Exploring Genetic Determinants: A Comprehensive Analysis of Serpin B Family SNPs and Prognosis in Glioblastoma Multiforme Patients.

Serpins are serine proteinase inhibitors, with several serpins being overexpressed in cancer cells. Thus, we aim to analyze the single-nucleotide polymorphism (SNP) of Serpinb11 and its association with GBM survival. A cohort of 63 GBM patients recruited from King Abdullah University Hospital in Jordan underwent polymorphism analysis and overall survival (OS) assessments. The Cancer Genome Atlas (GBM) cohort was useful for validation. We constructed a risk score using the principal component analysis for the following Serpin genes: Serpinb3, Serpinb5, Serpinb6, Serpinb11, and Serpinb12, and patients were grouped into high- vs. low-risk groups based on the median cutoff. Univariable Cox models were used to study the survival outcomes. We identified a significant association between rs4940595 and survival. In the TCGA cohort, Serpinb3 alterations showed worse OS. Univariable Cox showed worse PFS outcomes with higher SERPINB5 and SERPINB6 expression. A Serpin B 5-gene risk score showed a trend towards worse PFS in the high-risk group. Upregulated DEGs showed GO enrichment in cytokine regulation and production, positive regulation of leukocyte activation, and the MAPK cascade. The high-risk group showed a significantly higher infiltration of M2 macrophages and activated mast cells. Our findings showed a significant role of the Serpin B family in GBM survival in the Jordanian population.

Clinical and biomarker results from a phase II trial of combined cabozantinib and durvalumab in patients with chemotherapy-refractory colorectal cancer (CRC): CAMILLA CRC cohort.

CAMILLA is a basket trial (NCT03539822) evaluating cabozantinib plus the ICI durvalumab in chemorefractory gastrointestinal cancer. Herein, are the phase II colorectal cohort results. 29 patients were evaluable. 100% had confirmed pMMR/MSS tumors. Primary endpoint was met with ORR of 27.6% (95% CI 12.7-47.2%). Secondary endpoints of 4-month PFS rate was 44.83% (95% CI 26.5-64.3%); and median OS was 9.1 months (95% CI 5.8-20.2). Grade≥3 TRAE occurred in 39%. In post-hoc analysis of patients with RAS wild type tumors, ORR was 50% and median PFS and OS were 6.3 and 21.5 months respectively. Exploratory spatial transcriptomic profiling of pretreatment tumors showed upregulation of VEGF and MET signaling, increased extracellular matrix activity and preexisting anti-tumor immune responses coexisting with immune suppressive features like T cell migration barriers in responders versus non-responders. Cabozantinib plus durvalumab demonstrated anti-tumor activity, manageable toxicity, and have led to the activation of the phase III STELLAR-303 trial.

Antagonism of regulatory ISGs enhances the anti-melanoma efficacy of STING agonists.

Background: Stimulator of Interferon Genes (STING) is a dsDNA sensor that triggers type I inflammatory responses. Recent data from our group and others support the therapeutic efficacy of STING agonists applied intratumorally or systemically in a range of murine tumor models, with treatment benefits associated with tumor vascular normalization and improved immune cell recruitment and function within the tumor microenvironment (TME). However, such interventions are rarely curative and STING agonism coordinately upregulates expression of immunoregulatory interferon-stimulated genes (ISGs) including Arg2, Cox2, Isg15, Nos2, and Pdl1 that may limit treatment benefits. We hypothesized that combined treatment of melanoma-bearing mice with STING agonist ADU-S100 together with antagonists of regulatory ISGs would result in improved control of tumor growth vs. treatment with ADU-S100 alone. Methods: Mice bearing either B16 (BRAFWTPTENWT) or BPR20 (BRAFV600EPTEN-/-) melanomas were treated with STING agonist ADU-S100 plus various inhibitors of ARG2, COX2, NOS2, PD-L1, or ISG15. Tumor growth control and changes in the TME were evaluated for combination treatment vs ADU-S100 monotherapy by tumor area measurements and flow cytometry/transcriptional profiling, respectively. Results: In the B16 melanoma model, we noted improved antitumor efficacy only when ADU-S100 was combined with neutralizing/blocking antibodies against PD-L1 or ISG15, but not inhibitors of ARG2, COX2, or NOS2. Conversely, in the BPR20 melanoma model, improved tumor growth control vs. ADU-S100 monotherapy was only observed when combining ADU-S100 with ARG2i, COX2i, and NOS2i, but not anti-PD-L1 or anti-ISG15. Immune changes in the TME associated with improved treatment outcomes were subtle but included increases in proinflammatory innate immune cells and activated CD8+CD69+ T cells and varied between the two tumor models. Conclusions: These data suggest contextual differences in the relative contributions of individual regulatory ISGs that serve to operationally limit the anti-tumor efficacy of STING agonists which should be considered in future design of novel combination protocols for optimal treatment benefit.

