Telomerase inhibitors TMPyP4 and BIBR 1532 show synergistic antitumor activity in combination with chemotherapeutic drugs

Abstract

We investigated the in vitro pharmacodynamic interactions of telomerase inhibitors (TMPyP4 and BIBR1532) with three anticancer drugs (cisplatin, doxorubicin, and paclitaxel) on a broad range of human cancer cell lines (MCF-7, MDA-MB-231, HeLa, U-118 MG, OVCAR-3, MCF-12A), selected based on the basal level of hTERT. The drug combination approach was performed using a combination index (CI), the Chou-Talalay method. The HeLa cells show the highest level of hTERT among the studied cell lines, and the second level was noted in the MCF-7 cells. Almost all used combinations in this line revealed a synergistic effect, with the lowest CI for BIBR1532 and cisplatin. Interestingly, the highest synergistic effect, compared to other combinations, was shown by BIBR1532 and doxorubicin in U118 MG cells. Additionally, the highest effect of TMPyP4, compared to all combinations, was noted in conjunction with cisplatin in HeLa cells. The most impactful results were achieved by combining inhibitors with drugs that interact directly with DNA strands. Moreover, the different hTERT levels influence the response to treatments. This underscores the need for in vitro optimization to maximize the synergistic interaction of compounds. Combining genome-based medicine and drug screening using personalized models may fulfill the promise of precision medicine for every cancer type.

Introduction

Cancer is the main challenge in medicine, especially in aging societies. According to the latest GLOBOCAN (Global Cancer Statistics), an estimated 19.3 million new cancer cases and nearly 10.0 million deaths occurred worldwide in 2020¹. The most recent World Health Organization (WHO) report for the year 2019 ranks cancer as the first or second leading cause of death before the age of 70 years in more than 60% of the countries worldwide². In recent decades, there have been tremendous developments and improvements in cancer treatments. Unfortunately, resistance to anticancer therapies is still commonly observed in clinical practice³. Several strategies have been proposed to overcome this phenomenon, focusing on increasing the effectiveness of cancer treatment or reducing drug resistance. These include identifying biomarkers to predict resistance and drug response, defining new targets, and developing targeted therapies and polytherapy aiming at multiple signaling pathways⁴. Telomerase activity is one of these potential cancer biomarkers for therapeutic and diagnostic approaches. Telomerase is a ribonucleoprotein built of two essential components, i.e., a protein catalytic subunit—human telomerase reverse transcriptase (hTERT), and telomeric RNA – a template for RNA-dependent DNA synthesis. The holoenzyme is a critical element in cancer cell survival that prevents telomere shortening after cell divisions⁵. Telomeres are located at the ends of chromosomes, complex structures composed of tandem DNA repeats (TTAGGG)n in humans and a six-member protein complex known as shelterin. Telomerase activity is repressed in most somatic cells except the reproductive cells, intestinal stem cells, skin basal cells, hematopoietic progenitor cells, and activated lymphocytes⁶. The expression restoration or upregulation of telomerase is a critical step in over 90% of cancers⁷. This stage of carcinogenesis ensures that cancer cells have replicative immortality. Interestingly, the catalytic subunit, hTERT, exhibits telomere-independent regulation of cancer cell metabolism in critical ways for tumorigenesis. It participates in cell growth, signal transduction, drug response, and the regulation of gene expression⁸. Consequently, recent efforts in cancer therapy aim to target the telomerase/hTERT function. The approaches utilized are based on anti-telomerase immunotherapy, enzyme inhibition, competitive substrate incorporation, and telomere end stabilization^9,10. However, many anti-telomerase drugs produced only modest survival benefits for cancer patients in clinical trials. A handful of trials have initially demonstrated a potent telomere length-dependent effect, unfortunately, followed by the resistance induction caused by the switch to the ALT (alternative lengthening of telomeres) mechanism¹¹. Interestingly, our previous study revealed that lentiviral-mediated hTERT downregulation increased breast cancer cells’ sensitivity to doxorubicin¹², implicating the possibility of overcoming drug resistance by targeting hTERT, as suggested elsewhere^13–15. Thus, the idea of a therapeutic approach based on combining telomerase inhibitors with chemotherapeutics in breast cancer cells was born. The drug combination selection for clinical development should be based on a predefined understanding of drugs’ mechanisms of action, which would help to predict additive or synergistic side effects¹³. The main rationales for these types of strategies are to prolong progression-free survival (PFS) and overall survival (OS)¹⁴. Synergism of drugs with different modes of action may potentially lead to higher therapeutic efficacies and improved tolerability by lowering the dose and preventing the development or overturning of drug resistance mechanisms¹⁵.

Telomerase targeting-based strategy

The basic idea of the anticancer strategy is to use the most efficient and specific way to eliminate cancer cells. Consequently, targeting telomerase, supported by a combination with anticancer drugs, seems to provide a promising solution. This approach leads to disrupting telomere restoration and, thus, destabilization of the genome and induced susceptibility of cancer cells to a chemotherapeutic agent. Consequently, the drug concentrations used in therapy could be lower and, simultaneously, would show reduced adverse side effects with high efficacy. TMPyP4 and BIBR1532 are the compounds that show high potential in telomerase-targeting¹⁶. TMPyP4 is a cationic porphyrin (5,10,15,20-tetra-(N-methyl-4-pyridyl)porphyrin) (Fig. 1A) that has been reported to stabilize G-quadruplex structures (G4s), with the additional inhibition of telomerase activity in cancer cells¹⁷. Consequently, it interferes with the ability of telomerase to extend telomeres. Cell treatment leads to progressive telomere shortening that results in cancer cell senescence or apoptosis¹⁸. Moreover, the action of TMPyP4 in cancer cells includes modulation of the expression of genes involved in cell metabolism, proliferation, and survival¹⁶. It is selectively toxic for carcinoma cells over normal epithelial cells^19,20. The effect of the TMPyP4 is mediated through its ability to bind DNA and stabilize the guanine-quadruplexes. Due to its strong binding affinities for nucleic acids, this porphyrin can selectively photocleave DNA²¹. In addition, TMPyP4 has been considered a promising photosensitizer, especially since it shows high water solubility and high permeability through the cell membrane. As shown, TMPyP4-based photodynamic therapy led to the formation of reactive oxygen species and changes in the expression of genes involved in the response to oxidative stress²². Additionally, this porphyrin also impacts the adhesion and migration of cells. However, its metabolic effect is dose-dependent. As demonstrated, TMPyP4 at low concentrations (≤ 0.5 μM) increases cell–matrix adhesion and promotes the migration of tumor cells. However, a high TMPyP4 concentration (≥ 2 μM) inhibits cell proliferation and induces cell death¹⁹.

