Proteomic characterisations of ulcerative colitis endoscopic biopsies associate with clinically relevant histological measurements of disease severity.

AIMS AND METHODS: Accurate protein measurements using formalin-fixed biopsies are needed to improve disease characterisation. This feasibility study used targeted and global mass spectrometry (MS) to interrogate a spectrum of disease severities using 19 ulcerative colitis (UC) biopsies. RESULTS: Targeted assays for CD8, CD19, CD132 (interleukin-2 receptor subunit gamma/common cytokine receptor gamma chain), FOXP3 (forkhead box P3) and IL17RA (interleukin 17 receptor A) were successful; however, assays for IL17A (interleukin 17A), IL23 (p19) (interleukin 23, alpha subunit p19) and IL23R (interleukin 23 receptor) did not permit target detection. Global proteome analysis (4200 total proteins) was performed to identify pathways associated with UC progression. Positive correlation was observed between histological scores indicating active colitis and neutrophil-related measurements (R2=0.42-0.72); inverse relationships were detected with cell junction targets (R2=0.49-0.71) and ß-catenin (R2=0.51-0.55) attributed to crypt disruption. An exploratory accuracy assessment with Geboes Score and Robarts Histopathology Index cut-offs produced sensitivities/specificities of 72.7%/75.0% and 100.0%/81.8%, respectively. CONCLUSIONS: Pathologist-guided MS assessments provide a complementary approach to histological scoring systems. Additional studies are indicated to verify the utility of this novel approach.

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.

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. The highest predictive accuracy was noted in the cytogenetic intermediate risk patients (AUC(ROC) 0·88, P = 0·002), a subgroup that lacks prognostic/predictive biomarkers for induction response. Only white blood cell count and cytogenetic risk were associated with response to induction therapy in the validation set. After controlling for these variables, the SCNP classifier score was associated with complete remission (P = 0·017), indicating that the classifier provides information independent of other clinical variables that were jointly associated with response. This is the first validation of an SCNP classifier to predict response to induction chemotherapy. Herein we demonstrate the usefulness of quantitative SCNP under modulated conditions to provide independent information on AML disease biology and induction response.

Single cell network profiling assay in bladder cancer.

The aim of this study was to assess the feasibility of applying the single cell network profiling (SCNP) assay to the examination of signaling networks in epithelial cancer cells, using bladder washings from 29 bladder cancer (BC) and 15 nonbladder cancer (NC) subjects. This report describes the methods we developed to detect rare epithelial cells (within the cells we collected from bladder washings), distinguish cancer cells from normal epithelial cells, and reproducibly quantify signaling within these low frequency cancer cells. Specifically, antibodies against CD45, cytokeratin, EpCAM, and cleaved-PARP (cPARP) were used to differentiate nonapoptotic epithelial cells from leukocytes, while measurements of DNA content to determine aneuploidy (DAPI stain) allowed for distinction between tumor and normal epithelial cells. Signaling activity in the PI3K and MAPK pathways was assessed by measuring intracellular levels of p-AKT and p-ERK at baseline and in response to pathway modulation; 66% (N = 19) of BC samples and 27% (N = 4) of NC samples met the "evaluable" criteria, i.e., at least 400,000 total cells available upon sample receipt with >2% of cells showing an epithelial phenotype. The majority of epithelial cells detected in BC samples were nonapoptotic and all signaling data were generated from identified cPARP negative cells. In four of 19 BC samples but in none of the NC specimens, SCNP assay identified epithelial cancer cells with a quantifiable increase in epidermal growth factor-induced p-AKT and p-ERK levels. Furthermore, preincubation with the PI3K inhibitor GDC-0941 reduced or completely inhibited basal and epidermal growth factor-induced p-AKT but, as expected, had no effect on p-ERK levels. This study demonstrates the feasibility of applying SCNP assay using multiparametric flow cytometry to the functional characterization of rare, bladder cancer cells collected from bladder washing. Following assay standardization, this method could potentially serve as a tool for disease characterization and drug development in bladder cancer and other solid tumors.