Tumor organoids in translational cancer research: Models for personalized therapy

Abstract

BACKGROUND

Tumor organoids are 3D cell culture models derived from patient tumor tissues that replicate the complexity of the tumor microenvironment (TME). These models preserve the genetic and phenotypic features of the original tumor, making them superior to traditional 2D cultures and xenografts for cancer research.

AIM

To explore the role of tumor organoids in translational cancer research, with a focus on their applications in personalized therapy and drug testing.

METHODS

A comprehensive review of studies was conducted, including articles from PubMed, Scopus, and Web of Science, with a focus on tumor organoid models in cancer research, particularly in preclinical and clinical drug testing, personalized therapy, and biomarker identification.

RESULTS

Tumor organoids enable high-throughput drug screening, allowing the identification of effective therapies for individual patients. They provide insights into tumor behavior, metastasis, and resistance mechanisms. Additionally, organoids facilitate the evaluation of various therapeutic strategies, including chemotherapy, targeted therapies, and immunotherapies. Despite challenges like inconsistent success rates and ethical concerns with animal-derived matrices, advancements in organoid technology, including AI integration and multi-omics, promise to enhance their clinical applications.

CONCLUSION

Tumor organoids hold immense potential in precision oncology by providing more accurate, patient-specific models for studying cancer biology and predicting treatment responses. Their integration into clinical decision-making will enhance personalized treatment approaches and improve cancer therapy outcomes.

Key Words: Tumor organoids; Personalized therapy; Cancer research; Drug screening; Tumor microenvironment

Core Tip: Tumor organoids are a promising tool for personalized cancer therapy, offering patient-specific models that replicate the tumor’s genetic, phenotypic, and microenvironmental characteristics for more effective treatment strategies.

INTRODUCTION

The worldwide health problem of cancer persists because it shows resistance to standard treatments and exists in multiple forms which results in unfavorable treatment outcomes. The basic understanding of cancer biology has been achieved through traditional in vitro models which include 2D cell cultures and xenografts. The models fail to duplicate the complete complexity which exists in human tumors. The 3D structure together with cell interactions and tumor microenvironment (TME) that affect tumor behavior and metastasis and treatment responses cannot be replicated by these models. The 3D tumor structures known as organoids which are derived from patient tumor tissues provide a superior research alternative[1].

The genetic diversity together with cell types and molecular features of tumors remain intact in organoids which makes them a superior tool for cancer research. Through these organoids scientists can observe how cancer cells interact with their surroundings which results in more realistic tumor behavior simulations. Tumor organoid technology shows promise to enhance cancer research together with drug testing and personalized treatment approaches[2].

This mini-review examines how tumor organoids function in translational cancer research through personalized therapy applications. The review evaluates organoid-based models by discussing their benefits and obstacles while showing recent developments and explaining their potential clinical implementation for better treatment results. Tumor organoids will establish themselves as fundamental elements of precision oncology because they enable the development of personalized cancer treatments.

MATERIALS AND METHODS

A comprehensive literature search was conducted for this narrative review to identify studies on the use of tumor organoids in translational cancer research, particularly focusing on their applications in personalized therapy and drug testing. The search was performed across multiple electronic databases, including PubMed, Scopus, and Web of Science, from their inception until August 2025. Only peer-reviewed articles published in English were included. A combination of keywords and Medical Subject Headings terms was used: ("tumor organoids" OR "cancer organoids") AND ("personalized therapy" OR "precision medicine") AND ("translational cancer research"). Studies were eligible if they involved tumor organoid models derived from human cancer tissues, focusing on preclinical or clinical trials that evaluated the efficacy of tumor organoids in drug screening or personalized treatment strategies. Articles were excluded if they did not directly relate to tumor organoids or personalized therapy or if they were non-peer-reviewed, conference abstracts, or opinion pieces.

Data extraction was performed independently by two reviewers using a standardized form. The extracted data included study characteristics (e.g., authors, year of publication, journal name, country, study type), details about the organoid models (e.g., tumor type, source of tumor tissue, methodology), applications of personalized therapy (e.g., therapeutic agents tested), and outcome measures such as success rates of therapy, drug response, and any reported adverse effects. The quality of the included studies was assessed using SYRCLE’s Risk of Bias tool for preclinical studies and the Cochrane Risk of Bias tool for clinical trials. Each study was categorized as low, unclear, or high risk of bias based on criteria such as selection bias, performance bias, detection bias, reporting bias, and attrition bias.

Given the variability in study designs and methodologies across the included studies, a narrative synthesis approach was employed to summarize the findings. This approach identified common themes such as the role of TME factors in organoid-based models, drug resistance mechanisms observed in tumor organoids, and the integration of organoid models in clinical decision-making. Due to the heterogeneity of the studies, a meta-analysis was not conducted.

RESULTS

The initial literature search returned 1256 studies, of which 558 were deemed potentially relevant based on title and abstract screening. After a full-text review, 130 studies met the eligibility criteria for inclusion in this narrative review. The PRISMA flowchart illustrates the study selection process, and the studies covered various cancer types, with a particular focus on colorectal cancer, lung cancer, and breast cancer organoid models (Figure 1). These studies provided valuable insights into the role of tumor organoids in personalized drug testing and tumor behavior investigations.

Figure 1 PRISMA 2009 flow diagram.

Despite some limitations, such as the exclusion of non-English language articles and the inability to conduct a formal meta-analysis, this review provides a comprehensive synthesis of current evidence on the use of tumor organoids in cancer research and personalized therapy. The studies demonstrated that tumor organoids are effective models for drug screening and predicting treatment efficacy. For example, in colorectal cancer, organoids derived from patient tissues were successfully used to test the efficacy of standard chemotherapy agents such as 5-fluorouracil, irinotecan, and oxaliplatin. Organoids that were resistant to oxaliplatin showed a significantly shorter progression-free survival in patients, supporting the potential of organoid models in predicting treatment responses. Similarly, lung cancer organoids were tested for their sensitivity to EGFR inhibitors, revealing that EGFR-mutant organoids responded well to these targeted therapies, mirroring clinical outcomes in patients.

Despite their promise, tumor organoids face several challenges. One of the main limitations is their inability to fully replicate the TME, particularly in terms of vascularization and immune cell interactions. Current organoid models often lack essential elements of the TME, which limits their ability to replicate in vivo conditions, especially in terms of drug delivery and immune response. Additionally, the success rates of organoid cultures vary across different cancer types. For instance, glioblastoma organoid cultures often fail to thrive, whereas lung adenocarcinoma organoids exhibit higher success rates. The use of animal-derived matrices, such as Matrigel, also raises ethical concerns and impacts the reproducibility of results. Efforts are underway to develop animal-free matrices to improve consistency and ethical standards in organoid research.

Another significant issue is the genetic and epigenetic stability of organoids over long-term culture. Studies have shown that epigenetic drift and clonal evolution can occur during prolonged passaging, which may skew results from long-passage organoids and affect their fidelity as models for drug testing.

Despite these challenges, recent advancements in organoid technology are promising. For example, the integration of artificial intelligence (AI) and multi-omics approaches is enhancing the predictive power of organoid models by providing more accurate insights into drug resistance mechanisms and treatment outcomes. Additionally, new organoids-on-a-chip platforms are being developed to better replicate the TME, including vascularization and immune interactions, which will improve the clinical relevance of organoid models.

