Subpopulation Of Cancer Cells Research Articles

BreCML: identifying breast cancer cell state in scRNA-seq via machine learning

Breast cancer is a prevalent malignancy and one of the leading causes of cancer-related mortality among women worldwide. This disease typically manifests through the abnormal proliferation and dissemination of malignant cells within breast tissue. Current diagnostic and therapeutic strategies face significant challenges in accurately identifying and localizing specific subtypes of breast cancer. In this study, we developed a novel machine learning-based predictor, BreCML, designed to accurately classify subpopulations of breast cancer cells and their associated marker genes. BreCML exhibits outstanding predictive performance, achieving an accuracy of 98.92% on the training dataset. Utilizing the XGBoost algorithm, BreCML demonstrates superior accuracy (98.67%), precision (99.15%), recall (99.49%), and F1-score (99.79%) on the test dataset. Through the application of machine learning and feature selection techniques, BreCML successfully identified new key genes. This predictor not only serves as a powerful tool for assessing breast cancer cellular status but also offers a rapid and efficient means to uncover potential biomarkers, providing critical insights for precision medicine and therapeutic strategies.

Understanding the development of enzalutamide resistance based on a functional single-cell approach.

Single-cell RNA-Sequencing reveals heterogeneities of tumor cell populations. In most cases, however, the functional significance of the observed heterogeneity is not tested. In this study, we first identified a possible therapy-resistant prostate cancer cell subpopulation with scRNA-Seq, then confirmed the resistant phenotype with single cell and colony - based cloning and functional testing. In addition, we also identified a therapeutic vulnerability of the resistant cells.

Targeting Lipid Metabolism in Cancer Stem Cells for Anticancer Treatment.

Cancer stem cells (CSCs), or tumor-initiating cells (TICs), are small subpopulations (0.0001-0.1%) of cancer cells that are crucial for cancer relapse and therapy resistance. The elimination of each CSC is essential for achieving long-term remission. Metabolic reprogramming, particularly lipids, has a significant impact on drug efficacy by influencing drug diffusion, altering membrane permeability, modifying mitochondrial function, and adjusting the lipid composition within CSCs. These changes contribute to the development of chemoresistance in various cancers. The intricate relationship between lipid metabolism and drug resistance in CSCs is an emerging area of research, as different lipid species play essential roles in multiple stages of autophagy. However, the link between autophagy and lipid metabolism in the context of CSC regulation remains unclear. Understanding the interplay between autophagy and lipid reprogramming in CSCs could lead to the development of new approaches for enhancing therapies and reducing tumorigenicity in these cells. In this review, we explore the latest findings on lipid metabolism in CSCs, including the role of key regulatory enzymes, inhibitors, and the contribution of autophagy in maintaining lipid homeostasis. These recent findings may provide critical insights for identifying novel pharmacological targets for effective anticancer treatment.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression and there is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet systems to study these processes in vitro are limited. Single-cell RNA sequencing (scRNA-seq) has revealed that some cancer cell lines include distinct subpopulations. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. With clusterCleaver, ESAM and BST2/tetherin were experimentally validated as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification of surface markers for tracking and isolating transcriptomically distinct subpopulations within cell lines. This tool paves the way for studies on coexisting cancer cell subpopulations in well-defined in vitro systems.

Retraction: CD44 Expression Denotes a Subpopulation of Gastric Cancer Cells in Which Hedgehog Signaling Promotes Chemotherapy Resistance.

Retraction: CD44 Expression Denotes a Subpopulation of Gastric Cancer Cells in Which Hedgehog Signaling Promotes Chemotherapy Resistance.

Assessing the metastatic potential of circulating tumor cells using an organ-on-chip model.