Cancer-Associated Fibroblasts in Gastrointestinal Cancers: Unveiling Their Dynamic Roles in the Tumor Microenvironment.

Gastrointestinal cancers are highly aggressive malignancies with significant mortality rates. Recent research emphasizes the critical role of the tumor microenvironment (TME) in these cancers, which includes cancer-associated fibroblasts (CAFs), a key component of the TME that have diverse origins, including fibroblasts, mesenchymal stem cells, and endothelial cells. Several markers, such as α-SMA and FAP, have been identified to label CAFs, and some specific markers may serve as potential therapeutic targets. In this review article, we summarize the literature on the multifaceted role of CAFs in tumor progression, including their effects on angiogenesis, immune suppression, invasion, and metastasis. In addition, we highlight the use of single-cell transcriptomics to understand CAF heterogeneity and their interactions within the TME. Moreover, we discuss the dynamic interplay between CAFs and the immune system, which contributes to immunosuppression in the TME, and the potential for CAF-targeted therapies and combination approaches with immunotherapy to improve cancer treatment outcomes.

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review.

PURPOSE OF REVIEW: This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to understand the role of AI and ML in healthcare, specifically in medical diagnosis, statistics, and precision medicine, and to explore their applications in predicting and managing sudden cardiac arrest outcomes, especially in the context of prehospital emergency care. RECENT FINDINGS: The role of AI and ML in healthcare is expanding, with applications evident in medical diagnosis, statistics, and precision medicine. Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There's a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). Furthermore, the combination of AI with automated external defibrillators (AEDs) shows potential in better detecting shockable rhythms during cardiac arrest incidents. AI and ML hold immense promise in revolutionizing the prediction and management of sudden cardiac arrest, hinting at improved survival rates and more efficient healthcare interventions in the future. Sudden cardiac arrest (SCA) continues to be a major global cause of death, with survival rates remaining low despite advanced first responder systems. The ongoing challenge is the prediction and prevention of SCA. However, with the rise in the adoption of AI and ML tools in clinical electrophysiology in recent times, there is optimism about addressing these challenges more effectively.

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System.

Purpose: The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL) and compare its performance to the S.T.O.N.E. and Guy's stone scores. Patients and Methods: This is a retrospective study that included 320 PCNL patients. Pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each were mean bootstrap estimate, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC. Results: Out of the 320 patients who underwent PCNL, the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65-0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63-0.85], 0.759, 0.72, and 0.769, respectively. The SVM results were 0.70 [0.60-0.79], 0.725, 0.74, and 0.751, respectively. The AUC of Guy's stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81-0.92], an accuracy of 0.70, and an AUC of 0.795, While the XGBoost results were 0.84 [0.78-0.91], 0.74, and 0.84, respectively. The SVM results were 0.86 [0.80-0.91], 0.79, and 0.858, respectively. Conclusion: MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLMs we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E scores.

Next generation immuno-oncology biomarkers in gastrointestinal cancer: what does the future hold?

INTRODUCTION: Gastrointestinal (GI) cancers pose a significant health burden worldwide, necessitating advancements in diagnostic and treatment approaches. One promising avenue is the utilization of next-generation biomarkers, which hold the potential to revolutionize GI cancer management. AREAS COVERED: This review explores the latest breakthroughs and expert opinions surrounding the application of next-generation immunotherapy biomarkers. It encompasses various aspects of the currently utilized biomarkers of immunotherapy in the context of GI cancers focusing on microsatellite stable cancers. It explores the promising research on the next generation of biomarkers addressing the challenges associated with integrating them into clinical practice and the need for standardized protocols and regulatory guidelines. EXPERT OPINION: Immune profiling, multiplex immunohistochemistry, analysis of immune cell subsets, and novel genomic and epigenomic markers integrated with machine-learning approaches offer new avenues for identifying robust biomarkers. Liquid biopsy-based approaches, such as circulating tumor DNA (ctDNA) and exosome-based analyses, hold promise for real-time monitoring and early detection of treatment response.

Expression of lymphoid structure-associated cytokine/chemokine gene transcripts in tumor and protein in serum are prognostic of melanoma patient outcomes.

