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

Autor: Cesano A; Nodality, Inc., South San Francisco, California, United States of America., Willman CL; University of New Mexico Cancer Center, Albuquerque, New Mexico, United States of America., Kopecky KJ; SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America., Gayko U; Nodality, Inc., South San Francisco, California, United States of America., Putta S; Nodality, Inc., South San Francisco, California, United States of America., Louie B; Nodality, Inc., South San Francisco, California, United States of America., Westfall M; Nodality, Inc., South San Francisco, California, United States of America., Purvis N; Nodality, Inc., South San Francisco, California, United States of America., Spellmeyer DC; Nodality, Inc., South San Francisco, California, United States of America., Marimpietri C; Nodality, Inc., South San Francisco, California, United States of America., Cohen AC; Nodality, Inc., South San Francisco, California, United States of America., Hackett J; Nodality, Inc., South San Francisco, California, United States of America., Shi J; Nodality, Inc., South San Francisco, California, United States of America., Walker MG; Nodality, Inc., South San Francisco, California, United States of America., Sun Z; ECOG Coordinating Center, Frontier Science, Boston, Massachusetts, United States of America., Paietta E; Montefiore Medical Center North Division, Bronx, New York, United States of America., Tallman MS; Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America., Cripe LD; Indiana University Simon Cancer Center, Indianapolis, Indiana, United States of America., Atwater S; Stanford University, Palo Alto, California, United States of America., Appelbaum FR; Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America., Radich JP; Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2015 Apr 17; Vol. 10 (4), pp. e0118485. Date of Electronic Publication: 2015 Apr 17 (Print Publication: 2015).
DOI: 10.1371/journal.pone.0118485
Abstrakt: 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.
Databáze: MEDLINE
Externí odkaz: