6.9 months PFS: 8.5 months OS: 18.1 months		[106]
		HR+/HER2- metastatic BC	Phase Ib (NCT03238196), fulvestrant/palbociclib/erdafitinib (n = 13 in dose escalation phase, n = 22 in the expansion dose)	PR: 3 patients, one of whom has lasted more than 2.5 years		[105]
	Vismodegib (a hedgehog inhibitor)	Metastatic PDAC	Phase II (NCT01088815), vismodegib/AG (starting the second cycle) (n = 71)	PFS: 5.42 months OS: 9.79 months No significant improvement compared to historical rates		[110]
	Vismodegib (a hedgehog inhibitor)	Locally advanced, unresectable basal cell carcinoma	Phase II (NCT01835626), single-arm, vismodegib/RT	1-year PFS rate: 100% 1-year OS rate: 96% 5-year PFS rate: 78% 5-year OS rate: 83%		[111]
	Tocilizumab (an antibody against IL-6R)	PDAC	Phase II (NCT02767557), Tocilizumab/AG (n = 70) vs. AG (n = 71)	6-month OS: 68.6% vs. 62.0% (p = 0.409). 18-month OS: 27.1% v 7.0% (p = 0.001). No differences in median OS, PFS, or ORR were observed		[108]
	Tocilizumab (an antibody against IL-6R)	Refractory PDAC	Phase II (NCT04258150), tocilizumab/ ipilimumab/nivolumab/RT (n = 26)	PFS: 1.6 months OS: 5.3 months Did not meet the prespecified criteria for expansion for full accrual		[109]
	Defactinib (a small-molecule inhibitor of FAK)	Recurrent LGSOC	Phase II (NCT04625270), avutometinib/defactinib (n = 115) vs. avutometinib (n = 70)	ORR: 31% vs. 17% (combination vs. monotherapy) ORR: 44% in KRAS mt, 17% in KRAS wt in the combination treatment group ORR: 23% in KRAS mt, 13% in KRAS wt in monotherapy PFS: 22.0 in KRAS-mt vs. 12.8 in KRAS-wt in the combination group		[116]
		Recurrent LGSOC	Phase III (NCT06072781), avutometinib/defactinib vs. investigator’s choice of treatments	Ongoing clinical trial		[117]
		Advanced KRAS mutant NSCLC	Phase II (NCT01951690), defactinib monotherapy (n = 55)	12-week PFS (endpoint): 15/45 (28%), with one patient achieving a PR PFS: 45 days (modest clinical activity)		[113]
		Pleural mesothelioma	Phase II (NCT01870609), defactinib (n = 173) vs. placebo (n = 171)	PFS: 4.1 vs. 4.0 months OS:12.7 vs. 13.6 months (HR, 1.0; 95% CI, 0.7 to 1.4)		[114]
		Refractory PDAC	Phase I (NCT02546531), defactinib/pembrolizumab/gemcitabine (n = 20); maintenance cohort (n = 10)	Disease control rate: 80% (one PR and fifteen SDs) PFS: 3.6 months OS: 7.8 months maintenance cohort: disease control rate: 70% (one PR and six SDs)		[119]
		PDAC	Phase II (NCT04331041), defactinib/ RT	Enrollment completed		[120]
Neutralizing agents targeting CAF-related soluble mediators	MSC-1 (AZD0171) (a monoclonal antibody for LIF)	Advanced solid tumors	Phase I (NCT03490669), MSC-1 (n = 41); phase II (NCT04999969) is ongoing	PFS was 5.9 weeks 23.7% had PFS > 16 weeks	1. Advantages: relatively high molecular specificity 2. Limitations: targeting a single CAF-related factor 3. Major translation barriers: compensatory cytokine network, context-dependent heterogeneity, and dependence on combination regimens	[123, 124]
	NOX-A12 (a CXCL12 inhibitor)	Advanced PDAC, CRC	Phase I/II (NCT03168139), NOX-A12/pembrolizumab (n = 20)	PFS: 1.87 months OS: 3.97 months		[126]
	NOX-A12 (a CXCL12 inhibitor)	Glioblastoma	Phase I/II dose escalation and expansion (NCT04121455), RT/BEV/ NOX-A12 (n = 6) vs. RT/NOX-A12 (n = 10)	PFS: 9.1 vs. 5.7 months (HR 0.14, 95% CI, 0.02–0.60, p = 0.009) OS: 19.9 vs. 12.7 months (HR, 0.23, 95% CI, 0.05 to 0.79, p = 0.021)		[125]
	Pamrevlumab (a CTGF inhibitor)	Locally advanced, unresectable PDAC	Phase III (NCT03941093) Pamrevlumab/AG or FOLFIRINOX (n = 143) vs. placebo/AG or FOLFIRINOX (n = 141)	OS: 17.25 vs. 17.94 months (HR, 1.08; 95% CI, 0.83 to 1.41; p = 0.55).		[127]
CAF-reprogramming agents	ATRA (a retinoid that promotes pancreatic stellate-cell quiescence)	PDAC	Phase Ib (NCT03307148), ATRA/AG (n = 27); a randomized phase II (STARPAC2, NCT04241276) is ongoing	OS: 11.7 months (95%CI, 8.6 to 15.7)	1. Advantages: preservation of stromal integrity 2. Limitations: incomplete and reversible CAF conversion 3. Major translation barriers: lack of validated biomarkers for confirming CAF-state conversion, treatment-induced CAF adaptation, limited evidence, and dependence on combination regimens	[129, 130]
	Paricalcitol (a vitamin D receptor agonist that promotes stellate-cell normalization)	Metastatic PDAC	Phase II (NCT03520790), paricalcitol/AG	Terminated		[131]
	Losartan (an angiotensin receptor blocker that reduces activated CAFs)	PDAC	Phase III, terminated before full accrual, mFOLFIRINOX (n = 44) vs. mFOLFIRINOX/losartan (n = 44)	ORR: 22% vs. 23% 6-month OS rate: 72.7% vs. 73.2% OS: 10.4 vs. 9.1 months (HR, 0.76, 95% CI, 0.47 to 1.22, p = 0.392), terminated early		[132]
FAP-targeted bispecific and immunomodulatory biologics	RO7122290 (an FAP–4-1BB agonist)	Metastatic CRC	Phase Ib (NCT04826003), RO7122290/cibisatamab	13.5% of patients had confirmed partial responses, exhibiting preliminary antitumor activity	Advantages: localized immune activation in FAP-positive stroma, and enhanced T-cell engagement and costimulation. Limitations: Heterogeneous FAP expression and adaptive immune/stromal resistance Major translational barriers: Biomarker-guided patient selection, on-target/off-tumor toxicity, and combination dependency	[138]
	BI 765179 (an FAP–4-1BB bispecific antibody)	Advanced solid tumors; advanced head and neck cancer	Phase I (NCT04958239), BI 765179 vs. BI 765179/ezabenlimab in patients with advanced cancer; BI 765,179 /pembrolizumab in patients with advanced head and neck cancer	Ongoing trial		[139]
	RO7300490 (an FAP–CD40 agonist)	Advanced solid tumors	Phase I (NCT04857138), RO7300490 (n = 80)	No objective responses and limited clinical activity		[140]
	MP0317 (an FAP–CD40 Designed Ankyrin Repeat Protein (DARPin))	Advanced solid tumors	Phase I (NCT05098405), MP0317 (n = 46)	One patient achieved an unconfirmed partial response, and 14 patients had stable disease		[141]
	Simlukafusp α, an FAP-targeting IL-2 variant immunocytokine	Advanced/metastatic solid tumors	Phase I (NCT02627274), Simlukafusp α (n = 61)	ORR: 5.1%, disease control rate: 27.1%		[142]

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AG: nab-paclitaxel/gemcitabine; ATRA: all-trans retinoic acid; BC: breast cancer; BEV: bevacizumab; CAFs: cancer-associated fibroblasts; CCA: cholangiocarcinoma; CI: confidence interval; CR: complete response; CRC: colorectal cancer; CT: carboplatin/ paclitaxel; CTGF: connective tissue growth factor; CXCL12: C-X-C motif chemokine ligand 12; CXCR4: C-X-C chemokine receptor type 4; ECM: extracellular matrix; FAK: focal adhesion kinase; FGFR: fibroblast growth factor receptor; FGFR2/3: fibroblast growth factor receptors 2 and 3; FOLFIRI: 5-fluorouracil, leucovorin, and irinotecan; FOLFIRINOX: fluorouracil, leucovorin, irinotecan, and oxaliplatin; GC: gastric cancer; GEJ: gastroesophageal junction; HA: hyaluronan; HER2: human epidermal growth factor receptor 2; HR: hazard ratio; HR+: hormonal receptor-positive; IL-6R: interleukin-6 receptor; JAK1/JAK2: Janus kinases 1 and 2; LGSOC: low-grade serous ovarian cancer; LIF: leukemia inhibitory factor; LOXL2: lysyl oxidase-like 2; mFOLFIRINOX: modified FOLFIRINOX; mFOLFOX6: modified oxaliplatin, leucovorin, and fluorouracil; MMP-9: matrix metalloproteinase-9; mt: mutant; NCT: ClinicalTrials.gov identifier; NMIBC: non-muscle-invasive bladder cancer; NSCLC: non-small cell lung cancer; OC: ovarian cancer; OR: odds ratio; ORR: objective response rate; OS: overall survival; PDAC: pancreatic ductal adenocarcinoma; PEGPH20: pegvorhyaluronidase alfa; PFS: progression-free survival; PR: partial response; RT: radiotherapy; SD: stable disease; TGF-βR1: transforming growth factor-β receptor type 1; TME: tumor microenvironment; UC: uterine cancer; wt: wild type

ECM-targeting agents

ECM is a complex and dynamic network primarily composed of structural proteins (e.g., collagen and elastin), glycoproteins (e.g., fibronectin and laminin), and proteoglycans (e.g., aggrecan, perlecan, and hyaluronic acid (HA)) that provides mechanical support and mediates intercellular communication with surrounding cells [75, 76]. In cancer, CAFs disrupt the balance between ECM production and degradation by overproducing matrix components, thereby enhancing ECM deposition [77, 78]. For instance, CAFs express enzymes such as lysyl oxidase (LOX), which crosslink collagen fibers and markedly increase matrix stiffness [79, 80]. CAFs also secrete enzymes like matrix metalloproteinases (MMPs) and other proteases that cleave the ECM, which can enhance drug penetration but may also facilitate immune escape and metastasis of cancer cells from the primary site [77, 80]. Collectively, these CAF-driven alterations are closely associated with malignant progression and drug resistance.

PEGPH20, which degrades HA, has been among the most extensively investigated ECM-targeting enzymes. Despite promising preclinical findings, PEGPH20 has failed to demonstrate efficacy across multiple clinical trials [81, 82]. In a phase Ib/II trial involving metastatic pancreatic ductal adenocarcinoma (PDAC), PEGPH20 combined with modified fluorouracil, leucovorin, irinotecan, and oxaliplatin (mFOLFIRINOX) was associated with shorter overall survival (OS) and increased toxicity compared to mFOLFIRINOX alone (OS: 7.7 vs. 14.4 months) [82]. More recently, a phase II single-arm trial revealed that PEGPH20 plus pembrolizumab did not improve clinical outcomes in PDAC [83]. Despite promise in early-stage trials, PEGPH20 plus nab-paclitaxel/gemcitabine also failed to demonstrate clinical benefits in the phase III trial [84–86].

The unsuccessful clinical development of PEGPH20 is probably multifactorial. The clinical development of PEGPH20 is largely driven by preliminary efficacy in small or exploratory HA-high subgroups. In a phase Ib study, only six patients with HA-high tumors were evaluated for biomarker-associated outcomes [84]. In the phase II trial (HALO202), an exploratory analysis of the HA-high subgroup showed that median OS in the PEGPH20 plus nab-paclitaxel/gemcitabine group was 11.5 months compared with 8.5 months in chemotherapy alone, with a hazard ratio (HR) of 0.96 [85]. These results suggest that the early signals may have overestimated the predictive utility of HA-high status for selecting patients likely to benefit from PEGPH20. The subsequent phase III trial defined HA-high tumors as ECM HA staining covering at least 50% of the tumor surface at any intensity before treatment [86]. However, HA distribution varied substantially between primary and metastatic lesions and over the course of treatment. Therefore, a single biopsy may not represent the stromal biology of all clinically relevant lesions. Additionally, PEGPH20 plus nab-paclitaxel/gemcitabine was associated with a higher toxicity burden than placebo plus nab-paclitaxel/gemcitabine, particularly for musculoskeletal adverse events, occurring in 75.4% versus 46.2% of patients for any grade and 13.2% versus 5.1% for grade ≥ 3 events. Patients receiving PEGPH20 also experienced a higher incidence of infections (21.8% vs. 12.8%) and grade ≥ 3 bleeding events (4.6% vs. 1.9%) [86]. These adverse events may necessitate dose reductions or treatment discontinuation, potentially compromising treatment delivery and contributing to unfavorable clinical outcomes.

Other matrix-degrading or matrix-remodeling strategies have also shown limited clinical translatability. Collagenase has been shown to improve drug penetration in preclinical studies; however, its effects are cargo- and context-dependent, and its application for ECM modulation remains primarily experimental [87, 88]. Simtuzumab, an antibody against LOXL2, did not improve clinical outcomes when combined with chemotherapy in phase II trials involving colorectal cancer (CRC) and PDAC [89, 90]. The MMP inhibitor prinomastat failed in a phase III clinical trial in the early 2000s [91, 92], while andecaliximab did not improve overall response rate (ORR), progression-free survival (PFS), and OS in phase II and III trials [93, 94]. One potential explanation is that MMPs are thought to act primarily during early cancer progression, driving local invasion and micrometastasis, and may become less therapeutically relevant after macroscopic metastases are established [91]. This disease-stage mismatch may partly explain their lack of efficacy in advanced cancer. Furthermore, MMP inhibitors can cause severe musculoskeletal pain and stiffness, which frequently result in high rates of treatment discontinuation [92].

