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 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.

MicroRNAs related to intrinsic resistance to oxaliplatin and the irinotecan metabolite SN-38 in 10 colorectal cancer cell lines.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 524-524 ◽  
Author(s):  
Niels Frank Jensen ◽  
Rolf Soekilde ◽  
Jan Stenvang ◽  
Birgitte Sander Nielsen ◽  
Thomas Litman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Predictive Biomarkers ◽  
Cancer Cell Lines ◽  
Colorectal Cancer Cell ◽  
Intrinsic Resistance ◽  
Short Term ◽  
Colorectal Cancer Cell Lines

524 Background: Chemotherapy of metastatic colorectal cancer is based on 5-flourouracil combined with either oxaliplatin or irinotecan (active metabolite: SN-38). Identification of predictive biomarkers of drug response is needed to provide a better personalized treatment. In this study we aimed to identify microRNAs related to intrinsic resistance to oxaliplatin or irinotecan in a panel of ten colorectal cancer cell lines. Methods: Drug sensitivity towards oxaliplatin and SN-38 was determined for ten colorectal cancer cell lines (Colo-205, DLD-1, HCC-2998, HCT-15, HCT-116, HT-29, KM12, LoVo, LS-174T, and SW620), using the cell viability MTT assay and the cell death LDH assay. In addition, two cell lines (DLD-1 and LoVo) were exposed to the drugs for 6, 24 or 48 hours. MicroRNA expression profiles were generated using the Exiqon miRCURY LNA microarray platform (including 840 microRNAs), and four differentially expressed microRNAs were validated by independent qRT-PCR measurements. Results: The drug sensitivities of the ten colorectal cancer cell lines varied about 50 times between the least and most sensitive cell lines. Correlation of drug sensitivity data to microRNA expression data across the ten cell lines yielded about 25 microRNA biomarker candidates, for each of the drug/assay combinations. Following short-term drug treatment 10-20 microRNAs were altered for each drug/cell line combination. Validation by qRT-PCR showed a very good correlation to the microarray data. MicroRNAs identified by correlation to drug sensitivity and by short-term treatment were compared, and less than 10% were identified by both approaches, perhaps representing the most promising candidates. These candidates are for SN-38 miR-15a, miR-22, miR-24, miR-98, miR-142-3p, miR-1290, and let-7b, and for oxaliplatin miR-23b, miR-27a, miR-192, miR-200a, miR-222, miR-886-5p, and miR-1308. Conclusions: In the present study we identified a number of microRNAs that are potentially involved in intrinsic resistance and/or could be predictive biomarkers for either irinotecan or oxaliplatin.

scholarly journals Network-Based Matching of Patients and Targeted Therapies for Precision Oncology*

2019 ◽  
Author(s):  
Qingzhi Liu ◽  
Min Jin Ha ◽  
Rupam Bhattacharyya ◽  
Lana Garmire ◽  
Veerabhadran Baladandayuthapani
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Precision Oncology ◽  
Large Set ◽  
Sensitivity Data ◽  
The Right

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible – allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients’ responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the “missing link” between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.Data and code availabilityhttps://github.com/bayesrx/bayesrx.github.io/tree/master/authors/liu-q./ Shiny app (data and results visualization tool): https://qingzliu.shinyapps.io/psb-app/

scholarly journals Effects of a Novel Thiadiazole Derivative with High Anticancer Activity on Cancer Cell Immunogenic Markers: Mismatch Repair System, PD-L1 Expression, and Tumor Mutation Burden

Pharmaceutics ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 885
Author(s):  
Sofia Sagredou ◽  
Panagiotis Dalezis ◽  
Eirini Papadopoulou ◽  
Maria Voura ◽  
Maria V. Deligiorgi ◽  
...  
Keyword(s):  
Anticancer Activity ◽  
Mismatch Repair ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Biomarkers ◽  
Cancer Cell Lines ◽  
Repair System ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Ht 29

Microsatellite instability (MSI), tumor mutation burden (TMB), and programmed cell death ligand-1 (PD-L1) are particularly known as immunotherapy predictive biomarkers. MSI and TMB are closely related to DNA mismatch repair (MMR) pathway functionality, while the PD-L1 checkpoint mediates cancer cell evasion from immune surveillance via the PD-L1/PD-1 axis. Among all the novel triazolo[3,4-b]thiadiazole derivatives, the compound KA39 emerged as the most potent anticancer agent. In the present study, potential alterations in MSI, TMB, and/or PD-L1 expression upon cell treatment with KA39 are explored. We tested three MMR-deficient (DLD-1, LS174T, and DU-145) and two MMR-proficient (HT-29 and PC-3) human cancer cell lines. Our findings support KA39-induced PD-L1 overexpression in all cancer cell lines, although the most outstanding increase was observed in MMR-proficient HT-29 cells. MSI analysis showed that KA39 affects the MMR system, impairing its recognition or repair activity, particularly in MMR-deficient DLD-1 and DU-145 cells, enhancing oligonucleotide production. There were no remarkable alterations in the TMB between untreated and treated cells, indicating that KA39 does not belong to mutagenic agents. Taking together the significant in vitro anticancer activity with PD-L1 upregulation and MSI increase, KA39 should be investigated further for its implication in chemo-immunotherapy of cancer.

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 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.

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