scholarly journals Identifying Drug Sensitivity Subnetworks with NETPHLIX

2019 ◽  
Author(s):  
Yoo-Ah Kim ◽  
Rebecca Sarto Basso ◽  
Damian Wojtowicz ◽  
Dorit S. Hochbaum ◽  
Fabio Vandin ◽  
...  
Keyword(s):  
Drug Response ◽  
Genetic Alterations ◽  
Previous Method ◽  
Computational Method ◽  
Cancer Subtypes ◽  
Network Information ◽  
Genome Wide ◽  
Network Identification ◽  
Cancer Phenotype ◽  
Gene Modules

AbstractPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. One of the important challenges in the area is to predict drug response on a personalized level. The pathway-centric view of cancer significantly advanced the understanding of genotype-phenotype relationships. However, most of network identification methods in cancer focus on identifying subnetworks that include general cancer drivers or are associated with discrete features such as cancer subtypes, hence cannot be applied directly for the analysis of continuous features like drug response. On the other hand, existing genome wide association approaches do not fully utilize the complex proprieties of cancer mutational landscape. To address these challenges, we propose a computational method, named NETPHLIX (NETwork-to-PHenotpe mapping LeveragIng eXlusivity), which aims to identify mutated subnetworks that are associated with drug response (or any continuous cancer phenotype). Utilizing properties such as mutual exclusivity and interactions among genes, we formulate the problem as an integer linear program and solve it optimally to obtain a set of genes satisfying the constraints. NETPHLIX identified gene modules significantly associated with many drugs, including interesting response modules to MEK1/2 inhibitors in both directions (increased and decreased sensitivity to the drug) that the previous method, which does not utilize network information, failed to identify. The genes in the modules belong to MAPK/ERK signaling pathway, which is the targeted pathway of the drug.

scholarly journals Encircling the regions of the pharmacogenomic landscape that determine drug response

2018 ◽  
Author(s):  
Adrià Fernández-Torras ◽  
Miquel Duran-Frigola ◽  
Patrick Aloy
Keyword(s):  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Drug Repositioning ◽  
Gene Sets ◽  
Diffusion Analysis ◽  
Genome Wide ◽  
Molecular Determinants ◽  
Gene Modules

AbstractBackgroundThe integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.MethodsTo simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.ResultsWe apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.ConclusionsNetwork biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

scholarly journals DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 354
Author(s):  
Lu Zhang ◽  
Xinyi Qin ◽  
Min Liu ◽  
Ziwei Xu ◽  
Guangzhong Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Area Under The Curve ◽  
Nucleotide Composition ◽  
Computational Method ◽  
Feature Subset ◽  
Accurate Identification ◽  
Genome Wide ◽  
Dinucleotide Composition ◽  
Optimal Feature Subset

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58%–83.38% and an area under the curve (AUC) of 81.39%–91.04%. Furthermore, the independent datasets achieved an ACC of 72.95%–83.04% and an AUC of 80.79%–91.09%, which shows an excellent generalization ability of our proposed method.

scholarly journals Modular genome-wide gene expression architecture shared by early traits of osteoporosis and atherosclerosis in the Young Finns Study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Binisha H. Mishra ◽  
Pashupati P. Mishra ◽  
Emma Raitoharju ◽  
Saara Marttila ◽  
Nina Mononen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Smoking Habit ◽  
Intima Media Thickness ◽  
Mineral Density ◽  
Genome Wide ◽  
Study Support ◽  
Gene Modules ◽  
Study Participants ◽  
Genome Wide Gene Expression ◽  
Young Finns Study

AbstractWe analysed whole blood genome-wide expression data to identify gene co-expression modules shared by early traits of osteoporosis and atherosclerosis. Gene expression was profiled for the Young Finns Study participants. Bone mineral density and content were measured as early traits of osteoporosis. Carotid and bulbus intima media thickness were measured as early traits of atherosclerosis. Joint association of the modules, identified with weighted co-expression analysis, with early traits of the diseases was tested with multivariate analysis. Among the six modules significantly correlated with early traits of both the diseases, two had significant (adjusted p-values (p.adj) < 0.05) and another two had suggestively significant (p.adj < 0.25) joint association with the two diseases after adjusting for age, sex, body mass index, smoking habit, alcohol consumption, and physical activity. The three most significant member genes from the significant modules were NOSIP, GXYLT2, and TRIM63 (p.adj ≤ 0.18). Genes in the modules were enriched with biological processes that have separately been found to be involved in either bone metabolism or atherosclerosis. The gene modules and their most significant member genes identified in this study support the osteoporosis-atherosclerosis comorbidity hypothesis and can provide new joint biomarkers for both diseases and their dual prevention.