Theoretically, ECM-targeting agents can improve drug penetration by reducing physical stromal barriers. However, their limited clinical efficacy suggests that matrix remodeling alone is insufficient to improve therapeutic outcomes. Several factors may account for this discrepancy. First, individual ECM-targeting agents generally act on a single matrix component and may therefore fail to dismantle the complex, multilayered stromal barrier (Table 3). Second, limited tumor selectivity could result in systemic toxicity and may lead to subsequent dose reduction and treatment discontinuation, thereby substantially restricting their clinical utility [86, 92]. Translation is further complicated by the spatial and temporal heterogeneity of ECM composition arising from the context-dependent functions of CAFs. Moreover, although early-phase studies demonstrated intratumoral HA depletion in small paired-biopsy subsets, serial assessment of ECM remodeling was limited in later clinical trials, making it difficult to determine the magnitude and durability of intratumoral pharmacodynamic effects [86]. Third, ECM-targeting agents have predominantly been evaluated in patients with advanced and metastatic cancers, including recurrent or refractory cancers (Table 3). In such settings, enhanced drug penetration may not necessarily translate into tumor regression when cancer cells remain resistant to the co-administered therapy. Finally, excessive ECM degradation may also disrupt tumor-restraining matrix functions and, in some contexts, facilitate cancer invasion or metastasis [63, 64]. Collectively, these factors highlight the need to optimize biomarker-guided patient selection and more selective or tumor-responsive delivery strategies.

Inhibitors of CAF-activation receptors and intracellular signaling

The activation of CAFs is orchestrated by the signaling network initiated by soluble mediators, growth factors, and mechanical cues within the TME [15]. Although many of these pathways are triggered by extracellular ligands, this section focuses specifically on therapeutic agents that inhibit their cognate receptors or downstream intracellular signaling components. Transforming growth factor-β (TGF-β) is one of the main factors that drive fibroblast transformation into myofibroblasts, and therefore represents a crucial target in CAF-targeting strategies [5, 95, 96]. Upon binding to TGF-βR on fibroblasts, this signaling cascade triggers the upregulation of α-SMA and FAP, promoting the acquisition of a pro-tumorigenic, myofibroblastic phenotype [95, 96]. CAF-derived CXCL12 can bind to the CXCR4 on CAFs, establishing an autocrine amplification loop that sustains CAF activation [97]. In addition, increased ECM stiffness triggers integrin/FAK and Hippo–YAP/TAZ (YAP: Yes-associated protein; TAZ: transcriptional coactivator with a PDZ-binding motif) signaling pathways, promoting CAF proliferation and resistance to apoptosis and thereby further increasing matrix stiffness [96]. Inflammatory cytokines such as interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor α (TNF-α) activate CAFs via nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling, while growth factors, including hepatocyte growth factor (HGF) and fibroblast growth factor (FGF), activate the MAPK/PI3K/AKT (MAPK: mitogen-activated protein kinase; PI3K: phosphoinositide 3-kinase; AKT: protein kinase B) pathways [96]. Sonic hedgehog (Shh), secreted by cancer cells and other cells, activates CAFs through Hh signaling. Hh signaling is enriched in myCAFs relative to iCAFs, and its inhibition can shift the CAF composition from myCAF-like toward iCAF-like states [98]. Moreover, the Hh pathway exhibits synergistic interactions with the TGF-β pathway [99]. Collectively, these receptor-mediated and intracellular-signaling pathways provide multiple therapeutic entry points for suppressing tumor-promoting CAF functions.

Galunisertib, a transforming growth factor-β receptor type 1 (TGF-βR1) inhibitor, has demonstrated preliminary clinical activity in combination regimens. In a phase Ib study of uterine or ovarian carcinosarcoma, galunisertib plus carboplatin/paclitaxel achieved partial responses in 31% of patients with measurable disease, with median PFS and OS of 6.6 and 18.9 months, respectively [100]. In a phase II cohort of recurrent or refractory non-small cell lung cancer (NSCLC), galunisertib plus nivolumab produced confirmed partial responses in 24% of patients, with median PFS and OS of 5.26 and 11.99 months, respectively [101]. The CXCR4 inhibitor balixafortide showed promising results in phase I/II clinical trials; however, in a phase III trial of metastatic breast cancer, balixafortide plus eribulin did not outperform eribulin alone, with median PFS of 3.5 versus 4.0 months and OS of 11.0 versus 11.2 months, respectively [102]. Similarly, the Janus kinase 1/2 (JAK1/JAK2) inhibitor ruxolitinib combined with capecitabine failed to improve clinical outcomes in advanced or metastatic PDAC [103]. By contrast, the fibroblast growth factor receptor (FGFR) inhibitor erdafitinib combined with cetrelimab significantly improved ORR and OS in a phase II trial involving patients with metastatic urothelial carcinoma [104]. In a phase Ib study involving three metastatic breast cancer patients, one patient had a partial response for more than 2.5 years [105]. Erdafitinib is also under investigation in phase II trials for FGFR-altered non-muscle-invasive bladder cancer and cholangiocarcinoma [106, 107]. Tocilizumab, an antibody against the interleukin-6 receptor (IL-6R), failed to improve 6- and 18-month OS when added to gemcitabine and paclitaxel in a phase II trial [108]. Additionally, in another phase II trial, tocilizumab combined with ipilimumab, nivolumab, and radiotherapy did not meet the prespecified criteria for expansion to full accrual [109]. Hedgehog inhibitor vismodegib combined with nab-paclitaxel/gemcitabine in metastatic PDAC achieved a median PFS of 5.42 months and OS of 9.79 months in a phase II study; however, no significant improvement was observed compared with historical outcomes [110]. In contrast, vismodegib plus radiotherapy in basal cell carcinoma reported favorable long-term disease control, with 1-year PFS and OS rates of 100% and 96%, respectively, and 5-year PFS and OS rates of 78% and 83% [111].

FAK acts as a mechanosensor and signal transducer, converting physical pressure from the ECM into biochemical signals that modulate tumor biology [112]. FAK signaling can promote fibronectin deposition and activate CAFs and macrophages, reinforcing physical and immunosuppressive barriers within the TME. Recently, FAK inhibitor defactinib failed to demonstrate efficacy in Kirsten rat sarcoma viral oncogene homolog (KRAS)-mutant NSCLC and pleural mesothelioma [113, 114]. By contrast, its combination with the RAF/MEK (rapidly accelerated fibrosarcoma/mitogen-activated protein kinase kinase) inhibitor avutometinib was approved for the treatment of KRAS-mutant, recurrent low-grade serous ovarian cancer (LGSOC) [115]. In the phase II RAMP 201 trial, the combination achieved an ORR of 31% compared with 17% for avutometinib monotherapy in LGSOC [116]. Notably, the ORR was 44% in patients with KRAS-mutant tumors but 17% in those with KRAS-wild-type tumors; the corresponding PFS was 22.0 and 12.8 months, respectively. The combination has subsequently advanced to the randomized phase III RAMP 301 trial, in which it is being compared with investigator’s choice of therapy [117]. A phase I trial of this combination is also ongoing in other solid tumors [118]. In addition, defactinib combined with pembrolizumab and gemcitabine has also shown promising results in PDAC [119], and the combination of defactinib and radiotherapy in a phase II trial has completed enrollment [120]. These results indicate promising clinical activity of combined RAF/MEK and FAK inhibition, particularly in KRAS-mutant LGSOC, although its efficacy in other solid tumors requires further supportive evidence.

The variable clinical performance of signaling inhibitors likely reflects the redundancy and interconnectivity of CAF-activating pathways. Accordingly, inhibition of a single receptor or kinase may be bypassed by compensatory signaling, leading to limited efficacy or rapid therapeutic resistance [15]. Furthermore, many of these targets are expressed by cancer cells and other stromal populations, making it difficult to attribute clinical benefits specifically to CAF modulation. Notably, the greater activity observed in selected combination regimens supports the development of biomarker-guided, mechanism-based combinations rather than single-pathway inhibition.

Neutralizing agents targeting CAF-related soluble mediators

CAF activation is typically accompanied by the secretion of various soluble mediators, which interact with cancer cells and immune cells through paracrine or juxtacrine signaling, thereby contributing to the establishment of a tumor-promoting TME [96, 121, 122]. Unlike the therapeutics discussed in Sect. Inhibitors of CAF-activation receptors and intracellular signaling, the agents considered here act directly on extracellular soluble mediators by neutralizing the ligand itself before or during receptor engagement.

Several agents targeting CAF-related soluble mediators have entered clinical evaluation. A phase I clinical trial of MSC-1 (AZD0171), a monoclonal antibody against LIF, in patients with solid tumors reported a PFS of 5.9 weeks, with 23.7% of patients achieving PFS longer than 16 weeks [123]. The phase II trial of MSC-1 is ongoing [124]. The CXCL12 inhibitor NOX-A12, when combined with radiotherapy and bevacizumab, extended PFS from 5.7 to 9.1 months and OS from 12.7 to 19.9 months in newly diagnosed glioblastoma [125]. Notably, the combination of NOX-A12 and pembrolizumab has demonstrated a favorable safety profile in advanced PDAC and CRC, supporting further investigation of this synergistic strategy in later-phase clinical trials [126]. The CTGF inhibitor pamrevlumab achieved an OS of 17.25 months in a phase III trial; however, this was not statistically significant compared with 17.94 months in the control group [127].

Neutralizing agents may offer greater molecular specificity than broad signaling inhibitors. However, similar to signaling inhibitors, blockade of a single ligand may be insufficient because CAFs secrete multiple overlapping and compensatory mediators. Moreover, CAF-derived cytokines may also exert context-dependent effects on surrounding compartments, reflecting the functional heterogeneity of CAFs themselves [34]. Their clinical application may therefore require biomarker-defined evidence of pathway dependency and rational combinations that simultaneously address parallel immunosuppressive mechanisms.

CAF reprogramming agents

Given the context-dependent roles of CAFs within the TME, CAF reprogramming represents a conceptually rational alternative to indiscriminate stromal depletion and therefore has attracted increasing research interest [95, 128]. To date, only a limited number of small-molecule agents have been explored clinically for this purpose, including all-trans retinoic acid [129, 130], the vitamin D receptor agonist paricalcitol [131], and the angiotensin receptor blocker losartan [132]. These agents function primarily by normalizing activated stellate cells and associated stromal functions but have not yet demonstrated convincing clinical efficacy [130, 132]. Of note, their clinical effects remain difficult to interpret because CAF phenotypic conversion has rarely been directly confirmed or monitored longitudinally. Moreover, CAF reprogramming may be incomplete or reversible owing to CAF plasticity and persistent tumor-derived signals.

FAP-targeted bispecific and immunomodulatory biologics

In addition to conventional therapeutics mentioned above, FAP has increasingly been exploited as a stromal anchor for immune-engaging and immunostimulatory biologics. These agents generally combine an FAP-binding moiety with an immune-effector or immunostimulatory component, thereby coupling stromal recognition with localized immune activation within the TME [133].

FAP–CD3 T-cell engagers are designed to bind FAP on CAFs and CD3ε on T cells, thereby redirecting endogenous T cells toward FAP-expressing stromal cells and inducing local immune activation and stromal depletion [134, 135]. Recent preclinical evidence showed that FAP–CD3 T-cell engager treatment depleted FAP-positive CAFs, but was accompanied by the emergence of a mixed CAF population, potentially limiting sustained therapeutic efficacy [136]. Moreover, this treatment was associated with T-cell exhaustion, and the addition of PD-1 blockade did not fully overcome resistance.

FAP-targeted costimulatory agonists have progressed further into clinical evaluation. RO7122290, an FAP–4-1BB agonist, was evaluated as monotherapy and in combination with atezolizumab in patients with advanced solid tumors [137]. This treatment induced pharmacodynamic T-cell activation and demonstrated preliminary antitumor activity, although cytokine release syndrome, pneumonitis, and other immune-related toxicities were observed. A phase Ib trial evaluated RO7122290 in combination with carcinoembryonic antigen (CEA)-CD3 T-cell engager cibisatamab in patients with heavily pretreated microsatellite-stable metastatic colorectal cancer [138]. The results showed that 13.5% of patients had partial responses. Other FAP–4-1BB agonists, including the FAP–4-1BB bispecific antibody BI 765179, have also entered early-phase clinical evaluation [139].

FAP–CD40 agonists represent another strategy to localize CD40-mediated immune activation. RO7300490 was evaluated in a first-in-human phase I study in patients with advanced solid tumors [140]. Despite evidence of tumor uptake and pharmacodynamic immune activation, no objective responses were observed among 73 efficacy-evaluable patients, indicating limited antitumor activity in this population. Moreover, MP0317, an FAP–CD40 Designed Ankyrin Repeat Protein (DARPin), similarly induced localized immune activation, but showed limited antitumor activity in a phase I study, with one partial response and stable disease in 14 of 46 patients [141]. Simlukafusp alfa (FAP-IL2v), an FAP-targeted interleukin-2 (IL-2) variant immunocytokine, induced expansion of natural killer (NK) cells and CD8 + T cells in patients with advanced or metastatic solid tumors but achieved an ORR of only 5.1% in a phase I study [142].

Collectively, these studies demonstrate the feasibility of exploiting FAP to localize immune activation, although clinical responses remain heterogeneous [138, 141]. Importantly, FAP-directed immune engagement does not necessarily result in sustained antitumor immunity, as T-cell exhaustion and adaptive stromal remodeling have been observed following FAP–CD3 treatment [136]. Moreover, FAP expression does not reliably distinguish tumor-promoting from tumor-restraining CAF states, raising the possibility that indiscriminate targeting of FAP-positive stromal populations may disrupt beneficial stromal functions and induce on-target/off-tumor toxicity [15]. These limitations underscore the need to identify tumors with therapeutically relevant FAP-positive stroma and to integrate biomarker-guided patient selection and rational tumor-directed combination strategies.

