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.

High-dimensional analysis of the aging immune system: verification of age-associated differences in immune signaling responses in healthy donors.

BACKGROUND: Single-cell network profiling (SCNP) is a multiparametric flow cytometry-based approach that simultaneously measures evoked signaling in multiple cell subsets. Previously, using the SCNP approach, age-associated immune signaling responses were identified in a cohort of 60 healthy donors. METHODS: In the current study, a high-dimensional analysis of intracellular signaling was performed by measuring 24 signaling nodes in 7 distinct immune cell subsets within PBMCs in an independent cohort of 174 healthy donors [144 elderly (>65 yrs); 30 young (25-40 yrs)]. RESULTS: Associations between age and 9 immune signaling responses identified in the previously published 60 donor cohort were confirmed in the current study. Furthermore, within the current study cohort, 48 additional immune signaling responses differed significantly between young and elderly donors. These associations spanned all profiled modulators and immune cell subsets. CONCLUSIONS: These results demonstrate that SCNP, a systems-based approach, can capture the complexity of the cellular mechanisms underlying immunological aging. Further, the confirmation of age associations in an independent donor cohort supports the use of SCNP as a tool for identifying reproducible predictive biomarkers in areas such as vaccine response and response to cancer immunotherapies.

Immune system functional pathway analysis using single cell network profiling (SCNP): a novel tool in cancer immunotherapy.

The development of cancer immunotherapies has been ongoing for many years and has shown limited success. Novel biomarkers are needed to identify patients most likely to respond to anticancer immune-therapeutic approaches. Moreover, a systems-level approach is required for comprehensive understanding of the interconnected components, pathways, and cell types associated with an immune response. In this chapter, we describe single cell network profiling (SCNP), a novel method for assessing and measuring immune function/dysfunction at a systems level. SCNP is a multiparametric flow-cytometry-based analysis that can simultaneously measure, at the single cell level, both extracellular surface markers and changes in intracellular signaling proteins in response to extracellular modulators. Measuring changes in signaling proteins following the application of an external modulation informs on the functional capacity of the signaling network which cannot be assessed by the measurement of basal signaling alone. In addition, the simultaneous analysis of multiple pathways in multiple cell subsets can provide insight into the connectivity of both cell signaling networks and immune cell subtypes. The experimental steps associated with an SCNP assay are (1) pre-analytical sample preparation; (2) modulation for functional analysis; (3) staining with antibody cocktail; (4) data acquisition on flow cytometer; and (5) data analysis and metrics. Important considerations for each step of the assay will be discussed, and data demonstrating the utility of SCNP for immune monitoring applications will be summarized.

Functional pathway analysis in acute myeloid leukemia using single cell network profiling assay: effect of specimen source (bone marrow or peripheral blood) on assay readouts.

BACKGROUND: Single cell network profiling (SCNP) is used to simultaneously measure the effects of modulators on signaling networks at the single cell level. SCNP-based biomarker assays predictive of response to induction therapy and relapse risk in acute myeloid leukemia (AML) patients are being developed. Such assays have typically used bone marrow (BM) as the sample source of blasts. Because circulating peripheral blasts are detectable in ∼65% of AML patients and peripheral blood (PB) sampling is less invasive than BM sampling, this study was performed to assess the effect of sample source on AML blasts signaling as measured in SCNP assay. METHODS: SCNP using multiparametric flow cytometry was used to evaluate the activation state of intracellular signaling molecules in leukemic blasts under basal conditions and after treatment with modulators in 46 pairs of BM mononuclear cells/PB mononuclear cells. The relationship between readouts of modulated intracellular proteins ("nodes") was measured using linear regression, Bland-Altman method, and Lin's concordance correlation coefficient. RESULTS: The majority (156/161) of signaling nodes show strong correlations between paired PB and BM samples independently from the statistical method used. Notable exceptions were two PB samples with almost undetectable levels of circulating blasts compared with paired BM samples. CONCLUSIONS: Our results demonstrate that specimen source (BM or PB) does not significantly affect proteomic signaling in patients with AML and circulating blasts. The ability to use PB as a sample source will facilitate the monitoring of cellular signaling effects following administration of targeted therapies and at time points when BM aspirates are not clinically justifiable.

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

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.

Substituent Effects on Rates and Stereoselectivities of Conrotatory Electrocyclic Reactions of Cyclobutenes. A Theoretical Study.

Substituent effects on the geometries and conrotatory electrocyclic ring openings of cyclobutenes were studied. This work extends the original investigations to many more substituents and provides a comprehensive theory of substituent effects on geometries and reaction rates. The effects of substitution at the 1 position are minimal; donor substituents raise the activation energy slightly, and powerful acceptor substituents slightly lower the activation energy. Substituents on C(3) cause small distortions of the cyclobutene geometry, in the same direction as the favored stereochemistry of reaction. Donors prefer outward rotation, while strong acceptors prefer inward rotation. The activation energy changes and cyclobutene geometrical perturbations were found to correlate with Taft sigma(R)(0) parameters. Amino, hydroxy, fluoro, chloro, methyl, cyano, formyl, and vinyl substituents were studied in the 1 position. Boryl, dimethylboryl, nitroso, formyl, nitro, carboxyl (neutral, protonated, and deprotonated), cyano, trifluoromethyl, sulfoxyl, sulfonyl, sulfinic acid, imino, N-protonated imino, ammonio, ethynyl, methyl, mercapto, chloro, fluoro, hydroxyl, amino, lithium oxy, vinyl, and acetyl were calculated as substituents in the 3 position. Comparisons with experimental results are given when available, and predictions are made in other cases.