Drug Response In Patients Research Articles

Patient-Derived Bladder Cancer Organoids as a Valuable Tool for Understanding Tumor Biology and Developing PersonalizedTreatment.

Bladder cancer (BC) is a heterogeneous disease with high recurrence rates and variable treatment responses. To address these clinical challenges, the world's first bladder cancer patient-derived organoids (PDOs) biobank is established based on an Asian population. Thirty-six BC-PDOs are generated from 56 patients and demonstrated that the BC-PDOs can replicate the histological and genomic features of parental tumors. Drug screening tests are conducted with a broad spectrum of conventional chemotherapeutic and targeted therapy drugs and identified differential drug sensitivities among the BC-PDOs. These in vitro results are consistently supported by the PDO xenograft animal studies and patients' clinical treatment outcomes, thereby verifying the predictive power of PDOs for drug responses in BC patients. By analyzing the genetic profiles of the PDOs, specific driver genes that correlate with drug sensitivity to two stand-of-care chemotherapeutics, cisplatin, and gemcitabine, are identified. Additionally, the practicality of PDOs in investigating the tumor microenvironment has been demonstrated. This study underscores the utility of PDOs in advancing the understanding of bladder cancer and the development of personalized therapeutic strategies. The BC-PDOs biobank provides an ideal preclinical platform for supporting the development of personalized treatment strategies for BC patients. This study also provides insights into the potential mechanisms of drug resistance, paves the way for subsequent region-specific research, and demonstrates the possibility of using PDO-related models to direct future research in developing drugs targeting tumor microenvironments.

Prediction of Patient Drug Response via 3D Bioprinted Gastric Cancer Model Utilized Patient‐Derived Tissue Laden Tissue‐Specific Bioink

AbstractDespite significant research progress, tumor heterogeneity remains elusive, and its complexity poses a barrier to anticancer drug discovery and cancer treatment. Response to the same drug varies across patients, and the timing of treatment is an important factor in determining prognosis. Therefore, development of patient‐specific preclinical models that can predict a patient's drug response within a short period is imperative. In this study, a printed gastric cancer (pGC) model is developed for preclinical chemotherapy using extrusion‐based 3D bioprinting technology and tissue‐specific bioinks containing patient‐derived tumor chunks. The pGC model retained the original tumor characteristics and enabled rapid drug evaluation within 2 weeks of its isolation from the patient. In fact, it is confirmed that the drug response‐related gene profile of pGC tissues co‐cultured with human gastric fibroblasts (hGaFibro) is similar to that of patient tissues. This suggested that the application of the pGC model can potentially overcome the challenges associated with accurate drug evaluation in preclinical models (e.g., patient‐derived xenografts) owing to the deficiency of stromal cells derived from the patient. Consequently, the pGC model manifested a remarkable similarity with patients in terms of response to chemotherapy and prognostic predictability. Hence, it is considered a promising preclinical tool for personalized and precise treatments.

Exvivo imaging-based high content phenotyping of patients with rheumatoid arthritis.

High content imaging-based functional precision medicine approaches have been developed and successfully applied in the field of haemato-oncology. For rheumatoid arthritis (RA), treatment selection is still based on a trial-and-error principle, and biomarkers for patient stratification and drug response prediction are needed. A high content, high throughput microscopy-based phenotyping pipeline for peripheral blood mononuclear cells (PBMCs) was developed, allowing for the quantification of cell type frequencies, cell type specific morphology and intercellular interactions from patients with RA (n=65) and healthy controls (HC, n=33). Samples were exposed to a curated set of RA-specific small molecules, biologicals and reference stimuli for 24h to assess exvivo drug effects. Data on exvivo PBMC phenotypes were integrated with information on patients' invivo medication and disease activity. The unbiased data from in total 6.9e8 individual cells were collected and allowed for the identification of PBMC phenotypes specific to disease activity as well as invivo and exvivo treatment. The arrayed exvivo drug perturbation enabled the systematic characterization of drug effects, clustering by mode of action and uncovered morphologic alterations associated with biologic disease-modifying anti-rheumatic drug (DMARD) treatment. Individual invivo treatment regimens translated into altered immune cell abundances in patients with a comedication of conventional synthetic DMARDs when compared to HCs. Global integration of PBMC characteristics led to clustering of patients according to disease activity and correlation with clinical data. The application of the developed screening tool demonstrates a technical proof-of-concept for feasibility of a functional precision medicine approach to the exvivo immunophenotypic characterisation of patients with RA. This work was supported by the Austrian Academy of Sciences, the Medical University of Vienna and a grant (RMG2235 to L.X.H.) from the European Alliance of Associations for Rheumatology (EULAR).

