3D models to study therapy-induced senescence: where do we stand now?

Abstract

Cellular senescence (CS) is a crucial tumor-suppressive phenomenon, inhibiting proliferation of cancerous cells. However, cancer therapies can also induce tumor cell senescence, generating senescent cells in tumoral and normal tissues. While initially beneficial, these senescent cells can paradoxically contribute to tumor recurrence, metastasis, and therapy resistance via the senescence-associated secretory phenotype (SASP). Due to the diverse and critical roles, cellular senescence could be a potential target in cancer biomedicine. To extend our understanding of therapy-induced senescence (TIS), developing experimental models is necessary. Currently TIS established models can be categorized into animal-based and laboratory models. These models are essential for advancing our knowledge of aging mechanisms and developing new treatment modalities. In vivo models of TIS have faced limitations, including poor immune system representation, oversimplified stromal complexity, and an inability to model functional vascular networks. Incorporating cutting-edge technologies such as 3D cultures, co-culturing, and tissue engineering can help researchers in creating in vitro models that closely mimic physiologically conditions. This review highlighted the current TIS challenges and advanced senotherapeutics. Finally, we discussed how to develop reliable in vitro models to better understanding TIS mechanisms.

Introduction

Cellular senescence (CS) represent a condition of irreversible cell cycle arrest and altered gene expression pattern, triggered by diverse stressors such as DNA damage, oxidative stress, and chronic inflammation (Inci et al. 2022). This process is thought to play essential crucial role in the progress of age-related disorders (Ma et al. 2018; Ramakrishna et al. 2013). Recent advancements have highlighted the dual role of senescence in cancer. While it serves as a tumor-suppressive mechanism by stopping cell proliferation, therapy-induced senescence (TIS) can paradoxically contribute to tumor recurrence, metastasis, and resistance to treatment through the senescence-associated secretory phenotype (SASP; Liu et al. 2024; Yang et al. 2021).

The current experimental models for senescence-based research can be divided into animal-based and laboratory models that study the process of cellular senescence and its connections with different diseases and therapeutic modalities (Farhadnejad et al. 2019). These models are critical in advancing our knowledge of the underlying cellular and molecular mechanisms of aging and development of new treatments. The lack of robust models that accurately reflect TIS in vivo has limited advanced research in this field. While conventional animal modeling provides a more realistic representation of in vivo interactions, their ability to fully recapitulate dormant tumor cells alongside a fully functional immune system is often restricted by practical and biological limitations (Pastore et al. 2024; Shboul et al. 2022). Recent studies using humanized mouse models have attempted to overcome these limitations by integrating immune components into tumor models (Shboul et al. 2022).

Conversely, the majority of in vitro research on the reversibility TIS has been conducted using 2D mono-culture models, which do not encompass the crucial spatiotemporal interactions among tumor cells, immune cells, and stromal cells (Al Shboul et al. 2022; Prasanna et al. 2021). To fully understand the potential application of using senescence as a mechanism of tumor suppression, it is essential to demonstrate its reversibility in a more realistic 3D model that takes into account the tumor microenvironment (TME) and immune cells (Diekmann et al. 2016). Organoid models are generated by culturing primary cells in a 3D matrix that supports organoid formation (Shi et al. 2024). These models offer a more physiologically applicable platform to study organ biology in physiologic and pathologic conditions (Heydari et al. 2021a, b). Recent advancements in organoid technology have enabled researchers to simulate complex tissue architectures and study SASP-driven processes more effectively (Gu et al. 2025; Li et al. 2024).

Regardless of the model used, in vitro studies of TIS often use various stressors, like DNA damage or oxidative stress, to induce senescence. Researchers can then study the effects of different treatments, including drugs and natural compounds on the molecular mechanisms underlying the cellular senescence. For example, recent studies employing CRISPR-Cas9-based screens have identified novel regulators of senescence pathways that could serve as therapeutic targets (Wang et al. 2021). One of the major advantages of in vitro models for senescence-based therapy is that they enable researchers to perform experiments that would not be feasible or ethically approved in in vivo. For example, these models can be used to test the efficacy of new treatments in a controlled and reproducible manner, without the need for animal testing (Muthamil et al. 2024). Another advantage of in vitro models is that they can provide valuable insights into the molecular mechanisms responsible for senescence and its association with specific disease. The generated knowledge can be used to develop new therapeutic strategies that target specific molecular pathways involved in cellular senescence (Torrens-Mas et al. 2021; Ruoß et al. 2020).

In conclusion, current limitations of 2D monoculture models and conventional animal modeling have highlighted the need for developing more comprehensive and realistic experimental models to investigate the role of senescence in inducing tumor dormancy. A 3D model that is enriched with the TME and immune cells may provide a more robust and reliable method for testing the reversibility of senescence and evaluating the potential of using senolytics as cancer treatments (Yadav et al. 2021). In addition, given the growing consideration of using senescent cell-clearing compounds in cancer therapy, 3D modeling may offer a more reliable tool for evaluating their efficacy and developing new treatments (Short et al. 2019; Zhu et al. 2020). While 3D models address some limitations of 2D systems, their predictive value for in vivo outcomes remains under validation. Recent studies comparing 3D models with patient-derived xenografts (PDXs) have shown alignment in senescence marker expression divergences in immune-mediated SASP effects (Invrea et al. 2020; Wawrzak-Pienkowska et al. 2025).

Here, we provided an overview of current treatments and FDA-approved drugs specifically for senescence. Moreover, the concept of senescence therapy and current models were discussed. Finally, reliable in vitro models for the investigation of TIS were proposed.

Common ways to induce senescence in cancer related therapies

TIS has been recognized as a response to a diverse range of chemotherapeutic agents, including traditional and targeted therapies (Fig. 1). Over the years, a multitude of components has been identified to induce senescence through various pathways, including in vitro and in vivo conditions. These components target different mechanisms, including DNA damage, oxidative stress, telomere attrition, and oncogene activation (Torres et al. 2024). Cancer therapies often disrupt mitochondrial function, leading to increased production of reactive oxygen species (ROS). Elevated ROS levels cause oxidative stress, which damages cellular macromolecules, including DNA, proteins, and lipids. The cell responds to DNA damage by activating the DNA damage response (DDR; Mahmud et al. 2024). By activating these pathways, the components promote cell cycle arrest, growth inhibition, and ultimately, the induction of senescence. The identification of these components has paved the way for the development of new therapeutic strategies for various diseases, including cancer (Saleh et al. 2020). While strategies like gene therapy, and viral-based techniques hold promise for tumor suppression (Luo 2022; Saleh et al. 2023), recent advancements in molecular biology and experimental models have introduced novel tools like CRISPR-Cas9, lentiviral vectors, and oncolytic viruses to enhance the precision and efficacy of senescence induction (Chaurasiya et al. 2020; Singh 2024; Zhang et al. 2023a). A summary of FDA-approved senescence-inducing agents are summarized in Table 1.

