Renal cell carcinoma organoids for precision medicine: bridging the gap between models and patients

Abstract

Renal cell carcinoma (RCC) poses significant challenges to precision oncology due to its pronounced molecular heterogeneity and complex tumor microenvironment (TME). Traditional two-dimensional (2D) cultures and animal models fall short in capturing patient-specific tumor biology, limiting their translational relevance. In contrast, three-dimensional (3D) organoid platforms offer structurally and functionally representative models that retain key pathological and pharmacological features of RCC. Notably, RCC organoids enable pharmacokinetic assessment, including drug penetration, metabolic activation, and off-target toxicity in a spatially organized context. This review outlines current strategies for RCC organoid construction-including patient-derived organoids (PDOs), air–liquid interface (ALI) cultures, scaffold-based matrices, and microfluidic systems-and evaluates their applications in drug screening, resistance modeling, and immunotherapy prediction. We further discuss technical and biological limitations such as phenotypic drift, inter-sample variability, and TME reconstruction, alongside emerging solutions in synthetic scaffolds, immune co-cultures, and multi-omics integration. In conclusion, RCC organoids are rapidly evolving into clinically actionable platforms, offering a scalable and predictive approach to personalized therapy in renal oncology.

Introduction

Renal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, with a steadily rising global incidence over the past decades. According to the 2022 GLOBOCAN statistics, approximately 430,000 new RCC cases and 160,000 deaths occur annually worldwide, posing a major public health concern [1]. Although early-stage RCC can often be effectively managed with surgical resection, treatment options for advanced or metastatic RCC remain limited. The current standard first-line therapies for advanced clear cell renal cell carcinoma (ccRCC) consist of immune checkpoint inhibitors (ICIs) combined with tyrosine kinase inhibitors (TKIs), such as nivolumab plus cabozantinib or pembrolizumab plus axitinib. Recent extended follow-up from large phase III trials-CheckMate 9ER (2024) and KEYNOTE-426 (2023)-demonstrated objective response rates (ORRs) of 56.5% and 60%, respectively, with median progression-free survival (PFS) reaching 16.6 months for the nivolumab–cabozantinib arm and 15.7 months for the pembrolizumab–axitinib arm [2–6]. Despite these improvements, approximately 30%–40% of patients exhibit primary resistance, and a significant proportion eventually develop acquired resistance, underscoring the urgent need for predictive and mechanistically informative preclinical models, such as PDOs.

A hallmark of RCC is its pronounced histological and molecular heterogeneity, which further compounds the complexity of treatment resistance and disease modeling across different subtypes.The major subtypes include clear cell RCC (ccRCC, accounting for 75%–80% of cases), papillary RCC (pRCC, 10%–15%), and chromophobe RCC (chRCC, %) [7]. ccRCC is typically characterized by inactivation of the VHL gene, loss of chromosome 3p, and sustained activation of the HIF signaling pathway. pRCC is often associated with MET activation and chromosomal gains, whereas chRCC features widespread chromosomal losses [8]. These molecular alterations not only drive tumorigenesis but also remodel the tumor microenvironment (TME), shaping immune phenotypes and modulating therapeutic responses. As RCC progresses, tumor-associated macrophages (TAMs) tend to increase while the function of tumor-infiltrating lymphocytes (TILs) becomes markedly impaired, contributing to an immunologically exhausted TME [9, 10]. This immune heterogeneity is a major factor underlying variable responses to immunotherapy among RCC patients.

However, traditional preclinical models-including two-dimensional (2D) cell cultures and animal systems-fall short in capturing these intricate features. 2D cell lines are prone to genetic drift and fail to preserve tumor heterogeneity and tissue architecture, restricting their utility for simulating disease progression or predicting personalized treatment outcomes [11, 12]. While animal models can recapitulate some aspects of the tumor milieu, they are constrained by interspecies differences, ethical considerations, high costs, and long experimental timelines, limiting their translational applicability in clinical settings [13]. Consequently, there is a pressing need for advanced in vitro models that can faithfully replicate the structural and functional characteristics of primary tumors while accurately predicting therapeutic responses.

Recent breakthroughs in three-dimensional (3D) organoid systems have ushered in a new era for precision oncology in RCC. Organoids are self-organizing, miniaturized 3D structures derived from stem cells or tissue-resident cells, capable of recapitulating the spatial architecture, genetic background, and cellular heterogeneity of the original tumor [14]. Since the first successful generation of intestinal organoids from Lgr5 stem cells by Sato et al. in [107], this technology has been widely applied to various solid tumors, including breast and colorectal cancers, significantly improving the predictive value of preclinical drug screening [14–16]. The integration of organoids with cutting-edge platforms-such as single-cell sequencing, multi-omics profiling, and microfluidic systems-has further enhanced their fidelity in modeling the TME and their potential for clinical translation [17].

Notably, in the context of RCC, Chen et al. were among the first to demonstrate that PDOs not only preserve the genomic and histological features of primary tumors but also reliably predict individual responses to targeted agents and immunotherapies, underscoring their promise for personalized treatment [18]. Nonetheless, their clinical translation is still hampered by several critical challenges, including suboptimal establishment efficiency, phenotypic instability, and incomplete integration of the TME, which collectively limit their scalability and integration into routine clinical workflows.This review aims to provide a comprehensive overview of the current methodologies and progress in RCC organoid construction, highlight their applications in drug sensitivity testing, resistance mechanism analysis, and immunotherapy prediction, and discuss key technical and translational challenges. We further propose future directions for optimizing organoid platforms to enhance their clinical utility in RCC precision medicine.In recent years, organoid technologies have emerged as powerful tools for modeling tumor heterogeneity, predicting therapeutic response, and guiding individualized treatment strategies. In renal cell carcinoma (RCC), patient-derived organoids (PDOs) are increasingly recognized for their ability to preserve key histological, genomic, and pharmacologic characteristics of primary tumors. RCC PDOs have been successfully employed in drug sensitivity assays, tumor–immune interaction studies, and modeling of radiotherapy responses, offering a bridge between basic research and precision oncology. However, the translation of RCC PDOs into clinical applications remains constrained by variable success rates across subtypes, long culture durations, and lack of standardized protocols. The present review aims to critically evaluate the current state of RCC organoid research, identify methodological and translational bottlenecks, and highlight forward-looking strategies that can accelerate the clinical integration of these models.

Strategies and methodologies for constructing RCC organoids

Molecular basis of RCC subtypes and their implications for organoid modeling

Although the morphological and epidemiological characteristics of RCC have been outlined in the introduction, a subtype-specific molecular perspective is essential for organoid-based modeling strategies. Among the various subtypes of renal cell carcinoma, clear cell RCC (ccRCC) remains the most thoroughly investigated and effectively replicated through organoid-based approaches. A characteristic genomic alteration in ccRCC is the frequent deletion of chromosome 3p, which commonly results in the loss of function of the VHL tumor suppressor gene. This genetic disruption leads to continuous activation of hypoxia-inducible factor (HIF) signaling, thereby promoting metabolic alterations and facilitating cellular adaptation under hypoxic conditions [19, 20]. Furthermore, mutations in key epigenetic regulators-such as PBRM1, SETD2, and BAP1-are frequently observed in ccRCC, disrupting cell polarity, chromatin remodeling, and DNA damage repair mechanisms, thereby influencing both the differentiation capacity and long-term stability of organoid cultures [21].In contrast, papillary RCC (pRCC) presents greater challenges for organoid generation. Type I pRCC is characterized by chromosomal gains in chromosomes 7 and 17 and persistent MET activation, rendering it highly dependent on hepatocyte growth factor (HGF) signaling. As a result, culture conditions often require enhanced supplementation of proliferative factors such as EGF. Type II pRCC, commonly associated with CDKN2A deletions, is more aggressive and exhibits unstable growth dynamics in vitro, frequently leading to structural collapse and heterogeneous proliferation during organoid formation [22–24].

Chromophobe RCC (chRCC) is distinguished by widespread chromosomal losses, particularly involving chromosomes 1, 2, 6, 10, 13, 17, and 21 [25, 26]. Due to its weak cell cycle control and low proliferative activity, chRCC organoids are exceedingly difficult to establish and are currently considered the least stable subtype in RCC organoid systems.

Mechanistically, the difficulty in establishing chRCC organoids is closely related to its distinct molecular background. Loss-of-function mutations in TP53, observed in approximately 30–35% of chRCC cases, compromise genomic stability and disrupt G1/S and G2/M checkpoint regulation, leading to mitotic arrest or apoptotic escape. In parallel, concurrent HNF1B deficiency has been shown to impair expression of key spindle assembly checkpoint components such as MAD2L1 and BUB1B, as well as cell cycle regulators like p27 and RB1, thereby contributing to chromosomal instability and defective mitotic progression [27, 28]. These molecular alterations impair the fidelity of cell division and may explain the low proliferative capacity and fragility of chRCC organoids in vitro. In addition, chRCC cells maintain high mitochondrial content and rely on oxidative phosphorylation, which induces chronic oxidative stress and ER stress, further compromising cellular viability under ex vivo conditions [29]. Together, these features help explain the frequent failure of chRCC organoids to sustain long-term proliferation or structural integrity.

Studies have shown that organoid formation efficiency and maintenance vary significantly among RCC subtypes, each requiring subtype-specific niche factors for optimal growth [24].

Several biological and technical factors underlie the highly variable success rates of RCC organoid cultures. Necrotic tumor cores often lack viable epithelial progenitor cells, resulting in reduced regenerative capacity. Mucinous or fibrotic histologies, more frequently encountered in papillary and collecting duct RCC, further hinder matrix embedding and organoid initiation. Technical parameters, such as prolonged cold ischemia time during tissue processing, have been shown to compromise cell viability and organoid-forming potential. Moreover, the choice between mechanical and enzymatic dissociation significantly influences outcome: overly harsh mechanical dissociation may shear fragile tumor cells, while extended enzymatic digestion can degrade surface proteins necessary for self-organization. These observations are consistent with prior reports from colorectal and pancreatic organoid protocols, where tissue viability and digestion quality were key determinants of establishment efficiency [30, 31]. Standardization of tissue handling and optimization of dissociation protocols remain critical for improving reproducibility across RCC subtypes.

The 2022 WHO update on renal tumor taxonomy highlighted the existence of over 20 infrequent RCC variants, among which are collecting duct carcinoma (CDC), renal medullary carcinoma (RMC), and RCC arising in the context of acquired cystic kidney disease [32]. Although organoid modeling for these rare variants is still in its infancy, increasing sample availability and advances in molecular annotation are expected to facilitate future expansion of organoid applications in these subtypes.