Comparative analysis of CAF-targeting therapeutic classes

Pharmacological and biologic CAF-targeting therapeutics differ in their mechanisms of action and molecular specificity. Currently, no single CAF-targeting therapeutic has consistently produced durable clinical benefit across tumor types (Table 3). ECM-targeting agents provide the most direct approach to overcoming physical barriers but carry a relatively high risk of nonspecific stromal disruption [63, 64]. Signaling inhibition may trigger side effects by regulating multiple unintended downstream processes, whereas cytokine neutralization may provide greater molecular specificity than signaling inhibition; however, both approaches remain vulnerable to pathway redundancy and the pleiotropic effects of their targets across malignant, immune, and stromal compartments [15, 34]. CAF reprogramming may better preserve stromal integrity than depletion-based approaches, but its durable therapeutic benefit remains to be established clinically [34]. FAP-targeted bispecific and immunomodulatory biologics use FAP-positive stroma to localize immune activation, but their efficacy may be limited by heterogeneous FAP expression, insufficient discrimination between functionally distinct CAF states, and adaptive immune or stromal responses [136]. Collectively, these limitations illustrate the difficulty of achieving durable therapeutic modulation of the CAF compartment through a single molecular or stromal intervention.

Challenges for clinical translation of CAF-targeting therapeutics

Despite encouraging preclinical findings, the clinical translation of CAF-targeting therapeutics remains limited. This discrepancy has prompted us to re-evaluate these approaches at both patient-selection and molecular-mechanistic levels. Several interconnected factors have been identified (Fig. 1).

CAF heterogeneity and plasticity: The diverse origins and functional heterogeneity of CAFs may reduce targeting precision and lead to variable or even opposing therapeutic effects [34, 63, 64]. Although CAF-directed approaches are intended to enhance sensitivity to antitumor treatment, cancer treatment does not simply kill tumor cells; rather, it “hijacks” CAFs within the TME by altering the CAF secretome. Consequently, CAFs can transition to tumor-promoting states and facilitate tumor invasion and metastasis [34].
Patient-selection and biomarker limitations: Most CAF-targeting agents are directed against a single CAF-related factor, while patient selection is primarily based on clinicopathological characteristics rather than on specific CAF-related biomarkers. This lack of biomarker-guided selection may lead to a mismatch between enrolled patients and therapeutic target. Although some clinical trials assessed patients using available biomarkers (such as HA), their assessment was based on pretreatment samples and may not reflect the dynamic changes during cancer treatment [86]. Individual biomarkers capture only selected aspects of CAF biology and may not adequately represent spatial and temporal CAF heterogeneity or predict therapeutic response [86]. Notably, emerging circulating biomarkers, including soluble FAP, FAP-positive circulating CAFs, and FAP-positive extracellular vesicles (EVs), may offer minimally invasive approaches for longitudinal assessment of stromal alterations [143, 144]. In addition, imaging-based approaches may complement tissue biomarkers by capturing interlesional stromal heterogeneity. FAP-targeted positron emission tomography/computed tomography (PET/CT) has already been used to identify patients with sufficient FAP-positive tumor uptake for FAP-directed radiopharmaceutical therapy, while emerging radiomic approaches may enable noninvasive characterization of desmoplastic or CAF-associated stromal states [145, 146]. At the tissue level, transcriptional and spatial CAF signatures derived from single-cell and spatial profiling can distinguish functionally divergent CAF programs and may provide greater biological resolution than individual markers such as FAP or α-SMA [147]. These approaches may facilitate biomarker-guided selection for CAF-directed therapies, but prospective validation against treatment response will be required.
Preclinical–clinical mismatch and combination-treatment dependency: CAF-targeting strategies have demonstrated efficacy predominantly in treatment-naïve models of localized primary tumors, whereas clinical trials have frequently enrolled patients with advanced and recurrent cancers who have received multiple prior therapies (Table 3). Notably, these conventional subcutaneous and orthotopic models do not adequately reproduce metastatic progression, treatment-induced stromal evolution, multidrug resistance, or the spatial and functional heterogeneity between primary and metastatic lesions [4]. This mismatch may overestimate preclinical efficacy and contribute to the limited clinical performance of CAF-targeting agents. Moreover, CAF modulation generally serves as an adjunctive strategy, and its therapeutic benefit depends on the activity of the co-administered anticancer treatment. Improved stromal accessibility may therefore fail to induce tumor regression when malignant cells are already resistant to the combination partner.
Insufficient tumor penetration and systemic toxicity: Dense CAF-derived ECM and associated vascular compression can impair tumor perfusion and limit the intratumoral delivery of systemically administered CAF-targeting therapeutics, particularly in highly desmoplastic tumors [148, 149]. Consequently, tumor exposure may remain subtherapeutic, whereas off-target exposure in normal tissues can increase toxicity and necessitate dose reduction or treatment discontinuation.
Signaling redundancy and pathway compensation: CAF phenotypes are regulated by interconnected and partially redundant signaling networks, while most agents target a single ECM component, protein, or signaling pathway (Table 3). This inherent redundancy limits their capacity to manage the highly complex and dynamic TME, particularly in advanced and metastatic cancer. These factors render CAF-targeting approaches particularly challenging in real-world settings and have spurred the development of precision multifunctional platforms capable of concurrently targeting CAFs and eradicating tumor cells.

CAF-targeting NP platforms

As previously discussed, the limited clinical performance of CAF-targeting therapeutics has been attributed partly to their insufficient tumor exposure, off-target cytotoxicity, and a relatively singular mechanism of action. NPs offer the potential to integrate CAF modulation with direct antitumor activity through co-delivery, co-targeting, and stimulus-responsive drug release [61, 62]. Currently, CAF-targeting NPs, constructed from polymers, metals, non-metallic inorganic materials, proteins, and cell-derived components, have been developed to deplete or reprogram CAFs. Because CAF-targeting NPs are frequently multifunctional and may combine several therapeutic mechanisms, a strictly mutually exclusive classification is difficult. For organizational purposes and to facilitate comparison with conventional CAF-targeting therapeutics and biologically inspired or living platforms, we group representative NPs according to their dominant therapeutic cargo or functional component into three categories: (1) anti-ECM antitumor NPs, (2) nucleic acid-based antitumor NPs, and (3) metal-based multifunctional NPs. In addition to summarizing their design rationales and preclinical efficacy, we critically evaluate their physicochemical constraints, safety concerns, manufacturing requirements, and potential for clinical translation. Table 4 summarizes some representative examples of these NPs, along with the corresponding mechanisms of action and translational challenges (Fig. 2).

Table 4.

Representative CAF-targeting nanoparticles and their mechanisms of action

Anti-ECM antitumor NPs

Anti-ECM antitumor NPs integrate CAF-directed therapeutics with antitumor agents and represent one of the most extensively investigated CAF-targeting strategies in nanomedicine. For example, Liu et al. developed a sequential treatment protocol involving an α-mangostin-loaded chitosan-based thermosensitive hydrogel followed by administration of glutathione (GSH)-responsive doxorubicin (DOX)-loaded NPs (NPc/DOX) [65]. The hydrogel enables sustained local release of α-mangostin, which reprogrammed activated CAFs toward a quiescent state through the TGF-β/Smad signaling pathway. The resulting stromal remodeling enhanced the penetration and accumulation of separately administered NPc/DOX, thereby improving therapeutic efficacy in CAF-rich breast cancer. Theivendran et al. employed a similar strategy by loading antifibrotic drug pioglitazone into GSH-responsive dendritic mesoporous organosilica to reprogram CAFs, followed by DOX treatment [150]. Moreover, other CAF-modulating agents—such as the NADPH oxidase 4 (NOX4) inhibitor digoxin, tyrosine kinase inhibitor dasatinib, or navitoclax—have likewise been incorporated into CAF-targeting NPs in combination with DOX, epirubicin, or anti-programmed cell death protein 1 (anti-PD-1) inhibitor, based on the shared rationale of combining ECM modulation with direct tumor killing [151–154].

Metabolic reprogramming of CAFs has also been incorporated into multifunctional nanosystems. Liu et al. constructed OEMH NPs using HA-modified Materials of Institut Lavoisier-100 (MIL-100) that was co-loaded with epigallocatechin gallate (EGCG) and oxaliplatin [155]. OEMH NPs could target CRC–CAF crosstalk by recognizing the CD44 receptor and promote intratumoral drug penetration following HA-mediated ECM remodeling. EGCG suppressed free fatty acids produced by CAFs, thereby reducing metabolic support for cancer cells and reversing oxaliplatin resistance. Moreover, EGCG could inhibit the transformation of hepatic stellate cells into CAFs in the liver and downregulate vascular endothelial growth factor (VEGF)/interleukin-11 (IL-11)/angiogenin (ANG), thereby contributing to vascular normalization and suppression of liver metastasis in CRC. Similarly, Yu et al. co-assembled ATP-citrate lyase inhibitor NDI-091143 with either DOX or paclitaxel to disrupt fatty-acid metabolism in CAFs, thereby remodeling the TME and potentiating chemotherapy [156].

Although most CAF-targeting nanosystems aim to degrade or soften the ECM, matrix reinforcement has also been explored as a means of limiting tumor dissemination. Cui et al. designed arginine-glycine-aspartic acid (RGD)-modified liposomal NPs co-loaded with DOX and genipin (RGD-LNP-DG) [157]. The RGD peptide facilitated targeting of integrin αvβ3⁺ cancer cells, whereas genipin crosslinked collagen fibers to reinforce the ECM; this reinforcement effectively inhibited lung metastasis. However, because collagen crosslinking may also reconstruct a physical barrier to drug transport, the durability and broader therapeutic consequences of this strategy require further evaluation.

Hypoxia-responsive platforms provide another approach to integrating ECM modulation with antitumor therapy. Chen et al. developed multifunctional ECMT nanoscavengers through the self-assembly of the digestive enzyme chymotrypsin, catalase, calcium peroxide NPs, and the photosensitizer chlorin e6 (Ce6) [158]. These nanoscavengers achieve efficient accumulation in melanoma through a dual mechanism: fibrin–fibronectin complex-targeting peptide-mediated ECM recognition and chymotrypsin-mediated ECM degradation. Under acidic conditions within the TME, the calcium peroxide particles released Ca²⁺ and O₂, while catalase further enhanced O₂ production. The resulting increase in O₂ alleviated the hypoxia in the TME and improved Ce6-mediated generation of reactive oxygen species (ROS) during photodynamic therapy. High levels of Ca²⁺ and ROS induced cancer-cell apoptosis and ICD and promoted deep infiltration of CD8⁺ T cells, thereby inhibiting abscopal tumor growth and lung metastasis. Zhao et al. developed a smart lipid nanoprobe, termed HMME-RG3@PFH, in which ginsenoside RG3 (RG3) was incorporated into the lipid membrane, perfluorohexane (PFH) was encapsulated in the core, and hematoporphyrin monomethyl ether (HMME) was loaded into the lipid bilayer [159]. RG3 promoted CAF uptake under hypoxic conditions by targeting glucose transporter protein 1 on CAFs. Under low-intensity focused ultrasound, PFH enabled ultrasound imaging, while HMME produced ROS to eliminate CAFs. Concurrently, the increased exposure of RG3 moieties promoted the conversion of CAFs to a quiescent state. Notably, this nanoprobe enabled image-guided drug release and synergistic therapy, offering a new approach for early diagnosis and treatment of CAF-related diseases.

Although CAF-targeting nanomedicines have been investigated predominantly in solid tumors, this strategy has also been explored in hematological malignancies. In multiple myeloma, Wang et al. developed paclitaxel-loaded PEG–PLA NPs modified with a cyclic peptide targeting PDGFR-β, which is expressed by both myeloma cells and CAFs [160]. The resulting dual-targeting NPs showed enhanced uptake by both cell populations and greater antimyeloma activity than untargeted NPs or free paclitaxel in preclinical models, illustrating the potential of simultaneously targeting malignant cells and their supportive stromal compartment in hematological cancers.

To date, no anti-ECM antitumor NPs have entered clinical trials. These platforms offer the conceptual advantage of integrating stromal modulation and direct tumor-cell killing within a single delivery system, using CAF-directed therapeutics and antitumor agents [61, 62]. Nevertheless, their efficacy has been demonstrated predominantly in murine models with localized primary tumors, which do not adequately capture spatial CAF heterogeneity and drug resistance in metastatic and recurrent cancer (Table 4). It therefore remains uncertain whether this combinatorial approach can perform effectively in complex, real-world settings. Although these NP-based approaches offer improved targeting precision and tumor accumulation relative to pharmacological and biologic CAF-targeting therapeutics, they do not fully eliminate limitations, such as insufficient selectivity and pathway redundancy.

Nucleic acid-based antitumor NPs

Nucleic acid-based antitumor NPs can deliver plasmids, small interfering RNA (siRNA), clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 components, miRNAs, or miRNA sponges to CAFs [66, 161, 162]. Although these cargoes act through distinct molecular mechanisms, they share the capacity to alter gene expression in CAFs and reprogram CAF states, thereby disrupting tumor-promoting CAF–cancer-cell interactions and enhancing antitumor effects.