DISCUSSION

Organoid derivation methods

Tumor organoids are created by collecting patient tumor tissues through surgery, biopsies, or fluid samples. The tissue is processed to isolate cells that can form organoids[3]. The first step involves dissociating the tissue into single-cell suspensions using mechanical methods (e.g., scalpels) and enzymatic methods (e.g., collagenase, trypsin). The dissociation method depends on the tumor type, as some are harder to break down than others[4]. Soft tumors may require less mechanical disruption, while solid tumors need more enzymatic treatment[5].

The isolated cells need to be embedded into an extracellular matrix (ECM) which typically uses Matrigel to create a tissue-like environment that supports cell adhesion. The choice of matrix between hydrogel and collagen depends on the specific requirements of the tumor cells. The embedded cells need growth factors such as EGF, FGF and Wnt signaling molecules to preserve stem cell properties while preventing premature cell differentiation[6]. The culture system implements air-liquid interface technology to create conditions that match those found in living tissues by exposing the surface cells to air. Organoid formation success rates differ from one case to another. The success rate for lung adenocarcinoma organoid culture is high but glioblastoma cells prove difficult to maintain in culture[7]. The research applications of organoids include drug screening and resistance studies and cancer behavior investigations. Organoids serve as precise models for studying colon cancer as well as lung cancer and breast cancer[8]. The current models face two main obstacles because of their inconsistent success rates and their inability to perfectly replicate the TME. The use of animal-derived Matrigel in organoid culture creates ethical issues and the absence of immune cells and vascularization in current models represents a major limitation[9-11].

Researchers are working on overcoming these challenges by creating animal-free matrices and incorporating additional cell types to improve the TME. The time and cost required for organoid generation are also factors, but with ongoing advancements, organoids are becoming more accessible for research and clinical use.

Quality control and characterization

Quality control and characterization are crucial for ensuring tumor organoids accurately represent the original tumor. Organoids are only useful if they retain the genetic, phenotypic, and functional characteristics of the tumor. Without proper validation, they may not provide reliable data for cancer research or personalized therapy[12].

Histopathological analysis serves as the initial method for characterization. The organoids show the same tissue arrangement as the original tumor according to this method. The examination of tissue architecture through HE staining allows scientists to observe glandular structures in breast and colorectal cancer tissues. The analysis reveals both abnormal cell differentiation patterns and structural damage. The assessment of specific biomarkers through immunohistochemistry (IHC) helps identify HER2 in breast cancer and EGFR in lung cancer. The biomarkers enable researchers to determine differentiation status and stem cell markers and tumor-specific mutations. The IHC procedure verifies that the organoids match the molecular profile of the original tumor. Molecular profiling through RNA sequencing (RNA-seq) and DNA sequencing delivers advanced understanding of biological systems. Through RNA-seq analysis researchers can compare organoid gene expression to primary tumor expression to detect driver mutations and splicing variations. DNA sequencing technology identifies mutations which are unique to tumors including TP53 and KRAS. The techniques verify that organoids maintain the genetic diversity which characterizes the original tumor[13].

Functional validation assesses whether the organoids exhibit behaviors like cell proliferation, differentiation, and drug response. Proliferation assays, such as Ki-67 staining, measure cell division rates, which are critical for validating aggressive tumor models. Differentiation assays check for the presence of specific cell types, ensuring the organoids mimic the tumor’s heterogeneity[14].

The development of drug response assays stands vital for delivering personalized treatment approaches. The drug response of organoids is evaluated through cell viability and apoptosis measurements after drug exposure. The tests evaluate the accuracy of organoids in predicting patient drug reactions. Organoids that show resistance to particular treatments should demonstrate decreased sensitivity while those that are sensitive should either die or experience growth inhibition. A complete method integrates histopathology with molecular profiling and functional assays. The multi-parameter approach guarantees that organoids accurately duplicate the tumor's diverse characteristics. The assessment of long-term culture stability remains crucial because genetic drift together with epigenetic changes can negatively impact organoid fidelity during extended periods of cultivation[15]. Organoid models require cryopreservation as a fundamental method for their preservation. The correct storage and thawing protocols enable organoids to maintain their operational capabilities. The reliability of patient-specific models for future research depends on organoid biobanks because they provide consistent results.

Epigenetic drift in tumor organoids

Epigenetic drift refers to the gradual accumulation of epigenetic changes (such as DNA methylation, histone modification, and non-coding RNA expression) that can occur during long-term culture of organoids. While these changes may be adaptive in the context of the in vitro environment, they can also diverge from the original epigenetic landscape of the primary tumor. Tumor organoids are cultured in synthetic media and conditions that might not fully replicate the complex microenvironment of human tumors. Over time, this can lead to the silencing of specific tumor suppressor genes or the activation of oncogenes, causing a shift in the molecular profile of the organoid compared to the patient's original tumor[16].

This drift can have significant implications for drug screening and clinical prediction. For instance, if epigenetic alterations within the organoid result in the expression of different molecular markers or resistance mechanisms, the organoid may no longer accurately reflect the tumor's behavior or response to therapies in the patient. Epigenetic drift could lead to misrepresentation of drug efficacy, rendering organoid-based testing less reliable as a model for clinical decisions[17].

Clonal evolution and its impact on organoid culture

Clonal evolution is the process by which different subpopulations (clones) of cells within a tumor organoid evolve over time, driven by genetic mutations and selective pressures. Tumor cells are inherently heterogeneous, with multiple clones coexisting within a single tumor. These clones may vary in terms of their genetic makeup, phenotypic characteristics, and response to treatment. In long-term culture, certain clones may be selected for their ability to proliferate or survive under the specific conditions of the in vitro environment, leading to a skewed representation of the original tumor population[18].

As organoids are passaged over time, the dominant clone(s) may not represent the full spectrum of genetic and phenotypic heterogeneity present in the patient’s tumor. This selective clonal expansion could result in the loss of critical subpopulations that may have important roles in metastasis or therapy resistance. For instance, cancer stem cells, which are known for their resistance to conventional therapies and their ability to drive relapse, could be underrepresented or eliminated during passaging. This raises concerns about the organoid's ability to serve as a reliable model for studying tumor progression, therapy resistance, and metastasis[19].

The clonal evolution of organoids also complicates their use in drug screening. If the dominant clones in the organoid culture are not the same as those driving resistance in the patient, then the drug response observed in the organoid may not accurately predict the clinical outcome. Long-term passaging increases the risk that the organoid will diverge significantly from the patient’s tumor, leading to misinformed therapeutic decisions[20].

Alterations in gene expression during long-term culture

Long-term culture of tumor organoids can also lead to significant alterations in gene expression patterns. Organoids are cultured in artificial environments, often lacking the complex cellular interactions and ECM components that are present in the in vivo TME. Over time, this discrepancy can cause changes in gene expression that influence cell differentiation, drug sensitivity, and response to various stimuli[21].

The lack of key stromal cells, immune cells, and signaling molecules that are crucial for maintaining the tumor’s original gene expression profile can result in a shift towards a more “primitive” or altered phenotype. For example, the expression of genes involved in epithelial-to-mesenchymal transition (EMT), a process linked to tumor invasiveness and metastasis, might be upregulated or downregulated, depending on the culture conditions. Similarly, tumor organoids could lose their expression of genes associated with drug resistance or immunotherapy evasion if those signals are not continuously reinforced by the culture system[22].

In drug screening, these gene expression shifts may lead to inaccurate predictions of drug efficacy. A drug that is effective against a primary tumor in vivo might not be as effective against the organoid if the latter has undergone gene expression changes that alter its sensitivity. This makes it crucial to closely monitor gene expression and continually validate organoid models to ensure they reflect the clinical reality of the patient's tumor[23].

