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

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 LD, Atwater S, Appelbaum FR, Radich JP - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Nodality, Inc., South San Francisco, California, United States of America.

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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SWOG Patient Disposition.A flow diagram showing all patients enrolled onto the SWOG parent AML trials and rationale for exclusion of patients from the final analysis sets. Text boxes describe the characteristics of patients carried forward.
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pone.0118485.g002: SWOG Patient Disposition.A flow diagram showing all patients enrolled onto the SWOG parent AML trials and rationale for exclusion of patients from the final analysis sets. Text boxes describe the characteristics of patients carried forward.

Mentions: For patients enrolled in SWOG trials, inclusion criteria were age > 55 years, diagnosis of non-APL AML, and enrollment in one of four SWOG frontline treatment trials using Ara-C-based induction therapy (SWOG-9031 [12], SWOG-9333 [13] (Ara-C/daunorubicin arm only), S0112 [14] or S0301 [14] (S1 Table). Eligible patients had two or more vials of a pre-induction sample (BM, PB or both) remaining in the SWOG AML biorepository, received at least one dose of Ara-C and consented for research use of their samples. Fig 2 shows the SWOG patient disposition flowchart: of the 536 patients registered on the above mentioned SWOG trials, 266 patients contributed samples (i.e. pretreatment BM and/or PB) to the SCNP assay.


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

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 LD, Atwater S, Appelbaum FR, Radich JP - PLoS ONE (2015)

SWOG Patient Disposition.A flow diagram showing all patients enrolled onto the SWOG parent AML trials and rationale for exclusion of patients from the final analysis sets. Text boxes describe the characteristics of patients carried forward.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401549&req=5

pone.0118485.g002: SWOG Patient Disposition.A flow diagram showing all patients enrolled onto the SWOG parent AML trials and rationale for exclusion of patients from the final analysis sets. Text boxes describe the characteristics of patients carried forward.
Mentions: For patients enrolled in SWOG trials, inclusion criteria were age > 55 years, diagnosis of non-APL AML, and enrollment in one of four SWOG frontline treatment trials using Ara-C-based induction therapy (SWOG-9031 [12], SWOG-9333 [13] (Ara-C/daunorubicin arm only), S0112 [14] or S0301 [14] (S1 Table). Eligible patients had two or more vials of a pre-induction sample (BM, PB or both) remaining in the SWOG AML biorepository, received at least one dose of Ara-C and consented for research use of their samples. Fig 2 shows the SWOG patient disposition flowchart: of the 536 patients registered on the above mentioned SWOG trials, 266 patients contributed samples (i.e. pretreatment BM and/or PB) to the SCNP assay.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Nodality, Inc., South San Francisco, California, United States of America.

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.

Show MeSH