Abnormal Cell Growth Research Articles

DIAGNOSTIC BIOMARKERS IN ORAL CANCER: CURRENT APPROACHES

Oral cancer is due to over expression of growth of abnormal cells which rapidly multiply and divide. oral cancer is the sixth most common tumour in the world and the highest mortality and morbidity rate amongst all the human malignancies. Betal nut, chewing pan and smokeless tobacco, intake of alcohol and chronic viral infections like human papilloma virus are some of the etiological factors of oral cancer. The pathogenies of oral cancers depend upon the interaction between the genetic, epigenetic and carcinogenetic factors and their association with tumour growth and the progression of cancer. Genetic alteration changes give rise to a multiplex of oral mucosal changes like cellular and architectural and dysplastic changes which help in grading of tumour (well-differentiated, moderately differentiated and poorly differentiated) oral cancer. K-67 and cyclin D1 are strong genetic changes taking place in oral malignancies. Diagnostic biomarkers are an essential tool for the early detection and better prognosis of oral cancer. There are invasive and non-invasive procedures for the diagnosis of oral cancer which include invasive procedures like biopsy and the non-invasive or minimally invasive procedures are blood, plasma, serum and saliva. The oral cancer biomarkers can be detected by taking the samples from cancer patients and running the molecular laboratory techniques like real time PCR for liquid biopsy, DNA sequencing, hybridization technique, single nucleotide polymorphism analysis and immunofluorescence for FFPS.
 Methylation includes hypermethylation and hypomethylation in which there is a defect in the promotor region of CPG island. The methylation process takes place in the early phase of the initiation of cancer so it is an important biomarker for the early detection of oral cancer. HOXA9, NID2, P16 AND PTEN are the genes of interest for hypermethylation in early cancer detection.
 Viral infection load like Epstein bar virus (EBV)and human papilloma virus (HPV) DNA load have specific biomarkers which can help in the early detection of oral cancer when combines with hypermethylation. Interferon and microRNA related gene are also upregulated in tumour and a biomarker for early detection of oral cancer.

A Comparative Study of Breast Mass Detection Using YOLOv8 Deep Learning Model in Various Data Scenarios on Multi-View Digital Mammograms

Breast cancer is one of the most common types of cancer among women worldwide. It typically begins with abnormal cell growth in the breast glands or milk ducts and can spread to other tissues. Many breast cancer cases start with the presence of a mass and should be carefully examined. Masses can be monitored using X-ray-based digital mammography images, including right craniocaudal, left craniocaudal, right mediolateral oblique, and left mediolateral oblique views. In this study, automatic mass detection and localization were performed on mammography images taken from the full-field digital mammography VinDr-Mammo dataset using the YOLOv8 deep learning model. Three different scenarios were tested: raw data, data with preprocessing to crop breast regions, and data with only mass regions cropped to a 1.2x ratio. The data were divided into 80% for training and 10% each for validation and testing. The results were evaluated using performance metrics such as precision, recall, F1-score, mAP, and training graphs. At the end of the study, it is demonstrated that the YOLOv8 deep learning model provides successful results in mass detection and localization, indicating its potential use as a computer-based decision support system.

PolyDSS: computer-aided decision support system for multiclass polyp segmentation and classification using deep learning

Colorectal cancer (CRC) is a malignant condition that affects the colon or rectum, and it is distinguished by abnormal cell growth in these areas. Colon polyps, which are abnormalities, can turn into cancer. To stop the spread of cancer, early polyp detection is essential. The timely removal of polyps without submitting a sample for histology is made possible by computer-assisted polyp classification. In addition to Locally Shared Features (LSF) and ensemble learning majority voting, this paper introduces a computer-aided decision support system named PolyDSS to assist endoscopists in segmenting and classifying various polyp classes using deep learning models like ResUNet and ResUNet++ and transfer learning models like EfficientNet. The PICCOLO dataset is used to train and test the PolyDSS model. To address the issue of class imbalance, data augmentation techniques were used on the dataset. To investigate the impact of each technique on the model, extensive experiments were conducted. While the classification module achieved the highest accuracy of 0.9425 by utilizing the strength of ensemble learning using majority voting, the proposed segmenting module achieved the highest Dice Similarity Coefficient (DSC) of 0.9244 using ResUNet++ and LSF. In conjunction with the Paris classification system, the PolyDSS model, with its significant results, can assist clinicians in identifying polyps early and choosing the best approach to treatment.