Several nucleic acid-based platforms have been developed to modulate CAF-activation pathways or CAF-derived cytokine signaling. Chang et al. constructed interleukin-15 (IL-15) plasmid-loaded, FAP-sensitive MgCa/z-Gly-Pro-pamidronate acid NPs, termed PN/MCG NPs [66]. In a 4T1 murine breast cancer model, the NPs specifically recognized FAP⁺αSMA⁺ CAFs, converting them into an FAP^–αSMA^– phenotype and thereby remodeling the TME from a tumor-promoting to a tumor-suppressive state. Concurrently, the nanocarriers delivered the IL-15 gene to CAFs, enabling sustained local IL-15 expression that supported T-cell function and limited regulatory T cell (Treg) expansion, while reducing systemic toxicity. When combined with PD-1 blockade, this system demonstrated marked efficacy in inhibiting tumor progression and metastasis. Notably, PN/MCG NPs have also been shown to engineer FAP⁺αSMA⁺ human mammary fibroblasts in vitro, indicating their potential for clinical translation. Similarly, Zhang et al. developed poly-γ-glutamic acid (PGA)/PolyMet-pRLN NPs, which could encode relaxin in CAFs [163]. The overexpression of relaxin improved TGF-β-induced CAF activation and remodeled the TME, resulting in a 2.2-fold increase in cytotoxic T cell infiltration within the tumor and promoting antitumor effects. Lang et al. developed a peptide NP (PNP)-based nanosystem carrying CXCL12-silencing siRNA (siCXCL12) and an FAP-α antibody, termed PNP/siCXCL12/mAb [162]. Through FAP-α recognition, this nanosystem selectively delivered siCXCL12 to CAFs, leading to CXCL12 silencing and CAF inactivation. Consequently, CAF–cancer cell crosstalk mediated by the CXCL12/CXCR4 axis was disrupted, thereby remodeling the tumor microenvironment in prostate cancer. Zhang et al. designed a lipid NP (LNP)-based drug (termed siFAK/CRISPR-PD-L1-LNP), which could simultaneously suppress FAK and disrupt programmed death ligand 1 (PD-L1) expression [164]. Through FAK-mediated reduction of ECM stiffness, gene-editing efficiency was enhanced by more than 10-fold. Concurrently, PD-L1 downregulation potentiated antitumor immune responses. Together, these mechanisms establish a new synergistic approach for cancer treatment.

Nucleic acid delivery has also been employed to transform CAFs into apCAFs. Qiu et al. [165] developed PF9PEI@αPC NPs based on their findings that the upregulation of programmed death-ligand 2 (PD-L2) in CAFs promoted T cell exhaustion and contributed to breast cancer recurrence following adjuvant radiotherapy. This nanodrug delivered an αPD-L1-P2A-CD86 plasmid to PD-L2-expressing CAFs. Following transfection, the engineered CAFs expressed anti-PD-L1 antibodies and the costimulatory molecule CD86, thereby acquiring antigen-presenting cell-like properties and reactivating tumor-specific T-cell immunity. When combined with radiotherapy, this strategy generated central memory T cells capable of eradicating metastatic tumors and preventing recurrence. Additionally, the local production of anti-PD-L1 antibodies could suppress immune escape and further enhance the antitumor response. Similarly, Geng et al. [166] developed a spatiotemporally photo-controlled nanosystem (TNP@CS-A/plasmid(pDNA)) that engineers CAFs to overexpress CD86 and the PD-L1 trap protein. In a highly fibrotic breast cancer model, this strategy elicited a fourfold increase in CD8⁺ T cell infiltration, leading to approximately 85% tumor growth inhibition and an 83.3% survival rate at 60 days. Moreover, the nanosystem established long-term immune memory and provided durable protection against lung metastasis.

MicroRNA (miRNA)-based nanoplatforms represent another class of nucleic acid-based NPs and have been developed to modulate CAF-associated signaling and treatment resistance. For example, Zhou et al. developed IEVs-PFD/138—a BMSC-derived EV platform loaded with miR-138-5p and PFD, and modified with integrin α5-targeting peptide for CAF homing [167]. Mechanistically, miR-138-5p inhibited TGF-β-mediated CAF activation by disrupting the fermitin family member 2 (FERMT2)–TGFBR1 complex and reduced proline-dependent collagen synthesis by targeting the FERMT2–pyrroline-5-carboxylate reductase 1 (PYCR1) complex. Together with the antifibrotic activity of PFD, these effects synergistically reprogrammed CAFs and alleviated TME fibrosis, ultimately enhancing gemcitabine penetration through the ECM and chemotherapeutic response in preclinical models. In another study, Sun et al. found that CD63⁺ CAF-derived miR-20 could bind to and suppress retinoblastoma (RB1) mRNA, thereby conferring resistance to the cyclin-dependent kinase 4/6 (CDK4/6) inhibitors in breast cancer [168]. To counteract this mechanism, they developed cyclic RGD (cRGD)-modified miR-20 sponge NPs that deliver miRNA sponges to cancer cells through tumor-tissue targeting marker αvβ3. These NPs successfully restored RB1 and reversed the drug resistance of CDK4/6 inhibitors in breast cancer.

Currently, nucleic acid-based antitumor NPs remain at the preclinical stage. Their clinical translation is constrained by both shared delivery barriers and cargo-specific safety concerns. For genome-editing systems, unintended alterations in non-CAF populations within the TME or normal tissues remain a particular concern because permanent genomic changes are difficult to reverse, underscoring the need for rigorous cell-type-resolved assessment of editing specificity. By contrast, siRNA- and miRNA-based approaches generally provide transient gene regulation and may therefore reduce risks associated with permanent genome editing [165, 166]. Nevertheless, efficient intracellular delivery remains a common challenge across nucleic acid platforms, as inadequate endosomal escape can lead to lysosomal degradation or intracellular sequestration, resulting in incomplete or heterogeneous gene modulation [169, 170]. Immune responses to nucleic acid cargoes, gene-editing proteins, or carrier materials may further limit repeated administration [171, 172]. miRNA-based approaches introduce additional challenges because individual miRNAs can regulate multiple intended and unintended transcripts, whereas inhibition of a single miRNA or miRNA family may be bypassed through compensatory regulatory networks [173]. Moreover, synthetic miRNA sponges require careful sequence and structural optimization to maintain multiple stable and functional binding sites [174–176]. Collectively, improved targeting specificity, intracellular delivery, safety, and cargo stability will be required before CAF-targeting nucleic acid-based nanomedicines can advance toward clinical translation.

Metal-based multifunctional NPs

Metal-based multifunctional NPs exert excellent cytotoxic and antitumor effects, with gold NPs (AuNPs) being extensively investigated in CAF-targeting research [177]. Quiescent fibroblasts exhibit high lipid content and low proliferative capacity, whereas CAF activation is associated with lipid depletion and acquisition of a highly proliferative myofibroblast-like phenotype. AuNPs may partially reverse this transition by upregulating lipogenesis-related genes, including FASN, SREBP2, and FABP3, thereby reprogramming CAFs [178]. AuNPs have also been shown to reduce CAF density and ECM production by suppressing profibrotic signals, including CTGF, TGF-β1, and VEGF, through modulation of AKT signaling. The resulting stromal decompression and improved vessel perfusion can alleviate hypoxia and enhance sensitivity to cisplatin in CRC [179]. Furthermore, AuNPs can inhibit tumor angiogenesis by blocking VEGF-VEGF receptor 2 signaling between TME and endothelial cells [180]. In another study, the CAF uptake of AuNPs was 2.65-fold that in HeLa cells, whereas uptake by normal fibroblasts was only 7.55% of that in HeLa cells [181]. However, these values were calculated on a per-cell basis and were influenced by substantial differences in cell size; after normalization to cell volume, the AuNP uptake in CAFs and normal fibroblasts was approximately 50% and 5%, respectively, of that in HeLa cells. Following radiotherapy, AuNP exposure increased DNA damage foci in CAFs (13.5%) and HeLa cells (9.8%) more prominently than in normal fibroblasts (8.8%). These findings suggest differential AuNP uptake and radiosensitization among these cell types. Although other studies have also reported preferential AuNP accumulation in CAFs relative to cancer cells or normal fibroblasts, the magnitude of CAF-versus-normal-fibroblast difference observed remains to be further validated in physiologically relevant models [182].

Beyond AuNPs, other metal-based NPs have also been explored for CAF modulation. For example, gold-core silver-shell NPs (Au@Ag core–shell NPs) could suppress tumor growth and metastasis by modulating the gene expression and secretory profiles of CAFs, thereby disrupting their intrinsic crosstalk with cancer cells [183]. Magnetic Cu@Fe₃O₄ NPs, consisting of iron and copper, could enhance CAF ferroptosis and ameliorate the immunosuppressive TME. Moreover, the altered secretome of CAFs can facilitate dendritic cell (DC) maturation and CD8⁺ T cell activation, leading to enhanced immune responses against cancer treatment in CRC [184]. Similarly, CFpAD, consisting of AuNPs and ferric oxide NPs (FNPs), could reprogram CAFs and reduce ECM secretion, enabling deep penetration [185]. Additionally, CFpAD could disrupt nutrient supply to glioma cells, lower local pH, and generate free radicals, providing synergistic effects when combined with chemotherapy, photothermal therapy, and radiotherapy.

Metal NPs have also been integrated with conventional therapeutics to achieve coordinated stromal modulation and cancer-cell killing. For example, Z@C-D/P [186], a ZnO@CuS-based smart nanoplatform, remodeled the ECM via acid-triggered pirfenidone (PFD) release that inhibited CAF activation, thereby enhancing DOX penetration. Subsequently, the remaining ZnO@CuS nanostructure produced cascade-amplified ROS and photothermal effects to eliminate tumor cells. This dual-functional system yielded 89.7% tumor growth inhibition and prevented lung metastasis in vivo. In another study, a protein nanocage modified with FAP was designed by Zhen et al. for selectively photoinduced CAF depletion, which reduced CXCL12 secretion and ECM deposition [187].

Clinical trials of AuNPs have progressed in both oncological and non-oncological diseases [177, 188]. Several AuNP-based candidates for neurodegenerative diseases have advanced to phase II/III trials, demonstrating their encouraging preliminary efficacy and acceptable safety profiles. By contrast, the clinical development of AuNPs in oncology has been limited, with several early-stage (phase 0/I) trials being discontinued [177, 189]. Other metal-based NPs have also entered clinical evaluation, including NBTXR3 (a hafnium oxide NP, phase II/III [190] and ongoing phase III [191]), AGuIX (an ultrasmall gadolinium-based theranostic NP, phase I/II [192]). These studies have not specifically evaluated the role of metal-based NPs in CAF modulation, but their clinical development, administration strategies, and adverse-event profiles may provide valuable information for future CAF-targeting metal NPs. Notably, safety remains a major concern because the biodegradation and clearance of metal-based NPs depend strongly on their physicochemical properties, dose, and administration frequency [193]. Chemically persistent metal NPs can accumulate in the liver, kidneys, and other vital organs for extended periods, potentially leading to delayed or unpredictable toxicities [194]. Degradable metal NPs release constituent ions that may induce oxidative stress and inflammatory cascades in healthy tissues, and they may also activate the immune system, increasing susceptibility to hypersensitivity-like reactions and further complicating repeated systemic administration [193].

Comparative analysis of CAF-targeting NP platforms

CAF-targeting nanomedicine platforms share a general rationale of combining stromal modulation with direct antitumor activity, but they differ substantially in their cargoes, mechanisms, safety profiles, and translational readiness (Table 4). Anti-ECM antitumor NPs provide the most direct integration of stromal remodeling and cancer-cell killing, although their efficacy remains dependent on sensitivity to the co-delivered therapeutics [152]. Nucleic acid-based antitumor NPs enable programmable and potentially durable modulation of CAF phenotypes; however, their translational risks vary according to cargo type: genome-editing approaches raise concerns regarding irreversible off-target alterations, whereas transient RNA-based strategies remain limited by intracellular delivery efficiency, cargo stability, unintended gene regulation, and compensatory signaling [165, 166]. Metal-based multifunctional NPs provide additional catalytic, radiosensitizing, photothermal, and ion-mediated antitumor effects that are distinct from those of most conventional therapeutics. Although metal-based NPs have progressed to clinical trials, few studies have specifically evaluated their role in CAF modulation [188]. Overall, each NP class offers distinct functional advantages, but no single class has yet been shown to simultaneously address the challenges of CAF selectivity, therapeutic durability, safety, and clinical scalability.

Challenges for clinical translation of CAF-targeting NP platforms

CAF-targeting NPs remain predominantly at the preclinical stage. Although platform-specific limitations of each NP category have been discussed above, several shared challenges continue to hinder their clinical translation.

First, the therapeutic performance of CAF-targeting NPs is strongly influenced by their physicochemical properties, particularly particle size and surface charge [195]. Larger NPs may accommodate greater therapeutic payloads but often exhibit limited penetration into fibrotic tumor regions; Smaller NPs may diffuse more readily; however, they may undergo rapid renal clearance or exhibit insufficient tumor retention [195]. Positively charged NPs generally show enhanced membrane binding and cellular uptake. However, this may also increase nonspecific adsorption and systemic toxicity. Neutral or slightly negative surfaces may prolong circulation while reducing cellular internalization and enhancing accumulation in vital organs, thereby increasing long-term safety concerns [196]. In biological fluids, NPs can adsorb proteins, forming a protein corona that can alter the biological identity, biodistribution, and therapeutic efficacy [197].

Second, effective NP delivery remains a major translational barrier. The enhanced permeability and retention (EPR) effect is highly heterogeneous across patients, tumor types, and even different regions within the same tumor. Its contribution to NP accumulation is often weaker and less predictable in human tumors than in murine models, especially in CAF-rich desmoplastic tumors, in which abundant ECM can markedly restrict NP extravasation and intratumoral diffusion [198, 199]. These limitations have prompted a shift from passive EPR-dependent accumulation toward active targeting, TME-responsive delivery, stromal remodeling, and strategies that enhance tumor transport. However, these approaches cannot fully overcome inadequate vascular perfusion or the physical barriers imposed by a dense stroma.

