Protocol for monitoring phagocytosis of cancer cells by TAM-like macrophages using imaging cytometry.

Here, we present a protocol for monitoring phagocytosis by M2-type macrophages using automated counting of phagocytic events with an imaging cytometer. We describe steps for isolating and differentiating peripheral blood mononuclear cell (PBMC)-derived monocytes into M2-like macrophages, preparing cancer cells expressing a green fluorescence marker, labeling with a pH-sensitive dye, and co-culturing with macrophages. We then outline procedures for enumerating phagocytic events using an imaging cytometer. For complete details on the use and execution of this protocol, please refer to Mishra et al.1.

Identification of Rocaglate Acyl Sulfamides as Selective Inhibitors of Glioblastoma Stem Cells.

Glioblastoma (GBM) is the most aggressive and frequently occurring type of malignant brain tumor in adults. The initiation, progression, and recurrence of malignant tumors are known to be driven by a small subpopulation of cells known as tumor-initiating cells or cancer stem cells (CSCs). GBM CSCs play a pivotal role in orchestrating drug resistance and tumor relapse. As a prospective avenue for GBM intervention, the targeted suppression of GBM CSCs holds considerable promise. In this study, we found that rocaglates, compounds which are known to inhibit translation via targeting of the DEAD-box helicase eIF4A, exert a robust, dose-dependent cytotoxic impact on GBM CSCs with minimal killing of nonstem GBM cells. Subsequent optimization identified novel rocaglate derivatives (rocaglate acyl sulfamides or Roc ASFs) that selectively inhibit GBM CSCs with nanomolar EC50 values. Furthermore, comparative evaluation of a lead CSC-optimized Roc ASF across diverse mechanistic and target profiling assays revealed suppressed translation inhibition relative to that of other CSC-selective rocaglates, with enhanced targeting of the DEAD-box helicase DDX3X, a recently identified secondary target of rocaglates. Overall, these findings suggest a promising therapeutic strategy for targeting GBM CSCs.

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.

Identification of WNK1 as a therapeutic target to suppress IgH/MYC expression in multiple myeloma.

Multiple myeloma (MM) remains an incurable hematological malignancy demanding innovative therapeutic strategies. Targeting MYC, the notorious yet traditionally undruggable oncogene, presents an appealing avenue. Here, using a genome-scale CRISPR-Cas9 screen, we identify the WNK lysine-deficient protein kinase 1 (WNK1) as a regulator of MYC expression in MM cells. Genetic and pharmacological inhibition of WNK1 reduces MYC expression and, further, disrupts the MYC-dependent transcriptional program. Mechanistically, WNK1 inhibition attenuates the activity of the immunoglobulin heavy chain (IgH) enhancer, thus reducing MYC transcription when this locus is translocated near the MYC locus. WNK1 inhibition profoundly impacts MM cell behaviors, leading to growth inhibition, cell-cycle arrest, senescence, and apoptosis. Importantly, the WNK inhibitor WNK463 inhibits MM growth in primary patient samples as well as xenograft mouse models and exhibits synergistic effects with various anti-MM compounds. Collectively, our study uncovers WNK1 as a potential therapeutic target in MM.

Comparative metabolomics of MCF-7 and MCF-7/TAMR identifies potential metabolic pathways in tamoxifen resistant breast cancer cells.

OBJECTIVES: Breast cancer is the most common cancer and the leading cause of cancer-related death among women. An Estrogen Receptor (ER) antagonist called tamoxifen is used as an adjuvant therapy for ER-positive breast cancers. Approximately 40% of patients develop tamoxifen resistance (TAMR) while receiving treatment. Cancer cells can rewire their metabolism to develop resistant phenotypes, and their metabolic state determines how receptive they are to chemotherapy. METHODS: Metabolite extraction from human MCF-7 and MCF-7/TAMR cells was done using the methanol-methanol-water extraction method. After treating the dried samples with methoxamine hydrochloride in pyridine, the samples were derivatized with 2,2,2-Trifluoro-N-methyl-N-(trimethylsilyl)-acetamide, and Chlorotrimethylsilane (MSTFA + 1% TMCS). The Gas chromatography/mass spectrometry (GC-MS) raw data were processed using MSdial and Metaboanalyst for analysis. RESULTS: Univariate analysis revealed that 35 metabolites were elevated in TAMR cells whereas 25 metabolites were downregulated. N-acetyl-D-glucosamine, lysine, uracil, tyrosine, alanine, and o-phosphoserine were upregulated in TAMR cells, while hydroxyproline, glutamine, N-acetyl-L-aspartic acid, threonic acid, pyroglutamic acid, glutamine, o-phosphoethanolamine, oxoglutaric acid, and myoinositol were found to be downregulated. Multivariate analysis revealed a distinct separation between the two cell lines, as evidenced by their metabolite levels. The enriched pathways of deregulated metabolites included valine, leucine, and isoleucine degradation, Citric Acid Cycle, Warburg effect, Malate-Aspartate shuttle, glucose-alanine cycle, propanoate metabolism, and Phospholipid biosynthesis. CONCLUSION: This study revealed dysregulation of various metabolic processes in TAMR cells, which may be crucial in elucidating the molecular basis of the mechanisms underlying acquired tamoxifen resistance.