Role of tumor organoids in personalized therapy

Tumor organoids are crucial for personalized cancer therapy because they replicate patient-specific tumor biology. Researchers can use organoids to test various drugs and identify the most effective treatments for individual patients. Organoids allow testing of sensitivity to chemotherapy, targeted therapies, and immunotherapies, helping clinicians make informed treatment decisions based on the patient's tumor characteristics[24].

Organoid studies demonstrate their ability to forecast how patients will react to chemotherapy treatments. The drug responses of colon cancer organoids to 5-fluorouracil irinotecan and oxaliplatin matched the clinical results of patients. Patients who had organoids that were resistant to oxaliplatin experienced much shorter progression-free survival periods than patients with organoids that were sensitive to the drug. Organoids show promise as predictive tools for treatment responses which can enhance therapeutic approaches[25]. Organoids from tumors help doctors find suitable treatments when genomic testing does not provide enough information about the cancer. The combination of ibrutinib treatment with organoids resulted in tumor reduction and symptom improvement for patients with low-grade serous ovarian cancer. Organoids demonstrate their capability to discover novel therapeutic approaches which benefit patients with cancers that standard treatments cannot effectively manage[26].

Organoids are also valuable for high-throughput drug screening (HTS). Testing drug libraries on organoids allows the rapid assessment of hundreds of compounds. In ovarian cancer, organoids revealed differential responses to chemotherapy, reflecting the variability seen in clinical practice. This reinforces the importance of organoids in developing personalized treatment strategies[19,27-30].

Immunotherapy development and testing

Immunotherapy has revolutionized cancer treatment, offering the potential for long-lasting responses, especially in hard-to-treat cancers like melanoma, lung cancer, and certain hematological cancers. Unlike traditional therapies, immunotherapies harness the body's immune system to target and eliminate tumor cells. Approaches like immune checkpoint inhibitors, CAR-T cell therapy, and tumor vaccines are central to this strategy. However, the efficacy of immunotherapies varies among patients, and not all tumors respond. Tumor organoids have become vital in understanding how tumors interact with the immune system and in testing immunotherapies[31].

Organoids function as individual patient models to study cancer interactions with immune cells. Researchers can duplicate the TME through the process of co-culturing organoids with T cells and macrophages. Researchers can study immune evasion mechanisms and immune cell recruitment through organoids more accurately than traditional 2D cultures or animal models. The development of immunotherapy depends heavily on immune checkpoint inhibitors which target PD-1 and CTLA-4. The immune system uses these checkpoints for tumor cells to avoid detection by the immune system. Checkpoint inhibitors applied to organoids allow scientists to study tumor responses which helps determine the most suitable treatment options for each patient. The PD-1 inhibitors activated immune cells in organoid co-culture experiments of breast cancer which produced results that matched clinical treatment outcomes. The model enables researchers to evaluate different treatment combinations between checkpoint inhibitors and other drugs to boost immune system responses. CAR-T cell therapy demonstrates success in treating hematologic cancers yet faces challenges when applied to solid tumor cases[32]. The accurate representation of tumor environments in solid tumors makes organoids superior to 2D models for studying CAR-T cell interactions with these tumors. Organoids in glioblastoma research have enabled scientists to identify TME immune-suppressive factors which led to the creation of combination therapies to boost CAR-T cell effectiveness. Organoids function as valuable tools for studying tumor-associated antigens and neoantigens which represent cancer-specific mutations[33]. The identified targets serve as bases for developing personalized vaccines and T-cell therapies. Tumor organoids enable researchers to detect special mutations and evaluate neoantigen vaccine performance prior to starting clinical trials. Organoids serve as essential tools for studying immunotherapy resistance. Patients who receive immunotherapy treatment first show positive responses but eventually develop resistance to the therapy. The repeated application of immune therapy to organoids enables scientists to study resistance mechanisms that include immune checkpoint activation and MHC class I expression reduction. The identification of combination therapies to overcome resistance becomes possible through this approach.

Finally, organoids allow for the study of how the TME influences immune therapy responses. Components of the TME, like tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs), can inhibit immune function and limit immunotherapy effectiveness. Co-culturing organoids with immune cells and other TME components provides insights into these interactions and supports the development of therapies that aim to "normalize" the TME for better immune responses[34].

Multi-organoid systems that simulate metastatic disease are also emerging. These systems allow researchers to study how immunotherapy affects not just primary tumors but also metastases. By using organ-on-a-chip platforms, they can examine the immune response at different tumor sites and understand how treatments targeting the primary tumor affect distant metastases. This approach is crucial for developing effective immunotherapies that target both primary and metastatic cancers[35].

Biomarker discovery and validation

Biomarker discovery and validation are key to advancing cancer research by identifying molecular signatures for diagnosis, treatment prediction, and prognosis. Tumor organoids play a crucial role in this process by offering a more accurate representation of human tumors than traditional 2D cell cultures or animal models. Organoids mimic the genetic, phenotypic, and microenvironmental characteristics of original tumors, making them valuable for discovering biomarkers related to cancer progression, treatment response, and resistance mechanisms[36].

Cancer biomarker identification requires the detection of molecular elements that are directly associated with tumorigenesis along with metastasis and drug resistance. Traditional sequencing approaches applied to patient samples fail to detect the intricate tumor cell interactions within their microenvironment. Tumor organoids maintain the complete heterogeneity along with three-dimensional architecture of tumors which leads to improved biomarker detection that mirrors the natural tumor biology. The combination of multiple omics approaches which includes genomics transcriptomics proteomics and metabolomics leads to better biomarker discovery. RNA-seq technology enables researchers to examine gene expression patterns within organoid cells which helps identify cancer-specific or treatment-response linked genes. DNA sequencing and copy number variation (CNV) analysis reveal mutations and amplifications in key oncogenes which helps identify genetic drivers and potential therapy targets. The search for biomarkers which track tumor progression or drug resistance needs proteomic analysis. The analytical process of mass spectrometry determines protein expression and their modifications and interactions that determine cancer cell activities. The activity patterns of kinases together with EMT markers reveal essential information about metastasis and immune system avoidance. Tumor organoids serve as exceptional tools for studying how cancer cells evolve treatment resistance along with protein expressions that facilitate drug evasion or immune system evasion. The identification of neoantigens represents essential targets because they derive from cancer mutations. The genetic diversity present in original tumors makes tumor organoids the optimal platform for discovering these antigens. The combination of WES with neoantigen prediction algorithms enables the identification of candidate neoantigens. Organoid technology allows researchers to assess T-cell responses toward antigens while simultaneously evaluating personalized cancer vaccines and adoptive T-cell therapies. Organoids serve as systems to replicate the TME which functions as a fundamental driver of cancer development and drug resistance. The TME consists of cancer cells together with stromal cells and immune cells and fibroblasts as well as ECM components. The dynamic analysis of tumor behavior and treatment responses becomes possible through organoid cultures when these components of the TME are used for co-culture experiments. Tumor growth and angiogenesis and immune suppression can be studied through the use of cancer-associated fibroblasts (CAFs) and TAMs in the co-culture system. The biomarkers PD-L1 and TGF-β serve as crucial indicators for predicting the effects of immune checkpoint inhibitors on patients[37].

Once potential biomarkers are identified, validating them in organoid models is essential. Organoids provide a high-throughput platform for testing biomarkers in patient-specific tumor models. Biomarkers identified through transcriptomic profiling can be validated by testing their inhibition or overexpression in organoids. This ensures that the biomarkers are functionally relevant for tumor growth or treatment resistance. Furthermore, biomarkers linked to poor prognosis or resistance can help stratify patients for clinical trials, ensuring targeted treatments[38].

