Nongenotoxic ABCB1 activator tetraphenylphosphonium can contribute to doxorubicin resistance in MX-1 breast cancer cell line

A1BG-AS1 promotes adriamycin resistance of breast cancer by recruiting IGF2BP2 to upregulate ABCB1 in an m6A-dependent manner

Adriamycin (ADR) resistance is an obstacle for chemotherapy of breast cancer (BC). ATP binding cassette subfamily B member 1 (ABCB1) expression is indicated to be closely related to the drug resistance of cancer cells. The current work intended to explore the molecular mechanisms to regulate ABCB1 in BC cells with ADR resistance. We found that long noncoding RNA (lncRNA) A1BG antisense RNA 1 (A1BG-AS1) is upregulated in ADR resistant BC cell lines (MCF-7/ADR, MDA-MB-231/ADR). A1BG-AS1 knockdown enhanced the ADR sensitivity by suppressing the viability, proliferation potential and migration ability, and facilitating cell apoptosis in BC. Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is known to be an m6A reader to modulate the stability of mRNA transcripts in an m6A-dependent manner, which was a shared RNA binding protein (RBP) for A1BG-AS1 and ABCB1. The interaction of IGF2BP2 with A1BG-AS1 or ABCB1 was explored and verified using RNA pulldown and RNA immunoprecipitation (RIP) assays. ABCB1 mRNA and protein expression was positively regulated by A1BG-AS1 and IGF2BP2 in BC cells. ABCB1 mRNA expression was stabilized by A1BG-AS1 via recruiting IGF2BP2 in an m6A-dependent manner. Moreover, rescue assays demonstrated that A1BG-AS1 enhanced BC ADR resistance by positively modulating ABCB1. Xenograft mouse models were used to explore whether A1BG-AS1 affected the ADR resistance in BC in vivo. The findings indicated that A1BG-AS1 silencing inhibited tumor growth and alleviated ADR resistance in vivo. In conclusion, A1BG-AS1 enhances the ADR resistance of BC by recruiting IGF2BP2 to upregulate ABCB1 in an m6A-dependent manner.

Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

Breast Cancer Stem-Like Cells in Drug Resistance: A Review of Mechanisms and Novel Therapeutic Strategies to Overcome Drug Resistance

Breast cancer is the most frequent type of malignancy in women worldwide, and drug resistance to the available systemic therapies remains a major challenge. At the molecular level, breast cancer is heterogeneous, where the cancer-initiating stem-like cells (bCSCs) comprise a small yet distinct population of cells within the tumor microenvironment (TME) that can differentiate into cells of multiple lineages, displaying varying degrees of cellular differentiation, enhanced metastatic potential, invasiveness, and resistance to radio- and chemotherapy. Based on the expression of estrogen and progesterone hormone receptors, expression of human epidermal growth factor receptor 2 (HER2), and/or BRCA mutations, the breast cancer molecular subtypes are identified as TNBC, HER2 enriched, luminal A, and luminal B. Management of breast cancer primarily involves resection of the tumor, followed by radiotherapy, and systemic therapies including endocrine therapies for hormone-responsive breast cancers; HER2-targeted therapy for HER2-enriched breast cancers; chemotherapy and poly (ADP-ribose) polymerase inhibitors for TNBC, and the recent development of immunotherapy. However, the complex crosstalk between the malignant cells and stromal cells in the breast TME, rewiring of the many different signaling networks, and bCSC-mediated processes, all contribute to overall drug resistance in breast cancer. However, strategically targeting bCSCs to reverse chemoresistance and increase drug sensitivity is an underexplored stream in breast cancer research. The recent identification of dysregulated miRNAs/ncRNAs/mRNAs signatures in bCSCs and their crosstalk with many cellular signaling pathways has uncovered promising molecular leads to be used as potential therapeutic targets in drug-resistant situations. Moreover, therapies that can induce alternate forms of regulated cell death including ferroptosis, pyroptosis, and immunotherapy; drugs targeting bCSC metabolism; and nanoparticle therapy are the upcoming approaches to target the bCSCs overcome drug resistance. Thus, individualizing treatment strategies will eliminate the minimal residual disease, resulting in better pathological and complete response in drug-resistant scenarios. This review summarizes basic understanding of breast cancer subtypes, concept of bCSCs, molecular basis of drug resistance, dysregulated miRNAs/ncRNAs patterns in bCSCs, and future perspective of developing anticancer therapeutics to address breast cancer drug resistance.

