A clinician’s perspective: what tumor-organoid researchers ought to know

Jeong Uk Lim

doi:10.51335/organoid.2025.5.e1

Organoid > Volume 5; 2025 > Article

Lim: A clinician’s perspective: what tumor-organoid researchers ought to know

Review Article

Organoid 2025;5:e1.

Published online: January 25, 2025

DOI: https://doi.org/10.51335/organoid.2025.5.e1

A clinician’s perspective: what tumor-organoid researchers ought to know

Jeong Uk Lim

Division of Pulmonary, Critical Care and Allergy, Department of Internal Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

Correspondence to: Jeong Uk Lim Division of Pulmonary, Critical Care and Allergy, Department of Internal Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 10 63(yuksam)-ro, Yeongdeungpo-gu, Seoul 07345, Korea E-mail: cracovian@catholic.ac.kr

Received January 15, 2025 Revised January 17, 2025 Accepted January 20, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Patient-derived tumor organoids (PDTOs) provide powerful platforms for modeling human tumors, offering insights into cancer biology and personalized therapy development. Incorporating clinical knowledge—such as cancer staging, molecular profiling, and treatment outcomes—enhances the relevance of PDTO research. Lung cancer staging systems guide tumor sample selection, thereby influencing organoid growth and treatment response studies. Clinical endpoints and molecular characteristics further align organoid experiments with real-world therapeutic challenges and patient care. Despite their potential, PDTO research faces challenges in replicating tumor heterogeneity and the tumor microenvironment, both of which are critical for studying drug resistance and treatment efficacy. Addressing these complexities requires close collaboration between researchers and clinicians to ensure that organoid models accurately reflect the clinical course of the disease. This review provides information on integrating clinical insights into PDTO research, focusing on aspects such as sample selection, clinical endpoints, and the implications of tumor staging. By bridging the gap between basic organoid research and clinical oncology, the review aims to guide researchers in enhancing the translational value of their work. A clear understanding of these clinical aspects can support the development of more robust organoid models, ultimately advancing cancer treatment strategies and improving patient outcomes.

Keywords: Patient-derived tumor organoids; Lung neoplasm; Mutation profiling; TNM staging; Biomarkers

Introduction

Patient-derived tumor organoids (PDTOs) serve as an innovative and impactful model for advancing cancer research and therapeutic strategies. Owing to their structural and functional similarities to human tumors, PDTOs have gained considerable interest for applications in cancer modeling, personalized treatment, drug evaluation, tumor tissue preservation, investigation of tumorigenesis, and various translational research studies [1-5]. Furthermore, PDTOs are being explored as platforms for clinical applications—including biomarker identification and anticancer drug screening—to improve the effectiveness of personalized therapy [6,7].

Although the use of organoids holds immense potential, effective translation to clinical applications requires a robust understanding of real-world clinical priorities. PDTOs are invaluable for bridging the gap between laboratory models and clinical practice because they closely replicate the genetic and phenotypic characteristics of the original tumors. As such, they have become effective tools for drug screening and biomarker discovery. Studies have demonstrated that PDTOs can predict patient-specific responses to treatments, thereby enhancing personalized medicine approaches. For instance, research has shown that organoids derived from colorectal cancer patients can forecast individual responses to chemotherapy, aiding in the selection of effective treatment regimens [8]. Additionally, PDTOs have been utilized to assess the efficacy of oncolytic adenoviruses, providing insights into personalized virotherapy strategies [9].

Integrating clinical information—such as cancer staging, clinical endpoints (overall survival [OS] and progression-free survival [PFS]), methods for obtaining viable tumor specimens, and the implications of mutation studies and programmed cell death ligand 1 (PD-L1) expression—is crucial for the effective acquisition and interpretation of PDTOs. This comprehensive understanding enhances the translational potential of organoid research [10].

This review aims to offer tumor-organoid researchers insights into the clinical oncology landscape, with a particular focus on lung cancer.

