Review Article

AI‑Powered Drug Discovery for Cancer Stem Cells in Head and Neck Squamous Cell Carcinoma and Non‑Small Cell Lung Cancer: Integrating Structural Prediction and Organoid Models

Sei Young Lee¹, Seohee Park², Haeun Kim², Seo Lyn Choi^3,4, Chang-Whan Yoon^3,4,#, Sang-Hyuk Lee^3,4,#

Abstract

Background/Objectives: The persistence of cancer stem cells (CSCs) represents a formidable barrier to curative cancer therapy, with CD133⁺ and CD44⁺ populations playing central roles in tumor initiation, metastasis, therapeutic resistance, and recurrence. These CSCs interact dynamically with the tumor microenvironment, promoting epithelial–mesenchymal transition (EMT), immune evasion, and metabolic adaptation, particularly in aggressive cancers such as head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC). Despite their clinical relevance, effective targeting of CSCs remains elusive due to their phenotypic plasticity and the absence of high-resolution structural data for drug development.

Methods: Recent advances in artificial intelligence (AI)—notably AlphaFold2-driven protein structure prediction and deep-learning–based docking algorithms—have revolutionized the discovery of CSC-targeted therapies. These tools enable high-throughput, structure-informed virtual screening even for targets with unresolved crystal structures, such as CD133 and CD44 isoforms. AI-guided workflows now allow simulation of ligand binding to predicted 3D models, accelerating the identification of compounds that may disrupt CSC-specific signaling and surface interactions.

Results: When coupled with organoids derived from cancer stem cell marker–positive cells systems enriched for CD133⁺/CD44⁺ CSCs, these predictive models offer a biologically relevant platform for functional validation. Such integrative approaches bridge computational precision with translational oncology, providing new avenues for personalized therapy design.

Conclusions: This review discusses the latest findings on CSC biology, AI-enabled drug prediction, and organoid modeling, while also addressing current limitations and future needs—including explainable AI, multi-omics integration, and protocol standardization—to enable next-generation therapies targeting CSCs across solid tumors.

Keywords

Cancer stem cell, CD44, CD133, Artificial intelligence, Organoid

Introduction

Cancer stem cells (CSCs) have emerged as key drivers of tumor initiation, progression, metastasis, and therapeutic resistance [1-6]. Among them, CD133⁺ cells are widely recognized as a critical subpopulation with enhanced self-renewal capacity and the ability to shape the tumor microenvironment [5,7,8]. Traditional drug discovery platforms often fail to capture the complexity and heterogeneity of these cells, resulting in limited clinical translation. Advances in artificial intelligence (AI) have enabled high-throughput, structure-based screening and molecular docking approaches that can identify candidate compounds with high specificity. When coupled with CD133⁺-derived organoid models, these tools offer a powerful means to assess drug efficacy in a physiologically relevant context. This review explores how AI and organoid technologies are converging to overcome the challenges of targeting CD133⁺ CSCs and establish a new paradigm in precision oncology.

Cancer Stem cell marker CD133 and CD44 in Head and Neck and Lung Cancers AI in PET/CT

Cancer stem cells (CSCs) represent a subpopulation within tumors that possess self-renewal capabilities, high tumor-initiating potential, and resistance to conventional therapies [1,4,6,9–16]. Among various CSC markers, CD133 (Prominin-1) and CD44 have emerged as two of the most extensively studied in solid tumors, including head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC) [3,6,17,18]. These markers not only aid in the identification of CSCs but are also functionally implicated in tumor progression, metastasis, and therapeutic resistance [1,2,9,11,19].