Targeting the GPI transamidase subunit GPAA1 abrogates the CD24 immune checkpoint in ovarian cancer.

CD24 is frequently overexpressed in ovarian cancer and promotes immune evasion by interacting with its receptor Siglec10, present on tumor-associated macrophages, providing a "don't eat me" signal that prevents targeting and phagocytosis by macrophages. Factors promoting CD24 expression could represent novel immunotherapeutic targets for ovarian cancer. Here, using a genome-wide CRISPR knockout screen, we identify GPAA1 (glycosylphosphatidylinositol anchor attachment 1), a factor that catalyzes the attachment of a glycosylphosphatidylinositol (GPI) lipid anchor to substrate proteins, as a positive regulator of CD24 cell surface expression. Genetic ablation of GPAA1 abolishes CD24 cell surface expression, enhances macrophage-mediated phagocytosis, and inhibits ovarian tumor growth in mice. GPAA1 shares structural similarities with aminopeptidases. Consequently, we show that bestatin, a clinically advanced aminopeptidase inhibitor, binds to GPAA1 and blocks GPI attachment, resulting in reduced CD24 cell surface expression, increased macrophage-mediated phagocytosis, and suppressed growth of ovarian tumors. Our study highlights the potential of targeting GPAA1 as an immunotherapeutic approach for CD24+ ovarian cancers.

Induction of double-strand breaks with the non-steroidal androgen receptor ligand flutamide in patients on androgen suppression: a study protocol for a randomized, double-blind prospective trial.

BACKGROUND: Prostate cancer remains the most prevalent malignancy and the second-leading cause of cancer-related death in men in the USA. Radiation therapy, typically with androgen suppression, remains a mainstay in the treatment of intermediate- and high-risk, potentially lethal prostate cancers. However, local recurrence and treatment failure remain common. Basic and translational research has determined the potential for using androgen receptor (AR) ligands (e.g., dihydrotestosterone and flutamide) in the context of androgen-deprived prostate cancer to induce AR- and TOP2B-mediated DNA double-strand breaks (DSBs) and thereby synergistically enhance the effect of radiation therapy (RT). The primary aim of this study is to carry out pharmacodynamic translation of these findings to humans. METHODS: Patients with newly diagnosed, biopsy-confirmed localized prostatic adenocarcinoma will be recruited. Flutamide, an oral non-steroidal androgen receptor ligand, will be administered orally 6-12 h prior to prostate biopsy (performed under anesthesia prior to brachytherapy seed implantation). Key study parameters will include the assessment of DNA double-strand breaks by γH2A.x foci and AR localization to the nucleus. The initial 6 patients will be treated in a single-arm run-in phase to assess futility by establishing whether at least 2 subjects from this group develop γH2A.x foci in prostate cancer cells. If this criterion is met, the study will advance to a two-arm, randomized controlled phase in which 24 participants will be randomized 2:1 to either flutamide intervention or placebo standard-of-care (with all patients receiving definitive brachytherapy). The key pharmacodynamic endpoint will be to assess whether the extent of γH2A.x foci (proportion of cancer cells positive and number of foci per cancer cell) is greater in patients receiving flutamide versus placebo. Secondary outcomes of this study include an optional, exploratory analysis that will (a) describe cancer-specific methylation patterns of cell-free DNA in plasma and urine and (b) assess the utility of serum and urine samples as a DNA-based biomarker for tracking therapeutic response. DISCUSSION: This study will confirm in humans the pharmacodynamic effect of AR ligands to induce transient double-strand breaks when administered in the context of androgen deprivation as a novel therapy for prostate cancer. The findings of this study will permit the development of a larger trial evaluating flutamide pulsed-dose sequencing in association with fractionated external beam RT (+/- brachytherapy). The study is ongoing, and preliminary data collection and recruitment are underway; analysis has yet to be performed. TRIAL REGISTRATION: ClinicalTrials.gov NCT03507608. Prospectively registered on 25 April 2018.

