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Sensitivity Analysis of the NPM-ALK Signalling Network Reveals Important Pathways for Anaplastic Large Cell Lymphoma Combination Therapy

View Article: PubMed Central - PubMed

ABSTRACT

A large subset of anaplastic large cell lymphoma (ALCL) patients harbour a somatic aberration in which anaplastic lymphoma kinase (ALK) is fused to nucleophosmin (NPM) resulting in a constitutively active signalling fusion protein, NPM-ALK. We computationally simulated the signalling network which mediates pathological cell survival and proliferation through NPM-ALK to identify therapeutically targetable nodes through which it may be possible to regain control of the tumourigenic process. The simulations reveal the predominant role of the VAV1-CDC42 (cell division control protein 42) pathway in NPM-ALK-driven cellular proliferation and of the Ras / mitogen-activated ERK kinase (MEK) / extracellular signal-regulated kinase (ERK) cascade in controlling cell survival. Our results also highlight the importance of a group of interleukins together with the Janus kinase 3 (JAK3) / signal transducer and activator of transcription 3 (STAT3) signalling in the development of NPM-ALK derived ALCL. Depending on the activity of JAK3 and STAT3, the system may also be sensitive to activation of protein tyrosine phosphatase-1 (SHP1), which has an inhibitory effect on cell survival and proliferation. The identification of signalling pathways active in tumourigenic processes is of fundamental importance for effective therapies. The prediction of alternative pathways that circumvent classical therapeutic targets opens the way to preventive approaches for countering the emergence of cancer resistance.

No MeSH data available.


Sensitivity profile of the network.Sensitivity of the endpoints (“Cell Survival” and “Proliferation”, encircled in the network representation Fig 1 in red and black, respectively) is calculated according to [38, 39] and represented in red and black data points in the central plot. Each point in the central plot corresponds to one of  states that differ from each other in exactly one node (recall that each node may assume a value of low or high activity). This enables the calculation of the sensitivity of every endpoint with respect to each node by comparing all pairs of states systematically. Following this step, the states that contributed the most to the sensitivity (the top- or bottom-2% regions) were examined to view how often each node is associated with the outcome. The results of this calculation are presented in the bar plots. εmax (εmin) indicates the maximum (minimum) sensitivity value corresponding to each bar in the plots on the right and left-hand sides. The bar plots in the middle represent the fractional involvement of the network nodes associated with negligible sensitivity (between ±0.005).
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pone.0163011.g002: Sensitivity profile of the network.Sensitivity of the endpoints (“Cell Survival” and “Proliferation”, encircled in the network representation Fig 1 in red and black, respectively) is calculated according to [38, 39] and represented in red and black data points in the central plot. Each point in the central plot corresponds to one of states that differ from each other in exactly one node (recall that each node may assume a value of low or high activity). This enables the calculation of the sensitivity of every endpoint with respect to each node by comparing all pairs of states systematically. Following this step, the states that contributed the most to the sensitivity (the top- or bottom-2% regions) were examined to view how often each node is associated with the outcome. The results of this calculation are presented in the bar plots. εmax (εmin) indicates the maximum (minimum) sensitivity value corresponding to each bar in the plots on the right and left-hand sides. The bar plots in the middle represent the fractional involvement of the network nodes associated with negligible sensitivity (between ±0.005).

Mentions: The first aim in the analysis of the simulations was to identify the states (combinations of nodes with different activity levels, denoted as the network’s “control nodes”) that most strongly influence the pathological outcome, i.e., cell proliferation and survival (denoted as the network’s “endpoints”). Sensitivity analysis was therefore performed, and the states were sorted according to their influence on proliferation and cell survival (central plot in Fig 2). The nodes with high or low (i.e., negative) sensitivity values with respect to the endpoints constitute control points of the network for tumour development.


Sensitivity Analysis of the NPM-ALK Signalling Network Reveals Important Pathways for Anaplastic Large Cell Lymphoma Combination Therapy
Sensitivity profile of the network.Sensitivity of the endpoints (“Cell Survival” and “Proliferation”, encircled in the network representation Fig 1 in red and black, respectively) is calculated according to [38, 39] and represented in red and black data points in the central plot. Each point in the central plot corresponds to one of  states that differ from each other in exactly one node (recall that each node may assume a value of low or high activity). This enables the calculation of the sensitivity of every endpoint with respect to each node by comparing all pairs of states systematically. Following this step, the states that contributed the most to the sensitivity (the top- or bottom-2% regions) were examined to view how often each node is associated with the outcome. The results of this calculation are presented in the bar plots. εmax (εmin) indicates the maximum (minimum) sensitivity value corresponding to each bar in the plots on the right and left-hand sides. The bar plots in the middle represent the fractional involvement of the network nodes associated with negligible sensitivity (between ±0.005).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036789&req=5

pone.0163011.g002: Sensitivity profile of the network.Sensitivity of the endpoints (“Cell Survival” and “Proliferation”, encircled in the network representation Fig 1 in red and black, respectively) is calculated according to [38, 39] and represented in red and black data points in the central plot. Each point in the central plot corresponds to one of states that differ from each other in exactly one node (recall that each node may assume a value of low or high activity). This enables the calculation of the sensitivity of every endpoint with respect to each node by comparing all pairs of states systematically. Following this step, the states that contributed the most to the sensitivity (the top- or bottom-2% regions) were examined to view how often each node is associated with the outcome. The results of this calculation are presented in the bar plots. εmax (εmin) indicates the maximum (minimum) sensitivity value corresponding to each bar in the plots on the right and left-hand sides. The bar plots in the middle represent the fractional involvement of the network nodes associated with negligible sensitivity (between ±0.005).
Mentions: The first aim in the analysis of the simulations was to identify the states (combinations of nodes with different activity levels, denoted as the network’s “control nodes”) that most strongly influence the pathological outcome, i.e., cell proliferation and survival (denoted as the network’s “endpoints”). Sensitivity analysis was therefore performed, and the states were sorted according to their influence on proliferation and cell survival (central plot in Fig 2). The nodes with high or low (i.e., negative) sensitivity values with respect to the endpoints constitute control points of the network for tumour development.

View Article: PubMed Central - PubMed

ABSTRACT

A large subset of anaplastic large cell lymphoma (ALCL) patients harbour a somatic aberration in which anaplastic lymphoma kinase (ALK) is fused to nucleophosmin (NPM) resulting in a constitutively active signalling fusion protein, NPM-ALK. We computationally simulated the signalling network which mediates pathological cell survival and proliferation through NPM-ALK to identify therapeutically targetable nodes through which it may be possible to regain control of the tumourigenic process. The simulations reveal the predominant role of the VAV1-CDC42 (cell division control protein 42) pathway in NPM-ALK-driven cellular proliferation and of the Ras / mitogen-activated ERK kinase (MEK) / extracellular signal-regulated kinase (ERK) cascade in controlling cell survival. Our results also highlight the importance of a group of interleukins together with the Janus kinase 3 (JAK3) / signal transducer and activator of transcription 3 (STAT3) signalling in the development of NPM-ALK derived ALCL. Depending on the activity of JAK3 and STAT3, the system may also be sensitive to activation of protein tyrosine phosphatase-1 (SHP1), which has an inhibitory effect on cell survival and proliferation. The identification of signalling pathways active in tumourigenic processes is of fundamental importance for effective therapies. The prediction of alternative pathways that circumvent classical therapeutic targets opens the way to preventive approaches for countering the emergence of cancer resistance.

No MeSH data available.