Third, the multicomponent nature of CAF-targeting NPs substantially increases manufacturing complexity. These platforms often integrate multiple therapeutic cargoes, targeting ligands, stimulus-responsive components, and biomaterials, each of which requires rigorous and standardized quality control during production. Increased formulation complexity not only complicates physicochemical characterization and identification of critical quality attributes but also poses challenges to Good Manufacturing Practice (GMP)-compatible scale-up and batch-to-batch reproducibility [200, 201]. Additionally, NP formulations may be sensitive to environmental conditions, creating challenges for sterilization and long-term storage [202–204]. These limitations may raise production costs and delay clinical development.

Fourth, current NP design rationales do not fully reflect the latest understanding of CAF biology or the clinical complexity of advanced cancers. Three major gaps are identified: (1) Many platforms are functionalized with targeting ligands directed against broadly expressed CAF-associated surface proteins, most commonly FAP. However, FAP expression spans multiple functionally distinct CAF states and lacks selectivity for tumor-promoting CAFs by itself, thereby limiting the state specificity of FAP-targeted NPs [205]. (2) Reprogramming tumor-promoting CAFs through a single protein or associated pathways may be insufficient to yield durable efficacy in advanced cancers, as compensatory mechanisms in parallel axes may undermine such interventions. (3) Existing designs have predominantly prioritized the delivery of conventional chemotherapeutics [206], with insufficient innovation in antitumor agents themselves—a critical gap given the high frequency of multidrug resistance and cross-resistance in advanced and recurrent cancers. In this regard, CAF-targeting NPs may face similar clinical attrition when advanced to trials in metastatic and recurrent settings.

Finally, the available evidence remains dominated by positive findings from small-animal studies, whereas neutral or negative outcomes, failure to outperform simpler formulations, and long-term toxicity are less frequently reported [206]. Long-term safety also remains insufficiently characterized in many preclinical nanomedicine studies [206]. Such publication and reporting biases may overestimate the therapeutic potential of CAF-targeting NPs.

Biologically inspired and living platforms for CAF-targeting therapy

As exogenous substances, biomaterial-based NPs are readily recognized by the immune system. This recognition triggers the activation of a cascade of downstream immune responses and promotes uptake by the mononuclear phagocyte system, particularly in the liver and spleen, resulting in shorter systemic circulation and poor drug delivery efficacy [207]. These limitations have prompted the development of biologically inspired delivery strategies, including biomimetic nanomedicines and engineered living therapeutics.

Biomimetic nanomedicine seeks to reproduce selected structural or biological features of living cells or tissues to improve drug delivery and therapeutic interactions with the TME [208]. In membrane-coated nanoplatforms, natural cell membranes are used to coat synthetic NP cores, transferring part of the parent cell’s surface protein and lipid repertoire to the delivery system and thereby conferring properties such as immune evasion, prolonged circulation, and cell- or tissue-specific recognition. In selected formulations, membrane-mediated interactions can further facilitate cellular uptake and intracellular drug delivery [209, 210]. Other biomimetic CAF-targeting platforms include cell- or matrix-based traps that exploit endogenous biological interactions within the tumor–stroma interface [72, 211].

In parallel, engineered bacteria represent a distinct class of living therapeutics. Their intrinsic tumor tropism and capacity for localized production or delivery of therapeutic agents provide alternative strategies for modulating CAFs and the surrounding TME [67, 69]. CAF-targeted engineered cell therapies have been investigated in both solid and hematological cancers [212, 213]. Table 5 summarizes representative platforms in each category, including their design rationales, underlying mechanisms of action, and translational challenges.

Table 5.

Representative biologically inspired and living platforms for CAF-targeting therapy

Cell-membrane-based biomimetic platform	Membrane source	Name	Target site	Tumor model	Mechanisms	Therapeutic outcomes	Category-specific advantages and limitations	Shared challenges	References
Membrane-based	RBC membrane	RBC-Fn-NP	CAFs	PDAC	FnBPA5 enhances CAF targeting, RBC vesicles protect FnBPA5, while RA reprograms CAFs, enhancing the antitumor effects of doxorubicin.	Prolonged blood circulation, enhanced drug delivery, and antitumor effects	Advantages: prolonged circulation and favorable reproducibility Limitations: lack of CAF or tumor selectivity and requirement of targeting ligand	Challenges in manufacturing, quality control, standardization, GMP-compatible scale-up, immunological risks, and biosafety	[215]
	RBC membrane	SB-IR-PLGA@RM	CAFs	TNBC	SB431542 reprograms CAFs, TAMs, and neutrophils, while IR780 induces ICD, promoting dendritic cell maturation and cytotoxic lymphocyte infiltration.	Remodeled TME and enhanced antitumor immunity			[216]
	CAF membrane	JQ1&PFD@CTL	CAFs	PDAC	TAT peptide enhances targeting and penetration ability, while pirfenidone suppresses CAF activity.	Reduced ECM production, enhanced drug penetration, improved hypoxic environment and glycolysis, and enhanced antitumor efficacy	Advantages: stronger CAF recognition Limitations: biological and immunological uncertainty due to CAF heterogeneity		[217]
	CAF membrane	cM@lip-siACTN1	CAFs	NSCLC	CAFs are reprogrammed via ACTN1/IL-6 and ACTN/CCL2.	Remodeled TME and inhibited cancer growth			[218]
	Cancer membrane	CLip-PC@CO-LC NPs	Cancer cells	NSCLC	MMP-9 degrades ECM, while downregulation of PGAM1 suppresses aggressive cancer phenotypes and mTOR-mediated aerobic glycolysis.	Remodeled ECM, reduced glycolysis in tumor, remodeled TME, and enhanced therapeutic efficacy	Advantages: stronger tumor recognition Limitations: biological and immunological uncertainty due to tumor heterogeneity, lack of CAF selectivity, and requirement of CAF-targeting ligand		[220]
	Cancer membrane	FAP-CAR-CM@PLGA-AB	Activated CAFs and senescent CAFs	BC	FAP peptide enhances CAF targeting, while nintedanib remodels ECM by clearing CAFs and senescent CAFs, thereby enhancing the antitumor effects of ABT-263	Reversed immunosuppressive TME, improved radiation sensitization, and reduced cancer recurrence			[221]
	CAF-cancer hybrid membrane	PH20/CCM@PMCS	CAFs and cancer cells	OC	PH20 degrades ECM, while downregulation of p65 in CAFs inhibits the NF-κB pathway and further promotes ECM remodeling; meanwhile, the nanoparticles induce ICD	Dual-targeting toward CAFs and cancer cells, enhanced drug penetration, and enhanced immune activation	Advantages: broader recognition profile Limitations: biological and immunological uncertainty due to CAF and tumor heterogeneity, as well as increased manufacturing difficulties		[68]
	CAF-cancer hybrid membrane	SNAP/MOF@HCM	CAFs and cancer cells	HCC	High-surface-area MOF enriches cavity-internal ions, thereby enhancing local thermal efficiency; the nanoparticles enhance heat and mass transfer by inhibiting the activation of CAFs and degrading ECM; the zinc ions released by MOF can trigger Fenton-like reactions, generating hydroxyl radicals, amplifying oxidative damage, and inducing tumor cell apoptosis and ICD synergistically with SNAP	Remodeled ECM/TME and enhanced antitumor immunity			[222]
Engineered bacteria
Engineered E. coli chassis			CAFs	CRC and melanoma	FAP enhances targeting ability, while ClyA kills CAFs and adjacent cancer cells by forming holes in the membrane.	Enhanced targeting ability, improved immunosuppressive TME, and enhanced antitumor efficacy	Advantages: local production and delivery of therapeutic agents	Challenges: horizontal gene transfer, uncontrolled bacterial proliferation or dissemination, and immunological risks	[69]
Engineered Salmonella			ECM	Colon cancer	The VNP^NKase degrades fibronectin, facilitating the infiltration of CD103⁺ DCs and the presentation of tumor-associated antigens, recruiting sufficient CD8⁺ T lymphocytes to eradicate localized tumors specifically.	Amplified abscopal effect and a long-term immune memory effect, preventing cancer metastasis and recurrence			[70]
CAF-based/CAF-related biomimetic traps
Construction			Tumor model		Mechanisms	Therapeutic outcomes	Advantages	Challenges	References
Viable and metabolically active CAFs in alginate/gelatine beads (500–750 μm in diameter), with a non-biodegradable polyelectrolyte coating			Various models		ECM proteins are secreted by encapsulated CAFs and are retained by these non-biodegradable coatings to trap cancer cells.	Delayed postsurgical peritoneal metastasis and prolonged survival in animal models	Tumor-cell capturing in peritoneal cavity	No methods to eliminate tumor cells	[72]
A non-resorbable three-dimensional scaffold coated with collagen			OC		This device triggers a fibrous connective tissue response via foreign body reaction and promotes more collagen deposition, creating a pre-metastatic niche for cancer cells.	Extended survival time from 99 days to 199 days for preventative implantation and extended survival to 126 days for metastasis treatment	Tumor-cell capturing in peritoneal cavity	Invasive implantation and possible surgical removal that may increase risks, and uncertain long-term safety	[211]
An implantable stromal cell-derived factor-1α (SDF-1α)-loaded silk fibroin/hyaluronic acid/heparin aerogel scaffold			Glioblastoma		The scaffold is loaded with the chemoattractant SDF-1α and confines CXCR4-positive glioblastoma cells	No improvement in OS	Tumor-cell capturing in resection cavity	Limited clinical efficacy	[231]
FAP-targeted engineered cell therapies
FAP-targeting CAR-T cells			Pleural mesothelioma (phase I), and multiple myeloma		CAR-mediated recognition of FAP enables engineered immune cells to selectively target FAP-positive CAFs, leading to stromal depletion or remodeling and improving the antitumor immune microenvironment and therapeutic accessibility	Selective toxicity on CAFs and divergent safety profiles in preclinical models, as well as limited efficacy in clinical trials	Active and programmable targeting of FAP-positive stroma; potential for sustained CAF depletion and TME remodeling	On-target/off-tumor toxicity, limited trafficking and penetration in solid tumors, and manufacturing complexity	[213,238]
Anti-FAP CAR-NK cells			Cervical cancer; pleural and peritoneal mesothelioma (ongoing clinical trial)						[71,239]
FAP-CAR macrophages			Pancreatic cancer						[240]

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Note: unless otherwise specified, all platforms mentioned in this table were investigated in preclinical models

Abbreviations: BC: breast cancer; BET: bromodomain and extraterminal domain; CAF: cancer-associated fibroblast; CAR: chimeric antigen receptor; CCL2: C-C motif chemokine ligand 2; ClyA: cytolysin A; CRC: colorectal cancer; DC: dendritic cell; ECM: extracellular matrix; FAP: fibroblast activation protein α; HCC: hepatocellular carcinoma; ICD: immunogenic cell death; IL-6: interleukin-6; MMP-9: matrix metalloproteinase 9; MOF: metal–organic framework; mTOR: mechanistic target of rapamycin; NF-κB: nuclear factor kappa-light-chain-enhancer of activated B cells; NK: natural killer; NKase: nattokinase; NO: nitric oxide; NPs: nanoparticles; NSCLC: non-small cell lung cancer; OC: ovarian cancer; PDAC: pancreatic ductal adenocarcinoma; PGAM1: phosphoglycerate mutase 1; PH20: hyaluronidase PH-20; PLGA: poly(lactic-co-glycolic acid); RA: retinoic acid; RBC: red blood cell; siRNA: small interfering RNA; SNAP: S-nitroso-N-acetylpenicillamine; TAMs: tumor-associated macrophages; TGF-β: transforming growth factor-β; TME: tumor microenvironment; TNBC: triple-negative breast cancer; ZIF-8: zeolitic imidazolate framework-8

Biomimetic membrane-based nanoplatforms for CAF-targeting therapy

Although stem cell-, immune cell-, and platelet-derived membranes have also been widely studied in cancer nanomedicine [214], the biomimetic membrane-based platforms currently reported for direct CAF targeting or CAF-mediated TME remodeling mainly involve four membrane sources (Table 5): (1) red blood cell (RBC) membrane, (2) CAF or bone marrow stromal cell membrane, (3) cancer cell membrane, and (4) CAF–cancer cell hybrid membrane. Their source-dependent membrane properties can facilitate circulation, tumor or CAF recognition, and cellular uptake. Following internalization, these nanoplatforms release their therapeutic cargoes—such as anti-ECM drugs or genetic medicines—to remodel the ECM or reprogram CAFs. These processes can enhance drug penetration and alleviate immunosuppression, thereby improving antitumor efficacy.

RBC membranes have primarily been utilized to shield conventional NPs from immune surveillance, thereby prolonging their circulation. For example, Zhao et al. developed RBC-Fn-NP, an RBC-vesicle-based nanodrug in which the membrane partially protected CAF-targeting peptide FnBPA5 and facilitated the delivery of the CAF-reprogramming agent retinoic acid and the chemotherapeutic agent DOX [215]. In another study, Liao et al. developed SB-IR-PLGA@RM, an RBC membrane-coated platform containing the sonosensitizer IR780 and the TGF-β inhibitor SB431542 [216]. Under ultrasound irradiation, IR780 triggered sonodynamic therapy-induced ICD, while SB431542 suppressed TGF-β signaling and promoted the reprogramming of tumor-associated macrophages, neutrophils, and CAFs. When combined with anti-PD-L1 therapy, this dual-action system remodeled the immunosuppressive TME and enhanced antitumor activity.

