Understanding Cancer's Defense against Topoisomerase-Active Drugs: A Comprehensive Review

In recent years, the emergence of cancer drug resistance has been one of the crucial tumor hallmarks that are supported by the level of genetic heterogeneity and complexities at cellular levels. Oxidative stress, immune evasion, metabolic reprogramming, overexpression of ABC transporters, and stemness are among the several key contributing molecular and cellular response mechanisms. Topo-active drugs, e.g., doxorubicin and topotecan, are clinically active and are utilized extensively against a wide variety of human tumors and often result in the development of resistance and failure to therapy. Thus, there is an urgent need for an incremental and comprehensive understanding of mechanisms of cancer drug resistance specifically in the context of topo-active drugs. This review delves into the intricate mechanistic aspects of these intracellular and extracellular topo-active drug resistance mechanisms and explores the use of potential combinatorial approaches by utilizing various topo-active drugs and inhibitors of pathways involved in drug resistance. We believe that this review will help guide basic scientists, pre-clinicians, clinicians, and policymakers toward holistic and interdisciplinary strategies that transcend resistance, renewing optimism in the ongoing battle against cancer.

Tetrandrine for Targeting Therapy Resistance in Cancer

During the last five decades, there has been tremendous development in our understanding of cancer biology and the development of new and novel therapeutics to target cancer. However, despite these advances, cancer remains the second leading cause of death across the globe. Most cancer deaths are attributed to the development of resistance to current therapies. There is an urgent and unmet need to address cancer therapy resistance. Tetrandrine, a bis-benzyl iso-quinoline, has shown a promising role as an anti-cancer agent. Recent work from our laboratory and others suggests that tetrandrine and its derivatives could be an excellent adjuvant to the current arsenal of anti-cancer drugs. Herein, we provide an overview of resistance mechanisms to current therapeutics and review the existing literature on the anti-cancer effects of tetrandrine and its potential use for overcoming therapy resistance in cancer.

Characterization of SN38-resistant T47D breast cancer cell sublines overexpressing BCRP, MRP1, MRP2, MRP3, and MRP4

Background

Although several novel resistant breast cancer cell lines have been established, only a few resistant breast cancer cell lines overexpress breast cancer resistance proteins (BCRP). The aim of this study was to establish new resistant breast cancer cell lines overexpressing BCRP using SN38 (7-ethyl-10-hydroxycamptothecin), an active metabolite of irinotecan and was to discover genes and mechanisms associated with multidrug resistance.

Methods

SN38-resistant T47D breast cancer cell sublines were selected from the wild-type T47D cells by gradually increasing SN38 concentration. The sensitivity of the cells to anti-cancer drugs was assessed by 3-(4,5-methylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) assay. Expression profiles of the resistance-related transporters were examined using RT-qPCR, and western blot analysis. Intracellular fluorescent dye accumulation in the resistant cells was determined using flow cytometry.

Results

The SN38-resistant T47D breast cancer cell sublines T47D/SN120 and T47D/SN150 were established after long-term exposure (more than 16 months) of wild-type T47D cells to 120 nM and 150 nM SN38, respectively. T47D/SN120 and T47D/SN150 cells were more resistant to SN38 (14.5 and 59.1 times, respectively), irinotecan (1.5 and 3.7 times, respectively), and topotecan (4.9 and 12 times, respectively), than the wild-type parental cells. Both T47D/SN120 and T47D/SN150 sublines were cross-resistant to various anti-cancer drugs. These resistant sublines overexpressed mRNAs of MRP1, MRP2, MRP3, MRP4, and BCRP. The DNA methylase inhibitor 5-aza-2′-deoxycytidine and the histone deacetylase inhibitor trichostatin A increased the expression levels of BCRP, MRP1, MRP2, MRP3, and MRP4 transcripts in T47D/WT cells. Fluorescent dye accumulation was found to be lower in T47D/SN120 and T47D/SN150 cells, compared to that in T47D/WT cells. However, treatment with known chemosensitizers increased the intracellular fluorescent dye accumulation and sensitivity of anti-tumor agents.

Conclusion

T47D/SN120 and T47D/SN150 cells overexpressed MRP1, MRP2, MRP3, MRP4, and BCRP, which might be due to the suppression of epigenetic gene silencing via DNA hypermethylation and histone deacetylation. Although these resistant cells present a higher resistance to various anti-cancer drugs than their parental wild-type cells, multidrug resistance was overcome by treatment with chemosensitizers. These SN38 resistant T47D breast cancer cell sublines expressing resistance proteins can be useful for the development of new chemosensitizers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09446-y.

Machine Learning Models Combined with Virtual Screening and Molecular Docking to Predict Human Topoisomerase I Inhibitors

In this work, random forest (RF), support vector machine, k-nearest neighbor and C4.5 decision tree, were used to establish classification models for predicting whether an unknown molecule is an inhibitor of human topoisomerase I (Top1) protein. All these models have achieved satisfactory results, with total prediction accuracies from 89.70% to 97.12%. Through comparative analysis, it can be found that the RF model has the best forecasting effect. The parameters were further optimized to generate the best-performing RF model. At the same time, features selection was implemented to choose properties most relevant to the inhibition of Top1 from 189 molecular descriptors through a special RF procedure. Subsequently, a ligand-based virtual screening was performed from the Maybridge database by the optimal RF model and 596 hits were picked out. Then, 67 molecules with relative probability scores over 0.7 were selected based on the screening results. Next, the 67 molecules above were docked to Top1 using AutoDock Vina. Finally, six top-ranked molecules with binding energies less than −10.0 kcal/mol were screened out and a common backbone, which is entirely different from that of existing Top1 inhibitors reported in the literature, was found.