Correlation between Risk Factors and Protein Expression of ER, PR, and HER-2 in Breast Cancer Patients: A Population-Based Study in Makassar Indonesia

Background: Breast cancer is a condition characterized by abnormal and uncontrollable growth of cells in breast tissue. According to Global Cancer Statistics 2020, it ranks as the leading cause of female cancer mortality. The disease has four subtypes defined by protein expression, namely Luminal A (ER/PR (+), HER2 (-)), Luminal B (ER/PR (+), HER2 (+)), triple-negative/basal-like (ER, PR, HER2 (-)), and HER2-enriched (ER (-), PR (-), HER2 (+)), each with distinct characteristics influenced by various risk factors. Therefore, this study aimed to explore the correlation between breast cancer risk factors and protein expression of ER, PR, and HER2. Methods: The cross-sectional analytical study was conducted in Makassar, Indonesia, using secondary data from 259 breast cancer patients. Information on ER, PR, HER2 expression, and patient risk factors such as age, history of hormonal contraceptive use, and family history of breast cancer were extracted from medical records and subjected to statistical analysis using the Chi-square test, with significance set at p < 0.05. Results: The results showed that there was a significant correlation between age ≥ 40 years and the expression of ER, PR, and HER2 (p < 0.005). However, no significant correlations were observed between family history and hormonal contraceptive use with ER, PR, and HER2 expression. Conclusions: This study established a meaningful correlation between risk factors of age and breast cancer subtypes based on ER, PR, and HER2 expression.

Therapeutic potential of mangiferin in cancer: Unveiling regulatory pathways, mechanisms of action, and bioavailability enhancements - An updated review.

Mangiferin (MGF) is a phenolic compound, which is a major source of MGF is the mango tree. MGF possesses some antioxidant, anti-inflammatory, and cytoprotective properties, enabling it to play its role against various diseases such as diabetes, obesity, lung injuries, and cancer. The word "Cancer" depicts an uncontrolled and abnormal growth of cells. This review paper reveals MGF's therapeutic, curative and protective potential impact against lung, liver, ovarian, prostate, breast, stomach, and oral cancers. MGF is used in various types of research in the form of powder, liquid extract, intramuscular, intravenous, nanoparticles coated with gold, in the form of a solution, or in combination with other drugs to evaluate synergistic effects. Many studies showed that MGF is safe to use but has less bioavailability in the body and 0.111 mg/mL solubility in water. However, certain studies indicated that its bioavailability and retention time increased when taken in the form of nanoparticles and in combination with other drugs. MGF also increases the sensitivity of other drugs (i.e., cisplatin) resistant to tumors. MGF has different mechanisms of action for different cancers. It mainly targets enzymes, interleukins, tumor growth factors, signaling pathways, apoptotic proteins, and genes to inhibit the growth of tumors, volume, angiogenesis, cellular functionality, further progression, and movement to other areas of the body. Moreover, MGF increases apoptosis and body weight with no or fewer side effects on normal cells. MGF unveiled a novel gate toward the treatment of cancer. Further research and human trials are needed in this regard.

Identification of promising small-molecule inhibitors targeting STK17B for cancer therapeutics: molecular docking and molecular dynamics investigations

Cancer is a complex disease characterized by the uncontrolled growth of abnormal cells, leading to the formation of tumours. STK17B, a member of the DAPK family, has been implicated in various cancers and is considered a potential therapeutic target. However, no drug in the market has been approved for the treatment of STK17 B-associated cancer disease. This research aimed to identify direct inhibitors of STK17B using computational techniques. Ligand-based virtual screening and molecular docking were performed, resulting in the selection of three lead compounds (CID_135698391, CID_135453100, CID_136599608) with superior binding affinities compared to the reference compound dovitinib. While molecular docking simulation revealed specific interactions between the lead compounds and key amino acid residues at the binding pocket of STK17B, molecular dynamics simulations demonstrated that CID_135453100 and CID_136599608 exhibit stable conformations and comparable flexibility to dovitinib. However, CID_135698391 did not perform well using this metric as it displayed poor stability. Overall, small-molecule compounds CID_135453100 and CID_136599608 showed promising binding interactions and stability, suggesting their potential as direct inhibitors of STK17B. These findings could contribute to the exploration of novel therapeutic options targeting STK17B in cancer treatment. Communicated by Ramaswamy H. Sarma

ARV-825 Showed Antitumor Activity against BRD4-NUT Fusion Protein by Targeting the BRD4.