Organoids are also instrumental in drug screening. By testing the efficacy of chemotherapy, targeted therapies, and immunotherapies on organoids, researchers can identify biomarkers predictive of treatment success or failure. For example, ovarian cancer organoids can identify biomarkers associated with platinum sensitivity or resistance, while lung cancer organoids can predict responses to EGFR inhibitors. Organoids offer more accurate predictions of treatment responses, helping guide personalized therapy decisions[39].

The discovery of biomarkers undergoes transformation through the implementation of AI and machine learning (ML). AI systems process extensive multi-omics datasets to detect patterns and correlations which human researchers might miss. The analysis of gene expression and protein activity and drug response data through AI systems reveals new biomarkers that show strong predictive potential. AI technology accelerates biomarker discovery operations which shortens the duration and decreases the expenses associated with conventional methods. Tumor organoids serve as essential tools for biomarker-driven clinical trials. The identification of biomarkers which predict patient outcomes enables researchers to create clinical trials that are both efficient and focused. Organoids enable researchers to test patients for biomarker-related treatments which helps select only those patients who will gain benefit from the therapy. The individualized approach enhances clinical trial outcomes and decreases expenses through the elimination of treatments that do not work. Organoid biobanks serve as a valuable resource for identifying biomarkers which exist across different cancer types and treatment protocols[40].

Predicting responses to therapy

Tumor organoids are increasingly recognized for their ability to predict patient-specific responses to therapy. They preserve the genetic, phenotypic, and microenvironmental diversity of the original tumors, making them an ideal model for assessing treatment responses. Unlike traditional 2D cell cultures, which cannot replicate the complexity and heterogeneity of tumors, organoids provide a more accurate representation of tumor behavior in the body[41]. This makes them especially valuable in personalized cancer treatment, where therapies are tailored to the individual based on their tumor’s unique characteristics[42].

Genetic fidelity and predictive power

One key advantage of tumor organoids in predicting therapeutic responses is their ability to preserve the genetic fidelity of the original tumor. Studies show that organoids retain 92.3% to 97.7% of the genetic variants found in primary tumors, including mutations, CNV, and genetic rearrangements[43]. This genetic preservation is essential for accurately predicting tumor responses to various treatments. For example, lung cancer organoids can retain genetic alterations like EGFR mutations or ALK translocations, allowing researchers to evaluate the effectiveness of therapies targeting these specific mutations. Similarly, colorectal cancer organoids with KRAS mutations can be tested for their response to EGFR inhibitors, helping identify patients who may benefit from such treatments and those likely to resist them[44].

Organoids serve as valuable tools for drug sensitivity testing because they replicate the genetic characteristics of the original tumor. Researchers use organoids to test various drugs which helps them detect genetic mutations that affect drug response or resistance[45]. The HER2-positive breast cancer organoids respond well to trastuzumab therapy but KRAS-mutant colorectal cancer organoids do not respond to EGFR inhibitors. The genetic information obtained from these studies enables doctors to pick the most suitable treatments for patients based on their tumor genetics[35,46,47].

Phenotypic heterogeneity and drug sensitivity

Tumor organoids effectively capture the phenotypic heterogeneity of tumors, a crucial factor in predicting therapy responses. In a primary tumor, cancer cells are not identical; there are often subpopulations with varying sensitivities to treatment. Some cells may have stem-like properties that make them more resistant to therapy, while others are more differentiated and susceptible to treatment. This diversity can significantly influence the overall response to therapy. Organoids, by maintaining this phenotypic variability, help identify resistant or sensitive subpopulations, enabling researchers to predict how different parts of a tumor may respond to treatment[48].

For example, in breast cancer, organoids derived from patient tumors show distinct subpopulations with different EMT states. EMT is a process where epithelial cells acquire mesenchymal traits, which are linked to increased metastatic potential and drug resistance. Organoids reflecting these different EMT states can test how chemotherapy or targeted therapies impact both mesenchymal and epithelial cells within the tumor. This helps assess whether therapies effectively target the resistant mesenchymal subpopulations or if they only target the more differentiated epithelial cells, potentially leading to relapse and metastasis[49].

In glioblastoma, a particularly heterogeneous tumor, organoids are used to study how various cellular populations respond to chemoradiation. They help identify whether the more aggressive, stem-like populations within the tumor are resistant to treatment, which is often a cause of therapy failure. By detecting these resistant subpopulations early, organoid models allow researchers to refine therapeutic approaches and explore combination therapies that can target both the differentiated and stem-like cells in the tumor[20,50-54].

TME and therapy response

Another significant advantage of tumor organoids is their ability to preserve the TME, which plays a crucial role in therapy response. The TME is a complex network of cells, ECM components, and signaling molecules that support and surround tumor cells. It can influence how tumors respond to treatments by promoting immune evasion, drug resistance, and metastasis. Tumor organoids can be co-cultured with immune cells, stromal cells, and endothelial cells to mimic these interactions, offering a more realistic platform for evaluating tumor responses to immunotherapies, such as immune checkpoint inhibitors, and chemotherapies[21,55].

For example, in melanoma, immune cells like T cells and macrophages in the organoid culture can impact the tumor’s response to PD-1 inhibitors. Organoids with a more immunosuppressive TME may show resistance to PD-1 blockade, while those with a more immunologically active environment may respond positively. This testing helps identify biomarkers of immune resistance and informs clinicians about potential combination therapies, such as pairing PD-1 inhibitors with chemotherapy or therapies that modify the TME to enhance immune responses[56].

Organoids also allow testing of vascularization in the TME. Many tumors, especially solid tumors, have poor blood vessel networks, which can hinder drug delivery. By incorporating endothelial cells into organoid cultures, researchers can simulate the effects of vascular normalization therapies, which aim to improve blood vessel function and enhance drug delivery. For example, anti-angiogenic drugs like bevacizumab, used in cancers like non-small cell lung cancer (NSCLC) and ovarian cancer, can be tested in organoids to assess their impact on chemotherapy and targeted therapy efficacy[57].

Drug resistance mechanisms and therapy prediction

Drug resistance remains one of the most significant challenges in cancer treatment. Tumor organoids offer a powerful platform for studying these resistance mechanisms in ways that closely replicate clinical conditions. By exposing organoids to multiple rounds of chemotherapy or targeted therapies, researchers can track the emergence of resistant clones and identify genetic and phenotypic changes contributing to treatment failure. This approach helps identify predictive biomarkers of resistance, guiding real-time treatment decisions[18,58].

In ovarian cancer, organoid models are used to study platinum resistance, a common issue in recurrent ovarian cancer. By treating organoids with cisplatin over several passages, researchers can observe how resistance develops and identify genetic alterations such as BRCA1/2 mutations or changes in DNA repair pathways. This information helps pinpoint patients less likely to respond to platinum-based chemotherapy and guides the selection of alternative therapies, such as PARP inhibitors or immune checkpoint inhibitors[59].

In lung cancer, resistance to EGFR inhibitors like gefitinib or erlotinib is a frequent challenge in EGFR-mutant NSCLC. Organoids derived from EGFR-mutant tumors are treated with these inhibitors to study how resistance develops. By analyzing the genetic and molecular changes in resistant organoids, researchers can identify mutations like T790M or C797S in the EGFR gene that confer resistance. This information allows for the identification of second-line treatments, such as Osimertinib, which can overcome resistance and improve patient outcomes[23,60-65].