Fig. 1.

Application of therapy-induced senescence for enhanced cancer treatments. Different approaches like chemotherapy and gene-based therapies can be employed for cancer treatment. These approaches can induce senescence in cancer cells, rendering them unable to proliferate and less invasive than original cancer cells. This process leads to the secretion of specific factors known as the SASP. In such cases, the use of senotherapeutics becomes beneficial as they can mitigate the side effects associated with TIS. Senotherapeutics function by either eliminating the SASP or selectively removing senescent cells from the tumor microenvironment, promoting a more effective and targeted cancer treatment approach. SASP senescence-associated secretory phenotype, TIS therapy-induced senescence, VSV vesicular stomatitis virus

Table 1.

FDA-approved anticancer therapies to induce senescence

Drug category	Examples	References
Topoisomerase poisons/inhibitors	Doxorubicin, Daunorubicin, Etoposide, Mitoxantrone, Camptothecin, Irinotecan, Topotecan	Yosef et al. (2017) and Velichko et al. (2015)
Alkylating agents	Busulfan, Temozolomide, Carmustine, Dacarbazine, Cyclophosphamide, Melphalan, Mitomycin C	Mei et al. (2014) and Aasland et al. (2019)
Platinum-based	Cisplatin, Carboplatin, Oxaliplatin	Sun et al. (2018) and Seignez et al. (2014)
Antimetabolites	Methotrexate, Pemetrexed, Gemcitabine, Azacitidine, Bromodeoxyuridine, 5-Fluorouracil, Mycophenolic acid, Hydroxyurea, Actinomycin D	Dabrowska et al. (2019), Putri et al. (2017), and Milczarek et al. (2018)
Microtubule inhibitors/poisons	Paclitaxel, Docetaxel, Vincristine, Vinblastine	Khongkow et al. (2016) and Mitri et al. (2014)
Hormonal therapy	Tamoxifen, Fulvestrant, Androgen Deprivation (CSS, antiandrogen, and/or castration)	Kawata et al. (2017) and Dolfi et al. (2014)
Kinase inhibitors	Imatinib, Nilotinib, Trametinib, Vemurafenib, Dasatinib, Lapatinib, Neratinib, Afatinib, Gefitinib, Erlotinib, Sorafenib	Wang et al. (2018) and Peng et al. (2015)
mTOR inhibitors	Rapamycin	Weichhart (2018) and Ota et al. (2009)
Monoclonal antibodies	Rituximab, Obinutuzumab, Pertuzumab, Trastuzumab, Bevacizumab, Ranibizumab	Rosemblit et al. (2018) and Hasan et al. (2011)
CDK 4/6 inhibitors	Palbociclib, Palbociclib, Abemaciclib, Ribociclib	Valenzuela et al. (2017) and Guan et al. (2017)
PARP inhibitors	Olaparib, Niraparib, Rucaparib	Chatterjee et al. (2013) and Alotaibi et al. (2016)
Aurora Kinase Inhibitors	MLN8054, TAK901	Liu et al. (2013) and Vilgelm et al. (2016)

CSS charcoal-stripped-serum, CDKs cyclin-dependent kinases, PARP poly ADP ribose polymerase

One effective approach to trigger senescence could be gene therapy techniques. Retrovirus-based gene delivery is one of the commonly used techniques to induce senescence via activating oncogenes known as oncogene-induced senescence (OIS; Muraki et al. 2024; Saleh et al. 2022a). OIS is a complex cellular response triggered by aberrant oncogenic signaling, involves multiple pathways regulating cell cycle, DNA damage signaling, immune response, and bioenergetics (Liu et al. 2018). The inducible retroviral delivery of HRAS-G12V or myr-AKT1 triggered OIS and was associated with the upregulation of endogenous retroviruses (ERVs), such as ERV9-1, and the activation of interferon signaling pathways in fibroblasts. The senescence was validated through SA-β-gal staining and the presence of SASP markers, including IL1B (Giorgio et al. 2024). Murine stem cell virus (MSCV) carrying Bcl2 and dominant-negative caspase 9 (C9DN) were successfully used in several studies to provoke senescence in lymphoma cells (Eµ-myc, Schmitt et al. 2002). MSCV encoding oncoprotein H-Ras^v12 and MSCV with an oncogenic Ras-to-oestrogen receptor (ER) fusion were used to induced OIS in mouse embryo fibroblasts Tig3 (Dörr et al. 2013). Retro- and lentivirus vectors carrying senescence inducing payloads possess remarkable potential to initiate and study senescence in vitro and in preclinical models (Ahmad et al. 2021). However, the safety of this approach should be carefully examined before any clinical applications are made.

Lentiviral vectors have the ability to infect non-dividing cells, which makes them a promising candidate to specifically kill senescent cells. Genetically engineered lentiviral vectors loaded with pro-apoptotic genes may provide a unique opportunity for specifically eliminating senescent cells (US Patent 20150064137A1). Combinatorial approaches the using viral vectors, exosomes, polynucleotides and/or pharmaceutical compositions targeting senescent cells may potentiate the therapeutic effect necessary for killing cancer senescent cells.

Some viral infections may also induce senescence in infected cells. SARS-CoV-2 and vesicular stomatitis virus (VSV) trigger a senescence reaction in host cells after infection (Lee et al. 2021). In fact, cell senescence is an antiviral defense mechanism that helps to reduce viral infectivity and impair virus particle production (Baz-Martínez et al. 2016). Nevertheless, VIS accompanied by senescence-associated secretory phenotype (SASP) and pre-existing age-associated senescent cells may induce severe organ damage and hyper-inflammation (Schmitt et al. 2022). Epstein–Barr virus (EBV) and Kaposi’s sarcoma associated herpesvirus (KSHV) trigger senescence via replicative stress and DNA damage (Hafez and Luftig 2017). HIV is a notorious example of VIS by activating NF-kB, up-regulating miR34a and Nef protein mediated autophagy (Yuan et al. 2019). Therefore, senolytic removal of senescent cells caused by viral infection may represent an efficient approach to mitigating the risk of organ failure and the disease severing (Lee et al. 2021).

