B-cell receptor signaling activity identifies patients with mantle cell lymphoma at higher risk of progression.

Mantle cell lymphoma (MCL) is an incurable B-cell malignancy characterized by a high clinical variability. Therefore, there is a critical need to define parameters that identify high-risk patients for aggressive disease and therapy resistance. B-cell receptor (BCR) signaling is crucial for MCL initiation and progression and is a target for therapeutic intervention.

Prognostic Biomarkers for Hepatic Veno-Occlusive Disease/Sinusoidal Obstruction Syndrome in Myeloablative Allogeneic Hematopoietic Cell Transplantation: Results from the Blood and Marrow Transplant Clinical Trials Network 1202 Study.

Hepatic veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is a potentially life-threatening complication of hematopoietic cell transplantation (HCT). This study aimed to determine a blood biomarker signature early post-HCT that identifies patients at high risk for VOD/SOS. A set of 23 plasma biomarkers, selected from the VOD/SOS literature, was measured on days 0, 7, and 14 after myeloablative HCT using blood samples from patients enrolled in the Blood and Marrow Transplant Clinical Trials Network (BMT CTN) Protocol 1202.

Cell by cell immuno- and cancer marker profiling of non-small cell lung cancer tissue: Checkpoint marker expression on CD103+, CD4+ T-cells predicts circulating tumor cells.

Non-small cell lung cancer (NSCLC) has a poor prognosis. Targeted therapy and immunotherapy in recent years has significantly improved NSCLC patient outcome. In this study, we employed cell-by-cell immune and cancer marker profiling of the primary tumor cells to investigate possible signatures that might predict the presence or absence of circulating tumor cells (CTCs).

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.

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)].

Development and validation of a single-cell network profiling assay-based classifier to predict response to induction therapy in paediatric patients with de novo acute myeloid leukaemia: a report from the Children's Oncology Group.

Single cell network profiling (SCNP) is a multi-parameter flow cytometry technique for simultaneous interrogation of intracellular signalling pathways. Diagnostic paediatric acute myeloid leukaemia (AML) bone marrow samples were used to develop a classifier for response to induction therapy in 53 samples and validated in an independent set of 68 samples. The area under the curve of a receiver operating characteristic curve (AUC(ROC)) was calculated to be 0·85 in the training set and after exclusion of induction deaths, the AUC(ROC) of the classifier was 0·70 (P = 0·02) and 0·67 (P = 0·04) in the validation set when induction deaths (intent to treat) were included.

Functional pathway analysis using SCNP of FLT3 receptor pathway deregulation in AML provides prognostic information independent from mutational status.

FMS-like tyrosine kinase 3 receptor (FLT3) 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 FLT3 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.

AKT signaling as a novel factor associated with in vitro resistance of human AML to gemtuzumab ozogamicin.

Gemtuzumab ozogamicin (GO), an immunoconjugate between an anti-CD33 antibody and a calicheamicin-γ(1) derivative, induces remissions and improves survival in a subset of patients with acute myeloid leukemia (AML). As the mechanisms underlying GO and calicheamicin-γ(1) resistance are incompletely understood, we herein used flow cytometry-based single cell network profiling (SCNP) assays to study cellular responses of primary human AML cells to GO. Our data indicate that the extent of DNA damage is quantitatively impacted by CD33 expression and drug efflux activity.

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.

Single-cell network profiling of peripheral blood mononuclear cells from healthy donors reveals age- and race-associated differences in immune signaling pathway activation.

A greater understanding of the function of the human immune system at the single-cell level in healthy individuals is critical for discerning aberrant cellular behavior that occurs in settings such as autoimmunity, immunosenescence, and cancer. To achieve this goal, a systems-level approach capable of capturing the response of the interdependent immune cell types to external stimuli is required. In this study, an extensive characterization of signaling responses in multiple immune cell subpopulations within PBMCs from a cohort of 60 healthy donors was performed using single-cell network profiling (SCNP).

Functional characterization of FLT3 receptor signaling deregulation in acute myeloid leukemia by single cell network profiling (SCNP).

Background: Molecular characterization of the FMS-like tyrosine kinase 3 receptor (FLT3) in cytogenetically normal acute myeloid leukemia (AML) has recently been incorporated into clinical guidelines based on correlations between FLT3 internal tandem duplications (FLT3-ITD) and decreased disease-free and overall survival. These mutations result in constitutive activation of FLT3, and FLT3 inhibitors are currently undergoing trials in AML patients selected on FLT3 molecular status. However, the transient and partial responses observed suggest that FLT3 mutational status alone does not provide complete information on FLT3 biological activity at the individual patient level.