scholarly journals The role of epigenetic modifications, long-range contacts, enhancers and topologically associating domains in the regulation of glioma grade-specific genes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ilona E. Grabowicz ◽  
Bartek Wilczyński ◽  
Bożena Kamińska ◽  
Adria-Jaume Roura ◽  
Bartosz Wojtaś ◽  
...  
Keyword(s):  
Gene Expression ◽  
Long Range ◽  
Genetic Alterations ◽  
Chromatin Organization ◽  
Open Chromatin ◽  
Low Grade ◽  
Strong Impact ◽  
Cell Matrix ◽  
Topologically Associating Domains ◽  
Genome Wide

AbstractGenome-wide studies have uncovered specific genetic alterations, transcriptomic patterns and epigenetic profiles associated with different glioma types. We have recently created a unique atlas encompassing genome-wide profiles of open chromatin, histone H3K27ac and H3Kme3 modifications, DNA methylation and transcriptomes of 33 glioma samples of different grades. Here, we intersected genome-wide atlas data with topologically associating domains (TADs) and demonstrated that the chromatin organization and epigenetic landscape of enhancers have a strong impact on genes differentially expressed in WHO low grade versus high grade gliomas. We identified TADs enriched in glioma grade-specific genes and/or epigenetic marks. We found the set of transcription factors, including REST, E2F1 and NFKB1, that are most likely to regulate gene expression in multiple TADs, containing specific glioma-related genes. Moreover, many genes associated with the cell–matrix adhesion Gene Ontology group, in particular 14 PROTOCADHERINs, were found to be regulated by long-range contacts with enhancers. Presented results demonstrate the existence of epigenetic differences associated with chromatin organization driving differential gene expression in gliomas of different malignancy.

scholarly journals A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Istvan Petak ◽  
Maud Kamal ◽  
Anna Dirner ◽  
Ivan Bieche ◽  
Robert Doczi ◽  
...  
Keyword(s):  
Clinical Benefit ◽  
Progressive Disease ◽  
Genetic Alterations ◽  
Outcome Data ◽  
Computational Method ◽  
Molecular Profile ◽  
Precision Oncology ◽  
Driver Genes ◽  
Oncology Clinical Trial ◽  
Molecularly Targeted Agents

AbstractPrecision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

scholarly journals High-resolution single-cell 3D-models of chromatin ensembles during Drosophila embryogenesis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qiu Sun ◽  
Alan Perez-Rathke ◽  
Daniel M. Czajkowsky ◽  
Zhifeng Shao ◽  
Jie Liang
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
3D Models ◽  
Computational Method ◽  
Midblastula Transition ◽  
Genome Wide ◽  
Large Ensembles ◽  
Chromatin Folding ◽  
Function Relationship ◽  
Relationship Of

AbstractSingle-cell chromatin studies provide insights into how chromatin structure relates to functions of individual cells. However, balancing high-resolution and genome wide-coverage remains challenging. We describe a computational method for the reconstruction of large 3D-ensembles of single-cell (sc) chromatin conformations from population Hi-C that we apply to study embryogenesis in Drosophila. With minimal assumptions of physical properties and without adjustable parameters, our method generates large ensembles of chromatin conformations via deep-sampling. Our method identifies specific interactions, which constitute 5–6% of Hi-C frequencies, but surprisingly are sufficient to drive chromatin folding, giving rise to the observed Hi-C patterns. Modeled sc-chromatins quantify chromatin heterogeneity, revealing significant changes during embryogenesis. Furthermore, >50% of modeled sc-chromatin maintain topologically associating domains (TADs) in early embryos, when no population TADs are perceptible. Domain boundaries become fixated during development, with strong preference at binding-sites of insulator-complexes upon the midblastula transition. Overall, high-resolution 3D-ensembles of sc-chromatin conformations enable further in-depth interpretation of population Hi-C, improving understanding of the structure-function relationship of genome organization.

scholarly journals Integrative analysis reveals early and distinct genetic and epigenetic changes in intraductal papillary and tubulopapillary cholangiocarcinogenesis