Viruses can selectively induce senescence in cancer cells through mechanisms involving oxidative stress and DNA damage responses, similar to TIS (Lee et al. 2021). This approach may bypass apoptosis resistance in cancer cells (Bousset and Gil 2022). Senescent cells secrete SASP factors that recruit immune cells, potentially enhancing antitumor immunity. Oncolytic viruses further exploit this by preferentially killing senescent cancer cells (Xiao 2023). VIS could complement traditional treatments like chemotherapy or radiation, particularly in immunocompetent patients, by amplifying immune-mediated tumor suppression (Wang et al. 2022a).

While VIS offers a promising strategy to exploit senescence for immune activation and tumor suppression, there are risks of SASP-mediated tumor progression and therapy resistance. SASP from VIS cells promotes EMT, angiogenesis, and metastasis in neighboring cells (Xiao 2023). Persisting senescent cells post-treatment may evade immune clearance and contribute to tumor recurrence (Bousset and Gil 2022; Xiao 2023). However, VIS efficacy depends on tumor type, host immune status, and viral tropism. For example, VSV and pseudotyped SARS-CoV-2 viruses induce senescence in epithelial cells but may require ROS modulation to minimize off-target effects (Lee et al. 2021).

Oncolytic viruses demonstrate efficient infection and lysis of cancer cells. The results with measles virus (MeV) and HepG2 cells show that senescent hepatoma cells support virus replication and gene expression (Weiland et al. 2014). Several oncolytics (adenovirus, poxvirus, reovirus, VSV) have shown promising efficacy in targeting hepatocellular carcinoma in Phase I, II and III of clinical trials. Oncolytics are generally well tolerated and have demonstrated mild to moderate potential for cancer treatment and patient recovery (Malogolovkin et al. 2021). Spatial and temporal control of VIS seems to be difficult and additional regulators (e.g. optogenetics or chemogenetics) could be used to arm oncolytics and precisely control virus replication (Malogolovkin et al. 2022). TIS and virotherapy are an attractive approach for maximizing cancer treatment and minimizing the adverse effects of chemo and radiotherapy.

With the development of cutting-edge methods such as CRISPR-Cas9 and lentiviruses, inducing senescence through genetic manipulation has become increasingly feasible and promising. In recent years, the field of gene therapy has made significant strides in advancing these techniques, and researchers are continuously exploring new avenues for their application (Schepers et al. 2021). CRISPR-Cas9, in particular, has gained attention for its precision and versatility in editing genes, allowing targeted modifications to induce senescence in specific cells or tissues. A genome-wide CRISPR-based screening is a powerful tool used to identify novel genes that may play a crucial role in cellular senescence (Sánchez-Martín et al. 2024). Using this approach, several potential gene regulators have been found. In a study, KAT7, a histone acetyltransferase, was identified as a trigger for senescence. Inactivation of KAT7 has led to accelerated human embryonic stem cells senescence (Wang et al. 2021).

Lentiviral vector loaded with Cas9/sg targeting the KAT7 gene was able to alleviate hepatocyte senescence and liver aging. In a similar study using 22 cancer cell lines and lentivirus-based CRISPR/Cas9 screen, it was discovered that coagulation factor IX (F9) is a regulator of cellular senescence (Carpintero-Fernández et al. 2022). In a recent study using CRISPR-based screening library (208 sg RNA targeting 32 nucleoporins and 34 nuclear transport receptors), XPO7 was shown to positively regulate senescence in mesenchymal stem cells by destabilizing histone deacetylase 2 (HDAC2; Li et al. 2023).

Lentiviruses, on the other hand, are used as vectors for delivering genetic materials into cells, providing a promising tool for targeted gene therapy applications. In a study, researchers demonstrated that, they conducted lentivirus vector-mediated overexpression or knockdown of AGTR1 in HCC (Wang 2022). These cells then exposed to sorafenib to induce senescence. The results showed that AGTR1 suppression triggered cellular senescence in HCC cells by deactivating ERK signaling, leading to an attenuated proliferative capacity, amplified expression of p53 and p21, and higher proportion of SA-β gal- and SAHF-positive cells. This study suggests that AGTR1 suppression, in combination with sorafenib, could potentially serve as a therapeutic approach for HCC (Wang et al. 2022b).

CRISPR-Cas9 provides high precision and flexibility for targeted gene editing and senescence induction; it may present risks such as off-target effects and requires careful validation (Sánchez-Martín et al. 2024). Compared to retroviral and lentiviral vectors, which are efficient for gene delivery but may integrate randomly into the genome, CRISPR-Cas9 generally facilitates more controlled and specific genetic modifications (Lindel et al. 2019). Nevertheless, lentiviral vectors remain advantageous for transducing non-dividing cells and for stable, long-term gene expression (White et al. 2017). Overall, the progress in gene therapy techniques like CRISPR-Cas9 and lentiviruses represents a promising avenue for inducing senescence and potentially mitigating the effects of aging-related diseases. Figure 2 outlines the major pathways and markers involved in TIS, specifically within the context of 3D models.

Fig. 2.

The major pathways and markers involved in therapy-induced senescence, specifically within the context of 3D models

Methodological approaches for assessing senescence in 3D models

Several methodological approaches are employed to evaluate senescence in 3D models, including imaging techniques and molecular assays. β-Galactosidase staining is widely used to identify cells with increased lysosomal activity, a marker for senescent cells (Cai et al. 2020). p16INK4A functions as a potent inhibitor of cyclin-dependent kinase and is associated with senescence. Its expression can be measured using techniques such as immunohistochemistry or transcript analysis in 3D cultures. Studies show suppressed senescence signatures in certain 3D cultures compared to traditional 2D systems (Yadav et al. 2021). These assays correlate with in vivo outcomes by reflecting key features of senescence observed in living tissues. β-Galactosidase staining corresponds effectively with lysosomal changes observed in aged tissues, making it a reliable indicator of cellular aging both in vitro and in vivo. Also, the expression of p16INK4A correlates strongly with age-related diseases and acts as a significant biomarker for senescent cells in vivo, particularly in aging skin and other tissues (Lombardi et al. 2024). 3D models offer enhanced physiological relevance compared to 2D systems, as they more accurately replicate tissue architecture and cellular interactions, which influence senescence markers like β-galactosidase activity and p16INK4A expression (Yadav et al. 2021).