Fig. 1.

Chemical structure depiction of A) TMPyP4 tosylate, B) BIBR1532, C) cisplatin, D) doxorubicin, E) paclitaxel (https://pubchem.ncbi.nlm.nih.gov).

BIBR1532 is a non-nucleoside, non-peptide telomerase inhibitor (Fig. 1B) that blocks enzyme activity by binding to the active site of hTERT²³. Moreover, it induces downregulation of hTERT expression, reduces telomere restoration, and provides additional anticancer effects, including inhibition of proliferation and invasion of cancer cells²⁴. Both inhibitors have demonstrated antitumor activity in multiple preclinical cancer models. BIBR1532 showed potential efficacy as an adjuvant in combination with other anticancer agents (such as arsenic trioxide) and radiotherapy in lung and breast cancer and acute promyelocytic leukemia^25–27.

Anticancer drugs

Several types of chemotherapy drugs target different types of cancer²⁸. The most common types of antitumor compounds are alkylating agents, e.g., cisplatin, and antimicrotubular agents, e.g., doxorubicin and paclitaxel²⁹. Cisplatin (CIS, cisplatinum, also called cis-diamminedichloroplatinum(II)) is a metallic (platinum) compound (Fig. 1C) used extensively for the treatment of human cancers such as bladder, blood, breast, cervical, esophageal, head and neck, lung, ovarian, testicular cancers, and sarcoma³⁰. Cisplatin interacts with cellular macromolecules, binds to DNA, and forms intra-strand DNA adducts, inhibiting DNA synthesis and cell growth³¹. Moreover, cisplatin-induced DNA damage results in the activation of ERK1/2 via PKCδ. ERK is activated via a signaling pathway involving Ras, Raf, MEK, and ERK, transducing signals from the plasma membrane to the cell nucleus³². The compound can induce intrinsic and extrinsic apoptosis pathways, producing reactive oxygen species through lipid peroxidation, activation of various signal transduction pathways, e.g., p53, p38, and JNK signaling, and cell cycle arrest. Research has shown upregulation of pro-apoptotic genes/proteins and downregulation of proto-oncogenes and anti-apoptotic genes/proteins³⁰. The mechanism of action can also lead to necrosis or autophagy³³. Additionally, cisplatin activates Akt in lung cancer cells mediated by epidermal growth factor (EGF), Src, and PI3-kinase. Cancer cells can limit cisplatin-induced apoptosis through survivin,  an apoptosis-inhibiting protein that is overexpressed in many tumors but is absent in most normal adult tissues. However, cisplatin treatment reduces survivin expression, inhibiting Akt in a few cancer cell types³⁴. Treatment reveals side effects like neurotoxicity, ototoxicity, renal toxicity, cardiotoxicity, hepatotoxicity, and secondary malignancies^35,36.

Furthermore, one of the most efficient chemotherapy medications approved by the Food and Drug Administration (FDA) is doxorubicin (DOX). It is used to treat several cancer types, including breast, lung, gastric, ovarian, thyroid, non-Hodgkin’s, Hodgkin’s lymphoma, multiple myeloma, sarcoma, and pediatric cancers. DOX is an anthracycline extracted from Streptomyces peucetius var. caesius (Fig. 1D) in the 1970s^37,38. The principal cytotoxic mechanism of DOX is the poisoning of DNA topoisomerase 2 (Top2), which generates lethal DNA double-strand breaks³⁹. As a DNA intercalator, doxorubicin prefers the intercalation site containing adjacent GC base pairs, probably due to specific hydrogen-bond formation between doxorubicin and guanine⁴⁰. DOX causes the increased absorption of oxygen and produces multiple types of reactive oxygen species (ROS), which can trigger the pathological sarcoplasmic reticulum (SR)/Ca2 + leakage, further DNA damage, and interruption of autophagic flux, causing lipid peroxidation-dependent ferroptosis and many other types of regulated cell death types, including apoptosis^41,42. Moreover, the documented mechanism of action also includes membrane damage through altered sphingolipid metabolism⁴³. Like other anthracyclines, DOX triggers many side effects. The most common adverse effects of DOX include nausea, vomiting, stomatitis, loss of appetite, abdominal pain, diarrhea, unusual tiredness, dizziness, hair loss, nail loss, red urine discoloration, and, most limiting, cardiotoxicity⁴⁴.

Another type of anticancer compound is paclitaxel (PACL), a diterpenoid pseudoalkaloid composed of two molecules: a taxane ring with a four-membered oxetane side ring at the C4 and C5 positions and a homochiral ester side chain at the C13 position (Fig. 1E)⁴⁵. The side chain at C13 binds to microtubules, stabilizes tubulin bundles, and stimulates microtubule disassembly in guanosine triphosphate (GTP) independently. As a result, cell proliferation is inhibited by cell cycle arrest at the metaphase/anaphase border and the formation of an incomplete chromosome metaphase plate induced by the stabilization of microtubule dynamics. The intact taxane ring and ester side chain are responsible for the cytotoxic effect⁴⁶. PACL prevents cell division by supporting the formation of stable microtubules, especially from β-tubulin heterodimers, and inhibits their depolymerization; therefore, exposed cells become arrested in the G2/M phase of the cell cycle⁴⁷. Depending on the concentration and duration of exposure, PACL induces apoptosis. At a concentration of ≥ 10 nM and an exposure duration of at least 12 h, apoptosis in the S-phase cells can be induced without mitotic arrest. The activation of Raf-1, which is responsible for the control of apoptosis, is induced by a concentration of  ≥ 9 nM. In lower concentrations, there is no involvement of Raf-1 kinase, but apoptosis is still induced by p53 and p21⁴⁸. After 24 h exposure, PACL causes irreversible arrest of mitosis⁴⁹. The mechanism of action also involves the activation of multiple signal transduction pathways, which may be related to pro-apoptotic signaling. PACL-related pathways are TLR-4-dependent pathway, c-Jun N-terminal kinase (JNK), P38 mitogen-activated protein (MAP) kinase, nuclear factor kappa B (NF-κB), and activator of the transcription factor (STAT) pathway. Induction of cytokines and pro-inflammatory proteins leads to immunomodulatory effects of paclitaxel at low concentrations and cell death at higher doses^50–53. PACL also has a strong angiogenic inhibitory effect⁵⁴. In a highly vascularized transgenic murine breast cancer (Met-1), it reveals antiangiogenic properties associated with a down-regulation of vascular endothelial growth factor (VEGF)⁵⁵. PACL has also been reported to induce the production of reactive oxygen species (ROS) and increase hydroperoxide production by increasing NADPH oxidase activity, which contributes to oxidative stress and may play a role in the potency of the anticancer effects of PACL⁵⁶.

