Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data

Ryall, Karen A.; Shin, Jaehyun; Yoo, Minjae; Hinz, Trista K.; Kim, Jihye; Kang, Jaewoo; Heasley, Lynn E.; Tan, Aik Choon

doi:10.1093/bioinformatics/btv427

Cited by 18 publications

(23 citation statements)

References 37 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The successful interplay between annotated compound sets, phenotypic screening, genetic methods, and bioinformatics is well documented, and integration leads to new insights on targets and pathways of interest. [16][17][18][19][20][21][22][23] Herein we describe our progress towards the generation of a publicly available KCGS, and outline a collaborative plan to complete the set. This collaborative project between industrial and academic scientists will build a comprehensive KCGS composed only of potent, narrow spectrum inhibitors that collectively demonstrate full coverage of all human protein kinases for which there are assays available at one of the contract research organizations that offers broad kinome profiling ("the screenable kinome").…”

Section: Kinase Chemogenomicsmentioning

confidence: 99%

Progress Towards a Public Chemogenomic Set for Protein Kinases and a Call for Contributions

Drewry

Wells

Andrews

et al. 2017

Preprint

View full text Add to dashboard Cite

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases.

show abstract

Section: Kinase Chemogenomicsmentioning

confidence: 99%

Progress Towards a Public Chemogenomic Set for Protein Kinases and a Call for Contributions

Drewry

Wells

Andrews

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Motivated by these studies, Ryall et al, developed the Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data, and gene expression profiles to determine kinase dependency in cancer cells (74). As a proof of concept, they applied KAR to rank the kinase dependencies in 21 lung cancer cell lines using publicly available pharmacological screening data from the Genomics of Drug Sensitivity in Cancer (GDSC).…”

Section: Computational Approaches To Assess Kinase Co-dependenciesmentioning

confidence: 99%

Exploiting receptor tyrosine kinase co-activation for cancer therapy

Tan

Vyse

Huang

2017

Drug Discovery Today

View full text Add to dashboard Cite

Studies over the past decade have shown that Receptor Tyrosine Kinase (RTK) co-activation is prevalent in many cancer types. Compelling data demonstrates that cancers are likely to have evolved RTK co-activation as a generic means for driving tumour growth and providing a buffering system to limit the lethal effects of microenvironmental insults including therapy. In this review, we summarise the general principles of RTK co-activation gleaned from key studies over the last decade. We discuss direct and indirect approaches to exploit RTK co-activation for cancer therapy and describe recent developments in computational approaches to predict kinase co-dependencies by integrating drug screening data and kinase inhibitor selectivity profiles. We offer a perspective on the outstanding questions in the field focusing on the implications of RTK co-activation on tumour heterogeneity and cancer evolution and conclude by surveying emerging computational and experimental approaches that will provide further insights into the biology of RTK co-activation and deliver new developments in effective cancer therapies.

show abstract

“…Ryall et al . gathered publicly available gene expression data as well as kinase inhibition and drug sensitivity data and applied the kinase addiction ranker (KAR) on 21 lung cell lines and samples of 151 leukaemia patients tested with 66 kinase inhibitors. However, the validation of the predictive capacity of kinase dependency of the KAR algorithm was only probed in one epithelial lung cell line.…”

Section: Approaches To Identify Regulatory Kinasesmentioning

confidence: 99%

“…Integrative approaches may be able to translate the acquired knowledge to clinical management of cancer, in order to detect vulnerabilities of cancer cells that may be used to prioritise treatments in a personalised manner. Ryall et al [132] gathered publicly available gene expression data as well as kinase inhibition and drug sensitivity data and applied the kinase addiction ranker (KAR) on 21 lung cell lines and samples of 151 leukaemia patients tested with 66 kinase inhibitors. However, the validation of the predictive capacity of kinase dependency of the KAR algorithm was only probed in one epithelial lung cell line.…”

Section: Integration Of Different Types Of Molecular Datamentioning

confidence: 99%

Approaches to identify kinase dependencies in cancer signalling networks

2017

View full text Add to dashboard Cite

Edited by Wilhelm JustCells integrate extracellular signals into appropriate responses through a complex network of biochemical reactions driven by the activity of protein and lipid kinases, among other proteins. In order to understand this complexity, new approaches, both experimental and computational, have recently been developed with the aim to identify regulatory kinases and infer their activation status in the context of their signalling network. Here, we review such approaches with particular focus on those based on phosphoproteomics. Integration of kinase activity measurements inferred from phosphoproteomics data with other 'omics' datasets is starting to be used to identify regulatory nodes in biochemical networks. These methodologies may in the future be used to identify patient-specific targets and thus advance personalised cancer medicine.

show abstract

Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 18 publications

References 37 publications

Progress Towards a Public Chemogenomic Set for Protein Kinases and a Call for Contributions

Progress Towards a Public Chemogenomic Set for Protein Kinases and a Call for Contributions

Exploiting receptor tyrosine kinase co-activation for cancer therapy

Approaches to identify kinase dependencies in cancer signalling networks

Contact Info

Product

Resources

About