The validation of 3D models in TIS studies involves a variety of statistical methodologies. In 3D skin aging models, studies employed technical replicates (e.g., multiple measurements per sample) and biological replicates (independent experiments) to ensure the reliability of their findings. For example, fibroblasts treated with mitomycin-C were examined across separate trials to address variability (Lombardi et al. 2024). Comparative studies between 2 and 3D cultures used one-way ANOVA to assess differences in gene expression associated with senescence, cytoskeletal structure, and nucleolar volume. Post hoc tests, such as Tukey’s, were conducted to identify specific group differences. For example, 3D analysis of nucleolar volume under doxorubicin treatment revealed significant increases (P < 0.05) at higher doses, undetectable in 2D (Yadav et al. 2021; Lombardi et al. 2024). Traditional markers of senescence in 2D, such as flattened cell morphology, were suppressed in 3D models, highlighting the need for volumetric analysis (e.g., confocal microscopy) to detect subtle phenotypic changes (Yadav et al. 2021). Additionally, simulation-based studies, including TIS models, used sensitivity analysis to evaluate the robustness of parameters and the oscillatory interactions between tumor and immune cells (Serrano and Hagar 2021).

Therapy-induced senescence: a two-sided strategy

Here, we will address the two significant challenges associated with senescence-based therapies. First, although senescence therapy can be a reliable treatment for cancer, several studies have shown that tumor cells may re-enter the cell cycle after senescence induction by chemotherapy drugs. One study showed that a small population of breast tumor cells developed resistance to senescence after being exposed to adriamycin and continued to express cell cycle regulators that promoted cell proliferation (Elmore et al. 2005). Another study showed that a population of lung cancer cells induced into senescence by chemotherapy drugs recovered their proliferative capacity and shaped colonies after drug removal. The frequency of this escape was infrequent, but sustained expression of cdc2, a cell cycle regulator, was associated with the evasion of senescence in both studies (Roberson et al. 2005). G0-arrested cancer cells persist after treatment and resume proliferation under favorable conditions, as demonstrated by flow cytometry and drug response assays (Kluska et al. 2023). A recent study developed a transcriptional signature to quantify G0 arrest in 8005 primary tumors, linking reversible quiescence to therapy resistance and identifying mutational constraints for cell cycle re-entry (Wiecek et al. 2023).

Polyploidy is also a common feature of senescent cells induced by chemotherapy drugs. The polyploid senescent tumor cells, often termed polyploid giant cancer cells (PGCCs), can serve as a reservoir for relapse. These cells can eventually escape from growth arrest, contribute to tumor relapse, therapy resistance, and the emergence of more aggressive cancer cell populations. Targeting these cells is a promising direction for preventing recurrence after cancer therapy (Saleh et al. 2022b). These studies used different cancer cell lines and induced senescence using various treatments, including doxorubicin, cisplatin, and etoposide. Live cell imaging and fluorescent labeling techniques were used to visualize the behavior of the cells, and markers of cell proliferation and DNA synthesis were detected in polyploid senescent cells. These studies suggest that polyploid senescent cells can escape from senescence and contribute to tumor growth and relapse. The expression of various proteins, such as p53, p21Cip1, Cdc2, and cyclin B1, was found to be involved in the process of escape from senescence (Sabisz and Skladanowski 2009; Saleh et al. 2019).

To better understand the dual roles of TIS, researchers are increasingly utilizing 3D models to dissect the underlying mechanisms and signaling pathways involved. TIS is primarily mediated by two pathways of p53/p21 pathway and p16/RB pathway. These pathways are often co-activated in TIS, with stromal interactions and metabolic reprogramming further modulating senescence outcomes. 3D models offer promising insights into cancer biology and TIS, particularly in studying signaling pathways, SASP, and DNA damage responses (Micco et al. 2021; Shreeya et al. 2023). 3D models reveal how mechanical and biochemical cues regulate TIS-related signaling. For example, in breast cancer models, 3D cultures showed that malignant reversion requires coordinated inhibition of β1 integrin and EGFR pathways, which are dysregulated in TIS (Schmeichel and Bissell 2003). Co-culture models demonstrate stromal cells directly influence epithelial senescence through paracrine signaling and ECM remodeling (Pauty et al. 2021).

Moreover, SASP has dual roles; recruits immune cells to clear senescent cells and aids tissue repair and promotes chronic inflammation, EMT, and therapy resistance (Shreeya et al. 2023). For example, SASP factors like TGF-β enhance metastasis by remodeling the tumor microenvironment (Chambers et al. 2021). Furthermore, senescent fibroblasts in collagen-embedded vascular models trigger sprouting angiogenesis via SASP factors, sustained by ECM stiffening. In 3D skin models, ionizing radiation induces distinct 53BP1 foci patterns, with slower repair kinetics compared to 2D (Su et al. 2010). Incision or laser ablation techniques quantify stored elastic energy in tumors, linking mechanical compression to senescence evasion (Zhang et al. 2019a). By integrating optical, genetic, and computational tools, 3D models provide unparalleled resolution of TIS mechanisms, enabling targeted interventions such as SASP inhibitors or radioprotectants.

However, TIS can be exploited for therapeutic benefit through many strategies that address therapy resistance, prevent recurrence, or in combination with immunotherapies (Chembukavu and Lindsay 2024). For example, 3D models have identified senolytic drugs, such as BCL-2 inhibitors (navitoclax), capable of effectively targeting and eliminating senescent cells. 3D models also have been effective in discovering potential combination therapies that can enhance the efficacy of CDK 4/6 inhibitors (Zhang et al. 2021). Even so, challenges related to therapy evasion raise concerns about the optimal outcomes of this treatment, indicating that further efforts are required for clinical translation.

