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Objectives The minor allele of the common rs2231142 ABCG2 variant predicts inadequate response to allopurinol urate lowering therapy. We hypothesize that additional variants in genes encoding urate transporters and allopurinol-to-oxypurinol metabolic enzymes also predict allopurinol response. Methods This study included a subset of participants with gout from the Long-term Allopurinol Safety Study Evaluating Outcomes in Gout Patients (LASSO), whose whole genome was sequenced (n  = 563). Good responders had a 4:1 or 5:1 ratio of good [serum urate (SU) <0.36 mmol/l on allopurinol ≤300 mg/day] to poor (SU ≥0.36 mmol/l despite allopurinol >300 mg/day) responses over five to six time points, while inadequate responders had a 1:4 or 1:5 ratio of good to poor responses. Adherence to allopurinol was determined by pill counts, and for a subgroup (n  = 303), by plasma oxypurinol >20μmol/l. Using the sequence kernel association test (SKAT), we estimated the combined effect of rare and common variants in urate secretory (ABCC4 , ABCC5 , ABCG2 , SLC17A1 , SLC17A3 , SLC22A6 , SLC22A8) and reuptake genes (SLC2A9 , SLC22A11) and in allopurinol-to-oxypurinol metabolic genes (AOX1 , MOCOS , XDH) on allopurinol response. Results There was an association of rare and common variants in the allopurinol-to-oxypurinol gene group (P SKAT-C = 0.019), and in MOCOS , encoding molybdenum cofactor sulfurase, with allopurinol response (P SKAT-C = 0.011). Evidence for genetic association with allopurinol response in the allopurinol-to-oxypurinol gene group (P SKAT-C = 0.002) and MOCOS (P SKAT-C < 0.001) was stronger when adherence to allopurinol therapy was confirmed by plasma oxypurinol. Conclusion We provide evidence for common and rare genetic variation in MOCOS associating with allopurinol response. [ABSTRACT FROM AUTHOR]

Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity. A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery. Methods: In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery. Discussion: As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs. Conclusion: Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. [ABSTRACT FROM AUTHOR]

Introduction: Pharmacogenetics (PGx) holds promise for optimizing psychotropic medication use, with CYP2D6 and CYP2C19 identified as key genes in antidepressant treatment. However, few studies have explored the genetic variants of these genes in real-world settings for patients experiencing ineffectiveness or adverse drug reactions (ADRs) to antidepressants. Methods: This case series includes 40 patients who underwent PGx testing due to antidepressant ineffectiveness or ADRs between June 2020 and April 2022. We describe the patients' demographic, clinical, and genetic characteristics and assess the value of PGx testing based on feedback from their psychiatrists. Results: The most common diagnoses were major depressive disorder (60.0%) and post-traumatic stress disorder (30.0%). Ineffectiveness was reported in 65.0% of patients, ADRs in 2.5%, and both in 32.5%. The antidepressants involved included SSRIs (45.0%), SNRIs (27.5%), atypical antidepressants (20.0%), and tricyclics (17.5%). Only 17.5% of patients had normal CYP2D6 and CYP2C19 metabolic activity. Actionable genetic variants were identified in 22.0% of CYP2D6/CYP2C19-antidepressant-response pairs. PGx recommendations were followed in 92.7% of cases, with significant improvement in ADRs reported in 71.4% of patients and efficacy improvement in 79.5%. Discussion: Our findings suggest that PGx testing can guide prescribing decisions for patients with antidepressant ineffectiveness or ADRs. The relatively high prevalence of genetic variants affecting pharmacokinetics supports the broader adoption of PGx testing in psychiatric practice. [ABSTRACT FROM AUTHOR]

Objective: To analyze the local functional activity and connectivity features of the brain associated with drug response inpatients newly diagnosed with epilepsy (NDE) who are naïve to anti-seizure medication (ASM). Methods: Recruited patients, underwent functional magnetic resonance imaging at baseline, and were assigned to the well-controlled (WC, n = 28) or uncontrolled (UC, n = 11) groups based on their response to ASM. Healthy participants were included in the control group (HC, n = 29). The amplitudes of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were used to measure local functional activity, and voxel-wise degree centrality (DC) and seed-based functional connectivity (FC) were used to evaluate the connecting intensity of the brain areas. Results: Compared to the HC and WC groups, the UC group had higher ALFF values in the left posterior central gyrus (PoCG.L) and left inferior temporal gyrus (ITG.L) and higher DC in the bilateral PoCG (Gaussian random field correction, voxel-level P < 0.001, and cluster-level P < 0.05). Both PoCG and ITG.L in the UC group showed stronger FC with multiple brain regions, mainly located in the occipital and temporal lobes, compared to the HC or WC group, while the WC group showed decreased or similar FC compared to the HC group. Interpretation: Excessive enhancement of brain functional activity or connecting intensity in ASM-naïve patients with NDE may be associated with a higher risk of poor drug response. [ABSTRACT FROM AUTHOR]