Background: Proinflammatory chemokines/cytokines support development and maturation of tertiary lymphoid structures (TLS) within the tumor microenvironment (TME). In the current study, we sought to investigate the prognostic value of TLS-associated chemokines/cytokines (TLS-kines) expression levels in melanoma patients by performing serum protein and tissue transcriptomic analyses, and to then correlate these data with patients clinicopathological and TME characteristics. Methods: Levels of TLS-kines in patients' sera were quantitated using a custom Luminex Multiplex Assay. The Cancer Genomic Atlas melanoma cohort (TCGA-SKCM) and a Moffitt Melanoma cohort were used for tissue transcriptomic analyses. Associations between target analytes and survival outcomes, clinicopathological variables, and correlations between TLS-kines were statistically analyzed. Results: Serum of 95 patients with melanoma were evaluated; 48 (50%) female, median age of 63, IQR 51-70 years. Serum levels of APRIL/TNFSF13 were positively correlated with levels of both CXCL10 and CXCL13. In multivariate analyses, high levels of serum APRIL/TNFSF13 were associated with improved event-free survival after adjusting for age and stage (HR = 0.64, 95% CI 0.43-0.95; p = 0.03). High expression of APRIL/TNFSF13 tumor transcripts was significantly associated with improved OS in TCGA-SKCM (HR = 0.69, 95% CI 0.52-0.93; p = 0.01) and in Moffitt Melanoma patients (HR = 0.51, 95% CI: 0.32-0.82; p = 0.006). Further incorporation of CXCL13 and CXCL10 tumor transcript levels in a 3-gene index revealed that high APRIL/CXCL10/CXCL13 expression was associated with improved OS in the TCGA SKCM cohort (HR = 0.42, 95% CI 0.19-0.94; p = 0.035). Melanoma differentially expressed genes positively associated with high APRIL/CXCL10/CXCL13 tumor expression were linked to tumor infiltration by a diverse array of proinflammatory immune cell types. Conclusion: Serum protein and tumor transcript levels of APRIL/TNFSF13 are associated with improved survival outcomes. Patients exhibiting high coordinate expression of APRIL/CXCL10/CXCL13 transcripts in their tumors displayed superior OS. Further investigation of TLS-kine expression profiles related to clinical outcomes in larger cohort studies is warranted.

Compensatory Hyperhidrosis After Non-Surgical Treatment of Primary Focal Hyperhidrosis: Two-Year Single-Centered Prospective Study From Jordan.

BACKGROUND: Primary focal hyperhidrosis (PH) can be managed by a wide range of medical and surgical modalities. Compensatory hyperhidrosis (CH) is a well-documented complication of surgical treatment. We aimed to investigate the occurrence of compensatory hyperhidrosis (CH) in PH patients after nonsurgical treatment with botulinum toxin A (BTX- A) or iontophoresis. METHODOLOGY: We carried out a unicentric prospective study on PH patients from King Abdullah University Hospital (KAUH) in Jordan. PH patients were evaluated after 1-month of nonsurgical treatment. Patients who developed CH were re-assessed after 3-6 months through a telephone-based interview. RESULTS: A total of 86 patients with PH who underwent nonsurgical treatment with iontophoresis or botulinum toxin were recruited. Twenty-four (27.9%) patients developed subjective CH. It was mild in (75%), moderate in (21%), and severe in (4%) of patients affected, it was self-limiting within a few months in all patients. Patients with CH did not differ significantly in demographic or clinical variables from patients who did not develop CH except at the site of PH (p value = .05). CONCLUSION: The findings of this study indicate that more than quarter (27.9%) of patients with PH may develop minor compensatory sweating, however this didn't affect satisfaction with treatment.

Limbic and cortical regions as functional biomarkers associated with emotion regulation in bipolar disorder: A meta-analysis of neuroimaging studies.

BACKGROUND: Bipolar disorder (BD) is a psychiatric disorder characterized by episodes of depression and mania, associated with impaired emotion processing. Several functional MRI (fMRI) studies have been used to investigate the structural and functional alteration in BD. Here, we aim to investigate the current fMRI findings of brain activation during emotion-regulation tasks between BD patients and healthy controls (HC). METHODS: A systematic search through PubMed database for fMRI studies on bipolar patients and HC yielded 685 studies. We performed an activation likelihood estimation (ALE) on 21 studies for emotion regulation in BD patients and HC. Furthermore, we performed subgroup analyses for task performances in response time and accuracy between bipolar patients and HC. RESULTS: The total sample included 21 fMRI studies, comprising 543 BD patients, compared to 565 HC. ALE maps for emotion-related tasks showed hyperactivation in BD patients in the caudate, amygdala, precentral gyrus, middle frontal gyri, and sub-gyrus. Whereas hypoactivation was seen in the inferior frontal gyrus and anterior cingulate gyrus. LIMITATIONS: We could not apply a correction for p-value thresholds, as it needs large number of foci. Second, functional abnormalities were investigated for adult BD patients only, as BD patients have functional differences correlated with age. CONCLUSIONS: Our results showed that limbic and cortical regions can represent a potential biomarker for the diagnosis and management of BD, by showing clustered brain regions of abnormal patterns of increased activation between BD patients and HC.