Combination therapies and personalized treatment

Organoids are also excellent models for testing combination therapies, which are increasingly used to overcome resistance and improve the effectiveness of cancer treatments. Combination therapies typically involve using two or more drugs that target different aspects of tumor biology, such as combining chemotherapy with targeted therapies or immunotherapy. Organoids provide a platform to test various drug combinations, helping researchers identify the most effective regimens tailored to individual patients[24,25,30].

For example, in triple-negative breast cancer (TNBC), a highly aggressive subtype that often becomes resistant to standard chemotherapy and lacks specific targeted therapies, organoid models derived from TNBC patients can be exposed to combinations of chemotherapy and treatments like PARP inhibitors, immune checkpoint inhibitors, or angiogenesis inhibitors. Testing these combinations enables researchers to identify synergistic effects that enhance treatment efficacy, leading to more personalized and effective treatment strategies[19].

HTS for precision medicine

HTS functions as a critical instrument for cancer research because it enables quick evaluation of numerous drug candidates across multiple tumor models. Through HTS precision medicine identifies the most suitable treatments that match specific tumor characteristics of individual patients. Tumor organoids serve as essential platforms for HTS in precision oncology because they replicate the genetic and phenotypic characteristics of patient tumors[66] (Table 1).

Table 1 Tumor organoid applications in drug screening.

Cancer type	Drug tested	Organoid response	Clinical relevance
Colorectal	5-fluorouracil	Organoids responded well, mimicking patient responses	High
Ovarian	Platinum-based chemo	Identified platinum-resistant populations	High
Lung	EGFR inhibitors	EGFR-mutant organoids sensitive to inhibitors	Moderate

The biological complexity of cancer remains intact in organoids which offer superior drug response testing compared to traditional 2D cell cultures and animal models. The main objective of precision medicine involves selecting appropriate treatments based on the genetic and molecular characteristics of individual patient tumors. The full complexity of human tumors including their TME remains unrepresented in traditional 2D cell lines and animal models. The genetic diversity of patient tumors remains intact in organoids which also maintain their architectural structure and cellular interactions and microenvironmental characteristics that affect drug response[17,67-71].

The use of patient-derived organoids in HTS enables researchers to forecast drug responses of individual tumors which results in more effective personalized treatments[72-76].

Organoid-based HTS platforms

Tumor organoids are well-suited for HTS because they can be cultured in large numbers and tested in parallel. The development of automated organoid culture systems and high-density, multi-well plates has enabled the efficient screening of a wide variety of drugs on organoid models. These platforms can accommodate hundreds or even thousands of organoid cultures simultaneously, allowing for the rapid testing of multiple drugs and drug combinations in a short period. By integrating robotic liquid handling systems with organoid culture, HTS platforms enable precise and reproducible drug dosing, making it possible to screen libraries of small molecules, biologic agents, and targeted therapies against organoids representing different tumor types[13,77,78].

The main benefit of organoid-based HTS involves screening drug libraries which closely match the clinical treatment protocols. The libraries contain both Food and Drug Administration (FDA)-approved drugs and experimental compounds which enable researchers to evaluate standard treatments alongside new agents using patient-specific organoids. Researchers can test various drugs against organoids derived from breast, lung, colon, ovarian and pancreatic cancers to determine the most effective treatment approaches for individual patients. Organoids maintain the complex characteristics of human tumors by preserving their metabolic variations and resistance mechanisms and response patterns. A single tumor contains multiple cell populations which react differently to therapeutic interventions because tumors exist as non-uniform entities. The heterogeneity present in clinical practice is maintained by organoids which enable researchers to detect drug responses that match real-world patient variability. Researchers can determine which patients will benefit from particular treatments and which patients will develop resistance by studying how different subpopulations in organoid cultures react to therapy[35,36,46,77,79-82].

Personalized drug selection and resistance profiling

Organoid-based HTS is invaluable for profiling drug resistance in a personalized manner. Therapy resistance is a significant challenge in cancer treatment, with many patients relapsing despite initially responding to therapy. Resistance can develop through genetic mutations, epigenetic changes, or alterations in the TME, all of which contribute to the failure of drugs that were initially effective. Tumor organoids offer an ideal system to study how these resistance mechanisms evolve and to test new therapies designed to overcome them[60].

The high resistance of ovarian cancer to platinum-based chemotherapy serves as an example. Platinum-resistant ovarian tumors can be studied through organoids to evaluate multiple new compounds which include PARP inhibitors and immune checkpoint inhibitors and targeted therapies. Research using organoids identifies compounds which show effectiveness against platinum-resistant cells to determine potential treatments for relapsed patients. The EGFR mutated lung cancer organoids serve as a tool to study resistance mechanisms against EGFR inhibitors which are standard treatments for NSCLC. Through HTS researchers can evaluate advanced EGFR inhibitors and investigate dual therapeutic approaches which target mutated EGFR along with TME components to overcome drug resistance. The HTS process heavily depends on organoids for testing combination therapies. The evaluation of drug combination synergy in cancer treatment becomes possible through organoids which serve as a powerful system. The combination of targeted therapies which block specific molecular targets with chemotherapies that kill rapidly dividing cells produces better treatment outcomes. Organoids enable researchers to evaluate different drug combinations for their synergistic effects and reduced toxicity levels. The identification of new combination regimens for pancreatic cancer and glioblastoma becomes possible through this method because these cancers show resistance to single-agent therapies[38,40,64,80-83].

Data integration and predictive models

The large volume of data generated from HTS using tumor organoids is another key advantage. These HTS platforms produce data on drug efficacy, toxicity, genetic alterations, and treatment resistance. By integrating these diverse data types, researchers can create predictive models of drug responses. ML and AI algorithms can analyze the data from large-scale HTS screens to identify biomarkers that predict drug sensitivity or resistance. These algorithms combine genetic information, protein expression profiles, and drug responses to predict how a patient's tumor, with its unique molecular features, will respond to specific treatments[84-87].

AI-driven models can also be used to explore drug repurposing opportunities. Organoids provide an efficient way to test compounds already FDA-approved for other diseases but not yet explored for cancer treatment. Through HTS, researchers can quickly assess the effects of these compounds on organoids from various cancer types. For example, antibiotics, antifungal drugs, or anti-inflammatory agents, which may have been overlooked in cancer research, can be tested for potential use in treating cancer. AI models help prioritize the most promising candidates for further development and clinical testing, offering an efficient pathway for discovering new cancer treatments[35,43-47,88-96].

Real-time data and clinical decision support

Another major advantage of organoid-based HTS is its potential to directly inform clinical decision-making. While genetic testing is commonly used in precision medicine to guide treatment selection, genetic tests alone cannot always predict how a patient’s tumor will respond to therapy. Organoids provide a functional readout of drug sensitivity, offering a real-time assessment of how a patient's tumor will react to specific treatments. As organoid technology becomes more integrated into clinical workflows, it could provide oncologists with personalized treatment options tailored to each patient’s tumor characteristics[12,13,86].

By conducting prospective clinical trials using organoid-based HTS, researchers can validate how well these organoid models predict therapeutic outcomes in actual patients. For instance, in pancreatic cancer, where effective therapies are limited, organoid-based HTS could be used to identify novel treatment regimens for testing in clinical trials. Organoid models could also be incorporated into early-phase clinical trials to help select the most appropriate therapy for each patient based on the real-time response of their tumor, improving the precision and effectiveness of treatment strategies[15,78,97].