Materials and methods

Ethics statement: This study was a literature review of previously published studies and was therefore exempt from institutional review board approval.

This narrative review was conducted to provide a comprehensive understanding of PDTOs in the context of lung cancer research and clinical applications. References were identified through a systematic search of databases including PubMed, Scopus, and Web of Science. The search employed keywords such as “patient-derived tumor organoids,” “lung cancer,” "cancer staging,” “clinical endpoints,” “mutation profiling,” “biomarkers,” “drug resistance,” and “immunotherapy.” Boolean operators such as “AND,” “OR,” and “NOT” were used to refine search results and ensure their relevance to the review’s objectives.

Articles were selected based on their focus on PDTOs in cancer modeling, translational applications, and clinical integration. Priority was given to studies published in high-impact, peer-reviewed journals between 2015 and 2024.

Lung cancer pathologic types

When samples are acquired and examined under a microscope, professional hospital pathologists diagnose whether the tissue is cancerous. Once lung cancer is confirmed, pathologic typing is performed. Approximately 15% to 20% of lung cancers are diagnosed as small-cell lung cancer (SCLC), which exhibits neuroendocrine features and expresses specific markers characteristic of SCLC. This type is distinct for its high proliferation rate and aggressive metastatic potential. Despite an initially high response to chemotherapy, patients diagnosed with SCLC often have a poor prognosis [11].

Non-small cell lung cancer (NSCLC), which includes all lung cancers not classified as SCLC, accounts for approximately 80% to 85% of cases. Among the NSCLC subtypes, adenocarcinoma represents about 55% to 60% of cases, followed by squamous cell carcinoma at 30% to 35%. Other subtypes include unspecified NSCLC, large cell neuroendocrine carcinoma, and other less common types. Adenocarcinoma and squamous cell carcinoma together represent the majority of NSCLC cases [12]. Notably, squamous cell carcinoma is more strongly associated with smoking than other NSCLC subtypes and is commonly found in the central regions of the lung.

Differentiating pathologic types is important for two reasons: (1) different subtypes exhibit distinct clinical outcomes, and (2) treatment modalities vary based on the pathologic subtype.

Lung cancer staging and its clinical importance

1. Overview of lung cancer staging

Lung cancer is the most frequently diagnosed cancer worldwide and remains a leading cause of global mortality [13]. Histologically, lung cancer is categorized into SCLC and NSCLC. SCLC constitutes approximately 15% to 20% of cases, while NSCLC accounts for the remaining 80% to 85% [14,15].

Staging lung cancer is essential for assessing disease progression, predicting prognosis, and determining optimal treatment strategies [16]. The tumor, node, and metastasis (TNM) classification system plays a key role in evaluating disease burden, as it assesses the size of the primary tumor, the involvement of mediastinal lymph nodes, and the extent of metastatic spread. Treatment strategies are determined based on the cancer stage [17]. The ninth edition of the TNM Classification, developed by the International Association for the Study of Lung Cancer, is the most recent version used for lung cancer staging [18]. This system, derived from an extensive global dataset, provides detailed criteria for evaluating the extent of the disease.

The TNM system comprises three key components: (1) T (Tumor): Represents the size and extent of the primary tumor; (2) N (Node): Indicates whether the cancer has involved mediastinal lymph nodes; (3) M (Metastasis): Describes whether the cancer has spread to distant organs in the body.

The system uses letters and numbers to provide detailed information about the disease. For example, designations like T1N0MX or T3N1M0 indicate different aspects of the cancer’s characteristics [19]. The combined TNM stages are then used to classify cancer from stage I to stage IV. A higher number following T or N indicates a greater disease burden and is more likely to correspond to an advanced stage. If intrathoracic lesions, such as contralateral nodules or malignant pleural or pericardial effusions, are present (and no extrathoracic metastatic lesions exist), the M stage is classified as M1a. For patients with extrathoracic metastatic lesions (located outside the chest region), the classification will be M1b, M1c1, or M1c2, depending on the number and extent of the lesions. All metastatic lesions, whether intrathoracic or extrathoracic, are classified as stage IV [20].

