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A target-disease network model of second-generation BCR-ABL inhibitor action in Ph+ ALL.

Rix U, Colinge J, Blatt K, Gridling M, Remsing Rix LL, Parapatics K, Cerny-Reiterer S, Burkard TR, Jäger U, Melo JV, Bennett KL, Valent P, Superti-Furga G - PLoS ONE (2013)

Bottom Line: We then mapped those interactions along with known genetic lesions onto public protein-protein interactions.Computation of global scores through correlation of target affinity, network topology, and distance to disease-relevant nodes assigned the highest impact to dasatinib, which was subsequently confirmed by proliferation assays.In future, combination of patient-specific genomic information with detailed drug target knowledge and network-based computational analysis should allow for an accurate and individualized prediction of therapy.

View Article: PubMed Central - PubMed

Affiliation: CeMM - Research Center, Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.

ABSTRACT
Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) is in part driven by the tyrosine kinase bcr-abl, but imatinib does not produce long-term remission. Therefore, second-generation ABL inhibitors are currently in clinical investigation. Considering different target specificities and the pronounced genetic heterogeneity of Ph+ ALL, which contributes to the aggressiveness of the disease, drug candidates should be evaluated with regard to their effects on the entire Ph+ ALL-specific signaling network. Here, we applied an integrated experimental and computational approach that allowed us to estimate the differential impact of the bcr-abl inhibitors nilotinib, dasatinib, Bosutinib and Bafetinib. First, we determined drug-protein interactions in Ph+ ALL cell lines by chemical proteomics. We then mapped those interactions along with known genetic lesions onto public protein-protein interactions. Computation of global scores through correlation of target affinity, network topology, and distance to disease-relevant nodes assigned the highest impact to dasatinib, which was subsequently confirmed by proliferation assays. In future, combination of patient-specific genomic information with detailed drug target knowledge and network-based computational analysis should allow for an accurate and individualized prediction of therapy.

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Related in: MedlinePlus

Hybrid drug-protein/protein-protein interaction networks of specific drug binding proteins.Individual cellular target profiles of nilotinib (green), dasatinib (red), bosutinib (yellow) and bafetinib (blue) were intersected with each other and overlaid with PPI data from public databases. Protein kinases and the oxidoreductase NQO2, as a validated target of nilotinib and to lesser extent of bafetinib, were considered to be direct drug binders (solid lines) and color-coded according to the drug they were interacting with. Shared kinase targets display a split color code. All other non-kinase proteins were assumed to be indirect binders (dashed lines) and displayed in grey. The analysis reveals distinct protein complexes, which are enriched by particular drugs and which are highlighted with the respectively colored background. A. Z-119 drug-protein interaction network. B. BV-173 drug-protein interaction network.
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pone-0077155-g003: Hybrid drug-protein/protein-protein interaction networks of specific drug binding proteins.Individual cellular target profiles of nilotinib (green), dasatinib (red), bosutinib (yellow) and bafetinib (blue) were intersected with each other and overlaid with PPI data from public databases. Protein kinases and the oxidoreductase NQO2, as a validated target of nilotinib and to lesser extent of bafetinib, were considered to be direct drug binders (solid lines) and color-coded according to the drug they were interacting with. Shared kinase targets display a split color code. All other non-kinase proteins were assumed to be indirect binders (dashed lines) and displayed in grey. The analysis reveals distinct protein complexes, which are enriched by particular drugs and which are highlighted with the respectively colored background. A. Z-119 drug-protein interaction network. B. BV-173 drug-protein interaction network.

Mentions: For a proteome-wide understanding of the respective drug-protein interaction networks, we broadened our analysis by including non-kinase targets. Therefore, we performed drug affinity chromatography experiments in the presence of soluble drug (Tables S5 and S6 in File S1), which competes with the respective drug matrix for specific targets and their interaction partners while non-specific proteins remain unaffected. Next, we compared the average spectral counts of regular (uncompeted) and competition experiments and determined proteins that were specific for each drug (Figure 2). Proteins, which were not sufficiently competed by the respective soluble drug, but were otherwise specific with regard to other TKI, were rescued for this analysis. We thus identified 144 proteins, including the 79 mentioned kinases, that were specifically binding to any one or more given TKI in BV-173 and Z-119 cells. Assuming that protein kinases were direct drug binders, we generated hybrid drug-protein/protein-protein interaction networks for each cell type with these 144 proteins by mining of publicly available PPI databases (IntAct, HRPD, MINT, BioGRID, DIP) (Figure 3, Figure S3 in File S1). The majority of non-kinase proteins displayed at least one known interaction with another selected protein, which might serve as validation of the specificity assessment and support the assumption that these proteins are indirect drug binders. To confirm this, we considered that true indirect drug interactors should be in interaction with affinity-enriched kinases more frequently than random. Considering the targets we could map on the PPI network only, the 75 mapped kinases featured 4020 direct interactions. Forty-four of the 63 mapped non-kinases were among the kinase interactors. Given that the interactome contained 13350 proteins, we found a hypergeometric P-value of 2.57e-7.