Polytherapy with telomerase inhibitors

Most mechanisms of action of the chemotherapeutic drugs are not selective to cancer, consequently affecting normal cells. Telomerase has emerged as a unique molecular target for cancer treatment. A substantial disadvantage is the time delay observed in telomere attrition. Therefore, telomerase inhibitors are not likely to be used in monotherapy as the first-line treatment. The standard chemotherapies are usually associated with tumor recurrences related to cancer heterogeneity and acquired resistance. This urgent need to develop novel approaches to fight resistant types can be fulfilled by multidrug combination treatment. In this setting, the telomerase inhibitors might prevent the regrowth of residual cancer cells. Accordingly, we used telomerase inhibitors TI (TMPyP4 and BIBR1532) in combination with three commonly used anticancer drugs (CIS, DOX, and PACL). The study’s main aim was to evaluate the efficacy of the therapy approach based on a combination of TI and anticancer drugs and investigate the in vitro pharmacodynamic interactions of those combinations in a broad range of human cancer cell lines.

Materials and methods

Cell culture

This study used ten human cancer cell lines and one non-tumorigenic mammary epithelial cell line. All cell lines were purchased from the American Type Culture Collection (ATCC, USA). The breast adenocarcinoma cell lines MCF-7 (ATCC® HTB-22™) and MDA-MB-231 (ATCC® HTB-26™) were maintained as a monolayer in a complete growing medium RPMI-1640 (Biowest, France), supplemented with 10% (v/v) of fetal bovine serum (FBS) (Sigma-Aldrich, Germany). The breast cancer cell line SK-BR-3 (ATCC® HTB-30™) and ovarian cancer cell line SK-OV-3 (ATCC® HTB-77™) were cultured in McCoy’s 5 A medium (Biowest, USA) with 10% (v/v) FBS (Sigma-Aldrich, Germany) addition. The human cervical carcinoma HeLa (ATCC® CCL-2TM) and the breast cancer cell line T-47D (ATCC® HTB-133™) were maintained in Dulbecco’s Modified Eagle Medium (DMEM) with 10% (v/v) FBS (Sigma-Aldrich, Germany) addition. The glioblastoma cell lines U-118 MG (ATCC® HTB-15™) and U-138 MG (ATCC® HTB-16™) were cultured in DMEM and Eagle`s Minimum Essential Medium (EMEM) with 10% (v/v) FBS (Sigma-Aldrich, Germany), respectively. The colorectal adenocarcinoma cell line Caco-2 (ATCC® HTB-37™) was maintained in EMEM (ATCC®, USA) with 1% (v/v) non-essential amino acids (Sigma-Aldrich, Germany) and 10% (v/v) FBS (Sigma-Aldrich, Germany) addition. The ovarian cancer cell line OVCAR-3 (ATCC® HTB-161™) was cultured in RPMI-1640 medium (Biowest, France) supplemented with insulin (10 µg/mL) and 20% FBS (Sigma-Aldrich, Germany). The non-tumorigenic epithelial cell line MCF-12A (ATCC® CRL-10782™) was maintained in DMEM-F12 medium (Biowest, USA) supplemented with hydrocortisone (0.5 µg/mL), insulin (10.0 µg/mL), human epidermal growth factor (hEGF) (20.0 ng/mL), cholera toxin (0.1 µg/mL), and 5% (v/v) horse serum (all purchased from Sigma-Aldrich, Germany).

All cell lines were grown to a confluence of 80% in 100 × 15 mm Falcon® Petri dishes (Corning, Poland) at 37 °C in an atmosphere containing 5% (v/v) of CO₂ at 95% (v/v) of relative humidity. The absence of mycoplasma was checked routinely using the Mycoplasma Stain Kit (Sigma-Aldrich, Germany).

Immunodetection

The MCF-7, T-47D, SK-BR-3, MDA-MB-231, MCF-12A, Caco-2, OVCAR-3, SK-OV-3, U-118 MG, U-138 MG, and HeLa cells were seeded at a density of 7,5 × 10⁵ cells into 100 mm culture plates and maintained for 72 h. Whole-cell extracts were prepared using a modified RIPA lysis buffer (50 mM Tris–HCl, pH 8.0, 150 mM NaCl, 1% NP40, 0.1% SDS, 100 mM PMSF, 25 μg/ml Na3VO4, 25 μg/ml NaF, 25 μg/ml leupeptin, and 25 μg/ml aprotinin), as previously described (12). The protein concentration was measured using a Bradford assay (Sigma-Aldrich, USA), and 40 μg of each extract was loaded onto SDS-PAGE gels. According to the standard PVDF membrane procedure, western blotting was performed as previously described (Pierce Biotechnology, USA)¹². The primary antibodies used for detection were anti-hTERT (1:1000, Novus Biologicals, USA) and anti-GAPDH (1:2000, Santa Cruz Biotechnology, USA). After removing the antibodies, anti-rabbit IgG or anti-mouse IgG (1:1000, Cell Signaling Technology, Danvers, MA, USA) secondary antibodies labeled with horseradish peroxidase were added. The proteins were detected by Super Signal West Pico Chemiluminescent Substrate (Thermo Scientific, USA) using a CCD camera and VisionWorks LS software (version 8.20, UVP, Inc., USA; https://www.uvp.com/products/bioimaging-systems/uvp-gel-compact/). Additionally, results were analyzed semi-quantitatively using Image Studio Lite (LI-COR Biosciences, USA).

