Functional Pathway Analysis Using SCNP of <em>FLT3</em> Receptor Pathway Deregulation in AML Provides Prognostic Information Independent from Mutational Status

<div><p>FMS-like tyrosine kinase 3 receptor (<i>FLT3</i>) internal tandem duplication (ITD) mutations result in constitutive activation of this receptor and have been shown to increase the risk of relapse in patients with acute myeloid leukemia (AML); however, substantial heterogeneity in clinical outcomes still exists within both the ITD mutated and unmutated AML subgroups, suggesting alternative mechanisms of disease relapse not accounted by <i>FLT3</i> mutational status. Single cell network profiling (SCNP) is a multiparametric flow cytometry based assay that simultaneously measures, in a quantitative fashion and at the single cell level, both extracellular surface marker levels and changes in intracellular signaling proteins in response to extracellular modulators. We previously reported an initial characterization of FLT3 ITD-mediated signaling using SCNP. Herein SCNP was applied sequentially to two separate cohorts of samples collected from elderly AML patients at diagnosis. In the first (training) study, AML samples carrying unmutated, wild-type <i>FLT3</i> (<i>FLT3</i> WT) displayed a wide range of induced signaling, with a fraction having signaling profiles comparable to <i>FLT3</i> ITD AML samples. Conversely, the <i>FLT3</i> ITD AML samples displayed more homogeneous induced signaling, with the exception of patients with low (<40%) mutational load, which had profiles comparable to <i>FLT3</i> WT AML samples. This observation was then confirmed in an independent (verification) cohort. Data from the second cohort were also used to assess the association between SCNP data and disease-free survival (DFS) in the context of <i>FLT3</i> and nucleophosmin (<i>NPM1</i>) mutational status among patients who achieved complete remission (CR) to induction chemotherapy. The combination of SCNP read outs together with <i>FLT3 and NPM1</i> molecular status improved the DFS prediction accuracy of the latter. Taken together, these results emphasize the value of comprehensive functional assessment of biologically relevant signaling pathways in AML as a basis for the development of highly predictive tests for guidance of post-remission therapy.</p> </div

Nodes in combination with molecular characterization improve DFS modeling.

<p>The table displays the <i>p</i>-values for the models (<i>P</i> Model) as well as the components: node (<i>P</i> Node), molecular characterization (<i>P</i> MolChar), and the interaction term (<i>P</i> interaction term).</p><p>p-S6 indicates phosphorylated S6 ribosomal protein; G.CSF, granulocyte colony-stimulating factor; p-STAT3, phosphorylated signal transducer and activator of transcription 3; PMA, phorbol myristate acetate; p-ERK, phosphorylated endoplasmic reticulum kinase; SCF, Skp, Cullin, and F-box containing complex; CD34, cluster of differentiation 34; cPARP, cleaved poly(ADP-ribose) polymerase; and p-Chk2, phosphorylated checkpoint 2 protein kinase.</p><p>Sample size n = 39 for each row</p>*<p>Wald test used.</p>†<p>t-test (H0:Slope = 0). Significant p-value suggests influence of model component on hazard ratio.</p>‡<p>Log of hazard ratio fit of MolChar plus node with interaction term using Cox Proportional -hazards regression: log h(t) = β₀+β₁*FLT3 ITD+β₂*node+β₂*node*FLT3 ITD.</p>§<p>Signaling examined in non-apoptotic Leukemic cells (cPARP negative).</p>∥<p>Interaction term included in model to evaluate simultaneous influence of MolChar and node on hazard ratio.</p

Clinical characteristics: verification cohort (CR only).

<p>Modeling of DFS was performed only among patients who achieved CR to induction therapy. Each of the clinical co-variates, demographic characteristics and molecular characterics was tested for association with DFS using logrank test.</p><p>Logrank test was applied to compute p-values.</p>*<p>Cytogenetics was coded as continuous variable: Favorable = 1, Intermediate/Unknown = 2, Unfavorable = 3.</p><p>DFS indicates disease free survival; CR, complete responder; WBC, white blood cell; <i>NPM1</i> nucleophosmin 1; RAEB-t, refractory anemia with excess blasts in transformation; and RAEB, refractory anemia with excess blasts.</p

Comparison of <i>FLT3</i> ITD signaling versus <i>FLT3</i> WT signaling.

<p>Box and whisker plots of FLT3L-induced p-S6 with the log₂fold metric in increasing mutational load (ITD−[<i>FLT3</i> WT] = 0%, ITD+[<i>FLT3</i> ITD]≥0%, ITD+30≥30%, ITD+40≥40%, ITD+50≥50% ITD, respectively). This is the primary objective analysis.</p

Muted FLT3L-induced signaling in <i>FLT3</i> ITD samples.

<p>(A) <i>FLT3</i> ITD samples demonstrate lower FLT3L-induced PI3K, RAS and STAT signaling. Time-course of FLT3L-induced signaling of p-S6 (lower left), p-ERK (upper right), p-AKT (upper left), and p-STAT5 (lower right) at 5, 10 and 15 min time points in healthy bone marrow myeloblasts (BMMb) (left), and leukemic blasts from AML donors with (middle) or without (right) <i>FLT3</i> ITD mutation. Donors with low mutational load (<40%) are identified with an arrow.</p

PCA pathway analysis of <i>FLT3</i> ITD AML samples and healthy BMMb compared to <i>FLT3</i> WT AML.

<p>PCA analysis of FLT3L-induced signaling (PC 1) and AraC/Daunorubicin-induced apoptosis measured by cPARP (PC 2) in healthy BMMb (blue dots), <i>FLT3</i> ITD (red dots) and <i>FLT3</i> WT (green dots). Donors with low <i>FLT3</i> ITD mutational load (<40%) are indicated by arrows.</p

<i>In vitro</i> apoptosis responses in <i>FLT3</i> ITD samples.

<p>Staurosporine→cPARP U_a metric (left graph), Ara-C/Daunorubicin→cPARP U_a metric (middle graph), and etoposide→cPARP U_a metric (right graph) for healthy (left), <i>FLT3</i> ITD (middle) and <i>FLT3</i> WT (right) bone marrow. Samples with low mutational load (<40%) are identified with an arrow.</p

Association of <i>FLT3</i> ITD and <i>NPM1</i> mutation with DFS.

<p>(A) Patient cohort used for DFS modeling. (B) Cox-proportional hazards model for DFS using <i>FLT3</i> mutation data log h(t) = β₀+β₁*<i>FLT3</i> ITD. Probability of DFS versus days of complete disease response (CR) for <i>FLT3</i> ITD AML samples (solid line) and <i>FLT3</i> WT samples (dotted line). (C) Cox-proportional hazards model for DFS using <i>NPM1</i> data log h(t) = β₀+β₁*<i>NPM1</i> mutated. Probability of DFS versus days of complete disease response (CR) for <i>NPM1</i>-mutated AML samples (solid line) and <i>NPM1</i> WT samples (dotted line).</p

Study Design Diagram.

<p>Influence of <i>FLT3</i> ITD mutation status on functional signaling was studied in two independent data sets; observations made in the first set (training, N = 46) were verified in the second set (Verification, N = 104).</p

Clinical outcomes: verification cohort.

<p>Fisher's exact test was applied to categorical variables.</p><p>NR indicates non-responder; and CR, complete responder.</p