scholarly journals Fast and robust method for drug response biomarker identification and sample stratification

2019 ◽  
Author(s):  
Simanti Bhattacharya ◽  
Amit Das
Keyword(s):  
Bayesian Network ◽  
Drug Response ◽  
Functional Enrichment ◽  
Supervised Machine Learning ◽  
List Type ◽  
Robust Method ◽  
Biomarker Identification ◽  
Drug Response Biomarkers ◽  
Response Biomarker ◽  
Response Biomarkers

AbstractWith unprecedented progress of cancer research, the world is now prepared with versatile arsenal of drugs to combat cancer. However, individual’s response to any drug or combination treatment stands as a major challenge and hence there exists the sheer need for personalized medication. Identification of drug response biomarkers from a wholistic tumor microenvironment analysis would guide researchers to develop custom-tailored treatment regimen.In this study, a fast and robust method has been developed to identify drug response biomarkers from entire transcriptomics data analysis in a data-driven manner. The biomarkers which were identified by the method, were able to stratify patients between responders vs non-responders population. Furthermore, bayesian network (BN) analysis, done on the data, brought forth a mechanistic insight into the role of identified biomarkers in regulating drug’s efficacy.The importance of this work lies with the protocol that is time saving and requires less computation power, yet analyzes a whole system data and helps the researchers to take a step forward towards the development of personalized care in effective cancer treatment.HighlightsSupervised machine learning approach to analyze gene expression data.Drug response biomarker identification.Categorization of samples for their drug response with the help of identified biomarkers.Functional enrichment to understand the biomarkers association with biological processes.Bayesian network analysis to develop causal structure among identified biomarkers and drug targets.Time and cost-effective pipeline for fast and robust prediction of drug response biomarkers.

Abstract 4907: Cell line panel profiling reveals novel drug response biomarkers for BTK and CDK4/6 inhibitors

2018 ◽  
Author(s):  
Joost C. Uitdehaag ◽  
Jeffrey J. Kooijman ◽  
Jeroen A.D.M. de Roos ◽  
Martine B.W. Prinsen ◽  
Jelle Dylus ◽  
...  
Keyword(s):  
Cell Line ◽  
Drug Response ◽  
Cell Line Panel ◽  
Drug Response Biomarkers ◽  
Response Biomarkers ◽  
Novel Drug

scholarly journals Gemcitabine and Arabinosylcytosin Pharmacogenomics: Genome-Wide Association and Drug Response Biomarkers

PLoS ONE ◽  
2009 ◽  
Vol 4 (11) ◽  
pp. e7765 ◽  
Author(s):  
Liang Li ◽  
Brooke L. Fridley ◽  
Krishna Kalari ◽  
Gregory Jenkins ◽  
Anthony Batzler ◽  
...  
Keyword(s):  
Drug Response ◽  
Genome Wide Association ◽  
Genome Wide ◽  
Drug Response Biomarkers ◽  
Response Biomarkers

scholarly journals Combined Cellular and Biochemical Profiling to Identify Predictive Drug Response Biomarkers for Kinase Inhibitors Approved for Clinical Use between 2013 and 2017

2018 ◽  
Vol 18 (2) ◽  
pp. 470-481 ◽  
Author(s):  
Joost C.M. Uitdehaag ◽  
Jeffrey J. Kooijman ◽  
Jeroen A.D.M. de Roos ◽  
Martine B.W. Prinsen ◽  
Jelle Dylus ◽  
...  
Keyword(s):  
Drug Response ◽  
Kinase Inhibitors ◽  
Clinical Use ◽  
Drug Response Biomarkers ◽  
Response Biomarkers

scholarly journals Modeling Gene-Wise Dependencies Improves the Identification of Drug Response Biomarkers in Cancer Studies

2017 ◽  
pp. btw836 ◽  
Author(s):  
Olga Nikolova ◽  
Russell Moser ◽  
Christopher Kemp ◽  
Mehmet Gönen ◽  
Adam A. Margolin
Keyword(s):  
Drug Response ◽  
Drug Response Biomarkers ◽  
Response Biomarkers ◽  
Cancer Studies

scholarly journals A personalized network framework reveals predictive axis of anti-TNF response across diseases

2021 ◽  
Author(s):  
Shiran Gerassy-Vainberg ◽  
Elina Starosvetsky ◽  
Renaud Gaujoux ◽  
Alexandra Blatt ◽  
Naama Maimon ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Drug Response ◽  
Computational Approach ◽  
High Dimensional ◽  
Biomarker Identification ◽  
Individual Level ◽  
Medical Need ◽  
Inflammatory Bowel ◽  
Intermediate Monocytes ◽  
Blood Data

Personalized treatment of complex diseases is an unmet medical need pushing towards drug biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach for modeling cell-centered individual-level network dynamics from high-dimensional blood data to predict infliximab response and uncover individual variation of non-response. We identified and validated that the RAC1-PAK1 axis is predictive of infliximab response in inflammatory bowel disease. Intermediate monocytes, which closely correlated with inflammation state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in Rheumatoid arthritis, validated in three public cohorts. Our findings support pan-disease drug response diagnostics from blood, implicating common mechanisms of drug response or failure across diseases.

scholarly journals A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Dennis Wang ◽  
James Hensman ◽  
Ginte Kutkaite ◽  
Tzen S Toh ◽  
Ana Galhoz ◽  
...  
Keyword(s):  
Cell Lines ◽  
Dose Response ◽  
Biomarker Discovery ◽  
Statistical Framework ◽  
Curve Fit ◽  
Uncertainty Estimates ◽  
Drug Response Biomarkers ◽  
Novel Biomarkers ◽  
Response Biomarkers

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells’ response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

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