CD133: A Marker of Tumor Initiation and Therapy Resistance

D133 (Prominin-1) is a pentaspan transmembrane glycoprotein widely used to isolate CSCs across various tumor types [12,15,20–22]. CD133 is a widely recognized marker for cancer stem cells (CSCs) and plays a crucial role in promoting therapeutic resistance, epithelial–mesenchymal transition (EMT), and malignancy in lung cancers [15,20,23]. In non-small cell lung cancer (NSCLC), CD133⁺ CSCs exhibit increased resistance to platinum-based chemotherapy and epidermal growth factor receptor (EGFR)-targeted therapies [24]. Recent studies have demonstrated that CD133⁺ cells, particularly those co-expressing CXCR4, display elevated levels of cytochrome P450 enzymes (e.g., CYP1B1) and anti-apoptotic factors, contributing to chemoresistance and metastatic spread [25,26]. Furthermore, IL-6 signaling selectively enriches the CD133⁺ population by activating STAT3 and downstream EMT programs, further enhancing resistance and tumor aggressiveness [27]. Clinically, elevated CD133 expression is associated with shorter progression-free and overall survival in patients undergoing standard chemotherapy regimens, emphasizing its value as a predictive biomarker [28].
Mechanistically, CD133 plays a central role in driving EMT and malignant transformation in lung cancer [29]. In CD133⁺ NSCLC cells, EMT transcription factors such as Slug, Snail, and Bmi1 are upregulated, leading to the downregulation of epithelial markers like E-cadherin and the upregulation of mesenchymal markers such as vimentin [30]. This shift toward a mesenchymal phenotype enhances tumor invasiveness and metastatic capacity. Under hypoxic conditions, CD133 expression is upregulated through HIF-1α/2α-mediated activation of stemness-associated transcription factors, including SOX2, OCT4, and Nanog [31]. These factors bind to the CD133 promoter and reinforce self-renewal, motility, and resistance traits. In addition, CD133 engages multiple signaling cascades, including PI3K/AKT, WNT/β-catenin, and JNK pathways, to maintain the CSC phenotype and promote survival under therapeutic stress [32–35].
Emerging single-cell transcriptomic analyses have further revealed substantial heterogeneity within CD133⁺ CSC populations in lung tumors, identifying distinct subclones with specialized functions such as metabolic adaptation, immune evasion, and enhanced migratory capacity [36–38]. Circulating extracellular vesicles (EVs) enriched with CD133⁺ CD326⁻ markers have been detected in the plasma of lung cancer patients and correlate with tumor progression and poor prognosis, suggesting their utility as dynamic biomarkers for CSC burden and treatment response. These findings underscore the multifaceted role of CD133 in lung cancer pathogenesis and highlight the therapeutic potential of targeting CD133⁺ CSC niches through combinatorial strategies involving metabolic inhibitors, immunotherapies, and signaling pathway modulators. In lung cancers, particularly in EGFR-mutant NSCLC, CD44 is enriched in tyrosine kinase inhibitor (TKI)-resistant clones. CD44 variant isoforms (e.g., CD44v6 and CD44v9) have been associated with ROS defense and glutathione metabolism, facilitating chemoresistance [39]. Novel research highlights that CD44-expressing CSCs may contribute to an immunosuppressive tumor microenvironment by recruiting myeloid-derived suppressor cells (MDSCs) and regulatory T cells [40].

CD44 as a Cancer Stem Cell Marker in Head and Neck Cancer

CD44, a transmembrane glycoprotein that binds hyaluronic acid and other extracellular matrix components, is one of the most studied CSC markers in squamous cell carcinoma of the head and neck (HNSCC) [18]. Recent meta-analyses show that high CD44 expression in oral squamous cell carcinoma (OSCC) is significantly associated with lymph node metastasis, advanced TNM stage (III–IV), and poorer overall survival, with hazard ratios of ~1.7 for OS and ~1.66 for disease-free survival [22]. 

Notably, CD44 immunostaining shows divergent prognostic value depending on tumor location: it strongly predicts worse outcomes in pharyngeal or laryngeal cancers but has limited predictive utility in oral cavity tumors — possibly due to CD44 ectodomain shedding obscuring real expression levels.

Mechanistic studies reveal that CD44+ cells in HNSCC frequently display epithelial-to-mesenchymal transition (EMT) phenotypes, increased plasticity between EMT and MET states, and enhanced resilience to chemo‑ and radiotherapy [18,20,41]. One key pathway is ERK1/2–Nanog, which upregulates CD44 expression and sustains CSC traits, promoting tumorigenicity and metastasis in preclinical models of HNSCC.

Additionally, CD44+ cells in primary HNSCC exhibit constitutive activation of STAT3, driving selective expression of PD-L1 [42]. This confers immunosuppressive capacity: CD44+ CSCs evade cytotoxic CD8+ T‑cell responses unless PD‑1/PD‑L1 blockade or STAT3 inhibition is applied, which restores T‑cell immunogenicity [43,44].

