Application of organoid models in basic and translational research of lung cancer: a narrative review

Introduction

Lung cancer is the leading cause of cancer-related mortality worldwide (1). Precision medicine guided by lung cancer molecular profiling is pivotal for improving patient outcomes (2). Notably, the efficiency of transition from basic science to clinical application is vital for clinical practice innovation and in this process, the preclinical experimental models that faithfully mirror human disease biology are urgently needed.

Traditional experimental models include patient-derived xenografts (PDXs), genetically engineered mouse models (GEMMs), and circulating tumor cell-derived xenografts (CDXs). However, they possess inherent limitations including absence of a fully human tumor microenvironment (TME) in PDXs (3), restricted genomic complexity in GEMMs (4), and the lack of a functional human immune system in CDXs (5) etc. Consequently, their predictive power for clinical drug responses is often suboptimal. A recent announcement from U.S. Food and Drug Administration encourages testing novel drugs using new approach methodologies (NAMs)—encompassing artificial intelligence (AI) models, human cell systems, organoids, and organ-chips (6).

Patient-derived organoids (PDOs) are three-dimensional (3D) structures that self-organize from patient tissue and retain key pathological, genetic, and phenotypic features of the original tumor (7-9). Despite variability in establishment success rates across different tumor types and culture conditions, their short culture cycle and scalability make them exceptionally suited for high-throughput drug susceptibility testing (DST) (10). The ability of PDOs to recapitulate the multi-omics landscape and drug responses of primary tumors—when subjected to rigorous morphological, histopathological, and genomic validation—has established their utility across various clinical scenarios, from neoadjuvant therapy planning to preclinical drug evaluation (8).

Despite this promise, the broader application of lung cancer organoids (LCOs) faces several key challenges, including the standardization of culture and characterization protocols, the recapitulation of a complete TME (especially immune and vascular components), variable success rates influenced by sample type and tumor purity, and the validation of their predictive value in guiding clinical decisions. This review therefore aims to systematically delineate the progress in LCO research, with a focus on addressing these challenges. We will explore the evolution of LCO culture techniques, critically comparing conventional 3D platforms with emerging 2.5-dimensional (2.5D) systems that offer cost-effective and rapid alternatives for clinical translation. We will detail the standards for their comprehensive characterization—including morphological, histopathological, and genomic validation—and critically assess their expanding applications in deciphering tumor biology, predicting treatment efficacy for targeted agents, chemotherapy, and immunotherapy, and accelerating novel drug development. Ultimately, this review aims to provide a foundational resource for harnessing LCOs to accelerate the transition from bench-side discovery to clinical application in lung cancer. We present this article in accordance with the Narrative Review reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0063/rc).

Methods

A narrative review was conducted to systematically examine the methodologies, characterizations, and applications of LCOs. The literature search was performed in PubMed, Scopus, and Web of Science for articles published up to March 2026. The search strategy aimed to encompass the breadth of LCO research, from foundational culture techniques to translational applications. Key search terms and their combinations included: “lung cancer organoid/ LCO”, “patient-derived organoid/PDO”, “3D culture”, “2.5D culture”, “organoid characterization”, “drug susceptibility testing”, “high-throughput screening”, “precision medicine”, and “tumor microenvironment/ TME”. Medical Subject Headings (MeSH) terms such as “Lung Neoplasms”, “Organoids”, and “Precision Medicine” were also utilized.

Inclusion criteria focused on original research articles, reviews, and pivotal methodological studies published in English that reported on: (I) the establishment and optimization of LCO culture systems (including 3D, 2.5D, and advanced co-culture models); (II) multi-omics and functional characterization of LCOs; (III) the use of LCOs in basic research to elucidate tumor biology, signaling pathways, and mechanisms of drug resistance; and (IV) the application of LCOs in translational research for drug susceptibility prediction (targeted therapy, chemotherapy, immunotherapy) and novel therapeutic development [e.g., antibody-drug conjugates (ADCs)]. Editorials, commentaries, and studies not specific to lung cancer were excluded, although seminal studies on general organoid or tumor modeling technologies with broad applicability were considered where relevant to methodological discussions.

The selection process involved an initial screening of titles and abstracts by two authors to assess relevance, followed by a full-text review of selected articles to confirm alignment with the review’s objectives. Data regarding culture techniques, characterization methods, key findings, and clinical correlations were then synthesized into a coherent narrative structured around the central themes of technology development, model validation, and translational application, as presented in the subsequent sections.

The detailed search strategy is summarized in Table 1, and the complete search strategy used for PubMed is provided in Table S1.

An overview of LCOs

PDOs are 3D, self-organizing in vitro cell culture models capable of simulating the functions of tissues and organs. The development of lung organoids was first reported by Zimmermann et al. in 1987 (11). A significant breakthrough came in 2015, when Dye et al. successfully generated lung organoids from pluripotent stem cells. This technological advancement enabled the in vitro simulation of the complex physiological microenvironment of the human lung, thereby providing an unprecedentedly precise model for investigating lung development and disease pathogenesis (12).

A milestone in LCOs research was achieved in 2017, when Pauli et al. established 56 LCOs models from 769 patients and conducted high-throughput drug screening on them. This study marked the official debut of LCOs in lung cancer research, ushering in a new era of personalized drug screening (13). Since then, substantial progress has been made in multiple aspects of LCO research. In terms of culture technology, the optimization of medium components through the addition of specific cytokines (14,15) and improvements in digestion protocols (16,17) have significantly enhanced the success rate and stability of LCO culture. Regarding functional studies, gene editing technologies have facilitated the exploration of how key genes in LCOs regulate tumor cell proliferation, invasion, and metastasis (18,19). In drug development, LCO-based high-throughput screening platforms have demonstrated superior efficiency, with a screening throughput more than threefold higher than that of traditional methods, greatly accelerating the drug discovery process (13,20).

With the maturation of related technologies, LCO research has gradually extended to different pathological subtypes of lung cancer, demonstrating excellent fidelity in preserving pathological characteristics. For lung adenocarcinoma (LUAD), Li et al. successfully established 12 LUAD organoid lines, which were confirmed to reliably retain the histopathological features of the parental tumors. Genomic analysis revealed that, compared with normal lung organoids, the most enriched signaling pathways in LUAD organoids included the PI3K-Akt pathways, as well as pathways associated with focal adhesion, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interactions, providing crucial insights for developing novel targeted therapies (21). For lung squamous cell carcinoma (LSCC), Hai et al. identified multiple unique gene mutation sites in the Asian population through whole-exome sequencing of organoids. Small-molecule inhibitors designed to target these sites exhibited potent antitumor activity in organoid models (18). For small-cell lung cancer (SCLC), Choi et al. achieved long-term expansion of SCLC organoids by supplementing the culture with WNT3A or R-spondin1. Comprehensive genetic and histopathological analyses demonstrated that this organoid model stably maintained the genetic profile, molecular characteristics, and morphological structure of the original tumor, providing a valuable platform for studying this refractory malignancy (22).

