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

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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Flow Cytometry Data (FCS FIles) and Gating Image Files

For each sample data is include for three wells: unmodulated (UM) condition at 15 min timepoint, unmodulated condition at 24 hour timepoint and AraC+Daunorubicin treated condition at 24 hour timepoint. For each well the zipped archive file consists of one FCS files and one gating image file

Study Design Diagram.

<p>Flowchart of the study design with descriptive schematics of the patient sets in the Training, Verification and Validation analysis sets. SWOG samples were randomized into a Training and a Validation Analysis set and were sorted by tissue type (PB or BM). An initial subset of classifiers was trained separately in PB and BM samples in the Training Analysis sets and then PB classifiers were applied to BM and BM classifiers were applied to PB. From this training process 5 candidate classifiers were selected and applied to the ECOG Verification Analysis set. The final SCNP classifier was further refined and applied to 1) ECOG Verification Analysis set, 2) SWOG BM Validation Analysis set and 3) SWOG PB Validation Analysis set.</p

Single Cell Network Profiling (SCNP) Technology.

<p>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.</p

Comparison of predictions between paired PB and BM samples.

<p>Predicted probability of CR for BM and PB samples from donors with SCNP data with paired samples in the validation set. Denovo vs secondary AML subtypes are noted in the inset. A majority of the predictions were concordant between the tissues types of de novo. Of note, two RD donors that were discordant are secondary AML.</p

Locked DX_SCNP Classifier Inputs.

<p><i>Score =</i></p><p></p><p></p><p></p><p></p><p></p><p><mi>e</mi></p><p></p><p><mi>χ</mi><mo>'</mo></p><p><mi>β</mi><mo stretchy="true">^</mo></p><p></p><p></p><p></p><p><mn>1</mn><mo>+</mo></p><p><mi>e</mi></p><p></p><p><mi>χ</mi><mo>'</mo></p><p><mi>β</mi><mo stretchy="true">^</mo></p><p></p><p></p><p></p><p></p><p></p><p></p><p></p><i>where</i><p></p><p></p><p></p><p><mi>χ</mi><mo>'</mo></p><p></p><p></p><p></p><i>is the vector of node-metric values and</i><p></p><p></p><p></p><p><mi>β</mi><mo stretchy="true">^</mo></p><p></p><p></p><p></p><i>is the vector of regression coefficients</i><p></p><p>Locked DX_SCNP Classifier Inputs.</p

ECOG Patient Disposition.

<p>A flow diagram showing all patients enrolled onto the parent ECOG AML trials and rationale for exclusion of patients from the final analysis sets. Text boxes describe the characteristics of patients carried forward.</p