Faculty Opinions recommendation of TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.

2015 ◽  
Author(s):  
Zdeněk Valenta
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
R Package ◽  
Cancer Cell Lines ◽  
Drug Combinations ◽  
Targeted Drug

scholarly journals SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology

2020 ◽  
Vol 48 (W1) ◽  
pp. W494-W501 ◽  
Author(s):  
Heewon Seo ◽  
Denis Tkachuk ◽  
Chantal Ho ◽  
Anthony Mammoliti ◽  
Aria Rezaie ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Combination ◽  
Predictive Biomarkers ◽  
Cancer Cell Lines ◽  
Drug Combinations ◽  
Drug Synergism ◽  
Precision Oncology ◽  
Synergistic Drug Combinations ◽  
Pharmacological Profiles

Abstract Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients.

scholarly journals Comprehensive Live-cell Imaging Analysis of Cryptotanshinone and Synergistic Drug-Screening Effects in Various Human and Canine Cancer Cell Lines

2020 ◽  
Author(s):  
Michael L. Bittner ◽  
Rosana Lopes ◽  
Jianping Hua ◽  
Chao Sima ◽  
Aniruddha Datta ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Screening ◽  
Drug Exposure ◽  
Salvia Miltiorrhiza ◽  
Cancer Cell Lines ◽  
Drug Combinations ◽  
Live Cell ◽  
Canine Cancer

ABSTRACTBackgroundSeveral studies have highlighted both the extreme anticancer effects of Cryptotanshinone (CT), a Stat3 crippling component from Salvia miltiorrhiza, as well as other STAT3 inhibitors to fight cancer.MethodsData presented in this experiment incorporates 2 years of in vitro studies applying a comprehensive live-cell drug-screening analysis of human and canine cancer cells exposed to CT at 20 μM concentration, as well as to other drug combinations. As previously observed in other studies, dogs are natural cancer models, given to their similarity in cancer genetics, epidemiology and disease progression compared to humans.ResultsResults obtained from several types of human and canine cancer cells exposed to CT and varied drug combinations, verified CT efficacy at combating cancer by achieving an extremely high percentage of apoptosis within 24 hours of drug exposure.ConclusionsCT anticancer efficacy in various human and canine cancer cell lines denotes its ability to interact across different biological processes and cancer regulatory cell networks, driving inhibition of cancer cell survival.

scholarly journals A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Åsmund Flobak ◽  
Barbara Niederdorfer ◽  
Vu To Nakstad ◽  
Liv Thommesen ◽  
Geir Klinkenberg ◽  
...  
Keyword(s):  
Signal Transduction ◽  
Cell Viability ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Combination ◽  
Cancer Cell Lines ◽  
Drug Combinations ◽  
Clinical Investigations ◽  
Transduction Mechanisms ◽  
Targeting Signal

Abstract While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.

scholarly journals Combination treatment optimization using a pan-cancer pathway model

2021 ◽  
Vol 17 (12) ◽  
pp. e1009689
Author(s):  
Robin Schmucker ◽  
Gabriele Farina ◽  
James Faeder ◽  
Fabian Fröhlich ◽  
Ali Sinan Saglam ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Cell Lines ◽  
Cancer Cell ◽  
Complex Diseases ◽  
Cancer Cell Lines ◽  
Drug Combinations ◽  
Combination Therapies ◽  
Pathway Model ◽  
Treatment Optimization ◽  
Pan Cancer

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.

Investigation of cancer cell lines for peptide receptor-targeted drug development

2011 ◽  
Vol 19 (8) ◽  
pp. 719-730 ◽  
Author(s):  
Lichun Sun ◽  
Jing Luo ◽  
L. Vienna Mackey ◽  
Lynsie M. Morris ◽  
Laura G. Franko-Tobin ◽  
...  
Keyword(s):  
Drug Development ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Peptide Receptor ◽  
Targeted Drug

scholarly journals Comparing cellular uptake and cytotoxicity of targeted drug carriers in cancer cell lines with different drug resistance mechanisms

2011 ◽  
Vol 7 (3) ◽  
pp. 324-332 ◽  
Author(s):  
Tingjun Lei ◽  
Supriya Srinivasan ◽  
Yuan Tang ◽  
Romila Manchanda ◽  
Abhignyan Nagesetti ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cellular Uptake ◽  
Drug Carriers ◽  
Cancer Cell Lines ◽  
Resistance Mechanisms ◽  
Targeted Drug

scholarly journals Comprehensive live-cell imaging analysis of cryptotanshinone and synergistic drug-screening effects in various human and canine cancer cell lines

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0236074
Author(s):  
Michael L. Bittner ◽  
Rosana Lopes ◽  
Jianping Hua ◽  
Chao Sima ◽  
Aniruddha Datta ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Screening ◽  
Drug Exposure ◽  
Salvia Miltiorrhiza ◽  
Cancer Cell Lines ◽  
Drug Combinations ◽  
Live Cell ◽  
Canine Cancer

Background Several studies have highlighted both the extreme anticancer effects of Cryptotanshinone (CT), a Stat3 crippling component from Salvia miltiorrhiza, as well as other STAT3 inhibitors to fight cancer. Methods Data presented in this experiment incorporates 2 years of in vitro studies applying a comprehensive live-cell drug-screening analysis of human and canine cancer cells exposed to CT at 20 μM concentration, as well as to other drug combinations. As previously observed in other studies, dogs are natural cancer models, given to their similarity in cancer genetics, epidemiology and disease progression compared to humans. Results Results obtained from several types of human and canine cancer cells exposed to CT and varied drug combinations, verified CT efficacy at combating cancer by achieving an extremely high percentage of apoptosis within 24 hours of drug exposure. Conclusions CT anticancer efficacy in various human and canine cancer cell lines denotes its ability to interact across different biological processes and cancer regulatory cell networks, driving inhibition of cancer cell survival.

scholarly journals CELLector: Genomics Guided Selection of Cancer in vitro Models

2018 ◽  
Author(s):  
Hanna Najgebauer ◽  
Mi Yang ◽  
Hayley E Francies ◽  
Clare Pacini ◽  
Euan A Stronach ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
R Package ◽  
Cancer Cell Lines ◽  
In Vitro Models ◽  
Computational Method ◽  
R Shiny ◽  
Selection Of

The selection of appropriate cancer models is a key prerequisite for maximising translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: a computational method (implemented in an open source R Shiny application and R package) allowing researchers to select the most relevant cancer cell lines in a patient-genomic guided fashion. CELLector leverages tumour genomics data to identify recurrent sub-types with associated genomic signatures. It then evaluates these signatures in cancer cell lines to rank them and prioritise their selection. This enables users to choose appropriate models for inclusion/exclusion in retrospective analyses and future studies. Moreover, this allows bridging data from cancer cell line screens to precisely defined sub-cohorts of primary tumours. Here, we demonstrate usefulness and applicability of our method through example use cases, showing how it can be used to prioritise the development of new in vitro models and to effectively unveil patient-derived multivariate prognostic and therapeutic markers.

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