Kinases, due to their etiologic roles in many cancers, are considered potential targets for anti-cancer drug development. However, the acquired additional mutations, either in the drug target (i.e. kinase) itself or in its downstream or parallel pathways, are the primary cause for drug resistance in many cancer treatment therapies. Although cancer cells may harbor hundreds of genomic alterations in various biological pathways, only subsets of these alterations are driving the cancer initiation or progression in different patients, leading to variable treatment responses even across patients with similar disease types. Identification of such personalized driver mutations is the first step toward the development of more individualized cancer treatments. Current advances in omics technologies are able to identify such vulnerabilities in cancer cells. Simultaneously, efforts have been given on computational drug discovery in this context, but with giving less emphasis on systematic experimental-computational approaches that could pinpoint the most effective combinations to target. Therefore, novel rational approaches to develop multi-targeted therapies by selectively targeting the mutated kinases (e.g. inhibit a cancer-driving oncogene or re-activate a mutated cancer suppressor) without affecting their wild-type counterparts to attack the resistance problem and to provide personalized and clinically actionable therapeutic strategies.
The goal of the research topic includes bringing the scientific community together to discuss the most vulnerable and potential driver mutations in kinases that caused drug resistance and opportunities for anti-cancer drug development. The focus will be to understand drug-resistance mechanisms due to additional mutations in kinases and further identification of candidate compounds that selectively target the mutated kinases (e.g. inhibit a cancer-driving oncogene or re-activate a mutated cancer suppressor) without affecting their wild type counterparts. This topic will help in developing more personalized, effective, and safe anticancer treatment strategies by translating the cancer genome information into clinically actionable treatment options.
I include the following points under scope and information for authors. Manuscripts should include experimental validation of findings, particularly for biomarker discovery. Potential drug identification is highly encouraged. Please refer to the
Computational Genomics about page for more detail.
1) Omics approaches for identifying kinases and associated mutations as biomarkers for drug resistance in cancer treatment.
2) Machine learning and deep learning models for prediction of drug resistance due to kinase mutations, drug-kinase interactions, drug therapy response, etc.
3) Computational approaches to identify and understand druggable kinome.
4) Application of network algorithms to understand biological networks e.g. drug-kinase interactions, kinase-kinase interactions, miRNA-kinase interactions, etc. towards drug repurposing and biomarker identification.
Kinases, due to their etiologic roles in many cancers, are considered potential targets for anti-cancer drug development. However, the acquired additional mutations, either in the drug target (i.e. kinase) itself or in its downstream or parallel pathways, are the primary cause for drug resistance in many cancer treatment therapies. Although cancer cells may harbor hundreds of genomic alterations in various biological pathways, only subsets of these alterations are driving the cancer initiation or progression in different patients, leading to variable treatment responses even across patients with similar disease types. Identification of such personalized driver mutations is the first step toward the development of more individualized cancer treatments. Current advances in omics technologies are able to identify such vulnerabilities in cancer cells. Simultaneously, efforts have been given on computational drug discovery in this context, but with giving less emphasis on systematic experimental-computational approaches that could pinpoint the most effective combinations to target. Therefore, novel rational approaches to develop multi-targeted therapies by selectively targeting the mutated kinases (e.g. inhibit a cancer-driving oncogene or re-activate a mutated cancer suppressor) without affecting their wild-type counterparts to attack the resistance problem and to provide personalized and clinically actionable therapeutic strategies.
The goal of the research topic includes bringing the scientific community together to discuss the most vulnerable and potential driver mutations in kinases that caused drug resistance and opportunities for anti-cancer drug development. The focus will be to understand drug-resistance mechanisms due to additional mutations in kinases and further identification of candidate compounds that selectively target the mutated kinases (e.g. inhibit a cancer-driving oncogene or re-activate a mutated cancer suppressor) without affecting their wild type counterparts. This topic will help in developing more personalized, effective, and safe anticancer treatment strategies by translating the cancer genome information into clinically actionable treatment options.
I include the following points under scope and information for authors. Manuscripts should include experimental validation of findings, particularly for biomarker discovery. Potential drug identification is highly encouraged. Please refer to the
Computational Genomics about page for more detail.
1) Omics approaches for identifying kinases and associated mutations as biomarkers for drug resistance in cancer treatment.
2) Machine learning and deep learning models for prediction of drug resistance due to kinase mutations, drug-kinase interactions, drug therapy response, etc.
3) Computational approaches to identify and understand druggable kinome.
4) Application of network algorithms to understand biological networks e.g. drug-kinase interactions, kinase-kinase interactions, miRNA-kinase interactions, etc. towards drug repurposing and biomarker identification.