Metastatic lung cancer remains a leading cause of death worldwide, with its intricate metastatic cascade posing significant challenges to researchers and clinicians. Despite substantial progress in understanding this cascade, many aspects remain elusive. Microfluidic-based vasculature-on-chip models have emerged as powerful tools in cancer research, enabling the simulation of specific stages of tumor progression. In this study, we investigate the extravasation behaviors of A549 lung cancer cell subpopulations, revealing distinct differences based on their phenotypes. Our results show that holoclones, which exhibit an epithelial phenotype, do not undergo extravasation. In contrast, paraclones, characterized by a mesenchymal phenotype, demonstrate a notable capacity for extravasation. Furthermore, we observed that paraclones migrate significantly faster than holoclones within the microfluidic model. Importantly, we found that the depletion of vascular endothelial growth factor (VEGF) effectively inhibits the extravasation of paraclones. These findings highlight the utility of microfluidic-based models in replicating key aspects of the metastatic cascade. The insights gained from this study underscore the potential of these models to advance precision medicine by facilitating the assessment of patient-specific cancer cell dynamics and drug responses. This approach could lead to improved strategies for predicting metastatic risk and tailoring personalized cancer therapies, potentially involving the sampling of cancer cells from patients during tumor resection or biopsies.

Nanozyme-Based Pump-free Microfluidic Chip for Colorectal Cancer Diagnosis via Circulating Cancer Stem Cell Detection.

Circulating cancer stem cells (CCSCs) are subpopulations of cancer cells with high tumorigenicity, chemoresistance, and metastatic potential, which are also major drivers of disease progression. Herein, to achieve the prediction of tumor diagnosis and progression in colorectal cancer (CRC), a new, automated, and portable lateral displacement patterned pump-free (LP) microfluidic chip (LP-chip) with the CoPt3 nanozyme was established for CCSC capture and detection in peripheral blood and feces samples ex vivo. In this design, CoPt3@HA probes with functions of magnetic separation and colorimetric signal transduction by peroxidase-mimicking activity were applied for the capture of CCSCs and signal output in clinical samples. The generated colors of polydopamine (PDA) were quantifiable through the smartphone APP and visualizable by the naked eye in the test line (T line) and control line (C line) of the LP-chip. In the optimal experimental conditions, the CCSC concentration was sensitive to change in the range 0-105 cells mL-1, with a detection limit of 3 cells mL-1 (S/N = 3). Preliminary studies of clinical samples suggest that the platform has the potential for prediction of colorectal cancer progression and poor prognosis. Overall, the LP-chip provides potential strategies for timely diagnosis, therapeutic monitoring, and recurrence prediction to improve home-based patient care.

Multiplexed Glycan Immunofluorescence Identification of Pancreatic Cancer Cell Subpopulations in Both Tumor and Blood Samples.

Pancreatic ductal adenocarcinoma (PDAC) tumor heterogeneity impedes the development of biomarker assays suitable for early disease detection that would improve patient outcomes. The CA19-9 glycan is currently used as a standalone biomarker for PDAC. Furthermore, previous studies have shown that cancer cells may display aberrant membrane-associated glycans. We therefore hypothesized that PDAC cancer cell subpopulations could be distinguished by aberrant glycan signatures. We used multiplexed glycan immunofluorescence combined with pathologist annotation and automated image processing to distinguish between PDAC cancer cell subpopulations within tumor tissue. Using a training-set/test-set approach, we found that PDAC cancer cells may be identified by signatures comprising 4 aberrant glycans (VVL, CA19-9, sTRA, and GM2) and that there are three glycan-defined PDAC tumor types: sTRA type, CA19-9 type, and intermixed. To determine whether the aberrant glycan signatures could be detected in blood samples, we developed hybrid glycan sandwich assays for membrane-associated glycans. In both patient-matched tumor and blood samples, the proportion of aberrant glycans detected was consistent. Furthermore, our multiplexed glycan immunofluorescent approach proved to be more sensitive and more specific than CA19-9 alone. Our results provide proof of concept for a novel methodology to improve early PDAC detection and patient outcomes.