Beyond patient-derived samples, human induced pluripotent stem cell (hiPSC)-derived alveolar organoids have emerged as powerful tools for modeling the lung metastatic microenvironment. These organoids contain alveolar type I (AT1) and type II (AT2) cells, along with mesenchymal components, and can be co-cultured with cancer cells to study colonization. Studies have revealed that AT1 cells regulate indolent disseminated tumor cell (DTC) behavior via secreted frizzled-related protein 2, while AT2 cells promote DTC survival through fatty acid metabolism (23).

Despite the encouraging achievements of LCO models in precision medicine and translational research, their clinical utility in guiding treatment decisions requires further systematic evaluation through prospective studies.

Success rates of organoid establishment

Although the studies described above report successful establishment of LCO models, it is important to recognize that the interpretation of “success rate” varies significantly across different studies, sample types, and culture conditions. First, a distinction must be made between the “organoid formation rate” (i.e., the proportion of primary cultures in which visible organoids form) and the success rate of establishing pure tumor organoids confirmed by long-term culture and genetic validation. Multiple studies have demonstrated that the true success rate of LCOs that can be stably passaged and genetically confirmed as pure tumor is substantially lower than early optimistic estimates. Through systematic analysis of 58 non-small cell lung cancer (NSCLC) samples, Dijkstra et al. found that up to 80% of tumor-derived lung organoids were actually the result of normal airway epithelial cell overgrowth; after excluding these normal organoids, the overall establishment rate of pure NSCLC organoids was only 17% (24). Shi et al. similarly noted that although the primary organoid formation rate in their study reached 88%, 72% of these were short-term cultures (1–3 months, passages 1–9), and only 15% achieved long-term stable passaging (>3 months, >10 passages) (25). Using airway organoid (AO) medium containing Nutlin-3a, Yokota et al. successfully established three genetically confirmed lung tumor organoids capable of long-term culture (>13 months) from 41 lung cancer samples, yielding a success rate of 7%, and noted that this method could not establish organoid models from TP53 wild-type tumors (26). Ebisudani et al. reported establishment efficiencies of 13–14% for LUAD and LUSC organoids, compared to 78% for SCLC, suggesting that different histological subtypes exhibit varying adaptability to culture conditions (27). A recent study by Ehlen et al. also indicated that even with optimized culture protocols, the success rate for long-term lung tumor organoids remains only approximately 15%, and sample cryopreservation exceeding six months significantly reduces success rates (28).

Comparison of different culture protocols further reveals the underlying causes of success rate variability. Sachs et al. achieved a 94% success rate in establishing organoids from normal lung tissue using AO medium containing multiple growth factors (FGF7, FGF10, etc.), but the establishment of tumor organoids faced challenges of normal cell overgrowth and required Nutlin-3a selection for TP53-mutant tumors, with a success rate of 28% for metastasis-derived tumor organoids (29). In contrast, Kim et al. employed a simplified “minimum basal medium” that inhibited normal cell growth, achieving a tumor organoid establishment rate of up to 87% in samples subjected to cytological quality control, and successfully established an organoid biobank of 80 samples covering five histological subtypes (30). In a recent systematic comparison of different media combinations, Strocchi et al. found that while Clevers medium (similar to that used by Sachs et al.) achieved a tumor organoid growth rate of 71%, the success rate of true tumor organoids confirmed by immunohistochemistry and next-generation sequencing (NGS) quality control was only 20%; in contrast, their optimized OMC#5 medium, despite a slightly lower growth rate (55%), achieved a quality control pass rate of up to 84% (31). Using 2.5D culture conditions, Lan et al. achieved relatively true LCO production by screening or enriching tumor cells to reach 10⁵–10⁷ cells prior to organoid culture, with success rates ranging from 78.57% to 80% (32). Collectively, these studies demonstrate that LCO establishment success rates are influenced by multiple factors including sample source (primary vs. metastatic), genetic mutation status, histological subtype, timeliness of sample processing, and culture protocols. Therefore, when evaluating and comparing success rates across different studies, these contextual factors must be fully considered, and a clear distinction should be made between short-term cultures and pure tumor organoids confirmed by long-term genetic validation. Notably, whether clinical stage, histological grade, and tumor aggressiveness correlate with success rates has not been systematically investigated. However, detailed documentation of these clinicopathological features is essential for future multicenter comparative studies and standardization efforts.

Culture technologies of LCOs

Current mainstream technologies for culturing LCOs include 3D and 2.5D culture systems, which are often integrated with advanced engineering techniques such as biological scaffolds, 3D bioreactors, organ-on-a-chip devices, and microfluidics (Figure 1) (20,33). Furthermore, the incorporation of multiple cell types—such as immune cells and fibroblasts—via co-culture systems, along with attempts to construct vascular networks, is advancing organoid models toward closer physiological and structural resemblance to in vivo tumor tissues (9,34-37).

Figure 1 Schematic overview of current culture technologies for LCOs. The figure illustrates the two principal culture systems employed for LCO generation: 3D and 2.5D. The 3D culture system (above) encompasses several advanced engineering platforms, including organ-on-a-chip devices, 3D bioreactors utilizing ECM-coated microcarriers, and hydrogel-based biological scaffolds. Cells are either embedded within hydrogels or maintained in low-attachment plates to facilitate self-assembly into 3D clusters. The 2.5D culture system (below) presents a simplified and cost-effective alternative. The process involves digesting patient-derived samples (e.g., malignant pleural effusion), followed by centrifugation and filtration to obtain cells. These cells are then either (i) seeded onto a prepared scaffold cell layer and cultured in a customized medium, or (ii) mixed with a homemade thermosensitive semi-solid matrix gel (solidified at 4 ℃ and maintained in a semi-solid state at 37 ℃), eliminating the need for commercial hydrogel. This method successfully mimics the native pleural microenvironment while offering operational convenience for downstream applications such as drug susceptibility testing. Reproduced from Lan S, Ma L, Tang C, et al. A clinically translatable 2.5D lung adenocarcinoma organoid platform derived from malignant pleural effusions for precision drug screening and tumor-immune-stromal modeling. Biomedicine & Pharmacotherapy 2025;193:118712. Copyright ©2025 Elsevier Masson SAS. All rights reserved. 2.5D, two-point-five-dimensional; 3D, three-dimensional; ECM, extracellular matrix; LCO, lung cancer organoid.

3D organoid culture system

The core principle of 3D culture is to maintain cells in a suspension, preventing their adhesion to the culture dish surface, thereby facilitating spontaneous formation of 3D structures. This system can be categorized into scaffold-based and scaffold-free strategies. Scaffolds are typically composed of biological or synthetic hydrogels that mimic the native ECM.

Mazzocchi et al. generated LUAD organoids using hydrogel matrices, which recapitulate the physiological extracellular microenvironment by supporting cell cluster formation or encapsulating individual cells. A major advantage of this approach is the tunability of hydrogel physicochemical properties—such as porosity and stiffness—to match the requirements of specific tissues (38). Papp et al. isolated cells from NSCLC pleural effusions via gradient centrifugation and subsequently co-cultured with normal human lung fibroblasts (17). Price et al. demonstrated that low-concentration ECM facilitates large-scale expansion and long-term culture of organoids (39).

3D bioreactors are rotating culture systems that maintain continuous stirring to prevent cell sedimentation. A critical parameter is the optimization of stirring speed: excessively high speeds can damage organoids, whereas insufficient speed results in sedimentation. These systems often employ ECM-coated microcarrier beads as scaffolds to better simulate in vivo the 3D tissue architecture and function, though they typically require relatively long growth cycle (40).

Organ-on-a-chip systems use microfluidic technology to reconstitute physiological culture environments for organoids on chip platforms. Since the introduction of lung-on-a-chip model in 2010, this technology has evolved rapidly, enabling reconstruction of patient-like TMEs and supporting dynamic interactions between organoids and TME components (41). Park et al. developed a vascularized 3D LCO-on-a-chip model using decellularized lung ECM, A549 cells, human umbilical vein endothelial cells, and fibroblasts, and applied it in drug screening with doxorubicin (42). Astrocytes and cerebral vascular endothelial cells (CVECs) are key components of the TME in brain tumors. Kim et al. successfully modeled the complex immune microenvironment of brain metastases by co-culturing NSCLC brain metastatic cells with astrocytes and CVECs on a chip platform (43).

2.5D organoid culture system

Despite the widespread application of 3D organoid models, their complex culture procedure, high costs mainly due to expensive commercial Matrigel and multiple factors, fluctuating success rates, and significant batch-to-batch variations have limited their clinical translation (44). To overcome these obstacles, a more convenient and cost-effective 2.5D organoid culture system has emerged. The concept of 2.5D organoids was first proposed by Abugomaa et al. in 2020. They first transitioned from 3D to 2.5D organoids in canine bladder cancer and subsequently advanced the technology to generate them from cells, achieving a critical technical breakthrough (45,46). These models are straightforward to operate, retain physiological relevance close to that of 3D models, and are more convenient for downstream analysis, achieving a favorable balance between cost and effectiveness (45).

To date, 2.5D technology has been successfully explored in multiple species (cats, dogs) and various human tissues (e.g., pulmonary fibrosis, eye, retina, salivary gland, glial cells, and intestine) (45,47-53). Xu et al. pioneered the establishment of 2.5D lung bud organoids using single-cell and cell clump seeding methods (44). Recently, Lan et al. innovatively established a lung cancer 2.5D organoid culture system by using a mixture of patient-derived malignant pleural effusion (MPE) supernatant and complete medium to replace hydrogel and seeding cells on a scaffold cell layer, which successfully simulated the native microenvironment of MPE cells within the pleural cavity (32). This model effectively retains the genetic characteristics and immune microenvironment of the parental tumor, providing a practical platform for rapid drug sensitivity testing and clinical decision-making in patients with advanced lung cancer.

Comparison of 2.5D and 3D culture systems

The 2.5D system and conventional 3D culture system complement each other and are suitable for different research needs. In their review on pulmonary fibrosis models, Sundarakrishnan et al. clearly defined these two systems: in 2.5D culture, cells grow on an irregular ECM layer, providing an environment between 2D and 3D that facilitates imaging and analysis; whereas in 3D culture, cells are completely embedded within an ECM hydrogel, better recapitulating the 3D interactions between cells and the matrix in vivo (48). In a direct comparison of the two systems using a canine bladder cancer model, Abugomaa et al. found that the 2.5D system was simpler to operate, more cost-effective, and exhibited significantly faster cell proliferation than the 3D organoids from which they were derived, while still retaining key tumor marker expression and in vivo tumorigenicity (45,46). In a study on human iPSC-derived lung bud 2.5D organoids, Xu et al. reported that this method could induce the formation of functional organoids within 9 days, approximately three times faster than traditional 3D methods, and facilitated direct microscopic observation of luminal structures (44). In retinal organoid differentiation studies, Bartalska et al. found that 2.5D culture could simultaneously generate retinal cups and microglia-like cells within 3–4 weeks, a process difficult to achieve in pure 3D culture (50). Using MPE, Lan et al. established a 2.5D LUAD organoid platform, demonstrating over 90% genomic concordance with the original tumor and approximately 90% cost reduction compared to traditional 3D methods, making it particularly suitable for rapid drug sensitivity testing (32). The PhaseFIT system developed by Zhao et al. leverages the imaging-friendly nature of 2.5D intestinal organoids to achieve virtual fluorescence staining based on live-cell phase-contrast images (54).

In summary, the 2.5D system offers distinct advantages in terms of cost, operational simplicity, culture duration, scalability, and imaging convenience, making it particularly well-suited for studies requiring high-throughput drug screening, rapid clinical drug sensitivity testing, and dynamic observation of live cells. However, the 3D culture system retains irreplaceable value in simulating complex in vivo tissue structures. The 3D system better recapitulates the 3D interactions between cells and the ECM, establishes concentration gradients of nutrients and oxygen, and thus forms a TME closer to the in vivo reality. This is essential for studying complex biological processes such as tumor invasion, metastasis, angiogenesis, and cell-matrix signaling (48). Therefore, the research objective should guide the choice of model: if the focus is on rapid clinical translation and large-scale drug screening, the 2.5D system represents a more efficient and economical tool; if the aim is to delve into the complex mechanisms of tumorigenesis and microenvironmental interactions, the 3D system remains the preferred platform. A detailed comparison of the two systems is presented in Table 2.

Immune microenvironment and vascular system of organoids

Two major bottlenecks faced by current organoid models (including 3D and 2.5D) are the lack of functional immune microenvironment and vascular system. This limits the accuracy of predicting drug responses (especially immunotherapy) and restricts their large-scale culture.

For reconstructing the immune microenvironment, researchers primarily use co-culture strategies to simulate the TME. For example, James et al. successfully maintained the interaction between tumor-infiltrating lymphocytes and cancer cells in vitro by preserving endogenous immune and stromal components from primary tumors, which was used for immunotherapy modeling (55). Cattaneo et al. established a co-culture system of tumor organoids and peripheral blood lymphocytes, confirming that it can effectively expand tumor-reactive T cells and evaluate their cytotoxic capacity (56). Additionally, co-culture with fibroblasts (57-59) or with extracellular vesicles derived from stromal cells (60) has been proven to significantly enhance organoid growth, invasiveness, and remodel the microenvironment, demonstrating the effectiveness of co-culture strategies.