A staging system categorizes cancer patients from stage I (early, localized cancer) to stage IV (advanced cancer that has spread to distant areas). This classification assists healthcare providers in developing tailored treatment plans, optimizing patient care, and improving outcomes by aligning interventions with the cancer's progression stage. Lung cancer management employs multiple treatment approaches, including surgery, radiotherapy, and various anticancer therapies. The choice of treatment depends on factors such as the cancer stage, histological subtype, and the patient’s overall health status [21]. For early-stage cases (stages I or II), curative interventions such as surgery or stereotactic radiotherapy (for patients who are not surgical candidates) are commonly used. In contrast, advanced or metastatic lung cancers are typically managed with palliative chemotherapy and/or radiotherapy, as surgical options are generally not feasible [21].

For SCLC, although the TNM staging system is used, treatment decisions are primarily based on a unique staging system that classifies the disease as either “limited stage” (where all lesions are confined to a single radiation field with no metastases) or “extensive stage” (where lesions extend beyond a single radiation field and are often accompanied by metastatic lesions) [22].

2. Why staging matters for organoid research

Lung cancer staging is essential not only for patient treatment but also for researchers studying tumor organoids. Some studies have incorporated tumor burden—reflected by lung cancer staging at diagnosis—as one of the indices for predicting prognosis based on PDTOs [23].

Localized tumors (stage I-II): These cancers are usually small and confined to the lung, and they can often be removed surgically. For these stages, the priority is predicting early disease recurrence after complete resection. The five-year recurrence rate can be as high as 45% in stage 1b and 62% in stage II. Patients in these stages often receive adjuvant chemotherapy to reduce the risk of recurrence following surgery [24]. PDTOs can serve as a platform for selecting the most effective chemotherapy regimens.

Advanced tumors (stage III-IV): These cancers have spread further, either to nearby lymph nodes or to distant parts of the body. Although stage IIIA—an earlier stage within the advanced group—may be treated with complete surgical resection, most patients with stage III or IV disease typically receive non-surgical treatments, such as traditional chemotherapy, targeted therapy, immunotherapy, radiotherapy, or combinations thereof. These tumors are generally more complex, with higher mutation rates and greater resistance to treatment.

By understanding the stage of the cancer, researchers can select appropriate samples to create organoids that accurately represent the disease. This is important for testing drugs and discovering new treatments tailored to specific stages of lung cancer.

Cancer specimen acquisition and its challenges

1. Obtaining tumor samples from patients

Clinically, obtaining high-quality tumor specimens is essential for diagnostic and therapeutic decisions. Methods include biopsies, fine-needle aspirations, and surgical resections, depending on the tumor’s location, stage, and the patient’s condition. Researchers working with organoids should understand that each method yields samples with different viability, cellular diversity, and size. For example, surgical resections can provide large, viable samples suitable for organoid culture, while needle biopsies may yield smaller specimens with limited cellular diversity.

It is crucial to consider the timing of sample collection. In lung cancer, most samples are obtained at the time of diagnosis. Samples collected after complete resection are particularly valuable because they closely resemble the actual human tumor and are typically free from chemotherapy exposure, thus representing the patient’s native tumor microenvironment. Most diagnostic samples are obtained via percutaneous needle biopsy or aspiration, which involves extracting a small piece of tumor tissue by inserting a needle into the lung tumor. Alternatively, samples may be acquired through surgery, typically performed to obtain a larger specimen for diagnostic or curative purposes. Additionally, malignant pleural effusion samples can be collected, offering a relatively easy and safe option, as procedures like pigtail catheter insertion or thoracotomy provide access to a substantial number of tumor cells [25]. Tumor samples can also be obtained through bronchoscopy or endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). During bronchoscopy, clinicians access the patient’s airways to collect tumor tissue under direct visualization [26]. EBUS-TBNA allows for the collection of tissue from mediastinal or hilar lymph nodes using ultrasound guidance via a bronchoscope, providing a minimally invasive method for sampling tumors or lymph nodes in the mediastinum [27,28].