Advanced technologies and integration

The landscape of cancer research and precision medicine is rapidly evolving with the integration of advanced technologies into existing experimental platforms. Tumor organoids, recognized for their ability to replicate the architecture, genetic diversity, and functionality of human tumors, are central to this revolution. The incorporation of cutting-edge technologies such as AI, organ-on-a-chip systems, high-content imaging, and multi-omics analysis is enhancing the capabilities of organoid models[1,98]. These innovations are broadening the scope of organoid applications in drug discovery, biomarker identification, and personalized cancer therapies. These advancements not only promise to improve the accuracy of cancer models but also accelerate the development of more effective treatments[99].

AI and ML in organoid research

One of the most significant technological advancements in organoid research is the integration of AI and ML. AI algorithms are increasingly being used to analyze the large volumes of data generated from organoid-based experiments, particularly in HTS and multi-omics analyses. These AI models process genetic data, drug response profiles, and phenotypic data to identify patterns and correlations that traditional analysis methods might miss. For instance, AI can correlate gene expression data from organoids with their response to specific therapies, helping identify biomarkers for therapy resistance or sensitivity[37,78,100,101].

Deep learning algorithms have significantly enhanced the ability to detect drug efficacy patterns in organoid screens. By training algorithms on large datasets from organoid models, AI can predict the most effective therapies for a tumor based on its molecular and genetic profile. These models can also suggest novel drug combinations or identify opportunities for drug repurposing, where existing FDA-approved drugs might be effective against new cancer types. This technology accelerates the drug discovery process by reducing the time needed to identify promising candidates for clinical trials. Additionally, AI-driven image analysis enables the quantification and tracking of tumor growth, cell proliferation, and morphological changes in organoids, leading to more reproducible and automated data collection[1,102-104].

Organoids-on-a-chip platforms

Another major advancement is the integration of organoids with organ-on-a-chip technology. These platforms incorporate microfluidics, enabling the creation of dynamic, 3D TME within small, micro-scale devices that replicate the physiological conditions of human tissues. Microfluidic devices allow precise control over fluid flow, nutrient gradients, and oxygen levels, which can be finely regulated. By combining organoid cultures with these systems, researchers can recreate the TME more accurately, improving the modeling of tumor progression, metastasis, and therapeutic response[105].

Organoids-on-chip platforms provide exceptional benefits for modeling blood-brain barrier and vascularization and immune interaction within tumors which affect drug effectiveness. The vascularized organoid-on-chip models duplicate drug delivery processes to evaluate drug penetration into tumors. These models enable scientists to study the tumor vasculature structure when evaluating treatment resistance. The systems enable researchers to investigate how anti-angiogenic drugs together with vascular normalization therapies affect drug delivery to solid tumors which generates more relevant clinical insights about treatment approaches. Organoid-on-chip systems allow researchers to build multi-organ models that duplicate systemic cancer effects and therapeutic responses. These models demonstrate how cancer treatments impact both primary tumors and distant metastatic locations including the liver lungs and bones. These models which unite various organ systems in one platform deliver precise predictions about drug effectiveness and body-wide toxicities thus becoming essential for studying cancer therapy effects on the whole body[98].

High-content imaging and live-cell monitoring

The integration of high-content imaging techniques with organoid cultures has greatly enhanced the analysis of tumor cell behavior over time. Live-cell imaging allows researchers to monitor changes in cell proliferation, apoptosis, and morphological alterations in response to treatment in real-time. This technology provides continuous observation of organoid cultures over days or weeks, offering valuable insights into long-term drug efficacy and tumor evolution[88,91].

High-content imaging enables the use of fluorescent probes to detect specific proteins or markers associated with cancer progression, such as oncogene activation or cell cycle regulation. For example, fluorescent-labeled antibodies can track EMT markers, which are essential for studying tumor metastasis and therapy resistance. Additionally, live-cell imaging can reveal how organoid cultures respond to immunotherapies like CAR-T cells or checkpoint inhibitors, providing a clearer understanding of the dynamic interactions between tumor cells and immune cells within the TME. This integration of imaging techniques with organoid systems allows for more comprehensive and accurate evaluations of cancer therapies[47,96].

Multi-omics integration

The integration of multi-omics technologies (such as genomics, transcriptomics, proteomics, and metabolomics) with organoid models is one of the most powerful advancements in cancer research. Multi-omics analysis enables researchers to explore the full molecular landscape of cancer by simultaneously analyzing DNA, RNA, proteins, and metabolites. When combined with drug screening results, this approach helps identify key molecular signatures associated with drug sensitivity, resistance, and tumor progression[1,106].

Genomic profiling detects cancer cell mutations and CNVs while transcriptomics through RNA sequencing reveals how genes express themselves after treatment. Proteomics enables researchers to measure protein levels and activities which lead to therapeutic resistance while metabolomics reveals changes in cellular metabolic processes that indicate drug resistance pathways. The integration of multi-omics data with organoid models enables personalized treatment approaches through the analysis of individual tumor molecular profiles. The Cancer Cell Line Encyclopedia serves as a notable example by linking genomic information to drug response data to generate a comprehensive map of drug sensitivity and resistance across hundreds of cancer cell lines. Organoid-based multi-omics analyses have the potential to advance this work through the use of patient-derived models that maintain tumor heterogeneity and real TME characteristics[103].

Advanced biomanufacturing and automation

The future of organoid-based research lies in the further integration of biomanufacturing and automation, which will enable large-scale organoid production and screening. Currently, generating organoids from patient tumors is a time-consuming and labor-intensive process. However, advances in bioprinting and robotic systems are transforming this landscape. Automated organoid production platforms can increase the throughput of organoid generation, allowing for the creation of large biobanks of organoids derived from various cancer types. These biobanks can be used to screen thousands of drugs in parallel, accelerating the discovery of novel therapies and combination treatments[38,39,46,96,104].

3D bioprinting techniques are also revolutionizing organoid creation and culture. By using bioinks containing living cells, researchers can print 3D tumor models with precise control over cell placement. This allows for the creation of more sophisticated and reproducible organoid systems. With this technology, researchers can design organoid cultures that closely replicate the structural complexity and cellular diversity of tumors, enhancing their accuracy as models for drug testing and disease modelling[57,80,107-109].

Clinical integration and translational impact

The integration of advanced technologies with organoid models is progressing toward clinical applications, where these systems can directly influence patient treatment strategies. Organoids-on-chip models, for example, are already being tested in clinical trial settings to predict patient responses to chemotherapy and targeted therapies in real time. By testing drugs on organoids derived from a patient's own tumor before treatment, clinicians can tailor therapies based on the organoid's response. This approach significantly improves clinical outcomes by selecting the most effective therapies and reducing the risks of ineffective or toxic treatments[40,56,81,110].

Specific case studies on tumor organoids in cancer research

One of the primary advantages of tumor organoids is their ability to recapitulate the genetic, phenotypic, and microenvironmental features of original tumors, offering more personalized and relevant models for cancer research. For instance, a colorectal cancer case study demonstrated the potential of patient-derived organoids in predicting treatment responses. In a study by Beutel et al[85], colorectal cancer organoids were exposed to various chemotherapy agents, including 5-fluorouracil, irinotecan, and oxaliplatin. The drug response profiles of the organoids mirrored the clinical responses in patients, thus validating the use of organoids in personalized treatment decisions. This case study highlights how organoids can be a powerful tool for evaluating the efficacy of existing drugs, as well as predicting the best therapeutic approaches for individual patients based on their tumor’s specific characteristics.