To address the problem of necrosis in the core of organoids caused by limited nutrient diffusion, the construction of vascular networks is crucial. 3D bioprinting technology has enabled the generation of branched blood vessels and intestinal epithelial-like crypts in centimeter-scale intestinal-like tissues (61). Fischbach et al. introduced alginate into hydrogels to investigate the angiogenic capacity of cancer cells and found that upregulation of interleukin (IL)-8 promotes angiogenesis in oral squamous cell carcinoma (62). Furthermore, research on the unique mechanisms of tumor angiogenesis is expected to facilitate vascular system formation in LCOs for personalized medicine (63). In the field of lung cancer, a 2023 study used decellularized porcine lung matrix as bioink and co-cultured lung cancer cells, fibroblasts, and human umbilical vein endothelial cells (HUVECs) through 3D bioprinting, successfully constructing an organoid model with lumenized vascular networks, achieving preliminary “vascularization” (64). Another strategy involves transplanting spheroids formed by lung cancer cells, HUVECs, and mesenchymal stem cells onto the chick chorioallantoic membrane, leveraging the in vivo environment to induce angiogenesis and fusion, thus providing a platform for studying tumor-vascular interactions (65). Recent advances have enabled more sophisticated reconstruction of the TME, including 3D bioprinted vascularized LCO models and microfluidic platforms for co-culturing LCOs with autologous immune cells to quantitatively assess immune cell-mediated cytotoxicity (66). However, these vascularized models still face challenges regarding vascular functional integrity, immune cell integration, and standardization, which are key areas for future optimization.

Characterization techniques of LCOs

To ensure that LCO models faithfully recapitulate the biological characteristics of the original tumors, multi-dimensional and systematic characterization is indispensable, primarily encompassing morphological, histopathological, and molecular analyses (Figure 2) (9,67,68).

Figure 2 Multi-dimensional characterization of LCOs compared with original clinical tumor samples. The figure illustrates the parallel morphological, histopathological, and molecular analyses performed to validate the fidelity of LCO models. Both primary lung cancer tissue (clinical sample) and its derived organoids (LCOs) are subjected to identical characterization techniques. These include H&E staining for histopathological architecture, IHC for protein marker expression, and IF for spatial protein localization and cellular structures. For genomic characterization, DNA sequencing is performed on both samples to assess mutation concordance, while RNA sequencing enables transcriptomic profiling and analysis of differentially expressed genes (log₂ fold change). This comparative multi-omics approach demonstrates the ability of LCOs to recapitulate key morphological and molecular features of the parental tumor, establishing their reliability for downstream research and clinical applications. Reproduced from Lan S, Ma L, Tang C, et al. A clinically translatable 2.5D lung adenocarcinoma organoid platform derived from malignant pleural effusions for precision drug screening and tumor-immune-stromal modeling. Biomedicine & Pharmacotherapy 2025;193:118712. Copyright ©2025 Elsevier Masson SAS. All rights reserved. H&E, hematoxylin and eosin; IF, immunofluorescence; IHC, immunohistochemistry; LCO, lung cancer organoid.

Morphological and histopathological characterization

Morphological characterization serves as the foundation for evaluating whether organoids successfully reproduce the histological features of primary tumors. Qualified organoids should possess stable self-assembly capacity, forming compact, well-defined spherical or cystic 3D structures in hydrogel matrices, with diameters typically ranging from 100 to 300 µm (69). Phase-contrast microscopy allows preliminary observation of surface smoothness and translucency, while live-cell staining (e.g., Calcein-AM/EthD-1) enables accurate confirmation of cell viability (70).

Deeper histopathological characterization relies on specific staining techniques. Different staining reagents bind to chemical components of tissues or cells, enabling precise assessment of the structural similarity between organoids and primary tumors (71). Additionally, immunofluorescence staining can detect the spatial distribution patterns of cell polarity proteins (e.g., ZO-1, E-cadherin), revealing the integrity of the epithelial tissue structure (32,72).

For evaluating stability during long-term culture, the proliferation capacity and morphological consistency of organoids need to be monitored. Histochemical analysis can track changes in their cellular hierarchical structure and differentiation direction (73). Specific functional characterization includes assessing their potential for directed differentiation into alveolar or bronchial epithelium (74), as well as verification using lineage-specific markers, such as SP-C and thyroid transcription factor 1 (TTF-1) for lung epithelial cells (75,76). Notably, organoids of different pathological subtypes must retain their unique histological features. LUAD organoids should preserve acinar arrangements and intracellular mucin vacuoles, which can be confirmed by AB-PAS staining (77,78). LSCC organoids typically exhibit keratin pearls and intercellular bridges, a feature supported by CK5/6 immunohistochemistry (79,80). SCLC organoids express neuroendocrine markers (e.g., Syn, CgA) (81,82) and retain stem cell properties (e.g., CD44⁺/CD133⁺) (83,84).

Although the aforementioned in vitro characterizations [such as hematoxylin and eosin (H&E) staining, immunohistochemistry, and immunofluorescence] provide important evidence of similarity between LCOs and the original tumors, from a rigorous pathological perspective, in vitro analyses alone may be insufficient to fully confirm the histological authenticity of the model. The complexity of the in vivo microenvironment—including 3D tissue architecture, stromal components, nutrient and oxygen gradients, and physiological mechanical forces—is essential for maintaining the authentic growth patterns and differentiation states of tumors (48). Therefore, transplanting LCOs into immunodeficient mice for xenograft validation represents an important method for comprehensively assessing their pathological fidelity. Multiple studies have demonstrated that such in vivo models can provide critical information unattainable through in vitro analyses alone. For instance, Dijkstra et al. noted that in cases where the tumor origin of organoids is difficult to definitively determine, in vivo tumorigenicity testing can be considered for final confirmation, serving as a powerful means to validate the tumor origin of organoids (24). Ebisudani et al. transplanted LCOs of various histological subtypes into mice and confirmed that the pathological morphology and subtype marker expression of the resulting xenograft tumors remained consistent with the original tumors (27). In their study on large cell neuroendocrine carcinoma (LCNEC) of the lung, Yokota et al. further demonstrated that organoid-derived xenografts not only recapitulated the typical pathological structure of LCNEC but also faithfully reproduced the admixed adenocarcinoma components present in “combined LCNEC”, highlighting the unique value of in vivo models in capturing tumor heterogeneity and complexity (85). Collectively, these studies confirm the irreplaceable value of in vivo xenograft models in validating the pathological fidelity of organoids, capturing tumor heterogeneity, and assessing their malignant behavior.

Therefore, incorporating in vivo xenograft validation enables a more comprehensive and reliable assessment of the pathological fidelity of LCOs, confirming that they are not merely simple aggregates of tumor cells but truly represent “living models” capable of recapitulating the complex biological behavior of the original tumors. This is of paramount importance for the subsequent utilization of LCOs in drug evaluation, mechanistic studies, and translational applications in precision medicine.

Long-term stability and phenotypic evolution of LCOs

An important consideration for LCO-based studies is the potential for phenotypic and genetic changes during long-term culture. Evidence from literatures demonstrated that lung cancer PDOs could maintain major genomic and histological characteristics during long-term culture when maintained under optimized conditions. Kim et al. compared early passage (< passage 4) and late passage (> passage 10) organoids and found that major cancer driver genes, variant allele frequency distributions, and copy number alterations were preserved during long-term culture (30). Similarly, Shi et al. confirmed that NSCLC organoids retained mutation, copy number, and gene expression profiles of their parental tumors even after >10 passages (25). Strocchi et al. further demonstrated that one LCO line remained viable for >18 months and maintained histological and genetic concordance with the original tumor after freeze/thaw (31). These findings suggest that spontaneous histological transformation is not an inevitable consequence of long-term culture and can be minimized through proper culture conditions and rigorous quality control.

