Cell Signaling-Based Classifier Predicts Response to Induction Therapy in Elderly Patients with Acute Myeloid Leukemia
Year: 2015
Type: article
Abstract: 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.
Source: PLoS ONE
Authors: Alessandra Cesano, Cheryl L. Willman, Kenneth J. Kopecky, Urte Gayko, Santosh Putta, Brent Louie, Matt Westfall, Norman Purvis, David C. Spellmeyer, Carol Marimpietri, Aileen C. Cohen, James Hackett, Jing Shi, Michael G. Walker, Zhuoxin Sun, Elisabeth Paietta, Martin S. Tallman, Larry D. Cripe, Susan Atwater, Frederick R. Appelbaum, Jerald P. Radich
Institutions: Nodality (United States), UNM Comprehensive Cancer Center, Fred Hutch Cancer Center, Montefiore Medical Center, Memorial Sloan Kettering Cancer Center, Indiana University Health, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University, Stanford University
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,382
Awards: G1011812605, Eastern Cooperative Oncology Group, G1545560008, G1771393403, COOPERATIVE STUDY OF CANCER CHEMOTHERAPY, ECOG-ACRIN Network Group Operations Center, G1938688226, G1958973285, Network Lead Academic Participating Site: Memorial Sloan-Kettering Cancer Center, G2002724969, G2017411191, G2064543036, G2435896781, G2679961467, G2734683319, SOUTHWEST ONCOLOGY GROUP STATISTICAL CENTER, G345775953, G3475623468, G36541222, SWOG Network Group Operations Center of the NCTN, G379387698, ECOG Clinical Trials, Southwest Oncology Group, G4935167430, G5417201949, G608154585, ECOG-ACRIN Network Group Statistics and Data Management Center, G7061256793, G7495095258, G763325562, Eastern Cooperative Oncology Group Operations Office, G8763430483, G87770821, G9000028455, G9000076717, G9000083837, G9000254987, G92717670, G964706321
OpenAlex ID: W2009752967