Recent studies have shown that malignant pleural effusions are a reliable source of tumor cells for generating tumor organoids, possibly due to their high cancer cell purity. It is important to note the site of tumor sample collection.

The situation differs when samples are collected after chemotherapy has been initiated. In terms of understanding a patient’s clinical course, the response to actual drugs holds greater significance compared to samples collected at baseline. Two factors must be considered when interpreting PDTOs growth and drug responses: whether the samples were collected while the patient was responding well to anticancer treatment and whether they were influenced by the effects of the ongoing chemotherapy regimen.

Therefore, researchers working with PDTOs should have a clear understanding of the patient’s clinical endpoints and engage in discussions with the clinicians managing the patients.

2. Challenges in sample collection

In lung cancer, collecting representative samples can be challenging due to the tumor's location and heterogeneity. EBUS-TBNA is one technique used to access mediastinal lymph nodes in cases of metastasis. Understanding these techniques aids organoid researchers in collaborating with clinicians to obtain samples that best represent the tumor’s heterogeneity, which is important for studying drug resistance and mutation evolution.

Tumor tissues obtained from patients are processed for dissociation to isolate patient-derived cancer cells using a combination of mechanical and chemical dissociation techniques [29]. A notable issue is the presence of non-cancerous cells in the sample. During the process of generating organoids from tumor tissues, normal cells adjacent to tumor cells may proliferate more rapidly than cancer cells, resulting in contamination of lung cancer organoids and hindering their development and application. A study investigating the purity of lung adenocarcinoma organoids found that 58% of the samples were contaminated by healthy airway cells [30]. Therefore, obtaining samples with a high proportion of cancer cells is important.

Clinical endpoints: OS, PFS, and the objective response rate

1. OS

OS, defined as the time from treatment initiation to death from any cause, remains the gold standard for evaluating cancer treatment efficacy. From a clinician’s perspective, one of the primary goals in managing lung cancer patients is to prolong OS as much as possible [31]. Although OS is a clear and easily interpretable endpoint, it is less applicable to slowly progressive diseases or patients with a long expected survival [31].

2. PFS

PFS measures the duration during and after treatment that a patient lives without disease progression. In clinical trials, comparing the PFS of different treatment groups helps determine which treatment is most effective in delaying disease progression. According to the Response Evaluation Criteria in Solid Tumors (RECIST), disease progression is defined as a 20% increase in the sum of the diameters of target lesions [32].

3. Objective response rate

The objective response rate (ORR), which is the percentage of patients who experience a partial or complete response to treatment, provides immediate feedback on treatment efficacy. The ORR, defined by RECIST criteria, is one of the most frequently used endpoints [33].

After undergoing chemotherapy, patients typically receive computed tomography and/or magnetic resonance imaging scans every 2 to 3 months to monitor target lesions, which are specific areas tracked to assess treatment response. For lung cancer patients, these target lesions usually include the primary lung tumor and notable metastatic sites. A significant reduction in the size of these lesions indicates regression. Depending on the extent of the reduction, patients are categorized as having either a “partial response” or a “complete response.” If patients achieve either response at least once during treatment, they are considered to have demonstrated an Objective response [34]. Evaluating whether patients’ actual treatment responses align with the drug responses observed in PDTO models is important for assessing the translational relevance of organoid-based research.

Mutation studies in lung cancer: EGFR, KRAS, and ALK

1. Relevance of mutation profiling

One of the potential strengths of PDTOs in oncology is their ability to assess the efficacy of various drugs on a patient’s tumor cells prior to clinical administration, thereby facilitating more precise and effective treatment [35]. Understanding key genetic alterations in patients is crucial for this process, as targeted therapies are designed based on these genetic features [36-40].

