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

• Published in: British journal of haematology 2013-07, Vol.162 (2), p.250-262
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Adolescent; Antineoplastic Combined Chemotherapy Protocols - therapeutic use; Biological and medical sciences; Child; Child, Preschool; Cytarabine - administration & dosage; Daunorubicin - administration & dosage; Female; Flow Cytometry - methods; Hematologic and hematopoietic diseases; Humans; Induction response; Infant; Intracellular Signaling Peptides and Proteins; intracellular signalling; Leukemia, Myeloid, Acute - drug therapy; Leukemia, Myeloid, Acute - pathology; Leukemias. Malignant lymphomas. Malignant reticulosis. Myelofibrosis; Male; Medical sciences; Multiple tumors. Solid tumors. Tumors in childhood (general aspects); Neoadjuvant Therapy; paediatric acute myeloid leukaemia; Prognosis; Prospective Studies; Remission Induction; Retrospective Studies; single cell network profiling; Single-Cell Analysis - methods; Thioguanine - administration & dosage; Treatment Outcome; Tumors
• ISSN: 0007-1048; 1365-2141
• DOI: 10.1111/bjh.12370

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 (AUCROC) was calculated to be 0·85 in the training set and after exclusion of induction deaths, the AUCROC 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 (AUCROC 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.