Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia

• Published in: PloS one Vol. 10; no. 4; p. e0118485
• Main Authors: Cesano, Alessandra, Willman, Cheryl L, Kopecky, Kenneth J, Gayko, Urte, Putta, Santosh, Louie, Brent, Westfall, Matt, Purvis, Norman, Spellmeyer, David C, Marimpietri, Carol, Cohen, Aileen C, Hackett, James, Shi, Jing, Walker, Michael G, Sun, Zhuoxin, Paietta, Elisabeth, Tallman, Martin S, Cripe, Larry D, Atwater, Susan, Appelbaum, Frederick R, Radich, Jerald P
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17-04-2015; Public Library of Science (PLoS)
• Subjects: Acute myeloid leukemia; Aged; Aged, 80 and over; Analysis; Antineoplastic Combined Chemotherapy Protocols - therapeutic use; Apoptosis; Area Under Curve; Blood Cells - cytology; Blood Cells - metabolism; Bone marrow; Bone Marrow Cells - cytology; Bone Marrow Cells - metabolism; Cancer therapies; Cell survival; Chemotherapy; Classifiers; Clinical trials; Cytogenetics; Diagnostic systems; Elderly patients; Enrollments; Female; Flow cytometry; Geriatrics; Humans; Induction therapy; Leukemia; Leukemia, Myeloid, Acute - diagnosis; Leukemia, Myeloid, Acute - drug therapy; Leukemia, Myeloid, Acute - pathology; Male; Medical prognosis; Medical research; Metabolic Networks and Pathways; Middle Aged; Mutation; Myeloid leukemia; Older people; Oncology; Patients; Pediatrics; Peripheral blood; Predictive control; Prognosis; Remission Induction; ROC Curve; Signal classification; Signal Transduction; Signaling; Single-Cell Analysis; Studies; Survival analysis; Training; New York; South San Francisco California; California; Indiana; United States > US; New Mexico
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0118485

Single-cell network profiling (SCNP) data generated from multi-parametric flow cytometry analysis of bone marrow (BM) and peripheral blood (PB) samples collected from patients >55 years old with non-M3 AML were used to train and validate a diagnostic classifier (DXSCNP) for predicting response to standard induction chemotherapy (complete response [CR] or CR with incomplete hematologic recovery [CRi] versus resistant disease [RD]). SCNP-evaluable patients from four SWOG AML trials were randomized between Training (N = 74 patients with CR, CRi or RD; BM set = 43; PB set = 57) and Validation Analysis Sets (N = 71; BM set = 42, PB set = 53). Cell survival, differentiation, and apoptosis pathway signaling were used as potential inputs for DXSCNP. Five DXSCNP classifiers were developed on the SWOG Training set and tested for prediction accuracy in an independent BM verification sample set (N = 24) from ECOG AML trials to select the final classifier, which was a significant predictor of CR/CRi (area under the receiver operating characteristic curve AUROC = 0.76, p = 0.01). The selected classifier was then validated in the SWOG BM Validation Set (AUROC = 0.72, p = 0.02). Importantly, a classifier developed using only clinical and molecular inputs from the same sample set (DXCLINICAL2) lacked prediction accuracy: AUROC = 0.61 (p = 0.18) in the BM Verification Set and 0.53 (p = 0.38) in the BM Validation Set. Notably, the DXSCNP classifier was still significant in predicting response in the BM Validation Analysis Set after controlling for DXCLINICAL2 (p = 0.03), showing that DXSCNP provides information that is independent from that provided by currently used prognostic markers. Taken together, these data show that the proteomic classifier may provide prognostic information relevant to treatment planning beyond genetic mutations and traditional prognostic factors in elderly AML.