Recent pan-cancer profiling also indicates that CD44 especially certain variant isoforms) correlates with immunosuppressive microenvironments—high infiltration of Tregs and M2 macrophages—and may influence patient response to immune checkpoint inhibitors in HNSCC. Collectively, CD44 in HNSCC functions beyond a mere marker—it actively regulates CSC maintenance, EMT plasticity, metastasis, and immune evasion.

AI-Driven Drug Prediction and CD133/CD44 Docking Simulation

Artificial intelligence (AI) is transforming the landscape of drug discovery by enabling rapid, large-scale prediction of compound–target interactions, particularly for challenging or previously undruggable targets such as cancer stem cell (CSC) markers CD133 and CD44 [9,32,45]. With the integration of deep learning and structure-based computational algorithms, molecular docking and virtual screening can now be performed even for proteins that lack experimentally resolved structures. AI-based tools accelerate compound screening, enhance hit identification, and reduce development costs—facilitating rational drug design against CSCs in cancers such as HNSCC and NSCLC.

AlphaFold2, a breakthrough protein structure prediction platform, has emerged as a powerful tool for modeling the 3D structure of difficult targets [46]. In the case of CD44, while only the hyaluronan-binding domain (HABD) has been crystallized, AlphaFold2 and related algorithms such as RoseTTAFold and D-I-TASSER have been employed to generate full-length structural models of CD44 and its clinically relevant isoforms (e.g., CD44v3-v10, CD44v6-v10) [47]. These models provide high-confidence structures of both extracellular and transmembrane domains, revealing conformational flexibility introduced by alternative splicing in the variant isoforms. Molecular dynamics (MD) simulations have validated the stability of AlphaFold-predicted models, particularly in membrane-like environments, thus supporting their use in structure-based virtual screening for therapeutic targeting of CD44⁺ CSCs.

CD133 (Prominin-1), a pentaspan transmembrane glycoprotein, also lacks a crystal structure, but AlphaFold2 provides plausible models of its extracellular loops and membrane-embedded regions, which are key to ligand recognition and antibody binding [9,32,48,49]. These AI-generated models enable the simulation of interactions with small molecules or antibodies, including virtual screening of libraries to identify candidate compounds that can bind and block CD133 functional domains. Recent applications include in silico docking of antibody fragments against predicted extracellular domains and use of AI-guided fragment-based drug discovery to identify novel CD133 inhibitors [50]. Integration of docking simulations with biological validation—such as CSC sphere assays or CD133/CD44 knockdown—offers a robust framework for targeting CSC-specific pathways [51]. 

Furthermore, AI-driven docking platforms like GNINA, DeepDock, and DockThor-VS, which incorporate convolutional neural networks or graph neural networks, significantly enhance binding affinity predictions and pose selection for flexible targets such as CD133 and CD44. When paired with AlphaFold2 structures, these tools enable end-to-end virtual screening workflows: from structure prediction to docking, hit ranking, and selection of lead compounds. This integrative strategy not only expands the scope of druggable CSC markers but also holds promise for personalized therapy targeting CD133⁺/CD44⁺ populations in aggressive and resistant cancers.

The Scientific Rationale for CD44+/CD133⁺-Derived Organoid Models

In head and neck squamous cell carcinoma (HNSCC) and non‑small cell lung cancer (NSCLC), CD44⁺/CD133⁺ organoid models enable high‑content functional validation of candidate therapeutics identified via AI‑driven virtual screening. Notably, large-scale clinical–academic collaborations—such as the King’s College London–GSK Translational Oncology Research Hub—are developing comprehensive libraries of lung adenocarcinoma patient‑derived organoids (PDOs). These PDOs are integrated with genomic, transcriptomic, and response data, and leveraged by machine‑learning models to predict relapse risk and treatment outcomes, effectively creating patient‑specific “digital twin” models.

By employing AlphaFold2‑derived structural models of CD133 (notably its extracellular loops) and CD44 variant isoforms, advanced docking tools and AI‑guided virtual screening can prioritize compounds targeting CSC‑specific epitopes. These in silico–selected candidates are then empirically evaluated in CSC‑enriched organoid assays that simulate physiologically relevant conditions including gradients of hypoxia, cytokine signaling, and stromal interactions—thereby bridging computational predictions with biologically meaningful validation.