Study compounds

TMPyP4, 5,10,15,20-Tetrakis(1-methylpyridinium-4-yl)porphyrin tetra(p-toluenesulfonate) was purchased from Abcam Biochemicals (Cambridge, UK). Doxorubicin hydrochloride, paclitaxel, and cisplatin were obtained from Sigma–Aldrich (St. Louis, USA), and BIBR1532 from Cayman Chemical (Ann Arbor, USA). All procedures and storage were performed with minimal exposure to light due to TMPyP4’s susceptibility to light.

Cell viability assay

The cytotoxicity of the studied compounds was assessed in six selected cell lines (MCF-7, MDA-MB-231, HeLa, U-118 MG, OVCAR-3, MCF-12A) with a high level of hTERT (selection was based on WB results) using the MTT test. The assay was performed as previously described¹⁶. Briefly, 5 × 10³ cells were seeded into each well of 96-well plates in a total medium volume of 100 μL/well. The cells were treated with various concentrations of TMPyP4, BIBR1532, DOX, CIS, or PACL. The tested compounds were dissolved in 0.9% NaCl, DMSO, or water, respectively. Cells were incubated with the studied compounds for 24 h. Subsequently, 10 μL of MTT solution (5.0 mg/mL) (Sigma–Aldrich, Germany) was added to each well. The cells were incubated at 37 °C for 4 h, followed by adding 100 μL of solubilization buffer (10% SDS in 0.01 M HCl). Finally, the absorbance at 570 nm was measured using a Microplate Reader Multiscan FC (Thermo Scientific, USA) with a reference wavelength of 690 nm. Three separate experiments were performed, with three repeats for each concentration. Relative cell viability was determined using the following formula: %viability = (mean of A570-A690 of an experimental group)/(mean of A570-A690 of the control group) × 100%. The viability of the cells was calculated using Microsoft® Excel® (version 2108, Microsoft, USA; https://www.microsoft.com/pl-pl/microsoft-365/excel).

Inhibitor-drug interaction

Inhibitor-drug interaction was assessed in the mentioned cell lines using the MTT test. Briefly, 5 × 10³ cells were seeded into each well of 96-well plates in a total medium volume of 100 μL/well. The cells were exposed to TMPyP4 (20 µM) or BIBR1532 (20 µM) alone or in combination with DOX (1 µM, 5 µM), PACL (5 µM, 10 µM), or CIS (10 µM, 20 µM). The cells were exposed to inhibitors for 48 h and drugs for the next 24 h. Subsequently, 10 µL of MTT solution (5 mg/mL) (Sigma–Aldrich, USA) was added to each well. The cells were incubated at 37 °C for 4 h, followed by adding 100 μL of solubilization buffer (10% SDS in 0.01 M HCl). Finally, the absorbance at 570 nm was measured using a Microplate Reader Multiscan FC (Thermo Scientific, USA) with a reference wavelength of 690 nm. Three separate experiments were performed, with three repeats for each concentration. The results were analyzed for the drug combination approach based on cell viability assay using the combination index (CI) method developed by Chou and Talalay using Compusyn v1.0 software (ComboSyn, Inc., NJ, US). Combination indices, CI < 1, CI = 1, and CI > 1 indicate synergism, additive effects, and antagonism, respectively. The dose–response data were presented as a heatmap with single-drug and combination treatment responses. The inhibitory properties were visualized as dose–response matrices using SynergyFinder 3.0 (https://synergyfinder.fimm.fi), a free web application for multidrug combination response analysis.

Statistical analysis

The obtained data were expressed as the mean ± SD of at least three experiments. Differences were assessed for statistical significance using repeated-measures ANOVA. All statistical analyses were conducted using GraphPad Prism (version 10.2.1, GraphPad Software, USA; https://www.graphpad.com/). The threshold for significance was defined as p ≤ 0.05. The symbols *, **, and *** were used for p < 0.05, p < 0.01, and p < 0.001 respectively.

Results

The evaluation of the constitutive level of the hTERT in a broad range of human cancer cell lines

The first step of assessing the mode of action and efficacy of telomerase inhibitors in combination with chemotherapy drugs was establishing the constitutive level of the hTERT in the studied human cancer cell lines. As an experimental model, we used four breast cancer cell lines (MCF-7, T-47D, SK-BR-3, MDA-MB-231), two ovarian cancer cell lines (OVCAR-3, SK-OV-3), two glioblastoma cell lines (U-118 MG, U-138 MG), a colorectal adenocarcinoma cell line (Caco-2), human cervical carcinoma cell line (HeLa), and non-tumorigenic breast epithelial cell line (MCF-12A), selected as the control to confirm the specificity of action. Based on Western blot analysis, we have chosen six cell lines (MCF-7, MDA-MB-231, OVCAR-3, U-118 MG, HeLa, and MCF-12A) for further research (Fig. 2). The decision was made based on high hTERT levels and similar drugs used in the therapy of selected types of cancer. The human cervical carcinoma cell line, HeLa, exerted the highest level of protein subunit hTERT, and the ovarian cancer cell line SK-OV-3, the lowest, relative to the GAPDH housekeeping reference. Original Western blot images with full-length blots associated with this article can be found in the Supplementary Material.

Fig. 2.

Western blot analysis of the constitutive level of the hTERT in different human cancer cell lines. GAPDH was used as the loading standard (only one representative blot for each is shown) (A); densitometry analysis was performed on three scanned membranes from three independent experiments (B).

The assessment of the cytotoxic effect of studied drugs in six cell lines (MCF-7, MDA-MB-231, OVCAR-3, U-118 MG, HeLa, and MCF-12A) with MTT assay

The cytotoxic effect of two telomerase inhibitors, TMPyP4, BIBR1532, and three chemotherapeutic drugs, doxorubicin hydrochloride, cisplatin, and paclitaxel, was assessed after 24 h of treatment using an MTT assay.

