Dynamic single-cell network profiles in acute myelogenous leukemia are associated with patient response to standard induction therapy

Abstract

Purpose: Complete response to induction chemotherapy is observed in approximately 60% of patients with newly diagnosed non-M3 acute myelogenous leukemia (AML). However, no methods exist to predict with high accuracy at the individual patient level the response to standard AML induction therapy.

Experimental design: We applied single-cell network profiling (SCNP) using flow cytometry, a tool that allows a comprehensive functional assessment of intracellular signaling pathways in heterogeneous tissues, to two training cohorts of AML samples (n = 34 and 88) to predict the likelihood of response to induction chemotherapy.

Results: In the first study, univariate analysis identified multiple signaling "nodes" (readouts of modulated intracellular signaling proteins) that correlated with response (i.e., AUC(ROC) > or = 0.66; P < or = 0.05) at a level greater than age. After accounting for age, similar findings were observed in the second study. For patients <60 years old, complete response was associated with the presence of intact apoptotic pathways. In patients > or =60 years old, nonresponse was associated with FLT3 ligand-mediated increase in phosphorylated Akt and phosphorylated extracellular signal-regulated kinase. Results were independent of cytogenetics, FLT3 mutational status, and diagnosis of secondary AML.

Conclusions: These data emphasize the value of performing quantitative SCNP under modulated conditions as a basis for the development of tests highly predictive for response to induction chemotherapy. SCNP provides information distinct from other known prognostic factors such as age, secondary AML, cytogenetics, and molecular alterations and is potentially combinable with the latter to improve clinical decision making. Independent validation studies are warranted.

Fig. 1

Nodes, pathways, and biological categories evaluated in the two training studies. A node is defined as a combination of a specific proteomic readout in the presence or absence of a specific modulator. Text in blue indicates assay readouts (phosphoproteins, cleaved proteins, or receptors/drug transporters). The four biological categories of nodes are indicated by color: (a) chemokine, cytokine, and growth factor signaling pathways; (b) phosphatase activity; (c) surface markers; and (d) apoptosis.

Fig. 2

Role of each metric in assessing different aspects of signaling biology. Summary schema of all metrics used in the two studies and the role each has in assessing different aspects of signaling and pathway biology.

Fig. 3

Univariate analysis—first training study. Univariate analysis of modulated signaling and functional apoptosis nodes stratifies NR and CR patient groups. A, stratification of NR and CR patient groups with SCF modulated, but not basal, p-Akt signaling. B, stratification of NR and CR patients using functional apoptosis assays. Combinations of nodes can improve stratification by single nodes. Examples show how corners and thresholds for the classifiers are set. O, CR; X, NR. C, doublet combination (SCF→p-ERK | Fold and IL-27→p-STAT3 | Total). D, triplet combination (etoposide→p-Chk2⁻, c-PARP⁺ | Quad, IL-27→p-STAT3 | Total, and SCF→p-Akt | Fold).

Fig. 4

Nodes complement age in stratifying patient response to induction therapy—second training study. IL-27→p-STAT1 | Fold and IL-27→p-STAT3 | Fold improve sensitivity and specificity of age as a classifier for response to induction therapy. Significant P values (slope = 0) for the node found after accounting for age in the logistic regression. ROC plots illustrate CR and NR stratification using the model incorporating node/metrics IL-27→p-STAT3 | Fold and IL-27→p-STAT1 | Fold with age.