The bromodomain-containing 4 (BRD4) is a member of the bromodomain and extra terminal domain (BET) family, which is an important epigenetic reader. It is currently a promising oncology target. In some tumors, BET bromodomain inhibitors have demonstrated promising results. Proteolysis-targeting methods (PROTAC), which rapidly and effectively degrade BRD4, have displayed considerable potential in the treatment of tumors in recent years. The purpose of this study is to examine the potential impact of BRD4 PROTAC compounds ARV-825 on oncogene BRD4-NUT fused protein in NUT carcinoma. The effectiveness of ARV-825 was evaluated at the cellular level using the cell counting kit 8 test, wound healing, cell transfection, western blotting analysis, and RNA sequencing. The effectiveness of ARV-825 was also examined in vivo using a xenograft model. The BRD4-NUT fusion gene was overexpressed in 3T3 cells, and the pathogenic fusion gene was simulated. The results showed that the overexpression of BRD4-NUT could promote the proliferation and migration of 3T3 cells, but the expression of BRD4 protein was degraded after the addition of the novel cereblon-based PROTAC compound ARV-825 against BRD4, resulting in inhibition of BRD4-NUT 3T3 cell proliferation and migration. Further RNA-seq analysis showed that overexpression of BRD4-NUT was accompanied by increased expression of gene (e.g., Myc, E2F, TRAFs, Wnt, Gadd45g, and Sox6) with significantly enriched pathway (e.g., small cell lung cancer, NF-kappa B signaling pathway, and breast cancer), promoted cell cycle from G 1 phase to S phase, and increased cell proliferation and migration, activated the antiapoptosisi signal, led to abnormal cell growth, and ultimately led to tumorigenesis. The addition of ARV-825 effectively rescued this process and effectively inhibited cell vitality, proliferation, and migration. In vivo studies demonstrated that treatment with ARV-825 greatly suppressed tumor growth without causing harmful side effects and downregulated the BRD4-NUT expression level. Through the induction of BRD4 protein degradation, ARV-825 can successfully limit BRD4-NUT 3T3 cell proliferation in vitro and in vivo. These findings suggested that the BRD4 inhibitor ARV-825 would be an effective therapeutic strategy for treating NUT carcinoma that with the genetic feature of BRD4-NUT fusion event.

Assess the Attitudes of Cancer Patients Attending Oncology Clinics in Several Iraqi Governorates on the use of Chemotherapy, as well as their Awareness of its Side Effects

Background: Cancers are a class of illnesses characterized by the unchecked growth and spread of abnormal cells. Death may ensue if the metastasis, or the spread of cancer cells at this point, is not stopped. Methods & patients: In this cross-sectional descriptive study, one hundred patients participated, carried out in the oncology department at Salah Al Deen general hospital in Salah al Deen governorate and Baquba general hospital in Diyala governorate from 23 October 2022to23 February 2023, key inclusion criteria involved the oncology clinic sees all cancer patients, including inpatient and outpatient. who received chemotherapy. Results: It was observed through our study thatmost of participants were female (72%) and only 28% were male. About 72%, 64%, 58%, 56% of participants knew that chemotherapy could cause alopecia, nausea, vomiting and loss of appetite respectively. A50% of cancerd patient participate in these study who received chemotherapy got their information from their friends , 42% of participants received their information from doctor and only 8% of participants received their information from pharmacists Conclusion: The current study concluded that approximately more than half of the participants have acceptable knowledge about potential side effects of chemotherapy, but most of the participants were informed by their friends more than their doctor regarding side effects of chemotherapy. Aim of study: The purpose of the study is to look into and determine how cancer patients in particular Iraqi governorates feel about using chemotherapy and how informed they are of its side effects.

The cardio-oncology continuum: Bridging the gap between cancer and cardiovascular care.

Cancer and cardiovascular disease are two of the leading causes of death worldwide. Although cancer has historically been viewed as a condition characterized by abnormal cell growth and proliferation, it is now recognized that cancer can lead to a variety of cardiovascular diseases. This is due to the direct impact of cancer on the heart and blood vessels, which can cause myocarditis, pericarditis, and vasculitis. Additionally, cancer patients frequently experience systemic effects such as oxidative stress, inflammation, and metabolic dysregulation, which can contribute to the development of cardiovascular risk factors such as hypertension, dyslipidemia, and insulin resistance. It is important to closely monitor patients with cancer, especially those undergoing chemotherapy or radiation therapy, for cardiovascular risk factors and promptly address them. This article aims to explore the clinical implications of the underlying mechanisms connecting cancer and cardiovascular diseases. Our analysis highlights the need for improved cooperation between oncologists and cardiologists, and specialized treatment for cancer survivors.