Neonatal intensive care unit (NICU), particularly in treating developmental and epileptic encephalopathy (DEE) and metabolic epilepsy (ME), requires a deep understanding of their complex etiologies and treatment responses. After excluding treatable cases such as infectious or autoimmune encephalitis, our focus shifted to a more challenging subgroup of 59 patients for in-depth genetic analysis using exome sequencing (ES). The ES analysis identified 40 genetic abnormalities, significantly including de novo variants. Notably, we found structural variation as duplications in regions 2q24.3, including SCN1A and SCN2A were observed in 7 cases. These genetic variants, impacting ion channels, glucose transport, transcription regulation, and kinases, play a crucial role in determining medication efficacy. More than one-third (34.2%) of patients with DEE had an unfavorable response to anti-seizure medications (ASMs) in the chronic phase. However, since the ketogenic supplementary diet showed a positive effect, more than three-quarters (80%) of these drug-resistant patients improved during a 3-month follow-up. In contrast, the ME had a lower adverse reaction rate of 9.1% (2/22) to specialized medications, yet there were 5 fatalities and 10 cases with unidentified genetic etiologies. This study suggests the potential of categorizing drug-resistant variants and that a ketogenic diet could be beneficial in managing DEE and ME. It also opens new perspectives on the mechanisms of the ketogenic diet on the discovered genetic variants. [ABSTRACT FROM AUTHOR]

The effects of elexacaftor/tezacaftor/ivacaftor (ETI) on respiratory outcomes for people with cystic fibrosis (CF) were demonstrated by several clinical trials, mainly based on simple spirometry. However, gains in lung function may vary greatly between patients, and predictors of FEV 1 change after treatment have yet to be defined. Which ventilatory parameters are involved in the heterogeneity of FEV 1 change after 12-month ETI treatment in people with CF and advanced lung disease? This was a multicenter, observational, prospective cohort study at two major CF centers in Italy. We enrolled 47 adults with CF and advanced lung disease (FEV 1 < 40% or actively listed for lung transplant) who started ETI treatment between December 2019 and December 2021. At treatment initiation and after 12 months, patients underwent body plethysmography. Values were compared at the two time points. To assess the relationship between baseline plethysmography measurements and treatment-induced changes in FEV 1 , we used the Spearman rank correlation coefficient (r s) and median quantile regressions. After 12 months of ETI treatment, there was a significant increase in FEV 1 % predicted from a median value of 36.0 (25th-75th percentile, 33-39) to 52 (25th-75th percentile, 43-61) (P <.001). Inspiratory capacity/total lung capacity (TLC) ratio also increased from 32.0 (25th-75th percentile, 28.6-36.9) to 36.3 (25th-75th percentile, 33.4-41.3) (P <.001). Specific airway resistance decreased from 263 (25th-75th percentile, 182-405) to 207 (25th-75th percentile, 120-258) (P <.001). Functional residual capacity/TLC ratio decreased from 68.2 (25th-75th percentile, 63.3-71.9) to 63.9 (25th-75th percentile, 58.8-67.1) (P <.001), and residual volume/TLC ratio decreased from 53.1 (25th-75th percentile, 48.3-59.4) to 45.6 (25th-75th percentile, 39.4-49.8) (P <.001). Changes in FEV 1 % predicted negatively correlated with baseline functional residual capacity/TLC ratio (r s = −0.38, P =.009) and residual volume/TLC ratio (r s = −0.42, P =.004). After adjustment for age at treatment initiation and cystic fibrosis transmembrane conductance regulator genotype, we estimated that for each 10-unit increase in baseline residual volume/TLC ratio, the expected median change in FEV 1 decreased by 2.3 (95% CI, −5.8 to −0.8). ETI was associated with improvements in both static and dynamic volumes in people with CF and advanced lung disease. Heterogeneity in FEV 1 % predicted change after 12 months of treatment may be predicted by the severity of hyperinflation at baseline. [ABSTRACT FROM AUTHOR]

Drug responses heterogeneity is often highlighted to justify the need for precision medicine. However, due to the highly complex nature of cell phenotypes in many diseases, one of key challenges is how to obtain the high content features in a cellular population. Here we present a single-cell vibrational phenomics approach, integrating synchrotron infrared microspectroscopy and multivariate calculation, for quantitatively evaluating the cellular responses to drug perturbation with single cell resolution. In a human hepatocellular carcinoma HepG2 cell model, the phenotypic changes induced by two types of drugs, taxol (TAX) and protopanaxadiol (PPD), were analyzed and revealed the response heterogeneity in drug concentration and chemical components. These findings not only provide a label-free strategy for determining the drug response at the single cell level, but also demonstrate the great potential of vibrational phenomics as a drug discovery platform. [ABSTRACT FROM AUTHOR]

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG's surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy. [ABSTRACT FROM AUTHOR]

Abstract Neonatal intensive care unit (NICU), particularly in treating developmental and epileptic encephalopathy (DEE) and metabolic epilepsy (ME), requires a deep understanding of their complex etiologies and treatment responses. After excluding treatable cases such as infectious or autoimmune encephalitis, our focus shifted to a more challenging subgroup of 59 patients for in-depth genetic analysis using exome sequencing (ES). The ES analysis identified 40 genetic abnormalities, significantly including de novo variants. Notably, we found structural variation as duplications in regions 2q24.3, including SCN1A and SCN2A were observed in 7 cases. These genetic variants, impacting ion channels, glucose transport, transcription regulation, and kinases, play a crucial role in determining medication efficacy. More than one-third (34.2%) of patients with DEE had an unfavorable response to anti-seizure medications (ASMs) in the chronic phase. However, since the ketogenic supplementary diet showed a positive effect, more than three-quarters (80%) of these drug-resistant patients improved during a 3-month follow-up. In contrast, the ME had a lower adverse reaction rate of 9.1% (2/22) to specialized medications, yet there were 5 fatalities and 10 cases with unidentified genetic etiologies. This study suggests the potential of categorizing drug-resistant variants and that a ketogenic diet could be beneficial in managing DEE and ME. It also opens new perspectives on the mechanisms of the ketogenic diet on the discovered genetic variants.