CAF membranes have also been exploited to facilitate drug delivery to CAF-rich tumors. Zhang et al. developed a Trojan horse-like liposomal delivery system coated with activated CAF membranes and co-loaded with the bromodomain and extra-terminal (BET) inhibitor JQ1 and the antifibrotic drug PFD [217]. This platform was further functionalized with TAT (trans-activator of transcription) peptides to enhance penetration into the dense ECM in pancreatic tumors. PFD suppressed CAF activity and reduced ECM production, thereby alleviating hypoxia and glycolysis and enhancing intratumoral accumulation and activity of JQ1. Yan et al. designed a nanodrug termed cM@lip-siACTN1, consisting of a CAF membrane coating and a liposome core encapsulating the siRNA against ACTN1 [218]. In vitro and in vivo studies demonstrated that this system could inhibit cell growth, migration, and invasion of NSCLC by modulating ACTN1/IL-6 and ACTN1/CCL2 pathways. Kong et al. developed a bone marrow stromal cell membrane-coated biomimetic nanocomposite for the treatment of acute myeloid leukemia (AML) [219]. This platform comprised an oxygen-carrying perfluorooctyl bromide (PFOB)-loaded poly(lactic-co-glycolic acid) (PLGA) core decorated with platinum (Pt) nanozymes, and a DOX-loaded membrane coating to facilitate bone marrow homing and synergistic leukemia-cell killing. Importantly, the stromal cell membrane also disrupted CXCR4/CXCL12-mediated leukemia-cell homing, thereby reducing residual leukemia burden in the bone marrow and limiting infiltration into peripheral organs.

Cancer-cell membranes provide another source of biomimetic coating. Zhang et al. fabricated CLip-PC@CO-LC NPs using the lung cancer cell membrane and MMP-9-responsive, charge-reversal liposomal membranes [220]. These membranes coated a lipoic acid-modified polypeptide core co-loaded with docetaxel and siRNA targeting phosphoglycerate mutase 1 (PGAM1), a key glycolytic enzyme. This system responded to MMP-9, pH, and redox stimuli to enable tumor-specific drug release, achieving greater tumor-growth inhibition than free docetaxel (DTX) or siPGAM1 alone. Jian et al. constructed AP-CAR-CM@PLGA-AB NPs by employing 4T1 cell membranes modified with FAP [221]. These NPs co-encapsulated the anti-fibrotic drug nintedanib and the senolytic agent navitoclax (ABT-263). Consequently, by eliminating immunosuppressive and senescent CAFs, this nanoplatform not only overcame radioresistance but also significantly enhanced the radiosensitivity of breast cancer tumors.

Hybrid CAF–cancer-cell membranes have been designed to combine stromal and malignant-cell recognition. Yao et al. constructed PH20/CCM@PMCS, a hybrid CAF–cancer cell membrane-coated system containing carboplatin and siRNA against p65, a major transcription factor in the NF-κB pathway-mediated TME remodeling [68]. This hybrid membrane was engineered to overexpress hyaluronoglucosaminidase PH-20 (PH20), thereby promoting ECM degradation and improving drug delivery. Similarly, Ma et al. developed SNAP/MOF@HCM by coating a zeolitic imidazolate framework-8 (ZIF-8) metal–organic framework (MOF) platform with a hybrid cancer cell–CAF membrane and loading it with nitric oxide donor S-nitroso-N-acetylpenicillamine (SNAP) [222]. The hybrid membrane promoted recognition of both liver cancer cells and CAFs, while the MOF enhanced microwave-induced thermal effects. Furthermore, zinc ions released from the MOF triggered Fenton-like reactions to generate hydroxyl radicals, which synergized with SNAP-released nitric oxide to amplify oxidative damage, induce ICD, and activate systemic antitumor immunity. These findings suggest that hybrid membranes may broaden cellular recognition compared with single-source membranes.

Overall, the biological functions and translational potential of membrane-coated NPs depend strongly on the membrane source and encapsulated cargo. RBC membranes primarily provide immune camouflage and prolonged circulation; they may offer comparatively favorable biocompatibility and source accessibility, but lack intrinsic CAF or tumor specificity [223]. Therefore, these platforms generally require additional targeting ligands. CAF- and cancer-cell-derived membranes may support stronger source-dependent recognition, but they are associated with greater biological heterogeneity and immunological uncertainty [214, 224]. Hybrid CAF–cancer-cell membranes may extend recognition to both stromal and malignant compartments but also introduce greater compositional, manufacturing, and regulatory complexity [68]. In addition, although autologous membrane sources may reduce alloimmune responses, many currently reported biomimetic nanoplatforms rely on cultured cell lines or non-autologous membrane sources. Because cell-derived membranes retain source-cell proteins and other biologically active components, their immunogenicity and biological activity may vary with membrane origin and processing. These considerations underscore the need for rigorous membrane-source selection, compositional characterization, and immunogenicity assessment before clinical translation [225].

Engineered bacteria for CAF-targeting strategy

Bacteria can preferentially colonize tumors and elicit antitumor immune responses, making them potential vehicles for localized production and delivery of therapeutic agents. Through genetic modification, bacterial strains can be engineered to produce specific antitumor payloads, potentially addressing limitations of conventional therapies, such as off-target toxicity and poor penetration into deep tumor tissues [67]. Zhang et al. developed engineered Salmonella to secrete nattokinase (VNP^NKase), a fibrinolytic enzyme that degrades fibronectin, thereby remodeling the TME and enhancing radioimmunotherapy in colon cancer [70]. Xu et al. developed CAT-BLAST, an engineered Escherichia coli chassis expressing an FAP-specific synthetic adhesin to enhance precise targeting of CAFs and capable of secreting cytolysin A (ClyA), a pore-forming toxin that induces apoptosis in both CAFs and adjacent tumor cells [69]. Its FAP-targeting and antitumor efficacy were demonstrated in preclinical tumor models. Moreover, this system exhibited a favorable biosafety profile, as evidenced by stable body weights, transient fecal shedding, and normal histology of major organs as revealed by hematoxylin and eosin staining.

Despite their capacity for tumor colonization and localized production of therapeutic payloads, engineered bacteria raise substantial biosafety and regulatory concerns. These bacterial systems are genetically engineered living therapeutics; engineered genes or selectable markers may also be transferred to commensal or pathogenic microorganisms through horizontal gene transfer [226, 227]. Uncontrolled proliferation or dissemination beyond the tumor may cause systemic infection, particularly in immunocompromised patients, and may also disrupt the resident microbiota, leading to dysbiosis and related adverse effects. Accordingly, the biodistribution and persistence of engineered bacteria within the host, as well as their shedding from treated patients, warrant careful monitoring during clinical translation [227, 228]. Additionally, host immunity may either rapidly eliminate the bacterial vector, thereby reducing therapeutic efficacy, or induce excessive inflammatory responses, thereby increasing the risk of immune-mediated toxicity [226, 229].

CAF-based/CAF-related biomimetic tumor traps

De Vlieghere et al. leveraged the natural affinity of cancer cells for the ECM to develop a CAF-based biomimetic trap for delaying peritoneal metastasis [72]. This system consisted of viable and metabolically active CAFs encapsulated in alginate/gelatine beads (500–750 μm in diameter), which were subsequently coated with a non-biodegradable polyelectrolyte layer. This coating retained ECM proteins secreted by encapsulated CAFs, thereby creating an artificial receptive niche that attracted and trapped disseminated cancer cells in the peritoneal cavity. Intraperitoneal injection of the trap significantly delayed postsurgical peritoneal metastasis and prolonged survival in animal models. It could also capture free-floating cancer cells of different origins without adhering to intestinal organs and inducing abdominal inflammation.

Building on a similar concept, Alonso-Alconada et al. designed a biomimetic device to convert a disseminated disease into a localized condition [211]. The device consisted of a non-resorbable, collagen-coated three-dimensional scaffold that elicited a foreign body reaction after implantation. This response evolved from acute inflammation to fibroblast recruitment, collagen deposition, and fibrous connective tissue formation, thereby generating an artificial pre-metastatic niche for ovarian cancer cell colonization. The tumor-cell-capturing capacity of the uncoated scaffold increased progressively with implantation time, from 63.7% after 1 week to 97.6% after 6 months, whereas type I collagen coating maintained high capture rates over prolonged implantation (97.7%, 94.3%, and 93.3% at 1, 6, and 12 months, respectively). In the preventive setting, scaffold implantation extended survival from 99 to 199 days, whereas implantation after established metastasis prolonged survival to 126 days. Following surgical removal of the device, six of eight animals reached the 1-year endpoint, suggesting that the approach could potentially localize peritoneal disease and facilitate subsequent salvage surgery. However, in a first-in-human study involving 23 patients with advanced high-grade serous ovarian cancer, intraperitoneally implanted M-Trap devices captured tumor cells in at least one device in 62% of patients who developed recurrence; tumor infiltration was limited, and the devices failed to focalize recurrent disease [230]. At 6 months after implantation, 78.3% of patients remained free of device- and procedure-related major adverse events. These findings highlight a substantial preclinical–clinical discrepancy and suggest that the complex spatial distribution, fluid dynamics, and biological heterogeneity of human peritoneal metastases may limit the ability of an implanted artificial niche to compete effectively with endogenous metastatic sites.

In another study, an implantable stromal cell-derived factor 1-α (SDF-1α)–loaded silk fibroin/hyaluronic acid/heparin aerogel scaffold was developed to chemoattract and confine CXCR4-positive glioblastoma cells within the resection cavity, thereby reshaping infiltrative tumor growth into a more localized pattern. Although the scaffold was well tolerated and effectively recruited tumor cells in vitro and in vivo, it did not improve overall survival, indicating that additional tumor-eliminating mechanisms would be required for therapeutic benefit [231]. Preclinical studies have also explored the use of chemoattractant-containing hydrogels, mechanical ratchet structures, and engineered metastatic niches (which exploit the cancer cell–neutrophil interactions) to attract and confine disseminated cancer cells at local sites [73, 232–235]. Although these approaches are not directly CAF- or ECM-targeted, they may provide proof of concept that artificial niches can redirect or sequester disseminated tumor cells and may inform the future development of CAF- or stroma-based biomimetic traps.

CAF-based or related biomimetic traps represent a conceptually distinct strategy that intercepts disseminated cancer cells rather than directly delivering cytotoxic agents. However, their applicability may be restricted to anatomically accessible cavities, and the need for implantation and, in some designs, subsequent surgical removal may limit clinical feasibility [230]. The use of viable, metabolically active CAFs raises additional safety concerns because these cells may continue to secrete cytokines, growth factors, and other stromal mediators with potentially pro-inflammatory or proangiogenic effects. Although the polyelectrolyte coating was designed to be non-biodegradable and to retain the encapsulated CAFs, its long-term structural integrity and capacity to prevent CAF escape require further evaluation. Because these systems sequester cancer cells without directly eliminating them, the captured cells may remain viable and constitute a potential malignant reservoir, thereby limiting the overall therapeutic benefit [72]. The limited disease-localizing effect observed in the first-in-human M-Trap study further suggests that artificial niches may be insufficient to compete with the multiple endogenous metastatic sites and complex biological conditions encountered in human disease [230]. Moreover, most evidence has been generated in immunocompromised models, leaving the influence of host immunity insufficiently defined. Long-term implantation may provoke persistent foreign-body responses, chronic inflammation, fibrosis, and infection, thus warranting rigorous evaluation in immunocompetent models.

CAF-targeted engineered cell therapies

FAP has also been explored as a target for adoptive cellular therapies in both solid and hematological malignancies [212, 213]. FAP-chimeric antigen receptor (CAR)-T cells can selectively deplete FAP-positive stromal cells and remodel the TME; however, preclinical studies have reported divergent safety profiles. Wang et al. reported that FAP-targeting CAR-T cells could effectively deplete stroma without severe toxicity, whereas Tran et al. demonstrated cachexia and bone toxicity resulting from recognition of FAP-positive normal stromal populations [236, 237]. The phase I FAPME study demonstrated that intrapleural administration of autologous FAP-targeting CAR-T cells to patients with malignant pleural mesothelioma was feasible and was not associated with significant CAR-T-related toxicity, although the small cohort precluded conclusions regarding efficacy [238]. Sakemura et al. developed CAR-T cells co-targeting B-cell maturation antigen (BCMA) on malignant plasma cells and FAP or SLAM family member 7 (SLAMF7) on bone-marrow CAFs, which enhanced CAR-T-cell effector function and reduced CAF-mediated resistance in preclinical multiple myeloma models [213].

Anti-FAP CAR-NK cells have demonstrated selective cytotoxicity against FAP-positive CAFs in preclinical tumor models [71]. Another dual mesothelin/FAP CAR-NK strategy has entered early-phase clinical evaluation for pleural and peritoneal mesothelioma [239]. Moreover, FAP-CAR macrophages have been shown to selectively target FAP-positive CAFs and improve the penetration of therapeutic agents and immune cells, thereby enhancing the efficacy of chemotherapy and immunotherapy in pancreatic cancer models [240].

Collectively, these findings support the feasibility of CAF-directed engineered cell therapies. However, FAP expression in normal stromal populations creates a risk of on-target/off-tumor toxicity, while CAR-T cell therapy itself may cause systemic immune toxicities such as cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome [241]. In solid tumors, limited trafficking and intratumoral penetration of engineered immune cells may further restrict therapeutic efficacy. Therefore, improving CAF-state specificity, restricting CAR activity within the TME, and incorporating controllable safety mechanisms will be important for clinical translation.

Comparative analysis of biologically inspired and living platforms for CAF-targeting therapy

The biologically inspired and living platforms described above exploit distinct biological properties of source-cell membranes, tumor-colonizing bacteria, or tumor–stroma interactions; they therefore differ fundamentally in their design rationales and intended clinical applications.