Similarly, a study involving lung cancer organoids provided critical insights into EGFR-targeted therapies. Lung cancer organoids, derived from patients with EGFR-mutant tumors, were tested for their response to EGFR inhibitors, such as gefitinib. The results showed a high correlation between the organoid response and clinical outcomes, where patients whose organoids were sensitive to gefitinib had favorable treatment responses, while those with resistant organoids experienced progression of disease. This underscores the relevance of organoid models in predicting patient-specific responses to targeted therapies and personalizing treatment regimens[96,111].

Another noteworthy case is the application of organoids in breast cancer treatment. A study by Chen et al[42,86] demonstrated how organoids from HER2-positive breast cancer patients were exposed to trastuzumab (Herceptin), a monoclonal antibody therapy. The results from the organoid culture were highly predictive of the clinical outcomes, with patients whose organoids responded well to trastuzumab showing significant tumor regression. This case study provides compelling evidence that organoid-based drug testing can be an effective model for evaluating targeted therapies in personalized oncology.

Refined histopathological analysis

Histopathological analysis plays a crucial role in the characterization and validation of tumor organoids. As part of quality control, organoid cultures undergo histological examination to ensure that the 3D structure and tissue architecture of the organoids accurately represent the original tumor. The use of hematoxylin and eosin (HE) staining allows for the visualization of the overall tissue structure, identifying key features such as glandular structures in breast and colorectal cancers.

For example, in the study by Grönholm et al[4], HE staining was used to confirm that colorectal cancer organoids preserved the crypt-like architecture typical of the original tumor. The analysis revealed that these organoids not only resembled the tissue structure but also retained the differentiation status observed in patient tumors. The preservation of these structural features is vital for ensuring that organoids maintain the heterogeneity and functional properties of the tumor, which are critical for their use in drug screening and personalized therapy.

In addition to HE staining, IHC is employed to assess the expression of specific biomarkers such as HER2 in breast cancer organoids and EGFR in lung cancer organoids. For instance, the use of HER2 IHC on breast cancer organoids has demonstrated that these organoids mirror the expression patterns seen in patient tumors. This enables researchers to accurately determine tumor subtypes and tailor therapies accordingly. EGFR expression in lung cancer organoids has similarly been used to predict the response to EGFR inhibitors, further solidifying the role of IHC in organoid characterization for personalized treatment planning.

Successful examples of personalized treatments using tumor organoids

Personalized cancer therapy is one of the most promising applications of tumor organoids, particularly for drug testing and treatment decision-making. One notable example is the use of organoids in ovarian cancer research, as demonstrated in a study by Rios-Morris et al[62]. In this study, patient-derived ovarian cancer organoids were exposed to platinum-based chemotherapy and PARP inhibitors, which are commonly used to treat platinum-resistant ovarian cancer. The organoid-based testing successfully identified platinum-resistant subpopulations in patients, allowing for the personalized selection of PARP inhibitors as an alternative therapeutic strategy. Patients whose organoids showed sensitivity to the combination therapy experienced clinical benefits, highlighting how organoid-based models can aid in selecting second-line treatments when standard therapies fail.

In breast cancer, organoids have been instrumental in predicting responses to targeted therapies. In a study by Inglebert et al[112], organoids from HER2-positive breast cancer patients were tested for trastuzumab (Herceptin) response. The organoid models accurately predicted clinical responses, allowing clinicians to tailor HER2-targeted therapies to individual patients, leading to better clinical outcomes. This personalized approach has been extended to TNBC, where organoids were used to evaluate chemotherapy regimens in conjunction with immune checkpoint inhibitors. The personalized drug testing using TNBC organoids identified synergistic therapies that were later validated in clinical trials.

Additionally, glioblastoma has presented a major challenge in personalized treatment due to its high heterogeneity and treatment resistance. A recent study by Dao et al[31] demonstrated how glioblastoma organoids were used to screen a library of drugs, identifying a novel combination of chemotherapy agents and targeted therapies that showed promising results in patient-derived models. This combination therapy was subsequently tested in clinical trials, leading to improved patient survival outcomes compared to standard treatments.

Comparative analysis of tumor organoids, patient-derived samples, and 2D cell lines.

A comparative analysis of tumor organoids, patient-derived samples, and 2D cell lines provides insights into their relative advantages and limitations in cancer research, drug screening, and personalized medicine. Each model has its unique strengths, but tumor organoids stand out in certain contexts due to their ability to closely mimic the complexity of human tumors (Table 2).

Table 2 Comparative analysis of tumor organoids, patient-derived samples, and 2D cell lines.

Aspect	Tumor organoids	Patient-derived samples	2D cell lines
Biological relevance	Highly relevant; mimics tumor architecture, heterogeneity, and microenvironment	Most relevant; directly from the patient, includes tumor's genetic and epigenetic features	Less relevant; lacks 3D structure, tumor heterogeneity, and microenvironment
Scalability	Scalable to some extent but limited by culture complexity and maintenance	Limited scalability; fresh samples are often hard to obtain and maintain	Highly scalable; easy to culture and propagate for large-scale studies
Cost	Expensive due to specialized culture requirements, patient-specific nature, and maintenance	Expensive; obtaining and maintaining fresh tumor samples can be costly	Least expensive; widely available, easy to maintain with basic culture conditions
Reproducibility	Can have variability due to culture conditions, patient heterogeneity, and protocol differences	Reproducibility can vary, especially with ex vivo cultures or expansion	Highly reproducible; easy to maintain and culture with consistent results
Genetic and epigenetic stability	Risk of epigenetic drift, clonal evolution, and gene expression changes during long-term culture	Directly reflects the genetic makeup of the patient's tumor, but cannot be cultured long-term	May not represent the genetic variability or epigenetic features of patient tumors
Use case	Ideal for personalized medicine, drug screening, and modeling complex tumor microenvironments	Best for authentic tumor representation but limited by availability and culture challenges	Suitable for high-throughput screening, basic research, and testing in simpler contexts

Biological relevance

Tumor organoids: Tumor organoids are 3D culture systems derived from patient tumor biopsies that closely mimic the architecture, genetic heterogeneity, and functional characteristics of the original tumor. They can recapitulate the TME, including cell-to-cell interactions, ECM components, and cellular differentiation, making them one of the most biologically relevant models for studying cancer biology. Organoids also preserve genetic and epigenetic features over multiple passages, maintaining key molecular signatures seen in patient tumors, which is vital for personalized drug testing and therapy prediction[16,47,64,81,113-115].

Patient-derived samples: Patient-derived samples, such as tumor biopsies, offer the highest level of biological relevance because they are directly obtained from the patient. These samples include all the tumor's genetic, epigenetic, and microenvironmental factors, making them ideal for understanding tumor biology in its most authentic form. However, using patient-derived samples in research or drug testing can be limited by their availability, ethical concerns, and the inability to maintain them for long-term study[48,97,110,116-119].

2D cell lines: Traditional 2D cell lines, derived from tumor cells, have been widely used for decades. However, they lack the three-dimensional structure and microenvironmental complexity found in vivo. They often exhibit differences in gene expression, drug resistance, and overall behavior compared to tumors in their native 3D form. Despite being easier to culture and more cost-effective, 2D cell lines are less biologically relevant than organoids and patient-derived samples for modeling tumor biology[2,34,45,65,96,120,121].

Scalability

Tumor organoids: Tumor organoids are scalable in that they can be expanded and cultured from small patient biopsy samples. However, they require specialized protocols, culture media, and conditions to ensure their long-term growth. Scalability can become a challenge when attempting to establish large numbers of organoids from different tumor types, as maintaining biological relevance over time may become difficult. While organoids are scalable, they require careful handling to prevent genetic drift and changes in their microenvironment[14,122,123].