Mutation profiling is essential in lung cancer treatment because specific genetic alterations serve as therapeutic targets. Approximately 15% to 30% of NSCLC cases harbor Epidermal Growth Factor Receptor (EGFR) mutations, while Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations are present in about 25% of cases in Western populations. Although less frequent, ALK rearrangements are highly actionable [41].

Identifying these mutations enables the use of targeted therapies, which have significantly improved patient outcomes. For example, cancers with EGFR mutations can be treated with tyrosine kinase inhibitors (TKIs), leading to increased survival rates. Similarly, patients with ALK rearrangements benefit from ALK inhibitors, which have shown efficacy in halting disease progression [42]. Key genetic alterations can be identified through single-gene analysis methods or next-generation sequencing, which allows the detection of a panel of critical mutations in cancer tissue or patients’ blood samples [43].

Therefore, comprehensive genetic profiling is crucial for personalized treatment strategies in NSCLC, as it allows for the selection of appropriate targeted therapies based on individual tumor genetics. This approach not only improves therapeutic efficacy but also minimizes unnecessary side effects by avoiding ineffective treatments.

2. EGFR mutations

EGFR mutations are present in approximately 40% to 59% of NSCLC cases in Asian patients and 5% to 19.4% of NSCLC cases in Caucasian patients [44]. Certain genetic alterations in EGFR are amenable to treatment with TKIs. Common EGFR mutations in NSCLC that are targetable by TKIs include deletions in exon 19 and the single amino acid substitution L858R in exon 21 [45]. First-generation TKIs, such as erlotinib and gefitinib [46], were followed by second-generation TKIs like afatinib and dacomitinib [47,48]. Currently, third-generation TKIs, including osimertinib and lazertinib, are the standard of care for first-line treatment of common EGFR mutations [37,49].

Although EGFR mutations initially respond well to TKIs, resistance eventually develops due to additional mutations. Despite TKIs blocking cancer cell proliferation pathways, cancer cells adapt by activating alternative pathways or altering the target proteins to which TKIs bind, ultimately reducing drug efficacy. Numerous clinically significant resistance mutations exist, some of which will be discussed later.

Upon progression on osimertinib, approximately 15% of tumors develop on-target mutations [50], with EGFR C797X in exon 20 being the most prevalent. This mutation interferes with the covalent binding of osimertinib to the EGFR kinase domain. Other notable acquired mutations include L718Q/V, G719A, and G724S in exon 18 [51].

Another notable mechanism of resistance involves MET-dependent resistance, often activated by the formation of homodimers or through trans-activation by other tyrosine kinase receptors. MET amplification contributes to resistance in approximately 50% to 60% of cases treated with first- and second-generation EGFR TKIs [52,53], and in 15% to 19% of cases involving third-generation EGFR TKIs [54]. In EGFR-mutant NSCLC, MET amplification leads to resistance by persistently activating key downstream signaling pathways—such as MAPK, STAT, and PI3K/AKT—which continue to drive tumor cell proliferation and survival independent of EGFR signaling [52,55]. Overcoming this resistance requires concurrent targeting of both EGFR and MET receptors, highlighting the potential utility of anti‑MET agents in combination with EGFR TKIs to achieve a more effective antitumor response [56].

Another notable mutation involves the human epidermal growth factor receptor 2 (HER2), a tyrosine kinase receptor encoded by the ERBB2 gene [57]. HER2 amplification is observed in 5% of patients who develop resistance to second-line osimertinib and in 2% of cases using first-line osimertinib [58,59].

RET fusions have also been reported as an acquired resistance mechanism after EGFR TKI treatment [60,61]. In fact, RET fusions are more frequently linked to acquired resistance to third-generation EGFR TKIs compared to earlier generations [62]. BRAF V600E-mediated osimertinib resistance is also notable [63,64]. Recent studies indicate that the activation of the PI3K/AKT/mTOR signaling pathway contributes to the aggressiveness of lung cancer [65].