SASP often promoting therapy resistance through its ability to reshape TME. Key mechanisms include paracrine effects on surrounding cells, DNA damage and genotoxic stress (Liu et al. 2024; Chambers et al. 2021; Zhang et al. 2018). SASP factors such as interleukins (e.g., IL-6, IL-8), chemokines, and growth factors can induce EMT, promoting metastasis and therapy resistance. Chronic secretion of these molecules fosters an immunosuppressive milieu, reducing anti-tumor immune responses and enabling cancer cells to evade immune surveillance (Liu et al. 2024; Chambers et al. 2021). Moreover, persistent DNA damage response pathways (e.g., ATM-p38-mTOR signaling) sustain SASP expression, amplifying its effects over time (Zhang et al. 2018). 3D models enable researchers to study the impact of SASP factors secreted by senescent stromal cells on adjacent cancer cells, immune cells, and endothelial cells within a simulated TME (Liu et al. 2024; Coppé et al. 2010). Advanced imaging techniques in 3D models facilitate real-time tracking of SASP-induced changes such as EMT, immune suppression, or angiogenesis (Zhang et al. 2018; Saleh et al. 2018). For example, monitoring cytokine gradients within 3D matrices provides insights into the mechanisms by which SASP promotes localized resistance. Moreover, the immunosuppressive role of SASP can be evaluated in 3D models that include immune cells, revealing how TIS-induced senescence modifies immune surveillance mechanisms (Liu et al. 2024).

Immune cells play dual roles in TIS models as effectors of senescent cell clearance (via NK/CD8+ T cells) and mediators of resistance (via PD-L1 and M2 macrophages). Senescent cancer cells upregulate PD-L1 through transcriptional activation and glycosylation, mediated by ribophorin 1 (RPN1). Anti-PD-1 therapy reversed immune evasion in TIS models, restoring CTL-mediated elimination of senescent cells and reducing recurrence (Hwang et al. 2025). Also, senescent cells secrete chemokines and express NKG2D ligands that promote NK cell recruitment and cytotoxicity. In Kras-mutant liver cancer models, SASP factors like IL-15 and CXCL1 mobilized NK cells, leading to tumor regression (Chibaya et al. 2022). Moreover, SASP components recruit immunosuppressive macrophages (M2 phenotype), fostering angiogenesis and metastasis (Liu et al. 2024; Chibaya et al. 2022).

Second, aside from its potential role in promoting cell proliferation and contributing to disease recurrence, cell senescence can have other harmful effects caused by the senescent cell accumulation. These negative effects of senescence are caused by both cell-autonomous mechanisms, such as changes in gene expression, and cell non-autonomous mechanisms, like signaling through the SASP (Saleh et al. 2020). In a study, Demaria et al. showed that eliminating senescent cells after doxorubicin treatment diminished various side-effects like bone marrow suppression, inflammation, and reduced tumor recurrence (Demaria et al. 2017). The SASP consists of various soluble and insoluble elements, ranging from cytokines and chemokines to extracellular matrix components, and various signaling molecules. These factors enabling senescent cells to influence their microenvironment. However, the impact of this interaction on patient outcomes remains uncertain (Coppé et al. 2010).

Compelling evidence substantiate the SASP′s involvement in disease development, as it promotes the growth of neighboring non-senescent cells. This phenomenon has been observed in the context of melanoma and prostate tumor cells, where inducing senescence through cisplatin or doxorubicin in tumor cells led to heightened proliferation in untreated, non-senescent cancer cells (Sun et al. 2018; Ewald et al. 2008). It is worth noting the melanoma studies noted a similar impact both in vitro and in vivo, the prostate cancer study failed to replicate these finding in vitro setting. This implies that the impact of the SASP may vary depending on the type of tumor (Saleh et al. 2020). SASP can induce neighboring cells to become more invasive and develop stem-like properties, and stimulate new blood vessels formation. SASP can also have contradictory effects on immune cells, either increasing immune surveillance or suppressing it depending on the context. Senescent cells can also become more drug-resistant and aggressive through several mechanisms, including upregulation of anti-apoptotic proteins (such as BCL-2 family), secretion of SASP factors (such as IL-6 and TGF-β) that promote EMT and stemness, and release of extracellular vesicles that induce drug efflux transporter expression in neighboring cancer cells (Hu 2022; Vernot 2020). These changes accompanied by upregulation of stem cell markers and a pro-survival phenotype may confer resistance to chemotherapy, which could drastically influence patient outcomes (Ortiz-Montero et al. 2017; Ohanna et al. 2013).

PDXs are created by implanting human tumors into immunodeficient mice, allowing for the study of tumor biology and treatment responses (Siolas and Hannon 2013). PDXs have been shown to accurately replicate patient responses to therapies, making them valuable for assessing drug efficacy (Izumchenko et al. 2017). Furthermore, PDX models can be repeatedly sampled to study clonal evolution and drug resistance, enhancing personalized treatment strategies (Lodhia et al. 2015). Integrating TIS assessment in these models could provide insights into the therapeutic outcomes and mechanisms underlying cancer treatment responses.

Emerging approaches in senotherapeutics: senolytics and beyond

Senotherapeutics refer to a group of agents that aim to address the two key processes related to aging. Among these agents, senolytics are agents that selectively eliminate senescent cells by inducing apoptosis, while senomorphics modulate the phenotype of senescent cells, primarily by suppressing the SASP without killing the cells (Birch and Gil 2020) (Fig. 1).

Senolytics

Senolytic drugs have risen as a hopeful approach to eliminate senescent cells and delay or even reverse age-related pathology. The discovery of senolytic drugs began with the observation that removing senescent cells could delay the development of age-related diseases in mice (Krishnamurthy et al. 2004). Researchers then screened large libraries of compounds to identify those that could selectively kill senescent cells. They found that dasatinib and quercetin were effective in eliminating senescent cells in vitro and in vivo (Zhu et al. 2015). Later studies identified other compounds that could selectively eliminate senescent cells, including navitoclax (ABT263) and fisetin (Chang et al. 2016; Yousefzadeh et al. 2018).

Senolytic drugs act by triggering senescent cells to undergo apoptosis, a programmed cell death process. The mechanism of action of senolytic drugs varies depending on the specific compound. For example, dasatinib and quercetin act by inhibiting the pro-survival pathways that senescent cells use to escape apoptosis (Kirkland and Tchkonia 2020). Navitoclax, ABT263, and ABT737 act by inhibiting the anti-apoptotic protein Bcl-2, which is overexpressed in some senescent cells (Zhu et al. 2016; Wilson et al. 2010). Fisetin acts by inhibiting pro-survival pathways and activating apoptotic pathways (Zhu et al. 2017).

