Biology subjects

Gottlieb, A.

Publications and source records attributed to Gottlieb, A..

8 recordsLinked to original sources

bioRxiv2024

An estrogen receptor signaling transcriptional program linked to immune evasion in human hormone receptor-positive breast cancer

T cells are generally sparse in hormone receptor-positive (HR+) breast cancer, potentially due to limited antigen presentation, but the driving mechanisms of low T cell abundance remains unclear. Therefore, we defined and investigated programs ( gene modules), related to estrogen receptor signaling (ERS) and immune signaling using bulk and single-cell transcriptome and multiplexed immunofluorescence of breast cancer tissues from multiple clinical sources and human cell lines. The ERS gene module, dominantly expressed in cancer cells, was negatively associated with immune-related gene modules TNF/NF-{kappa}B signaling and type-I interferon (IFN-I) response, which were expressed in distinct stromal and immune cell types, but also, in part, expressed and preserved as a cancer cell-intrinsic mechanisms. Spatial analysis revealed that ERS strongly correlated with reduced T cell infiltration, potentially due to its association with suppression of TNF/NF-{kappa}B-induced angiogenesis and IFN-I-induced HLA expression in macrophages. Preoperative endocrine therapy in ER+/HER2-breast cancer patients produced better responses in ERS-high patients, with TNF/NF-{kappa}B expression associated with reduced ERS. Targeting these pathways may enhance T cell infiltration in HR+ breast cancer patients. Statement of SignificanceThis study elucidates the immunosuppressive role of ER signaling in breast cancer, highlighting a complex interplay between cancer, stromal, and immune cells and reveals potential approaches to enhance immunogenicity in HR+ breast cancer. These findings offer crucial insights into immune evasion in breast cancer and identify strategies to enhance T cell abundance.

cancer biology↗

bioRxiv2020

PathDSP: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment

Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Using a deep neural network, we outperforming state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia (CCLE) as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.

bioinformatics↗

bioRxiv2020

The efficacy of a virtual reality exposure therapy treatment for fear of flying: A retrospective study

BackgroundFear of flying (FoF) is an anxiety disorder classified as a phobia. Its prevalence is estimated at 10-40% in the industrialized world, and it is accompanied by severe economic, social, vocational and emotional consequences. In recent years, virtual reality-based exposure therapy (VRET) for FoF has been introduced. One such FoF-VRET is offered as a paid clinical service at the Center of Advanced Technologies in Rehabilitation (CATR), Sheba Medical Center, Israel. Positive long-term efficacy of FoF-VRET has been found in several studies. However, these studies are limited by relatively small, non-representative samples and a lack of comparative pre/post functional efficacy outcome measures. MethodsTo address these limitations, we conducted a retrospective survey of self-referred individuals treated with FoF-VRET at CATR over the previous four years. The aim of the present study was to evaluate the efficacy of our FoF-VRET in this representative real-world sample. Of 274 individuals who received the treatment, 214 met inclusion/criteria, and 103 agreed to participate. The survey focused mainly on collecting information regarding flight activity before and after treatment. The primary outcome measures were: (1) number of flights per month (FpM); (2) number of flight hours per month (FHpM). For each participant, these outcomes were computed for the post-treatment period ([≥]6 months after FoF-VRET) and the corresponding pre-treatment period. ResultsFpM (mean{+/-}SD) increased from .05{+/-}.07 to .16{+/-}.07 flights (p<.0001). FHpM rose from.22{+/-}.41 to .80{+/-}.86 hours per month (p<.0001). ConclusionsThese results are indicative of FoF-VRET treatment efficacy. Future studies should evaluate long-term maintenance of the treatment effect and thus identify the optimal frequency for delivery of periodic booster treatments.

neuroscience↗

bioRxiv2020

Multimodal immersive trail making - virtual reality paradigm to study cognitive-motor interactions

BackgroundNeuropsychological tests of executive function have limited real-world predictive and functional relevance. An emerging solution for this limitation is to adapt the tests for implementation in virtual reality (VR). We thus developed two VR-based versions of the classic Color-Trails Test (CTT), a well-validated pencil-and-paper executive function test assessing sustained (Trails A) and divided (Trails B) attention - one for a large-scale VR system (DOME-CTT) and the other for a portable head-mount display VR system (HMD-CTT). We then evaluated construct validity, test-retest reliability, and age-related discriminant validity of the VR-based versions and explored effects on motor function. MethodsHealthy adults (n=147) in three age groups (young: n=50; middle-aged: n=80; older: n=17) participated. All participants were administered the original CTT, some completing the DOME-CTT (14 young, 29 middle-aged) and the rest completing the HMD-CTT. Primary outcomes were Trails A and B completion times (tA, tB). Spatiotemporal characteristics of upper-limb reaching movements during VR test performance were reconstructed from motion capture data. Statistics included correlations and repeated measures analysis of variance. ResultsConstruct validity was substantiated by moderate correlations between the gold standard pencil-and-paper CTT and the VR adaptations (DOME-CTT: tA 0.58, tB 0.71; HMD-CTT: tA 0.62, tB 0.69). VR versions showed relatively high test-retest reliability (intraclass correlation; VR: tA 0.60-0.75, tB 0.59-0.89; original: tA 0.75-0.85, tB 0.77-0.80) and discriminant validity (area under the curve; VR: tA 0.70-0.92, tB 0.71-0.92; original: tA 0.73-0.95, tB 0.77-0.95). VR completion times were longer than for the original pencil-and-paper test; completion times were longer with advanced age. Compared with Trails A, Trails B target-to-target VR hand trajectories were characterized by delayed, more erratic acceleration and deceleration, consistent with the greater executive function demands of divided vs. sustained attention; acceleration onset later for older participants. ConclusionsThe present study demonstrates the feasibility and validity of converting a neuropsychological test from two-dimensional pencil-and-paper to three-dimensional VR-based format while preserving core neuropsychological task features. Findings on the spatiotemporal morphology of motor planning/execution during the cognitive tasks may lead to multimodal analysis methods that enrich the ecological validity of VR-based neuropsychological testing, representing a novel paradigm for studying cognitive-motor interactions.