Integration of RNA Editing with Multiomics Data Improves Machine Learning Models for Predicting Drug Responses in Breast Cancer Patients.

The integration of conventional omics data such as genomics and transcriptomics data into artificial intelligence models has advanced significantly in recent years; however, their low applicability in clinical contexts, due to the high complexity of models, has been limited in their direct use inpatients. We integrated classic omics, including DNA mutation and RNA gene expression, added a novel focus on promising omics methods based on A>I(G) RNA editing, and developed a drug response prediction model. We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (NCT02022202). This study was used to train (70%) with 10-fold cross-validation and test (30%) the drug response classification models. We assess the performance of the random forest (RF), generalized linear model (GLM), and support vector machine (SVM) with the Caret package in classifying therapy response via various combinations of clinical data, tumoral and germline mutation data, gene expression data, and RNA editing data via the LASSO and PCA strategies. First, we characterized the cohort on the basis of clinical data, mutation landscapes, differential gene expression, and RNAediting sites in 69 nonresponders and 35 responders to therapy. Second, regarding the prediction models, we demonstrated that RNA editing data improved or maintained the performance of the RF model for predicting drug response across all combinations. To select the final model, we compared the score between models with different data combinations, highlighting an score of 0.96 (95% CI: 0.957--0.961) and an AUC of 0.922, using LASSO for feature selection. Finally, we developed a nonresponse risk score on the basis of features that contributed to the selected model, focusing on three RNA-edited sites in the genes KDM4B, miRNA200/TTLL10-AS1, and BEST1. The score was created to facilitate the clinical translation of our findings, presenting a probability of therapy response according to RNA editing site patterns. Our study highlights the potential of RNA editing as a valuable addition to predictive modeling for drug response in patients with breast cancer. The nonresponse risk score could represent a tool for clinical translation, offering a probability-based assessment of therapy response. These findings suggest that incorporating RNA editing into predictive models could enhance personalized treatment strategies and improve decision-making in oncology.

Unveiling KLHL23 as a key immune regulator in hepatocellular carcinoma through integrated analysis.

Age-related cancers are characterized by impaired protein homeostasis, where Kelch protein superfamily members have showed accumulating clues as critical regulators. In this paper, the cancerous role of Kelch-like family member 23 (KLHL23) was comprehensively analyzed with TCGA and single cell GEO database across overall 33 cancer types. By multi-omics analysis upon the transcriptomic, genomic, and methylation data, the current study explored the association of KLHL23 with patient survival, gene ontology, tumor-infiltrating lymphocytes, and drug responses. The correlation of copy number variations and methylation with dysregulated expression of KLHL23 were also addressed. Notably, KLHL23 levels correlated with survival in cancers such as hepatocellular carcinoma and low-grade glioma. The study also highlighted how reduced KLHL23 expression is linked to increased immune activity and sensitivity to chemotherapy, suggesting its potential as a biomarker for cancer prognosis and treatment responsiveness.

Murine Xenograft Models as Preclinical Tools in Endometrial Cancer Research.

