scholarly journals Linking drug target and pathway activation for effective therapy using multi-task learning

2017 ◽  
Author(s):  
Mi Yang ◽  
Jaak Simm ◽  
Chi Chung Lam ◽  
Pooya Zakeri ◽  
Gerard J.P. van Westen ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Drug Targets ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
Machine Learning Algorithms ◽  
Response Data ◽  
Combination Strategies ◽  
Pathway Activation ◽  
Protein X

ABSTRACTDespite the abundance of large-scale molecular and drug-response data, the insights gained about the mechanisms underlying treatment efficacy in cancer has been in general limited. Machine learning algorithms applied to those datasets most often are used to provide predictions without interpretation, or reveal single drug-gene association and fail to derive robust insights. We propose to use Macau, a bayesian multitask multi-relational algorithm to generalize from individual drugs and genes and explore the interactions between the drug targets and signaling pathways’ activation. A typical insight would be: “Activation of pathway Y will confer sensitivity to any drug targeting protein X”. We applied our methodology to the Genomics of Drug Sensitivity in Cancer (GDSC) screening, using gene expression of 990 cancer cell lines, activity scores of 11 signaling pathways derived from the tool PROGENy as cell line input and 228 nominal targets for 265 drugs as drug input. These interactions can guide a tissue-specific combination treatment strategy, for example suggesting to modulate a certain pathway to maximize the drug response for a given tissue. We confirmed in literature drug combination strategies derived from our result for brain, skin and stomach tissues. Such an analysis of interactions across tissues might help target discovery, drug repurposing and patient stratification strategies.

scholarly journals Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
High Accuracy ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

scholarly journals Identifying Nootropic Drug Targets via Large-Scale Cognitive GWAS and Transcriptomics

2020 ◽  
Author(s):  
Max Lam ◽  
Chen Chia-Yen ◽  
Xia Yan ◽  
W. David Hill ◽  
Joey W. Trampush ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Drug Repurposing ◽  
Gene Identification ◽  
Summary Statistics ◽  
General Cognitive Ability ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Meta Analyses

AbstractBackgroundCognitive traits demonstrate significant genetic correlations with many psychiatric disorders and other health-related traits. Many neuropsychiatric and neurodegenerative disorders are marked by cognitive deficits. Therefore, genome-wide association studies (GWAS) of general cognitive ability might suggest potential targets for nootropic drug repurposing. Our previous effort to identify “druggable genes” (i.e., GWAS-identified genes that produce proteins targeted by known small molecules) was modestly powered due to the small cognitive GWAS sample available at the time. Since then, two large cognitive GWAS meta-analyses have reported 148 and 205 genome-wide significant loci, respectively. Additionally, large-scale gene expression databases, derived from post-mortem human brain, have recently been made available for GWAS annotation. Here, we 1) reconcile results from these two cognitive GWAS meta-analyses to further enhance power for locus discovery; 2) employ several complementary transcriptomic methods to identify genes in these loci with variants that are credibly associated with cognition; and 3) further annotate the resulting genes to identify “druggable” targets.MethodsGWAS summary statistics were harmonized and jointly analysed using Multi-Trait Analysis of GWAS [MTAG], which is optimized for handling sample overlaps. Downstream gene identification was carried out using MAGMA, S-PrediXcan/S-TissueXcan Transcriptomic Wide Analysis, and eQTL mapping, as well as more recently developed methods that integrate GWAS and eQTL data via Summary-statistics Mendelian Randomization [SMR] and linkage methods [HEIDI], Available brain-specific eQTL databases included GTEXv7, BrainEAC, CommonMind, ROSMAP, and PsychENCODE. Intersecting credible genes were then annotated against multiple chemoinformatic databases [DGIdb, KI, and a published review on “druggability”].ResultsUsing our meta-analytic data set (N = 373,617) we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. 26 genes were associated with general cognitive ability via SMR, 16 genes via STissueXcan/S-PrediXcan, 47 genes via eQTL mapping, and 68 genes via MAGMA pathway analysis. The use of the HEIDI test permitted the exclusion of candidate genes that may have been artifactually associated to cognition due to linkage, rather than direct causal or indirect pleiotropic effects. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging on our various transcriptome and pathway analyses, as well as available chemoinformatic databases, we identified 16 putative genes that may suggest drug targets with nootropic properties.DiscussionResults converged on several categories of significant drug targets, including serotonergic and glutamatergic genes, voltage-gated ion channel genes, carbonic anhydrase genes, and phosphodiesterase genes. The current results represent the first efforts to apply a multi-method approach to integrate gene expression and SNP level data to identify credible actionable genes for general cognitive ability.

scholarly journals An Integrating Approach for Genome-Wide Screening of MicroRNA Polymorphisms Mediated Drug Response Alterations

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xianyue Wang ◽  
Hong Jiang ◽  
Wei Wu ◽  
Rongxin Zhang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Drug Response ◽  
Mirna Target ◽  
Mrna Translation ◽  
Gene Interaction ◽  
Mirna Regulation ◽  
Small Noncoding Rnas ◽  
Target Sites ◽  
Drug Efficiency

MicroRNAs (miRNAs) are a class of evolutionarily conserved small noncoding RNAs, ~22 nt in length, and found in diverse organisms and play important roles in the regulation of mRNA translation and degradation. It was shown that miRNAs were involved in many key biological processes through regulating the expression of targets. Genetic polymorphisms in miRNA target sites may alter miRNA regulation and therefore result in the alterations of the drug targets. Recent studies have demonstrated that SNPs in miRNA target sites can affect drug efficiency. However, there are still a large number of specific genetic variants related to drug efficiency that are yet to be discovered. We integrated large scale of genetic variations, drug targets, gene interaction networks, biological pathways, and seeds region of miRNA to identify miRNA polymorphisms affecting drug response. In addition, harnessing the abundant high quality biological network/pathways, we evaluated the cascade distribution of tarSNP impacts. We showed that the predictions can uncover most of the known experimentally supported cases as well as provide informative candidates complementary to existing methods/tools. Although there are several existing databases predicting the gain or loss of targeting function of miRNA mediated by SNPs, such as PolymiRTS, miRNASNP, MicroSNiPer, and MirSNP, none of them evaluated the influences of tarSNPs on drug response alterations. We developed a user-friendly online database of this approach named Mir2Drug.