Plasma Levels of Organochlorine Pesticides and Tumor Necrosis Factor-Alpha: A Potential Risk Factor for Developing Acquired Aplastic Anemia in the North Indian Population.

Background Pesticide exposure might have a contributory role in the development of acquired aplastic anemia (AA). However, the precise mechanisms of pesticide-induced AA remain unknown. In this case-control study, we conducted a comparative analysis of plasma levels of organochlorine pesticides (OCP) and tumor necrosis factor-alpha (TNF-alpha) between Indian patients diagnosed with AA and an age- and sex-matched control group. Methods This is an observational case-control study conducted at a tertiary care hospital in North India. In this study, 90 subjects were included, out of which 45 were diagnosed with AA according to the criteria of the International Agranulocytosis and Aplastic Anemia Study. Cases were compared with 45 controls. A trained interviewer gave all study subjects a questionnaire to collect data regarding demographic details, exposure to pesticides, and clinical history. Physical examination and routine laboratory investigations of each subject were performed. Both cases and controls were tested for their plasma levels of organochlorines as per established protocol by gas chromatography-mass spectrometry. TNF-alpha level was measured by enzyme-linked immunosorbent assay in each subject. Results There was a significant increase in plasma levels of delta hexachlorocyclohexane (delta HCH) (p = 0.02) and heptachlor (p = 0.00) in patients with AA as compared to controls. We observed nonsignificant trends towards higher levels of beta HCH (p = 0.643), aldrin (p = 0.399), and p,p'-Dichlorodiphenyltrichloroethane (p,p'-DDT) (p = 0.453) in patients with AA when compared to the controls. There were significantly higher TNF-alpha levels (p = 0.024) in cases as compared to the controls. Conclusion Our study concludes that patients with AA exhibited higher levels of delta-HCH, heptachlor, and TNF-alpha in comparison to the control group. There is a significant positive correlation of TNF alpha with OCPs (alpha HCH, lindane, delta HCH, heptachlor, aldrin, p,p'- DDD, and methoxychlor pesticides). These organochlorines may have accumulated in the fatty tissue of bone marrow because of their lipophilic nature. This suggests that they might have served as a neoantigen to trigger an increase in TNF-alpha production, which may have led to disrupted bone marrow function through cell-mediated immunity, leading to AA.

Lung anomaly detection from respiratory sound database (sound signals).

Chest or upper body auscultation has long been considered a useful part of the physical examination going back to the time of Hippocrates. However, it did not become a prevalent practice until the invention of the stethoscope by Rene Laennec in 1816, which made the practice suitable and hygienic. Pulmonary disease is a kind of sickness that affects the lungs and various parts of the respiratory system. Lung diseases are the third largest cause of death in the world. According to the World Health Organization (WHO), the five major respiratory diseases, namely chronic obstructive pulmonary disease (COPD), tuberculosis, acute lower respiratory tract infection (LRTI), asthma, and lung cancer, cause the death of more than 3 million people each year worldwide. Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963(96.3%), 0.932,(93.2%) and 0.948(94.8%), respectively.

Aging, oxidative stress and degenerative diseases: mechanisms, complications and emerging therapeutic strategies.

