UNASSIGNED: We sought to evaluate the impact of baseline anxiety levels on drug placebo separation and drug and placebo response in acutely psychotic schizophrenic subjects.
UNASSIGNED: In this post-hoc analysis, modified intent-to-treat Positive and Negative Syndrome Scale data were obtained from a phase 2, multi-center, 5 week, randomized, double-blind, placebo-controlled trial of KarXT in hospitalized adults with DSM-5 schizophrenia experiencing an acute exacerbation or relapse of symptoms. We investigated the impact of anxiety on drug placebo separation and drug and placebo response in 2 ways. In the first set of analyses, we dichotomized the data based on the absence or presence of anxiety symptoms. In the second set of analyses, we categorized subjects by levels of anxiety. All analyses were conducted using generalized linear models with normal distribution and identity link function.
UNASSIGNED: On average, subjects entering the trial were suffering from a moderate level of anxiety. Subjects with no baseline anxiety had a significant increase in placebo response, a decrease in drug response and did not separate drugs from placebo. With increasing levels of baseline anxiety, a larger drug placebo difference was observed.
UNASSIGNED: Our analyses identified that absence of anxiety at baseline was associated with a loss of signal at end of treatment between drug and placebo driven by a differential effect on placebo and treatment response. The effect observed was not related to the overall baseline symptom severity and was not mediated by improvement in anxiety itself. Interpretation of the results is caveated by the retrospective nature of the analyses.

OBJECTIVE: 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, 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 5-6 timepoints, 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 (PSKAT-C = 0.019), and in MOCOS, encoding molybdenum cofactor sulphurase, with allopurinol response (PSKAT-C = 0.011). Evidence for genetic association with allopurinol response in the allopurinol-to-oxypurinol gene group (PSKAT-C = 0.002) and MOCOS (PSKAT-C < 0.001) was stronger when adherence to allopurinol therapy was confirmed by plasma oxypurinol.
CONCLUSIONS: We provide evidence for common and rare genetic variation in MOCOS associating with allopurinol response.

暂无摘要。

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.

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.

BACKGROUND: Neoadjuvant treatment has been developed as a systematic approach for patients with early breast cancer and has resulted in improved breast-conserving rate and survival. However, identifying treatment-sensitive patients at the early phase of therapy remains a problem, hampering disease management and raising the possibility of disease progression during treatment.
METHODS: In this retrospective analysis, we collected 2-deoxy-2-[F-18] fluoro-d-glucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) images of primary tumor sites and axillary areas and reciprocal clinical pathological data from 121 patients who underwent neoadjuvant treatment and surgery in our center. The univariate and multivariate logistic regression analyses were performed to investigate features associated with pathological complete response (pCR). An 18F-FDG PET/CT-based prediction model was trained, and the performance was evaluated by receiver operating characteristic curves (ROC).
RESULTS: The maximum standard uptake values (SUVmax) of 18F-FDG PET/CT were a powerful indicator of tumor status. The SUVmax values of axillary areas were closely related to metastatic lymph node counts (R = 0.62). Moreover, the early SUVmax reduction rates (between baseline and second cycle of neoadjuvant treatment) were statistically different between pCR and non-pCR patients. The early SUVmax reduction rates-based model showed great ability to predict pCR (AUC = 0.89), with all molecular subtypes (HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2-) considered.
CONCLUSIONS: Our research proved that the SUVmax reduction rate of 18F-FDG PET/CT contributed to the early prediction of pCR, providing rationales for utilizing PET/CT in NAT in the future.

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.

Head and Neck Squamous Cell Carcinoma (HNSCC) remains a significant health burden due to tumor heterogeneity and treatment resistance, emphasizing the need for improved biological understanding and tailored therapies. This study enrolled 31 HNSCC patients for the establishment of patient-derived tumor organoids (PDOs), which faithfully maintained genomic features and histopathological traits of primary tumors. Long-term culture preserved key characteristics, affirming PDOs as robust representative models. PDOs demonstrated predictive capability for cisplatin treatment responses, correlating ex vivo drug sensitivity with patient outcomes. Bulk and single-cell RNA sequencing unveiled molecular subtypes and intratumor heterogeneity (ITH) in PDOs, paralleling patient tumors. Notably, a hybrid epithelial-mesenchymal transition (hEMT)-like ITH program is associated with cisplatin resistance and poor patient survival. Functional analyses identified amphiregulin (AREG) as a potential regulator of the hybrid epithelial/mesenchymal state. Moreover, AREG contributes to cisplatin resistance via EGFR pathway activation, corroborated by clinical samples. In summary, HNSCC PDOs serve as reliable and versatile models, offer predictive insights into ITH programs and treatment responses, and uncover potential therapeutic targets for personalized medicine.

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.

The metabolism of glioma cells exhibits significant heterogeneity and is partially responsible for treatment outcomes. Given this variability, we hypothesized that the effectiveness of treatments targeting various metabolic pathways depends on the bioenergetic profiles and mitochondrial status of glioma cells. To this end, we analyzed mitochondrial biomass, mitochondrial protein density, oxidative phosphorylation (OXPHOS), and glycolysis in a panel of eight glioma cell lines. Our findings revealed considerable variability: mitochondrial biomass varied by up to 3.2-fold, the density of mitochondrial proteins by up to 2.1-fold, and OXPHOS levels by up to 7.3-fold across the cell lines. Subsequently, we stratified glioma cell lines based on their mitochondrial status, OXPHOS, and bioenergetic fitness. Following this stratification, we utilized 16 compounds targeting key bioenergetic, mitochondrial, and related pathways to analyze the associations between induced changes in cell numbers, proliferation, and apoptosis with respect to their steady-state mitochondrial and bioenergetic metrics. Remarkably, a significant fraction of the treatments showed strong correlations with mitochondrial biomass and the density of mitochondrial proteins, suggesting that mitochondrial status may reflect glioma cell sensitivity to specific treatments. Overall, our results indicate that mitochondrial status and bioenergetics are linked to the efficacy of treatments targeting metabolic pathways in glioma.