However, a persistent challenge across RCC organoid cultures is the tendency for long-term instability due to both genetic and epigenetic evolution. Over extended passages, organoids can undergo genetic drift–the stochastic accumulation of new mutations–as well as clonal selection driven by in vitro conditions [33, 34]. These processes often lead to loss of intratumoral heterogeneity and can precipitate growth instability (e.g. some aggressive organoid lines undergoing structural collapse and uneven proliferation). Epigenetic alterations compound this instability: prolonged culture is linked to broad transcriptomic shifts and chromatin modifications as cells adapt to artificial conditions [35]. Collectively, these genomic and epigenomic changes may cause RCC organoids to diverge from the original tumor profile, undermining their predictive accuracy for therapy responses – for instance, if a rare subclone outgrows others, the organoid’s drug sensitivities may no longer reflect the patient’s tumo [33]. To mitigate phenotypic drift and genetic instability, current best practices include limiting organoid passage number (typically P5) and cryopreserving early-passage cultures to periodically “reset” the phenotype and genomic state, thus preserving fidelity for downstream assays [15].

Optimizing defined media to minimize undue selection pressure and incorporating supportive niche factors are also being explored. For example, co-culturing organoids with stromal or immune cells is hypothesized to better maintain the tumor’s original microenvironment signals and genomic fidelity [36].

In summary, the distinct molecular landscapes of RCC subtypes dictate their specific nutritional requirements, signaling dependencies, and microenvironmental adaptations. Accordingly, the rational design of organoid models must be tailored to the genomic and phenotypic profiles of each subtype to enhance modeling fidelity and efficiency. This level of precision is critical for downstream applications such as high-throughput drug screening, immune co-culture assays, and patient-specific therapeutic testing.

To support this framework, we constructed Table 1, which compares the molecular characteristics of major RCC subtypes and highlights key considerations for successful organoid establishment. To improve clarity and reproducibility, organoid success rates in Table 1 are stratified into five quantitative categories based on reported establishment efficiencies in original studies: Very low (<10%), Low (10–30%), Moderate (30–60%), Moderate to high (60–80%), and High (>80%). These thresholds are informed by empirical data from RCC organoid studies and benchmarked against organoid establishment rates in other epithelial tumors, including colorectal, gastric, and glioblastoma models [15, 31, 37–41].

Table 1.

Molecular characteristics of renal cell carcinoma subtypes and key considerations for organoid modeling

Subtype	Molecular features	Key signaling pathways	Organoid modeling challenges	Optimization strategies	Reported organoid success rate	References
ccRCC	VHL gene inactivation; 3p chromosomal loss	HIF, VEGF/ANGPT2	Requires hypoxic conditions and Wnt support	Optimized using 5% O tension + R-spondin1/Wnt3a	Moderate to high(60-–80%)	[30][38–40][129]
pRCC-I	Chromosome 7/17 amplification; MET pathway activation	c-MET/HGF	Exhibits unstable proliferation	EGF and HGF supplementation	Moderate(30-–60%)	[22–24] [85]
pRCC-II	CDKN2A deletion; metabolic dysregulation	c-MET/HGF	Prone to structural collapse	Low-serum medium recommended	Low(10-–30%)	[22–24] [85]
chRCC	Broad loss of chromosomes (1, 2, 6, 10, 13, 17, 21)	DNA replication stress; oxidative stress; KIT	Very slow growth and low viability	Long-term culture and ECM customization	Very low(<10%)	[24–26] [88]
Rare RCCs (e.g., CDC, RMC)	Subtype-specific mutations or translocations	Subtype-dependent	Scarce samples and undefined protocols	Stromal/immune co-culture and custom nutrient environments recommended	Not systematically reported	[30]

ccRCC: clear cell renal cell carcinoma; pRCC: papillary RCC; chRCC: chromophobe RCC; CDC: collecting duct carcinoma; RMC: renal medullary carcinoma; ECM: extracellular matrix; HGF: hepatocyte growth factor.Organoid success rate categories are defined as: Very low (<10%), Low (10–30%), Moderate (30–60%), Moderate to high (60–80%), High (>80%), based on reported establishment efficiencies in original RCC organoid studies and comparable epithelial tumor models

Looking forward, the development of subtype-specific modeling strategies is essential to overcoming the biological limitations discussed above. For example, type I papillary RCC (pRCC) organoids with MET amplification may benefit from media supplemented with selective MET inhibitors (e.g., crizotinib or capmatinib) to promote controlled proliferation while maintaining oncogenic signaling [42, 43]. For chromophobe RCC (chRCC), the incorporation of antioxidant-enriched scaffolds or low-ROS culture media may help mitigate the oxidative stress–induced growth arrest observed in vitro [44]. In addition, the application of ROCK inhibitors or mitotic checkpoint modulators could partially restore proliferative capacity in chRCC lines with TP53 and HNF1B deficiencies [27, 45]. These strategies, although currently unvalidated in RCC, are inspired by similar approaches in pancreatic, lung, and neuroendocrine organoid systems and may offer a conceptual roadmap for improving RCC subtype representation in future organoid biobanks.

Renal cancer PDOs are most commonly derived from tumor tissues harvested during surgery or biopsy, followed by enzymatic dissociation into epithelial aggregates or single cells. They are then maintained in 3D culture systems using ECM-derived hydrogels like Matrigel or BME to support organoid growth and structural organization. [46, 47]. Growth media enriched with factors like Wnt3a, R-spondin1, and Noggin support epithelial regeneration. This approach preserves tumor-specific genomic alterations and partially recapitulates elements of the tumor microenvironment, facilitating personalized therapeutic assessments.

Organoids derived through this process preserve the genomic integrity and heterogeneity of the original tumor and may partially recapitulate elements of the TME, including immune and stromal interactions, thus enabling personalized assessment of drug and immunotherapeutic responses [48–50].

A key study demonstrated that PDOs exhibit high concordance with their corresponding primary tumors in terms of histoarchitecture, mutational landscape, and transcriptomic profiles, as verified by histological staining, immunofluorescence, whole-exome sequencing, RNA-seq, and single-cell RNA-seq analyses [24].

Recent studies have demonstrated that the tissue source from which PDOs are derived significantly influences organoid establishment rates, growth kinetics, and fidelity to the original tumor architecture. For instance, samples from radical nephrectomy often provide larger viable tumor areas with fewer necrotic cores, facilitating higher PDO formation efficiency. In contrast, needle biopsies frequently yield limited, heterogeneous samples that are more susceptible to culture failure due to low tumor cell density or sampling bias toward non-representative regions [30, 31]. Partial nephrectomy tissues, while surgically conservative, may include adjacent non-malignant parenchyma, increasing the risk of normal epithelial overgrowth in culture. These variations in specimen origin underscore the need to optimize dissociation protocols and pre-analytical handling based on tissue type to maximize organoid viability and tumor-specific representation.

However, this approach presents several technical challenges. One major issue is the overgrowth of non-malignant epithelial cells during in vitro expansion, which can compromise tumor specificity. For instance, a study on lung cancer PDOs reported that 58% of culture failures were due to the dominance of normal epithelial cells [48]. Although optimized culture conditions have been shown to minimize this problem in RCC PDOs, inter-tumor and inter-patient variability in culture responsiveness remains substantial [51].

Moreover, PDO generation is inherently reliant on access to surgical or biopsy-derived tissue, which limits its application in clinical settings involving sampling difficulties or significant intratumoral heterogeneity. This limitation underscores the need for more flexible tissue acquisition and culture strategies to broaden PDO utility across diverse clinical contexts.

In light of these limitations, future efforts should focus on tailoring culture strategies to address the subtype-specific biological features of RCC. For example, clear cell RCC (ccRCC), which is characterized by VHL inactivation and HIF pathway activation, creates a pseudohypoxic microenvironment that may require low-oxygen tension or hypoxia-responsive scaffolds to improve organoid viability and preserve molecular fidelity. Emerging biomaterials such as PEG- or GelMA-based hydrogels with tunable oxygen diffusivity have been successfully used to simulate low-oxygen tumor conditions in other organoid systems and may be adapted to ccRCC models in future studies [52, 53]. Conversely, chromophobe RCC (chRCC), which shows strong oxidative stress signatures and frequent TP53 mutations, may benefit from antioxidant-enriched matrices that counteract reactive oxygen species–induced differentiation arrest [54, 55]. Although such adaptations are still under development, their integration could enhance subtype-specific fidelity in RCC organoid cultures.

Although technically more challenging to establish than PDOs, iPSC-derived cancer organoids offer unique advantages for studying hereditary cancers. By integrating CRISPR-based genome editing, specific cancer-associated mutations can be introduced into normal organoid systems to generate subtype-specific tumor models [56–58]

For patients who carry oncogenic germline mutations but have not yet developed overt disease, iPSC technology enables the creation of organoid models that recapitulate early oncogenesis. For example, organoids have been successfully derived from iPSCs of patients with Li-Fraumeni syndrome [59]. Recent studies have also demonstrated that overexpression of the KRAS G21D oncogene can induce pancreatic tumorigenesis in vitro [60], and that RAS-mutated alveolar type II (AT2) cells derived from iPSCs can model lung adenocarcinoma-like genomic programs in 0% of cases [61]. While iPSC-derived organoids have been successfully applied to model pancreatic and lung cancers, these systems typically benefit from well-defined epithelial lineage markers and robust tumor-initiating mutations. In contrast, developing reliable iPSC-derived organoid models of RCC-particularly the clear cell subtype (ccRCC)-remains especially challenging due to the lack of lineage-specific differentiation protocols and the requirement for multiple cooperating driver mutations, setting it apart from cancers like pancreatic or lung tumors.Compared to other tumor types or normal kidney organoids, the development of iPSC-derived ccRCC organoids is hindered by several unique biological and technical barriers. First, the precise cell of origin for ccRCC remains unclear, complicating efforts to direct iPSCs toward a lineage capable of recapitulating key pathological and molecular features [62]. Second, faithful modeling of ccRCC requires the stable introduction of multiple driver mutations (e.g., VHL, PBRM1, BAP1, SETD2), a process that remains inefficient and prone to off-target effects or genomic instability in iPSCs [63, 64]. Furthermore, iPSC-derived kidney organoids tend to preferentially differentiate into distal nephron lineages, limiting the generation of proximal tubular epithelial cells-the presumed origin of ccRCC [65]. To overcome these differentiation barriers and facilitate the study of hereditary ccRCC, recent studies have proposed the integration of iPSC technology with CRISPR/Cas9-mediated gene editing to model germline mutations such as VHL. For example, Stransky et al. successfully established a Vhl-deficient mouse model using AAV-delivered CRISPR/Cas9 under kidney-specific promoters (Cdh16, Pax8), which recapitulated key features of human ccRCC, including HIF pathway activation and lipid-rich clear cell histology [66]. Although developed in vivo, this strategy provides a conceptual framework that can be extended to human iPSC-derived kidney organoids. By introducing pathogenic VHL mutations into iPSCs from healthy individuals or patients with von Hippel–Lindau syndrome, researchers can generate pre-malignant organoid models that reflect early tumorigenic events under controlled genetic backgrounds. Such models would be particularly valuable for dissecting stepwise disease progression, evaluating mutation cooperativity, and testing early intervention strategies in a personalized context.

Even when tumorigenic mutations are successfully introduced, tumorigenic cells are often outcompeted by normal epithelial progenitors during prolonged culture, leading to the progressive loss of oncogenic phenotypes [67]. Finally, ccRCC is characterized by a complex TME enriched in vasculature, stromal elements, and immune infiltrates, none of which are faithfully reproduced in current iPSC-based systems [57]. Collectively, these limitations underscore the complexity of modeling ccRCC from iPSCs and highlight the need for refined differentiation protocols, multi-lineage co-culture platforms, and spatial tissue engineering strategies.