Murine xenograft models are valuable and increasingly used preclinical tools in cancer research to understand disease pathogenesis and guide treatment options. The aim of this narrative review is to summarize the studies that employed mouse xenograft models, using cell lines, patient-derived tumors, or organoids, in endometrial cancer (EC) research, detailing their methodology and main findings. We identified 27 articles reporting on heterotopic EC xenografts, including subcutaneous, subrenal capsule, intraperitoneal, and retro-orbital models, and 18 articles using orthotopic xenografts. Subcutaneous xenografts generated using either cell lines or patient tumors have been widely used; however, their low engraftment rates and the inability to recapitulate main clinical features such as metastases limit their translational value. Subrenal capsule models showed improved engraftment rates compared to subcutaneous models, but tumors exhibited slower and constrained tumor growth. Orthotopic models are technically more challenging to generate and monitor, but tumor growth occurs in a relevant microenvironment and EC ortho-xenografts exhibit high engraftment rates and metastases to clinically relevant sites. Cell line-based xenograft (CDX) models are attractive tools because they are convenient, easy to use, and amenable to genetic modifications, making them suitable for proof-of-concept approaches and large-scale studies. EC xenografts developed from patient tumors (PDTXs) are more labor/cost-intensive for their establishment but can capture the genetic and molecular heterogeneity within and across histologic subtypes and can inform personalized patient treatment. EC organoid-based xenograft (PDOX) models combine the advantages of both CDXs and PDTXs since they are more time- and cost-effective, faithfully maintain tumor characteristics and therapeutic responses, and can be genetically modified. Despite substantial progress in EC management, there are still several unmet needs. Efficient targeted treatments are currently indicated only for a small subgroup of patients, while women with recurrent or advanced-stage EC have very few therapeutic options and their prognosis remains unfavorable. Novel (targeted) drugs, combinational regimens and tools to predict the real drug response in patients are urgently needed. Xenograft models are expected to inform about disease mechanisms and to help identify novel therapeutic options and suitable target patients.

Monitoring drug Efficacy through Multi-Omics Research initiative in Alzheimer’s Disease (MEMORI-AD): A protocol for a multisite exploratory prospective cohort study on the drug response-related clinical, genetic, microbial and metabolomic signatures in Filipino patients with Alzheimer’s disease

IntroductionDementia is one of the leading causes of disability among older people aged 60 years and above, with majority eventually being diagnosed with Alzheimer’s disease (AD). Pharmacological agents approved for...

Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer.

The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.

Brain Activity Flow and Machine Learning for Predicting Drug Response in Patients With Major Depressive Disorder

Brain Activity Flow and Machine Learning for Predicting Drug Response in Patients With Major Depressive Disorder

PANoptosis-Relevant Subgroups Predicts Prognosis and Characterizes the Tumour Microenvironment in Ovarian Cancer.

Ovarian cancer (OC) poses a significant health burden with high mortality rates among female reproductive malignancies. Variability in treatment responses underscores the need for reliable prognostic markers to refine risk stratification. PANoptosis, a novel form of programmed cell death, plays pivotal roles in cancer pathogenesis and therapy. However, its prognostic relevance in OC remains unclear. Utilizing data from The Cancer Genome Atlas (TCGA), we analyzed transcriptomic and clinical signatures of OC patients. Through consensus clustering, we delineated molecular subtypes associated with PANoptosis-related genes (PRGs). We constructed and validated prognostic models using LASSO and Cox regression analyses, corroborated with GEO dataset validation. CIBERSORT assessed immune cell infiltration by risk score, and a predictive algorithm evaluated chemotherapy responses. Additionally, we investigated the biological role of the key gene CXCL13 in OC and its response to immunotherapy. Based on 19 PRGs, we identified two OC subtypes (PAN-Cluster1, PAN-Cluster2). Machine learning-derived risk scores using PAN-Cluster differentially expressed genes emerged as an independent prognostic indicator. Distinct risk groups exhibited varying clinical outcomes, immune profiles, drug sensitivities, and mutational landscapes. Notably, we confirmed CXCL13 as a model key gene and explored its role in OC regulation. In OC cells, suppression of CXCL13 expression enhances cell proliferation and migration, while patients with high CXCL13 expression show an improved response to immunotherapy. We initially identified the molecular subtypes associated with PRGs and established a prognostic model related to PRGs to predict survival and drug response in OC patients. Although further validation is required, these findings offer valuable insights into the development of personalized treatment strategies for OC patients.

Organoids research progress in gynecological cancers: a bibliometric analysis.