Convolutional neural network-based skin cancer classification with transfer learning models

Skin cancer is a medical condition characterized by abnormal growth of skin cells. This occurs when the DNA within these skin cells becomes damaged. In addition, it is a prevalent form of cancer that can result in fatalities if not identified in its early stages. A skin biopsy is a necessary step in determining the presence of skin cancer. However, this procedure requires time and expertise. In recent times, artificial intelligence and deep learning algorithms have exhibited superior performance compared with humans in visual tasks. This result can be attributed to improved processing capabilities and the availability of vast datasets. Automated classification driven by these advancements has the potential to facilitate the early identification of skin cancer. Traditional diagnostic methods might overlook certain cases, whereas artificial intelligence-powered approaches offer a broader perspective. Transfer learning is a widely used technique in deep learning, involving the use of pre-trained models. These models are extensively implemented in healthcare, especially in diagnosing and studying skin lesions. Similarly, convolutional neural networks (CNNs) have recently established themselves as highly robust autonomous feature extractors that can achieve excellent accuracy in skin cancer detection because of their high potential. The primary goal of this study was to build deep-learning models designed to perform binary classification of skin cancer into benign and malignant categories. The tasks to resolve are as follows: partitioning the database, allocating 80% of the images to the training set, assigning the remaining 20% to the test set, and applying a preprocessing procedure to the images, aiming to optimize their suitability for our analysis. This involved augmenting the dataset and resizing the images to align them with the specific requirements of each model used in our research; finally, building deep learning models to enable them to perform the classification task. The methods used are a CNNs model and two transfer learning models, i.e., Visual Geometry Group 16 (VGG16) and Visual Geometry Group 19 (VGG19). They are applied to dermoscopic images from the International Skin Image Collaboration Archive (ISIC) dataset to classify skin lesions into two classes and to conduct a comparative analysis. Our results indicated that the VGG16 model outperformed the others, achieving an accuracy of 87% and a loss of 38%. Additionally, the VGG16 model demonstrated the best recall, precision, and F1- score. Comparatively, the VGG16 and VGG19 models displayed superior performance in this classification task compared with the CNN model. Conclusions. The significance of this study stems from the fact that deep learning-based clinical decision support systems have proven to be highly beneficial, offering valuable recommendations to dermatologists during their diagnostic procedures.

Klasifikasi Tumor Otak Menggunakan Local Binary Pattern dan SVM Classifier

Brain tumors are abnormal cell growths in brain tissue that can be life-threatening. This study aims to classify brain tumors to help early diagnosis. The method used is to extract features from brain MRI images using Local Binary Pattern (LBP) and then classified with Support Vector Machine (SVM). The data used were 2044 brain MRI images consisting of 3 classes namely meningioma, no tumor, and pituitary. The best results were obtained using LBP with a radius of 1 and the number of neighbors 8, while the best SVM model used the RBF kernel with a C value of 50, resulting in 88% accuracy, 86% precision, and 87% recall. It can be concluded that the combination of LBP and SVM methods is effective enough to classify brain tumor types to support early diagnosis.

AML alters bone marrow stromal cell osteogenic commitment via Notch signaling.

Acute myeloid leukemia (AML) is a highly heterogeneous malignancy caused by various genetic alterations and characterized by the accumulation of immature myeloid blasts in the bone marrow (BM). This abnormal growth of AML cells disrupts normal hematopoiesis and alters the BM microenvironment components, establishing a niche supportive of leukemogenesis. Bone marrow stromal cells (BMSCs) play a pivotal role in giving rise to essential elements of the BM niche, including adipocytes and osteogenic cells. Animal models have shown that the BM microenvironment is significantly remodeled by AML cells, which skew BMSCs toward an ineffective osteogenic differentiation with an accumulation of osteoprogenitors. However, little is known about the mechanisms by which AML cells affect osteogenesis. We studied the effect of AML cells on the osteogenic commitment of normal BMSCs, using a 2D co-culture system. We found that AML cell lines and primary blasts, but not normal hematopoietic CD34+ cells, induced in BMSCs an ineffective osteogenic commitment, with an increase of the early-osteogenic marker tissue non-specific alkaline phosphatase (TNAP) in the absence of the late-osteogenic gene up-regulation. Moreover, the direct interaction of AML cells and BMSCs was indispensable in influencing osteogenic differentiation. Mechanistic studies identified a role for AML-mediated Notch activation in BMSCs contributing to their ineffective osteogenic commitment. Inhibition of Notch using a γ-secretase inhibitor strongly influenced Notch signaling in BMSCs and abrogated the AML-induced TNAP up-regulation. Together, our data support the hypothesis that AML infiltration produces a leukemia-supportive pre-osteoblast-rich niche in the BM, which can be partially ascribed to AML-induced activation of Notch signaling in BMSCs.

Mitocurcumin utilizes oxidative stress to upregulate JNK/p38 signaling and overcomes Cytarabine resistance in acute myeloid leukemia

Acute myeloid leukemia (AML) is a type of blood cancer that is characterized by the rapid growth of abnormal myeloid cells. The goal of AML treatment is to eliminate the leukemic blasts, which is accomplished through intensive chemotherapy. Cytarabine is a key component of the standard induction chemotherapy regimen for AML. However, despite a high remission rate, 70–80% of AML patients relapse and develop resistance to Cytarabine, leading to poor clinical outcomes. Mitocurcumin (MitoC), a derivative of curcumin that enters mitochondria, leading to a drop in mitochondrial membrane potential and mitophagy induction. Further, it activates oxidative stress-mediated JNK/p38 signaling to induce apoptosis. MitoC demonstrated a preferential ability to kill leukemic cells from AML cell lines and patient-derived leukemic blasts. RNA sequencing data suggests perturbation of DNA damage response and cell proliferation pathways in MitoC-treated AML. Elevated reactive oxygen species (ROS) in MitoC-treated AML cells resulted in significant DNA damage and cell cycle arrest. Further, MitoC treatment resulted in ROS-mediated enhanced levels of p21, which leads to suppression of CHK1, RAD51, Cyclin-D and c-Myc oncoproteins, potentially contributing to Cytarabine resistance. Combinatorial treatment of MitoC and Cytarabine has shown synergism, increased apoptosis, and enhanced DNA damage. Using AML xenografts, a significant reduction of hCD45+ cells was observed in AML mice bone marrow treated with MitoC (mean 0.6%; range0.04%–3.56%) compared to control (mean 38.2%; range10.1%–78%), p = 0.03. The data suggest that MitoC exploits stress-induced leukemic oxidative environment to up-regulate JNK/p38 signaling to lead to apoptosis and can potentially overcome Cytarabine resistance via ROS/p21/CHK1 axis.

Enhancement of medical images diagnosis using fuzzy convolutional neural network

Brain diseases are primarily brought on by abnormal brain cell growth, which can harm the structure of the brain and eventually result in malignant brain cancer. Major challenges exist when using a computer aided diagnosis (CAD) system for an early diagnosis that enables decisive treatment, particularly when it comes to the accurate detection of various diseases in the pictures for magnetic resonance imaging (MRI). In this study, the fuzzy convolutional neural networks (FCNN) were proposed for accurate diagnosis of brain tumors (glioma, meningioma, pituitary and non-tumor) which is implemented using Keras and TensorFlow. This approach follows three steps, training, testing, and evaluation. In training process, it builds a smart model and the structure consists of seven blocks (convolution, rectified linear unit (ReLU), batch normalization, and max pooling) then use flatten, fuzzy inferences layer, and dense layer with dropout. An international dataset with 7,022 brain tumor MRI images, was tested. The evaluation model attained a high performance with training accuracy of 99.84% and validation accuracy is 98.63% with low complexity and time is 58 s per epoch. The suggested approach performs better than the other known algorithms and may be quickly and accurately used for medical picture diagnosis.

Studi Kasus Deskripsi Asuhan Keperawatan Gangguan Rasa Nyaman Nyeri Pada Pasien Yang Mengalami Gangguan Sistem Reproduksi di Rumah Sakit Wilayah Jakarta

Disorders of the female reproductive system can occur due to various risk factor, one of which is abnormal cell growth. Abnormal cells that occur depending on the location and can spread if accompanied by malignan, this condition can cause discomfort that affects patients who experience pain, so nursing care is needed so that these conditions do not affect the impact and quality of life of patients. The aim is to provide nursing care to patients with reproductive disorders who experience discomfort and pain. This scientific writing method uses a case study method with an analytical descriptive study design. This method is used to describe and analyze 2nd patients, with a holistic approach to nursing care. This case study was conducted in one of the hospitals in the Jakarta area, and set the inclusion criteria for the subjects to be used. The author chose 2nd patients, namely patients with reproductive disorders with postoperative endometriosis and ovarian cancer who experienced discomfort and pain. Nursing care is carried out for 3x24 hours starting from assessment to documentation. Results: In Mrs. N and Mrs. I, the pain was felt in the postoperative area and the pain was partially resolved, but the patient was able to control the pain he felt. In Mrs. N and Mrs. I, the pain scale on the first day was a scale of 5 and a scale of 4, on the second day a scale of 3rd and a scale of 3rd, on the third day a scale of 0 and a scale of 1st after collaboration between analgesics and non-pharmacological therapy of musical distraction. Conclusion: In reproductive disorders patients who experience pain there is the ability to control pain in nursing care provided by nurse.