The long and short non-coding RNAs modulating EZH2 signaling in cancer

Non-coding RNAs (ncRNAs) are a large family of RNA molecules with no capability in encoding proteins. However, they participate in developmental and biological processes and their abnormal expression affects cancer progression. These RNA molecules can function as upstream mediators of different signaling pathways and enhancer of zeste homolog 2 (EZH2) is among them. Briefly, EZH2 belongs to PRCs family and can exert functional roles in cells due to its methyltransferase activity. EZH2 affects gene expression via inducing H3K27me3. In the present review, our aim is to provide a mechanistic discussion of ncRNAs role in regulating EZH2 expression in different cancers. MiRNAs can dually induce/inhibit EZH2 in cancer cells to affect downstream targets such as Wnt, STAT3 and EMT. Furthermore, miRNAs can regulate therapy response of cancer cells via affecting EZH2 signaling. It is noteworthy that EZH2 can reduce miRNA expression by binding to promoter and exerting its methyltransferase activity. Small-interfering RNA (siRNA) and short-hairpin RNA (shRNA) are synthetic, short ncRNAs capable of reducing EZH2 expression and suppressing cancer progression. LncRNAs mainly regulate EZH2 expression via targeting miRNAs. Furthermore, lncRNAs induce EZH2 by modulating miRNA expression. Circular RNAs (CircRNAs), like lncRNAs, affect EZH2 expression via targeting miRNAs. These areas are discussed in the present review with a focus on molecular pathways leading to clinical translation.

Long non-coding RNAs as the critical regulators of doxorubicin resistance in tumor cells

Resistance against conventional chemotherapeutic agents is one of the main reasons for tumor relapse and poor clinical outcomes in cancer patients. Various mechanisms are associated with drug resistance, including drug efflux, cell cycle, DNA repair and apoptosis. Doxorubicin (DOX) is a widely used first-line anti-cancer drug that functions as a DNA topoisomerase II inhibitor. However, DOX resistance has emerged as a large hurdle in efficient tumor therapy. Furthermore, despite its wide clinical application, DOX is a double-edged sword: it can damage normal tissues and affect the quality of patients’ lives during and after treatment. It is essential to clarify the molecular basis of DOX resistance to support the development of novel therapeutic modalities with fewer and/or lower-impact side effects in cancer patients. Long non-coding RNAs (lncRNAs) have critical roles in the drug resistance of various tumors. In this review, we summarize the state of knowledge on all the lncRNAs associated with DOX resistance. The majority are involved in promoting DOX resistance. This review paves the way to introducing an lncRNA panel marker for the prediction of the DOX response and clinical outcomes for cancer patients.

Structure activity relationships and the binding mode of quinolinone-pyrimidine hybrids as reversal agents of multidrug resistance mediated by P-gp

P-gp-associated multidrug resistance is a major impediment to the success of chemotherapy. With the aim of finding non-toxic and effective P-gp inhibitors, we investigated a panel of quinolin-2-one-pyrimidine hybrids. Among the active compounds, two of them significantly increased intracellular doxorubicin and rhodamine 123 accumulation by inhibiting the efflux mediated by P-gp and restored doxorubicin toxicity at nanomolar range. Structure-activity relationships showed that the number of methoxy groups, an optimal length of the molecule in its extended conformation, and at least one flexible methylene group bridging the quinolinone to the moiety bearing the pyrimidine favored the inhibitory potency of P-gp. The best compounds showed a similar binding pattern and interactions to those of doxorubicin and tariquidar, as revealed by MD and hybrid QM/MM simulations performed with the recent experimental structure of P-gp co-crystallized with paclitaxel. Analysis of the molecular interactions stabilizing the different molecular complexes determined by MD and QTAIM showed that binding to key residues from TMH 4-7 and 12 is required for inhibition.