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

2008 ◽  
Vol 26 (5) ◽  
pp. 531-539 ◽  
Author(s):  
Zoltán Kutalik ◽  
Jacques S Beckmann ◽  
Sven Bergmann
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Drug Response ◽  
Integrative Analysis ◽  
Modular Approach ◽  
Response Data

scholarly journals Compendium of Synovial Signatures Identifies Pathologic Characteristics for Predicting Treatment Response in Rheumatoid Arthritis

2018 ◽  
Author(s):  
Ki-Jo Kim ◽  
Minseung Kim ◽  
Ilias Tagkopoulos
Keyword(s):  
Rheumatoid Arthritis ◽  
Predictive Model ◽  
Treatment Response ◽  
Drug Targets ◽  
Drug Response ◽  
Factorization Method ◽  
Drug Responses ◽  
Machine Learning Classification ◽  
Pathway Activation ◽  
Genome Scale

ABSTRACTTreatment of patients with rheumatoid arthritis (RA) is challenging due to clinical heterogeneity and variability. Integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts can provide insights on the causal basis of drug responses. A normalized compendium was built that consists of 256 RA synovial samples that cover an intersection of 11,769 genes from 11 datasets. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. Finally, we built a predictive model for treatment response by using RA-relevant pathway activation scores and four machine learning classification techniques. We identified 876 up-regulated DEGs including 24 known genetic risk factors and 8 drug targets. DEG-based subgrouping revealed 3 distinct RA patient clusters with distinct activity signatures for RA-relevant pathways. In the case of infliximab, we constructed a classifier of drug response that was highly accurate with an AUC/AUPR of 0.92/0.86. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients. The efficacy of a predictive model for personalized drug response has been demonstrated and can be generalized to several drugs, co-morbidities, and other relevant features.

scholarly journals Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

2021 ◽  
Author(s):  
Max Lam ◽  
Chia-Yen Chen ◽  
Tian Ge ◽  
Yan Xia ◽  
David W. Hill ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Drug Repurposing ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Nootropic Drug ◽  
Gene Sets ◽  
Genome Wide ◽  
Novel Drug

AbstractBroad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify “druggable” targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

scholarly journals Assessment of pharmacogenomic agreement

2016 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

AbstractIn 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer1(GDSC) and Cancer Cell Line Encyclopedia2(CCLE), were inconsistent3. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response4, seemingly contradicting our previous findings3. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent5. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

Chemoresistance in Ovarian Cancer: Prospects for New Drugs

2020 ◽  
Vol 20 ◽  
Author(s):  
Shivani Tendulkar ◽  
Suneel Dodamani
Keyword(s):  
Ovarian Cancer ◽  
Drug Resistance ◽  
Signaling Pathways ◽  
Drug Targets ◽  
Cellular Level ◽  
New Drugs ◽  
Therapeutic Outcomes ◽  
Pathway Activation ◽  
Efflux Pathway ◽  
Clinical Trails

: This review focuses on conventional treatment overview, signaling pathways and various reasons for drug resistance with understanding novel methods that can lead to effective therapies. Ovarian cancer is amongst the most common gynecological and lethal cancers in women from the age of 20-60. The survival rate is limited to 5 years due to diagnosis in subsequent stages with reoccurrence of tumor and resistance of chemotherapeutic. The recent clinical trails use combinatorial treatment of carboplatin and paclitaxel on ovarian cancer after cytoreduction of tumor. Predominantly patients are responsive initially to therapy and later develop metastases due to drug resistance. Chemotherapy also leads to drug resistance causing enormous variations at cellular level. Multifaceted mechanisms like drug resistance are associated with number of genes and signaling pathways that process the proliferation of cells. Reasons for resistance include epithelial-mesenchyme, DNA repair activation, autophagy, drug efflux, pathway activation, and so on. Determining the routes on molecular mechanism that target chemoresistance pathways are necessary for controlling the treatment and understanding efficient drug targets can open light on improve therapeutic outcomes. Most common drug used for ovarian cancer is Cisplatin, which activates various chemoresistance pathways ultimately causing drug resistance. There have been substantial improvements in understanding the mechanisms of cisplatin resistance or chemo sensitizing cisplatin for effective treatment. Using therapies with combination that involve phytochemical or novel drug delivery system involving the phytochemicals would be a novel treatment in cancer. Phytochemicals are plant-derived compounds that exhibit anticancer, anti-oxidative, anti-inflammatory properties that minimalize side effects exerted from chemotherapeutics.

scholarly journals Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

2017 ◽  
Author(s):  
Fabian Fröhlich ◽  
Thomas Kessler ◽  
Daniel Weindl ◽  
Alexey Shadrin ◽  
Leonard Schmiester ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Drug Response ◽  
Mechanistic Model ◽  
Cancer Cell Lines ◽  
Mechanistic Models ◽  
Starting Point ◽  
Parameterized Model

The response of cancer cells to drugs is determined by various factors, including the cells’ mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs.

scholarly journals Assessment of pharmacogenomic agreement

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 825 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

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