Notably, significant progress has been achieved in modeling hereditary kidney diseases.For instance, the Nishinakamura group successfully recapitulated congenital nephrotic syndrome pathology using iPSC-derived kidney organoids and corrected the causative mutation to restore organoid function [68].

With the ongoing maturation of iPSC-based techniques across organ systems [69, 70], it is anticipated that iPSC-derived RCC models will be increasingly utilized over the next decade.

Urine-Derived Tubuloids: A Noninvasive Approach

To circumvent invasive tissue acquisition, Frans and colleagues were the

first to establish renal tubuloids by isolating tubular epithelial cells

from human urine, offering a promising strategy for noninvasive organoid

modeling [71]. This approach overcomes the limitations of surgical or

biopsy sampling and is particularly suitable for RCC patients who are

inoperable, harbor occult tumors, or require longitudinal disease

monitoring.

Although no successful reports of RCC organoid generation from urine

have been published to date, the approach holds future potential. However, several biological and technical obstacles limit its current feasibility. Although urine is a non-invasive and readily accessible source of exfoliated cells, its utility in generating renal cell carcinoma (RCC) organoids remains unproven and technically challenging. In contrast to urothelial malignancies such as bladder cancer-where exfoliated tumor cells are relatively abundant and have enabled the successful generation of urine-derived organoids (urinoids) in over 50% of cases-RCC tumor cells are rarely shed into urine, resulting in markedly low cell yields and limited viability for downstream culture [72, 73]. Moreover, urine typically contains a substantial population of normal renal epithelial cells, particularly from the distal and collecting tubules, which exhibit strong proliferative capacity and can rapidly dominate in vitro cultures, thereby masking or outcompeting malignant clones [71, 74]. This cellular heterogeneity presents a major barrier to selectively enriching RCC cells. Distinguishing tumor cells from normal epithelial contaminants is further complicated by the absence of reliable surface markers or lineage reporters specific to malignant renal epithelial subtypes.

In addition, multiple studies have shown that renal cell carcinoma (RCC) is a relatively low-shedding tumor, Of all extra-cranial tumors, RCC sheds the least amount of cfDNA [75]. Detection of tumor-derived material in urine is even more challenging due to its lower analyte concentration and contamination by normal epithelial cells, particularly from the distal nephron, which further complicates downstream isolation and expansion of malignant clones [76]. These limitations highlight the current inadequacy of urine as a source for either RCC organoid generation or noninvasive liquid biopsy in clinical practice.

While the use of PCR-Activated Cell Sorting (PACS) or single-cell genomic profiling may offer potential avenues for prospective isolation, such approaches remain labor-intensive and are not yet standardized [77, 78]. Consequently, the utility of urine-derived cells for RCC organoid generation is currently limited, and further methodological advances are required to overcome these biological and technical constraints.

By enriching for circulating tumor cells (CTCs) or exfoliated tumor cells and integrating advanced techniques such as magnetic bead sorting and microfluidic culture systems, this strategy may enable noninvasive, precision modeling of urologic cancers in clinical practice.

Optimization of culture systems and key technical considerations

Matrigel is one of the most widely used commercial extracellular matrix (ECM) substitutes in tumor and three-dimensional (3D) culture systems. It plays a more critical role in organoid development than previously recognized. Matrigel is primarily composed of collagen, fibronectin, laminin, proteoglycans, and other ECM proteins and glycoproteins [79].

The ECM not only provides mechanical support for cell adhesion and migration but also serves as a reservoir for growth factors. Through mechanotransduction, it regulates key signaling pathways that control cellular proliferation, survival, morphology, adhesion, and motility [79]. During tumor progression, stromal fibroblasts within the TME are activated by hypoxia, oxidative stress, and various cytokines and growth factors, differentiating into cancer-associated fibroblasts (CAFs). These CAFs secrete aberrant ECM components that facilitate tumor growth and metastasis, making them critical structural and functional contributors to the tumor niche [79]. Aberrant activation of ECM-remodeling gene programs has been significantly correlated with poor prognosis across multiple malignancies, including those of the breast, lung, and stomach [80, 81]. Therefore, accurately replicating the ECM is essential for improving the predictive fidelity of tumor organoid models in precision medicine.

At present, the majority of tumor organoids are grown within basement membrane extracts (BMEs) obtained from Engelbreth-Holm-Swarm (EHS) mouse sarcomas, including commonly used matrices such as Matrigel, Geltrex, and Cultrex BME. These natural ECMs contain essential proteins for organoid growth but suffer from batch-to-batch variability and undefined composition, posing challenges for reproducibility and potential immunogenicity [82]. Among ECM components, collagen is the most abundant and plays a crucial role in maintaining tissue integrity and modulating the TME [83].

To overcome the inherent limitations of traditional matrices, synthetic hydrogels have emerged as promising alternatives. These three-dimensional polymeric frameworks are designed to recapitulate the biochemical and biomechanical characteristics of the native extracellular matrix (ECM), while also facilitating the efficient exchange of gases, nutrients, metabolites, and waste. Among them, polyethylene glycol (PEG) is widely employed due to its high hydrophilicity and adjustable mechanical properties. By integrating PEG-based polymers with natural ECM constituents, hybrid hydrogel systems have been engineered to provide a stable and tunable microenvironment conducive to organoid growth [84]. These matrices have been validated in intestinal and colonic organoid systems [84, 85], and their application to RCC models offers opportunities for improving physiological relevance. PEG-fibrinogen hydrogels, for instance, enhance epithelial polarity and support lumen formation in kidney-derived organoids [85, 86], although their comparative efficacy in supporting subtype-specific features of RCC PDOs-such as preserving cell polarity, metabolic gradients, and architecture of clear cell RCC-remains insufficiently characterized and warrants further investigation in a subtype-stratified manner. However, comparative benchmarking of these scaffolds in RCC PDOs remains largely anecdotal, underscoring the need for standardized validation protocols.

Emerging hydrogel platforms such as gelatin-methacryloyl (GelMA), alginate, and hyaluronic acid–based matrices are gaining increasing attention due to their tunable biophysical properties, photopolymerization compatibility, and excellent biocompatibility. GelMA hydrogels, in particular, have been shown to support nephron-like structure formation and promote differentiation in hiPSC-derived kidney organoids [87]. Additionally, hybrid GelMA–hyaluronic acid hydrogels exhibit enhanced mechanical robustness and controlled molecular transport suitable for epithelial organoid cultures [88]. Despite this promising evidence, their application in RCC PDOs is still limited, and subtype-specific validation studies are currently lacking. Therefore, establishing standardized protocols for comparative benchmarking of these materials across RCC subtypes is critical for translational adoption.

To better replicate the metabolic and biophysical diversity of RCC subtypes, future iterations of synthetic matrices may incorporate features such as ROS-scavenging capability, stiffness modulation, or hypoxia-mimetic gradients. For instance, soft hydrogels with enhanced oxygen permeability may better simulate the pseudohypoxic microenvironment in ccRCC, while antioxidant-enriched scaffolds may provide a more supportive milieu for chRCC cultures burdened by oxidative stress [44, 89]. Additionally, integration of microfluidic perfusion systems has shown promise in enhancing nutrient and gas exchange and may serve as a complementary platform to optimize matrix–cell interactions in RCC organoid cultures [90].

The Wnt/-catenin signaling cascade is integral to the maintenance of tumor organoid cultures, influencing essential cellular events including proliferation, structural patterning, and metastatic behavior through its control over cell cycle and tissue morphogenesis [91]. Maintenance of cancer stem cell (CSC) self-renewal and pluripotency critically depends on active Wnt signaling [92, 93]. This involves not only transcriptional reprogramming but also modulation of cytoskeletal dynamics and mitotic spindle integrity.

R-spondin acts as a Wnt signal enhancer by binding to Lgr5 receptors, inhibiting Wnt receptor degradation, and sustaining pathway activation, thereby promoting stem cell expansion [94] Noggin is a key inhibitor of the bone morphogenetic protein (BMP) pathway. It antagonizes BMP family members by preventing their interaction with receptors, modulating cellular responses to differentiation signals. In intestinal organoid models, Noggin depletion leads to decreased Lgr5 expression, compromising stem cell proliferation and maintenance [14].

Epidermal growth factor (EGF) binds to its receptor EGFR and activates downstream MAPK, PI3K/AKT, and JAK/STAT pathways, which markedly enhance cell proliferation and survival. Notably, EGF signaling exhibits dynamic feedback regulation, which may influence organoid dependence on EGF during extended culture [95].

Importantly, distinct RCC subtypes exhibit differential dependence on growth factor cues. Papillary RCC (pRCC) organoids often require hepatocyte growth factor (HGF) to maintain MET signaling activity and epithelial proliferation, reflecting MET pathway activation in pRCC tumors [96] In contrast, clear cell RCC (ccRCC) organoids tend to respond more robustly to EGF supplementation, consistent with EGFR overexpression driving rapid cell proliferation in ccRCC tissues [97, 98].Chromophobe RCC (chRCC), although less prevalent, demonstrates consistent overexpression of KIT, indicating potential reliance on receptor tyrosine kinase signaling for growth and survival [99]. These subtype-specific requirements underscore the need for tailored growth factor cocktails for effective organoid derivation and expansion.

Collectively, Wnt, R-spondin, Noggin, and EGF orchestrate a network of signaling pathways that maintain stemness and structural integrity during tumor organoid culture. Their coordinated activity is essential for successful organoid establishment and long-term maintenance. These subtype-specific requirements are also summarized in Table 1 for further reference in media optimization strategies.

Sato et al. first proposed a systematic organoid culture system using DMEM/F12 as the basal medium [14], which has since become widely adopted across various organoid platforms [100]. While some studies have shown that replacing DMEM/F12 with physiologic media does not significantly alter chemosensitivity in pancreatic cancer organoids [101], nutrient compositions vary considerably across media. For instance, MEM, DMEM, RPMI, and Ham’s F12 differ notably in amino acid concentrations, ranked from highest to lowest: MEM >DMEM >RPMI >Ham’s F12.

Interestingly, tumor cells with greater invasive potential often demonstrate reduced dependence on nutrient-rich media. DMEM supports rapid proliferation, whereas Ham’s F12 promotes differentiation and the formation of stable 3D structures [102]. The DMEM/F12 mixture balances these attributes by sustaining both proliferative capacity and structural integrity, making it a favorable choice for tumor organoid culture.

There remains ongoing debate about whether physiologic culture media can more faithfully mimic the in vivo TME. In reality, tumors develop unique metabolic microenvironments that are distinct from standard in vitro conditions [103]. Due to its nutrient richness and physicochemical stability, DMEM/F12 remains the standard medium for constructing tumor organoids and mimicking tumor-like microenvironments. However, tumors exhibit intrinsic differences in metabolic demands, cell-cell interactions, and signaling dynamics. Future advances may involve customizing basal media formulations-such as adjusting glucose/amino acid ratios or adding tumor-specific cytokines-to enhance predictive accuracy in personalized medicine.

