Single Cell Network Profiling (SCNP)-Based Classifier to Predict Response to Induction Therapy in Pediatric Patients with De Novo Acute Myeloid Leukemia (AML): Validation Study Results,
Authors: Lacayo Norman J. , Alonzo Todd A. , Gayko Urte , Westfall Matt , Purvis Norman , Putta Santosh , Hackett James , Cohen Aileen Cleary , Gerbing Robert B. , Dahl Gary V , Gamis Alan S. , Meshinchi Soheil 
Published: October 2019

Abstract 3544

Background:

In pediatric de novo AML, cytarabine (Ara-C)-based induction therapy results in above 80% complete response (CR) rates but nearly half of those who achieve an initial remission relapse die of their disease. Accurate prediction of initial response to chemotherapy at the time of diagnosis as well as identification of those at high risk of relapse despite initial remission would allow for patient specific therapy and improved clinical outcome. SCNP is a multiparametric flow cytometry-based assay that provides simultaneously quantitative measurements of extracellular surface markers as well as changes in intracellular signaling proteins in response to extracellular modulators at the single cell level (Kornblau et al. Clin Cancer Res 2010). In a previous study, we used this assay to define two distinct classifiers associated with response to standard induction therapy and risk of relapse in diagnostic bone marrow mononuclear cells from pediatric patients (pts) with non-M3 AML (ASH 2010;116:Abstract 954). This study is intended to confirm the validity of the pre-specified response to induction therapy classifier in an independent set of AML pediatric pts.

Methods:

The SCNP-based response classifier developed using 53 AML cryopreserved samples from patients enrolled on POG (now COG) trial 9421 was comprised of a combination of three SCNP readouts that measure apoptosis, MAPK signaling, and PI3K signaling and had a bootstrapped out-of-bag estimated Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.84 (95% CI 0.67– 0.96). The classifier was tested on 68 cryopreserved samples (20 non–responders (NR) and 48 CRs) from patients enrolled on COG trials AAML0531 (samples from patients randomized to Ara-C, Daunomycin and Etoposide [ADE] induction therapy) and AAML03P1 (samples from patients treated with ADE plus Gemtuzumab Ozogamicin induction therapy). The primary hypothesis was that the prediction of induction response by the continuous score from the pre-specified classifier would yield an AUROC significantly greater than 0.5.

Results:

The primary objective of the study was met with an AUROC of 0.66 (n=68) p=0.042 (see table). The primary analysis used an NR classification that combined patients with either induction failure (n=14) or induction death (n=6). A pre-specified analysis in which induction deaths were removed resulted in an AUROC of 0.70 (n=62) p=0.021, suggesting that the underlying disease biology may be different for induction death vs. induction failure. In this study, WBC and cytogenetics risk groups were associated with induction response while age, gender and FLT3-ITD status were not. In a multivariate analysis of induction response that included WBC, cytogenetics and the pre-specified continuous SCNP classifier score, only cytogenetic risk group (p=0.001) and SCNP score (p=0.017) remained significant. Exploratory analyses excluding induction deaths suggest that the relationship between the SCNP score and induction response is strong among patients with an intermediate cytogenetic classification (n=23) (AUROC=0.88, p=0.002), while no relationship (AUC=0.48, p=0.959) is seen in those patients with a poor cytogenetic classification (n=17). Among the three SCNP signaling nodes contributing to the score, the node measuring drug-induced apoptosis performs most consistently across the training and validation sets.

Conclusion:

This study is the first validation of a SCNP-based classifier that predicts response to induction Rx. It shows that performing quantitative SCNP under modulated conditions can serve as the basis for developing biologically based tests in leukemia that offer new insights into the individual patient's disease biology. Additionally, this technology could prove useful in guiding alternative therapeutic strategies.

Disclosures:

Gayko: Nodality Inc.: Employment, Equity Ownership. Westfall:Nodality Inc.: Employment, Equity Ownership. Purvis:Nodality Inc.: Employment, Equity Ownership. Putta:Nodality Inc.: Employment, Equity Ownership. Hackett:Nodality Inc.: Employment, Equity Ownership. Cleary Cohen:Nodality Inc.: Consultancy, Equity Ownership.