Regarding the acquisition of mesenchymal phenotypes, single-cell multi-omic analyses of Kras/p53-driven tumor organoids have revealed that epithelial-mesenchymal transition (EMT)-like states (Hmga2-high) exist alongside AT2-like states (SPC-high) in early-stage organoids, reflecting pre-existing tumor heterogeneity and early oncogenic reprogramming rather than culture-induced artifacts (86). Dost et al. similarly demonstrated that loss of AT2 differentiation markers and upregulation of developmental genes including Hmga2 occur early after oncogenic KRAS activation (87). These studies indicate that EMT-like phenotypes observed in LCOs often represent intrinsic tumor heterogeneity rather than spontaneous transformation during culture.

A more common challenge identified across multiple studies is contamination with normal airway epithelial cells, which can outgrow tumor cells in culture (25). To address this, rigorous quality controls including histology, immunohistochemistry for lineage markers (TTF-1, p63, CK5/6, CK7), and genomic validation are essential to confirm fidelity to the original tumor (30,31). Optimized media formulations such as OMC#5 have been shown to improve selectivity for tumor organoids (31).

Based on these findings, we recommend: (I) using early-passage LCOs (typically <10 passages) for drug sensitivity testing; (II) regular characterization using histology, immunohistochemistry, and genomic validation to confirm tumor origin and detect potential contamination; (III) monitoring of key lineage markers (e.g., TTF-1, p63, CK5/6) to ensure maintenance of tumor-specific features; and (IV) for studies investigating EMT-related phenotypes, considering that observed heterogeneity may reflect pre-existing tumor biology rather than culture-induced changes. These practices help maintain the fidelity of LCO models and ensure that observed phenotypes reflect tumor biology rather than experimental artifacts.

Molecular characterization

Molecular characterization aims to evaluate the genetic consistency between organoids and parental tumors at the genomic and transcriptomic levels, which represents a core step in ensuring model fidelity. Genomic analysis not only reveals the origin of organoids but also their high similarity to the genetic profile of parental samples, a fundamental requirement for reliable disease modeling (88). In contrast, transcriptomic analysis focuses more on the functional maturity of organoids, specifically, whether they possess tumor subtype-specific transcriptional signatures (89).

Multiple studies have confirmed the high molecular fidelity of LCOs. Wang et al. successfully established 162 LCOs from malignant serous effusions (MSE) of 107 patients, with 103 pleural fluid samples among 132 MSE samples. Genomic comparative analysis showed no significant difference in the detection rate of somatic mutations between LCOs and pleural effusion samples, with a high consistency in the median maximum allele frequency, demonstrating the robust ability of LCOs to recapitulate the genetic profile (14). A study by the Fukushima team on 40 SCLC organoids drew similar conclusions. These organoids not only commonly carried mutations in core SCLC driver genes such as TP53 and RB1, but also recapitulated key genetic alterations like MYC amplification and CDKN2A deletion. More importantly, they successfully reproduced the characteristic features of the four SCLC molecular subtypes (ASCL1, NEUROD1, POU2F3, and YAP1) at the transcriptomic level (15).

These findings collectively demonstrate that rigorous molecular characterization is a prerequisite for applying organoids in downstream experiments such as high-throughput drug screening and mechanism research. The fidelity of organoid models to the molecular profiles of their parent tumors directly determines their value in mechanistic studies and clinical prediction.

Comparison of validation methods and quality control standards

Current studies employ diverse methods and criteria for validating LCO fidelity. To systematically assess the quality of LCO models and provide a reference for future researchers, this review summarizes the commonly used validation methods, markers, and exclusion criteria in representative studies (Table 3).

Morphological/histopathological validation

Nearly all studies employ H&E staining to compare the histological structure of organoids with that of the original tumors. In addition, immunohistochemistry is used to verify the expression of key lineage markers. For LUAD, commonly used markers include TTF-1, Napsin A, and CK7 (25,30,32). Liu et al. further demonstrated that LUAD PDOs exhibited similar morphological features, proliferation rates (Ki-67), and stemness characteristics (SOX2, ALDH1A1) as their parental tumors, confirming the fidelity of organoid models through multi-dimensional characterization (9). For LSCC, commonly used markers include p40, p63, and CK5/6 (25,30,32). Consistently, Pan et al. demonstrated that Kmt2d-deficient lung basal cell organoids exhibited squamous differentiation with keratinization and strong expression of ΔNp63 (p40) and KRT5 upon malignant transformation, further validating these markers for LUSC organoid identification (90). For SCLC, commonly used markers include NCAM1 (CD56), synaptophysin, and CgA (27). Dijkstra et al. further emphasized that the polarized staining pattern of p63 can be used to distinguish normal cystic airway epithelial organoids from solid tumor organoids (24).

Genomic validation

Whole-exome sequencing and targeted gene panel sequencing are the primary tools for assessing genomic concordance between organoids and original tumors. Researchers typically quantify similarity by comparing mutation profiles, copy number variations, and allele frequencies (25,27). Kim et al. used SNP fingerprinting to verify the matching of all LCOs with their parental tumors (30). Yokota et al. innovatively employed FISH karyotyping to confirm the tumor origin of organoids based on chromosomal numerical and structural abnormalities, thereby excluding karyotypically normal cells (26).

Quality control and exclusion criteria

Clear exclusion criteria are essential for ensuring the reliability of LCO studies. The most common issue is the overgrowth of normal airway epithelial cells which results in organoids that are genetically inconsistent with the original tumors (24,25). To address this, as described above, Sachs et al., Kim et al., Strocchi et al., and Lan et al. have also proposed quality control methods in their studies to inhibit normal cell growth and promote tumor cell proliferation (29-32). Notably, to ensure high organoid fidelity, Lan et al. directly excluded samples containing fewer than 10⁵ tumor cells (32).

Collectively, these studies demonstrate that establishing and adhering to a rigorous, multi-dimensional (morphological, histopathological, and genomic) set of validation methods and quality control standards constitutes the foundation for obtaining reliable LCO models and conducting subsequent translational research.

Applications of LCOs in basic research

LCOs models provide a powerful platform for dissecting tumor biology in an environment that closely mimics human physiology. They have been widely utilized in basic research areas such as genomics, epigenetics, and metabolic reprogramming (Figure 3).

Figure 3 Applications of LCOs in basic and translational research. This schematic summarizes the versatile utility of LCOs across research domains. In basic research (left panel), LCOs enable modeling of precancerous lesions and investigation of tumor metabolic reprogramming. In translational research (right panel), LCOs serve as platforms for: (I) drug susceptibility testing, showing dose-response curves of organoids treated with inhibitors versus control; (II) immunotherapy modeling, illustrating T-cell-mediated immune attack leading to tumor cell death; and (III) novel therapeutic development, such as evaluating ADC therapies that induce tumor cell apoptosis. Together, these applications highlight the role of LCOs in bridging mechanistic discovery and clinical translation. ADC, antibody-drug conjugate; ATP, adenosine triphosphate; LCO, lung cancer organoid; LUAD, lung adenocarcinoma; PD-1, programmed cell death protein 1; SCLC, small cell lung cancer; TCA, tricarboxylic acid (cycle); TPC, tumor-propagating cells.

LCOs in genomics and signaling pathway research

Organoid models have been widely used in genomics and signaling pathway studies. Fukushima et al. successfully modeled the preneoplastic stage of the YAP1 subtype of SCLC by knocking out TP53 and RB1 and blocking epidermal growth factor receptor (EGFR) signaling in human alveolar organoids. This model demonstrated loss of alveolar genes and upregulation of YAP1 expression, offering a new perspective for understanding the origin of SCLC (15). Windmöller et al. demonstrated that CD133(+)CD44(+) NSCLC organoids overexpressed NF-κB and MYC, suggesting the potential therapeutic value of targeting these pathways (69). Furthermore, Taverna et al. revealed that CD133(+) cell colonies in NSCLC organoids also display highly invasive characteristics, a mechanism closely associated with the activation of the AXL, TGFβ, and JAK1 pathways. The AXL inhibitor TP-0903 and the JAK inhibitor Ruxolitinib effectively inhibited their growth, validating these pathways as viable therapeutic targets (91). Using NSCLC organoid models, Chen et al. confirmed that reactive oxygen species (ROS) play a critical role in EMT, cell invasion, and migration. They also discovered that the small-molecule drug fangchinoline effectively suppresses tumor progression by reducing cytoplasmic ROS levels and inhibiting the Akt-mTOR signaling pathway (92).

LCOs in epigenetics research

Organoid models have also contributed significant findings in epigenetics research. Rowbotham et al. identified a Sca-1(+)CD24(+) cell subpopulation in LUAD organoids. This subpopulation exhibits a highly proliferative and invasive phenotype due to inhibited function of the histone methyltransferase G9a and shows sensitivity to demethylase inhibitors. This provided a theoretical basis for intervening in tumor progression through epigenetic regulation (74).

LCOs in tumor metabolic reprogramming research

The application of organoids in tumor metabolic reprogramming research has uncovered new metabolic dependency mechanisms. Glutamine synthetase (GS) is frequently overexpressed in lung cancer and promotes tumor growth through glutamine synthesis metabolism. Zhao et al. confirmed that knockdown GS significantly inhibits organoid growth, while depleting GS restores organoid sensitivity to paclitaxel, highlighting the potential of targeting tumor metabolism to overcome chemotherapy resistance (93).

LCOs in clinical drug vulnerability testing

Drug susceptibility prediction is one of the most promising applications of organoid technology in translational clinical research for solid tumors (Figure 3). A substantial body of evidence has demonstrated a high consistency between drug responses in organoids and the phenotypic responses of the primary tumors from which they were derived. The reliability of LCOs as a novel tool, serving as an alternative to cell lines and PDX models, has led to their adoption in multiple clinical trials (94-97).

Targeted drugs

Targeted therapy is the cornerstone of precision medicine for NSCLC, yet drug resistance remains a major challenge (98). Organoid-based drug screening platforms provide a powerful predictive tool for assessing resistance mechanisms, exploring combination therapies, and developing new drugs (99-102).

Combination strategies for EGFR-tyrosine kinase inhibitors (TKIs) resistance

Wang et al. utilized patient-derived LCOs from MPEs to evaluate patient responses to various treatment regimens in a real-world study (14). Their results showed that LCO-based drug sensitivity tests (LCO-DSTs) accurately predicted the efficacy of osimertinib, chemotherapy, and dual-targeted therapy. For instance, in one patient with concurrent EGFR L858R mutation and MET amplification, LCO-DST indicated a synergistic effect of osimertinib combined with savolitinib, which was subsequently confirmed by effective clinical response. In another case with EGFR 19del and RET-CCDC6 fusion, LCO-DST also accurately predicted the efficacy of osimertinib combined with either cabozantinib or BLU-667 (14).

Efficacy prediction of anaplastic lymphoma kinase (ALK)-TKIs and novel ADC drugs

Lee et al. accurately predicted patient responses to multiple ALK-TKIs using PDOs from ALK-rearranged NSCLC. The sensitivity of organoids to crizotinib, alectinib, and brigatinib was 100% for all three drugs, with specificities of 66.6%, 83.3%, and 83.3%, respectively, demonstrating the precise predictive power of organoids in guiding treatment for rare driver gene mutations (103). To address MET amplification, a key resistance mechanism to EGFR-TKIs, the novel ADC REGN5093-M114 was evaluated in patient-derived MET-amplified NSCLC organoid models. The results demonstrated that REGN5093-M114 exhibited significant anti-tumor activity against MET-amplified NSCLC, with efficacy increasing in correlation with higher MET copy numbers (104).

Subtype-specific therapy for SCLC

Fukushima et al. discovered that the YAP1 subtype of SCLC exhibits a subtype-dependent reliance on IGF-1 using PDO models, and the PI3K inhibitor linperlisib effectively inhibited the activity of organoids of this subtype, providing new insights for subtype-specific SCLC therapy (15). Recent studies have also utilized PDX-derived organoids to demonstrate the synergistic antitumor activity of lurbinectedin combined with irinotecan in relapsed SCLC, further supporting the utility of organoid models in evaluating novel drug combinations (105).

These studies indicate that organoid-based drug screening holds significant potential for predicting individualized treatment responses, offering particular clinical translational value in assessing complex resistance mechanisms and combination strategies.

Clinical studies incorporating LCO-DST

To further illustrate the translational application of LCOs in drug development and treatment guidance, we summarize representative clinical studies that have incorporated patient-derived LCOs, either as parallel preclinical models supporting interventional trials or as integral components of observational studies aiming to validate drug sensitivity predictions (Table 4). These include interventional trials with published efficacy data that utilized organoids for preclinical validation—such as the Phase 2 trial of pyrotinib in human epidermal growth factor receptor 2 (HER2)-mutant NSCLC (NCT02535507) (106) and the phase 1 trial of amivantamab in EGFR Exon20ins NSCLC (NCT02609776) (107)—as well as several ongoing prospective observational studies that are building biobanks and evaluating the correlation between ex vivo organoid drug responses and patient outcomes. While randomized controlled trials with prospective organoid-guided therapy are still awaited, these studies collectively support the potential of LCO-based drug sensitivity testing to inform personalized treatment decisions, especially in patients with advanced or drug-resistant lung cancer.

Chemotherapy and immune checkpoint inhibitors (ICIs)

For patients with advanced driver gene-negative lung cancer, chemotherapy and ICIs are integral components of standard treatment. LCO models have demonstrated considerable value in predicting chemotherapy responses. Wang et al. reported an accuracy of up to 83.3% for patient-derived LCO-based prediction of chemotherapy drug susceptibility, with complete concordance between organoid sensitivity and clinical outcome in all four patients treated with the EP regimen (etoposide + cisplatin) (14). Hu et al. conducted drug susceptibility tests using patient-derived SCLC organoids cultured on superhydrophobic microwell array chips to successfully predict sensitivity to the GC regimen (gemcitabine + cisplatin), which was confirmed by subsequent clinical tumor regression (20).