Gynecological cancers (GC) pose a severe threat to the health and safety of women's lives, and organoids, as in-vitro research models, have demonstrated significant advantages in simulating tissue characteristics and drug screening. In recent years, there has been a rapid increase in research outcomes related to organoids in GC. However, there has been no bibliometric study concerning. Publications related to GC and organoids from 2010-2023 were retrieved from the Web of Science Core Collection (WoSCC). We conducted a bibliometric analysis and visualization using CiteSpace, VOSviewer, and the Bibliometrix R Package. This analysis included the spatiotemporal distribution, author, sources, references, and keywords. A total of 333 publications were included. The number of annual publications indicated an explosive phase of development since 2019. The USA was the most important country in terms of cooperation, publication output, citation and centrality. University of California system ranked first in productivity among institutions, and HIPPO Y is the most relevant author in the research field. CANCERS published the most documents, and NATURE is the most cited sources. Analysis of Keywords and References, it is possible to establish the trend, and find the hotspots in the research field. This bibliometric analysis delineated global landscapes and progress trends in GC organoids research. This study emphasized that organoids can effectively replicate the original tissue or tumors, providing a good in-vitro model for research on tumor-related mechanisms and showing significant advantages in drug screening and efficacy clinical prediction. Additionally, as preclinical models, they provide compelling evidence for personalized therapy and prediction of patient drug responses.

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

FOXN3-AS1: A Candidate Prognostic Marker and Epigenetic Target with Immunotherapeutic Implications in Acute Myeloid Leukemia.

We focused on the FOXN3 gene and selected its antisense transcripts (FOXN3-AS1) to investigate its potential involvement in acute myeloid leukemia (AML). Several integrated multi-omics datasets have expanded the horizons of the cancer landscape. With the emergence of new high-throughput technologies, a large number of non-coding RNAs have been confirmed to be involved in the pathogenesis of different types of hematological malignancies. We conducted experimental validation using quantitative polymerase chain reaction (qPCR) with bone marrow specimens from AML patients. Then, Kaplan-Meier (KM) and Receiver Operating Characteristic (ROC) curves were used to substantiate the prognostic association between FOXN3-AS1 and AML patients within the TCGA database. Correlation between FOXN3-AS1 expression and gene mutation, immune, and immune function using Spearman correlation analysis. To explore the physical and functional interaction between FOXN3-AS1 and the DNMT1 protein, we utilized the RPISeq web tool from Iowa State University. Subsequently, we performed qPCR experiments to test the effect of 5AzaC (DNMT1 inhibitor) on FOXN3-AS1 expression AML cell lines (THP1 and OCI-AML3). We leveraged the "OncoPredict" R package in conjunction with the Genomics of Drug Sensitivity (GDSC) database to predict drug response in AML patients expressing FOXN3-AS1. We observed a significant upregulation of FOXN3-AS1 expression in AML patients compared to healthy controls using clinical samples. The TCGA database revealed an association between high FOXN3-AS1 expression and adverse prognosis. In our subsequent analysis, genes with poor prognostic implications in AML patients were exclusively identified in the FOXN3-AS1 high-expression group, further corroborating this relationship. AML patients with higher FOXN3-AS1 expression levels may respond less optimally to immunotherapy than patients with lower levels. Besides, we computationally predicted the interaction of FOXN3- AS1 and DNMT1 protein and experimentally confirmed that DNMT1i (GSK-3484862) affects the expression level of FOXN3-AS1. We also found that the chemotherapy drugs (5-Fluorouralic, Cisplatin, Dactolisib, Sapitinib, Temozolomide, Ulixertinib, Vinorelbine, Ruxolitinib, Osimertinib and Cisplatin) showed favorable responses in AML patients with high FOXN3-AS1 expression levels. Our candidate approach identifies FOXN3-AS1 as a prognostic indicator of survival in AML with a potential immune-related role. The preliminary observations we made on FOXN3-AS1/DNMT1 crosstalk warrant more in-depth invested immunotherapeutic approaches in AML.

Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer.