Notably, the metabolic profiles of RCC subtypes vary considerably, and future refinement of basal media formulations may further enhance modeling fidelity. ccRCC, for instance, demonstrates a marked dependence on glutamine metabolism and exhibits altered glycolytic flux due to VHL loss and HIF- stabilization [104, 105]. Customized media incorporating subtype-specific metabolite concentrations-such as low-glucose, glutamine-rich, or lactate-buffered formulations-may offer improved support for tumor-specific growth kinetics. These innovations, especially when combined with microfluidic bioreactors, could also accelerate culture timelines for personalized therapeutic testing.

Advanced modeling strategies and technological innovations in RCC organoids

Comparison of three-dimensional culture platforms

Matrix-embedded culture

The conventional method for generating RCC organoids involves mechanical and enzymatic dissociation of tumor tissues obtained from surgical resection or biopsy to yield cell clusters or single-cell suspensions. These are then mixed with Matrigel or other basement membrane extracts (BMEs) to form semi-solid domes and cultured in defined media that support epithelial cell adhesion, expansion, and three-dimensional differentiation into organoid structures [106]. Matrigel serves as a scaffold that mimics the extracellular matrix (ECM), supporting spatial cell growth and epithelial morphogenesis.

The culture medium is typically supplemented with Wnt pathway agonists (e.g., R-spondin1), epidermal growth factor (EGF), and fibroblast growth factors to maintain stemness and promote organoid development. This classical approach was originally developed by Sato et al. for intestinal stem cells and has since been widely adopted for modeling various solid tumors, including RCC [14, 107]. In 2022, Li et al. successfully established multiple RCC organoid lines using this technique, which preserved both the histological architecture and genomic characteristics of their tumors of origin [24].

Despite its maturity and broad application, matrix-based culture systems face limitations such as batch-to-batch variability, undefined composition, and potential immunogenicity of natural ECM components [108]. To address these concerns, synthetic hydrogels-such as polyethylene glycol (PEG)-based and self-assembling peptide hydrogels-have been developed as alternatives to more precisely mimic the mechanical environment of renal tissues [109].

Tumor microenvironment reconstruction: ALI and microfluidics

The air-liquid interface (ALI) culture system allows more comprehensive reconstruction of the TME. By establishing an interface between air and culture medium on a Transwell membrane, the ALI method enables preservation of native tissue architecture, including epithelial layers, immune cells, and stromal components, without dissociation [38].

Neal et al. utilized the ALI culture system to establish patient-derived organoids (PDOs) from non-small cell lung cancer that retained autologous tumor-infiltrating lymphocytes (TILs) and native stromal components, enabling robust evaluation of immunotherapeutic responses. Importantly, their study demonstrated functional T cell-mediated cytotoxicity within the organoids, characterized by granzyme B and IFN- secretion, tumor-specific killing, and recapitulation of immune exhaustion markers such as PD-1 and TIM-3 on CD8 T cells [38]. These findings confirm that ALI organoids can preserve not only the spatial architecture of the tumor-immune microenvironment but also the functional competence of intratumoral T cells. This has important implications for modeling immune checkpoint blockade and developing personalized immunotherapy strategies. More recently, Esser et al. adapted the ALI platform for RCC organoids, preserving immune-stromal crosstalk and enabling individualized drug sensitivity profiling [41].Microfluidic technology, on the other hand, provides a platform to simulate key physical and biochemical aspects of the TME-such as vascularization, nutrient gradients, and shear forces. Homan’s team developed a microfluidic chip that supports nephron maturation and angiogenesis without the need for in vivo transplantation [86]. These platforms enable fine-tuned environmental control and improved biomimicry, thus enhancing the physiological fidelity of RCC organoid cultures.

Despite these technical strengths, the clinical translation of microfluidic-based organoid systems faces several practical hurdles. Most devices rely on microfabrication techniques such as photolithography or soft lithography, which are labor-intensive, expensive, and require specialized infrastructure such as cleanrooms and dedicated equipment [110]. Moreover, many microfluidic platforms are incompatible with standardized multiwell formats, complicating their integration into automated, high-throughput drug screening workflows commonly used in clinical settings [111].

Operational complexity also limits scalability: precise flow regulation, device priming, and troubleshooting demand technical expertise and hands-on adjustment. In addition, inter-batch variability in microchannel geometry, fluid dynamics, and surface coatings can result in inconsistent assay performance, thereby raising concerns regarding reproducibility and regulatory compliance [112].

Until these barriers are overcome through design standardization, automation, and cost-effective manufacturing, microfluidic-based organoid systems are likely to remain confined to research applications rather than routine clinical use.

Together, ALI and microfluidic systems offer promising avenues for enhancing physiological relevance, immunological integration, and pharmacodynamic testing of RCC organoid models.

Engineering approaches: scaffolds and 3D bioprinting

Engineering technologies such as scaffolds and 3D bioprinting provide structural customization and spatial control in organoid modeling. Scaffold-based techniques use natural or synthetic materials to create three-dimensional frameworks that support tumor cell growth, spatial organization, and functional zonation. For example, Batchelder et al. seeded RCC cells onto decellularized renal matrices and polysaccharide scaffolds, successfully inducing nephron-like structures [113].

Bioprinting employs bio-inks to layer-by-layer reconstruct tumor tissues, mimicking spatial patterns of tumor–stroma–vasculature interactions [114]. Clark and colleagues introduced an immersion bioprinting approach that enhanced the reproducibility and throughput of organoid generation [115]. Although applications in RCC remain nascent, recent studies have achieved high organoid formation efficiency and preserved tumor heterogeneity using scaffold-free, magnetically assisted suspension cultures [116].With ongoing advancements in printing resolution and biomaterial development, 3D bioprinting holds significant promise for high-fidelity microenvironment reconstruction and personalized RCC modeling.

Despite these advances, both platforms face critical translational hurdles:

Decellularized scaffold platforms, while preserving native ECM architecture, exhibit significant batch-to-batch variability. This stems from donor heterogeneity, inconsistencies in decellularization protocols, and loss or retention of bioactive components, which affects matrix stiffness, antigenicity, and functional signaling-ultimately influencing organoid morphology and viability. The lack of standardized quality metrics-such as residual DNA quantification, collagen integrity, and mechanical assessment-further limits reproducibility and poses challenges for regulatory acceptance and clinical-grade scalability [117].

Though promising in spatial control, 3D bioprinting confronts resolution limits for microvasculature, which remains a critical barrier, as highlighted by recent reviews on achieving micron-scale vascular fidelity in bioprinted tissues [118, 119].

Cell viability decline during prolonged printing has been documented due to shear stress and thermal effects; experimental studies and reviews have noted reductions in viability associated with extrusion-based techniques [120].In addition to hardware and bioink costs, the absence of consensus on GMP-compliant protocols and validated materials creates significant interlaboratory variability-further complicating reproducibility, scalability, and regulatory clearance [121].

A comparative summary of the key features, advantages, and application scenarios of these three-dimensional organoid platforms is provided in Table 2. Future efforts should prioritize subtype-specific benchmarking of these platforms-particularly assessing their ability to preserve spatially organized architecture and phenotypic traits in ccRCC, pRCC, and chRCC organoids.

Table 2.

Comparative overview of 3D culture techniques for renal cancer organoids

Culture method	Principle	Advantages	Limitations	Microenvironment fidelity	Suitable applications	References
Matrigel-embedded culture	Embedding cells within basement membrane extract (e.g., Matrigel) to support 3D growth	Technically mature; preserves tumor architecture and heterogeneity	Batch variability; potential immunogenicity	Moderate	Drug screening; heterogeneity studies	[14]
Air–liquid interface (ALI)	Transwell system preserving epithelial–stromal–immune compartments at the interface	Maintains immune cell viability (e.g., TILs); enables long-term co-culture	Technically demanding; requires osmotic fine-tuning	High	Immunotherapy testing; TIME modeling	[37]
Microfluidic chip culture	Perfusion-based platform simulating vascularized conditions with flow and shear stress	Recapitulates blood flow; supports immune and endothelial cell co-culture	Costly and technically complex	High	Angiogenesis studies; dynamic drug evaluation	[77]
Decellularized scaffold	Recellularization of native kidney ECM scaffolds	Anatomical fidelity; supports nephron-like structures	Tedious preparation; low reproducibility	Moderate to high	Invasion modeling; organ regeneration	[100]
3D bioprinting	Layer-by-layer deposition of bioinks for spatial control over tumor-stroma-vasculature	Tunable architecture; customizable composition	Limited bioink diversity; current systems lack high resolution	Variable	Personalized modeling; barrier penetration assays	[102]
Suspension culture	Formation of spheroids/organoids without matrix in low-adhesion/rotary systems	Low-cost; scalable and simple	Lacks ECM and spatial support; limited TME fidelity	Low	Preliminary drug testing; mechanistic studies	[42]

TME: tumor microenvironment; ECM: extracellular matrix; ALI: air–liquid interface; TILs: tumor-infiltrating lymphocytes; TIME: tumor immune microenvironment

Organoid co-culture models and reconstruction of the tumor microenvironment in drug resistance mechanisms

Compared with traditional two-dimensional cell lines, tumor organoids retain the architectural, genetic, and pharmacological features of primary tumors, making them especially suitable for modeling intratumoral heterogeneity and personalized therapeutic responses. The kidney, being a highly vascularized organ, often exhibits abundant infiltration of immune cells-such as T cells, macrophages, and NK cells-within its tumor tissues [10, 122]. However, conventional patient-derived organoid (PDO) models frequently lack key components of the TME, including immune, stromal, and vascular elements, thus limiting their ability to faithfully recapitulate in vivo conditions. Recently, co-culture systems incorporating immune or stromal cells have emerged as valuable tools to reconstruct the TME, investigate resistance mechanisms, and guide precision immunotherapy. Renal cell carcinoma-especially the clear-cell subtype— is renowned for a uniquely hypoxia-driven TME marked by rich vasculature, distinctive immune infiltration, and profound metabolic reprogramming. In over 80% of ccRCC cases, loss of VHL creates a “pseudohypoxic” state in which HIF- remains constitutively active even under normoxia, driving excessive VEGF-mediated angiogenesis and metabolic shifts [123, 124]. This hyper-vascular yet metabolically abnormal milieu is characterized by oxygen gradients, acidosis, and immunosuppressive metabolites (e.g. lactate, adenosine), which collectively exhaust effector T cells and support immune evasion [123]. Recapitulating these RCC-specific microenvironmental features in vitro remains challenging – conventional PDO cultures seldom capture the hypoxic, pro-angiogenic conditions or the dynamic vascular–metabolic interplay of ccRCC. Nevertheless, emerging strategies are making headway: co-culturing with endothelial cells can introduce rudimentary vascular structures [124]. and advanced platforms like microfluidic organ-on-a-chip devices now enable controlled nutrient flow and oxygen gradients [36, 125]. By better mimicking the hypoxia and vascular metabolic switch of ccRCC’s TME, these next-generation models promise to enhance the fidelity of drug resistance studies and immunotherapy testing in RCC. Notably, Neal et al. demonstrated that autologous co-culture of tumor organoids with peripheral blood lymphocytes enabled patient-specific immunotherapy assessment in gastrointestinal malignancies, establishing a precedent for functional TME modeling in vitro [38]. Applying similar strategies to RCC may facilitate the discovery of TME-dependent resistance biomarkers and inform personalized therapeutic design.