Notably, the efficacy of chemotherapeutic agents is profoundly influenced by the TME, particularly the ECM. Dense, collagen‑rich ECM can impede drug penetration and contribute to resistance. Ismail et al. employed ECM‑mimetic hydrogels to establish a free‑floating patient‑derived organotypic tumor spheroid (PDOTS) system that preserved both the morphological features and the stromal/immune components of the original tumors; 58% of PDOTS retained cytokine and growth factor expression profiles similar to the parental tumors, offering a valuable tool to study ECMdependent drug responses (108). Incorporating such ECM components into LCO culture systems may therefore enable a more faithful recapitulation of in vivo drug efficacy.

In the context of immunotherapy, co-culture models that combine LCOs with immune cells have become indispensable for studying ICIs. Jenkins et al. developed a microfluidics-based patient and mouse-derived PDOTS platform in lung cancer using fresh tumor tissues, this system successfully retained autologous lymphocyte populations (including CD4⁺ and CD8⁺ T cells) and, upon programmed cell death protein 1 (PD1) blockade, revealed a specific upregulation of the chemokines CCL19 and CXCL13 in most PDOTS samples (109). Cattaneo et al. established a co-culture system of NSCLC patient-derived tumor organoids and autologous peripheral blood lymphocytes, confirming the expansion of tumor-reactive CD8⁺ T cells that killed autologous tumor organoids in a Major Histocompatibility Complex-dependent manner while sparing normal lung organoids (56,59). Liu et al. further dissected the local immune response using a function‑associated single‑cell RNA‑sequencing platform on 171 individual primary LCOs; they demonstrated that αPD‑1‑induced cytotoxicity is predominantly mediated by CD8⁺ T cells and identified a 10‑gene signature associated with tumorreactive T cells (68). Novel immune checkpoints, such as T cell immunoreceptor with Ig and ITIM domains (TIGIT), have also emerged as promising targets. Luo et al. observed an increased frequency of TIGIT⁺CD8⁺ T cells with impaired effector function in LUAD and showed, in PDO models, that combining TIGIT blockade with IL‑15 stimulation synergistically enhanced the cytotoxic activity of CD8⁺ tumorinfiltrating lymphocytes (110). Collectively, these studies underscore the central role of CD8⁺ T cells in ICI efficacy and provide a rationale for combination immunotherapeutic strategies.

Furthermore, the genetic landscape of a tumor profoundly influences its response to both chemotherapy and immunotherapy. Li et al. used patient‑derived organoids from a stage IIIA adenocarcinoma patient with a novel LRRTM4-ALK fusion to guide adjuvant crizotinib therapy, achieving >3‑year diseasefree survival; notably, the variant allele frequency of the driver mutation was enriched in organoids compared to the primary tumor, highlighting the value of NGS in validating genetic fidelity and identifying critical cancer cell populations (111). Kim et al. established a biobank of 80 LCOs covering five histological subtypes and used targeted sequencing of 164 cancerrelated genes to demonstrate >77% concordance of somatic mutations between organoids and parental tumors (30). Shi et al. employed whole‑exome sequencing (WES) and RNA‑seq to show that long‑term cultured NSCLC organoids preserved the mutation, copy number, and gene expression profiles of their parental tumors, and demonstrated strong synergy between FGFR and MEK inhibitors in FGFR1amplified LUSC organoids (25). Ebisudani et al. integrated WES, RNA‑seq, ATAC‑seq, and methylation analysis to reveal that NKX2-1 expression defines Wnt dependency in LUAD, demonstrating the power of multiomics approaches in LCO-based mechanistic studies (27). In their comprehensive review, Taverna et al. highlighted the utility of LCOs for ex vivo drug screening with chemotherapy, targeted therapy, and immunotherapy, emphasizing that PDOs consistently recapitulate the genomic alterations and drug sensitivity of primary tumors (102). Tang and Tian highlighted the capability of PDOs in elucidating resistance mechanisms associated with driver gene mutations and co-mutations, including EGFR-TKI resistance mediated through DCLK1-dependent Wnt/β-catenin signaling activation (112,113). Therefore, integrating NGS of LCOs and their matched parental tumors not only validates genetic fidelity but also provides critical insights into drug response mechanisms, identifies novel biomarkers, and guides rational combination therapies.

Novel drug development

ADCs have emerged as a powerful class of therapeutics in oncology, combining the specificity of monoclonal antibodies with the potent cytotoxicity of chemical payloads (114,115). Recent advances have expanded their application to lung cancer, where multiple ADCs targeting diverse antigens are under preclinical and clinical investigation. PDO models have proven invaluable for evaluating ADC efficacy, penetration, and resistance mechanisms in a personalized manner.

Human epidermal growth factor receptor 3 (HER3)-targeting ADCs: AMT-562 overcomes resistance in low-expression tumors

Patritumab deruxtecan (U3-1402) is the first HER3-targeted ADC to demonstrate clinical efficacy in EGFR-mutant NSCLC, yet over 60% of patients do not respond, largely due to low HER3 expression (116). To address this limitation, AMT-562 was developed by conjugating a novel HER3 antibody Ab562 with exatecan via a hydrophilic T moiety, resulting in enhanced potency and bystander killing. In PDO models of colon and lung cancer, including those with low HER3 expression and resistance to patritumab deruxtecan, AMT-562 exerted potent cytotoxic activity. Specifically, in low HER3-expressing organoids, AMT-562 achieved cell viability inhibition rates ≥70%, whereas patritumab deruxtecan showed ≤40% inhibition. Moreover, AMT-562 induced apoptosis in ≥60% of resistant organoid cells and demonstrated efficient penetration into the 3D organoid structure, in contrast to the limited penetration observed with patritumab deruxtecan (116). These findings highlight the potential of AMT-562 to expand the therapeutic window for HER3-expressing tumors.

HER2-targeting ADCs: organoid-based drug sensitivity testing guides personalized therapy

HER2 mutations occur in approximately 3% of NSCLC and represent an actionable target. Luan et al. reported a case of HER2-mutant LUAD where the patient developed severe interstitial lung disease after trastuzumab deruxtecan (T-DXd) therapy, necessitating alternative treatment. Using PDOs established from a bronchoscopic biopsy, drug sensitivity testing was performed. The organoid model revealed that sunvozertinib, an EGFR/HER2 inhibitor, exhibited potent activity with a half-maximal inhibitory concentration (IC₅₀) of 0.28 µM, while T-DXd showed resistance (IC₅₀ =2.96 µM). Guided by these results, the patient received sunvozertinib and achieved a partial response with sustained disease control (117). This case illustrates the utility of PDOs in selecting effective therapies when standard options fail due to toxicity or resistance.