Colorectal cancer (CRC) is a common intestinal malignancy worldwide, posing a serious threat to public health. Due to its high heterogeneity, prognosis and drug response of different CRC patients vary widely, limiting the effectiveness of traditional treatment. Therefore, this study aims to construct a novel CRC prognostic signature using machine learning algorithms to assist in making informed clinical decisions and improving treatment outcomes. Gene expression matrix and clinical information of CRC patients were obtained from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then, genes with prognostic value were identified through univariate Cox regression analysis. Next, nine machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), gradient boosting machine (GBM), CoxBoost, plsRcox, Ridge, Enet, StepCox, SuperPC and survivalSVM were integrated to form 97 combinations, which was employed to screen the best strategy for building a prognostic model based on the average C-index in the three CRC cohorts. Kaplan Meier survival analysis, receiver operating curve (ROC) analysis and multivariate regression analysis were conducted to assess the predictive performance of the constructed signature. Furthermore, the CIBERSORT and ESTIMATE algorithms were utilized to quantify the infiltration level of immune cells. Besides, a nomogram were developed to predict 1-, 2-, and 3-year overall survival (OS) probabilities for individual patient. A prognostic signature consisting of 13 genes was developed utilizing LASSO Cox regression and GBM methods. Across both the training and validation datasets, the performance evaluation consistently indicated the signature's capacity to accurately predict the prognosis of CRC patients. Especially, compared with 30 published signatures, the 13-gene model exhibited dramatically superior predictive power. Even within clinical subgroups, it could still precisely stratify the prognosis. Functional analysis revealed a robust association between the signature and the immune status as well as chemotherapy response in CRC patients. Furthermore, a nomogram was created based on the signature-derived risk score, which demonstrated a strong predictive ability for OS in CRC patients. The 13-gene prognostic signature is expected to be a valuable tool for risk stratification, survival prediction, and treatment evaluation of patients with CRC.

Quantitative Systems Pharmacology Models: Potential Tools for Advancing Drug Development for Rare Diseases.

Rare diseases, affecting millions globally, present significant drug development challenges. This is due to the limited patient populations and the unique pathophysiology of these diseases, which can make traditional clinical trial designs unfeasible. Quantitative Systems Pharmacology (QSP) models offer a promising approach to expedite drug development, particularly in rare diseases. QSP models provide a mechanistic representation of the disease and drug response in virtual patients that can complement routinely applied empirical modeling and simulation approaches. QSP models can generate digital twins of actual patients and mechanistically simulate the disease progression of rare diseases, accounting for phenotypic heterogeneity. QSP models can also support drug development in various drug modalities, such as gene therapy. Impactful QSP models case studies are presented here to illustrate their value in supporting various aspects of drug development in rare indications. As these QSP model applications continue to mature, there is a growing possibility that they could be more widely integrated into routine drug development steps. This integration could provide a robust framework for addressing some of the inherent challenges in rare disease drug development.

MicroRNA-506 as a tumor suppressor in prostate cancer by regulation of Hippo signaling pathway

MicroRNA-506 as a tumor suppressor in prostate cancer by regulation of Hippo signaling pathway

Allele frequency of genetic variations related to the UGT1A1 gene-drug pair in a group of Iranian population.

The efficacy and safety of drug treatments vary widely due to genetic variations. Pharmacogenomics investigates the impact of genetic variations on patient drug response. This research investigates the frequency of UGT1A1 genetic variations in the Iranian population, comparing them with global data to provide insights into the pharmacogenomic approach in the Iranian population. The study was conducted using the data of the Bushehr Elderly Health (BEH) program, a population-based cohort study of the elderly population aged ≥ 60 years. Genotyping of three UGT1A1 variant alleles (UGT1A1*6, UGT1A1*27, and UGT1A1*80) was performed on a group of 2730 elderly Iranian participants with the Infinium Global Screening Array. The genotyping analysis revealed significant differences compared to major global populations that were addressed in the gnomAD database. UGT1A1*80 was found at a high frequency (32.34%), and followed by UGT1A1*6 (0.76%) and UGT1A1*27 (0.018) at a low frequency in the Iranian group. The UGT1A1*80 was the more prevalent allele between investigated alleles in the present study which can be considered as an important allele for pharmacogenomic testing.

