Population-scale variability of the human UDP-glycosyltransferase gene family.

Human UDP-glycosyltransferases (UGTs) are responsible for the glucuronidation of a wide variety of endogenous substrates and multiple commonly prescribed drugs. Different genetic polymorphisms in UGT genes are implicated in interindividual differences in drug response and cancer risk. However, the genetic complexity beyond these variants has not been comprehensively assessed. We here leveraged whole-exome and whole-genome sequencing data from 141,456 unrelated individuals across seven major human populations to provide a comprehensive profile of genetic variability across the human UGT gene family. Overall, 9666 exonic variants were observed of which 98.9% were rare. To interpret the functional impact of UGT missense variants, we developed a gene family-specific variant effect predictor. This algorithm identified a total of 1208 deleterious variants, most of which were found in African and South Asian populations. Structural analysis corroborated the predicted effects for multiple variations in substrate binding sites. Combined, our analyses provide a systematic overview of UGT variability, which can yield insights into inter-individual differences in phase 2 metabolism and facilitate the translation of sequencing data into personalized predictions of UGT substrate disposition.

Effectiveness of PD-1 inhibitor-based first-line therapy in Chinese patients with metastatic gastric cancer: a retrospective real-world study.

Objective: Programmed cell death protein-1 (PD-1) inhibitor-based therapy has demonstrated promising results in metastatic gastric cancer (MGC). However, the previous researches are mostly clinical trials and have reached various conclusions. Our objective is to investigate the efficacy of PD-1 inhibitor-based treatment as first-line therapy for MGC, utilizing real-world data from China, and further analyze predictive biomarkers for efficacy. Methods: This retrospective study comprised 105 patients diagnosed with MGC who underwent various PD-1 inhibitor-based treatments as first-line therapy at West China Hospital of Sichuan University from January 2018 to December 2022. Patient characteristics, treatment regimens, and tumor responses were extracted. We also conducted univariate and multivariate analyses to assess the relationship between clinical features and treatment outcomes. Additionally, we evaluated the predictive efficacy of several commonly used biomarkers for PD-1 inhibitor treatments. Results: Overall, after 28.0 months of follow-up among the 105 patients included in our study, the objective response rate (ORR) was 30.5%, and the disease control rate (DCR) was 89.5% post-treatment, with two individuals (1.9%) achieving complete response (CR). The median progression-free survival (mPFS) was 9.0 months, and the median overall survival (mOS) was 22.0 months. According to both univariate and multivariate analyses, favorable OS was associated with patients having Eastern Cooperative Oncology Group performance status (ECOG PS) of 0-1. Additionally, normal baseline levels of carcinoembryonic antigen (CEA), as well as the combination of PD-1 inhibitors with chemotherapy and trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive MGC, independently predicted longer PFS and OS. However, microsatellite instability/mismatch repair (MSI/MMR) status and Epstein-Barr virus (EBV) infection status were not significantly correlated with PFS or OS extension. Conclusion: As the first-line treatment, PD-1 inhibitors, either as monotherapy or in combination therapy, are promising to prolong survival for patients with metastatic gastric cancer. Additionally, baseline level of CEA is a potential predictive biomarker for identifying patients mostly responsive to PD-1 inhibitors.

Unraveling the Drug Response Heterogeneity with Single-Cell Vibrational Phenomics.

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.

Precision Medicine for Asthma Treatment: Unlocking the Potential of the Epigenome and Microbiome.

