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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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Single Cell Network Profiling (SCNP) Technology.Bone marrow or blood cells (1) are modulated, fixed and permeabilized (2), then stained with an antibody cocktail containing antibodies directed against both cell surface markers as well as post-translational modifications of intra-cellular proteins(3). Cells are acquired using multiparametric flow cytometry (4) thus allowing quantification of intracellular pathway activity in cell subsets identified by gating on lineage surface markers (5). Various metrics to quantify basal and induced signaling and to assess association with biologic and clinical outcomes are applied.
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pone.0118485.g001: Single Cell Network Profiling (SCNP) Technology.Bone marrow or blood cells (1) are modulated, fixed and permeabilized (2), then stained with an antibody cocktail containing antibodies directed against both cell surface markers as well as post-translational modifications of intra-cellular proteins(3). Cells are acquired using multiparametric flow cytometry (4) thus allowing quantification of intracellular pathway activity in cell subsets identified by gating on lineage surface markers (5). Various metrics to quantify basal and induced signaling and to assess association with biologic and clinical outcomes are applied.

Mentions: Single-cell network profiling (SCNP) technology uses multi-parameter flow cytometry to study signaling pathways and networks at the single-cell level (Fig 1). Assaying cells at this level of resolution allows the identification of rare cell populations and reveals differences in the capacity of signaling pathways among cell subtypes, as well as between and within patient samples. The feasibility of applying SCNP in various hematologic malignancies has previously been documented [7], [8]. Specifically, an SCNP assay predicting the likelihood of response to induction therapy in pediatric AML has been validated and published [9]. Functional characterization of the disease samples at the single cell level may add information that is independent of existing molecular prognostic factors like FLT3-ITD mutation status [10], [11]. The current study presents the development and validation of a SCNP classifier (DXSCNP) for the prediction of response to Ara-C-based induction chemotherapy in elderly (> 55 year old) patients with newly diagnosed AML.


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)

Single Cell Network Profiling (SCNP) Technology.Bone marrow or blood cells (1) are modulated, fixed and permeabilized (2), then stained with an antibody cocktail containing antibodies directed against both cell surface markers as well as post-translational modifications of intra-cellular proteins(3). Cells are acquired using multiparametric flow cytometry (4) thus allowing quantification of intracellular pathway activity in cell subsets identified by gating on lineage surface markers (5). Various metrics to quantify basal and induced signaling and to assess association with biologic and clinical outcomes are applied.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0118485.g001: Single Cell Network Profiling (SCNP) Technology.Bone marrow or blood cells (1) are modulated, fixed and permeabilized (2), then stained with an antibody cocktail containing antibodies directed against both cell surface markers as well as post-translational modifications of intra-cellular proteins(3). Cells are acquired using multiparametric flow cytometry (4) thus allowing quantification of intracellular pathway activity in cell subsets identified by gating on lineage surface markers (5). Various metrics to quantify basal and induced signaling and to assess association with biologic and clinical outcomes are applied.
Mentions: Single-cell network profiling (SCNP) technology uses multi-parameter flow cytometry to study signaling pathways and networks at the single-cell level (Fig 1). Assaying cells at this level of resolution allows the identification of rare cell populations and reveals differences in the capacity of signaling pathways among cell subtypes, as well as between and within patient samples. The feasibility of applying SCNP in various hematologic malignancies has previously been documented [7], [8]. Specifically, an SCNP assay predicting the likelihood of response to induction therapy in pediatric AML has been validated and published [9]. Functional characterization of the disease samples at the single cell level may add information that is independent of existing molecular prognostic factors like FLT3-ITD mutation status [10], [11]. The current study presents the development and validation of a SCNP classifier (DXSCNP) for the prediction of response to Ara-C-based induction chemotherapy in elderly (> 55 year old) patients with newly diagnosed AML.

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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