Because the majority of NSCLC patients have targetable EGFR mutations and many eventually develop resistance to EGFR TKIs, there is a significant unmet need for detecting resistance-related genetic alterations and identifying effective subsequent treatments to overcome this challenge.

3. ALK mutations

ALK gene fusion was first recognized for its clinical relevance in lung cancer in 2007 [66]. Patients with ALK rearrangements represent approximately 2-5% of all NSCLC cases [67]. These rearrangements, often caused by an inversion of the ALK gene with the echinoderm microtubule-associated protein-like 4 (EML4) gene, render patients eligible for treatment with ALK TKIs [68-70].

4. KRAS

The KRAS gene, part of the RAS oncogene family, is mutated in approximately 25% to 35% of newly diagnosed NSCLC cases [71]. Among these mutations, G12C is the most prevalent, accounting for roughly 40% of cases, followed by G12V (21%), G12D (17%), and G12A (10%) [72].

One of the most notable KRAS mutations is KRAS G12C. Based on the results of the CodeBreak 100 trial, sotorasib—a KRAS G12C inhibitor—demonstrated an ORR of 36% in patients with KRAS G12C-mutated NSCLC and has been approved for clinical use [73,74]. Similarly, adagrasib, another KRAS G12C inhibitor, has received United States Food and Drug Administration approval for the treatment of NSCLC patients who have received at least one prior systemic therapy [41,75]. Despite the introduction of these groundbreaking KRAS inhibitors, resistance inevitably develops, as with EGFR TKIs. Primary resistance mechanisms have been linked to loss-of-function mutations in KEAP1, SMARCA4, CDKN2A, and STK11. These mutations are significantly enriched in patients with KRAS G12C-mutated NSCLC who experience early progression (within 3 months without achieving an objective response) following treatment with sotorasib or adagrasib. Interestingly, STK11 loss did not adversely affect treatment outcomes in patients with KEAP1 wild-type tumors, suggesting that the negative effects of STK11 mutations in patients treated with G12C inhibitors are largely driven by concurrent KEAP1 mutations [76]. There is an unmet need to identify key resistance mechanisms and develop effective next-line treatments for patients who experience progression after KRAS inhibitor therapy.

Understanding PD-L1 as a biomarker

Molecular markers play a crucial role in lung cancer immunotherapy, with proteins such as programmed cell death protein 1 (PD-1) and its ligand PD-L1 serving as key predictors of response to immunotherapeutic agents [77,78]. PD-1 is a critical immune checkpoint that enables cancer cells to evade immune responses by interacting with PD-L1 expressed on tumor cells, thereby inhibiting the tumor-killing activity of CD8 T cells [79].

Immune checkpoint inhibitors (ICIs) are a mainstay modality in the treatment of metastatic NSCLC. Used either alone or in combination with platinum-based chemotherapy, these therapies have demonstrated significant benefits—including prolonged OS and PFS—in patients with locally advanced or metastatic NSCLC [80]. PD-L1 expression serves as a predictive marker for response to ICIs such as pembrolizumab and nivolumab in NSCLC [81]. For organoid researchers, incorporating PD-L1 expression into immunotherapy model development is crucial, as it can influence the models’ responsiveness to immune-based treatments.

Integrating tumor-organoid models into patient care

Clinically relevant tumor-organoid models must reflect patient-specific characteristics, such as mutation profiles, histological features, and response patterns. By correlating organoid responses with clinical outcomes like OS, PFS, and ORR, researchers can validate the predictive potential of organoids. Longitudinal studies that track changes in organoid models over time could simulate a patient’s clinical course, providing insights into disease progression and treatment responses.