Asthma is a leading worldwide biomedical concern. Patients can experience life-threatening worsening episodes (exacerbations) usually controlled by anti-inflammatory and bronchodilator drugs. However, substantial heterogeneity in treatment response exists and a subset of patients with unresolved asthma carry the major burden of this disease. The study of the epigenome and microbiome might bridge the gap between human genetics and environmental exposures to partially explain the heterogeneity in drug response. This review aims to provide a critical examination of the existing literature on the microbiome and epigenetic studies examining associations with asthma treatments and drug response, highlight convergent pathways, address current challenges, and offer future perspectives. Current epigenetic and microbiome studies have shown the bilateral relationship between asthma pharmacological interventions and the human epigenome and microbiome. These studies, focusing on corticosteroids and to a lesser extent on bronchodilators, azithromycin, immunotherapy, and mepolizumab, have improved the understanding of the molecular basis of treatment response and identified promising biomarkers for drug response prediction. Immune and inflammatory pathways (i.e., IL-2, TNF-α, NF-κB, and CEBPs) underlie microbiome-epigenetic associations with asthma treatment, representing potential therapeutic pathways to be targeted. A comprehensive evaluation of these omic biomarkers could significantly contribute to precision medicine and new therapeutic target discovery.

µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics.

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.

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

Promotion of maturation of human pluripotent stem cell-derived cardiomyocytes via treatment with the peroxisome proliferator-activated receptor alpha agonist Fenofibrate.

As research on in vitro cardiotoxicity assessment and cardiac disease modeling becomes more important, the demand for human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) is increasing. However, it has been reported that differentiated hPSC-CMs are in a physiologically immature state compared to in vivo adult CMs. Since immaturity of hPSC-CMs can lead to poor drug response and loss of acquired heart disease modeling, various approaches have been attempted to promote maturation of CMs. Here, we confirm that peroxisome proliferator-activated receptor alpha (PPARα), one of the representative mechanisms of CM metabolism and cardioprotective effect also affects maturation of CMs. To upregulate PPARα expression, we treated hPSC-CMs with fenofibrate (Feno), a PPARα agonist used in clinical hyperlipidemia treatment, and demonstrated that the structure, mitochondria-mediated metabolism, and electrophysiology-based functions of hPSC-CMs were all mature. Furthermore, as a result of multi electrode array (MEA)-based cardiotoxicity evaluation between control and Feno groups according to treatment with arrhythmia-inducing drugs, drug response was similar in a dose-dependent manner. However, main parameters such as field potential duration, beat period, and spike amplitude were different between the 2 groups. Overall, these results emphasize that applying matured hPSC-CMs to the field of preclinical cardiotoxicity evaluation, which has become an essential procedure for new drug development, is necessary.

A co-culture system of macrophages with breast cancer tumoroids to study cell interactions and therapeutic responses.

3D tumoroids have revolutionized in vitro/ex vivo cancer biology by recapitulating the complex diversity of tumors. While tumoroids provide new insights into cancer development and treatment response, several limitations remain. As the tumor microenvironment, especially the immune system, strongly influences tumor development, the absence of immune cells in tumoroids may lead to inappropriate conclusions. Macrophages, key players in tumor progression, are particularly challenging to integrate into the tumoroids. In this study, we established three optimized and standardized methods for co-culturing human macrophages with breast cancer tumoroids: a semi-liquid model and two matrix-embedded models tailored for specific applications. We then tracked interactions and macrophage infiltration in these systems using flow cytometry and light sheet microscopy and showed that macrophages influenced not only tumoroid molecular profiles but also chemotherapy response. This underscores the importance of increasing the complexity of 3D models to more accurately reflect in vivo conditions.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

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.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

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.

Data Valuation with Gradient Similarity.

High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks.

Tumor-matched and unmatched cancer associated fibroblasts exhibit differential effect on proliferation and FMOD and MMP9 gene expression in head and neck squamous cell carcinoma cells when cocultured in spheroids.

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, PDGFRß, 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.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response.

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/.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Efficacy of Recombinant Methioninase on Late-stage Patient Cancer in the Histoculture Drug Response Assay (HDRA) as a Potential Functional Biomarker of Sensitivity to Methionine-restriction Therapy in the Clinic.