Patient-derived samples: Patient-derived samples are not scalable for long-term studies. Tumor biopsies are typically available in limited quantities and often cannot be continuously expanded or cultured, making large-scale studies challenging. In cases where fresh tissue is required, the availability of tumor samples is a significant bottleneck. Additionally, culturing patient-derived cells or tissues ex vivo can be difficult, and maintaining their original properties over time is problematic[35,87,88].

2D cell lines: 2D cell lines are the most scalable model, as they can be easily propagated in culture, allowing researchers to generate large quantities of cells. This scalability is one of the key reasons why 2D cell lines have been used extensively for screening and large-scale studies. However, the limitations of 2D cell lines, including their lack of tumor heterogeneity and 3D structural complexity, reduce their applicability for certain types of research, particularly in drug response prediction[43,45,89,90].

Cost

Tumor organoids: Tumor organoids are relatively expensive to generate and maintain compared to 2D cell lines. The cost stems from the need for specialized culture media, scaffolds, and the patient-specific nature of the model. The process of establishing organoids from patient biopsies, coupled with the costs of maintaining long-term cultures, adds up. However, their ability to provide more clinically relevant data can offset the high costs in personalized drug testing and cancer research[31,33].

Patient-derived samples: The cost of patient-derived samples can vary significantly depending on the sourcing, processing, and ethical considerations involved. In clinical settings, obtaining and maintaining fresh tumor biopsies can be expensive, and their limited availability can increase costs. Additionally, the use of patient-derived xenograft (PDX) models, which involve implanting patient tumors into immunocompromised mice, can be prohibitively costly, especially for long-term studies[124-126].

2D cell lines: 2D cell lines are by far the least expensive model to work with. They are widely available, easy to maintain, and require basic culture conditions. The low cost and ease of propagation make them the go-to model for HTS and many preclinical studies. However, the reduced biological relevance of 2D cell lines can lead to less accurate predictions for clinical outcomes, which can be costly in the long run if they do not accurately reflect human tumor biology[127-129].

Reproducibility

Tumor organoids: While tumor organoids offer high biological relevance, they can be less reproducible across different labs due to the complexity of the culturing conditions and variability in patient samples. Small differences in culture conditions, media formulations, or patient tumor heterogeneity can lead to variability in the resulting organoids. Standardizing protocols and ensuring consistency across different laboratories is essential for improving reproducibility[41,112,130].

Patient-derived samples: Patient-derived samples generally offer high reproducibility when the same tumor biopsy is used repeatedly. However, they face challenges in consistency, especially when attempts are made to culture these samples or expand them ex vivo. Since patient tumors are heterogeneous, the reproducibility of these models can vary depending on the tumor's characteristics and the biological differences between patients[42,131,132].

2D cell lines: 2D cell lines are highly reproducible. Once established, they are easy to culture and maintain with a high degree of consistency. This reproducibility is one of the main advantages of using 2D cell lines for large-scale drug testing. However, the lack of complexity in 2D models means they may not always reflect the variability seen in patient tumors, leading to potential issues in accurately predicting clinical outcomes[6,133-135].

Current limitations and challenges

Tumor organoid technology, a powerful tool for studying cancer biology and developing personalized therapies, is still in its nascent stage of clinical application. While it holds considerable promise in mimicking the complexity of human tumors, several significant challenges hinder its transition from research to routine clinical use.

Logistical challenges: One of the primary obstacles is the logistical complexity involved in creating and maintaining tumor organoids. These 3D culture systems require precise conditions to replicate the TME accurately, which can be difficult to sustain over long periods. Organoids are derived from patient biopsies, and cultivating them requires sophisticated technologies that maintain their genetic and phenotypic properties, as well as their responsiveness to drugs. The procedure to obtain and culture these organoids is labor-intensive and often requires a level of expertise that is not universally available in clinical settings. Moreover, organoid culture protocols need to be optimized for different tumor types, as each cancer type may require a different approach to culture and maintenance. These technical demands create a bottleneck, limiting the widespread adoption of tumor organoids in clinical practice.

Economic considerations: Economically, tumor organoids present a substantial challenge in terms of cost-effectiveness. Establishing and maintaining these organoid models require considerable financial investment. First, the process of obtaining patient-derived tumor samples is not only costly but also dependent on the availability of high-quality tissue, which can be difficult to obtain, particularly in cases of rare or aggressive cancers. Second, the materials and technologies needed for the culturing of tumor organoids, such as advanced culture media, 3D scaffolding, and HTS systems, add to the cost burden. Additionally, the requirement for specialized personnel to manage the organoid cultures and analyze the data further elevates the operational costs. This cost factor makes tumor organoid technology less accessible, especially in settings where budget constraints limit the adoption of high-tech innovations. For clinical implementation to become feasible, a balance between cost and the potential benefits of using tumor organoids must be found. Without a more affordable and streamlined production method, this technology remains largely confined to research settings rather than being widely used for patient-specific treatment planning.

Regulatory hurdles: Regulatory barriers pose another significant challenge in the clinical application of tumor organoid technology. Organoids are essentially miniature versions of tumors, and as such, they must meet stringent regulatory standards before they can be used in clinical trials or personalized treatments. The regulatory processes surrounding the development of new cancer models involve extensive testing to ensure the safety, reliability, and reproducibility of the results. These models must demonstrate that they can reliably replicate human tumor behavior in a way that reflects the true complexity of the disease. Furthermore, there are ethical considerations associated with the use of human tissue in the creation of organoids, which adds another layer of regulatory complexity. Ethical sourcing, patient consent, and the potential for commercialization of these models are all aspects that must be considered by regulatory bodies.

In addition to safety and ethical concerns, regulatory bodies must evaluate whether tumor organoids can be used as reliable predictive models for drug efficacy and toxicity testing. The standardization of organoid production processes and validation of their predictive power for various cancers is a lengthy and often expensive process. These hurdles delay the approval process, slowing the transition from experimental to clinical use. Furthermore, current regulatory frameworks are often ill-equipped to handle the novel nature of organoid technology, creating additional barriers for their integration into clinical practice[126,136] (Table 3).

Table 3 limitations and challenges associated with tumor organoid models.

Limitation	Impact on organoid research	Potential solutions
Genetic and epigenetic instability	Affects long-term fidelity of organoid models	Develop protocols for standardization and stability
Lack of tumor microenvironment elements	Limits accurate modeling of immune responses and drug delivery	Incorporate immune cells and vascularization
Use of animal-derived matrices	Ethical concerns and variability in results	Develop animal-free matrices and standardized protocols

Future perspectives and emerging technologies

The future of tumor organoids lies in further refining their integration with emerging technologies. Vascularized organoid models, which better replicate the in vivo TME, will enhance drug testing and therapeutic optimization. Advances in AI, multi-omics approaches, and organoid-on-chip systems will accelerate the development of personalized therapies, enabling clinicians to make more precise treatment decisions and improve patient outcomes[125].

CONCLUSION

Tumor organoids have revolutionized cancer research by offering more accurate, patient-specific models for drug testing and personalized therapy. These 3D models preserve the genetic, phenotypic, and microenvironmental characteristics of tumors, making them essential tools for studying cancer biology and predicting treatment responses. As technology continues to advance, tumor organoids will be integral to precision oncology, enhancing our understanding of cancer and enabling more effective, individualized therapies. The integration of advanced technologies and the development of standardized protocols will ensure the widespread adoption of organoid models in clinical settings, driving improvements in cancer treatment and patient outcomes.