Aging accompanied by several age-related complications, is a multifaceted inevitable biological progression involving various genetic, environmental, and lifestyle factors. The major factor in this process is oxidative stress, caused by an abundance of reactive oxygen species (ROS) generated in the mitochondria and endoplasmic reticulum (ER). ROS and RNS pose a threat by disrupting signaling mechanisms and causing oxidative damage to cellular components. This oxidative stress affects both the ER and mitochondria, causing proteopathies (abnormal protein aggregation), initiation of unfolded protein response, mitochondrial dysfunction, abnormal cellular senescence, ultimately leading to inflammaging (chronic inflammation associated with aging) and, in rare cases, metastasis. RONS during oxidative stress dysregulate multiple metabolic pathways like NF-κB, MAPK, Nrf-2/Keap-1/ARE and PI3K/Akt which may lead to inappropriate cell death through apoptosis and necrosis. Inflammaging contributes to the development of inflammatory and degenerative diseases such as neurodegenerative diseases, diabetes, cardiovascular disease, chronic kidney disease, and retinopathy. The body's antioxidant systems, sirtuins, autophagy, apoptosis, and biogenesis play a role in maintaining homeostasis, but they have limitations and cannot achieve an ideal state of balance. Certain interventions, such as calorie restriction, intermittent fasting, dietary habits, and regular exercise, have shown beneficial effects in counteracting the aging process. In addition, interventions like senotherapy (targeting senescent cells) and sirtuin-activating compounds (STACs) enhance autophagy and apoptosis for efficient removal of damaged oxidative products and organelles. Further, STACs enhance biogenesis for the regeneration of required organelles to maintain homeostasis. This review article explores the various aspects of oxidative damage, the associated complications, and potential strategies to mitigate these effects.

Next-Generation Sequencing Technology: Current Trends and Advancements.

The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.

Advancing cellular immunotherapy with macrophages.

Cell-based immunotherapies have become an exciting avenue for cancer treatment, particularly CAR T cells, which have shown great success in treating hematological malignancies. However, the limited success of T cell-based approaches in treating solid tumors has sparked interest in alternative cell types that could be used for solid tumor immunotherapy. Recent research has pointed to macrophages as a potential solution, given their ability to infiltrate solid tumors, exhibit a strong anti-tumor response, and persist long-term in the tumor microenvironment. Although early attempts with ex-vivo activated macrophage-based therapies failed to translate into clinical success, the field has revolutionized with the recent development of chimeric antigen receptor-expressing macrophages (CAR-M). While CAR-M therapy has reached the clinical trial stage, several challenges still need to be overcome before the therapy can become a reality. Here we review the evolution of macrophage-based cell therapy and evaluate recent studies and developments, emphasizing the potential of macrophages as cellular therapeutics. Furthermore, we also discuss the challenges and opportunities associated with using macrophages as a basis for therapeutic interventions.

Appraisal of the Possible Role of PPARγ Upregulation by CLA of Probiotic Pediococcus pentosaceus GS4 in Colon Cancer Mitigation.

The prevalence of colon cancer (CC) is increasing at the endemic scale, which is accompanied by subsequent morbidity and mortality. Although there have been noteworthy achievements in the therapeutic strategies in recent years, the treatment of patients with CC remains a formidable task. The current study focused on to study role of biohydrogenation-derived conjugated linoleic acid (CLA) of probiotic Pediococcus pentosaceus GS4 (CLAGS4) against CC, which induced peroxisome proliferator-activated receptor gamma (PPARγ) expression in human CC HCT-116 cells. Pre-treatment with PPARγ antagonist bisphenol A diglycidyl ether has significantly reduced the inhibitory efficacy of enhanced cell viability of HCT-116 cells, suggesting the PPARγ-dependent cell death. The cancer cells treated with CLA/CLAGS4 demonstrated the reduced level of Prostaglandin E2 PGE2 in association with reduced COX-2 and 5-LOX expressions. Moreover, these consequences were found to be associated with PPARγ-dependent. Furthermore, delineation of mitochondrial dependent apoptosis with the help of molecular docking LigPlot analysis showed that CLA can bind with hexokinase-II (hHK-II) (highly expressed in cancer cells) and that this association underlies voltage dependent anionic channel to open, thereby causing mitochondrial membrane depolarization, a condition that initiates intrinsic apoptotic events. Apoptosis was further confirmed by annexin V staining and elevation of caspase 1p10 expression. Taken all together, it is deduced that, mechanistically, the upregulation of PPARγ by CLAGS4 of P. pentosaceus GS4 can alter cancer cell metabolism in association with triggering apoptosis in CC.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

Emerging Trends in Immunotherapy for Cancer.