Abstract Considering the key role of myeloid cell differentiation—related genes in the tumor microenvironment (TME), we aimed to build a prognostic risk model using these genes for Lung adenocarcinoma (LUAD). The mRNA gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded as the training and validation sets. Then, “edgeR” R package was applied to screen out the differentially expressed genes (DEGs) and univariate cox regression, backward stepwise selection analyses were performed to construct a prognostic model for LUAD. ESTIMATE, TIMER, XCELL, CIBERSORT abs, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT algorithms were conducted to access the association of risk levels with the stromal and immune cell infiltration levels in LUAD. Six genes (F2RL1, PRKDC, TNFSF11, INHA, PLA2G3 and TUBB1) were utilized to construct the prognostic model. The risk model showed excellent prognostic performance for LUAD in both TCGA and GEO datasets. Also, compared to the low-risk patients, the high-risk patients had higher expression of immune checkpoint molecules and showed a lower IC50 value to the chemotherapy agents. Our findings provided a myeloid cell differentiation—related gene signature that could effectively predict prognosis and guide treatment strategies for LUAD patients.

Abstract Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos (‘microPhos’), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in 90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.

Abstract We report a 4‐year‐old girl developing therapy refractory paronychia induced by selumetinib, an oral selective inhibitor of mitogen‐activated protein kinase 1/2 prescribed for a large submandibular plexiform neurofibroma. Although this cutaneous reaction is well‐known and more prevalent in children than in adults, no histopathological characterisations of nail unit toxicity in children on selumetinib have been reported so far. We show histopathological studies on patient‐derived periungual inflamed skin to investigate the cutaneous impact of selumetinib therapy. Our findings are consistent with those of epidermal growth factor receptor inhibitors and support the role of a non‐specific immune activation rather than opportunistic infection in the underlying mechanism of the disease. Partial bilateral matricectomies with electrocautery were resolutive, and the child restarted selumetinib with no recurrence of paronychia during a follow‐up period of 3 months after nail surgery.

Copy number alterations (CNAs) are prevalent across breast cancer (BCa) subtypes, with triple-negative BCa exhibiting the most heterogeneous CN profile, while luminal A/estrogen-receptor-positive tumors demonstrate the least heterogeneity. While chromosome arm-level CNAs impact 25% of the typical cancer genome, only 10% of the genome is affected by focal CNAs. The most frequent CNAs in BCa include losses of 8p, 13q, 16q, and 17p and gains of 1q and 8q. CNAs influence tumor biology, including cell proliferation, invasion, and metastasis. They can also serve as prognostic markers, with specific CNAs associated with disease aggressiveness and poor clinical outcomes. While CNAs offer promise as biomarkers for personalized treatments, challenges such as tumor heterogeneity and clonal evolution must be addressed. Technological advances and bioinformatics present opportunities for refining our understanding of CNAs and translating this knowledge into clinical practice. Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact. [ABSTRACT FROM AUTHOR]

Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation. Synopsis: The study introduces µPhos, an accessible, plate-based workflow for low-input mass spectrometry-based phosphoproteomics. µPhos greatly facilitates high-dimensional experimental designs, as demonstrated by a 96-well drug response screen and analysis of small anatomical regions in the mouse brain. Systematic optimization results in sensitive and selective enrichment of phosphopeptides in <100 µL, allowing direct processing of 96-well plates within one working day. µPhos combined with dia-PASEF reproducibly quantifies 6000 to 17,000 confidently localized phosphosites from 1 to 20 µg protein starting material. Sub-region resolved analysis of the mouse brain phosphoproteome infers spatially distinct kinase activity. Time-course analysis of chronic myeloid leukemia cells treated with tyrosine kinase inhibitors reveals drug-specific responses. The study introduces µPhos, an accessible, plate-based workflow for low-input mass spectrometry-based phosphoproteomics. µPhos greatly facilitates high-dimensional experimental designs, as demonstrated by a 96-well drug response screen and analysis of small anatomical regions in the mouse brain. [ABSTRACT FROM AUTHOR]

Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques. [ABSTRACT FROM AUTHOR]

Numerous aspects of cellular signaling are regulated by the kinome—the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation driving many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. In this work, we attempt to link the state of the human kinome, or kinotype, with cell viability in treated, patient-derived PDAC tumor and cancer-associated fibroblast cell lines. We applied classification models to independent kinome perturbation and kinase inhibitor cell screen data, and found that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines, including the understudied kinases CSNK2A1/3, CAMKK2, and PIP4K2C. We next utilized these models to predict the response of new, clinical kinase inhibitors that were not present in the initial dataset for model devlopment and conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the perturbed state of the human protein kinome provides significant opportunity for better understanding of signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapeutic strategies for PDAC. [ABSTRACT FROM AUTHOR]

