A Bayesian network classification methodology for gene expression data

We present new techniques for the application of a Bayesian network learning framework to the problem of classifying gene expression data. The focus on classification permits us to develop techniques that address in several ways the complexities of learning Bayesian nets. Our classification model reduces the Bayesian network learning problem to the problem of learning multiple subnetworks, each consisting of a class label node and its set of parent genes. We argue that this classification model is more appropriate for the gene expression domain than are other structurally similar Bayesian network classification models, such as Naive Bayes and Tree Augmented Naive Bayes (TAN), because our model is consistent with prior domain experience suggesting that a relatively small number of genes, taken in different combinations, is required to predict most clinical classes of interest. Within this framework, we consider two different approaches to identifying parent sets which are supported by the gene expression observations and any other currently available evidence. One approach employs a simple greedy algorithm to search the universe of all genes; the second approach develops and applies a gene selection algorithm whose results are incorporated as a prior to enable an exhaustive search for parent sets over a restricted universe of genes. Two other significant contributions are the construction of classifiers from multiple, competing Bayesian network hypotheses and algorithmic methods for normalizing and binning gene expression data in th

Microencapsulated adult porcine islets transplanted intraperitoneally in streptozotocin-diabetic non-human primates

Xenogeneic donors would provide an unlimited source of islets for the treatment of type 1 diabetes (T1D). The goal of this study was to assess the function of microencapsulated adult porcine islets (APIs) transplanted ip in streptozotocin (STZ)-diabetic non-human primates (NHPs) given targeted immunosuppression. APIs were encapsulated in: (a) single barium-gelled alginate capsules or (b) double alginate capsules with an inner, islet-containing compartment and a durable, biocompatible outer alginate layer. Immunosuppressed, streptozotocin-diabetic NHPs were transplanted ip with encapsulated APIs, and graft function was monitored by measuring blood glucose, %HbA1c, and porcine C-peptide. At graft failure, explanted capsules were assessed for biocompatibility and durability plus islet viability and functionality. Host immune responses were evaluated by phenotyping peritoneal cell populations, quantitation of peritoneal cytokines and chemokines, and measurement of anti-porcine IgG and IgM plus anti-Gal IgG. NHP recipients had reduced hyperglycemia, decreased exogenous insulin requirements, and lower percent hemoglobin A1c (%HbA1c) levels. Porcine C-peptide was detected in plasma of all recipients, but these levels diminished with time. However, relatively high levels of porcine C-peptide were detected locally in the peritoneal graft site of some recipients at sacrifice. IV glucose tolerance tests demonstrated metabolic function, but the grafts eventually failed in all diabetic NHPs regardless of the type of encapsulation or the host immunosuppression regimen. Explanted microcapsules were intact, "clean," and free-floating without evidence of fibrosis at graft failure, and some reversed diabetes when re-implanted ip in diabetic immunoincompetent mice. Histology of explanted capsules showed scant evidence of a host cellular response, and viable islets could be found. Flow cytometric analyses of peritoneal cells and peripheral blood showed similarly minimal evidence of a host immune response. Preformed anti-porcine IgG and IgM antibodies were present in recipient plasma, but these levels did not rise post-transplant. Peritoneal graft site cytokine or chemokine levels were equivalent to normal controls, with the exception of minimal elevation observed for IL-6 or IL-1β, GRO-α, I-309, IP-10, and MCP-1. However, we found central necrosis in many of the encapsulated islets after graft failure, and explanted islets expressed endogenous markers of hypoxia (HIF-1α, osteopontin, and GLUT-1), suggesting a role for non-immunologic factors, likely hypoxia, in graft failure. With donor xenoislet microencapsulation and host immunosuppression, APIs corrected hyperglycemia after ip transplantation in STZ-diabetic NHPs in the short term. The islet xenografts lost efficacy gradually, but at graft failure, some viable islets remained, substantial porcine C-peptide was detected in the peritoneal graft site, and there was very little evidence of a host immune response. We postulate that chronic effects of non-immunologic factors, such as in vivo hypoxic and hyperglycemic conditions, damaged the encapsulated islet xenografts. To achieve long-term function, new approaches must be developed to prevent this damage, for example, by increasing the oxygen supply to microencapsulated islets in the ip space

Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: A Children's Oncology Group study

Gene expression profiling was performed on 97 cases of infant ALL from Children's Oncology Group Trial P9407. Statistical modeling of an outcome predictor revealed 3 genes highly predictive of event-free survival (EFS), beyond age and MLL status: FLT3, IRX2, and TACC2. Low FLT3 expression was found in a group of infants with excellent outcome (n ∇ 11; 5-year EFS of 100%), whereas differential expression of IRX2 and TACC2 partitioned the remaining infants into 2 groups with significantly different survivals (5-year EFS of 16% vs 64%; P < .001). When infants with MLL-AFF1 were analyzed separately, a 7-gene classifier was developed that split them into 2 distinct groups with significantly different outcomes (5-year EFS of 20% vs 65%; P < .001). In this classifier, elevated expression of NEGR1 was associated with better EFS, whereas IRX2, EPS8, and TPD52 expression were correlated with worse outcome. This classifier also predicted EFS in an independent infant ALL cohort from the Interfant-99 trial. When evaluating expression profiles as a continuous variable relative to patient age, we further identified striking differences in profiles in infants less than or equal to 90 days of age and those more than 90 days of age. These age-related patterns suggest different mechanisms of leukemogenesis and may underlie the differential outcomes historically seen in these age groups

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