Collaboration between clinicians and researchers is crucial for optimizing the utility of organoid models. Clinicians can provide vital information regarding patient selection criteria, sample acquisition methods, and clinical endpoints. Conversely, organoid researchers can offer clinicians personalized insights into patient-specific treatment responses. This collaboration supports clinicians by providing a more accurate depiction of the patient’s tumor microenvironment and by identifying potentially effective treatment modalities. The checklist of key clinical points to consider when processing PDTOs is provided in Table 1.

Future directions and challenges

Advances in technology have facilitated the development and application of targeted therapies and immunotherapies for lung cancer treatment. The combination of immunotherapy with chemotherapy is expected to heighten the demand for personalized drug regimens [82]. Consequently, the importance of drug efficacy prediction analyses is likely to expand. Lung cancer organoid models offer a valuable platform for assessing the effectiveness of various anticancer agents, including immunotherapies, and for evaluating combinations of multiple drugs. Additionally, these models serve as predictive tools for the efficacy of next-generation anticancer drugs currently in development.

One challenge in utilizing PDTOs for personalized lung cancer treatment is ensuring their effective and consistent application, with tumor heterogeneity presenting a potential limitation. Single biopsies may not capture the full genetic diversity of a tumor. Employing multi-region sampling or integrating circulating tumor DNA into organoid cultures could enhance model accuracy. By representing multiple tumor sites, organoids could become more robust predictors of patient response, particularly for metastasized and heterogeneous tumors like advanced NSCLC.

Furthermore, there is an unmet need to replicate a tumor microenvironment that is suitable for immunotherapy drug testing. To address this, co-culturing immune cells is essential. However, accurately simulating a tumor-immune microenvironment that closely resembles that of the patient who provided the cancer tissue remains a significant challenge.

Conclusion

This review highlights the essential clinical considerations that tumor-organoid researchers should understand to bridge the gap between laboratory models and patient care. By integrating knowledge of lung cancer staging, specimen acquisition, clinical endpoints, mutation studies, and immunotherapy markers, researchers can increase the translational value of organoid models. Collaborative efforts between clinicians and researchers, combined with advancements in organoid technology, have the potential to transform the role of organoids in precision oncology. As research progresses, tumor organoids could facilitate a personalized approach to cancer treatment, supporting clinical decision-making and improving patient outcomes.

NOTES

Conflict of interest

No potential conflict of interest relevant to this article was reported.

Funding

This research was supported by a grant (RS-2024-00332142) from ministry of food and drug safety.

Data availability

Not applicable.

Table 1.

Checklist of clinical features for lung cancer PDTOs

Step	Checklist item
Patient and sample selection	- Confirm lung cancer histological subtype (e.g., adenocarcinoma, squamous cell carcinoma)
	- Include detailed patient staging information (TNM classification)
	- Confirm EGFR, ALK, ROS-1, KRAS, or other mutation status to mimic targeted treatment scenarios
	- Collect clinical data on PD-1/PD-L1 expression for immunotherapy relevance
	- Ensure ethical compliance and patient consent for sample use
Sample collection and handling	- Check which samples are used to acquire patient-derived tumors
	- Minimize sample handling time to preserve cell integrity
	- Check whether samples include abundant non-cancerous cells
	- Use optimized preservation media to maintain sample viability during transport
Organoid culture and maintenance	- Select culture media and conditions specific to lung tissue requirements
	- Consider co-culture with immune cells to study the immune response in organoids
	- Validate growth and morphology periodically to ensure that patient-derived characteristics are maintained
Genetic and molecular analysis	- Verify key genetic or molecular alteration profiles of the patients who provide samples, and confirm the timing of the tests (before or after organoid sample collection).
	- For PDTOs, consider performing tests for key genetic alterations to determine whether the patient’s tumor characteristics are replicated.
	- Compare organoid genetic alterations with the primary tumor.

PDTO, patient-derived tumor organoid; TNM, tumor-node-metastasis classification; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; KRAS, Kirsten rat sarcoma viral oncogene homolog; PD-1, programmed cell death protein 1; PD-L1, programmed cell death ligand 1.

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