Recent advances in cancer immunology have enabled the discovery of promising immunotherapies for various malignancies that have shifted the cancer treatment paradigm. The innovative research and clinical advancements of immunotherapy approaches have prolonged the survival of patients with relapsed or refractory metastatic cancers. Since the U.S. FDA approved the first immune checkpoint inhibitor in 2011, the field of cancer immunotherapy has grown exponentially. Multiple therapeutic approaches or agents to manipulate different aspects of the immune system are currently in development. These include cancer vaccines, adoptive cell therapies (such as CAR-T or NK cell therapy), monoclonal antibodies, cytokine therapies, oncolytic viruses, and inhibitors targeting immune checkpoints that have demonstrated promising clinical efficacy. Multiple immunotherapeutic approaches have been approved for specific cancer treatments, while others are currently in preclinical and clinical trial stages. Given the success of immunotherapy, there has been a tremendous thrust to improve the clinical efficacy of various agents and strategies implemented so far. Here, we present a comprehensive overview of the development and clinical implementation of various immunotherapy approaches currently being used to treat cancer. We also highlight the latest developments, emerging trends, limitations, and future promises of cancer immunotherapy.

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers.

It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient's previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.

Macrophages as a Potential Immunotherapeutic Target in Solid Cancers.

The revolution in cancer immunotherapy over the last few decades has resulted in a paradigm shift in the clinical care of cancer. Most of the cancer immunotherapeutic regimens approved so far have relied on modulating the adaptive immune system. In recent years, strategies and approaches targeting the components of innate immunity have become widely recognized for their efficacy in targeting solid cancers. Macrophages are effector cells of the innate immune system, which can play a crucial role in the generation of anti-tumor immunity through their ability to phagocytose cancer cells and present tumor antigens to the cells of adaptive immunity. However, the macrophages that are recruited to the tumor microenvironment predominantly play pro-tumorigenic roles. Several strategies targeting pro-tumorigenic functions and harnessing the anti-tumorigenic properties of macrophages have shown promising results in preclinical studies, and a few of them have also advanced to clinical trials. In this review, we present a comprehensive overview of the pathobiology of TAMs and their role in the progression of solid malignancies. We discuss various mechanisms through which TAMs promote tumor progression, such as inflammation, genomic instability, tumor growth, cancer stem cell formation, angiogenesis, EMT and metastasis, tissue remodeling, and immunosuppression, etc. In addition, we also discuss potential therapeutic strategies for targeting TAMs and explore how macrophages can be used as a tool for next-generation immunotherapy for the treatment of solid malignancies.

Suppression of DNA Polymerase ß Activity Is Synthetically Lethal in BRCA1-Deficient Cells.

People whose cells express mutated forms of the BRCA1 tumor suppressor are at a higher risk for developing cancer. BRCA1-deficient cells are defective in DNA double-strand break repair. The inhibition of poly(ADP-ribose) polymerase 1 in such cells is a synthetically lethal, cytotoxic effect that has been exploited to produce anticancer drugs such as Olaparib. However, alternative synthetic lethal approaches are necessary. We report that DNA polymerase ß (Pol ß) forms a synthetically lethal interaction with BRCA1. The SiRNA knockdown of Pol ß or the treatment with a Pol ß pro-inhibitor (pro-1) is cytotoxic in BRCA1-deficient ovarian cancer cells. BRCA1-complemented cells are significantly less susceptible to either treatment. pro-1 is also toxic to BRCA1-deficient breast cancer cells, and its toxicity in BRCA1-deficient cells is comparable to that of Olaparib. These experiments establish Pol ß as a synthetically lethal target within BRCA1-deficient cells and a potentially useful one for treating cancer.

Metabolic reprograming confers tamoxifen resistance in breast cancer.

Breast cancer is the most common cancer among females and the leading cause of cancer-related deaths. Approximately 70 % of breast cancers are estrogen receptor (ER) positive. An ER antagonist such as tamoxifen is used as adjuvant therapy in ER-positive patients. The major problem with endocrine therapy is the emergence of acquired resistance in approximately 40 % of patients receiving tamoxifen. Metabolic alteration is one of the hallmarks of cancer cells. Rapidly proliferating cancer cells require increased nutritional support to fuel various functions such as proliferation, cell migration, and metastasis. Recent studies have established that the metabolic state of cancer cells influences their susceptibility to chemotherapeutic drugs and that cancer cells reprogram their metabolism to develop into resistant phenotypes. In this review, we discuss the major findings on metabolic pathway alterations in tamoxifen-resistant (TAMR) breast cancer and the molecular mechanisms known to regulate the expression and function of metabolic enzymes and the respective metabolite levels upon tamoxifen treatment. It is anticipated that this in-depth analysis of specific metabolic pathways in TAMR cancer might be exploited therapeutically.