DOX was used in the 0.05–5 µM concentration range. The value of IC50 (the half-maximal inhibitory concentration) was reached only by HeLa cell lines, and it was 1 µM (Table 1). The most resilient to doxorubicin was the glioblastoma cell line U-118 MG; the highest concentration resulted in a 15% decrease in viability (Fig. 3a). The viability of none of the cell lines was significantly reduced by the concentration of 0.05 or 0.1 µM. Higher concentrations led to various effects depending on the cell line. The viability of OVCAR-3 cells was not affected by 2 µM concentration and lower, while DOX at 5 µM provoked almost a 40% decrease in viability. A similar effect was observed in the MDA-MB-231 cells. The second breast cancer line used, MCF-7, responded to 2 µM and 5 µM DOX treatment with around a 40% decrease in cell metabolism. The viability of the non-tumorigenic breast epithelial cell line, MCF-12A, started to decrease with the treatment of 1 µM, reaching 70% and less than 60% with 5 µM concentration, relative to control (untreated cells).

Table 1.

In vitro cytotoxic activity (IC50) of doxorubicin or cisplatin after 24 h treatment.

Cell line	Compound	IC50
HeLa	Doxorubicin	1 µM
OVCAR-3	Cisplatin	33.7 µM
HeLa	Cisplatin	31.9 µM

Fig. 3.

Viability assessment of cancer cells subjected to selected telomerase inhibitors or chemotherapeutic drugs. Cells (MCF7, MDA-MB-231, MCF-12A, HeLa, OVCAR-3, and U-118 MG) were treated with DOX (a), CIS (b), PACL (c), TMPyP4 (d), or BIBR1532 (e) for 24 h. The cell viability is expressed as a percentage of the non-treated control cells. The mean of three experiments ± SD is shown.

CIS was used in the 1–50 µM concentration range. The IC 50 value was estimated for OVCAR-3 (33.7 µM) and HeLa (31.9 µM) cell lines (Table 1). The most resistant to cisplatin was the breast adenocarcinoma MDA-MB-231 cell line; the highest 50 µM CIS concentration caused less than 20% diminishment in cell viability (Fig. 3b). Similar responses were noticed with MCF-7 and U118-MG cell lines. The viability inhibition of MCF-12A starts to increase with the 5 µM CIS treatment, reaching 15% and then over 40% with a 50 µM concentration.

PACL was used in the 0.1–20 µM concentration range. The highest concentration used significantly reduced the viability of MCF-7 cells, causing a decrease of over 40% (Fig. 3c). A similar response to this concentration was observed in OVCAR-3 and MCF-12A cells. The viability of U-118 MG decreased by about 25% after treatment with 20 µM PACL concentration. The value of IC50 (the half-maximal inhibitory concentration) was not reached in any tested cell line. The MDA-MB-231 and HeLa cell lines were the least susceptible to the cytotoxic activity of paclitaxel; the 20 µM concentration resulted in less than a 10% drop.

The cationic porphyrin TMPyP4, a G-quadruplex structure stabilizer, was used in the 1–50 µM concentration range. No significant effect on the viability of the tested cell lines was noted (Fig. 3d).

BIBR1532, a non-competitive telomerase inhibitor, was used in the 0.1–50 µM concentration range. Both breast cancer cell lines responded with a 10% decrease in cell viability throughout the concentration selection (Fig. 3e). The 50 µM BIBR1532 increased metabolic inhibition to 25% in the OVCAR-3 cell line. We noticed a pro-proliferative effect of the highest concentration in HeLa and U-118 MG cell lines, ~ 135%. At the same time, the opposite effect and an almost 50% decrease in proliferation were observed in the non-tumorigenic epithelial cell line MCF-12A. Cytotoxicity assessment enabled a partial evaluation of IC values in distinguished cell lines after treatment with respective compounds (Table 1).

Investigating the efficacy of combination treatment and the sensitizing effects of telomerase inhibitors TMPyP4 and BIBR1532 on DOX, CIS, or PACL

To investigate the in vitro pharmacodynamic interactions of combining telomere inhibitors TMPyP4 and BIBR1532 with DOX, CIS, and PACL, we used the Chou-Talalay method. This method for drug combination is based on the median-effect equation, which provides the theoretical basis for the combination index (CI) equation^57,58. It measures the specific effect of the combination concentrations of drugs A and B, normalized to their corresponding concentrations with the same effect as a single agent. Based on this method, the computer software CompuSyn determined the type of relationship. This algorithm allows quantitative assessment of drug interaction, which can be defined as synergism (CI < 1), additive (CI = 1), and antagonistic effect (CI > 1). An additional indicator calculated by the CompuSyn program is the dose reduction index (DRI). The DRI estimates the extent to which the dose of one or more agents in the combination can be reduced to achieve effect levels comparable with those achieved with single agents. DRI determines how many folds of dose reduction are possible for each drug in synergistic combinations.

The first part of the experiment was the 48 h incubation with a 20 µM concentration of the chosen telomerase inhibitor (TMPyP4 or BIBR1532). This step aimed to sensitize the cells to the chemotherapy drugs used in the following stage. The second part was a 24 h treatment with two concentrations of tested compounds: DOX (1 or 5 µM), CIS (10 or 20 µM), and PACL (5 or 10 µM). The concentration for TMPyP4 has been chosen based on our previous experiments showing that 20 µM TMPyP4 significantly diminished telomerase activity and expression in MCF-7 and MDA-MB-321 cell lines¹⁶. Interestingly, BIBR1532 in 20 µM concentration effectively inhibited telomerase activity, resulting in disruption of chromosomal stability and inhibition of the ATM/CHK1 (ataxia-telangiectasia-mutated/Checkpoint kinase 1) pathway, which impaired the DNA damage repair pathway²⁷.