Oncofetal morphogenesis similar to embryonic gut formation by a subpopulation of DLD-1 human colon cancer cells

Cancer stem cells (CSC) are thought to be responsible for cancer phenotypes and cellular heterogeneity. Here we demonstrate that the human colon cancer cell line DLD1 contains two types of CSC-like cells that undergo distinct morphogenesis in the reconstituted basement membrane gel Matrigel. In our method with cancer cell spheroids, the parent cell line (DLD1-P) developed grape-like budding structures, whereas the other (DLD1-Wm) and its single-cell clones dynamically developed worm-like ones. Gene expression analysis suggested that the former mimicked intestinal crypt-villus morphogenesis, while the latter mimicked embryonic hindgut development. The organoids of DLD1-Wm cells rapidly extended in two opposite directions by expressing dipolar proteolytic activity. The invasive morphogenesis required the expression of MMP-2 and CD133 genes and ROCK activity. These cells also exhibited gastrula-like morphogenesis even in two-dimensional cultures without Matrigel. Moreover, the two DLD1 cell lines showed clear differences in cellular growth, tumor growth and susceptibility to paclitaxel. This study also provides a simple organoid culture method for human cancer cell lines. HT-29 and other cancer cell lines underwent characteristic morphogenesis in direct contact with normal fibroblasts. Such organoid cultures would be useful for investigating the nature of CSCs and for screening anti-cancer drugs. Our results lead to the hypothesis that CSC-like cells with both invasive activity and a fetal phenotype, i. e. oncofetal CSCs, are generated in some types of colon cancers.

Drug tolerant persister cell plasticity in cancer: A revolutionary strategy for more effective anticancer therapies.

Non-genetic mechanisms have recently emerged as important drivers of anticancer drug resistance. Among these, the drug tolerant persister (DTP) cell phenotype is attracting more and more attention and giving a predominant non-genetic role in cancer therapy resistance. The DTP phenotype is characterized by a quiescent or slow-cell-cycle reversible state of the cancer cell subpopulation and inert specialization to stimuli, which tolerates anticancer drug exposure to some extent through the interaction of multiple underlying mechanisms and recovering growth and proliferation after drug withdrawal, ultimately leading to treatment resistance and cancer recurrence. Therefore, targeting DTP cells is anticipated to provide new treatment opportunities for cancer patients, although our current knowledge of these DTP cells in treatment resistance remains limited. In this review, we provide a comprehensive overview of the formation characteristics and underlying drug tolerant mechanisms of DTP cells, investigate the potential drugs for DTP (including preclinical drugs, novel use for old drugs, and natural products) based on different medicine models, and discuss the necessity and feasibility of anti-DTP therapy, related application forms, and future issues that will need to be addressed to advance this emerging field towards clinical applications. Nonetheless, understanding the novel functions of DTP cells may enable us to develop new more effective anticancer therapy and improve clinical outcomes for cancer patients.

Single-cell sensor analyses reveal signaling programs enabling Ras-G12C drug resistance.

Clinical resistance to rat sarcoma virus (Ras)-G12C inhibitors is a challenge. A subpopulation of cancer cells has been shown to undergo genomic and transcriptional alterations to facilitate drug resistance but the immediate adaptive effects on Ras signaling in response to these drugs at the single-cell level is not well understood. Here, we used Ras biosensors to profile the activity and signaling environment of endogenous Ras at the single-cell level. We found that a subpopulation of KRas-G12C cells treated with Ras-G12C-guanosine-diphosphate inhibitors underwent adaptive signaling and metabolic changes driven by wild-type Ras at the Golgi and mutant KRas at the mitochondria, respectively. Our Ras biosensors identified major vault protein as a mediator of Ras activation through its scaffolding of Ras signaling pathway components and metabolite channels. Overall, methods including ours that facilitate direct analysis on the single-cell level can report the adaptations that subpopulations of cells adopt in response to cancer therapies, thus providing insight into drug resistance.