An ensemble learning-based framework for breast cancer prediction

Breast cancer is a disease characterised by abnormal cell growth in the breast, with various types and unique characteristics depending on the malignant cells involved. It predominantly affects women, and researchers in the field of clinical sciences are increasingly interested in leveraging Artificial Intelligence (AI) techniques to predict and diagnose breast cancer. This study utilises the widely-used Wisconsin Breast Cancer Dataset (WBCD) from the University of California Irvine (UCI) machine learning repository. The dataset contains 30 features, including mean, standard error, and “worst” values, providing insights into different aspects of breast cancer. A stacked ensemble classifier is proposed to evaluate the effectiveness of the proposed model, incorporating multiple machine learning algorithms such as the Decision Tree Classifier, AdaBoost Classifier, Gaussian Naive Bayes (GaussianNB), and Multi-layer Perceptron Classifier (MLPClassifier). Various performance measures, including the Receiver Operating Characteristic Curve (ROC curve), Area Under the Curve (AUC), specificity, F1-score, sensitivity, and accuracy, are used to assess the model’s performance. The results demonstrate that the proposed ensemble technique achieves an accuracy of 97.66%, surpassing the performance reported in existing literature. This highlights the superior performance of the developed model compared to previous approaches. The study underscores the potential of AI techniques and machine learning algorithms in advancing breast cancer prediction and diagnosis, offering promising avenues for improving clinical decision-making and patient outcomes.

Tagraxofusp Shows Promising Anti-Tumoral Efficacy in Preclinical in Vitro Models of Myelofibrosis, Both As a Single Agent and in Combination with Janus Kinase Inhibitors

BACKGROUND: Myelofibrosis (MF) is an aggressive myeloproliferative neoplasm. Abnormal growth of hematopoietic stem and progenitor cells in MF is driven by somatic mutations affecting the Janus Kinase (JAK) signaling pathway with JAK2 V617F occurring in 50-60% of patients (Cardoso et al, Plos One 2015). The role of the JAK/Signal transducer and activator of transcriptions (STAT) pathway in MF has led to the recent approval of three different JAK2 inhibitors (ruxolitinib, pacritinib, and fedratinib) for MF treatment. However, despite limiting the symptoms of the disease, JAK2 inhibitors do not result in remission due to persistence of JAK2 activation and MF-driving cells (Pandey et al, Blood Cancer J 2022). Thus, there remains a high unmet need for treatment strategies for patients with MF. Tagraxofusp, a first-in-class CD123-directed therapy, is a recombinant fusion protein consisting of human interleukin-3 conjugated to a truncated diphtheria toxin payload approved (US/EU) for the treatment of Blastic Plasmacytoid Dendritic Cell Neoplasm. In MF, CD123 is expressed on malignant cells and bone marrow (BM) accessory cells that support the proliferation of neoplastic cells (Bao et al, Hematol Oncol Stem Cell Ther 2019; Lasho et al, Blood 2014). Based on CD123 expression, tagraxofusp is a potential, novel approach for MF treatment either alone or in combination. Initial results from a two-stage multicenter, phase 2 study (STML-0401-0314; NCT02268253) of single-agent tagraxofusp in pts with relapsed or refractory (R/R) MF demonstrated encouraging clinical efficacy and a manageable safety profile (Yacoub et al. ASH 2021). We aim to further investigate the antitumoral efficacy of tagraxofusp in MF preclinical models and the potential improvement of antitumoral efficacy by combining tagraxofusp with the JAK inhibitors, ruxolitinib, and pacritinib. METHODS: MF cell lines (SET2, UKE1, HEL: JAK2 V617F; UT7: JAK2 wild type [wt]) and bone marrow mononuclear cell (BMMC) samples from healthy donors (HD) were characterized for the extracellular expression of CD123 by flow cytometry staining. In vitro cytotoxicity experiments in CD123-positive MF cell lines (SET2, UT7, UKE-1) tested tagraxofusp and ruxolitinib or pacritinib either as single agents or in combination (MTS assay; 72 h). The combination index (CI) was calculated according to the Compusyn-Chou method (Chou TC., Pharmacol Rev, 2006) and Combenefit platform. In vitro co-culture experiments were performed with MF cell lines incubated in the presence of human primary HD-BMMCs. Cell death, as measured by LiveDead/AnnV staining, was monitored by FACS analysis through differential staining of MF cells and BMMCs with Cell Trace Violet dye (CTV). A signaling pathway analysis (JAK/STAT, S6, Mcl1) was performed through immunocapillary electrophoresis on MF cell lines treated with single agents or in combination. RESULTS: The MF cell lines showed different levels of CD123 expression. Except for the CD123 negative cell line (HEL), CD123 positive cell lines were all sensitive to tagraxofusp independent of CD123 expression intensity. As expected, both ruxolitinib and pacritinib were active in the presence of JAK2 wt pathway. A synergistic drug interaction was observed (mean CI range: 0.59-1.12) in most combinations of tagraxofusp with JAK inhibitors, as shown in a representative CD123+ cell line in Figure 1. When BMMCs, which are a subpopulation of BM Accessory Cells partially expressing CD123, were co-cultured with MF cell lines, the cytotoxic effect induced by tagraxofusp was enhanced, and this improvement was maintained in combination with JAK2 inhibition. Molecular biomarkers, such as JAK/STAT, S6, and Mcl1, were evaluated in MF cell lines treated with tagraxofusp and JAK inhibitors and supported the MoA of this combination. CONCLUSIONS: Our studies showed high sensitivity of MF cell lines to tagraxofusp as a single agent, which is expected given phase 2 results demonstrated clinical efficacy of single-agent tagraxofusp in R/R MF (Yacoub et al. ASH 2021). In addition, synergism was observed in combination with JAK inhibitors. The in vitro antitumoral effect was more pronounced in the presence of BM accessory cells, suggesting an extra targeting of CD123-positive BM cells by tagraxofusp in vivo. These results support potential clinical development of TAG in combination with JAK inhibitors for the treatment of MF.