Combination of TMPyP4 (20 µM) with DOX revealed the highest synergistic effect in MDA-MB-231 cells, with CI 0.44 (for combination with 1 µM DOX; p < 0.01) and 0.39 (for combination with 5 µM DOX; p < 0.01) (Fig. 4A1, Table 2). Interestingly, these two drugs caused the strongest response in this cell line compared to other combinations. Moreover, we observed extremely high DRI for DOX (Table 2). Another synergistic effect was noticed in the MDA-MB-231 cell line after treatment with TMPyP4 (20 µM) and 20 µM CIS (CI 0.72) (Fig. 4A2, Table 2). Other combinations produced antagonistic effects (Fig. 4A3,4,5,6); the strongest one was with BIBR1532 (20 µM) and DOX (1 µM, 5 µM DOX; p < 0.01) (Fig. 4A4). Interestingly, almost all studied combinations revealed synergistic effects in the MCF-7 cell line (Fig. 4B). Treatment with TMPyP4 and 1 µM DOX revealed CI = 0.45 (p < 0.01) (Fig. 4B1) and with 20 µM CIS, 0.56 (p < 0.05) (Fig. 4B2, Table 2). Interestingly, the effect of TMPyP4 combination with paclitaxel has changed from synergistic with 5 µM PACL (CI 0.81) to antagonistic with the concentration of 10 µM (CI 1.1) (Fig. 4B3, Table 2). In the case of BIBR1532 (20 µM) and DOX conjunction, we observed synergistic effects of the same intensity CI = 0.5 with both concentrations (1 and 5 µM) (Fig. 4B4, Table 2). The lowest CI (0.43) was noted for BIBR1532 and 20 µM CIS co-treatment (p < 0.01) (Fig. 4B5), and combination with 5 µM PACL (CI 0.45) produced a similar outcome (Fig. 4B6, Table 2). It is worth noting that MCF7 cells showed a significantly higher telomerase catalytic subunit basal level than MDA-MB-231 cells (Fig. 2). In the MCF-12A cell line, we observed antagonistic effects after treatment with TMPyP4 and DOX (Fig. 4C1) and with TMPyP4 and CIS (Fig. 4C2). Only co-treatment with PACL showed a synergistic result (Fig. 4C3). Additionally, all BIBR1532 combinations have shown a synergistic effect (Fig. 4C4,5,6). Interestingly, the TMPyP4 combination with 5 µM DOX exerted an additive response in HeLa cells (Fig. 4D1). This cell line revealed the highest level of the hTERT protein subunit (Fig. 2). One of the strongest synergistic interactions was noted for TMPyP4 and both concentrations of CIS (CI 0.30, CI 0.27; p < 0.01) (Fig. 4D2, Table 2). Interestingly, the effect of the TMPyP4 combination with PACL has changed from antagonistic with 1 µM PACL (CI 1.1) to synergistic with the concentration of 5 µM (CI 0.74) (Fig. 4D3, Table 2). The HeLa cells responded best to BIBR1532 and 5 µM DOX combination (CI 0.12; p < 0.001) (Fig. 4D4, Table 2). The co-treatment with CIS also showed a synergistic response (Fig. 4D5) as well as the conjunction with 5 µM PACL (Fig. 4D6). Treatment of the U118 MG cell line revealed synergistic effects in all administered combinations with TMPyP4 (Fig. 4E1,2,3). Interestingly, we noticed the most effective response to the TMPyP4 and PACL combination in this cell line compared to other cancer cell lines (Fig. 4E3). The highest effect in U118 MG cells has been obtained after BIBR1532 and 5 µM DOX co-treatment, creating the lowest IC compared to all combinations, leading to a high DRI (BIBR1532 935.7; DOX 71.8) (Table 2, Fig. 4E4). We also noted a strong response to BIBR 1532 and CIS treatments (p < 0.001) (Fig. 4E5). However, it did not show the highest anti-tumor effect compared to other combinations. The conjunction with PACL revealed an antagonistic reaction (CI 1.33) (Fig. 4E6, Table 2). The synergistic effect in the OVCAR-3 cell line was caused by combinations of TMPyP4 with DOX (Fig. 4F1) and TMPyP4 with CIS (Fig. 4F2). Other approaches showed an antagonistic effect (Fig. 4F3,4,5,6). The lowest CI = 0.49 was revealed after treatment with TMPyP4 and 20 µM CIS (p < 0.01) (Fig. 4F2).

Fig. 4.

Dose–response matrices of proliferation inhibition after 48 h pre-treatment with TMPyP4 (20 µM) or BIBR1532 (20 µM), followed by 24 h combination treatment with TI and CIS (10 µM and 20 µM), DOX (1 µM and 5 µM), and PACL (5 µM and 10 µM), and single drug treatments in A) MDA-MB-231, B) MCF7, C) MCF-12A, D) HeLa, E) U-118 MG, F) OVCAR-3. Values on the heatmap represent the average of growth inhibition from 3 separate experiments, n = 3; * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 2.

Combination index (CI) and dose reduction index (DRI) after 48 h pre-treatment with TMPyP4 (20 µM) or BIBR1532 (20 µM) and 24 h combination treatment with TI and CIS (10 µM and 20 µM), DOX (1 µM and 5 µM), and PACL (5 µM and 10 µM) in HeLa, MCF-7, MCF-12A, MDA-MB-231, OVCAR-3, and U-118 MG cells. CI < 1 – synergism, CI = 1 – addition, and CI > 1—antagonistic effect. NaN-not a number.