Considering the key role of myeloid cell differentiation—related genes in the tumor microenvironment (TME), we aimed to build a prognostic risk model using these genes for Lung adenocarcinoma (LUAD). The mRNA gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded as the training and validation sets. Then, "edgeR" R package was applied to screen out the differentially expressed genes (DEGs) and univariate cox regression, backward stepwise selection analyses were performed to construct a prognostic model for LUAD. ESTIMATE, TIMER, XCELL, CIBERSORT abs, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT algorithms were conducted to access the association of risk levels with the stromal and immune cell infiltration levels in LUAD. Six genes (F2RL1, PRKDC, TNFSF11, INHA, PLA2G3 and TUBB1) were utilized to construct the prognostic model. The risk model showed excellent prognostic performance for LUAD in both TCGA and GEO datasets. Also, compared to the low-risk patients, the high-risk patients had higher expression of immune checkpoint molecules and showed a lower IC50 value to the chemotherapy agents. Our findings provided a myeloid cell differentiation—related gene signature that could effectively predict prognosis and guide treatment strategies for LUAD patients. [ABSTRACT FROM AUTHOR]

Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein–protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers. [ABSTRACT FROM AUTHOR]

By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of −4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response. [ABSTRACT FROM AUTHOR]

Epitranscriptomics is a field that delves into post-transcriptional changes. Among these modifications, the conversion of adenosine to inosine, traduced as guanosine (A>I(G)), is one of the known RNA-editing mechanisms, catalyzed by ADARs. This type of RNA editing is the most common type of editing in mammals and contributes to biological diversity. Disruption in the A>I(G) RNA-editing balance has been linked to diseases, including several types of cancer. Drug resistance in patients with cancer represents a significant public health concern, contributing to increased mortality rates resulting from therapy non-responsiveness and disease progression, representing the greatest challenge for researchers in this field. The A>I(G) RNA editing is involved in several mechanisms over the immunotherapy and genotoxic drug response and drug resistance. This review investigates the relationship between ADAR1 and specific A>I(G) RNA-edited sites, focusing particularly on breast cancer, and the impact of these sites on DNA damage repair and the immune response over anti-cancer therapy. We address the underlying mechanisms, bioinformatics, and in vitro strategies for the identification and validation of A>I(G) RNA-edited sites. We gathered databases related to A>I(G) RNA editing and cancer and discussed the potential clinical and research implications of understanding A>I(G) RNA-editing patterns. Understanding the intricate role of ADAR1-mediated A>I(G) RNA editing in breast cancer holds significant promise for the development of personalized treatment approaches tailored to individual patients' A>I(G) RNA-editing profiles. [ABSTRACT FROM AUTHOR]

The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments. Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management. The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries. As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced. [ABSTRACT FROM AUTHOR]

ObjectiveTo investigate the clinical efficacy, preservation of laryngeal function, and safety differences between PD-1 inhibitors combined with chemotherapy, and targeted therapy combined with chemotherapy in LA HPSCC patients.MethodsThis was a retrospective analysis of patients with LA HPSCC treated at Beijing Tongren Hospital, Capital Medical University from October, 2020 to March, 2024. A total of 110 eligible patients were included, 56 in the PD-1 inhibitors combined with chemotherapy group (Group A), and 54 in the targeted therapy combined with chemotherapy group (Group B). Relevant clinical data were collected, and the clinical efficacy, preservation of laryngeal function, complete response (CR) rate, pathological complete response (pCR) rate, major pathological response (MPR), and treatment-related adverse events (TRAEs) of the two groups were analyzed and compared.ResultsIn both groups A and B, the objective response rate (ORR) and disease control rate (DCR) were similar with no significant differences, but the pCR rate in Group A was much higher than that in Group B, at 37.5% and 7.4%, respectively (p

BackgroundAs targeted drug development in myasthenia gravis (MG) continues to advance, it is important to compare the efficacy of these drugs for better clinical decision-making. However, due to the varied regimens and dosages used in clinical trials for different drugs, a standardized comparison between them is necessary.MethodsThis study enrolled participants in phase II and III trials of innovative targeted drugs for MG. The primary outcome was the change in Quantitative Myasthenia Gravis score (MG-QMG) from baseline. The efficacy of all drugs at four time points was separately analyzed at four time points: initiation 1 week, initiation 4 weeks, maximized response, and post last dose 4 weeks. A network meta-analysis was conducted to compare the results of the different drugs.ResultsA total of 9 drugs, including Efgartigimod, Rozanolixizumab, Batoclimab, Eculizumab, Belimumab, Zilucoplan, Ravulizumab, Nipocalimab, Rituximab, derived from 12 studies were analyzed. At the initiation 1-week time point, three drugs exhibited significant improvement compared to the placebo effect: Efgartigimod, Zilucoplan, Rozanolixizumab. At the initiation 4-week time point, four drugs showed significant improvement compared to the placebo effect: Efgartigimod, Rozanolixizumab, Batoclimab, Zilucoplan. At the maximized response time point, six drugs achieved significant improvement compared to the placebo effect: Efgartigimod, Rozanolixizumab, Batoclimab, Eculizumab, Zilucoplan, Ravulizumab. At the post last dose 4-week point, all drugs statistically showed no significant difference from the placebo.ConclusionAlthough the MG subtypes were not consistent across trials, within the regimen design of each trial, neonatal Fc receptor inhibitors—represented by Efgartigimod, Rozanolixizumab, and Batoclimab—exhibited the most effective response rates when compared to complement and B-cell inhibitor drugs.

Single-cell multiomics (sc-multiomics) is a burgeoning field that simultaneously integrates multiple layers of molecular information, enabling the characterization of dynamic cell states and activities in development and disease as well as treatment response. Studying drug actions and responses using sc-multiomics technologies has revolutionized our understanding of how small molecules intervene for specific cell types in cancer treatment and how they are linked with disease etiology and progression. Here, we summarize recent advances in sc-multiomics technologies that have been adapted and improved in drug research and development, with a focus on genome-wide examination of drug-chromatin engagement and the applications in drug response and the mechanisms of drug resistance. Furthermore, we discuss how state-of-the-art technologies can be taken forward to devise innovative personalized treatment modalities in biomedical research.