This integrated translational pipeline—combining AI‑based protein structure prediction, virtual screening, and functional testing in CSC‑enriched organoid models—supports rapid hit prioritization and the identification of biomarkers capable of stratifying responsive patient populations. Emerging evidence reaffirms CD133 as a predictor of CSC‑driven relapse and metastasis, while CD44 is increasingly implicated in therapy resistance and maintenance of stemness9,52.

Collectively, these approaches represent a powerful precision‑oncology strategy aimed at eradicating the CSC compartment and preventing tumor recurrence.

Challenges and Future Directions

Despite progress in understanding and targeting CD44⁺ and CD133⁺ CSCs, clinical translation faces challenges. Structural ambiguity of CD133, absence of crystal structures, and heterogeneous marker expression complicate therapeutic design. High-purity isolation and maintenance of CD133⁺ CSCs remain difficult, limiting reproducibility in drug screening. Organoid culture protocols require optimization and standardization across tumor types to faithfully model CSC biology [52,54,56,57].

AI models often function as ‘black boxes’ lacking interpretability, necessitating rigorous biological validation to confirm predicted efficacy. Addressing these issues requires integrative approaches combining high-resolution structural prediction (e.g., AlphaFold2), explainable AI frameworks, and comprehensive multi-omics profiling of organoids [53,58]. This will improve candidate identification accuracy and mechanistic understanding.

Future efforts should focus on refining CD133⁺ organoid platforms to enhance physiological relevance and adaptability for personalized screening. Incorporating stromal, immune, and dynamic microenvironmental components will increase translational fidelity. Integration of patient-specific genomic and transcriptomic data with AI-driven screening holds promise for developing precision therapies to eradicate CSCs. Collectively, these innovations will accelerate discovery of novel CSC-targeted therapeutics, advancing precision oncology and improving patient outcomes.

Discussion

This review highlights the critical role of CD133⁺ and CD44⁺ cancer stem cells (CSCs) in sustaining tumorigenesis, metastasis, and therapeutic resistance in head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC). Advances in AI-driven protein structure prediction and molecular docking provide unprecedented opportunities to identify druggable epitopes in CSC-associated targets, even in the absence of crystal structures. When combined with organoid models derived from CSC populations, these computational methods enable functional validation and bridge the gap between in silico predictions and translational oncology.

Conclusion

AI-enabled structural modeling combined with CSC-derived organoid systems provides a transformative platform for CSC-targeted drug discovery. By integrating computational precision with functional validation, these approaches hold promise for overcoming drug resistance and reducing recurrence driven by CD44⁺ or CD133⁺ populations in HNSCC and NSCLC. Future innovations incorporating explainable AI, multi-omics integration, and standardized organoid protocols will be essential for advancing personalized therapies that effectively eradicate CSCs and improve patient outcomes.

Funding

This work was supported by the Korea–US Collaborative Research Fund (KUCRF), funded by the Ministry of Science and ICT and the Ministry of Health & Welfare, Republic of Korea (grant number RS-2024-00468417), and by the KBSMC–SKKU Future Clinical Convergence Academic Research Program (Kangbuk Samsung Hospital & Sungkyunkwan University).

Acknowledgments

We sincerely thank Chung-Ang University Hospital and Kangbuk Samsung Hospital.

Conflict of Interest

The authors have no conflicts of interest to declare and agreed to the published version of the manuscript.

Author Contributions

The contributions of each author to this study are summarized as follows. SYL, CWY, and SHL conceptualized and designed the overall research framework. Data curation was conducted by SYL, SHP, and SYL, while SYL, SLC, and CWY were responsible for formal data analysis. Funding for the study was acquired by SHL. The experimental investigation was carried out by CWY and SHL. Methodological design and refinement were performed collaboratively by SYL, SHP, HEK, SLC, and CWY. Validation and visualization of the results were undertaken by SYL, SHP, HEK, SLC, and CWY. The original draft of the manuscript was written by SYL, CWY, and SHL, and the final version of the manuscript was reviewed and edited by SYL, CWY, and SHL.

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