scholarly journals Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction

2020 ◽  
Vol 11 ◽  
Author(s):  
Barbara Niederdorfer ◽  
Vasundra Touré ◽  
Miguel Vazquez ◽  
Liv Thommesen ◽  
Martin Kuiper ◽  
...  
Keyword(s):  
Cell Line ◽  
Specific Drug ◽  
Logic Modeling ◽  
Drug Synergy ◽  
Synergy Prediction

Preclinical evaluation of the ETS inhibitor TK216 against relapsed and refractory childhood leukemia.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10033-10033
Author(s):  
Ritul Sharma ◽  
Satbir Thakur ◽  
Mohit Jain ◽  
Chunfen Zhang ◽  
Anne-Marie Langevin ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Line ◽  
Drug Combination ◽  
Childhood Leukemia ◽  
Sensitive Cell Line ◽  
Pediatric Leukemia ◽  
Drug Synergy ◽  
Molecular Abnormalities ◽  
Anti Cancer ◽  
Combination Studies

10033 Background: Although survival rates have improved in the recent past, relapse and refractory disease remain a significant cause of death in children with leukemia. This calls for an urgent need for the development of novel therapies that could effectively treat leukemias in children. The E26 transformation specific (ETS) family of transcription factors regulate various normal cellular functions but are abnormally expressed in various cancers, including leukemia. TK216 is an ETS inhibitor, that has shown pre-clinical activity and clinical efficacy in solid tumors. In this study, we explore the feasibility of using TK216 as a therapeutic agent for the treatment of high risk refractory pediatric leukemia. Methods: A panel of pediatric leukemia derived cell lines and primary blast cells representing a spectrum of molecular abnormalities seen in pediatric leukemia were treated in vitro with TK216 to determine cytotoxicity. Normal lymphocytes were used as controls and cell viability was determined 72 hours post-treatment by Alamar blue assay. The induction of tumor cell apoptosis and target modulation were detected by Western blotting. Alterations in the cell cycle were assessed by FACS analysis with PI staining. Drug combination studies were carried out with established anti-leukemic agents to identify synergy for greater therapeutic efficiency. Results: TK216 decreased cell viability in leukemia cells compared to normal lymphocyte controls in a dose-dependent manner with variations in sensitivity noted with inherent molecular abnormalities. The IC50 values observed ranged from 0.22 µM for the most sensitive cell line, MV4-11 to 0.95 µM for least sensitive cell line, SUP-B15. Apoptosis induction upon TK216 treatment was confirmed by PARP cleavage and caspase 3 activation. Cell cycle analysis demonstrated increased sub-G1 population of cells after TK216 treatment. A strong correlation between sub-G1 population and sensitivity of the cell line towards TK216 (47% in MV4-11 vs 3.72% in SUP-B15) was observed. Screening of a panel of 200 FDA approved anti-cancer agents in drug combination studies identified potential agents for drug synergy. Significant drug synergy was noted with TK216 in combination with the epigenetic modifier 5-azacytidine and the Bcl-2 inhibitor, Venetoclax. [Combination Index for Venetoclax and TK216, mean = 0.65 for MV4-11 and 0.33 for SUP-B15]. Conclusions: Data from our study demonstrate that the ETS inhibitor TK216 induces apoptosis and cell cycle arrest in pediatric leukemia cells at physiologically relevant concentrations. Our combination studies identified distinct anti-cancer agents that could be used for developing effective drug combination regimens with TK216. Overall, our findings provide essential preclinical data for the consideration of TK216 in early phase clinical trials for the treatment of selected high-risk and refractory childhood leukemia.

scholarly journals The Selectivity of Austocystin D Arises from Cell-Line-Specific Drug Activation by Cytochrome P450 Enzymes

2011 ◽  
Vol 74 (4) ◽  
pp. 567-573 ◽  
Author(s):  
Kevin M. Marks ◽  
Eun Sun Park ◽  
Alexander Arefolov ◽  
Katie Russo ◽  
Keiko Ishihara ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Cell Line ◽  
Specific Drug ◽  
Cytochrome P450 Enzymes ◽  
Drug Activation

scholarly journals Anti-cancer Drug Synergy Prediction in Understudied Tissues using Transfer Learning

2020 ◽  
Author(s):  
Yejin Kim ◽  
Shuyu Zheng ◽  
Jing Tang ◽  
W. Jim Zheng ◽  
Zhao Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Transfer Learning ◽  
Treatment Options ◽  
Cancer Drug ◽  
Drug Synergy ◽  
Learning From Data ◽  
Cancer Types ◽  
Improved Accuracy ◽  
Synergy Prediction