Immune Cells, Cytokines, and Gut Microbiota Landscape Along Monoclonal Gammopathy of Undetermined Significance (MGUS) to Multiple Myeloma (MM) Evolution

Introduction: Multiple myeloma (MM) is an incurable hematologic cancer characterized by abnormal plasma cell growth in the bone marrow (BM). It is preceded by two premalignant conditions: monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM). Understanding the transition from MGUS to MM is crucial for better risk stratification, early interventions, and targeted treatments. This study aims to gain insights into immunological changes and fecal microbiota variations along the MGUS to MM transition. Methods: To comprehensively evaluate the immune composition (myeloid/lymphoid T/B/NK subpopulations and immune checkpoint distribution) in both bone marrow (BM) and peripheral blood (PB), we designed six 10-color custom and two 8-color flow cytometry panels. We analyzed samples from 13 MGUS, 12 SMM, and 63 newly diagnosed MM patients. Additionally, we evaluated cytokine/chemokine abundance in BM and PB using a 48-plex Luminex plate on 72 samples from the same MGUS, SMM, and MM patients, along with 4 healthy donors (HD). Furthermore, we performed fecal microbiome profiling on samples from 3 MGUS, 6 SMM, and 9 MM patients to investigate potential relationships between bacterial composition and MM evolution. Statistical analysis was performed in R by using specific packages (such as FlowCT). Results: Through unsupervised analysis of T cells by flow cytometry, we identified a significant increase in circulating TEMRA CD8 T cells, mostly for CD57+ cells (mean, 7.5% for MGUS vs 20.1% for SMM vs 21.6% for MM; p<0.05) and a significant reduction in naïve CD8 T TIGIT+ cells (mean, 12.4% for MGUS vs 5.98% for SMM vs 4.16% for MM; p<0.001) and naïve CD8 T TIGIT+ TIM3+ (mean, 2.18% for MGUS vs 1.24% for SMM vs 1.17% for MM; p<0.001). Regarding CD4 T subsets, we observed a reduction in BM effector memory phenotype along disease evolution (mean, 7.07% for MGUS vs 4.54% for SMM vs 3.72% for MM; p<0.05). Interestingly, we confirmed the increase of BM CD4 T cells production of IL17 (mean: 6.7% for MGUS vs 22% for MM; p<0.05) in MM patients compared to those with MGUS. In the context of myeloid cell subsets, circulating non-classical monocytes expressing HLA-DR and CD11c (mean, 1.45% for MGUS vs 0.95% for SMM vs 0.69% for MM; p<0.05) as well as granulocytes (mean, 74.18% MGUS vs 73.86% SMM vs 65.59% MM; p<0.05) were found to be reduced along MM progression. No major differences were observed for other evaluated lineages. Regarding cytokines and chemokines, a concurrent decrease in GM-CSF, IFN-a2, IFN-g, IL-1b, IL-2, IL-2Ra, IL-3, IL-10, IL-13, and MCP-1/CCL2 levels within the BM plasma throughout MM progression was observed, thus supporting a global reduction in myeloid function as well as in T cell effector activity. Moreover, PDGF-BB, IFN-a2, IL-16, and IL-8 decrease was observed in PB plasma along MM progression. Lastly, while no differences were observed in term of alpha and beta diversity, we observed an overall increase in total abundance of fecal bacteria along MM evolution (maybe related to loss of myeloid/lymphoid control according to what previously described), with several bacterial genus significantly over-represented in MM patients, including Bariatricus, CAG-83, Alistipes, Bifidobacterium, Streptococcus, Ruminococcus D, Slackia A, and Coprococcus A, while others (such as Parabacteroides B) was found to be more represented in MGUS and SMM patients. The analysis of phyla abundance indicated Firmicutes D and Lactobacillales as more abundant in MM patients, while Actinobacteriota dominated in SMM patients, and Proteobacteria and Bacteroidota were more prominent in MGUS patients. Conclusions: Overall, this study demonstrated a progressive impairment of both myeloid and lymphoid compartment in term of cellular composition, cytokine production and release and control over gut microbiome. Recovering from T cell exhaustion and IL17 production remains a major challenge for these patients.