Membrane-coated systems are primarily delivery-oriented platforms in which a synthetic nanocarrier is encapsulated with a biologically derived membrane, and they may be particularly suitable for systemic treatment of CAF-rich tumors requiring simultaneous stromal remodeling and direct tumor-cell killing [214, 224]. However, their therapeutic performance remains dependent on the encapsulated cargo. Engineered bacteria are living therapeutic factories designed to colonize tumors and continuously produce therapeutic agents in situ and may therefore be particularly valuable for tumors that are poorly accessible to conventional drug-delivery systems and require localized, sustained modulation of the TME [67, 69, 70]. Compared with membrane-coated NPs, they may provide more active penetration into poorly perfused or hypoxic tumor regions and enable sustained local production of selected therapeutics. However, their biological activity is more difficult to control, and their therapeutic benefits must be balanced against their biosafety risks. CAF-based and related biomimetic traps follow an interception-oriented strategy by creating an artificial premetastatic niche that redirects disseminated cancer cells toward a localized and potentially removable site [72, 242]. Because tumor-cell capture does not necessarily depend on drug sensitivity, these systems may theoretically retain activity against treatment-resistant cells and thereby offer a complementary strategy for advanced cancers with peritoneal metastasis. However, the first-in-human study failed to reproduce the pronounced disease-focalizing effects observed in animal models, suggesting substantial gaps between preclinical and clinical studies [230]. These systems are also invasive and generally require both implantation and subsequent surgical removal, which may limit their clinical applicability. Moreover, further evidence from immunocompetent models is needed to validate the long-term safety of foreign device implantation. FAP-targeted engineered cell therapies provide active and programmable targeting of CAFs; however, their clinical translation remains constrained by their on-target/off-tumor toxicities due to the expression of FAP in normal stromal populations, systemic immune toxicities, as well as the limited trafficking and penetration of engineered immune cells into solid tumors [212, 213].

In summary, the clinical value of these biomimetic approaches will depend not simply on their biological sophistication, but on whether their platform-specific advantages justify the additional challenges in biosafety and clinical implementation.

Challenges for clinical translation of biologically inspired and living platforms

Biologically inspired and living platforms have been investigated in both preclinical and clinical studies. In addition to category-specific limitations noted above, several overarching challenges associated with CAF-targeting biomimetic platforms remain unresolved.

First, the inherent heterogeneity of biological components creates substantial challenges for manufacturing, quality control, and standardization. Membrane composition may vary according to donor, tumor type, treatment history, and cellular state. These factors directly contribute to differences in surface protein profiles, biodistribution, cellular uptake, and therapeutic efficacy. Moreover, this variability is particularly relevant to CAF-derived membranes because of CAF heterogeneity and plasticity [224]. Membrane-coated systems inherit the manufacturing constraints of their synthetic NP cores but also introduce additional requirements for source-cell expansion, membrane isolation and purification, and coating reproducibility [209]. Engineered bacteria additionally require control of genetic stability, replication, containment, and potency [227, 228]. Engineered cell therapies require standardized cell sourcing, ex vivo expansion, phenotypic and functional consistency, and potency testing, while patient-to-patient variability, along with complex manufacturing and distribution workflows, may further complicate large-scale implementation [243]. Biomimetic traps require reproducible scaffold fabrication, mechanical stability, and long-term biocompatibility. Collectively, these requirements complicate GMP-compatible scale-up, cost-effective production, and regulatory approval.

Second, host–platform interactions represent a shared translational concern. Because these systems contain exogenous biomaterials, biological membranes, or living microorganisms and cells, they introduce additional platform-specific immune interactions that may alter biodistribution, clearance, efficacy, and tolerability [244]. It should be noted that the nature and magnitude of these immune responses may evolve after repeated administration or prolonged implantation [244]. Therefore, standardized immune profiling and long-term studies in immunocompetent models are required before clinical translation.

Third, the clinical advantage of biomimetic systems over simpler approaches remains inadequately demonstrated. Most reported platforms are evaluated primarily in preclinical models, while rigorous comparisons with simpler nanoplatforms, standard-of-care treatments, or other clinically established targeting strategies remain limited [206].

Cross-modality comparison and translational lessons

Across pharmacological and biologic CAF-targeting therapeutics, nanomedicines, and biologically inspired and living platforms, technological evolution has expanded how CAFs and the surrounding stroma can be therapeutically manipulated (Tables 3, 4 and 5). Pharmacological approaches primarily act through direct modulation of the ECM, cytokines, or signaling pathways. Nanomedicines build on many of these biological mechanisms by improving cargo delivery and integrating stromal modulation with direct antitumor effects. Biologically inspired and living platforms further extend this concept by exploiting biological functions such as immune camouflage, multivalent cellular recognition, tumor colonization, tumor cell capturing, or tumor–immune cell interaction. Thus, the major differences among these modalities lie not only in their therapeutic mechanisms but also in how, where, and for how long the intervention is delivered.

Despite these technological advances, several biological limitations persist across modalities. Clinical evidence with conventional CAF-targeting therapeutics indicates that CAF heterogeneity and plasticity, pathway redundancy, and dependence on an effective tumor-directed therapeutic mechanism remain common challenges (Table 3). However, many nanomedicines and membrane-coated nanoplatforms continue to deliver cargoes or modulate pathways similar to those targeted by conventional therapeutics; consequently, improved delivery does not necessarily overcome resistance arising from inappropriate target selection or compensatory signaling (Table 4). At the same time, each technological advance introduces additional platform-specific constraints. For example, nanomedicines add challenges related to physicochemical properties, intratumoral transport, long-term safety, and manufacturing, whereas biologically inspired and living platforms introduce further complexity associated with biological-source variability, immune interactions, standardization, and clinical implementation.

These trade-offs are particularly evident among biomimetic platforms. Membrane-coated nanoplatforms can provide multivalent recognition beyond conventional ligand–receptor targeting, potentially mitigating limitations such as high selectivity associated with ligand-receptor targeting in cancer research (Table 5). However, this potential has not been fully exploited in existing designs, whose primary goals are to deliver conventional chemotherapeutics. Compared to nucleic acid-based antitumor NPs, engineered bacteria offer active tumor colonization, but this advantage should be balanced by concerns regarding biocontainment, persistence, and host immune responses. Biomimetic traps represent an even more distinct strategy by physically redirecting disseminated tumor cells rather than delivering therapeutic agents; however, their applicability is constrained by anatomical accessibility, implantation requirements, and limited preliminary efficacy in real-world settings. Engineered cell therapies, particularly CAR-T cells, have achieved substantial clinical success in several hematological malignancies; however, the clinical efficacy of FAP-targeted engineered cell therapies in solid tumors remains insufficiently established [238, 245]. Thus, the functional advantages gained by each platform are accompanied by a corresponding set of new translational liabilities.

These comparisons indicate that technological evolution has expanded the functional repertoire of CAF-targeting strategies but may not be sufficient to eliminate the core biological and clinical barriers identified in clinical practice of conventional therapeutics. Each modality addresses selected limitations while introducing new platform-specific constraints. Thus, technological complexity should be justified by its ability to overcome a clearly defined biological or clinical barrier rather than by technological sophistication alone. These cross-modality lessons provide the basis for the translational roadmap and research priorities discussed below.

Future perspectives

Translational roadmap from CAF-state discovery to clinical implementation

Based on the cross-modality lessons outlined above, we propose a biomarker-driven translational roadmap linking CAF-state discovery to clinical implementation (Fig. 3). Rather than treating biological discovery, therapeutic development, and clinical evaluation as separate steps, this framework integrates CAF-state validation and biomarker development with clinically relevant preclinical testing, manufacturing and safety assessment, and biomarker-enriched clinical trials. Importantly, translation should operate as an iterative process in which clinical findings continuously inform CAF-state definition, biomarker refinement, model optimization, and therapeutic redesign.

Fig. 3 — Translational roadmap from CAF-state discovery to clinical implementation. The roadmap integrates CAF-state discovery and biomarker development, clinically relevant preclinical evaluation, therapeutic optimization, and biomarker-guided clinical translation within an iterative refinement framework

Key translational priorities for CAF-targeting therapy over the next decade

Within this translational framework, three priorities warrant particular emphasis over the next decade: defining therapeutically relevant CAF states, developing clinically representative models of advanced disease, and designing therapeutic platforms around clearly defined clinical needs.

First, CAF-targeting strategies should increasingly focus on therapeutically relevant functional states rather than relying exclusively on rigidly defined subpopulations. CAF populations are highly heterogeneous and plastic, and phenotypically distinct subpopulations may transition between functional states and share overlapping tumor-promoting programs during tumor progression or treatment [34]. Consequently, targeting a single CAF subpopulation may be insufficient if other populations retain or acquire similar functions, although direct evidence of compensatory functional replacement remains limited. State-based targeting remains challenging because CAFs displaying the same functional programs may arise from different cell origins and are rarely defined by a single, universally applicable surface marker. Future research should therefore prioritize the development of functionally validated biomarker signatures that integrate state-associated surface proteins, transcriptional programs, and spatial context across tumor types and disease stages.

Second, clinically relevant models of advanced disease are essential for the preclinical development of CAF-targeting strategies. Although spatial and functional heterogeneity have been increasingly demonstrated at the molecular level, many proof-of-concept studies still rely on localized, treatment-naïve models that fail to capture the biological complexity and treatment history of late-stage disease [4, 14, 34]. This mismatch may overestimate the preclinical efficacy of CAF-targeting therapies and provide inaccurate information for subsequent clinical studies. Future models should therefore be optimized to closely reflect real-world features in clinical settings, although their development remains challenging due to lengthy establishment procedures, biological variability, and limited reproducibility.

Third, CAF-targeting platform design should prioritize an effective tumor-directed therapeutic mechanism, with CAF modulation serving as a context-specific therapeutic enabler. Many CAF-targeting nanomedicines are designed to remodel the stroma, improve drug penetration, or alleviate immunosuppression, thereby sensitizing cancer cells to co-delivered or subsequently administered therapies [61, 62]. This design rationale emphasizes the contribution of CAF modulation to antitumor efficacy, representing a CAF–cancer loop. Consequently, the therapeutic benefit of these platforms often remains dependent on the activity of the tumor-directed treatment. However, existing systems frequently combine CAF modulation with conventional chemotherapeutics, which may be insufficient in clinically advanced and recurrent cancers where multidrug resistance is already established. Future designs should therefore prioritize the development of effective tumor-directed agents as the principal therapeutic component, with state-specific CAF modulation serving as a complementary strategy.

Box 1. Outstanding translational questions in CAF-targeting therapy.

How can therapeutically actionable CAF states be distinguished from correlative phenotypes and validated as causal drivers of tumor progression, immune suppression, or treatment resistance across specific cancer types and disease stages?
Which biomarker strategies can reliably identify and longitudinally track therapeutically relevant CAF states despite their spatial, temporal, and treatment-induced plasticity?
Which preclinical models, or combinations of models, most accurately predict CAF evolution and therapeutic response in metastatic, recurrent, and treatment-experienced human cancers?
In which clinical contexts does CAF modulation provide meaningful benefit beyond that achieved by an effective tumor-directed therapy alone, and which patients are most likely to benefit?
Under what conditions do multifunctional nanomedicine or biomimetic platforms provide a clinically meaningful advantage over simpler formulations, ligand-targeting systems, antibody-based therapies, or conventional combination strategies?
What quantitative go/no-go criteria for biodistribution, immunological safety, long-term toxicity, reproducibility, scalability, and GMP-compatible manufacturing should be met before complex CAF-targeting platforms advance to clinical testing?

Emerging technologies and interdisciplinary collaboration for CAF state-associated biomarker discovery and precision nanomedicine

Emerging technologies and interdisciplinary collaboration will be essential for translating state-based CAF biology into clinically applicable biomarkers and precision nanomedicine. Single-cell sequencing may help characterize transcriptionally distinct CAF programs, whereas spatial transcriptomics, spatial proteomics, and multiplex imaging may provide complementary information on their anatomical distribution and interactions with malignant, immune, vascular, and other stromal compartments [54, 55]. Artificial intelligence-assisted digital pathology may further enable scalable assessment of clinically relevant stromal features from routinely collected tissue sections, including stromal architecture, tumor–stroma boundaries, immune exclusion, and spatial organization [246, 247]. Integrating these technologies with longitudinal clinical samples and well-annotated treatment histories may enable the identification and validation of CAF-state-associated biomarker signatures that capture spatial, temporal, and treatment-induced variation.

Importantly, translating these discoveries into precision nanomedicine will require close collaboration among cancer biologists, pathologists, clinicians, bioinformaticians, materials scientists, and pharmaceutical and manufacturing specialists. Biological and clinical studies should define which CAF states are causally relevant, in which disease contexts they should be targeted, and which biomarkers are suitable for patient selection and pharmacodynamic monitoring; in parallel, nanomedicine development should adapt therapeutic cargoes, physicochemical properties, and delivery mechanisms to these clinically defined requirements. Iterative integration of molecular profiling, spatial pathology, therapeutic engineering, and clinical feedback could therefore link CAF-state discovery with patient stratification and platform design, enabling CAF-targeting nanomedicine to evolve from technology-driven formulation toward biomarker-guided precision therapy.

A hypothesis-generating sequential cancer cell/CAF dual targeting strategy

Building on the multivalent recognition properties of membrane-coated platforms and the cargo-related limitations identified above, we propose a hypothesis-generating sequential cancer cell/CAF dual-targeting strategy that exploits reciprocal interactions between malignant cells and CAFs (Fig. 4).

