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

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 Cripe, Susan Atwater, Frederick R. AppelbaumJerald P. Radich

Research output: Contribution to journalArticle

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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.

Original languageEnglish (US)
Article numbere0118485
JournalPLoS One
Volume10
Issue number4
DOIs
StatePublished - Apr 17 2015

Fingerprint

Cell signaling
myeloid leukemia
Acute Myeloid Leukemia
bone marrow
Bone
Classifiers
Bone Marrow
therapeutics
cells
Blood
Therapeutics
Bone Marrow Diseases
blood
Induction Chemotherapy
Chemotherapy
prediction
Flow cytometry
ROC Curve
Proteomics
Cell Differentiation

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Cesano, A., Willman, C. L., Kopecky, K. J., Gayko, U., Putta, S., Louie, B., ... Radich, J. P. (2015). Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia. PLoS One, 10(4), [e0118485]. https://doi.org/10.1371/journal.pone.0118485

Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia. / 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; Atwater, Susan; Appelbaum, Frederick R.; Radich, Jerald P.

In: PLoS One, Vol. 10, No. 4, e0118485, 17.04.2015.

Research output: Contribution to journalArticle

Cesano, A, Willman, CL, Kopecky, KJ, Gayko, U, Putta, S, Louie, B, Westfall, M, Purvis, N, Spellmeyer, DC, Marimpietri, C, Cohen, AC, Hackett, J, Shi, J, Walker, MG, Sun, Z, Paietta, E, Tallman, MS, Cripe, L, Atwater, S, Appelbaum, FR & Radich, JP 2015, 'Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia', PLoS One, vol. 10, no. 4, e0118485. https://doi.org/10.1371/journal.pone.0118485
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 ; Atwater, Susan ; Appelbaum, Frederick R. ; Radich, Jerald P. / Cell signaling-based classifier predicts response to induction therapy in elderly patients with acute myeloid leukemia. In: PLoS One. 2015 ; Vol. 10, No. 4.
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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.",
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AU - Putta, Santosh

AU - Louie, Brent

AU - Westfall, Matt

AU - Purvis, Norman

AU - Spellmeyer, David C.

AU - Marimpietri, Carol

AU - Cohen, Aileen C.

AU - Hackett, James

AU - Shi, Jing

AU - Walker, Michael G.

AU - Sun, Zhuoxin

AU - Paietta, Elisabeth

AU - Tallman, Martin S.

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AU - Radich, Jerald P.

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