Organoid–immune cell co-culture

The tumor immune microenvironment (TIME) plays a pivotal role in the initiation, progression, and treatment response of renal cell carcinoma (RCC). Conventional PDO models are typically devoid of immune components, limiting their utility in immunology research. To overcome this, immune cells have been incorporated into organoid systems to create co-culture models that more accurately simulate immune surveillance, immune evasion, and immunotherapy responses within a three-dimensional framework.

Modeling T cell–mediated cytotoxicity in vitro typically involves combining RCC-derived organoids with either self- or donor-origin T cells. This co-culture approach supports the reconstruction of immune-tumor interactions in a 3D framework that preserves cellular architecture and contextual signaling.Under optimized conditions—such as IL-2 supplementation and effector-to-target ratios of 5–10:1—CD8 T cells can effectively lyse RCC organoid cells [38, 126]. Engineered T cells, including CAR-T and TCR-T cells, have also been co-cultured with PDOs to provide ex vivo platforms for personalized immunotherapy evaluation [127, 128]. The air–liquid interface (ALI) method further preserves native immune subpopulations within tissue, including CD8PD-1 exhausted T cells and regulatory T cells (Tregs). In ALI-based organoid models, PD-1 inhibition restores cytotoxic T cell activity and induces tumor cell death, offering a promising approach to evaluate immune checkpoint efficacy. This platform offers translational relevance for evaluating immune therapeutic responsiveness [39]. The observed inter-organoid variability in response supports the model’s potential for predicting immunotherapeutic efficacy and identifying resistance mechanisms.

Beyond T cells, natural killer (NK) cells offer unique advantages in RCC, particularly under conditions of downregulated MHC-I expression. IL-15–activated NK cells have demonstrated robust cytotoxicity in 3D models, potentially via the STAT5/Akt/c-Myc signaling axis and mitochondrial preservation [129, 130]. Macrophage–organoid co-culture systems simulate TAM-dominant immunosuppressive niches and are valuable for evaluating immune checkpoint inhibitors (ICIs) under escape-prone conditions [131, 132].

Although co-culture studies involving B cells, dendritic cells (DCs), and neutrophils in RCC remain in early stages, ALI platforms have shown potential for maintaining diverse immune subsets [38], offering a basis for studying antibody-mediated immunity, antigen presentation, and immunogenic regulation.

In summary, organoid–immune cell co-culture systems expand the depth and translational relevance of RCC research by enabling accurate reconstruction of patient-specific TIME profiles. These systems hold strong potential for predicting immunotherapy responses, elucidating resistance pathways, and screening combinatorial strategies in future precision oncology applications.

Looking forward, the development of next-generation co-culture platforms is essential for enhancing the physiological relevance and clinical utility of RCC organoid–immune models. For instance, microfluidic-based organoid-on-a-chip systems can recapitulate dynamic perfusion, immune cell trafficking, and oxygen/nutrient gradients, thereby offering a controllable microenvironment for TIME reconstruction [133]. Bioprinting and synthetic extracellular matrices also enable spatially resolved immune–tumor co-localization, which facilitates immune synapse formation and T cell–mediated killing. Additionally, preserving autologous tumor-infiltrating lymphocytes (TILs) during organoid expansion may more accurately recapitulate patient-specific immune landscapes compared with donor-derived PBMCs [133]. Recently, vascularized co-culture models using endothelial progenitors or HUVECs have demonstrated improved immune cell infiltration and drug response fidelity in other cancer types and may be adapted for RCC [134]. Integrating single-cell multi-omics profiling within these platforms would further enable dissection of immune exhaustion, clonal dynamics, and checkpoint dependencies at high resolution.

These advanced strategies offer conceptual and technical innovations that may overcome current limitations in RCC immunotherapy modeling and facilitate translational immuno-oncology research.

Organoid–stromal cell co-culture

Stromal cells are essential for reconstructing a physiologically relevant TME. In RCC, cancer-associated fibroblasts (CAFs) and endothelial cells not only form physical ECM barriers but also regulate immunosuppression, angiogenesis, and drug resistance via the secretion of cytokines, chemokines, and proteases. Standard PDO systems, being enriched in epithelial cells, often fail to capture these dynamic tumor–stroma interactions, limiting their utility in mechanistic and translational studies. As a result, co-culture models integrating stromal components are increasingly employed to investigate tumor–stroma crosstalk and therapy resistance mechanisms.

CAF–organoid co-culture is one of the most extensively explored approaches. CAFs can be isolated from RCC tissues and sorted based on -SMA or FAP expression, then co-embedded with PDOs in Matrigel at defined ratios (typically 1:1 or 1:2) [135, 136]. It has been demonstrated that cancer-associated fibroblasts play a central role in establishing tumor-promoting conditions. This process entails a shift in cellular metabolism characterized by elevated lactate production, concurrent activation of key immunosuppressive mediators such as TGF- and IL-6, and extensive remodeling of the extracellular matrix through matrix metalloproteinases, particularly MMP-2 and MMP-9 [137–139]. In addition to promoting ECM remodeling, CAFs actively secrete a repertoire of cytokines and growth factors that modulate immune cell polarization and angiogenic mimicry. Specifically, TGF- and IL-6 secreted by CAFs have been shown to skew macrophages toward an immunosuppressive M2-like phenotype, thereby facilitating tumor-associated macrophage (TAM) recruitment and dysfunction in ccRCC [140]. CAF-derived CXCL12 also plays a role in the exclusion of cytotoxic T cells, while VEGF and MMP-9 secretion foster vascular mimicry and the formation of leaky, aberrant vasculature, which collectively impair effective immune cell infiltration [140, 141]. Incorporating these stromal cues into organoid co-cultures improves the capacity of RCC models to capture immunosuppressive and pro-angiogenic features of the tumor microenvironment.

Moreover, CAFs have been implicated in resistance to VEGF- and mTOR-targeted therapies, underscoring their role as key mediators of treatment failure in RCC [142, 143].

Endothelial co-culture systems are designed to simulate the highly vascularized nature of ccRCC, which is driven by signaling axes such as VEGF-A and ANGPT2 between tumor and endothelial cells [144]. Incorporating human umbilical vein endothelial cells (HUVECs) or iPSC-derived endothelial progenitors into PDO cultures has been shown to induce lumen-like vascular structures [145].

Such strategies have been applied across various organ systems—including heart, brain, and kidney organoids [146–149]. When combined with microfluidic platforms, these systems allow for the simulation of shear stress and vascular permeability, enabling studies of how tumor-derived factors (e.g., VEGF, MMPs) regulate angiogenesis dynamics [150]. Some ALI–PDO systems have also demonstrated long-term survival of CD31 endothelial cells, further validating their capacity for in vitro vasculature reconstruction [38].

In conclusion, organoid–stromal co-culture systems significantly enhance the physiological fidelity and functional depth of RCC organoid models.

To visually illustrate the 3D architecture and functional integration of organoids with immune and stromal components, see Fig. 1, which presents a schematic overview of the tumor microenvironment co-culture model. The integration of multiple stromal cell types—including CAFs, endothelial cells, and normal epithelial cells—provides a robust experimental foundation for investigating RCC pathogenesis, identifying anti-resistance targets, and optimizing combination treatment strategies. With the advancement of microfluidics, bioprinting, and single-cell omics, these co-culture systems are expected to evolve into highly sophisticated platforms for modeling RCC–stroma interactions.

Fig. 1.

Schematic diagram of the co-culture tumor microenvironment model. This diagram illustrates a renal cell carcinoma (RCC) tumor organoid–immune co-culture platform developed to recapitulate the tumor microenvironment (TME) and assess immunotherapeutic mechanisms. A Immune–tumor interactions within the TME. Dendritic cells (DCs) initiate CD4 T cell activation and priming, while tumor cells suppress cytotoxic responses via PD-1/PD-L1 signaling. Natural killer (NK) cells recognize stress ligands on tumor cells and contribute to cytotoxic T lymphocyte (CTL) activation. Immunosuppressive cytokines such as IL-10, TGF- and VEGF promote immune dysfunction and facilitate tumor immune escape. B CAF- and cytokine-mediated immunosuppressive remodeling. Cancer-associated fibroblasts (CAFs), macrophages, and DCs release soluble factors (e.g., VEGF, PGF) that upregulate PD-L1 on both tumor and immune cells. In addition to dampening T cell activation, CAFs actively orchestrate the recruitment of suppressive immune subsets—such as regulatory T cells and M2 macrophages—thus reinforcing immune evasion within the tumor milieu. C Application of the RCC organoid–immune co-culture model in immunotherapy assessment. Incorporation of immune checkpoint inhibitors (ICIs), such as anti–PD-1 antibodies, allows functional evaluation of immunotherapeutic efficacy. Reactivation of exhausted CD8 T cells within this model recapitulates clinical responses, supporting its utility as a preclinical platform for drug screening and individualized treatment prediction. The organoid system integrates Matrigel-based 3D extracellular matrix, RCC tumor epithelial cells, and multiple immune subsets (T cells, B cells, NK cells, DCs, macrophages), enabling high-fidelity modeling of patient-specific immune–tumor crosstalk. Image Created in https://BioRender.com

Research and translational applications of RCC organoids

Building upon the conceptual framework introduced in the Introduction, this section summarizes the current applications of RCC organoids in translational research and discusses key challenges and future directions for clinical implementation.

Organoid biobanking and pediatric RCC modeling

Significance and current status of organoid biobanks

Traditional drug sensitivity testing methods—such as short-term ex vivo tumor slice cultures and patient-derived xenograft (PDX) models—have provided preliminary insights into individualized therapy [151, 152]. However, tumor slice cultures are limited by poor proliferative capacity, and PDX models are time-consuming, resource-intensive, and difficult to scale due to their reliance on animal hosts. In recent years, organoid technology has emerged as a promising alternative due to its high fidelity and scalability, offering a robust technical foundation for the establishment of living tumor organoid biobanks.

Researchers have successfully developed organoid biobanks for a wide range of solid tumors, including glioblastoma [153], gastric cancer [154], bladder cancer [30], neuroendocrine tumors [155], and pediatric renal cancers [156]. These biobanks preserve the histological and molecular features of the original tumors and are widely used for high-throughput drug screening, resistance mechanism exploration, and novel target discovery. Organoid biobanks serve as a vital bridge between basic science and clinical applications, facilitating the study of tumor heterogeneity and supporting data-driven approaches for personalized medicine.

