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

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.

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.

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.

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.