Single-Cell Network Profiling (SCNP)-Based Classifier to Predict Response to Induction Therapy in Elderly Patients with Acute Myeloid Leukemia (AML): Validation in Two Independent Sample Sets From ECOG and SWOG Trials.
Authors: Cesano Alessandra , Willman Cheryl L , Kopecky Kenneth J. , Gayko Urte , Putta Santosh , Louie Brent , Westfall Matt , Purvis Norman , Spellmeyer David , Marimpietri Carol , Hackett James , Shi Jing , Paietta Elisabeth , Tallman Martin S. , Cripe Larry D , Atwater Susan K. , Appelbaum Frederick R. , Radich Jerald P. 
Published: October 2019

Abstract 2489

Purpose:

Standard Induction chemotherapy (Ara-C/daunorubicin, 3+7 regimen) in elderly patients (pts) with AML results in approximately 35–45% complete remission (CR) rate, and pts with resistant disease (RD) have a median survival of only 1–3 months. Developing a test for accurate prediction of response to standard induction therapy at the time of diagnosis may help inform treatment selection and improve clinical trial design.

Methods:

Single cell network profiling (SCNP) is a multi-parameter flow cytometry based approach for simultaneous interrogation of intracellular signaling pathways at a single cell level. SCNP was used to evaluate signaling profiles in leukemic blasts and to develop a classifier (DXSCNP) of response to induction therapy (CR/CRi, i=incomplete) in a Training Set of cryopreserved diagnostic samples (57 PB and 43 BM) collected from 74 non-M3 AML pts, aged 56+ treated with 3+7-based regimens on 4 SWOG clinical trials. SCNP intracellular readouts quantifying apoptotic response after 24 hrs in vitro treatment with Ara-C/Daunorubicin formed the inputs for DXSCNP. Pt and disease characteristics available either at diagnosis, i.e. relevant to induction therapy choices (e.g., age, WBC counts, FAB class, secondary AML, performance status – CLINICAL1), or available after start of induction therapy (CLINICAL2) were used to develop 2 clinical predictors (DXCLINICAL1 and DXCLINICAL2) of CR/CRi in the Training Set. The performance characteristics of these 3 classifiers were then tested separately in BM and PB sample sets. Specifically, classifier validation was performed in 2 independent BM sample sets (A: n=24 BM samples from ECOG E3999 trial; B: n=42 independent BM samples from the same 4 SWOG trials from which the Training samples were derived) and in 1 PB sample set (C: n=53, from the 4 SWOG trials; notably only 24 patients were shared between Set B and C)). The area under the receiver operating characteristic curve (AUROC) was used to measure each classifier's ability to predict response to 3+7 induction therapy. Out of bag estimates (OOB) of AUROC were calculated using the Training Set, and H0:AUROC=0.5 was tested against HA:AUROC>0.5 for each classifier in the Validation Sets.

Results:

As shown in Table 1, DXSCNP was a significant predictor of CR/CRi in BM samples. The AUROC for the DXSCNP classifier was 0.81 in the Training Set and 0.76 (p=0.01) and 0.72 (p=0.02) in Validation Sets A and B, respectively. No significant DXCLINICAL1 was identified from the 74 pts in the Training Set(OOB AUROC∼0.5). By contrast the AUROC of DXCLINICAL2 (which included inputs for cytogenetics, Flt3ITD, and NPM1 mutational status) was 0.63 (OOB) in the Training Set, and 0.61 (p=0.18) and 0.53 (p=0.38) in Validation Sets A and B, respectively. Of note, DXSCNP remained significant (p=0.03 and 0.04) when controlling for DXCLINICAL2 in both sample sets. Similar performance of DXSCNPwas observed in different pre-specified subgroups (although of small sample sizes) defined by pt, sample or disease characteristics.

Using PB samples, the AUROCs for the DXSCNP classifier were, 0.87 (OOB) in the Training Set and 0.53 (p=ns) in the Validation Set C. However, the performance of DXSCNP in PB samples differed significantly between secondary vs. De novo AML i.e. AUROC= 0.24 vs 0.8 respectively. Of note, DXSCNPperformed similarly for the subset of de novo patients for whom paired BM with PB samples were tested (AUC=0.78 (PB) and 0.74 (BM)).

Conclusion:

This is the first study describing the successful validation of an SCNP-based classifier to predict response to standard induction chemotherapy in elderly AML pts with performance superior to clinical variables available at diagnosis. A similar SCNP classifier with overlapping cellular biology inputs was previously validated for pediatric AML. These results confirm the ability of quantitative SCNP readouts to provide independent and actionable information on disease biology and pt treatment choices. Independent validation in prospective studies is warranted.

Disclosures:

Cesano: Nodality, Inc: Employment, Equity Ownership. Gayko:Nodality, Inc.: Employment, Equity Ownership. Putta:Nodality, Inc.: Employment, Equity Ownership. Louie:Nodality, Inc.: Employment, Equity Ownership. Westfall:Nodality, Inc.: Employment, Equity Ownership. Purvis:Nodality, Inc.: Employment, Equity Ownership. Spellmeyer:Nodality, Inc.: Employment, Equity Ownership. Marimpietri:Nodality, Inc.: Employment, Equity Ownership. Hackett:Nodality, Inc.: Employment, Equity Ownership. Shi:Nodality, Inc.: Employment, Equity Ownership.