Preclinical researches have revealed that senolytic drugs prolong the health span and lifespan of mice by delaying or reversing age-related pathology (Yosef et al. 2016). Senolytic drugs have been tested in mouse models of age-related diseases, including osteoarthritis, atherosclerosis, and neurodegenerative diseases, and have shown promising results (Xu et al. 2017; Roos et al. 2016; Zhang et al. 2019b). In clinical studies, senolytic drugs have been tested on small groups of patients with age-related diseases, such as idiopathic pulmonary fibrosis and diabetic kidney disease (Justice et al. 2019; Hickson et al. 2019), and studies for several other diseases have been planned or have already started (Kirkland and Tchkonia 2020). These studies have presented that senolytic drugs increase functional outcomes and reduce inflammation in some patients. Importantly, recent preclinical and early clinical research suggests that senolytics are also highly relevant to cancer therapy: by selectively eliminating TIS tumor cells, senolytics may help reduce tumor recurrence, limit pro-tumorigenic effects of the SASP, and improve the efficacy of conventional cancer treatments when used as adjuncts (Czajkowski et al. 2025; Malayaperumal et al. 2023).

Developing senolytic drugs involves several obstacles, including the need to identify compounds that are safe and effective for humans, the need to develop drugs that can target specific cell types, and the need to optimize dosing regimens. In addition, the use of senolytic drugs in healthy individuals for anti-aging purposes raises ethical and safety concerns. Nevertheless, the potential benefits of senolytic drugs are significant, and this field is rapidly evolving with the development of new compounds and initiation of clinical trials (Table 2).

Table 2.

In progress clinical trials for senolytic drugs

Condition or disease	Drugs	Phase	Study ID
Stem Cell Transplant	Dasatinib + Quercetin	N/A	NCT02652052
Alzheimer Disease	Dasatinib + Quercetin	Phase I/II	NCT04063124
Chronic Kidney Disease	Dasatinib + Quercetin	Phase II	NCT02848131
Healthy	Dasatinib + Quercetin	Phase II	NCT04313634
Mild Cognitive Impairment, Alzheimer Disease	Dasatinib + Quercetin	Phase I/II	NCT04785300
Alzheimer Disease, Early Onset Mild Cognitive Impairment	Dasatinib + Quercetin	Phase II	NCT04685590
Frailty Childhood Cancer	Dasatinib + Quercetin + Fisetin	Phase II	NCT04733534
Osteoarthritis, Knee	Fisetin + Losartan (anti-fibrotic agent)	Phase I/II	NCT04815902
COVID-19	Quercetin Phytosome	Phase III	NCT028749819
Osteoarthritis, Knee	Fisetin	Phase I/II	NCT04210986
COVID-19	Fisetin	Phase II	NCT04476953 NCT04771611
Frail Elderly Syndrome	Fisetin	Phase II	NCT03675724 NCT03430037
Chronic Kidney Diseases Diabetes Mellitus Diabetic Nephropathies	Fisetin	Phase II	NCT03325322
COVID-19 SARS-CoV Infection	Fisetin	Phase II	NCT04537299
Osteoarthritis, Knee	UBX0101	Phase I	NCT04229225
Osteoarthritis, Knee	UBX0101	Phase II	NCT04129944

Senomorphics

An alternative pharmaceutical strategy to trigger cellular senescence is provided by treatment with senomorphics. These medical components can suppress the harmful effects of SASPs that are secreted by senescent cells. Initially, senomorphics such as rapamycin, metformin, resveratrol, and aspirin have long been discovered.

Rapamycin, a macrolide compound, has the potential to diminish cellular senescence, inhibit SASPs, and increase longevity. It stands as one of the most extensively studies senomorphics for controlling senescence and SASPs. Rapamycin effectively suppress TORC1 activity, primarily through its interaction with the intracellular protein FKBP12 (Zhang et al. 2023b). The senescence-modulating and longevity-promoting properties of rapamycin are predominantly attributed to its ability to inhibit mTOR signaling by decreasing phosphorylation of S6K and 4E-BP downstream of TORC1 (Papadopoli et al. 2019). Proposal suggestion has been put forth that the adverse effects associated with rapamycin might be due to its off-target inhibition of mTORC2. Hence, developing new rapamycin analogs or rapalogues holds the potential to address rapamycin's side effects while enhancing its effectiveness, solubility, and pharmacokinetic characteristics (Schreiber et al. 2019). Metformin, initially designed for managing type 2 diabetes, has revealed it therapeutic potential in a range of age-related conditions, like insulin resistance, liver disorders, cancer, cardiovascular issues, neurodegenerative disorders and kidney diseases. It has also been shown to suppress cellular senescence and SASPs and to increase the health-span and lifespan of various model organisms. Metformin is currently undergoing evaluation for its potential age-targeting effects in the TAME clinical trial. While the precise mechanisms of action for metformin remain incompletely elucidated, it is believed to intricately modulate various pathways and molecular targets (Noren Hooten et al. 2016; Jiang et al. 2020). Metformin has been observed to impact all the fundamental aspects of biological aging. This includes its role in mediating nutrient-signaling pathways, reducing oxidative damage and genome instability, enhancing proteostasis, improving mitochondrial dysfunction, inducing stem cell rejuvenation, and modulating gut microbiota to enhance metabolism while reducing inflammation (Kulkarni et al. 2020; Hu et al. 2021). SIRT1, also known as silencing information regulator 2-related enzyme 1, is a deacetylase that relies on NAD+ and controls various signaling and transcriptional pathways related to aging and senescence. Resveratrol and other SIRT1 activators are known to extend lifespan and reduce cellular senescence by regulating signaling and transcriptional pathways associated with aging (Chen et al. 2020). However, the effect of resveratrol on cellular senescence is biphasic, with low concentrations preventing cellular senescence and suppressing SASPs (Xia et al. 2008), while high concentrations trigger growth arrest and induce senescence or apoptotic death (Shaito et al. 2020). The impact of resveratrol on extending the lifespan on mice also varies depending on several factors such as diet, timing and route of dosing, genetic background, and cell/tissue types (Bhullar and Hubbard 2015; Pallauf et al. 2016). Despite resveratrol’s limitations in terms of bioavailability and stability, alternative SIRT1 activators featuring unique structures, including SRT1720, SRT17201460, and SRT17202183, which are more potent than resveratrol and have entered clinical trials for age-related disease treatment (Hubbard and Sinclair 2014). Aside from its traditional role as a non-steroidal anti-inflammatory drug, aspirin offers various therapeutic advantages in addressing age-related conditions like atherosclerosis, arthritis, osteoporosis, and cancer (Hybiak et al. 2020). Studies show that aspirin can extend the lifespan and health span of worms and mice, potentially through inhibiting senescence and SASPs (Lushchak et al. 2021). While aspirin has demonstrated the ability to delay the initiation of replicative senescence in endothelial cells and ameliorated chemotherapy's adverse effects in mice, it has also been observed to heighten senescence in human colorectal cancer cells. This recommends that aspirin′s impact on senescence might differ depending on cellular context, the types of stress, and the dosage of treatment (Jung et al. 2015).