Single-cell profiling of surface glycosphingolipids opens a new dimension for deconvolution of breast cancer intratumoral heterogeneity and phenotypic plasticity

Glycosylated sphingolipids (GSLs) are a diverse group of cellular lipids typically reported as being rare in normal mammary tissue. In breast cancer (BCa), GSLs have emerged as noteworthy markers associated with breast cancer stem cells, mediators of phenotypic plasticity, and contributors to cancer cell chemoresistance. GSLs are potential surface markers that can uniquely characterize the heterogeneity of the tumor microenvironment, including cancer cell subpopulations and epithelial–mesenchymal plasticity (EMP). In this study, mass spectrometry analyses of the total sphingolipidome in breast epithelial cells and their mesenchymal counterparts revealed increased levels of Gb3 in epithelial cells and significantly elevated GD2 levels in the mesenchymal phenotype. To elucidate if GSL-related epitopes on BCa cell surfaces reflect EMP and cancer status, we developed and rigorously validated a 12-color spectral flow cytometry panel. This panel enables the simultaneous detection of native GSL epitopes (Gb3, SSEA1, SSEA3, SSEA4, and GD2), epithelial–mesenchymal transition markers (EpCAM, TROP2, and CD9), and lineage markers (CD45, CD31, and CD90) at the single-cell level. Next, the established panel was used for the analysis of BCa primary tumors and revealed surface heterogeneity in SSEA1, SSEA3, SSEA4, GD2, and Gb3, indicative of native epitope presence also on non-tumor cells. These findings further highlighted the phenotype-dependent alterations in GSL surface profiles, with differences between epithelial and stromal cells in the tumor. This study provides novel insights into BCa heterogeneity, shedding light on the potential of native GSL-related epitopes as markers for EMP and cancer status in fresh clinical samples. The developed single-cell approach offers promising avenues for further exploration.

Signaling dynamics in coexisting monoclonal cell subpopulations unveil mechanisms of resistance to anti-cancer compounds

BackgroundTumor heterogeneity is a main contributor of resistance to anti-cancer targeted agents though it has proven difficult to study. Unfortunately, model systems to functionally characterize and mechanistically study dynamic responses to treatment across coexisting subpopulations of cancer cells remain a missing need in oncology.MethodsUsing single cell cloning and expansion techniques, we established monoclonal cell subpopulations (MCPs) from a commercially available epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer cell line. We then used this model sensitivity to the EGFR inhibitor osimertinib across coexisting cell populations within the same tumor. Pathway-centered signaling dynamics associated with response to treatment and morphological characteristics of the MCPs were assessed using Reverse Phase Protein Microarray. Signaling nodes differentially activated in MCPs less sensitive to treatment were then pharmacologically inhibited to identify target signaling proteins putatively implicated in promoting drug resistance.ResultsMCPs demonstrated highly heterogeneous sensitivities to osimertinib. Cell viability after treatment increased > 20% compared to the parental line in selected MCPs, whereas viability decreased by 75% in other MCPs. Reduced treatment response was detected in MCPs with higher proliferation rates, EGFR L858R expression, activation of EGFR binding partners and downstream signaling molecules, and expression of epithelial-to-mesenchymal transition markers. Levels of activation of EGFR binding partners and MCPs’ proliferation rates were also associated with response to c-MET and IGFR inhibitors.ConclusionsMCPs represent a suitable model system to characterize heterogeneous biomolecular behaviors in preclinical studies and identify and functionally test biological mechanisms associated with resistance to targeted therapeutics.

In vivo single-cell CRISPR uncovers distinct TNF programmes in tumour evolution

The tumour evolution model posits that malignant transformation is preceded by randomly distributed driver mutations in cancer genes, which cause clonal expansions in phenotypically normal tissues. Although clonal expansions can remodel entire tissues1–3, the mechanisms that result in only a small number of clones transforming into malignant tumours remain unknown. Here we develop an in vivo single-cell CRISPR strategy to systematically investigate tissue-wide clonal dynamics of the 150 most frequently mutated squamous cell carcinoma genes. We couple ultrasound-guided in utero lentiviral microinjections, single-cell RNA sequencing and guide capture to longitudinally monitor clonal expansions and document their underlying gene programmes at single-cell transcriptomic resolution. We uncover a tumour necrosis factor (TNF) signalling module, which is dependent on TNF receptor 1 and involving macrophages, that acts as a generalizable driver of clonal expansions in epithelial tissues. Conversely, during tumorigenesis, the TNF signalling module is downregulated. Instead, we identify a subpopulation of invasive cancer cells that switch to an autocrine TNF gene programme associated with epithelial–mesenchymal transition. Finally, we provide in vivo evidence that the autocrine TNF gene programme is sufficient to mediate invasive properties and show that the TNF signature correlates with shorter overall survival of patients with squamous cell carcinoma. Collectively, our study demonstrates the power of applying in vivo single-cell CRISPR screening to mammalian tissues, unveils distinct TNF programmes in tumour evolution and highlights the importance of understanding the relationship between clonal expansions in epithelia and tumorigenesis.

