Impact of variants in ATP-binding cassette transporters on breast cancer treatment

Advances in the structure, mechanism and targeting of chemoresistance-linked ABC transporters

Cancer cells frequently display intrinsic or acquired resistance to chemically diverse anticancer drugs, limiting therapeutic success. Among the main mechanisms of this multidrug resistance is the overexpression of ATP-binding cassette (ABC) transporters that mediate drug efflux, and, specifically, ABCB1, ABCG2 and ABCC1 are known to cause cancer chemoresistance. High-resolution structures, biophysical and in silico studies have led to tremendous progress in understanding the mechanism of drug transport by these ABC transporters, and several promising therapies, including irradiation-based immune and thermal therapies, and nanomedicine have been used to overcome ABC transporter-mediated cancer chemoresistance. In this Review, we highlight the progress achieved in the past 5 years on the three transporters, ABCB1, ABCG2 and ABCC1, that are known to be of clinical importance. We address the molecular basis of their broad substrate specificity gleaned from structural information and discuss novel approaches to block the function of ABC transporters. Furthermore, genetic modification of ABC transporters by CRISPR-Cas9 and approaches to re-engineer amino acid sequences to change the direction of transport from efflux to import are briefly discussed. We suggest that current information regarding the structure, mechanism and regulation of ABC transporters should be used in clinical trials to improve the efficiency of chemotherapeutics for patients with cancer.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Role of Drug Transporters in Elucidating Inter-Individual Variability in Pediatric Chemotherapy-Related Toxicities and Response

Pediatric cancer treatment has evolved significantly in recent decades. The implementation of risk stratification strategies and the selection of evidence-based chemotherapy combinations have improved survival outcomes. However, there is large interindividual variability in terms of chemotherapy-related toxicities and, sometimes, the response among this population. This variability is partly attributed to the functional variability of drug-metabolizing enzymes (DME) and drug transporters (DTS) involved in the process of absorption, distribution, metabolism and excretion (ADME). The DTS, being ubiquitous, affects drug disposition across membranes and has relevance in determining chemotherapy response in pediatric cancer patients. Among the factors affecting DTS function, ontogeny or maturation is important in the pediatric population. In this narrative review, we describe the role of drug uptake/efflux transporters in defining pediatric chemotherapy-treatment-related toxicities and responses. Developmental differences in DTS and the consequent implications are also briefly discussed for the most commonly used chemotherapeutic drugs in the pediatric population.

Application progress of immobilized biomembrane in the discovery of active compounds of natural products

Natural products (NPs) are an important source of bioactive compounds. Considering their complex matrix effects, the development of suitable methodologies for the fast identification and analysis of active substances from NPs played a significant role in controlling their quality and discovering new drugs. In recent years, the technology of immobilized biomembrane has attracted increasing attention, due to its peculiarities such as multi-target efficiency, accuracy and/or time-saving compared with traditional activity-guided separation and ligand fishing methods. This article first provides a systematic review of the latest advances in screening technologies based on biomembrane in the field of NPs. It includes detailed discussions of these technologies, including cell membrane chromatography, artificial membrane chromatography, cell membrane fishing, living cell fishing methods, and their applications in screening various active molecules from NPs. Their limitations and future development prospects were further discussed.

CFTR reduces the proliferation of lung adenocarcinoma and is a strong predictor of survival in both smokers and non-smokers

Background

One of the main hurdles of oncological therapy is the development of drug resistance. The ABC transporter gene family contributes majorly to cancer chemoresistance. However, effects of somatic expression of most ABC transporters on cancer outcomes remain largely unclear.

Methods

We systematically analyzed expression signatures of all 48 human ABC transporters in samples from 8562 patients across 14 different cancer types. The association between CFTR (ABCC7) expression and outcomes was analyzed experimentally using knock-downs and pharmacological CFTR stimulation.

Results

Across 720 analyzed clinical associations with patient outcomes, 363 were nominally significant of which 29 remained significant after stringent Bonferroni correction. Among those were various previously known associations, as well as a multitude of novel factors that correlated with poor prognosis or predicted improved outcomes. The association between low CFTR levels and reduced survival in lung adenocarcinoma was confirmed in two independent cohorts of 246 patients with a history of smoking (logrank P = 0.0021, hazard ratio [HR], 0.49) and 143 never-smokers (logrank P = 0.0023, HR 0.31). Further in vitro experiments using naturally CFTR expressing lung adenocarcinoma cells showed that treatment with CFTR potentiators significantly reduced proliferation at therapeutically relevant concentrations.

Conclusions

These results suggest that CFTR acts as a pharmacologically activatable tumor suppressor and constitutes a promising target for adjuvant therapy in lung adenocarcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-04106-x.

Risk for relapse and death after adjuvant chemotherapy associated with SNPs in patients with breast cancer - A retrospective study

For the women breast cancer (BC) patients included in this retrospective study, the first line of systemic treatment in adjuvant modality for breast cancer (BC) after surgery was fluorouracil, epirubicin and cyclophosphamide (FEC). The aim of our investigation was to analyze the prognostic biomarkers for relapse and death of patients eight to ten years after chemotherapy in association with nausea and vomiting.

METHOD

This retrospective study included 114 patients treated between 2010 and 2013. Blood samples for Single Nucleotide Polymorphism (SNP) analysis before the chemotherapy treatment were collected. The medical records were used to determine relapses and death.

RESULTS

Sixteen percent relapsed and 9 % died during the follow-up period. SNPs located in the genes ESR and CASP9 were associated with both relapse and death.

CONCLUSIONS

Relapse and death were at a relative moderate level and consistent with other studies. Two SNPs in the Estrogen hormone receptor gene ESR1 and the apoptosis execution gene Caspases 9 (Casp9) were found to be associated with a higher risk of relapse and death. These findings suggest the possible value of blood biomarkers in the selection of individual treatments in the clinical setting.

Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.