Beyond conventional biobanking of RCC PDOs, emerging strategies emphasize the integration of multi-omics profiling—including scRNA-seq, ATAC-seq, and proteomics—to enhance the granularity and translational utility of biobanked models. Recent pilot studies have demonstrated that single-cell transcriptomic mapping of RCC organoids can uncover intra-tumoral heterogeneity, clonal evolution, and treatment-induced lineage plasticity, which are otherwise masked in bulk analyses. Moving forward, a standardized framework for RCC organoid biobanks may incorporate longitudinal sampling, spatial indexing, and metadata harmonization with clinical registries to enable predictive modeling of therapeutic outcomes. This direction differentiates our review by proposing a forward-looking roadmap for biobank design that extends beyond current static repositories.

The unique value of pediatric RCC organoids

Pediatric renal cancers are characterized by early onset, diverse histological subtypes, and unique genetic backgrounds, presenting heightened requirements for research platforms. Calandrini et al. established the first comprehensive pediatric renal cancer organoid biobank, encompassing multiple subtypes including Wilms tumor, malignant rhabdoid tumor, pediatric RCC, and mesoblastic nephroma [156]. These organoids preserved essential phenotypic, genomic, epigenomic, and transcriptomic features of the primary tumors, providing high-fidelity models for basic research, drug screening, and molecular classification of pediatric renal cancers.

Notably, pediatric RCC comprises rare and aggressive subtypes, such as translocation-associated RCC (tRCC) and renal medullary carcinoma (RMC). tRCC is characterized by TFE3 or TFEB gene fusions that activate lineage-inappropriate transcriptional programs and confer distinct therapeutic vulnerabilities [157]. In contrast, RMC often involves biallelic loss of SMARCB1, leading to oncogenic activation of MYC-driven transcriptional circuits such as the TFCP2L1–MYC axis [158, 159]. These tumors are largely resistant to conventional therapies and exhibit age-specific clinical courses, underscoring the urgency of pediatric-tailored approaches. Pediatric RCC organoids provide a tractable system to model these rare subtypes, enabling functional interrogation of fusion-driven or MYC-driven oncogenesis. In addition, they allow high-throughput screening of age-appropriate targeted agents, such as MET/ALK inhibitors for TFE3-fusion tRCC and BET inhibitors or CDK9 antagonists for MYC-overexpressing RMC [158, 159]. These platforms also support the evaluation of toxicity and efficacy profiles in a pediatric context, bridging a critical gap in translational pediatric oncology research.

Beyond modeling oncogenic fusions or MYC-driven programs, pediatric RCC organoids offer unique functional advantages over adult-derived models. Notably, they retain developmental signaling networks—such as WNT, Notch, and Hippo pathways—that are either absent or epigenetically silenced in adult RCC organoids [156]. These features allow pediatric organoids to model tumor–developmental crosstalk, which may influence both tumor biology and therapeutic vulnerability. Furthermore, pediatric organoids provide a relevant platform to assess age-specific pharmacokinetics and toxicity profiles, including nephrotoxicity during critical nephrogenic windows [160]. Compared with adult organoids, they enable investigation of long-term drug impact on renal progenitor maintenance, epithelial differentiation, and regenerative capacity. As such, pediatric RCC organoids are not merely histological replicas but functionally distinct systems that facilitate the design of developmentally appropriate therapeutic strategies and safety evaluations in children.

In summary, the development of pediatric RCC organoid biobanks fills a critical gap in childhood solid tumor modeling and accelerates mechanistic and therapeutic studies of rare subtypes such as pediatric RCC. These achievements highlight the translational potential of organoid technologies in pediatric oncology and provide a new experimental platform for designing precision treatment strategies in children.

Our laboratory’s efforts in building a ccRCC organoid biobank

Our research group is currently establishing a systematic biobank of patient-derived organoids (PDOs) from individuals with clear cell renal cell carcinoma (ccRCC), utilizing nephrectomy-derived tumor specimens. Under an optimized air-liquid interface (ALI) culture system, we employ a customized medium supplemented with R-spondin1, Wnt3a, and 5% hypoxic tension to preserve tumor stemness and maintain epigenetic stability.

To date, we have successfully generated PDO models from over 20 patients. Primary cultures show high morphological and molecular concordance with the original tumors, including VHL mutations and activation of the HIF signaling pathway. This PDO biobank will be applied to downstream applications such as drug sensitivity testing, immunotherapy response prediction, and investigation of resistance mechanisms. In parallel, we are conducting systematic validation using histopathology, whole-exome sequencing (WES), and transcriptomic analyses. Our aim is to contribute to the standardization of ccRCC organoid culture protocols and provide reliable models and clinical translational pathways for individualized therapeutic strategies.

Applications of RCC organoids in predicting therapeutic response and investigating resistance mechanisms

To systematically illustrate the translational trajectory of RCC organoid-based platforms in precision medicine, we constructed a workflow diagram highlighting key stages from tissue acquisition and model establishment to therapeutic evaluation and clinical feedback (Fig. 2). Serving as a roadmap, This schematic illustrates how organoid models are positioned across translational stages to support precision-guided therapeutic decisions.

Fig. 2.

Schematic workflow of renal cancer organoid-based precision medicine pipeline. This diagram illustrates the translational application roadmap of renal cell carcinoma (RCC) organoids in precision oncology. The workflow begins with the acquisition of tumor specimens via surgical resection or biopsy (Step 1), followed by tissue dissociation and 3D culture establishment using Matrigel, ALI, or microfluidic platforms (Step 2). Successfully cultured organoids undergo histological, genomic, and transcriptomic validation to ensure tumor fidelity (Step 3). Subsequent high-throughput drug screening and immune co-culture assays (Step 4) enable assessment of drug sensitivity, resistance mechanisms, and immunotherapy response (Step 5). Finally, integrated multi-omics and pharmacological results provide individualized clinical decision support (Step 6), potentially guiding patient-specific therapeutic strategies. By linking experimental tumor modeling with clinical application, this organoid-driven pipeline facilitates iterative, patient-tailored treatment strategies and enhances translational precision in RCC therapy.Image Created in https://BioRender.com

Drug sensitivity and targeted therapy response prediction

Drug resistance remains a leading cause of treatment failure in renal cell carcinoma (RCC), driven by mechanisms such as altered drug metabolism, target mutations, enhanced drug efflux, increased DNA repair capacity, and evasion of programmed cell death [161–163]. Traditional 2D cell lines lack the spatial architecture and microenvironmental context of primary tumors, making them inadequate for modeling these complex resistance pathways. In contrast, patient-derived organoids (PDOs)—as highly biomimetic three-dimensional systems—retain patient-specific features at structural, genomic, and heterogeneity levels, and have demonstrated superior predictive value in drug testing [67]. Their advantages include:

1. the ability to recapitulate physical features such as angiogenesis and oxygen gradients;

2. structural stability that supports activation of resistance pathways;

3. compatibility with high-throughput screening of patient-specific samples.

Studies have shown that multi-target tyrosine kinase inhibitors (TKIs)—including SU11274, foretinib, cabozantinib, and lenvatinib—exhibit subtype-selective efficacy in different RCC PDOs, with marked inter-patient variability [164]. The sensitivity profile of foretinib differed significantly between tumor and normal tissue–derived models. Compared with their normal counterparts, ccRCC organoids demonstrated significantly higher susceptibility to the therapeutic agent, suggesting selective efficacy toward malignant cell populations [67]. Additionally, Wnt pathway inhibitors showed selective anti-tumor effects in RCC PDOs, highlighting the Wnt axis as a potential therapeutic target [165].

Esser et al. established patient-derived organoid (PDO) models using an air–liquid interface (ALI) culture system to systematically evaluate the therapeutic efficacy of cabozantinib combined with a PD-1 inhibitor, and observed heterogeneous responses among the ALI-PDOs to this treatment [41]. Reustle et al. further applied a 3D-ALI platform integrated with multi-omics profiling to identify nicotinamide N-methyltransferase (NNMT) as a potential resistance biomarker [166]. Organoid co-culture strategies have also improved the modeling of complex drug responses. For instance, fibroblast-, endothelial-, and T cell-integrated PDO models of sunitinib resistance responded to low-dose combination treatments with Rapta-C, metformin, erlotinib, and parthenolide, effectively reversing resistance phenotypes [167].

Another study co-cultured RCC PDOs with HGF-secreting fibroblasts to replicate MET signaling activation in papillary RCC and evaluate response to the MET inhibitor capmatinib [168].

Collectively, PDOs have become pivotal tools for individualized drug screening and resistance mechanism exploration. To summarize recent progress, Table 3 presents representative studies on drug testing using RCC PDOs, including modeling strategies, drug classes, key findings, and clinical implications.

Table 3.

Representative studies utilizing renal cancer organoids for drug screening and treatment response prediction

Study (First Author, Year)	Organoid model type	Application scope	Key findings	References
Grassi et al. [67]	RCC and normal kidney-derived PDOs cultured in Matrigel	Model establishment and tumorigenicity validation	Organoids retained original tumor histology and demonstrated successful xenograft potential	[58]
Kazama et al. [164]	RCC PDOs for ex vivo drug testing	Tyrosine kinase inhibitor (TKI) sensitivity profiling	Differential responses to TKIs were observed, correlating with tumor mutational profiles	[149]
Li et al. [24]	RCC PDO biobank (ccRCC, pRCC, chRCC) using Matrigel-based culture	Personalized treatment screening and CAR-T cell evaluation	Most lines exhibited resistance to TKIs; CD70-targeted CAR-T cells induced effective tumor cell lysis	[24]
Xue et al. [39]	Air–liquid interface (ALI) PDOs with preserved immune/stromal compartments	Immune checkpoint inhibitor (ICI) efficacy assessment	PD-1 blockade restored T-cell functionality and induced apoptosis in PDOs, supporting immune co-culture modeling	[38]
Cesana et al. [116]	Scaffold-free tumoroids from ccRCC via magnetic suspension	Drug screening, invasion assay, biomarker discovery	Matrix-free culture system maintained tumor heterogeneity and demonstrated high establishment efficiency	[103]
Tse et al. [186]	Matrigel-based PDOs validated by patient-derived xenografts	Therapy response profiling and precision medicine application	In vitro PDO drug response profiles aligned with clinical treatment outcomes, guiding therapeutic adjustments	[169]

Summary of selected key studies demonstrating the use of renal cell carcinoma (RCC) organoid models for drug screening and therapeutic response prediction. These models include Matrigel-based patient-derived organoids (PDOs), air–liquid interface (ALI) systems, and scaffold-free tumoroids. Applications span TKI profiling, immune checkpoint blockade evaluation, CAR-T therapy testing, and phenotypic screening. The table highlights the methodological diversity and translational potential of organoid-based platforms in modeling RCC heterogeneity and predicting individualized treatment efficacy

Toward Clinical Validation: Future Directions for Translational Integration

Despite promising preclinical results, large-scale clinical validation of RCC organoid–based drug sensitivity testing remains limited. Most published studies have focused on drug screening and mechanistic insights in isolated PDO platforms, without paired clinical correlation to actual patient outcomes. Moving forward, prospective cohort studies comparing organoid-derived drug sensitivity profiles with in vivo treatment responses (e.g., RECIST-based assessment or progression-free survival) are urgently needed to establish clinical concordance. Recent pilot studies in gastrointestinal and breast cancers have demonstrated the feasibility of this approach, reporting >80% predictive accuracy for organoid-based assays [37, 169]. For RCC, building multi-institutional PDO biobanks linked to annotated clinical registries may facilitate such validation. Additionally, integration of organoid pharmacotyping with circulating biomarkers, imaging, and AI-based prediction tools may enhance the translational utility of PDOs in guiding precision therapy [170].

