Revolutionizing pediatric neuroblastoma treatment: unraveling new molecular targets for precision interventions.

Neuroblastoma (NB) is the most frequent solid tumor in pediatric cases, contributing to around 15% of childhood cancer-related deaths. The wide-ranging genetic, morphological, and clinical diversity within NB complicates the success of current treatment methods. Acquiring an in-depth understanding of genetic alterations implicated in the development of NB is essential for creating safer and more efficient therapies for this severe condition. Several molecular signatures are being studied as potential targets for developing new treatments for NB patients. In this article, we have examined the molecular factors and genetic irregularities, including those within insulin gene enhancer binding protein 1 (ISL1), dihydropyrimidinase-like 3 (DPYSL3), receptor tyrosine kinase-like orphan receptor 1 (ROR1) and murine double minute 2-tumor protein 53 (MDM2-P53) that play an essential role in the development of NB. A thorough summary of the molecular targeted treatments currently being studied in pre-clinical and clinical trials has been described. Recent studies of immunotherapeutic agents used in NB are also studied in this article. Moreover, we explore potential future directions to discover new targets and treatments to enhance existing therapies and ultimately improve treatment outcomes and survival rates for NB patients.

Computational insights into KRAS G12C inhibition: exploring possible repurposing of Azacitidine and Ribavirin.

Kirsten rat sarcoma (KRAS) stands out as the most prevalent mutated oncogene, playing a crucial role in the initiation and progression of various cancer types, including colorectal, lung and pancreatic cancer. The oncogenic modifications of KRAS are intricately linked to tumor development and are identified in 22% of cancer patients. This has spurred the necessity to explore inhibition mechanisms, with the aim of investigating and repurposing existing drugs for diagnosing cancers dependent on KRAS G12C In this investigation, 26 nucleoside-based drugs were collected from literature to assess their effectiveness against KRAS G12C. The study incorporates in-silico molecular simulations and molecular docking examinations of these nucleoside-derived drugs with the KRAS G12C protein using Protein Data Bank (PDB) ID: 5V71. The docking outcomes indicated that two drugs, Azacitidine and Ribavirin, exhibited substantial binding affinities of -8.7 and -8.3 kcal/mol, respectively. These drugs demonstrated stability in binding to the active site of the protein during simulation studies. Root mean square deviation (RMSD) analyses indicated that the complexes closely adhered to an equilibrium RMSD value ranging from 0.17 to 0.2 nm. Additionally, % occupancies, bond angles and the length of hydrogen bonds were calculated. These findings suggest that Azacitidine and Ribavirin may potentially serve as candidates for repurposing in individuals with KRAS-dependent cancers.Communicated by Ramaswamy H. Sarma.

Intraconal Retroorbital Cavernous Hemangioma - A Case Report.

Cavernous hemangioma of orbit is a benign, noninfiltrative, slowly progressive vascular neoplasm. It is usually asymptomatic but patients may present with proptosis and diminished vision due to compression of second cranial nerve, optic nerve. This can be usually diagnosed with the help of clinical examination and computed tomography (CT) or magnetic resonance imaging (MRI). Small sized tumours are worth wait and watch while large ones need surgical excision. In our case report, A 65-year-old male patient presented to the head and neck surgery with proptosis of left eye since 5 years along with decreased vision since 4 years. MDCT scan (orbits plain) suggestive of large solid retroocular, intraconal mass in left orbit leading to proptosis of left eyeball. The patient underwent excision of tumour through a transnasal endoscopic approach. Histopathological examination of the tumour identified as cavernous hemangioma. It is safe and effective way to access and excise the orbital tumours through the transnasal endoscopic approach. It is essential to have experienced surgeon in endoscopic procedures. The patient had satisfactory results at three months follow up and showed no symptoms or relapse on CT scans of orbital region. Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-023-03984-y.

Investigation of the Utility of Features in a Clinical De-identification Model: A Demonstration Using EHR Pathology Reports for Advanced NSCLC Patients.

BACKGROUND: Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification. OBJECTIVE: The performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model. METHODS: Using open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data. RESULTS: Among the 10 feature extractors explored in this study, n-gram, prefix-suffix, word embedding, and word shape performed the best. A model using combination of these four feature sets yielded precision, recall, and F1-score for each PHI as follows: NAME (0.80; 0.79; 0.80), LOCATION (0.85; 0.83; 0.84), DATE (0.86; 0.79; 0.82), HOSPITAL (0.96; 0.93; 0.95), ID (0.99; 0.82; 0.90), and INITIALS (0.97; 0.49; 0.65). We also found that the model's performance becomes saturated when the training data size is beyond 200. CONCLUSION: Manual de-identification of large-scale data is an impractical procedure since it is time-consuming and subject to human errors. Analysis of the NER model's performance in this study sheds light on a semi-automatic clinical de-identification pipeline for enterprise-wide data warehousing.

Machine Learning to Predict Tamoxifen Nonadherence Among US Commercially Insured Patients With Metastatic Breast Cancer.