	MDA-MB-231		MCF-7		MCF-12A		HeLa		U-118 MG		OVCAR-3
Dose TMPyP4 [µM]	20
Dose DOX [µM]	1	5	1	5	1	5	1	5	1	5	1	5
CI	0.44	0.39	0.45	0.64	1.16	1.36	0.98	1.00	0.51	0.67	0.54	0.88
DRI TMPyP4	2.26	2.57	2.88	3.94	1.69	2.00	1.65	4.72	2.72	3.46	2.29	2.55
DRI DOX	5.80E + 08	1.78E + 10	9.82	2.56	1.76	1.16	2.70	1.26	7.05	2.64	9.49	2.04
Dose CIS [µM]	10	20	10	20	10	20	10	20	10	20	10	20
CI	1.05	0.72	0.83	0.56	1.21	1.11	0.30	0.27	0.76	0.74	0.52	0.49
DRI TMPyP4	1.04	1.44	1.29	2.15	1.36	1.61	3.30	3.76	2.72	3.46	2.35	2.96
DRI CIS	11.58	32.52	16.45	10.85	2.13	2.05	3.90E + 37	6.40E + 40	2.56	2.23	10.28	6.73
Dose PACL [µM]	5	10	5	10	5	10	5	10	5	10	5	10
CI	NaN	NaN	0.81	1.10	0.62	0.65	1.10	0.74	0.57	0.48	1.66	1.76
DRI TMPyP4	0.70	0.80	2.76	3.70	1.69	1.69	1.14	2.69	3.16	4.39	0.82	1.10
DRI PACL	NaN	NaN	2.25	1.20	33.81	17.14	4.40	2.74	3.98	3.95	2.30	1.18
Dose BIBR1532 [µM]	20
Dose DOX [µM]	1	5	1	5	1	5	1	5	1	5	1	5
CI	1,641,633	2,100,426	0.50	0.50	0.15	0.03	0.67	0.12	0.01	0.01	1.63	1.06
DRI BIBR1532	2.37407	2.47841	2.69	6.03	7.55	68.05	2.55	96.09	438.27	935.74	0.71	2.07
DRI DOX	6.09E-07	4.76E-07	8.10	3.04	79.27	59.34	3.61	9.00	169.31	71.84	4.62	1.72
Dose CIS [µM]	10	20	10	20	10	20	10	20	10	20	10	20
CI	6.09	7.94	0.53	0.44	0.16	0.05	0.76	0.64	0.07	0.06	2.15	1.34
DRI BIBR1532	1.06	1.15	2.09	2.85	7.41	44.70	2.97	5.07	149.67	332.74	0.58	1.11
DRI CIS	0.19	0.14	19.82	11.60	38.28	34.35	2.34	2.25	16.59	16.71	2.34	2.25
Dose PACL [µM]	5	10	5	10	5	10	5	10	5	10	5	10
CI	NaN	NaN	0.45	0.78	0.37	0.30	0.95	1.03	1.33	1.33	1.09	1.31
DRI BIBR1532	1.39	1.93	10.04	11.28	2.73	3.31	1.37	1.62	4.14	18.32	1.48	1.98
DRI PACL	NaN	NaN	2.84	1.45	675.73	377.01	4.63	2.40	0.92	1.49	2.44	1.25

Discussion

Currently, two different approaches have emerged in cancer treatment algorithms: combination therapies and tumor-agnostic therapies, due to the enormous expansion of immuno-oncology and precision oncology. Molecular specific, aka tumor-agnostic therapies, target specific genomic anomalies or molecular features regardless of histological origin. In this case, cancer therapies are approved for biomarkers rather than cancer types.

Since most cancers result from the accumulation of multiple mutations, combination therapies are a more common approach than those based on single drug. They may consist of chemotherapy and immunotherapy, chemotherapy combined with targeted therapy, or immunotherapy plus targeted therapy, as well as a combination of two chemotherapeutic agents. The main goal of these configurations is to increase the efficacy of compounds, limit negative side effects (due to lower drug concentration applied), and prevent or overcome the development of resistance. However, the benefits gained from a drug combination approach, if not based on the presence of a specific genetic alteration, may not reveal synergistic effects. This can mean a"loss of precision"and, consequently, over-treatment of certain subgroups of patients. Oncology has changed dramatically with the advent of precision medicine, guided by the discovery of “treatable” gene mutations or immune targets assessed by next-generation sequencing (NGS)⁵⁹. The utility of precision medicine based on molecular and phenotypic profiling has been widely described in the literature and is slowly mastering the clinical approach^60–62. High hTERT expression or restored telomerase activity is a good example of an oncologic biomarker with therapeutic potential. Our goal was to investigate the in vitro pharmacodynamic interactions of telomerase inhibitors (TMPyP4 and BIBR1532) with three common anticancer drugs (CIS, DOX, and PACL) on a broad range of human cancer cell lines. The multidrug approach is a popular therapeutic method to prevent or fight drug resistance, which is one of the major causes of treatment failure and poor patient survival. Various mechanisms of this phenomenon have been discovered, including drug target alteration, reduced uptake or drug accumulation, increased efflux, intensification of DNA repair, and activation of cell survival pathways. Additionally, numerous studies show a relationship between telomerase activity and the response of cancer cells to therapy. Moreover, inhibition of telomerase activity and/or hTERT expression causes increased sensitivity of cancer cells to chemotherapy and radiation^12,16,63. The most likely cause of this phenomenon is telomerase’s contribution to genome stabilization. However, telomerase inhibition does not always result in the direct induction of cancer cell death. It affects the number of mechanisms associated with telomerase and telomeres, including proliferation control, DNA repair support, or even mitochondria protection^64–66.

A growing number of epidemiological and tumor genomic studies have identified an important role for telomere maintenance in cancer susceptibility, initiation, and prognosis. Telomere length has long been studied in relation to malignancies. Significantly, the relationship between telomere length and increased cancer risk varies in different types of tumors. Restoration of telomerase activity in cancer cells is essential for telomere maintenance and underlies the ability to divide continuously. Interestingly, telomere length in cancer cells is often shorter than in normal cells, most likely because cells divide rapidly at an early stage of tumor development, before telomerase expression and activity are restored^67,68. As a result of telomerase blockage, telomeres reach a critical length, triggering the DNA damage response (DDR). This pathway activates cellular checkpoints that lead to cell cycle arrest, one of the programmed types of cell death (apoptosis, autophagy, or other) or senescence (which involves growth arrest). This effect makes cancer cells more susceptible to chemotherapeutic drugs, especially cell cycle-dependent ones. Moreover, emerging evidence highlights additional functions of telomerase outside of the nucleus. Specifically, hTERT has been detected in the cytosol and mitochondria. Several studies have suggested that elevated levels of this protein in mitochondria occur in response to oxidative stress, and have proposed a protective role of hTERT in this context^69–73. Disruption of this mechanism by interfering with telomerase/hTERT may be involved in sensitizing cancer cells to doxorubicin, which causes the increased absorption of oxygen and produces multiple types of ROS, leading to oxidative stress and cancer cell death⁷³.

