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Development and validation of a single‐cell network profiling assay‐based classifier to predict response to induction therapy in paediatric patients with de novo acute myeloid leukaemia: a report from the Children's Oncology Group
Author(s) -
Lacayo Norman J.,
Alonzo Todd A.,
Gayko Urte,
Rosen David B.,
Westfall Matt,
Purvis Norman,
Putta Santosh,
Louie Brent,
Hackett James,
Cohen Aileen Cleary,
Cesano Alessandra,
Gerbing Robert,
Ravindranath Yaddanapudi,
Dahl Gary V.,
Gamis Alan,
Meshinchi Soheil
Publication year - 2013
Publication title -
british journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.907
H-Index - 186
eISSN - 1365-2141
pISSN - 0007-1048
DOI - 10.1111/bjh.12370
Subject(s) - receiver operating characteristic , induction chemotherapy , medicine , classifier (uml) , myeloid , oncology , area under the curve , artificial intelligence , chemotherapy , computer science
Summary Single cell network profiling ( SCNP ) is a multi‐parameter flow cytometry technique for simultaneous interrogation of intracellular signalling pathways. Diagnostic paediatric acute myeloid leukaemia ( AML ) bone marrow samples were used to develop a classifier for response to induction therapy in 53 samples and validated in an independent set of 68 samples. The area under the curve of a receiver operating characteristic curve ( AUC ROC ) was calculated to be 0·85 in the training set and after exclusion of induction deaths, the AUC ROC of the classifier was 0·70 ( P = 0·02) and 0·67 ( P = 0·04) in the validation set when induction deaths (intent to treat) were included. The highest predictive accuracy was noted in the cytogenetic intermediate risk patients ( AUC ROC 0·88, P = 0·002), a subgroup that lacks prognostic/predictive biomarkers for induction response. Only white blood cell count and cytogenetic risk were associated with response to induction therapy in the validation set. After controlling for these variables, the SCNP classifier score was associated with complete remission ( P = 0·017), indicating that the classifier provides information independent of other clinical variables that were jointly associated with response. This is the first validation of an SCNP classifier to predict response to induction chemotherapy. Herein we demonstrate the usefulness of quantitative SCNP under modulated conditions to provide independent information on AML disease biology and induction response.