Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

Seashore‐Ludlow, Brinton; Rees, Matthew G.; Cheah, Jaime H.; Çokol, Murat; Price, Edmund; Coletti, Matthew E.; Jones, Victor; Bodycombe, Nicole E.; Soule, Christian K.; Gould, Joshua; Alexander, Benjamin R.; Li, Ava; Montgomery, Philip; Wawer, Mathias; Kuru, Nurdan; Kotz, Joanne; Hon, C. Suk-Yee; Muñoz, Benito; Liefeld, Ted; Dančík, Vlado; Bittker, Joshua A.; Palmer, Michelle; Bradner, James E.; Shamji, Alykhan F.; Clemons, Paul A.; Schreiber, Stuart L.

doi:10.1158/2159-8290.cd-15-0235

Cited by 643 publications

(713 citation statements)

References 51 publications

(78 reference statements)

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“…Several types of medical researchers reviewed for small-molecule treatment in CCL models react differently to cancer cells. As believed, small-molecule and CCL models can be fully controlled through cancer cells by effective analysis methods and sensitivity profiling studies [23]. With this reason, we measured the Small-Molecule Cancer CellLine Sensitivity Profiling Data to identify sensitive drug or compound for each CCL.…”

Section: Discussionmentioning

confidence: 99%

“…Cancer Cell Line profiling of small-molecule sensitivity has appeared as a balanced method to measure the connections between genetic or cellular features of CCLs and small-molecule reaction [23]. The Cancer Therapeutics Response Portal (CTRP) [24] analyzed a recognized pathway with major transmissions between degrees of difference gene dependency, and sensitive and nonsensitive cell lines.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Analysis of Anticancer Drug Sensitivity of Lung Cancer Cell Lines by using Machine Learning Clustering Techniques

Wanigasooriya¹,

Halgamuge²,

Mohammad³

2017

ijacsa

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Analysis of Anticancer Drug Sensitivity of Lung Cancer Cell Lines by using Machine Learning Clustering Techniques

Wanigasooriya¹,

Halgamuge²,

Mohammad³

2017

ijacsa

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“…The challenges aimed at developing in silico methods to rank 91 drug combinations from most synergistic to most antagonistic. In the first DREAM drug combination challenge, the combinations were tested on OCI-LY3 human diffuse large B-cell lymphoma cell line [6]. AstraZeneca-Sanger Drug Combination DREAM Challenge was launched using 85 cancer cell lines and 11,759 drug combination screening for 118 drugs [8].…”

Section: Mathematical Optimization Methodsmentioning

confidence: 99%

“…It is not practical to screen all possible drug combinations since the number of possible combinations will increase exponentially with the increasing number of single drugs. In silico methods are developed for predicting new drug combinations before combination composition and practical test in the lab [6,7]. Some of the computational approaches are based on detailed mathematical modelling that concentrate on established cancer pathways and metabolic network constructions.…”

Section: Introductionmentioning

confidence: 99%

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear. It is not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer and discuss the limitations and challenges of these methods.

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“…Cancer Therapeutics Response Portal (CTRP v2.0, https://portals.broadinstitute.org/ctrp/) provided screenings of more than 481 small molecules on 664 cancer cell lines [15]. Drug response (AUC) data for 481 candidate cancer drugs in 41 pancreatic cancer cell lines were collected from CTRP.…”

Section: Genomics Datasets and Analysismentioning

confidence: 99%

PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target‐Drug Selection in cancer

et al. 2017

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Background: Precision medicine attempts to tailor the right therapy for the right patient. Recent progress in largescale collection of patents' tumor molecular profiles in The Cancer Genome Atlas (TCGA) provides a foundation for systematic discovery of potential drug targets specific to different types of cancer. However, we still lack powerful computational methods to effectively integrate multiple omics data and protein-protein interaction network technology for an optimum target and drug recommendation for an individual patient. Methods: In this study, a computation method, Precision Medicine Target-Drug Selection (PMTDS) based on genetic interaction networks is developed to select the optimum targets and associated drugs for precision medicine style treatment of cancer. The PMTDS system includes three parts: a personalized medicine knowledgebase for each cancer type, a genetic interaction network-based algorithm and a single patient molecular profiles. The knowledgebase integrates cancer drugs, drug-target databases and gene biological pathway networks. The molecular profiles of each tumor consists of DNA copy number alteration, gene mutation, and tumor gene expression variation compared to its adjacent normal tissue. Results: The novel integrated PMTDS system is applied to select candidate target-drug pairs for 178 TCGA pancreatic adenocarcinoma (PDAC) tumors. The experiment results show known drug targets (EGFR, IGF1R, ERBB2, NR1I2 and AKR1B1) of PDAC treatment are identified, which provides important evidence of the PMTDS algorithm's accuracy. Other potential targets PTK6, ATF, SYK are, also, recommended for PDAC. Further validation is provided by comparison of selected targets with, both, cell line molecular profiles from the Cancer Cell Line Encyclopedia (CCLE) and drug response data from the Cancer Therapeutics Response Portal (CTRP). Results from experimental analysis of forty six individual pancreatic cancer samples show that drugs selected by PMTDS have more sample-specific efficacy than the current clinical PDAC therapies. Conclusions: A novelty target and drug priority algorithm PMTDS is developed to identify optimum target-drug pairs by integrating the knowledgebase base with a single patient's genomics. The PMTDS system provides an accurate and reliable source for target and off-label drug selection for precision cancer medicine.

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Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

Cited by 643 publications

References 51 publications

The Analysis of Anticancer Drug Sensitivity of Lung Cancer Cell Lines by using Machine Learning Clustering Techniques

The Analysis of Anticancer Drug Sensitivity of Lung Cancer Cell Lines by using Machine Learning Clustering Techniques

In-Silico Methodologies for Cancer Multidrug Optimization

PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target‐Drug Selection in cancer

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