In addition to the types of senomorphics discussed above, there are several others, such as p38MAPK inhibitors, JAK/STAT inhibitors, NF-ƘB inhibitors, ATM inhibitors, and statins (reviewed here Zhang et al. 2023b).

Potential side effects and risks of senotherapeutics include gastrointestinal symptoms (such as nausea and diarrhea), fatigue, headaches, skin rashes, and fluctuations in blood pressure. There is also limited long-term safety data in humans, and concerns remain about off-target effects, drug interactions, and the lack of standardized dosing protocols. These factors highlight the need for careful monitoring and further clinical studies to fully assess the risk profile of senotherapeutics in cancer and other indications (Park and Shin 2022; Raffaele and Vinciguerra 2022).

In conclusion, the advancement of senotherapeutics for addressing treat age-related disorders and enhancing overall health span is rapidly evolving, with over 20 senolytic trials ongoing. This field is expected to expand with the identification of more optimal drugs and combinations.

Could 3D cultures and engineering approaches revolutionize our understanding of senescence in cancer therapy?

Currently, pre-clinical models employed to study tumor cell responses to cancer therapy (e.g. TIS) rely heavily on 2D-cultured cancer cells (Fig. 3). However, these models fall short in replicating essential elements like cellular diversity and tumor microenvironment, leading to oversimplification. As a result, it is imperative to develop more physiologically relevant tissue models for developing the preclinical and clinical evaluation of cancer therapy outcomes and for advancing our comprehension of senescence and its role in cancer biology (Inci et al. 2022; Torrens-Mas et al. 2021). While 2D cultures have long been fundamental to study senescence, recent literature suggests that 3D cultures, such as spheroids and organoids, offer a more realistic representation of senescence induction. These models can be characterized as by the presence of typical senescence marks, such as SA-β-gal activity, p16INK4a/p21Cip1 upregulation, SASP expression (Xie et al. 2018; Peng et al. 2020).

Fig. 3.

Current models for TIS study. Current models for studying TIS primarily rely on 2D cell cultures, which lack the complexity of in vivo conditions. To overcome this limitation, 3D cultures have been proposed as more reliable modalities. However, there are still challenges associated with 3D cultures. To enhance the development of improved models, incorporating 3D cultures with other advanced techniques including microfluidic systems and bioprinting is more promising. TIS therapy-induced senescence, SASP senescence-associated secretory phenotype, ECM extracellular matrix, CS cellular senescence

Intestinal epithelial organoids have been suggested as an appropriate model for aging studies, as evidenced by the accumulation of SA-β-gal, decreased expression of DNA methyltransferases, and the increased expression of p21 in organoids from aged mice (Uchida et al. 2018). This was demonstrated in an experimental setup that involved contracting organoids derived from young and old samples. The finding revealed epigenetic alterations leading to stem cell impairment and -diminished efficiency in generating organoid in aged mice and humans compared to the younger ones (Uchida et al. 2018; He et al. 2020). Additionally, aged organoids exhibited reduced levels of DNA methyltransferases and elevated markers of senescence, such as SA-β-galactivity, as well as increased expressions of p16 and p21 (Lewis et al. 2020). Nevertheless, these studies are exploring SC in various tissues and organs, rather than focusing specifically on TIS. 3D models that have been used to study TIS and provide experimental evidence that better mimics in vivo conditions compared to 2D cultures. For example, 3D polymer scaffolds have been shown to influence senescence signatures, cytoskeletal organization, and gene expression in irradiated cancer cells, resulting in reduced expression of senescence-associated genes and more physiologically relevant cell morphology (Yadav et al. 2021, 2024). However, 3D cultures also have notable limitations such as the lack of functional vasculature and limited nutrient and oxygen diffusion, which can affect cell viability and experimental outcomes. These challenges highlight the need for further model refinement to more accurately replicate the tumor microenvironment (TME; Habanjar et al. 2021).

While these studies have closely mirrored findings from 2D cultures, we expect that as more research is conducted, important differences between conventional and 3D models of TIS will emerge. According to a study, it was discovered that endometrial mesenchymal stem/stromal cells exposed to H₂O₂ exhibit different responses depending on their growth pattern: they undergo apoptosis when cultured as spheroids and experiences senescence when grown in a 2D culture condition (Domnina et al. 2020). Another study has revealed that growing cells induced into senescence by IR in a 3D scaffold instead of a 2D substrate reduces the expression of numerous characteristics associated with senescence, such as SASP (Yadav et al. 2021). These findings suggest that senescence might operate in a distinct manner in cells that have a 3D contact framework. These differences will be further highlighted as more promising models that incorporate various cell types, such as endothelial cells, immune cells and so on in a 3D format, emerge. Ultimately, physiologically accurate tissue models are essential for improving the preclinical and clinical efficacy of cancer therapy and elucidating the precise mechanisms by which TIS influences the overall response to treatment. In addition to endothelial and immune cells, the microenvironment of native cancer is more complicated and contains several crucial elements such as the ECM and cancer-associated fibroblasts (CAFs; Hirsch and Schildknecht 2019).