Genomic and Transcriptomic Predictors of Response to Immune Checkpoint Inhibitors in Melanoma Patients: A Machine Learning Approach.

Immune checkpoint inhibitors (ICIs) became one of the most revolutionary cancer treatments, especially in melanoma. While they have been proven to prolong survival with lesser side effects compared to chemotherapy, the accurate prediction of response remains to be an unmet gap. Thus, we aim to identify accurate clinical and transcriptomic biomarkers for ICI response in melanoma. We also provide mechanistic insight into how high-performing markers impose their effect on the tumor microenvironment (TME). Clinical and transcriptomic data were retrieved from melanoma studies administering ICIs from cBioportal and GEO databases. Four machine learning models were developed using random-forest classification (RFC) entailing clinical and genomic features (RFC7), differentially expressed genes (DEGs, RFC-Seq), survival-related DEGs (RFC-Surv) and a combination model. The xCELL algorithm was used to investigate the TME. A total of 212 ICI-treated melanoma patients were identified. All models achieved a high area under the curve (AUC) and bootstrap estimate (RFC7: 0.71, 0.74; RFC-Seq: 0.87, 0.75; RFC-Surv: 0.76, 0.76, respectively). Tumor mutation burden, GSTA3, and VNN2 were the highest contributing features. Tumor infiltration analyses revealed a direct correlation between upregulated genes and CD8+, CD4+ T cells, and B cells and inversely correlated with myeloid-derived suppressor cells. Our findings confirmed the accuracy of several genomic, clinical, and transcriptomic-based RFC models, that could further support the use of TMB in predicting response to ICIs. Novel genes (GSTA3 and VNN2) were identified through RFC-seq and RFC-surv models that could serve as genomic biomarkers after robust validation.

Meta-analytical evidence of functional and structural abnormalities associated with pain processing in migraine patients: An activation likelihood estimation.

BACKGROUND: Migraine is a primary headache disorder that causes debilitating throbbing pain. Several functional MRI (fMRI) and voxel-based morphometry (VBM) studies have been used to investigate the structural and functional alteration in migraine. Here, we aim to study the converged brain regions of functional and structural abnormalities in gray matter volume (GMV) associated with pain processing and management in migraineurs and healthy controls (HC). METHODS: A systematic search through PubMed and Sleuth was carried out for peer-reviewed functional and structural neuroimaging studies on migraine patients and HC yielded a total of 1136 studies. We performed an activation likelihood estimation (ALE) meta-analysis on VBM and pain stimulation task-based fMRI studies to investigate the converged areas of GMV and functional abnormalities between migraineurs and HC. We performed two subgroup analyses between migraine with aura (MwA) and migraine without aura (MwoA) relative to HC, and between chronic migraine (CM) and episodic migraine (EM) compared to HC. RESULTS: The total sample included 16 fMRI and 22 VBM studies, consisting of 1295 migraine patients, compared to 995 HC. In fMRI analysis, ALE maps for pain stimulation tasks revealed hyperactivation in migraineurs in the substantia nigra compared to HC, whereas hypoactivation was seen in the cerebellum. For the VBM analysis, ALE clusters of increased GMV in migraineurs were observed in the parahippocampus and putamen nucleus. Whereas clusters of reduced GMV in migraineurs were seen in the frontal gyri. Compared to HC, MwoA patients showed a GMV reduction in the insula, and anterior cingulate, whereas MwA patients showed GMV reduction in the cerebellum, cingulate gyrus, and insula. CM patients showed decreased GMV in the precentral gyrus, whereas EM patients showed decreased GMV in the parahippocampus, and inferior frontal gyrus when compared to HC. CONCLUSIONS: Our findings represent a potential biomarker for the diagnosis and management of migraine, by showing clustered brain regions of abnormal patterns of activation and GMV changes between migraineurs and HC which might be associated with hyposensitivity to pain in migraineurs. Further studies are required to determine disease progression or therapeutic interventions' effect on migraine.