Broad-spectrum T moiety-exatecan ADCs: overcoming multidrug resistance and enhancing tumor penetration

A novel ADC platform utilizing a self-immolative T moiety to conjugate exatecan, a potent topoisomerase I inhibitor, has demonstrated broad antitumor activity across multiple targets, including HER2, HER3, TROP2, and cadherin-6 (118). Compared to DXd-based ADCs, T moiety-exatecan conjugates exhibit higher stability, deeper tumor penetration, and enhanced bystander killing. In patient-derived colon cancer organoids, T moiety-exatecan ADCs (e.g., MTX-1000 targeting HER2) induced cell death extending from the surface to the core of organoids, whereas DS-8201a (T-DXd) caused only peripheral cell death. These ADCs also overcame multidrug resistance mediated by ABCG2/P-gp transporters, which limit the efficacy of DXd/SN-38 ADCs. In xenograft models of lung cancer with EGFR exon 20 insertion mutations and MET amplification, T moiety-exatecan ADCs produced complete responses, outperforming their DXd counterparts (118).

Carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6)-targeting ADC: combined efficacy with immunotherapy

CEACAM6 is overexpressed in multiple solid tumors, including lung cancer. Kogai et al. developed 84-EBET, an ADC targeting CEACAM6 with a bromodomain and extra-terminal protein degrader payload. In preclinical lung cancer models, 84-EBET showed potent antitumor activity superior to standard chemotherapies and ADCs with conventional payloads (DXd, SN38, MMAE). Importantly, combination with anti-PD-1 antibody led to complete tumor regression in syngeneic mouse models of lung cancer resistant to PD-1 blockade. Mechanistically, 84-EBET degraded BRD4 in both cancer and stromal cells via bystander effect, reducing inflammatory phenotypes and increasing tumor-infiltrating CD8⁺ T cells (119). This study underscores the potential of ADC-based delivery of epigenetic modulators to remodel the TME.

Organoid models for ADC toxicity screening

Predicting off-target toxicity, particularly drug-induced interstitial lung disease (DIILD), remains a challenge in ADC development. McCray et al. established bronchioalveolar organoids from human, rat, and nonhuman primate alveolar epithelial cells to assess ADC-associated lung injury. Organoids were exposed to ADCs with different linker-payload combinations (mafodotin, vedotin, deruxtecan). The results mirrored clinical observations: deruxtecan-based ADCs caused significant cytotoxicity and induced an inflammatory response (upregulation of CXCL8, CXCL5, CCL2, and other cytokines), while mafodotin and vedotin ADCs showed minimal effects. RNA sequencing revealed activation of inflammatory pathways, suggesting that lung epithelial cells themselves may contribute to DIILD through immune recruitment (120). This organoid platform offers a valuable preclinical tool for evaluating pulmonary toxicity of ADCs.

Collectively, these studies demonstrate the critical role of PDO models in advancing ADC development for lung cancer. Organoids enable functional assessment of ADC efficacy, penetration, and resistance in a patient-specific context, and can guide personalized therapeutic decisions. The integration of novel ADC platforms—such as T moiety-exatecan conjugates, CEACAM6-targeted degraders, and next-generation HER3 ADCs—with organoid-based testing holds promise for overcoming intrinsic and acquired resistance, expanding the reach of ADC therapy to broader patient populations.

Conclusions

LCOs, encompassing both 3D and 2.5D models, are capable of highly recapitulating the characteristics and heterogeneity of primary tumor tissues, and have emerged as a pivotal tool in advancing precision medicine and novel drug development for lung cancer. As shown in Table 5, although LCO research has made significant progress over the past few years, considerable heterogeneity exists across studies in terms of culture protocols, sample sources, success rates, validation methods, and primary applications. Collectively, these differences underscore several core challenges currently facing the LCO field: standardization of culture protocols, uniform definition of success rates, harmonization of validation methods, and how to strike a balance between model complexity and experimental throughput based on specific research objectives. Notably, the 2.5D culture system offers a new approach for reducing costs and shortening turnaround times while maintaining a certain level of complexity; however, its scope of application and standardization require further exploration. Only by systematically addressing these key issues can LCO technology truly achieve effective translation from the laboratory to clinical practice.

This review systematically summarizes recent progress in LCO culture techniques—including a critical comparison of 3D and emerging 2.5D platforms—characterization methods with an emphasis on validation strategies and quality control standards, basic research applications, and drug vulnerability prediction, highlighting the translational potential and remaining challenges of this rapidly evolving field. Drug efficacy prediction models based on LCOs have shown considerable value in assessing responses to targeted therapy, chemotherapy, ICIs, and other treatment modalities, underscoring their significant potential for clinical translation. Technically, the use of MPE samples has facilitated the establishment of high-quality LCOs, while the integration of bioengineering technologies such as microfluidic chips has markedly accelerated the standardization of organoid culture and high-throughput screening.

Nevertheless, organoid technology still faces several challenges that hinder its broader application in personalized medicine and drug development. A core limitation is the inadequate complexity of current models. First, the absence of a functional vascular system in most organoids restricts the internal transport of nutrients and metabolic waste, often leading to central necrosis and thereby compromising long-term culture and drug response simulations (121,122). Second, the immune microenvironment in most organoids remains incomplete. Key components of the TME, such as diverse immune cells and fibroblasts, are difficult to fully reconstitute in conventional cultures, which limits their utility in immunotherapy research (123). Finally, technical and standardization bottlenecks persist. Current culture systems are hampered by variable success rates, high costs, and significant batch-to-batch variation (24,124). Moreover, substantial heterogeneity in organoid size, structure, and cellular composition, coupled with a lack of unified quality control standards, severely impedes data reproducibility and large-scale application (125,126).

Looking forward, LCO research is expected to follow a trend of deep interdisciplinary integration. First, the integration of AI and radiomics will help overcome the limitations of traditional morphological analysis. By deeply mining the dynamic changes in organoids before and after drug treatment, more accurate models for predicting drug efficacy and toxicity can be constructed. Second, innovations of microphysiological systems and high-throughput drug screening platforms will further empower LCOs. The use of technologies such as microfluidic chips will enable construction of more complex TMEs and even the simulation of multi-organ interactions, providing a novel platform for systematic drug evaluation. Finally, the advancement of LCOs will increasingly rely on close collaboration among biomedical engineering, materials science, computational science, and clinical medicine. By developing novel biomaterials, such as highly biocompatible hydrogels, to assemble complex structures containing diverse microenvironmental cells, and by leveraging powerful AI-based data analytics, LCO technology is poised to build a complete bridge from basic research to translational drug development and ultimately to clinical personalized treatment, thereby accelerating the realization of precision medicine.

Acknowledgments

The authors thank the reviewers for their constructive comments and suggestions, which significantly improved the quality of this manuscript.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0063/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0063/prf

Funding: This work was supported by the Department of Science and Technology of Jilin Province (No. YDZJ202201ZYTS189) and the Scientific Research Funds of Jilin Province of Health and Family Planning Commission (No. 2023JC064).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-1-0063/coif). The authors have no conflicts of interest to declare.

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