IntroductionLiver cancer, particularly Hepatocellular carcinoma (HCC), remains a significant global health concern due to its high prevalence and heterogeneous nature. Despite the existence of approved drugs for HCC treatment, the scarcity of predictive biomarkers limits their effective utilization. Integrating diverse data types to revolutionize drug response prediction, ultimately enabling personalized HCC management.MethodIn this study, we developed multiple supervised machine learning models to predict treatment response. These models utilized classifiers such as logistic regression (LR), k-nearest neighbors (kNN), neural networks (NN), support vector machines (SVM), and random forests (RF) using a comprehensive set of molecular, biochemical, and immunohistochemical features as targets of three drugs: Pantoprazole, Cyanidin 3-glycoside (Cyan), and Hesperidin. A set of performance metrics for the complete and reduced models were reported including accuracy, precision, recall (sensitivity), specificity, and the Matthews Correlation Coefficient (MCC).Results and DiscussionNotably, (NN) achieved the best prediction accuracy where the combined model using molecular and biochemical features exhibited exceptional predictive power, achieving solid accuracy of 0.9693 ∓ 0.0105 and average area under the ROC curve (AUC) of 0.94 ∓ 0.06 coming from three cross-validation iterations. Also, found seven molecular features, seven biochemical features, and one immunohistochemistry feature as promising biomarkers of treatment response. This comprehensive method has the potential to significantly advance personalized HCC therapy by allowing for more precise drug response estimation and assisting in the identification of effective treatment strategies.

IntroductionPharmacogenetics (PGx) holds promise for optimizing psychotropic medication use, with CYP2D6 and CYP2C19 identified as key genes in antidepressant treatment. However, few studies have explored the genetic variants of these genes in real-world settings for patients experiencing ineffectiveness or adverse drug reactions (ADRs) to antidepressants.MethodsThis case series includes 40 patients who underwent PGx testing due to antidepressant ineffectiveness or ADRs between June 2020 and April 2022. We describe the patients' demographic, clinical, and genetic characteristics and assess the value of PGx testing based on feedback from their psychiatrists.ResultsThe most common diagnoses were major depressive disorder (60.0%) and post-traumatic stress disorder (30.0%). Ineffectiveness was reported in 65.0% of patients, ADRs in 2.5%, and both in 32.5%. The antidepressants involved included SSRIs (45.0%), SNRIs (27.5%), atypical antidepressants (20.0%), and tricyclics (17.5%). Only 17.5% of patients had normal CYP2D6 and CYP2C19 metabolic activity. Actionable genetic variants were identified in 22.0% of CYP2D6/CYP2C19-antidepressant-response pairs. PGx recommendations were followed in 92.7% of cases, with significant improvement in ADRs reported in 71.4% of patients and efficacy improvement in 79.5%.DiscussionOur findings suggest that PGx testing can guide prescribing decisions for patients with antidepressant ineffectiveness or ADRs. The relatively high prevalence of genetic variants affecting pharmacokinetics supports the broader adoption of PGx testing in psychiatric practice.

The article discusses a randomized study comparing the efficacy and safety of pregabalin versus gabapentin in chronic pruritus patients. Chronic pruritus, a persistent itching sensation lasting over 6 weeks, significantly impacts physical, psychological, and social well-being. The study found that pregabalin significantly improved chronic pruritus compared to gabapentin, with both drugs being safe and tolerable. Future research should explore long-term effects and conduct health economic analyses for cost-effectiveness. [Extracted from the article]

Abstract Background Cancer-associated fibroblasts (CAFs) are the major cellular component of the tumor microenvironment and are known to affect tumor growth and response to various treatments. This study was undertaken to investigate the crosstalk between tumor-matched or unmatched CAFs and head and neck squamous cell carcinoma (HNSCC) cells regarding tumor growth and treatment response. Methods Three HNSCC cell lines (LK0412, LK0902 and LK0923), were cocultured in 2D or in 3D with their tumor-matched CAFs, site matched CAFs from other tumors or normal oral fibroblasts (NOFs). Cell proliferation was assessed as the amount of Ki67 positive cells/ spheroid area in formalin-fixed- paraffin-embedded 3D spheroids stained with Ki67 antibody. Viability after seven days of cisplatin treatment was measured with CellTiter-Glo 3D Viability Assay. The mRNA expression of CAF-associated markers (ACTA2, COL1A2, FAP, PDGFRα, PDGFRβ, PDPN, POSTN and S100A4) in CAFs before and after coculture with tumor cells as well as mRNA expression of CAF-induced genes (MMP1, MMP9 and FMOD) in tumor cells separated from CAFs after co-culture was measured with RT-qPCR. The expression of selected protein biomarkers was validated with immunohistochemistry based on previous mRNA expression results. Results The proliferation of the LK0412 and LK0902 tumor spheroids varied significantly when cocultured with different CAFs and NOFs as shown by Ki-67 positive cells. RT‒qPCR analysis revealed different molecular profile of the analyzed HNSCC-derived CAFs concerning the expression of CAF-associated markers. The interaction between CAFs and HNSCC cells was more pronounced after coculture with unmatched CAFs as shown by changes in mRNA expression pattern of CAF-specific markers. Additionally, the unmatched CAFs significantly upregulated the mRNA expression of MMP1, MMP9 and FMOD in tumor cells compared to tumor-matched CAFs. Conclusion Our results indicate that tumor-matched CAFs are unique for each tumor and affect the proliferation and the gene/protein expression of tumor cells in a distinct manner. The interaction between tumor unmatched CAFs and HNSCC cells in the tumor spheroids is associated with significant changes in the mRNA expression of CAF-specific markers and significant increases in FMOD and MMP9 in tumor cells compared to when cocultured with tumor-matched CAFs. Taken together, our results show how important the selection of CAFs is to get a reliable in vitro model that mimics the patients’ tumor.