Pharmacogenomic Analysis in Determining Optimal Drug Dosage in Chronic Disease Patients in Indonesia

. Pharmacogenomics, the science that combines genetics with pharmacology, plays a vital role in determining the optimal dosage of drugs for patients, especially those with chronic diseases. In Indonesia, the use of pharmacogenomic approaches is still limited even though the genetically diverse population requires more personalized dosage adjustments. This study aims to analyze the effect of genetic variation on drug response in patients with chronic diseases, such as diabetes, hypertension, and cardiovascular disease, in Indonesia. By collecting and analyzing genetic data and patient clinical data, this study will identify genetic biomarkers associated with drug metabolism and therapeutic effectiveness. The results of this study are expected to provide guidance for more accurate and personalized drug dosage determination, which will ultimately improve therapeutic outcomes and reduce the risk of side effects. This study is also expected to encourage wider adoption of pharmacogenomic approaches in clinical practice in Indonesia, as part of the effort towards more personalized and effective medicine.

Change in prostate tissue gene expression following finasteride or doxazosin administration in the medical therapy for prostatic symptoms (MTOPS) study

Benign prostatic hyperplasia (BPH) may decrease patient quality of life and often leads to acute urinary retention and surgical intervention. While effective treatments are available, many BPH patients do not respond or develop resistance to treatment. To understand molecular determinants of clinical symptom persistence after initiating BPH treatment, we investigated gene expression profiles before and after treatments in the prostate transitional zone of 108 participants in the Medical Therapy of Prostatic Symptoms (MTOPS) Trial. Unsupervised clustering revealed molecular subgroups characterized by expression changes in a large set of genes associated with resistance to finasteride, a 5α-reductase inhibitor. Pathway analyses within this gene cluster found finasteride administration induced changes in fatty acid metabolism, amino acid metabolism, immune response, steroid hormone metabolism, and kinase activity within the transitional zone. We found that patients without this transcriptional response were highly likely to develop clinical progression, which is expected in 13.2% of finasteride-treated patients. Importantly, a patient’s transcriptional response to finasteride was associated with their pre-treatment kinase expression. Further, we identified novel expression signatures of finasteride resistance among the transcriptionally responded patients. These patients showed different gene expression profiles at baseline and increased prostate transitional zone volume compared to the patients who responded to the treatment. Our work suggests molecular mechanisms of clinical resistance to finasteride treatment that could be potentially helpful for personalized BPH treatment as well as new drug development to increase patient drug response.

Drug Response of Iranian Alzheimer's Patients to Rivastigmine Concerning Their Genotype for VDR rs11568820 and MTHFR C677T Variants: A Pharmacogenetic and Association Study.

Alzheimer's disease is a neurodegenerative disorder with polygenic etiology. Genetic risk variants for Alzheimer's disease differ among populations. Thus, discovering them in each population is clinically important. A total of 118 patients and 97 controls for VDR rs11568820 and 88 patients and 100 healthy controls for MTHFR C677T polymorphism were genotyped to evaluate the association of these polymorphisms with late-onset Alzheimer's disease in the Iranian population, along with their impacts on the response to Rivastigmine treatment. The VDR C allele was significantly associated with Alzheimer's disease and provided protection against it (P = 0.003, RR = 1.14, 95% CI 1.04-1.24), while the T allele increased susceptibility (P = 0.003, RR = 1.93, 95% CI 1.23-3.02). These results were also considerable upon excluding the effect of APOE ε4 allele. The Prevalence-corrected Positive Predictive Value was 1.71% for the VDR CC genotype and 4% for the VDR CT genotype, indicating lower and almost twofold higher chances of developing Alzheimer's disease, respectively. No significant correlation was observed between MTHFR C677T and Alzheimer's disease. Based on our pharmacogenetic study, MTHFR T allele carriers lacking APOE ε4 allele showed a better response to Rivastigmine treatment after a 2-year follow-up. Moreover, patients with VDR CC genotype displayed milder Alzheimer's disease, particularly when coincided with the APOE ε4 allele. The VDR rs11568820 polymorphism affects both Alzheimer's disease risk and the response to Rivastigmine in Iranian patients. Also, MTHFR C677T polymorphism may play a role in the response to Rivastigmine, through a pathway that needs to be elucidated in future studies.