PURPOSE: Adherence to tamoxifen citrate among women diagnosed with metastatic breast cancer can improve survival and minimize recurrence. This study aimed to use real-world data and machine learning (ML) methods to classify tamoxifen nonadherence. METHODS: A cohort of women diagnosed with metastatic breast cancer from 2012 to 2017 were identified from IBM MarketScan Commercial Claims and Encounters and Medicare claims databases. Patients with < 80% proportion of days coverage in the year following treatment initiation were classified as nonadherent. Training and internal validation cohorts were randomly generated (4:1 ratio). Clinical procedures, comorbidity, treatment, and health care encounter features in the year before tamoxifen initiation were used to train logistic regression, boosted logistic regression, random forest, and feedforward neural network models and were internally validated on the basis of area under receiver operating characteristic curve. The most predictive ML approach was evaluated to assess feature importance. RESULTS: A total of 3,022 patients were included with 40% classified as nonadherent. All models had moderate predictive accuracy. Logistic regression (area under receiver operating characteristic 0.64) was interpreted with 94% sensitivity (95% CI, 89 to 92) and 0.31 specificity (95% CI, 29 to 33). The model accurately classified adherence (negative predictive value 89%) but was nondiscriminate for nonadherence (positive predictive value 48%). Variable importance identified top predictive factors, including age ≥ 55 years and pretreatment procedures (lymphatic nuclear medicine, radiation oncology, and arterial surgery). CONCLUSION: ML using baseline administrative data predicts tamoxifen nonadherence. Screening at treatment initiation may support personalized care, improve health outcomes, and minimize cost. Baseline claims may not be sufficient to discriminate adherence. Further validation with enriched longitudinal data may improve model performance.

Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

OBJECTIVE: Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the "meta-model" and apply the meta-model to patient-specific cancer prognosis. MATERIALS AND METHODS: Using real-world data, we developed a suite of individual random survival forest models to predict survival in patients with advanced lung cancer, colorectal cancer, and breast cancer. Individual models varied by the predictor data used. We combined models for each cancer type into a meta-model that predicted survival for each patient using a weighted mean of the individual models for which the patient had all requisite predictors. RESULTS: The meta-model significantly outperformed many of the individual models and performed similarly to the best performing individual models. Comparisons of the meta-model to a more traditional imputation-based method of addressing missing data supported the meta-model's utility. CONCLUSIONS: We developed a novel machine learning-based strategy to underlie clinical decision support and predict survival in cancer patients, despite missing data. The meta-model may more generally provide a tool for addressing missing data across a variety of clinical prediction problems. Moreover, the meta-model may address other challenges in clinical predictive modeling including model extensibility and integration of predictive algorithms trained across different institutions and datasets.

Production, Purification and Characterization of a Novel Bacteriocin Produced by Bacillus subtilis VS Isolated from Mango (Mangifera indica L)

Abstract Bacteriocin has been identified as an excellent alternative to chemical preservatives due to its astonishing antimicrobial activity against food spoiling and food-borne pathogens. So there is a need to identify the newer and potent sources of bacteriocin producers. This study aims the isolation of potent bacteriocin producing microorganism from fresh fruits and vegetables, its production, purification, and characterization. Firstly, 43 isolates were analysed for its antimicrobial potential, out of which7 were found to inhibit the growth of various pathogens. Considering the results of antimicrobial activity; the microorganism isolated from mango was regarded as the most potent one; which was identified as Bacillus subtilis VS.70% ammonium sulphate precipitated and dialysed bacteriocin was purified using DEAE cellulose and sephadex G75 chromatography. Bacteriocin was purified by 24.64 fold with 8.65% recovery and its molecular weight was found to be 31.2kDa. The Purified bacteriocin was found to be stable at broad pH and temperature. It was found to be degraded by various proteases studied confirming its proteinaceous nature. Considering all these attributes; the purified bacteriocin isolated from Bacillus subtilis VS can be exploited by various food industries.

Eye-Tracking Study to Enhance Usability of Molecular Diagnostics Reports in Cancer Precision Medicine.

PURPOSE: We conducted usability studies on commercially available molecular diagnostic (MDX) test reports to identify strengths and weaknesses in content and form that drive clinical decision making. Given routine genomic testing in cancer medicine, oncologists must interpret MDX reports as well as evidence concerning clinical utility of biomarkers accurately for treatment or trial selection. This work aims to evaluate effectiveness of MDX reports in facilitating cancer treatment planning. METHODS: Fourteen clinicians at an academic tertiary care medical facility, with a wide range of experience in oncology and in the use of molecular testing, participated in this study. Three commercially available, widely used, Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists (CAP)-accredited test reports (labeled Laboratories A, B, and C) were used. Eye tracking, surveys, and think-aloud protocols were used to collect usability data for these MDX reports focusing on ease of comprehension and actionability. RESULTS: Clinicians found two primary areas in molecular diagnostic reports most useful for patient care: therapy options with benefit or lack of benefit to patients, including enrolling clinical trials; and pathogenic tumor molecular anomalies detected. Therapeutic implications and therapy classes such as US Food and Drug Administration-approved off-label, on-label, clinical trials were critical for decision making. However, all reports had usability and comprehension issues in these areas and could be improved. CONCLUSION: Focused usability studies can help drive our understanding of the clinical workflow for use of molecular diagnostic tests in cancer care. This in turn can have major effects on quality of care, outcomes, costs, and patient satisfaction. This study demonstrates the use of specific usability techniques (eye tracking and think-aloud protocols) to help clinical laboratories improve MDX report design in a precision oncology treatment setting.