Abstract Background The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations. Methods To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses. Results By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails. Conclusions In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.

Regulated cell death (RCD) has been documented to have great potentials for discovering novel biomarkers and therapeutic targets in malignancies. But its role and clinical value in HR+/HER2- breast cancer, the most common subtype of breast cancer, are obscure. In this study, we comprehensively explored 12 types of RCD patterns and found extensive mutations and dysregulations of RCD genes in HR+/HER2- breast cancer. A prognostic RCD scoring system (CDScore) based on six critical genes (LEF1, SLC7A11, SFRP1, IGFBP6, CXCL2, STXBP1) was constructed, in which a high CDScore predicts poor prognosis. The expressions and prognostic value of LEF1 and SFRP1were also validated in our tissue microarrays. The nomogram established basing on CDScore, age and TNM stage performed satisfactory in predicting overall survival, with an area under the ROC curve of 0.89, 0.82 and 0.8 in predicting 1-year, 3-year and 5-year overall survival rates, respectively. Furthermore, CDScore was identified to be correlated with tumor microenvironments and immune checkpoints by excavation of bulk and single-cell sequencing data. Patients in CDScore high group might be resistant to standard chemotherapy and target therapy. Our results underlined the potential effects and importance of RCD in HR+/HER2- breast cancer and provided novel biomarkers and therapeutic targets for HR+/HER2- breast cancer patients.

Background: Cancer-associated fibroblasts (CAFs) are the major cellular component of the tumor microenvironment and are known to affect tumor growth and response to various treatments. This study was undertaken to investigate the crosstalk between tumor-matched or unmatched CAFs and head and neck squamous cell carcinoma (HNSCC) cells regarding tumor growth and treatment response. Methods: Three HNSCC cell lines (LK0412, LK0902 and LK0923), were cocultured in 2D or in 3D with their tumor-matched CAFs, site matched CAFs from other tumors or normal oral fibroblasts (NOFs). Cell proliferation was assessed as the amount of Ki67 positive cells/ spheroid area in formalin-fixed- paraffin-embedded 3D spheroids stained with Ki67 antibody. Viability after seven days of cisplatin treatment was measured with CellTiter-Glo 3D Viability Assay. The mRNA expression of CAF-associated markers (ACTA2, COL1A2, FAP, PDGFRα, PDGFRβ, PDPN, POSTN and S100A4) in CAFs before and after coculture with tumor cells as well as mRNA expression of CAF-induced genes (MMP1, MMP9 and FMOD) in tumor cells separated from CAFs after co-culture was measured with RT-qPCR. The expression of selected protein biomarkers was validated with immunohistochemistry based on previous mRNA expression results. Results: The proliferation of the LK0412 and LK0902 tumor spheroids varied significantly when cocultured with different CAFs and NOFs as shown by Ki-67 positive cells. RT‒qPCR analysis revealed different molecular profile of the analyzed HNSCC-derived CAFs concerning the expression of CAF-associated markers. The interaction between CAFs and HNSCC cells was more pronounced after coculture with unmatched CAFs as shown by changes in mRNA expression pattern of CAF-specific markers. Additionally, the unmatched CAFs significantly upregulated the mRNA expression of MMP1, MMP9 and FMOD in tumor cells compared to tumor-matched CAFs. Conclusion: Our results indicate that tumor-matched CAFs are unique for each tumor and affect the proliferation and the gene/protein expression of tumor cells in a distinct manner. The interaction between tumor unmatched CAFs and HNSCC cells in the tumor spheroids is associated with significant changes in the mRNA expression of CAF-specific markers and significant increases in FMOD and MMP9 in tumor cells compared to when cocultured with tumor-matched CAFs. Taken together, our results show how important the selection of CAFs is to get a reliable in vitro model that mimics the patients' tumor. [ABSTRACT FROM AUTHOR]

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion: Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis. [ABSTRACT FROM AUTHOR]

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server. [ABSTRACT FROM AUTHOR]

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia–reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc−/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/. [ABSTRACT FROM AUTHOR]

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients. [ABSTRACT FROM AUTHOR]

Cancer is a significant global health threat and a leading cause of death worldwide. Effective early-stage interventions, particularly surgery, can potentially cure many solid tumors. However, the risk of postoperative cancer recurrence remains high. Recent research highlights the influence of perioperative anesthetic and analgesic choices on the fate of residual cancer cells, potentially affecting recurrence risks. Among these agents, ketamine—a well-known anesthetic and analgesic—has garnered interest due to its antitumor properties, mainly through inhibiting the N-methyl-D-aspartate (NMDA) receptor found in various cancer tissues. Additionally, ketamine's potential immunomodulatory effects, given the expression of NMDA receptors on immune cells, suggest that it plays a significant role during the perioperative period. This review synthesizes current evidence on ketamine's impact on cancer cell biology, inflammation, immune modulation, and the role of the gut microbiota, proposing ketamine as a promising agent for enhancing oncological outcomes. [ABSTRACT FROM AUTHOR]