Modeling response to radiotherapy and chemotherapy

RCC is traditionally resistant to conventional fractionated radiotherapy; however, stereotactic body radiation therapy (SBRT), a form of hypofractionated radiotherapy, has shown promising local control rates of up to 94.1% and 5-year progression-free survival as high as 80.5% [171, 172]. Given the substantial variability in radiotherapy response, developing ex vivo models for predicting radiosensitivity has become essential. Recent studies have employed tumor spheroids and PDOs to assess radiation response, providing experimental support for precision radiotherapy approaches [16].

High-linear energy transfer (LET) modalities such as carbon ion radiotherapy (CIRT) are gaining attention for their efficacy against radioresistant tumors [173]. While still in early-phase research, PDOs hold potential for simulating radiotherapeutic response and guiding dose optimization.

Although no studies have directly assessed radiotherapy responses in ccRCC-derived organoids, genomic profiling has revealed frequent alterations in DNA damage response (DDR) pathways—particularly involving ATM and CHEK2—in ccRCC tissues and patient-derived xenografts. These alterations are associated with elevated tumor mutational burden and reduced therapeutic sensitivity [174]. Mechanistically, CHEK2 has been shown to promote radioresistance in ccRCC via the LINC01094/miR-577/CHEK2/FOXM1 signaling axis [175], reinforcing the rationale for targeting DDR networks in radioresistant tumors.

In contrast to this knowledge gap in RCC, patient-derived organoids (PDOs) from other malignancies—particularly colorectal cancer (CRC)—have already been employed to predict radiotherapy responses. Hsu et al. demonstrated that CRC PDOs recapitulate intrinsic patient radiosensitivity, as evidenced by H2AX foci kinetics and clonogenic survival correlating with clinical outcomes [176]. Furthermore, Krause et al. reported that targeting DDR mechanisms via ATM inhibition sensitized CRC PDOs to both photon and proton irradiation, providing a proof-of-concept for combination strategy optimization [177].

To functionally assess radiosensitivity in PDO models, several validated assays have been employed in colorectal and pancreatic cancer organoids, and may be readily adapted to RCC PDOs. These include -H2AX foci quantification, which monitors DNA double-strand break repair kinetics post-irradiation; clonogenic survival assays, which assess long-term proliferative capacity after radiation exposure; and the alkaline comet assay, which evaluates residual DNA damage at the single-cell level. These techniques have demonstrated high fidelity in recapitulating patient-specific responses and are considered gold-standard approaches in functional radiogenomic studies. Their integration into RCC PDO workflows would enable systematic DDR phenotyping, facilitate radiosensitizer screening, and guide patient stratification for personalized radiotherapy regimens [176, 177].

Collectively, these precedents highlight the translational potential of PDO platforms in radiotherapy research and emphasize the urgent need to establish RCC-specific organoid systems with DDR phenotyping capabilities for individualized treatment guidance.

In terms of chemotherapy, RCC is largely resistant to conventional cytotoxic agents. Mechanisms include regulation of drug targets and cell cycle pathways by non-coding RNAs (e.g., miRNAs, lncRNAs, circRNAs) [178, 179]. Although chemotherapy is not routinely used in RCC, PDOs offer a unique platform for dissecting chemoresistance mechanisms. Through CRISPR-Cas9, RNA interference, and other genetic manipulation techniques, PDOs can be used to identify resistance-driving genes, screen synergistic drug combinations, and develop novel chemoresistant models [37, 180].

Immunotherapy response prediction and immune co-culture models

Immune checkpoint blockade has been established as the preferred first-line therapeutic strategy for patients with advanced renal cell carcinoma (RCC) [181, 182]. An objective response rate of 42% and enhanced overall survival were observed in intermediate- and poor-risk metastatic ccRCC patients receiving nivolumab combined with ipilimumab, as demonstrated in the CheckMate 214 trial [183].

Despite these advances, substantial inter-patient variability in ICI response necessitates reliable predictive models. PDO–immune cell co-culture systems incorporating tumor-infiltrating lymphocytes (TILs) or peripheral blood mononuclear cells (PBMCs) enable reconstruction of patient-specific tumor–immune interactions and provide a platform for assessing ICI efficacy.

The cancer immune cycle comprises antigen release, presentation, T cell activation, infiltration, and tumor clearance [184, 185]. Disruption at any stage may lead to immune evasion. ICIs aim to restore these pathways, yet RCC’s heterogeneity complicates biomarker development. In this context, PDO–immune co-cultures retain tumor heterogeneity and allow integration of functional immune cells, providing a physiologically relevant model for evaluating ICI responses.

Neal et al. developed an ALI–PDO system that retained native TILs, enabling immune response assessment [38]. Addition of PD-1 inhibitors (e.g., nivolumab) enhanced T cell activity and induced tumor cell apoptosis. Xue et al. further reported that the addition of the PD-1 antibody toripalimab to the ccRCC PDO–ALI system significantly restored T cell functionality and induced tumor cell apoptosis. [39].

These findings validate the PDO–immune co-culture platform as a predictive tool for ICI responsiveness and a clinically relevant model for immunotherapy stratification [186]. In addition to conventional TIL/PBMC-based co-cultures, engineered immune cells—such as CAR-T, TCR-T, and IL-15–activated NK cells—have shown promise in RCC PDO systems (see Section 3.2.1). Notably, NK cells exhibit anti-tumor activity even under MHC-I downregulation, suggesting their therapeutic potential.

In summary, PDO–immune co-culture platforms are powerful tools for reconstructing individualized tumor immune microenvironments (TIME), and hold promise for immunotherapy prediction, resistance profiling, and the development of novel combinatorial strategies.

Immune evasion mechanisms and translational exploration

Beyond predicting immunotherapy efficacy, PDO platforms can also be used to investigate immune evasion mechanisms. Major categories of tumor immune escape include:

Expansion of immunosuppressive cells: Regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs) suppress T cell function via secretion of IL-10, TGF-, and other factors [187].

Upregulation of immune checkpoints: Overexpression of PD-L1, LAG-3, and TIM-3 on tumor and immune cells contributes to T cell exhaustion [188, 189].

Defective antigen presentation: Downregulation of MHC-I or antigen loss prevents immune recognition [190, 191].

In renal cell carcinoma (RCC), especially clear cell RCC (ccRCC), tumor-specific immune evasion mechanisms are closely linked to its molecular landscape. A defining feature of ccRCC is the loss of the von Hippel–Lindau (VHL) tumor suppressor gene, leading to constitutive activation of hypoxia-inducible factor-2 alpha (HIF-2). This transcription factor directly enhances PD-L1 expression, facilitating T cell exhaustion and promoting immune escape [192]. Re-expression of functional VHL or pharmacological inhibition of HIF-2 (e.g., belzutifan) reduces PD-L1 levels, confirming the functional relevance of the VHL–HIF–PD-L1 axis in immune evasion [193].

Additionally, ccRCC often displays deficiency in surface MHC class I (MHC-I), which impairs CD8 T cell recognition. This loss is frequently driven by epigenetic or mutational downregulation of NLRC5, a master transcriptional regulator of MHC-I machinery, and occasionally by 2-microglobulin (B2M) alterations. A landmark study employed a CRISPR–Cas9–based targeted demethylation system, termed TRED-I, to reactivate NLRC5 expression in tumor cells. This approach restored MHC-I on the cell surface, increased CD8 T cell infiltration and activation, and synergized with anti-PD-1 therapy in murine tumor models [194]. Although direct CRISPR-mediated restoration of B2M expression in RCC organoids remains unreported, these findings collectively highlight CRISPR-based recovery of MHC-I as a credible strategy against immune evasion in ccRCC.

Metabolic suppression: Glucose depletion and lactic acid accumulation in the TME impair T/NK cell activity [195, 196].

Palacios et al. proposed that invariant natural killer T (iNKT) cell signaling may regulate tumor immunogenicity, and that PDOs represent an ideal platform for studying innate immune cell function [197].

By recapitulating these mechanisms in vitro, PDO–immune cell co-cultures allow real-time observation of immune suppression and evaluation of therapeutic strategies (e.g., ICIs) to reverse escape phenotypes. These models can facilitate identification of novel immunosuppressive targets and support the rational design of combination immunotherapies.

Limitations and future clinical translation of RCC PDO models

Technical barriers and time constraints in RCC PDO construction

Despite the tremendous potential of PDO technology in cancer research, its construction and application continue to face several technical challenges. One notable issue is the low establishment success rate of RCC PDOs, particularly those derived from non–clear cell subtypes, which is closely related to the quality of the starting tissue and the tumor cell content. [67, 198–200]. Moreover, commonly used ECM matrices such as Matrigel exhibit significant batch-to-batch variability, which affects organoid morphology and growth, and compromises the reproducibility of experimental outcomes [200].

In addition, PDO culture requires complex media supplemented with multiple expensive growth factors, creating cost barriers for large-scale applications. To address these limitations, researchers have developed customized matrix formulations and culture conditions. For instance, collagen-based matrices have been used to support stable ccRCC PDO growth for up to 21 days, underscoring the importance of extracellular matrix optimization for organoid stability [51]. Synthetic hydrogels have also been introduced to reduce batch variability and improve model consistency [200, 201].

Standardization remains a major bottleneck in the field of PDO–immune cell co-culture. Substantial heterogeneity exists among studies in terms of immune cell sources (e.g., TILs, PBMCs, NK cells), cell ratios, activation protocols, and culture conditions, making cross-study comparisons and reproducibility difficult. To address this, several optimization strategies have been proposed, including: unified protocols for immune cell isolation and activation, standardized effector-to-target cell ratios, and harmonized co-culture durations and assessment metrics.

Engineering platforms such as microfluidic chips offer technical solutions for more accurate simulation of the tumor–immune microenvironment. The ALI (air–liquid interface) system developed by Neal et al. successfully preserved native immune cell populations within PDOs and exhibited more physiologically relevant immune-tumor interactions [38], offering a model for future standardization. The integration of automated microfluidics, dynamic perfusion culture, and real-time imaging holds potential to further enhance throughput, stability, and translational feasibility of co-culture systems.

Beyond technical complexity, the time-intensive nature of RCC PDO cultivation also presents a significant hurdle for clinical application.

Several obstacles remain in translating PDO technology from the bench to the bedside. One major issue is the time-consuming nature of organoid culture. Standard protocols for RCC PDO establishment typically require 10 to 21 days, including tissue dissociation, organoid embedding, and initial outgrowth [202]. This timeframe often exceeds the clinical decision-making window in oncology, particularly for aggressive or progressing cases. While rapid drug screening assays can provide preliminary results within a few days, longer durations are still required for comprehensive response evaluation and resistance mechanism analysis [143].