The most significant effect and the lowest IC compared to all combinations were noted for BIBR 1532 and DOX in U118 MG cells. BIBR1532 binds to hTERT at the non-catalytic site and inhibits telomerase activity in a non-competitive manner⁷⁴. Its cytotoxicity is primarily caused by damaging the telomere structure, resulting in the loss of TRF2 binding, which induces telomere dysfunction, acts as a telomere end-to-end fusion, and increases p53 activation^75,76. Additionally, BIBR1532 disrupts chromosomal stability and the DNA damage response by inhibiting the ATM/CHK1 pathway²⁷. Interestingly, Bashash et al. reported that BIBR1532 induced caspase-dependent apoptosis through increased ROS levels and strongly enhanced the pro-oxidative property of DOX⁷⁷. Another highly synergistic outcome was obtained in co-treatment with cisplatin. In this case, most likely the combination with a DNA-damaging drug generates damage beyond repair, as cisplatin forms a covalent bond with the purine bases guanine and adenine, which leads to intra-strand and inter-strand crosslinks, causing subsequent strand breaks⁷⁸. Additionally, all combinations with TMPyP4 revealed an enhanced effect and CI < 1. The development of targeted strategies for glioblastoma has been extremely challenging, mostly since cancerogenic mutations are relatively rare and, when they happen, exhibit significant intra-tumoral heterogeneity. Interestingly, the most common oncogenic mutation in glioblastoma is hTERT promoter mutations (commonly c.−146C > T and c.−124C > T) occurring in up to 80% of cases. These mutations create GABP transcription factor binding sites, upregulating telomerase and enabling cellular immortality. Interestingly, unlike other genetic changes, the hTERT promoter mutations were observed to be clonal events and remained consistent between the samples taken before and after standard treatment⁷⁹. Moreover, a number of studies have demonstrated that GBMs’ telomeres are shorter than normal brain tissue, and together with higher telomerase activity, they seem to be associated with malignancy and poor outcome⁸⁰. Amen et al. showed that cancer-cell–specific inhibition of hTERT through GABPB1L-reduction increased sensitivity to temozolomide in glioblastoma tumors⁸¹. Furthermore, almost all studied combinations revealed synergistic effects in the MCF-7 cell line, with the lowest CI (0.43) for BIBR1532 and cisplatin (20 µM) co-treatment. Shi et al. studied the combination of BIBR 1532 and PACL in a broad range of breast cancer cell lines. MCF-7 cells revealed a synergistic response to this combination treatment (after 72 h incubation) and suggested p21-mediated apoptosis induction and DNA damage response. According to this new finding, telomere erosion induced by BIBR1532 strengthens the cytotoxicity of PACL⁸². It has also been shown that BIBR1532 suppresses telomerase activity in breast cancer stem cells and modulates the mTOR signaling pathway⁸³. The examination of telomere length and telomerase activity in breast cancer cell lines with various levels of invasiveness revealed an enhanced telomerase activity and short telomeres in the most aggressive cell lines⁸⁴. Furthermore, a clinical study examining a total of 44 breast cancer tissues, including 15 papillotubular, 17 scirrhous, and 12 solid-tubular carcinomas, determined that telomeres measured using quantitative FISH were significantly shorter than those of normal epithelial cells⁸⁵. These observations may be a confirmation of the utility of telomerase inhibitors in therapeutic combinations for breast cancer tumors.

On the other hand, we discovered that the highest synergistic effect of TMPyP4 was noted in HeLa cells in conjunction with CIS. This cervical cancer cell line shows the highest level of protein subunit hTERT among the ones studied in our project (Fig. 2). The second highest level was noted in the MCF-7 cell line. Interestingly, only these two lines responded to the combination used, with mostly synergistic effects. TMPyP4 is a small-molecule ligand that binds and stabilizes the G-quadruplex (G4) structure at telomere ends, blocking access to telomerase and preventing telomere elongation. G-quadruplex is a secondary DNA structure formed by guanine-rich nucleic acids. The stabilization of this structure can inhibit gene transcription^86,87. Our previous study on short-term TMPyP4 treatment showed no sensitization effect on MCF-7 and MDA-MB-231 to DOX (0.1 µM) but altered cell adhesion and migration abilities¹⁶. Interestingly, studies have shown that TMPyP4 decreased c-MYC expression at the RNA and protein levels, which has been shown to trigger rapid tumor regression in mice^88,89. Additionally, c-Myc is a transcription factor of hTERT, so TMPyP4 can indirectly decrease the expression of hTERT by downregulation of c-Myc. Moreover, TMPyP4 leads to telomere DNA damage response and activates cellular senescence and apoptosis⁹⁰.

The results indicate that the different landscapes of tumor types may directly influence the response type in various combination treatments. This demonstrates the need for in vitro optimization to maximize drug cytotoxicity and synergistic compound interactions. Due to genome-based and individualized model-based drug screening, many common cancers will receive precision treatment options. A promising method to start personalized treatment is the use of liquid biopsy in the diagnostic process. Strategies include detecting and monitoring of circulating tumor cells, cell-free DNA, and extracellular vesicles. Liquid biopsy allows minimally invasive molecular characterization of tumors, patient stratification to therapy, and treatment effectiveness monitoring⁹¹. Additionally, three-dimensional organoid culture and patient-derived xenografts have been studied as tools to select the most efficacious treatment^92,93. Combining genome-based medicine with drug screening based on personalized models may fulfill the promise of precision medicine for every cancer type.

Author contributions

Conceptualization, B.R. and A.R.-D.; methodology, A.R.-D., E.T., N.L.; software, N.L. A.R.-D.; formal analysis, E.T., N.L., M.I.; investigation, E.T., N.L. A.R.-D.; writing—original draft preparation, A.R.-D.; writing—review and editing, B.R., N.L., E.T.; visualization, E.T.; supervision, B.R. All authors have read and agreed to the published version of the manuscript.

Funding

The National Science Centre 2016/21/B/NZ7/01079 andUMO-2023/49/B/NZ7/00744.

Data availability

The data presented in this study are available on request from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-13496-0.