neuroscience↗

bioRxiv2020

Gait asymmetry, and bilateral coordination of gait during a six-minute walk test in persons with multiple sclerosis

Gait impairments in persons with multiple sclerosis (pwMS) underlying reduced walking endurance are still poorly understood. Thus, our objective was to assessed gait asymmetry (GA) and bilateral coordination of gait (BCG), among pwMS during the six-minute walk test (6MWT) and their association with disease severity. For this aim, we recruited ninety-two pwMS (age: 46.6 {+/-} 7.9; 83% females) with a broad range of clinical disability who completed the 6MWT wearing gait analysis system. GA was assessed by comparing left and right swing times, and BCG by using the phase coordination index (PCI). Several functional and subjective gait assessments were performed. Results show that gait is more asymmetric and less coordinated as the disease progresses (p<.0001). Participants with mild MS showed significant better BCG as reflected by lower PCI values in comparison to the other two MS severity groups (severe: p =.001, moderate: p=.02). GA and PCI also deteriorated significantly with time during the 6MWT (p<.0001). GA and PCI (i.e., BCG) show somewhat weaker associations with clinical MS status than associations observed between functional and subjective gait assessments and MS status. Similar to other neurological cohorts, GA and PCI are important parameters to assess and to target in interventions among pwMS.

physiology↗

bioRxiv2018

TF-TWAS: Transcription-factor polymorphism associated with tissue-specific gene expression

Transcriptional regulation is associated with a broad range of diseases. Methods associating genetic polymorphism with gene transcription levels offer key insights for understanding the transcriptional regulation plan. The majority of gene imputation methods focus on modeling polymorphism in the cis regions of the gene, partially owing to the large genetic search space. We hypothesize that polymorphism within transcription factors (TFs) may help explain transcription levels of their transcribed genes.\n\nHere, we test this hypothesis by developing TF-TWAS: imputation models that integrate transcription factor information with transcription-wide association study methodology. By comparing TF-TWAS models to base models that use only gene cis information, we are able to estimate possible mechanisms of the TF polymorphism effect - TF expression or binding affinity within four tissues - whole blood, liver, brain hippocampus and coronary artery.\n\nWe identified 48 genes where the TF-TWAS models explain significantly better their expression than cis models alone in at least one of the four tissues. Sixteen of these genes are associated with various diseases, including cancer, neurological, psychiatric and rare genetic diseases. Our method is a new expansion to transcriptome-wide association studies and enables the identification of new associations between polymorphism in transcription factor and gene transcription levels.

genomics↗

bioRxiv2017

Estimating Effects Of Second Line Therapy For Type 2 Diabetes Mellitus: Retrospective Cohort Study

ObjectiveMetformin is the recommended initial drug treatment in type 2 Diabetes Mellitus, but there is no clearly preferred choice for an additional drug when indicated. We use electronic health records to infer the counterfactual drug effectiveness in reducing HbA1c levels and effect on body-mass index (BMI) of four second line diabetes drug classes.\n\nStudy design and settingRetrospective analysis of the electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for censored patients and confounders.\n\nParticipants and ExposuresOur cohort consisted of roughly 25,000 patients with type 2 diabetes, prescribed metformin along with a drug out of four second line drug classes - sulfonylureas, thiazolidinediones, DPP-4 inhibitors and GLP-1 agonists, during the years 2000-2013.\n\nMain outcome measuresGlycated hemoglobin (HbA1c) and BMI of these patients after six and twelve months of treatment.\n\nResultsWe show that all four drug classes reduce glycated hemoglobin levels, but the effect of sulfonylureas after 12 months of treatment is less pronounced compared to other classes. We also predict that thiazolidinediones increase body weight while DPP-4 inhibitors decrease it.\n\nConclusionOur results are in line with current knowledge on second line drug effectiveness and effect on BMI. They demonstrate that causal inference from Electronic health records is an effective way for conducting multi-treatment causal inference studies.

epidemiology↗

bioRxiv2017

Population-specific imputation of gene expression improves prediction of pharmacogenomic traits for African Americans

Genome-wide association studies (GWAS) are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power, and allows the effects of both protein coding variation as well as variation in expression to be combined into \"gene level\" effects.\n\nPrevious work has shown that warfarin dose can be predicted using information from genetic variation that affects protein coding regions. Here, we introduce a method that improves the predicted dose by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression--on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic (PK) and pharmacodynamic (PD) pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose, and identify a signature of eleven gene-tissue pairs that significantly augment the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction, in a population-specific manner.

genomics↗