Distinct patterns of DNA damage response and apoptosis correlate with Jak/Stat and PI3kinase response profiles in human acute myelogenous leukemia.

Background: Single cell network profiling (SCNP) utilizing flow cytometry measures alterations in intracellular signaling responses. Here SCNP was used to characterize Acute Myeloid Leukemia (AML) disease subtypes based on survival, DNA damage response and apoptosis pathways.

Methodology And Principal Findings: Thirty four diagnostic non-M3 AML samples from patients with known clinical outcome were treated with a panel of myeloid growth factors and cytokines, as well as with apoptosis-inducing agents.

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.

Single cell network profiling (SCNP): mapping drug and target interactions.

Measuring target coverage of small molecule inhibitors is paramount-first, for selection of molecules to progress through the drug development process and second, once a candidate drug moves to clinical testing, for guiding dose/schedule selection. Single cell network profiling (SCNP) using multiparameter flow cytometry can measure compound effects on multiple signaling cascades in a cell-type-specific manner. We applied SCNP to a panel of compounds with reported inhibitory effects on Jak/Stat signaling using a novel system where modulation of multiple signaling cascades are simultaneously measured in discrete cell subsets in whole (ie, unfractionated) blood.

Shapes of things: computer modeling of molecular shape in drug discovery.

We review recent advances in computer modeling of molecular shape in drug discovery. We summarize the ways of representing shape computationally, discuss the various means of aligning molecules and shapes, consider the various ways of scoring similarity of shapes, and describe the ways in which these shapes can be used to construct molecular descriptors. Finally, we evaluate the success of these methods to date, suggest when they are best applied, and provide our recommendations for the direction of future work.

Feature-map vectors: a new class of informative descriptors for computational drug discovery.

In order to develop robust machine-learning or statistical models for predicting biological activity, descriptors that capture the essence of the protein-ligand interaction are required. In the absence of structural information from X-ray or NMR experiments, deriving informative descriptors can be difficult. We have developed feature-map vectors (FMVs), a new class of descriptors based on chemical features, to address this challenge.

Conformation mining: an algorithm for finding biologically relevant conformations.

Discovering essential features shared by active compounds, an important step in drug-design, is complicated by conformational flexibility. We present a new algorithm to efficiently mine the conformational space of multiple actives and find small subsets of conformations likely to be biologically relevant. The approach identifies chemical and steric similarities between actives, providing insight into features important for binding when structural data are absent.

Building predictive ADMET models for early decisions in drug discovery.

This review discusses the current challenges facing researchers developing computational models to predict absorption, distribution, metabolism, excretion and toxicity (ADMET) for early drug discovery. The strengths and weaknesses of different modeling approaches are reviewed and a survey of recent strategies to model several key ADMET parameters, including intestinal permeability, blood-brain barrier penetration, metabolism, bioavailability and drug toxicities, is presented.

Performance of 3D-database molecular docking studies into homology models.

The performance of docking studies into protein active sites constructed by homology model building was investigated using CDK2 and factor VIIa screening data sets. When the sequence identity between model and template near the binding site area is greater than approximately 50%, roughly 5 times more active compounds are identified than would be found randomly. This performance is comparable to docking to crystal structures.

Using ensembles to classify compounds for drug discovery.

This paper introduces Signal, a novel method for classifying activity against a small molecule drug target. Signal creates an ensemble, or collection, of meaningful descriptors chosen from a much larger property space. The method works with a variety of descriptor types, including fingerprints that represent four-point pharmacophores or shape descriptors.

A novel subshape molecular descriptor.

Molecules with similar shapes and features often have similar biological activity. Several computational approaches search chemical databases for new leads or templates based on overall molecular shape similarity. However, active molecules often present critical subshapes that are required for binding, which may be missed by comparing overall shape similarity.

Active learning with support vector machines in the drug discovery process.

We investigate the following data mining problem from computer-aided drug design: From a large collection of compounds, find those that bind to a target molecule in as few iterations of biochemical testing as possible. In each iteration a comparatively small batch of compounds is screened for binding activity toward this target. We employed the so-called "active learning paradigm" from Machine Learning for selecting the successive batches.

A novel shape-feature based approach to virtual library screening.

The shape of and the chemical features of a ligand are both critical for biological activity. This paper presents a strategy that uses these descriptors to build a computational model for virtual screening of bioactive compounds. Molecules are represented in a binary shape-feature descriptor space as bit-strings, and their relative activities are used to identify the subset of the bit-string that is most relevant to bioactivity.