Moreover, variability in sample acquisition and handling introduces additional inconsistencies. Differences in tissue transport, cold chain logistics, ECM matrix composition, and medium formulation can all impact outcomes. To enhance clinical applicability, development of engineered culture systems and workflow standardization is essential. On one hand, synthetic or well-characterized peptide-based matrices and optimized media formulations can reduce batch effects and increase reproducibility [200, 201]. On the other hand, automation and high-throughput technologies can shorten culture time and improve processing efficiency.

To further address clinical time constraints, mini-organoid platforms have emerged that enable robust drug response profiling within 72 h. For example, Tebon et al. developed a high-throughput workflow integrating bioprinted mini-organoids with high-speed live-cell interferometry (HSLCI), enabling mass profiling of thousands of single breast cancer organoids and discriminating treatment responses within 72 h—a timeframe compatible with clinical decisions such as neoadjuvant therapy selection [203]. For RCC, early-stage studies have also explored microfluidic-based organoid systems capable of partial maturation and treatment evaluation within 5–7 days, owing to improved nutrient exchange and perfusion dynamics [204, 205]. These emerging systems, when coupled with real-time imaging and automated data acquisition, hold promise for integration into time-sensitive therapeutic workflows.

In parallel, microfluidic perfusion systems have gained attention for their ability to mimic vascular flow, enhance nutrient exchange, and support partial organoid maturation within significantly reduced timeframes. These platforms support dynamic culture conditions that enhance drug diffusion and allow real-time monitoring of treatment responses [205]. Furthermore, they can be integrated with live imaging and biosensors to automate data capture and accelerate therapeutic decision-making [204].

Although these rapid protocols may sacrifice some of the architectural complexity of fully matured organoids, they represent a valuable compromise between speed and biological relevance, particularly when immediate therapeutic guidance is required.

Establishing quality control standards and participating in multi-center collaborations are crucial to harmonizing methodologies and enhancing reproducibility across institutions [206, 207].

Ethical, regulatory, and translational roadmap for RCC PDOs

As organoid technologies move toward clinical implementation, ethical and regulatory concerns are becoming increasingly prominent. Since organoids are derived from patient tissues (e.g., surgical specimens, biopsies), strict adherence to informed consent and data privacy regulations is essential. The scope of tissue usage, data-sharing policies, and post-expansion management of organoids must be explicitly addressed in ethical review processes.

Data integration and traceability also pose challenges. The large volume of multi-omics data generated in clinical organoid studies requires standardized data management frameworks to ensure quality and reliability. Additionally, unresolved ethical issues—including legal status of cell-derived models and protection of genomic data privacy—remain critical concerns [208]. In particular, organoid biobanking raises specific consent, storage, and data-sharing issues. Long-term storage of patient-derived organoids and associated multi-omics data requires explicit, broad consent that covers future unspecified research uses, potential commercialization, and cross-institutional data sharing [209]. Moreover, governance frameworks must address donor autonomy and withdrawal rights—even post–cell line establishment—as highlighted by the ISSCR’s 2023 Standards for Human Stem Cell Use, which emphasize dynamic consent processes, transparent material transfer agreements (MTAs), and secure genomic data protection [210]. Failing to standardize these practices risks compromising both ethical integrity and legal compliance in multi-centre or public–private collaborative settings.

Moving forward, a dual-track approach combining policy development and technological innovation is needed. Regulatory systems specific to organoid-based research and clinical trials should be established, with clearly defined standards for informed consent, data sharing, and safety compliance during commercialization.

Beyond addressing ethical and governance frameworks, an equally critical frontier involves charting a robust translational pipeline for RCC PDOs. The following strategies outline how multi-omics integration, regulatory advancements, and real-time functional testing can collectively bridge the gap from bench to bedside.

To accelerate clinical translation of RCC organoids, we propose a three-phase integrative model to bridge research, validation, and clinical application:

Basic research phase: Establish high-fidelity PDO models and use multi-omics profiling to define subtype-specific drug response signatures.

To maximize the predictive utility of RCC organoids, multi-omics data integration—spanning genomics, transcriptomics, proteomics, and metabolomics—has become an emerging strategy for identifying drug response patterns and resistance mechanisms [211]. Studies in organoids from colorectal and prostate cancers have shown that integrating gene expression, mutation, and proteomic profiles can outperform mutation-based predictors alone in forecasting therapy efficacy [212, 213]. In RCC, which is driven by metabolic reprogramming and chromatin dysregulation, multi-omics integration is particularly valuable [214]. Recent RCC-focused studies have demonstrated the clinical utility of multi-omics frameworks. For instance, Yao et al. (2024) integrated transcriptomic and proteomic datasets from ccRCC patient samples to define metabolic and immune-related subtypes, which exhibited differential responses to VEGFR-TKIs and immune checkpoint inhibitors [215]. Similarly, Qu et al. [216] developed a proteogenomic classifier in RCC that outperformed mRNA-only models in predicting recurrence risk and therapy resistance [216]. These examples illustrate how RCC-specific multi-omics pipelines can enhance stratification accuracy and guide personalized therapeutic choices. Machine learning models such as MOLI have shown success in merging multi-omics layers to improve drug response prediction [211]. These approaches, though not yet RCC-specific, are increasingly being adopted to enhance the translational accuracy of PDO-based screening platforms [215].

Translational validation phase: Incorporate PDOs into clinical trial workflows, pairing them with NGS-based molecular subtyping to predict therapy responses and resistance patterns.Clinical decision support phase: Miniaturize and standardize PDOs into “tumor-on-chip” platforms for dynamic drug sensitivity testing to support individualized treatment selection.Notably, in 2023, the U.S. FDA issued new regulations allowing the use of non-animal models for preclinical drug testing, providing a regulatory basis for the standardized application of PDOs in precision oncology [217]. In addition to the FDA’s stance, other regulators and international bodies are advancing guidelines for organoids and organ-on-chip systems in clinical decision-making. For example, the European Medicines Agency (EMA) has established a specialized expert community on “new approach methodologies” to facilitate integrating organoids and microphysiological systems into drug development (with a pathway for qualifying novel in vitro models). Similarly, China’s National Medical Products Administration (NMPA) in 2024 issued technical guidance that endorses using organoid and “tumor-on-chip” platforms to supplement conventional efficacy and safety testing data [218]. While no dedicated International Council for Harmonisation (ICH) guideline exists yet, global stakeholders are calling for harmonized policies and standards for these emerging models [219], In parallel, international standardization initiatives are underway—exemplified by a 2024 European organ-on-chip standardization roadmap and a NIST-led working group—to establish unified quality benchmarks for organoid-based ‘tumor-on-chip” platforms [220]

Looking ahead, the establishment of PDO biobanks, integration of ethical and multi-omics data systems, and development of high-throughput standardization protocols will be key prerequisites for mainstreaming PDOs into routine clinical workflows.

To transcend descriptive summaries of RCC PDO platforms, future innovation may converge on integrating artificial intelligence (AI) with single-cell omics from patient-matched tumor–organoid pairs. Machine learning algorithms, trained on scRNA-seq–derived transcriptional trajectories, can be harnessed to predict drug resistance phenotypes or classify immune subtypes based on PDO behavior. Preliminary work in colorectal and pancreatic cancers suggests that such AI-guided models can accurately stratify treatment responders and non-responders. Applying similar pipelines in RCC could unlock the predictive value of PDOs and establish a high-resolution interface between bench and bedside.

Conclusion

Renal cell carcinoma (RCC) organoids are emerging as powerful preclinical platforms that faithfully preserve tumor-specific histology, genetics, and pharmacologic responses. However, critical challenges—including subtype-specific modeling inefficiencies, phenotypic drift, and incomplete immune–stromal reconstruction—still hinder their clinical applicability.

Looking ahead, key priorities include the development of robust subtype-tailored PDO systems, the integration of single-cell and multi-omics profiling, and the deployment of AI-assisted phenotyping pipelines to refine drug sensitivity prediction. Establishing large-scale RCC organoid biobanks, aligning protocols with regulatory standards, and embedding PDOs within prospective clinical trial frameworks will be essential to translate benchside modeling into bedside decisions.

With these advancements, RCC organoids are poised to become indispensable tools in precision oncology—facilitating biomarker discovery, accelerating therapeutic development, and enabling individualized patient care.

Ps:Tables 1, 2, 3 are in the attachment

Abbreviations

Abbreviation

Full term

RCC

Renal cell carcinoma

ccRCC

Clear cell renal cell carcinoma

pRCC

Papillary renal cell carcinoma

chRCC

Chromophobe renal cell carcinoma

CDC

Collecting duct carcinoma

RMC

Renal medullary carcinoma

PDO

Patient-derived organoid

ALI

Air–liquid interface

TME

Tumor microenvironment

TIME

Tumor immune microenvironment

TKI

Tyrosine kinase inhibitor

ICIs

Immune checkpoint inhibitors

CAR-T

Chimeric antigen receptor T-cell

TCR-T

T-cell receptor-engineered T-cell

TILs

Tumor-infiltrating lymphocytes

PBMCs

Peripheral blood mononuclear cells

ECM

Extracellular matrix

HGF

Hepatocyte growth factor

VEGF

Vascular endothelial growth factor

ANGPT2

Angiopoietin-2

iPSC

Induced pluripotent stem cell

NK cells

Natural killer cells

CAFs

Cancer-associated fibroblasts

PDX

Patient-derived xenograft

CTCs

Circulating tumor cells

SBRT

Stereotactic body radiotherapy

LET

Linear energy transfer

FDA

U.S. food and drug administration

Wnt

Wingless/Integrated signaling

BMP

Bone morphogenetic protein

ISSCR

International society for stem cell research

HSLCI

High-speed live-cell interferometry

MTAs

Material transfer agreements

MET

Mesenchymal–epithelial transition factor (relevant in pRCC context)

CDKN2A

Cyclin-dependent kinase inhibitor 2A (gene)

TP53

Tumor protein p53

HNF1B

Hepatocyte nuclear factor 1-beta

PBRM1

Polybromo 1

SETD2

SET domain containing 2

BAP1

BRCA1 associated protein-1

MMPs

Matrix metalloproteinases

EGF

Epidermal growth factor

PD-L1

Programmed death-ligand 1

HIF-1

Hypoxia-inducible factor 1-alpha

CRISPR

Clustered regularly interspaced short palindromic repeats

scRNA-seq

Single-cell RNA sequencing

Author contributions

GL and SC conceived the overall framework of the review and supervised its development. JG and HL drafted the main sections of the manuscript and performed the literature review. SW, CZ, ZL, and CK contributed to data organization and table preparation. All authors revised and refined the manuscript, and read and approved the final version.

Funding

This study was supported by the Guizhou Science and Technology Department (Grant No. ZK2021380)

and the Doctoral Foundation of the Affiliated Hospital of Zunyi Medical University (Grant No. 201801).

Data Aavailability

Not applicable. This article is a review and does not contain primary data.

Declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable. This article does not contain individual person’sa in any form.

Competing interest

The authors declare that they have no Conflict of interest.