PARP1 Differentially Interacts with Promoter region of DUX4 Gene in FSHD Myoblasts.

OBJECTIVE: The goal of the study is to identity proteins, which interact with the promoter region of double homeobox protein 4 (DUX4) gene known to be causative for the autosomal dominant disorder Facioscapulohumeral Muscular Dystrophy (FSHD). METHODS: We performed a DNA pull down assay coupled with mass spectrometry analysis to identify proteins that interact with a DUX4 promoter probe in Rhabdomyosarcomca (RD) cells. We selected the top ranked protein poly (ADP-ribose) polymerase 1 (PARP1) from our mass spectrometry data for further ChIP-qPCR validation using patients' myoblasts. We then treated FSHD myoblasts with PARP1 inhibitors to investigate the role of PARP1 in the FSHD myoblasts. RESULTS: In our mass spectrometry analysis, PARP1 was found to be the top ranked protein interacting preferentially with the DUX4 promoter probe in RD cells. We further validated this interaction by immunoblotting in RD cells (2-fold enrichment compared to proteins pulled down by a control probe, p<0.05) and ChIP-qPCR in patients' myoblasts (65-fold enrichment, p<0.01). Interestingly, the interaction was only observed in FSHD myoblasts but not in the control myoblasts. Upon further treatment of FSHD myoblasts with PARP1 inhibitors, we showed that treatment with a PARP1 inhibitor, 3-aminobenzamide (0.5 mM), for 24 h had a suppression of DUX4 (2.6 fold, p<0.05) and ZSCAN4, a gene previously shown to be upregulated by DUX4, (1.6 fold, p<0.01) in FSHD myoblasts. Treatment with fisetin (0.5 mM), a polyphenol compound with PARP1 inhibitory property, for 24 h also suppressed the expression of DUX4 (44.8 fold, p<0.01) and ZSCAN4 (2.2 fold, p<0.05) in the FSHD myoblasts. We further showed that DNA methyltransferase 1 (DNMT1), a gene regulated by PARP1 was also enriched at the DUX4 promoter in RD cells through immunoblotting (2-fold, p<0.01) and immortalized FSHD myoblasts (42-fold, p<0.01) but not control myoblasts through ChIP qPCR. CONCLUSION: Our results showed that PARP1 and DNMT1 interacted with DUX4 promoter and may be involved in modulating DUX4 expression in FSHD.

DUX4 differentially regulates transcriptomes of human rhabdomyosarcoma and mouse C2C12 cells.

Facioscapulohumeral muscular dystrophy (FSHD) is linked to the deletion of the D4Z4 arrays at chromosome 4q35. Recent studies suggested that aberrant expression of double homeobox 4 (DUX4) from the last D4Z4 repeat causes FSHD. The aim of this study is to determine transcriptomic responses to ectopically expressed DUX4 in human and mouse cells of muscle lineage. We expression profiled human rhabdomyosarcoma (RD) cells and mouse C2C12 cells transfected with expression vectors of DUX4 using the Affymetrix Human Genome U133 Plus 2.0 Arrays and Mouse Genome 430 2.0 Arrays, respectively. A total of 2267 and 150 transcripts were identified to be differentially expressed in the RD and C2C12 cells, respectively. Amongst the transcripts differentially expressed in the RD cells, MYOD and MYOG (2 fold, p<0.05), and six MYOD downstream targets were up-regulated in RD but not C2C12 cells. Furthermore, 13 transcripts involved in germline function were dramatically induced only in the RD cells expressing DUX4. The top 3 IPA canonical pathways affected by DUX4 were different between the RD (inflammation, BMP signaling and NRF-2 mediated oxidative stress) and the C2C12 cells (p53 signaling, cell cycle regulation and cellular energy metabolism). Amongst the 40 transcripts shared by the RD and C2C12 cells, UTS2 was significantly induced by 76 fold and 224 fold in the RD and C2C12 cells, respectively. The differential expression of MYOD, MYOG and UTS2 were validated using real-time quantitative RT-PCR. We further validated the differentially expressed genes in immortalized FSHD myoblasts and showed up-regulation of MYOD, MYOG, ZSCAN4 and UTS2. The results suggest that DUX4 regulates overlapped and distinct groups of genes and pathways in human and mouse cells as evident by the selective up-regulation of genes involved in myogenesis and gametogenesis in human RD and immortalized cells as well as the different molecular pathways identified in the cells.

Lichen planus arising in radiation therapy treatment sites.

A 59-year-old woman with a history of metastatic breast cancer presented for evaluation of hyperpigmented, reticulated, pruritic plaques on her left arm and left thigh after undergoing localized radiation therapy to the left breast and left thigh. These painful plaques with surrounding erythema appeared to follow Blaschko lines and punch biopsy results were histologically consistent with lichen planus. Herein we report a case of radiation-induced lichen planus.