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Exposing Therapeutic Vulnerabilities in Cancer through Bioinformatics

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 2380

Special Issue Editor


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Guest Editor
Department of Molecular Medicine, Mayo Clinic, Rochester, MN 55905, USA
Interests: bioinformatics; therapy resistance in cancer; neuroendocrine lung; prostate cancers

Special Issue Information

Dear Colleagues,

Modern advances in genomic technologies have led to effective therapies against tumors with specific mutations. However, in a majority of cases, identifications of genomic alterations that dictate the fate of tumors and can be effectively targeted remain elusive. Additionally, while current immunotherapy-based approaches—such as immune checkpoint-blockade therapies—have resulted in improved survival in many patients, these therapies have significant liabilities due to the unpredictability of patient response and their high toxicity. This Special Issue of IJMS will discuss pathway/systems biology approaches aimed at the identification of tumor vulnerabilities to targeted or immune-based therapy. Topics include but are not limited to approaches that:

  • Predict tumor response/resistance to standard of care drugs;
  • Identify effective combinatorial therapies;
  • Predict the effectiveness of alternative approaches such as fasting or ascorbic acid therapy;
  • Develop immunograms that predict patient response to immune-based therapies;
  • Predict individualized based toxicity in standard of care therapies.

Dr. Farhad Kosari
Guest Editor

Manuscript Submission Information

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Keywords

  • systems biology
  • pathway analysis
  • immunograms
  • drug toxicity
  • combinatorial therapies
  • individualized therapy
  • responders and nonresponders

Published Papers (1 paper)

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Research

23 pages, 6124 KiB  
Article
Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model
by Liang Yu, Yayong Shi, Quan Zou, Shuhang Wang, Liping Zheng and Lin Gao
Int. J. Mol. Sci. 2020, 21(14), 5014; https://doi.org/10.3390/ijms21145014 - 16 Jul 2020
Cited by 22 | Viewed by 1989
Abstract
Some drugs can be used to treat multiple diseases, suggesting potential patterns in drug treatment. Determination of drug treatment patterns can improve our understanding of the mechanisms of drug action, enabling drug repurposing. A drug can be associated with a multilayer tissue-specific protein–protein [...] Read more.
Some drugs can be used to treat multiple diseases, suggesting potential patterns in drug treatment. Determination of drug treatment patterns can improve our understanding of the mechanisms of drug action, enabling drug repurposing. A drug can be associated with a multilayer tissue-specific protein–protein interaction (TSPPI) network for the diseases it is used to treat. Proteins usually interact with other proteins to achieve functions that cause diseases. Hence, studying drug treatment patterns is similar to studying common module structures in multilayer TSPPI networks. Therefore, we propose a network-based model to study the treatment patterns of drugs. The method was designated SDTP (studying drug treatment pattern) and was based on drug effects and a multilayer network model. To demonstrate the application of the SDTP method, we focused on analysis of trichostatin A (TSA) in leukemia, breast cancer, and prostate cancer. We constructed a TSPPI multilayer network and obtained candidate drug-target modules from the network. Gene ontology analysis provided insights into the significance of the drug-target modules and co-expression networks. Finally, two modules were obtained as potential treatment patterns for TSA. Through analysis of the significance, composition, and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed SDTP method for identifying drug treatment patterns. In summary, our novel approach used a multilayer network model to overcome the shortcomings of single-layer networks and combined the network with information on drug activity. Based on the discovered drug treatment patterns, we can predict the potential diseases that the drug can treat. That is, if a disease-related protein module has a similar structure, then the drug is likely to be a potential drug for the treatment of the disease. Full article
(This article belongs to the Special Issue Exposing Therapeutic Vulnerabilities in Cancer through Bioinformatics)
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