Non-genetic heterogeneity and immune subtyping in breast cancer: Implications for immunotherapy and targeted therapeutics

Breast cancer (BC) is a complex and multifactorial disease, driven by genetic alterations that promote tumor growth and progression. However, recent research has highlighted the importance of non-genetic factors in shaping cancer evolution and influencing therapeutic outcomes. Non-genetic heterogeneity refers to diverse subpopulations of cancer cells within breast tumors, exhibiting distinct phenotypic and functional properties. These subpopulations can arise through various mechanisms, including clonal evolution, genetic changes, epigenetic changes, and reversible phenotypic transitions. Although genetic and epigenetic changes are important points of the pathology of breast cancer yet, the immune system also plays a crucial role in its progression. In clinical management, histologic and molecular classification of BC are used. Immunological subtyping of BC has gained attention in recent years as compared to traditional techniques. Intratumoral heterogeneity revealed by immunological microenvironment (IME) has opened novel opportunities for immunotherapy research. This systematic review is focused on non-genetic variability to identify and interlink immunological subgroups in breast cancer. This review provides a deep understanding of adaptive methods adopted by tumor cells to withstand changes in the tumor microenvironment and selective pressure imposed by medications. These adaptive methods include alterations in drug targets, immune system evasion, activation of survival pathways, and alterations in metabolism. Understanding non-genetic heterogeneity is essential for the development of targeted therapies.

Characterization of EpCAM in thyroid cancer biology by three-dimensional spheroids in vitro model

BackgroundThyroid cancer (TC) is the most common endocrine malignancy. Nowadays, undifferentiated thyroid cancers (UTCs) are still lethal, mostly due to the insurgence of therapy resistance and disease relapse. These events are believed to be caused by a subpopulation of cancer cells with stem-like phenotype and specific tumor-initiating abilities, known as tumor-initiating cells (TICs). A comprehensive understanding of how to isolate and target these cells is necessary. Here we provide insights into the role that the protein Epithelial Cell Adhesion Molecule (EpCAM), a known TICs marker for other solid tumors, may have in TC biology, thus considering EpCAM a potential marker of thyroid TICs in UTCs.MethodsThe characterization of EpCAM was accomplished through Western Blot and Immunofluorescence on patient-derived tissue samples, adherent cell cultures, and 3D sphere cultures of poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC) cell lines. The frequency of tumor cells with putative tumor-initiating ability within the 3D cultures was assessed through extreme limiting dilution analysis (ELDA). EpCAM proteolytic cleavages were studied through treatments with different cleavages’ inhibitors. To evaluate the involvement of EpCAM in inducing drug resistance, Vemurafenib (PLX-4032) treatments were assessed through MTT assay.ResultsVariable EpCAM expression pattern was observed in TC tissue samples, with increased cleavage in the more UTC. We demonstrated that EpCAM is subjected to an intense cleavage process in ATC-derived 3D tumor spheres and that the 3D model faithfully mimics what was observed in patient’s samples. We also proved that the integrity of the protein appears to be crucial for the generation of 3D spheres, and its expression and cleavage in a 3D system could contribute to drug resistance in thyroid TICs.ConclusionsOur data provide novel information on the role of EpCAM expression and cleavage in the biology of thyroid TICs, and our 3D model reflects the variability of EpCAM cleavage observed in tissue samples. EpCAM evaluation could play a role in clinical decisions regarding patient therapy since its expression and cleavage may have a fundamental role in the switch to a drug-resistant phenotype of UTC cells.