Gut ◽  
2021 ◽  
pp. gutjnl-2020-322983
Author(s):  
Benjamin Goeppert ◽  
Damian Stichel ◽  
Reka Toth ◽  
Sarah Fritzsche ◽  
Moritz Anton Loeffler ◽  
...  
Keyword(s):  
Treatment Options ◽  
Genetic Alterations ◽  
Integrative Analysis ◽  
Intermediate Form ◽  
Precursor Lesions ◽  
Molecular Alterations ◽  
Genome Wide ◽  
Cells Of Origin ◽  
Sample Set ◽  
Methylation Profiling

ObjectiveA detailed understanding of the molecular alterations in different forms of cholangiocarcinogenesis is crucial for a better understanding of cholangiocarcinoma (CCA) and may pave the way to early diagnosis and better treatment options.DesignWe analysed a clinicopathologically well-characterised patient cohort (n=54) with high-grade intraductal papillary (IPNB) or tubulopapillary (ITPN) neoplastic precursor lesions of the biliary tract and correlated the results with an independent non-IPNB/ITPN associated CCA cohort (n=294). The triplet sample set of non-neoplastic biliary epithelium, precursor and invasive CCA was analysed by next generation sequencing, DNA copy number and genome-wide methylation profiling.ResultsPatients with invasive CCA arising from IPNB/ITPN had better prognosis than patients with CCA not associated with IPNB/ITPN. ITPN was localised mostly intrahepatic, whereas IPNB was mostly of extrahepatic origin. IPNB/ITPN were equally associated with small-duct and large-duct type intrahepatic CCA. IPNB exhibited mutational profiles of extrahepatic CCA, while ITPN had significantly fewer mutations. Most mutations were shared between precursor lesions and corresponding invasive CCA but ROBO2 mutations occurred exclusively in invasive CCA and CTNNB1 mutations were mainly present in precursor lesions. In addition, IPNB and ITPN differed in their DNA methylation profiles and analyses of latent methylation components suggested that IPNB and ITPN may have different cells-of-origin.ConclusionIntegrative analysis revealed that IPNB and ITPN harbour distinct early genetic alterations, IPNB are enriched in mutations typical for extrahepatic CCA, whereas ITPN exhibited few genetic alterations and showed distinct epigenetic profiles. In conclusion, IPNB/ITPN may represent a distinctive, intermediate form of intrahepatic and extrahepatic cholangiocarcinogenesis.

scholarly journals Stem and progenitor cells in myelodysplastic syndromes show aberrant stage-specific expansion and harbor genetic and epigenetic alterations

Blood ◽  
2012 ◽  
Vol 120 (10) ◽  
pp. 2076-2086 ◽  
Author(s):  
Britta Will ◽  
Li Zhou ◽  
Thomas O. Vogler ◽  
Susanna Ben-Neriah ◽  
Carolina Schinke ◽  
...  
Keyword(s):  
Progenitor Cells ◽  
Genetic Alterations ◽  
Fish Analysis ◽  
Exact Nature ◽  
Clinical Relapse ◽  
Hematopoietic Stem ◽  
Molecular Alterations ◽  
Genome Wide ◽  
Stem And Progenitor Cells ◽  
Specific Expansion

Abstract Even though hematopoietic stem cell (HSC) dysfunction is presumed in myelodysplastic syndrome (MDS), the exact nature of quantitative and qualitative alterations is unknown. We conducted a study of phenotypic and molecular alterations in highly fractionated stem and progenitor populations in a variety of MDS subtypes. We observed an expansion of the phenotypically primitive long-term HSCs (lineage−/CD34+/CD38−/CD90+) in MDS, which was most pronounced in higher-risk cases. These MDS HSCs demonstrated dysplastic clonogenic activity. Examination of progenitors revealed that lower-risk MDS is characterized by expansion of phenotypic common myeloid progenitors, whereas higher-risk cases revealed expansion of granulocyte-monocyte progenitors. Genome-wide analysis of sorted MDS HSCs revealed widespread methylomic and transcriptomic alterations. STAT3 was an aberrantly hypomethylated and overexpressed target that was validated in an independent cohort and found to be functionally relevant in MDS HSCs. FISH analysis demonstrated that a very high percentage of MDS HSC (92% ± 4%) carry cytogenetic abnormalities. Longitudinal analysis in a patient treated with 5-azacytidine revealed that karyotypically abnormal HSCs persist even during complete morphologic remission and that expansion of clonotypic HSCs precedes clinical relapse. This study demonstrates that stem and progenitor cells in MDS are characterized by stage-specific expansions and contain epigenetic and genetic alterations.

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