Histopathologic regression of cutaneous melanoma is considered a favorable prognostic factor, but its significance in clinical practice remains controversial. To investigate the prognostic importance of regression in patients with primary cutaneous melanoma undergoing sentinel lymph node (SLN) biopsy and to assess its significance in patients progressing to an unresectable stage requiring systemic therapy. We retrospectively reviewed patients with newly diagnosed melanoma undergoing SLN biopsy between 2010 and 2015 and available information on histopathologic regression (n = 1179). Survival data and associations of clinical variables with SLN status were assessed. Patients with regressive melanoma showed favorable relapse-free (hazard ratio [HR], 0.52; P =.00013), distant metastasis–free (HR, 0.56; P =.0020), and melanoma-specific survival (HR, 0.35; P =.00053). Regression was associated with negative SLN (odds ratio, 0.48; P =.0077). In patients who progressed to an unresectable stage, regression was associated with favorable progression-free survival under immune checkpoint inhibition (HR, 0.43; P =.031) but not under targeted therapy (HR, 1.14; P =.73) or chemotherapy (HR, 3.65; P =.0095). Retrospective, single-institutional design. Regression of cutaneous melanoma is associated with improved prognosis in patients eligible for SLN biopsy as well as in patients with unresectable disease receiving systemic therapy with immune checkpoint inhibitors. [ABSTRACT FROM AUTHOR]

Despite technical and pharmacological advancements in recent years, including optimized therapies and personalized medicine, postoperative pain management remains challenging and sometimes undertreated. This review aims to summarize and update how genotype-guided therapeutics within personalized medicine can enhance postoperative pain management. Several studies in the area have demonstrated that genotype-guided therapy has the ability to lower opioid consumption and improve postoperative pain. Gene mutations, primarily OPRM1, CYP2D6, CYP2C9, COMT and ABCB1, have been shown to exert nuanced influences on analgesic response and related pharmacological outcomes. This review underscores the integration of pharmacogenetic-guided personalized medicine into perioperative care, particularly when there is uncertainty regarding opioid prescriptions. This approach leads to superior outcomes in terms of postoperative pain relief and reduced morbidity for numerous patients. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) displays diverse intra-tumoral traits, impacting its progression and treatment outcomes. This study aimed to refine PCa cell culture conditions for dynamic monitoring of androgen receptor (AR) activity at the single-cell level. We introduced an extracellular matrix-Matrigel (ECM-M) culture model, enhancing cellular tracking during bioluminescence single-cell imaging while improving cell viability. ECM-M notably tripled the traceability of poorly adherent PCa cells, facilitating robust single-cell tracking, without impeding substrate permeability or AR response. This model effectively monitored AR modulation by antiandrogens across various PCa cell lines. Single-cell imaging unveiled heterogeneous antiandrogen responses within populations, correlating non-responsive cell proportions with drug IC50 values. Integrating ECM-M culture with the PSEBC-TSTA biosensor enabled precise characterization of ARi responsiveness within diverse cell populations. Our ECM-M model stands as a promising tool to assess heterogeneous single-cell treatment responses in cancer, offering insights to link drug responses to intracellular signaling dynamics. This approach enhances our comprehension of the nuanced and dynamic nature of PCa treatment responses. [ABSTRACT FROM AUTHOR]

Glioblastoma, a deadly brain tumor, shows limited response to standard therapies like temozolomide (TMZ). Recent findings from the REGOMA trial underscore a significant survival improvement offered by Regorafenib (REGO) in recurrent glioblastoma. Our study aimed to propose a 3D ex vivo drug response precision medicine approach to investigate recurrent glioblastoma sensitivity to REGO and elucidate the underlying molecular mechanisms involved in tumor resistance or responsiveness to treatment. Three-dimensional glioblastoma organoids (GB-EXPs) obtained from 18 patients' resected recurrent glioblastoma tumors were treated with TMZ and REGO. Drug responses were evaluated using NAD(P)H FLIM, stratifying tumors as responders (Resp) or non-responders (NRs). Whole-exome sequencing was performed on 16 tissue samples, and whole-transcriptome analysis on 13 GB-EXPs treated and untreated. We found 35% (n = 9) and 77% (n = 20) of tumors responded to TMZ and REGO, respectively, with no instances of TMZ-Resp being REGO-NRs. Exome analysis revealed a unique mutational profile in REGO-Resp tumors compared to NR tumors. Transcriptome analysis identified distinct expression patterns in Resp and NR tumors, impacting Rho GTPase and NOTCH signaling, known to be involved in drug response. In conclusion, recurrent glioblastoma tumors were more responsive to REGO compared to TMZ treatment. Importantly, our approach enables a comprehensive longitudinal exploration of the molecular changes induced by treatment, unveiling promising biomarkers indicative of drug response. [ABSTRACT FROM AUTHOR]

Background: The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations. Methods: To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses. Results: By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails. Conclusions: In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response. [ABSTRACT FROM AUTHOR]