Background/Aim: The present study utilized the three-dimensional histoculture drug response assay (HDRA) to determine the efficacy of recombinant methioninase (rMETase) on tumor tissue resected from patients with late-stage cancer, as a functional biomarker of sensitivity to methionine restriction therapy. Patients and Methods: Resected peritoneal-metastatic cancer, including colorectal cancer, pancreatic cancer, ovarian cancer, and pseudomyxoma were placed on Gelform in RPMI 1640 medium for seven days and treated with rMETase from 2.5 U/ml to 20 U/ml. Cell viability was determined using the MTT assay. A total of 48 patients with late-stage cancer underwent testing for rMETase responsiveness using the HDRA. Results: Colorectal cancer and pseudomyxoma had the highest sensitivity to rMETase. Pancreatic and ovarian cancer also responded to rMETase, but to a lesser degree. Conclusion: Patients with tumors with at least 40% sensitivity to rMETase in the HDRA are being considered as candidates for methionine restriction therapy, which includes the use of rMETase in combination with a low-methionine diet.

Detection accuracy and clinical applications of DP-TOF mass spectrometry.

OBJECTIVE: Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently used in clinical microbiology laboratories. This study aimed to determine whether dual-polarity time-of-flight mass spectrometry (DP-TOF MS) could be applied to clinical nucleotide detection. METHODS: This prospective study included 40 healthy individuals and 110 patients diagnosed with cardiovascular diseases. We used DP-TOF MS and Sanger sequencing to evaluate 17 loci across 11 genes associated with cardiovascular drug responses. In addition, we used DP-TOF MS to test 998 retrospectively collected clinical DNA samples with known results. RESULTS: A, T, and G nucleotide detection by DP-TOF MS and Sanger sequencing revealed 100% concordance, whereas the C nucleotide concordance was 99.86%. Genotyping based on the results of the two methods showed 99.96% concordance. Regarding clinical applications, DP-TOF MS yielded a 99.91% concordance rate for known loci. The minimum detection limit for DNA was 0.4 ng; the inter-assay and intra-assay precision rates were both 100%. Anti-interference analysis showed that aerosol contamination greater than 1013 copies/µL in the laboratory environment could influence the results of DP-TOF MS. CONCLUSIONS: The DP-TOF MS platform displayed good detection performance, as demonstrated by its 99.96% concordance rate with Sanger sequencing. Thus, it may be applied to clinical nucleotide detection.

Applied models and molecular characteristics of small cell lung cancer.

Small cell lung cancer (SCLC) is a highly aggressive type of cancer frequently diagnosed with metastatic spread, rendering it surgically unresectable for the majority of patients. Although initial responses to platinum-based therapies are often observed, SCLC invariably relapses within months, frequently developing drug-resistance ultimately contributing to short overall survival rates. Recently, SCLC research aimed to elucidate the dynamic changes in the genetic and epigenetic landscape. These have revealed distinct subtypes of SCLC, each characterized by unique molecular signatures. The recent understanding of the molecular heterogeneity of SCLC has opened up potential avenues for precision medicine, enabling the development of targeted therapeutic strategies. In this review, we delve into the applied models and computational approaches that have been instrumental in the identification of promising drug candidates. We also explore the emerging molecular diagnostic tools that hold the potential to transform clinical practice and patient care.

Estimating heterogeneity of treatment effect in psychiatric clinical trials.

Currently, placebo-controlled clinical trials report mean change and effect sizes, which masks information about heterogeneity of treatment effects (HTE). Here, we present a method to estimate HTE and evaluate the null hypothesis (H0) that a drug has equal benefit for all participants (HTE=0). We developed measure termed 'estimated heterogeneity of treatment effect' or eHTE, which estimates variability in drug response by comparing distributions between study arms. This approach was tested across numerous large placebo-controlled clinical trials. In contrast with variance-based methods which have not identified heterogeneity in psychiatric trials, reproducible instances of treatment heterogeneity were found. For example, heterogeneous response was found in a trial of venlafaxine for depression (peHTE=0.034), and two trials of dasotraline for binge eating disorder (Phase 2, peHTE=0.002; Phase 3, 4mg peHTE=0.011; Phase 3, 6mg peHTE=0.003). Significant response heterogeneity was detected in other datasets as well, often despite no difference in variance between placebo and drug arms. The implications of eHTE as a clinical trial outcomes independent from central tendency of the group is considered and the important of the eHTE method and results for drug developers, providers, and patients is discussed.