Mitophagy and cancer: role of BNIP3/BNIP3L as energetic drivers of stemness features, ATP production, proliferation, and cell migration.

Mitophagy is a selective form of autophagy which permits the removal of dysfunctional or excess mitochondria. This occurs as an adaptative response to physiological stressors, such as hypoxia, nutrient deprivation, or DNA damage. Mitophagy is promoted by specific mitochondrial outer membrane receptors, among which are BNIP3 and BNIP3L. The role of mitophagy in cancer is being widely studied, and more specifically in the maintenance of cancer stem cell (CSC) properties, such as self-renewal. Given that CSCs are responsible for treatment failure and metastatic capacity, targeting mitophagy could be an interesting approach for CSC elimination. Herein, we describe a new model system to enrich sub-populations of cancer cells with high basal levels of mitophagy, based on the functional transcriptional activity of BNIP3 and BNIP3L. Briefly, we employed a BNIP3(L)-promoter-eGFP-reporter system to isolate cancer cells with high BNIP3/BNIP3L transcriptional activity by flow cytometry (FACS). The model was validated by using complementary lysosomal and mitophagy-specific probes, as well as the mitochondrially-targeted red fluorescent protein (RFP), namely mt-Keima. High BNIP3/BNIP3L transcriptional activity was accompanied by increases in i) BNIP3/BNIP3L protein levels, ii) lysosomal mass, and iii) basal mitophagy activity. Furthermore, cancer cells with increased BNIP3/BNIP3L transcriptional activity exhibited CSC features, such as greater mammosphere-forming ability and high CD44 levels. To further explore the model, we also analysed other stemness characteristics in MCF7 and MDA-MB-231 breast cancer cell lines, directly demonstrating that BNIP3(L)-high cells were more metabolically active, proliferative, migratory, and drug-resistant, with elevated anti-oxidant capacity. Therefore, high levels of basal mitophagy appear to enhance CSC features.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.

Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools, capable of classifying cancer cells based on their functions, may substantially enhance therapy and extend patient life span. The connection between cell biomechanics and cancer cell functions is used here to classify cells through mechanical measurements, via particle uptake. Machine learning (ML) was used to classify cells based on single-cell patterns of uptake of particles with diverse sizes. Three pairs of human cancer cell subpopulations, varied in their level of drug resistance or malignancy, were studied. Cells were allowed to interact with fluorescently labeled polystyrene particles ranging in size from 0.04 to 3.36 μm and analyzed for their uptake patterns using flow cytometry. ML algorithms accurately classified cancer cell subtypes with accuracy rates exceeding 95%. The uptake data were especially advantageous for morphologically similar cell subpopulations. Moreover, the uptake data were found to serve as a form of "normalization" that could reduce variation in repeated experiments.

Unravelling the Mysteries of the Sonic Hedgehog Pathway in Cancer Stem Cells: Activity, Crosstalk and Regulation.

The Sonic Hedgehog (Shh) signalling pathway plays a critical role in normal development and tissue homeostasis, guiding cell differentiation, proliferation, and survival. Aberrant activation of this pathway, however, has been implicated in the pathogenesis of various cancers, largely due to its role in regulating cancer stem cells (CSCs). CSCs are a subpopulation of cancer cells with the ability to self-renew, differentiate, and initiate tumour growth, contributing significantly to tumorigenesis, recurrence, and resistance to therapy. This review focuses on the intricate activity of the Shh pathway within the context of CSCs, detailing the molecular mechanisms through which Shh signalling influences CSC properties, including self-renewal, differentiation, and survival. It further explores the regulatory crosstalk between the Shh pathway and other signalling pathways in CSCs, highlighting the complexity of this regulatory network. Here, we delve into the upstream regulators and downstream effectors that modulate Shh pathway activity in CSCs. This review aims to cast a specific focus on the role of the Shh pathway in CSCs, provide a detailed exploration of molecular mechanisms and regulatory crosstalk, and discuss current and developing inhibitors. By summarising key findings and insights gained, we wish to emphasise the importance of further elucidating the interplay between the Shh pathway and CSCs to develop more effective cancer therapies.