Advances in the field of genomics and transcriptomics have enabled researchers to identify gene signatures related to development and treatment of Small Cell Lung Cancer. In most cases, complex gene expression patterns are identified, comprising of genes with differential behavior. Most tools use single-genes as predictors of drug response, with only limited options for multi-gene use. Here we examine the potential of predicting drug response using these complex gene expression signatures by employing clustering and signal enrichment in Small Cell Lung Cancer. Our results demonstrate clustering genes from complex expression patterns helps identify differential activity of gene groups with alternate function which can then be used to predict drug response. • Clustering genes in expression signatures before enrichment analysis helps find key groups • Most small cell lung cancer cell lines are enriched for epithelial genes • A subset of inflamed signature genes used to predict immunotherapy response are not expressed in cell lines [ABSTRACT FROM AUTHOR]

Introduction: We evaluated baseline Clearance of anti-tumor necrosis factors and human leukocyte antigen variant (HLA DQA1*05) in combination as poor prognostic factors (PPF) of pharmacokinetic (PK) origin impacting immune response (formation of antidrug antibodies) and disease control of inflammatory bowel disease (IBD) patients treated with infliximab or adalimumab. Methods: Baseline Clearance was estimated in IBD patients before starting treatment using weight and serum albumin concentrations. HLA DQA1*05 carrier status (rs2097432 A/G or G/G variant) was measured using real time polymerase chain reaction. The outcomes consisted of immune response, clinical and biochemical remission (C-reactive protein<3 mg/L in the absence of symptoms), and endoscopic remission (SES-CD<3). Statistical analysis consisted of logistic regression and nonlinear mixed effect models. Results and discussion: In 415 patients enrolled from 4 different cohorts (median age 27 [IQR: 15-43] years, 46% females), Clearance>0.326 L/day and HLA DQA1*05 carrier status were 2-fold more likely to have antidrug antibodies (OR=2.3, 95%CI: 1.7-3.4; p<0.001, and OR=1.9, 95%CI: 1.4-2.8; p<0.001, respectively). Overall, each incremental PPF of PK origin resulted in a 2-fold (OR=2.16, 95%CI: 1.7-2.7; p<0.01) higher likelihood of antidrug antibody formation. The presence of both PPF of PK origin resulted in higher rates of antidrug antibodies (p<0.01) and lower clinical and biochemical remission (p<0.01). Each incremental increase in PPF of PK origin associated with lower likelihood of endoscopic remission (OR=0.4, 95%CI: 0.2-0.7; p<0.001). Prior biologic experience heightened the negative impact of PPF of PK origin on clinical and biochemical remission (p<0.01). Implementation of proactive therapeutic drug monitoring reduced it, particularly during maintenance and in the presence of higher drug concentrations (p<0.001). We conclude that PPF of PK origin, including both higher Clearance and carriage of HLA DQA1*05, impact outcomes in patients with IBD. [ABSTRACT FROM AUTHOR]

Hyperactive ribosome biogenesis (RiboSis) fuels unrestricted cell proliferation, whereas genomic hallmarks and therapeutic targets of RiboSis in cancers remain elusive, and efficient approaches to quantify RiboSis activity are still limited. Here, we have established an in silico approach to conveniently score RiboSis activity based on individual transcriptome data. By employing this novel approach and RNA-seq data of 14 645 samples from TCGA/GTEx dataset and 917 294 single-cell expression profiles across 13 cancer types, we observed the elevated activity of RiboSis in malignant cells of various human cancers, and high risk of severe outcomes in patients with high RiboSis activity. Our mining of pan-cancer multi-omics data characterized numerous molecular alterations of RiboSis, and unveiled the predominant somatic alteration in RiboSis genes was copy number variation. A total of 128 RiboSis genes, including EXOSC4 , BOP1 , RPLP0P6 and UTP23 , were identified as potential therapeutic targets. Interestingly, we observed that the activity of RiboSis was associated with TP53 mutations, and hyperactive RiboSis was associated with poor outcomes in lung cancer patients without TP53 mutations, highlighting the importance of considering TP53 mutations during therapy by impairing RiboSis. Moreover, we predicted 23 compounds, including methotrexate and CX-5461, associated with the expression signature of RiboSis genes. The current study generates a comprehensive blueprint of molecular alterations in RiboSis genes across cancers, which provides a valuable resource for RiboSis-based anti-tumor therapy. [ABSTRACT FROM AUTHOR]

Bromelain is a complex natural mixture of sulfhydryl-containing proteolytic enzymes that can be extracted from the stem or fruit of the pineapple. This compound is considered a safe nutraceutical, has been used to treat various health problems, and is also popular as a health-promoting dietary supplement. There is continued interest in bromelain due to its remarkable therapeutic properties. The mechanism of action of bromelain appears to extend beyond its proteolytic activity as a digestive enzyme, encompassing a range of effects (mucolytic, anti-inflammatory, anticoagulant, and antiedematous effects). Little is known about the clinical use of bromelain in pediatrics, as most of the available data come from in vitro and animal studies, as well as a few RCTs in adults. This narrative review was aimed at highlighting the main aspects of the use of bromelain in children, which still appears to be limited compared to its potential. Relevant articles were identified through searches in MEDLINE, PubMed, and EMBASE. There is no conclusive evidence to support the use of bromelain in children, but the limited literature data suggest that its addition to standard therapy may be beneficial in treating conditions such as upper respiratory tract infections, specific dental conditions, and burns. Further studies, including RCTs in pediatric settings, are needed to better elucidate the mechanism of action and properties of bromelain in various therapeutic areas. [ABSTRACT FROM AUTHOR]