<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bojic, Sanja</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M</style></author><author><style face="normal" font="default" size="100%">Stojkovic, Petra</style></author><author><style face="normal" font="default" size="100%">Ljujic, Biljana</style></author><author><style face="normal" font="default" size="100%">Gazdic Jankovic, Marina</style></author><author><style face="normal" font="default" size="100%">Armstrong, Lyle</style></author><author><style face="normal" font="default" size="100%">Markovic, Nebojsa</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lako, Majlinda</style></author><author><style face="normal" font="default" size="100%">Bauer, Roman</style></author><author><style face="normal" font="default" size="100%">Stojkovic, Miodrag</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Platform to study intracellular polystyrene nanoplastic pollution and clinical outcomes.</style></title><secondary-title><style face="normal" font="default" size="100%">Stem Cells</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stem Cells</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Environmental Pollution</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Induced Pluripotent Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Intracellular Space</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanoparticles</style></keyword><keyword><style  face="normal" font="default" size="100%">Plastics</style></keyword><keyword><style  face="normal" font="default" size="100%">Polystyrenes</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Treatment Outcome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 10 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">1321-1325</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Increased pollution by plastics has become a serious global environmental problem, but the concerns for human health have been raised after reported presence of microplastics (MPs) and nanoplastics (NPs) in food and beverages. Unfortunately, few studies have investigate the potentially harmful effects of MPs/NPs on early human development and human health. Therefore, we used a new platform to study possible effects of polystyrene NPs (PSNPs) on the transcription profile of preimplantation human embryos and human induced pluripotent stem cells (hiPSCs). Two pluripotency genes, LEFTY1 and LEFTY2, which encode secreted ligands of the transforming growth factor-beta, were downregulated, while CA4 and OCLM, which are related to eye development, were upregulated in both samples. The gene set enrichment analysis showed that the development of atrioventricular heart valves and the dysfunction of cellular components, including extracellular matrix, were significantly affected after exposure of hiPSCs to PSNPs. Finally, using the HiPathia method, which uncovers disease mechanisms and predicts clinical outcomes, we determined the APOC3 circuit, which is responsible for increased risk for ischemic cardiovascular disease. These results clearly demonstrate that better understanding of NPs bioactivities and its implications for human health is of extreme importance. Thus, the presented platform opens further aspects to study interactions between different environmental and intracellular pollutions with the aim to decipher the mechanism and origin of human diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32614127?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Menden, Michael P</style></author><author><style face="normal" font="default" size="100%">Wang, Dennis</style></author><author><style face="normal" font="default" size="100%">Mason, Mike J</style></author><author><style face="normal" font="default" size="100%">Szalai, Bence</style></author><author><style face="normal" font="default" size="100%">Bulusu, Krishna C</style></author><author><style face="normal" font="default" size="100%">Guan, Yuanfang</style></author><author><style face="normal" font="default" size="100%">Yu, Thomas</style></author><author><style face="normal" font="default" size="100%">Kang, Jaewoo</style></author><author><style face="normal" font="default" size="100%">Jeon, Minji</style></author><author><style face="normal" font="default" size="100%">Wolfinger, Russ</style></author><author><style face="normal" font="default" size="100%">Nguyen, Tin</style></author><author><style face="normal" font="default" size="100%">Zaslavskiy, Mikhail</style></author><author><style face="normal" font="default" size="100%">Jang, In Sock</style></author><author><style face="normal" font="default" size="100%">Ghazoui, Zara</style></author><author><style face="normal" font="default" size="100%">Ahsen, Mehmet Eren</style></author><author><style face="normal" font="default" size="100%">Vogel, Robert</style></author><author><style face="normal" font="default" size="100%">Neto, Elias Chaibub</style></author><author><style face="normal" font="default" size="100%">Norman, Thea</style></author><author><style face="normal" font="default" size="100%">Tang, Eric K Y</style></author><author><style face="normal" font="default" size="100%">Garnett, Mathew J</style></author><author><style face="normal" font="default" size="100%">Veroli, Giovanni Y Di</style></author><author><style face="normal" font="default" size="100%">Fawell, Stephen</style></author><author><style face="normal" font="default" size="100%">Stolovitzky, Gustavo</style></author><author><style face="normal" font="default" size="100%">Guinney, Justin</style></author><author><style face="normal" font="default" size="100%">Dry, Jonathan R</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">AstraZeneca-Sanger Drug Combination DREAM Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ADAM17 Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Antineoplastic Combined Chemotherapy Protocols</style></keyword><keyword><style  face="normal" font="default" size="100%">Benchmarking</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomarkers, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Datasets as Topic</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Antagonism</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Resistance, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Synergism</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Targeted Therapy</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">pharmacogenetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphatidylinositol 3-Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphoinositide-3 Kinase Inhibitors</style></keyword><keyword><style  face="normal" font="default" size="100%">Treatment Outcome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 06 17</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">2674</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for &gt;60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31209238?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel A</style></author><author><style face="normal" font="default" size="100%">Mateo, Francesca</style></author><author><style face="normal" font="default" size="100%">Herranz, Carmen</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cancer Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Kaplan-Meier Estimate</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolome</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Oncogenes</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Small Interfering</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, RNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Treatment Outcome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Nov 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">6059-6072</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">21</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30135189?dopt=Abstract</style></custom1></record></records></xml>