Fig. 4 — Hypothesis-generating sequential cancer cell–CAF dual-targeting strategy for advanced and recurrent cancers. The schematic integrates the clinical rationale, proposed sequential intervention, reciprocal cancer cell–CAF interactions, and key requirements for future validation. Sequential tumor-directed therapy followed by CAF-directed stromal modulation is hypothesized to establish a self-reinforcing cancer cell–CAF–cancer cell loop that enhances subsequent tumor-directed treatment. This conceptual strategy remains to be validated experimentally with respect to treatment sequencing, comparative efficacy, durability, biosafety, and manufacturing feasibility

In the initial stage, broad-spectrum tumor-directed agents, such as pore-forming proteins or functionally analogous nanomaterials, could be delivered using cancer-cell membrane-coated platforms to promote homologous recognition and cancer-cell elimination. Reducing the malignant-cell population may also attenuate cancer-derived signals that sustain tumor-promoting CAF states, thereby potentially creating a more favorable context for subsequent stromal modulation. In the second stage, CAF-directed nanoplatforms could deliver reprogramming agents to matrix-producing CAFs, thereby reducing stromal barriers and potentially improving the penetration and efficacy of subsequent rounds of tumor-directed therapy. Conceptually, this strategy differs from simultaneous dual targeting by attempting to exploit the dynamic dependence between malignant cells and CAFs in a defined therapeutic sequence and may establish a self-reinforcing cancer–CAF–cancer loop.

This framework requires systematic experimental validation. Key questions include whether sequential treatment provides an advantage over simultaneous intervention, which tumor-directed and CAF-directed agents are most appropriate for different disease contexts, how treatment timing should be optimized, and whether repeated modulation produces durable therapeutic benefit without unacceptable toxicity. For example, in highly desmoplastic tumors, where a dense CAF-derived stroma already restricts drug penetration, prior CAF reprogramming or stromal normalization may be required before effective tumor-directed treatment, whereas a tumor-first approach may be more feasible when sufficient initial access to malignant cells can be achieved. Moreover, membrane-mediated homotypic recognition is not absolutely specific, and both tumor-directed and CAF-directed platforms may exhibit off-target interactions with normal or inflammatory cells, necessitating more selective or multivalent targeting strategies. Pore-forming agents released after cancer-cell lysis can damage adjacent stromal or normal cells and may consequently introduce an additional safety concern; therefore, localized and dose-controllable toxicity should be considered an untested assumption requiring direct in vivo validation. Translation may also require coordinated optimization of the pharmacokinetics, biodistribution, tumor accumulation, half-lives, and dosing intervals of both therapeutic stages, together with reproducible manufacturing and validation in clinically representative models of advanced and treatment-resistant disease.

Conclusions

CAF-targeting strategies have expanded from direct stromal depletion and pathway inhibition to multifunctional nanomedicines and biologically inspired platforms, yet successful clinical translation remains limited. Across these modalities, increasing technological sophistication has not eliminated the fundamental challenges imposed by CAF heterogeneity and plasticity, disease-context dependence, therapeutic resistance, and inadequate patient stratification. Future progress will therefore require integration of functionally defined CAF states with clinically relevant disease models, effective tumor-directed therapeutic mechanisms, feasible delivery or intervention strategies, and biomarker-guided clinical development. Such a biologically and clinically driven framework may help translate the expanding technological repertoire of CAF-targeting strategies into meaningful patient benefit.

Acknowledgements

The authors would like to express their gratitude to PubMed for providing valuable resources and information.

Abbreviations

AG: Nab-paclitaxel plus gemcitabine
AKT: Protein kinase B
AML: Acute myeloid leukemia
ANG: Angiogenin
apCAF: Antigen-presenting CAF
ATP: Adenosine triphosphate
ATRA: All-trans retinoic acid
AuNPs: Gold nanoparticles
BC: Breast cancer
BCMA: B-cell maturation antigen
BET: Bromodomain and extra-terminal domain
BEV: Bevacizumab
BMSCs: Bone marrow-derived mesenchymal stromal cells
CAFs: Cancer-associated fibroblasts
CAR: Chimeric antigen receptor
CCA: Cholangiocarcinoma
CCL2: C-C motif chemokine ligand 2
CD: Cluster of differentiation
CDK4/6: Cyclin-dependent kinases 4 and 6
Ce6: Chlorin e6
CEA: Carcinoembryonic antigen
CI: Confidence interval
ClyA: Cytolysin A
CR: Complete response
CRC: Colorectal cancer
cRGD: Cyclic RGD
CRISPR: Clustered regularly interspaced short palindromic repeats
CRT: Calreticulin
CT: Carboplatin/paclitaxel
CTGF: Connective tissue growth factor
CXCL12: C-X-C motif chemokine ligand 12
CXCR4: C-X-C motif chemokine receptor type 4
DARPin: Designed Ankyrin Repeat Protein
DAS: Dasatinib
DC: Dendritic cell
DCN: Decorin
DES: Desmin
DOX: Doxorubicin
DTX: Docetaxel
ECM: Extracellular matrix
EGCG: Epigallocatechin gallate
EVs: Extracellular vesicles
Epi: Epirubicin
EPR: Enhanced permeability and retention
FAK: Focal adhesion kinase
FAP: Fibroblast activation proteinα
FAP-IL2v: FAP-targeted IL-2 variant immunocytokine
FERMT2: Fermitin family member 2
FGF: Fibroblast growth factor
FGF1: Fibroblast growth factor 1
FGFR: Fibroblast growth factor receptor
FGFR2/3: Fibroblast growth factor receptors 2 and 3
FNPs: Ferric oxide NPs
FOLFIRI: 5-Fluorouracil, leucovorin, and irinotecan
FOLFIRINOX: Fluorouracil, leucovorin, irinotecan, and oxaliplatin
FSP1: Fibroblast-specific protein 1
GC: Gastric cancer
GEJ: Gastroesophageal junction
GLUT1: Glucose transporter 1
glyCAF: Glycolytic CAF
GMP: Good Manufacturing Practice
GSH: Glutathione
HA: Hyaluronan
HCC: Hepatocellular carcinoma
HER2: Human epidermal growth factor receptor 2
HGF: Hepatocyte growth factor
Hh: Hedgehog
HIF: Hypoxia-inducible factor
HLA: Human leukocyte antigen
HMGB1: High-mobility group box 1
HMME: Hematoporphyrin monomethyl ether
HR: Hazard ratio
HR+: Hormone receptor-positive
iCAF: Inflammatory CAF
ICD: Immunogenic cell death
IFN-γ: Interferon gamma
IL: Interleukin
IL-1: Interleukin-1
IL-11: Interleukin-11
IL-2: Interleukin-2
IL-15: Interleukin-15
IL-6: Interleukin-6
IL-6R: Interleukin-6 receptor
IL-8: Interleukin-8
IM-CAFs: Immunomodulatory CAFs
JAK: Janus kinase
JAK1/JAK2: Janus kinases 1 and 2
KRAS: Kirsten rat sarcoma viral oncogene homolog
LGSOC: Low-grade serous ovarian cancer
LIF: Leukemia inhibitory factor
LNP: Lipid nanoparticle
LOX: Lysyl oxidase
LOXL2: Lysyl oxidase-like 2
M1: M1-type macrophage
M2: M2-type macrophage
mAb: Monoclonal antibody
MAPK: Mitogen-activated protein kinase
MDSC: Myeloid-derived suppressor cell
meCAF: Metabolic CAF
MEK: Mitogen-activated protein kinase kinase
mesCAF: Mesothelial-derived CAF
mFOLFIRINOX: Modified FOLFIRINOX
mFOLFOX6: Modified FOLFOX6 regimen comprising oxaliplatin, leucovorin, and fluorouracil
MHC II: Major histocompatibility complex class II
MIL-100: Materials of Institut Lavoisier‑100
miRNA: MicroRNA
MMP: Matrix metalloproteinase
MMP-9: Matrix metalloproteinase 9
MOF: Metal-organic framework
MR-CAF: Mechanoresponsive CAF
mRNA: Messenger RNA
MSC: Mesenchymal stromal cell
mt: Mutant
mTOR: Mechanistic target of rapamycin
myCAF: Myofibroblastic CAF
NADPH: Nicotinamide adenine dinucleotide phosphate
NCT: ClinicalTrials.gov identifier
NF-κB: Nuclear factor kappa-light-chain-enhancer of activated B cells
NG2: Neuron-glial antigen 2
NK: Natural killer
NKase: Nattokinase
NMIBC: Non-muscle-invasive bladder cancer
NO: Nitric oxide
NOX4: NADPH oxidase 4
NPs: Nanoparticles
NRG1: Neuregulin 1
NSCLC: Non-small cell lung cancer
OC: Ovarian cancer
OR: Odds ratio
ORR: Objective response rate
OS: Overall survival
PAI-1: Plasminogen activator inhibitor 1
PD-1: Programmed cell death protein 1
PD-L1: Programmed death-ligand 1
PD-L2: Programmed death-ligand 2
PDAC: Pancreatic ductal adenocarcinoma
PDGF: Platelet-derived growth factor
PDGFR: Platelet-derived growth factor receptor
PDGFRα/β: Platelet-derived growth factor receptors α andβ
pDNA: Plasmid DNA
PDPN: Podoplanin
PDT: Photodynamic therapy
PEGPH20: Pegvorhyaluronidase alfa
PET/CT: Positron emission tomography/computed tomography
PFD: Pirfenidone
PFH: Perfluorohexane
PFOB: Perfluorooctyl bromide
PFS: Progression-free survival
PGA: Poly-γ-glutamic acid
PGAM1: Phosphoglycerate mutase 1
PH20: Pyaluronidase PH-20
PI3K: Phosphoinositide 3-kinase
PLGA: Poly(lactic-co-glycolic acid)
PNP: Peptide nanoparticle
PR: Partial response
Pt: Platinum
PYCR1: Pyrroline-5-carboxylate reductase 1
RA: Retinoic acid
RAF: Rapidly accelerated fibrosarcoma
RB1: Retinoblastoma 1
RBC: Red blood cell
RG3: Ginsenoside Rg3
RGD: Arginine-glycine-aspartic acid
RhoA: Ras homolog family member A
ROCK: Rho-associated coiled-coil-containing protein kinase
ROS: Reactive oxygen species
RT: Radiotherapy
SA-β-gal: Senescence-associated β-galactosidase
SASP: Senescence-associated secretory phenotype
SD: Stable disease
SDF-1α: Stromal cell-derived factor-1α
SDT: Sonodynamic therapy
senCAF: Senescent CAF
SFRP2: Secreted frizzled-related protein 2
Shh: Sonic hedgehog
siRNA: Small interfering RNA
SLAMF7: SLAM family member 7
SNAP: S-nitroso-N-acetylpenicillamine
SSL-CAF: Steady-state-like CAF
STAT: Signal transducer and activator of transcription
TAMs: Tumor-associated macrophages
TAT: Transactivator of transcription
TAZ: Transcriptional coactivator with PDZ-binding motif
TGF-β: Transforming growth factor-β
TGF-βR: Transforming growth factor-β receptor
TGF-βR1: Transforming growth factor-β receptor type 1
TME: Tumor microenvironment
TLS: Tertiary lymphoid structure
TNBC: Triple-negative breast cancer
TNF-α: Tumor necrosis factorα
Treg: Regulatory T cell
UC: Uterine cancer
vCAFs: Vascular/perivascular CAFs
VEGF: Vascular endothelial growth factor
VIM: Vimentin
wt: Wild type
YAP: Yes-associated protein
ZIF-8: Zeolitic imidazolate framework-8
α-SMA: α-Smooth muscle actin

Author contributions

Y. J. conceptualized this work and drafted the original manuscript. B. D., H. Y., Y. H., and N. M. conducted the literature search and analysis, and critically revised the manuscript for important intellectual content. P. H. and J. D. developed the figures and visual illustrations. F. W. and Y. S. provided supervision, resources, and final approval of the version to be submitted.

Funding

This study was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2025ZD0545600), the National Clinical Key Discipline Construction Funds (No. Gynecology czxm-zk-40), the National Natural Science Foundation of China (No. 82372126, 82072078), Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital (No. 2023GSPKY11).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Fu-Gen Wu, Email: wufg@seu.edu.cn.

Yang Shen, Email: shenyang@seu.edu.cn.

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Associated Data

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

Data Availability Statement

No datasets were generated or analysed during the current study.

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PERMALINK

Targeting cancer-associated fibroblasts: therapeutic strategies, translational challenges, and future perspectives

Yanping Jiang

Bo Ding

Han Yin

Yue Hua

Ning Ma

Pengcheng Hu

Jianbin Deng

Fu-Gen Wu

Yang Shen

Abstract

Introduction

Table 1.

Table 2.

Conceptual frameworks of CAF-targeting strategies

Pharmacological and biologic CAF-targeting therapeutics

Fig. 1.

Table 3.

ECM-targeting agents

Inhibitors of CAF-activation receptors and intracellular signaling

Neutralizing agents targeting CAF-related soluble mediators

CAF reprogramming agents

FAP-targeted bispecific and immunomodulatory biologics

Comparative analysis of CAF-targeting therapeutic classes

Challenges for clinical translation of CAF-targeting therapeutics

CAF-targeting NP platforms

Table 4.

Fig. 2.

Anti-ECM antitumor NPs

Nucleic acid-based antitumor NPs

Metal-based multifunctional NPs

Comparative analysis of CAF-targeting NP platforms

Challenges for clinical translation of CAF-targeting NP platforms

Biologically inspired and living platforms for CAF-targeting therapy

Table 5.

Biomimetic membrane-based nanoplatforms for CAF-targeting therapy

Engineered bacteria for CAF-targeting strategy

CAF-based/CAF-related biomimetic tumor traps

CAF-targeted engineered cell therapies

Comparative analysis of biologically inspired and living platforms for CAF-targeting therapy

Challenges for clinical translation of biologically inspired and living platforms

Cross-modality comparison and translational lessons

Future perspectives

Translational roadmap from CAF-state discovery to clinical implementation

Fig. 3.

Key translational priorities for CAF-targeting therapy over the next decade

Emerging technologies and interdisciplinary collaboration for CAF state-associated biomarker discovery and precision nanomedicine

A hypothesis-generating sequential cancer cell/CAF dual targeting strategy

Fig. 4.

Conclusions

Acknowledgements

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Data Availability Statement

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