A target-disease network model of second-generation BCR-ABL inhibitor action in Ph+ ALL.

Rix U, Colinge J, Blatt K, Gridling M, Remsing Rix LL, Parapatics K, Cerny-Reiterer S, Burkard TR, Jäger U, Melo JV, Bennett KL, Valent P, Superti-Furga G - PLoS ONE (2013)

Hybrid drug-protein/protein-protein interaction networks of specific drug binding proteins.Individual cellular target profiles of nilotinib (green), dasatinib (red), bosutinib (yellow) and bafetinib (blue) were intersected with each other and overlaid with PPI data from public databases. Protein kinases and the oxidoreductase NQO2, as a validated target of nilotinib and to lesser extent of bafetinib, were considered to be direct drug binders (solid lines) and color-coded according to the drug they were interacting with. Shared kinase targets display a split color code. All other non-kinase proteins were assumed to be indirect binders (dashed lines) and displayed in grey. The analysis reveals distinct protein complexes, which are enriched by particular drugs and which are highlighted with the respectively colored background. A. Z-119 drug-protein interaction network. B. BV-173 drug-protein interaction network.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3795025&req=5

pone-0077155-g003: Hybrid drug-protein/protein-protein interaction networks of specific drug binding proteins.Individual cellular target profiles of nilotinib (green), dasatinib (red), bosutinib (yellow) and bafetinib (blue) were intersected with each other and overlaid with PPI data from public databases. Protein kinases and the oxidoreductase NQO2, as a validated target of nilotinib and to lesser extent of bafetinib, were considered to be direct drug binders (solid lines) and color-coded according to the drug they were interacting with. Shared kinase targets display a split color code. All other non-kinase proteins were assumed to be indirect binders (dashed lines) and displayed in grey. The analysis reveals distinct protein complexes, which are enriched by particular drugs and which are highlighted with the respectively colored background. A. Z-119 drug-protein interaction network. B. BV-173 drug-protein interaction network.
Mentions: For a proteome-wide understanding of the respective drug-protein interaction networks, we broadened our analysis by including non-kinase targets. Therefore, we performed drug affinity chromatography experiments in the presence of soluble drug (Tables S5 and S6 in File S1), which competes with the respective drug matrix for specific targets and their interaction partners while non-specific proteins remain unaffected. Next, we compared the average spectral counts of regular (uncompeted) and competition experiments and determined proteins that were specific for each drug (Figure 2). Proteins, which were not sufficiently competed by the respective soluble drug, but were otherwise specific with regard to other TKI, were rescued for this analysis. We thus identified 144 proteins, including the 79 mentioned kinases, that were specifically binding to any one or more given TKI in BV-173 and Z-119 cells. Assuming that protein kinases were direct drug binders, we generated hybrid drug-protein/protein-protein interaction networks for each cell type with these 144 proteins by mining of publicly available PPI databases (IntAct, HRPD, MINT, BioGRID, DIP) (Figure 3, Figure S3 in File S1). The majority of non-kinase proteins displayed at least one known interaction with another selected protein, which might serve as validation of the specificity assessment and support the assumption that these proteins are indirect drug binders. To confirm this, we considered that true indirect drug interactors should be in interaction with affinity-enriched kinases more frequently than random. Considering the targets we could map on the PPI network only, the 75 mapped kinases featured 4020 direct interactions. Forty-four of the 63 mapped non-kinases were among the kinase interactors. Given that the interactome contained 13350 proteins, we found a hypergeometric P-value of 2.57e-7.

Bottom Line: We then mapped those interactions along with known genetic lesions onto public protein-protein interactions.Computation of global scores through correlation of target affinity, network topology, and distance to disease-relevant nodes assigned the highest impact to dasatinib, which was subsequently confirmed by proliferation assays.In future, combination of patient-specific genomic information with detailed drug target knowledge and network-based computational analysis should allow for an accurate and individualized prediction of therapy.

View Article: PubMed Central - PubMed

Affiliation: CeMM - Research Center, Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.

ABSTRACT
Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) is in part driven by the tyrosine kinase bcr-abl, but imatinib does not produce long-term remission. Therefore, second-generation ABL inhibitors are currently in clinical investigation. Considering different target specificities and the pronounced genetic heterogeneity of Ph+ ALL, which contributes to the aggressiveness of the disease, drug candidates should be evaluated with regard to their effects on the entire Ph+ ALL-specific signaling network. Here, we applied an integrated experimental and computational approach that allowed us to estimate the differential impact of the bcr-abl inhibitors nilotinib, dasatinib, Bosutinib and Bafetinib. First, we determined drug-protein interactions in Ph+ ALL cell lines by chemical proteomics. We then mapped those interactions along with known genetic lesions onto public protein-protein interactions. Computation of global scores through correlation of target affinity, network topology, and distance to disease-relevant nodes assigned the highest impact to dasatinib, which was subsequently confirmed by proliferation assays. In future, combination of patient-specific genomic information with detailed drug target knowledge and network-based computational analysis should allow for an accurate and individualized prediction of therapy.

Show MeSH
Related in: MedlinePlus