While co-culturing various cell types and utilizing 3D models have improved certain drawbacks of 2D cell culture, these methods quiet face a few limitations. For example, they may lack sufficient ECM, cannot fully replicate complex in vivo processes such as metastasis, and are often limited in size and have optimized flow rates for nutrients and waste removal (Heydari et al. 2020, 2021b) (Fig. 2). To address these challenges, advanced techniques like bioprinting and microfluidic chips have been developed (Heydari et al. 2022). Bioprinting provides development of complex, spatial arrangement of 3D tissue models and ECM components that better mimic the cell–cell interactions, resulting in more physiologically accurate tissues with greater consistency. Meanwhile, microfluidic offer several benefits, such as the ability to generate the gradients of nutrients and oxygen to better mimic in vivo conditions and the possibility of integrating multiple organs-on-chip to model complex physiological interactions (Inci et al. 2022; Al Shboul et al. 2022). By integrating these innovative approaches with conventional organoid culture methods, we can advance the field of tissue engineering and create more realistic models for disease modeling and drug development approaches.

Limitations of 3D models in TIS research

Key limitations of current 3D models in TIS research are concerns on challenges in mimicking TME and differences between 3D models and aligning with in vivo systems (Chibaya et al. 2022; Ruhland and Alspach 2021). A notable deficiency in most 3D models is the absence of immune cell populations, such as T cells and macrophages, which are essential for investigating SASP-mediated immune modulation (Liu et al. 2024). Recent studies using co-culture systems with peripheral blood mononuclear cells (PBMCs) or CAR-T cells have shown improved immune relevance, however, these approaches encounter challenges related to scalability. Furthermore, native stromal components (such as cancer-associated fibroblasts, extracellular matrix remodeling) are often oversimplified in 3D spheroids. These complexities limit the capacity of 3D systems to replicate TME dynamics (Liu et al. 2024; Aasland et al. 2019; Ruhland and Alspach 2021). Although advancements in bioengineered matrices and co-culture systems are promising but still face scalability and reproducibility issues (Ruhland and Alspach 2021). Functional vasculature is also critical for studying drug delivery and hypoxia-induced senescence. Emerging technologies, such as vascularized organoid platforms and microfluidic systems, show potential but require further optimization for broader use (Liu et al. 2024; Zhang et al. 2024).

While 3D models can represent a certain level of heterogeneity, they often fail to reflect the genomic and phenotypic diversity observed in patient tumors (Liu et al. 2024). Single-cell RNA-Seq studies reveal significant differences between 3D cultures and in vivo systems. Dense structures in 3D models hinders drug penetration, resulting in underestimation of therapeutic efficacy (Liu et al. 2024). Recent studies using advanced imaging techniques has quantified these limitations and proposed alternatives, including dynamic culture conditions. In vivo senescence involves continuous interactions with neighboring tissues over time, whereas most 3D models capture short-term effects. Therefore, longitudinal studies that compare senescence dynamics between 3D and in vivo systems are necessary (Liu et al. 2024; Ruhland and Alspach 2021).

Conclusion and future prospects

CS is a complex process implicated in aging and age-related diseases. Although numerous molecular mechanisms of senescence have been unveiled, the absence of a standard marker for its detection remains a challenge. Hence, forthcoming researches are anticipated to discover novel molecular pathways and biomarkers tailored to specific diseases or tissues. Additionally, the combination of results from various models, including 3D patient-derived organoid studies, may provide a better understanding of CS (Uchida et al. 2018). While there has been progress in the progress of senotherapeutic approaches to cure age-related diseases, their possible side effects and long-term complications are still concerns. Therefore, new therapeutic methods such as immune surveillance and senomorphics are being explored (Balistreri et al. 2021). Effective translation of preclinical research, using alternative models and methodology, may facilitate the development of innovative therapeutic strategies and contribute to enhancing life expectancy. Additionally, these experimental models tend to simplify larger and intricately structured organs, like the brain. For instance, movement of metastatic cancer cells to other tissues via hematogenous and/or lymphatic diffusion is frequently inadequately represented in monolayer cultures and numerous 3D models due to lack of vasculature system.

Tumor-on-a-chip models have overcome some of the mentioned limitations and become valuable tools for analyzing the possibility of cancer metastases and drug discovery (Pauty et al. 2021; Ribas et al. 2017). However, there are still some concerns to be addressed, including the elevated production and maintenance costs, the absence of standardized protocols ensuring high reproducibility, the necessity for specialized expertise, and the restrict range of materials used in their construction. Moreover, understanding molecular signaling pathways involved in cancer cell senescence can provide valuable insights for discovering novel anti-tumor modalities and identifying novel molecular biomarkers. Besides, combination therapies using gene-based approaches and FDA-approved drugs might be a potential therapeutic approach to limit cancer progression through TIS (Wang et al. 2022b).

Despite advances in current research methodologies, they have notable limitations, including the oversimplification of 2D models, technical challenges in fully replicating the TME, and the lack of reliable systems to study interactions between senescent and non-senescent cells, as well as ECM dynamics. These gaps highlight the need for more physiologically relevant and technically sophisticated 3D models to advance our understanding of therapy-induced senescence (Shboul et al. 2022).

To further advance 3D models, it is essential to integrate functional vasculature and immune elements, employ bioprinting and microfluidic technologies to more accurately mimic the TME, implement co-culture systems (e.g. immune cells, endothelial cells, and other stromal cells) that enriched with the ECM, develop standardized protocols for modeling TIS and drug testing, and employ combination therapy. These improvements in 3D models will enhance their physiological relevance and translational potential for studying senescence and TIS in future. Future therapies could integrate TIS targeting with immunotherapy by combining senolytic drugs with immune checkpoint inhibitors to eliminate immunosuppressive senescent cells, restore effective antitumor immune responses, and improve immunotherapy efficacy (Chibaya et al. 2022; Oesterreich and Aird 2023).

Author contributions

ZH was involved in writing original draft and conceptualization. AM involved in writing gene therapy techniques. DL and AF helped in developing table. AS and OS were involved in visualization. PT and MV were involved in conceptualization, editing, funding, and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Ministry of Science and Higher Education of the Russian Federation under grant agreement N. 075-15-2024-640 (Sechenov University).

Data availability

Not applicable.

Declarations

Conflict of interest

The authors declare no competing interests.

Ethical approval

This study does not contain any studies with human or animal subjects performed by any of the authors.