title: 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.
abstract: 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.
authors.affiliation: Children's Oncology Group, Arcadia, CA, USA. lacayon@stanford.edu
authors.forename: Norman J
authors.initials: NJ
authors.lastname: Lacayo
authors.forename: Todd A
authors.initials: TA
authors.lastname: Alonzo
authors.forename: Urte
authors.initials: U
authors.lastname: Gayko
authors.forename: David B
authors.initials: DB
authors.lastname: Rosen
authors.forename: Matt
authors.initials: M
authors.lastname: Westfall
authors.forename: Norman
authors.initials: N
authors.lastname: Purvis
authors.forename: Santosh
authors.initials: S
authors.lastname: Putta
authors.forename: Brent
authors.initials: B
authors.lastname: Louie
authors.forename: James
authors.initials: J
authors.lastname: Hackett
authors.forename: Aileen Cleary
authors.initials: AC
authors.lastname: Cohen
authors.forename: Alessandra
authors.initials: A
authors.lastname: Cesano
authors.forename: Robert
authors.initials: R
authors.lastname: Gerbing
authors.forename: Yaddanapudi
authors.initials: Y
authors.lastname: Ravindranath
authors.forename: Gary V
authors.initials: GV
authors.lastname: Dahl
authors.forename: Alan
authors.initials: A
authors.lastname: Gamis
authors.forename: Soheil
authors.initials: S
authors.lastname: Meshinchi
mesh_terms: D000293:Adolescent; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D002648:Child; D002675:Child, Preschool; D003561:Cytarabine / Q000008:administration & dosage; D003630:Daunorubicin / Q000008:administration & dosage; D005260:Female; D005434:Flow Cytometry / Q000379:methods; D006801:Humans; D007223:Infant; D047908:Intracellular Signaling Peptides and Proteins; D015470:Leukemia, Myeloid, Acute / Q000188:drug therapy* / Q000473:pathology*; D008297:Male; D020360:Neoadjuvant Therapy; D011379:Prognosis; D011446:Prospective Studies; D012074:Remission Induction; D012189:Retrospective Studies; D059010:Single-Cell Analysis / Q000379:methods; D013866:Thioguanine / Q000008:administration & dosage; D016896:Treatment Outcome
chemical_list: D047908:Intracellular Signaling Peptides and Proteins; D003561:Cytarabine; D013866:Thioguanine; D003630:Daunorubicin
keywords: Induction response; intracellular signalling; paediatric acute myeloid leukaemia; single cell network profiling