AbstractMotivationExploring an exponentially increasing yet more promising space, high-throughput combinatorial drug screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues (such as bone and prostate) are understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied data-poor tissues as overcoming data scarcity problem.ResultsWe collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines from six different databases. We developed a drug synergy prediction model based on deep neural networks to integrate multi-modal input and utilize transfer learning from data-rich tissues to data-poor tissues. We showed improved accuracy in predicting drug synergy in understudied tissues without enough drug combination screening data nor after-treatment transcriptome. Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help prioritizing future in-vitro experiments.Availability and ImplementationOur algorithm will be publicly available via https://github.com/yejinjkim/drug-synergy-prediction

scholarly journals Anticancer drug synergy prediction in understudied tissues using transfer learning

2020 ◽  
Author(s):  
Yejin Kim ◽  
Shuyu Zheng ◽  
Jing Tang ◽  
Wenjin Jim Zheng ◽  
Zhao Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Transfer Learning ◽  
Learning Strategy ◽  
Mean Squared Error ◽  
Treatment Options ◽  
Drug Synergy ◽  
Learning From Data ◽  
Improved Accuracy ◽  
Synergy Prediction

Abstract Objective Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. Materials and Methods We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. Results We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. Conclusions Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.

Abstract PR04: A network-based approach for drug synergy prediction from gene expression data

2013 ◽  
Author(s):  
Mariano J. Alvarez ◽  
Yao Shen ◽  
Charles Karan ◽  
Mukesh Bansal ◽  
Michela Mattioli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Drug Synergy ◽  
Synergy Prediction

scholarly journals Stratification and prediction of drug synergy based on target functional similarity

2019 ◽  
Author(s):  
Mi Yang ◽  
Michael P. Menden ◽  
Patricia Jaaks ◽  
Jonathan Dry ◽  
Mathew Garnett ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
High Throughput ◽  
Large Scale ◽  
Cancer Cell Lines ◽  
Functional Similarity ◽  
Breast Cancer Cell Lines ◽  
Drug Synergy ◽  
High Throughput Screens ◽  
Synergy Prediction

ABSTRACTTargeted mono-therapies in cancer are hampered by the ability of tumor cells to escape inhibition through rewiring or alternative pathways. Drug combination approaches can provide a means to overcome these resistance mechanisms. Effective use of combinations requires strategies to select combinations from the enormous space of combinations, and to stratify patients according to their likelihood to respond. We here introduce two complementary workflows: One prioritising experiments in high-throughput screens for drug synergy enrichment, and a consecutive workflow to predict hypothesis-driven synergy stratification. Both approaches only need data of efficacy of single drugs. They rely on the notion of target functional similarity between two target proteins. This notion reflects how similarly effective drugs are on different cancer cells as a function of cancer signaling pathways’ activities on those cells. Our synergy prediction workflow revealed that two drugs targeting either the same or functionally opposite pathways are more likely to be synergistic. This enables experimental prioritisation in high-throughput screens and supports the notion that synergy can be achieved by either redundant pathway inhibition or targeting independent compensatory mechanisms. We tested the synergy stratification workflow on seven target protein pairs (AKT/EGFR, AKT/MTOR, BCL2/MTOR, EGFR/MTOR, AKT/BCL2, AKT/ALK and AKT/PARP1, representing 29 combinations and predicted their synergies in 33 breast cancer cell lines (Pearson’s correlation r=0.27). Additionally, we experimentally validated predicted synergy of the BRAF/Insulin Receptor combination (Dabrafenib/BMS−754807) in 48 colorectal cancer cell lines (r=0.5). In conclusion, our synergy prediction workflow can support compound prioritization in large scale drug screenings, and our synergy stratification workflow can select where the efficacy of drugs already known for inducing synergy is higher.

Ultrastructure of NPC/HK1 cells heterotransplanted in athymic nude mice

1982 ◽  
Vol 40 ◽  
pp. 236-237
Author(s):  
E.C. Chew ◽  
C.L. Li ◽  
D.P. Huang ◽  
H.C. Ho ◽  
L.S. Mak ◽  
...  
Keyword(s):  
Cell Line ◽  
Nude Mice ◽  
Biopsy Specimen ◽  
Epithelial Cell Line ◽  
Present Communication ◽  
Recurrent Tumour ◽  
Athymic Nude Mice ◽  
Cell Line Establishment ◽  
Fine Structures ◽  
Well Differentiated

An epithelial cell line, NPC/HK1, has recently been established from a biopsy specimen of a recurrent tumour of the nasopharynx which was histologically diagnosed as a moderately to well differentiated squamous cell carcinoma. A definite decrease in the amount of tonofilaments and desmosomes in the NPC/HK1 cells during the cell line establishment was observed. The present communication reports on the fine structures of the NPC/HK1 cells heterotraneplanted in athymic nude mice.

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