Histone Methyltranferase SETD8 Stimulates Proliferation and Anti-Apoptotic Effect in Acute Myeloid Leukemia through Epigenetically Upregulation of CXCR4

Acute myeloid leukemia (AML) is the most common type of leukemia., characterized by the rapid and uncontrolled growth of abnormal cells. Epigenetic modifications control gene expression without affecting its DNA sequence. H4K20me1 is the mono-methylation occurring on lysine 20 of histone H4, acting as an epigenetic mark that promotes gene expression. SETD8 is responsible for catalyzing the mono-methylation of lysine 20 on histone H4. While SETD8's role and mechanisms have been studied in various types of cancer, its function in AML remains unclear. This study is based on the screening of a small molecule compound library, which identified the inhibitor UNC0379 targeting the histone methyltransferase SETD8, showing clear inhibitory effects on three non-M3 AML cell lines. Bioinformatical analysis revealed elevated SETD8 expression in AML patients compared to normal individuals and higher SETD8 expression corresponded with lower overall survival rates (OS). Further investigations using AML cell lines and normal peripheral blood mononuclear cells (PBMCs) demonstrated that SETD8 knockdown or UNC0379 intervention inhibited cell proliferation and increased apoptosis in AML cells, while PBMC cells remained unaffected. In an AML model constructed with C1498 cell injection in C57BL/6J mice, we observed that SETD8 knockdown and treatment with UNC0379 inhibited tumor formation and infiltration. To elucidate the molecular mechanism of SETD8's impact on AML, we conducted transcriptome sequencing on SETD8 knockdown AML cells and found significantly decreased mRNA expression of CXCR4, which was further validated at the protein level. Also, BeatAML and TCGA datasets demonstrated a positive correlation between SETD8 and CXCR4 mRNA expression. These phenomena motivated us to clarify the regulation relationship between SETD8 and CXCR4. CXCR4 is a crucial membrane receptor. After stimulation, it promotes AML cell proliferation and survival. CHIP experiments in SETD8 knockdown cells revealed reduced H4K20me1 enrichment in the CXCR4 promoter region. Subsequent rescue experiments demonstrated that overexpressing CXCR4 or using its corresponding agonist SDF-1alpha in SETD8 knockdown cells moderately restored cell proliferation and reduced apoptosis. In summary, we discovered that SETD8 promotes CXCR4 transcriptional activation through catalyzing the H4K20me1 mark, thereby influencing AML cell proliferation and apoptosis. SETD8 presents a promising therapeutic target through genetic or pharmacological intervention.

Formulation and Evaluation of Herbal Anticancer Chewing Gum

The objective of the present study is the formulation and evaluation of herbal anticancer Chewing gum. Cancer is the abnormal growth of cells in the body. With emerging trends in herbal medicine and complementary alternative medicine, India is considered one of the world's largest producers of herbal medicine. Many plants and their products have anticancer agents. Ginger is considered an important spice with many clinical potential activities. Ginger and its compounds display anti-inflammatory, antioxidant, antimetastatic, and anticancer activity. The anticancer activity of ginger components is reviewed in this article due to its versatile therapeutic nature. The medicated chewing gum of gingerol was prepared by direct compression mold method. Five formulations were selected for study which was showing good physicochemical properties and drug release. Formulations were characterized for physical evaluation, weight variation, stickiness, hardness/plasticity, in vitro drug release, estimation of chewing gum consistency, and drug release study in saliva. All the formulations showed satisfactory results in physical evaluation, weight variation, stickiness, and hardness. The formulation medicated chewing gum 2 (F2 Batch) showed the best in-vitro drug release of 96% in 30 min and it is also accepted by people. Drug release in saliva also indicated that more than 50% of drug release occurs within 15 min. Medicated Chewing Gum is cost-effective and also shows better compliance with an increase in bioavailability.