Dynamic Rearrangement of Cell States Detected by Systematic Screening of Sequential Anticancer Treatments
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Dynamic Rearrangement of Cell States Detected by Systematic Screening of Sequential Anticancer Treatments. / Koplev, Simon; Longden, James; Ferkinghoff-Borg, Jesper; Blicher Bjerregård, Mathias; Cox, Thomas R.; Erler, Janine T.; Pedersen, Jesper T.; Voellmy, Franziska; Sommer, Morten O.A.; Linding, Rune.
In: Cell Reports, Vol. 20, No. 12, 19.09.2017, p. 2784-2791.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Dynamic Rearrangement of Cell States Detected by Systematic Screening of Sequential Anticancer Treatments
AU - Koplev, Simon
AU - Longden, James
AU - Ferkinghoff-Borg, Jesper
AU - Blicher Bjerregård, Mathias
AU - Cox, Thomas R.
AU - Erler, Janine T.
AU - Pedersen, Jesper T.
AU - Voellmy, Franziska
AU - Sommer, Morten O.A.
AU - Linding, Rune
PY - 2017/9/19
Y1 - 2017/9/19
N2 - Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood.
AB - Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood.
KW - cancer
KW - chemotherapy
KW - sequential
KW - time-stagger
U2 - 10.1016/j.celrep.2017.08.095
DO - 10.1016/j.celrep.2017.08.095
M3 - Journal article
C2 - 28930675
AN - SCOPUS:85029583390
VL - 20
SP - 2784
EP - 2791
JO - Cell Reports
JF - Cell Reports
SN - 2211-1247
IS - 12
ER -
ID: 185267999