Data quality is a recognized problem for high-throughput genomics platforms, as evinced by the proliferation of methods attempting to filter out lower quality data points. Different filtering methods lead to discordant results, raising the question, which methods are best? Astonishingly, little computational support is offered to analysts to decide which filtering methods are optimal for the research question at hand. To evaluate them, we begin with a pair of expression data sets, transcriptomic and proteomic, on the same samples. The pair of data sets form a test-bed for the evaluation. Identifier mapping between the data sets creates a collection of feature pairs, with correlations calculated for each pair. To evaluate a filtering strategy, we estimate posterior probabilities for the correctness of probesets accepted by the method. An analyst can set expected utilities that represent the trade-off between the quality and quantity of accepted features. We tested nine published probeset filtering methods and combination strategies. We used two test-beds from cancer studies providing transcriptomic and proteomic data. For reasonable utility settings, the Jetset filtering method was optimal for probeset filtering on both test-beds, even though both assay platforms were different. Further intersection with a second filtering method was indicated on one test-bed but not the other.
O308* Aims: Tolerance assays were performed in patients (pts) successfully withdrawn from IS after LTx. We hypothesized that tolerant pts would exhibit plasmacytoid dendritic cell (DC) lineage (pDC2), lack donor specific alloantibody, and demonstrate genotypes associated with low producers of pro-inflammatory cytokines. Methods: Pts withdrawn from IS (Group A) underwent DC subsets, donor specific alloantibody, and cytokine gene polymorphism evaluation. Pts undergoing prospective drug withdrawal (Group B) and those who required maintenance IS (Group C) were also studied. Assay methods: DC Xwere identified as HLA-DR positive (+) and lineage (lin) marker negative (-) by four-color flow cytometric analysis. Precursor (p) DC2 and (p) DC1 subpopulations were defined respectively as HLA-DR+, lin-, CD123 hi (IL3R a hi), CD11c- and as HLA-DR+, lin-, CD123 lo (IL3R a lo), CD11c+. Alloantibody by ELISA was performed for the presence (ELISA+) and donor-specificity of HLA antibodies (DS HLA Ab). Cytokine Polymorphism Allelic variations in tumor necrosis factor (TNF)-α and IL-10 were determined using PCR-SSP technique. Results: 25 pts withdrawn from IS by protocol (13), emergently (8), or because of non-compliance (4) have maintained normal liver function and freedom from IS for a mean of 7.6+/- 5.7 years. 82 patients (25 Group A, 35 Group B, and 22 Group C) were assayed.Figure#p<0.05 between Groups A and B compared to C *P<0.05 Group A versus Group C Although the number of pts with both TNF-α low and IL-10 high/intermediate polymorphism was slightly greater (19/25, 76%) in group A than in group C (13/20, 65%), this difference was not statistically significant. Conclusions: Clinically tolerant liver transplant recipients demonstrated higher pDC2/pDC1 ratio, and lacked donor specific antibody as compared to pts requiring ongoing IS. The DC subset ratio was similar to those undergoing prospective weaning (Group B) but the groups differed in incidence of DS HLA Ab. Results of prospective weaning may help determine the relative importance of each of these findings and contribute to immunologically characterizing the tolerant pts. Acknowledgment: This research was performed as a project of the Immune Tolerance Network, headquartered at UCSF and supported by the NIAID, NIDDK, and JDRF.
P1014 Aims: A new therapeutic protocol consisting of pre-transplant (pre-Tx) T cell depletion (Thymoglobulin (Thy) or Campath prior to allograft revascularization) and minimal post-Tx immunosuppression was initiated in lung transplant recipients. This protocol is associated with a sharp decline of all T cell subsets immediate post treatment and the potential loss of T helper memory responses. Methods: In this study, we measured the recovery of T cell responses to ConA, CMV and EBV in a cohort of 41 lung transplant recipients (82 samples) followed for 3 months up to 18 months post-Tx. We also determined the T cell reactivity in a group of 11 normal volunteers. We used a new Cylex ImmuKnow-CD3 assay to assess T cell activity by ATP production. Results: 34 lung recipients were treated pre-Tx with Thy and 7 received Campath. The ConA responses post-Tx in the Thy group were significantly lower in lung recipients as compared to control subjects up to 10 months post-Tx: control ATP levels were 360+132 ng/ml (range 174-484 ng/ml) while in the Tx cohort the ATP levels were at 1-3 months 144+90 ng/ml, at 4-6 months 128+87 ng/ml and 7-9 months 144+45 ng/ml. The ConA responses seem to recover 10 months post-Tx. with a mean of 230+76 ng/ml at 10-12 months and 275+136 ng/ml > 12 months post-Tx. For CMV reactivity, we analyzed the results based on the CMV serological status of the recipients. In CMV negative recipients who were Tx with a CMV positive donor (R-/D+), the mean ATP level was 5+2 ng/ml (n=8) which was considered negative based on the observation in CMV negative controls (ATP<10ng/ml). In contrast, the CMV seropositive recipients (R+) had a mean ATP level of 20+24 ng/ml and 7/10 samples exhibited ATP levels >10 ng/ml. After 7 months post –Tx, the CMV-specific memory responses seem to recover in both groups; 13/15 patients in the R-/D+ exhibited an ATP response >10 ng/ml (mean 32+25 ng/ml) and 21/21 samples in R+ recipients had ATP >10 (mean 39+36ng/ml). The R-D- group had negative responses during the entire period (n=8, mean 5+3 ng/ml ATP). In control samples, the ATP levels were 125+93 ng/ml (range 22-262). For EBV-specific T cell memory, results greater than 15 ng/ml ATP were considered positive. Most of the recipients were EBV positive and the responses in the first 6 months post-Tx were suppressed (mean 29-20, with 8/19 expressing levels <15 ng/ml). Following 6 months post-Tx, only 4/44 samples exhibited levels of ATP <15 (mean 80+82, n=44). Similar results were seen with Campath treated patients although the number of samples was smaller and the patients had a shorter follow-up (less than 6 months). Conclusions: We have evaluated the ImmuKnow CD3-assay to monitor the functional recovery of T cell immunity to various pathogens following T cell depletion therapy. The CD3 assay may be used as adjunct to the assessment of viral load to monitor T cell immunity. This would allow for adjusting immunosuppression in Tx recipients to prevent rejection and to avoid infection.
Bioinformatics can be divided into two phases, the first phase is conversion of raw data into processed data and the second phase is using processed data to obtain scientific results. It is important to consider the first “workflow” phase carefully, as there are many paths on the way to a final processed dataset. Some workflow paths may be different enough to influence the second phase, thereby, leading to ambiguity in the scientific literature. Workflow evaluation in bioinformatics enables the investigator to carefully plan how to process their data. A system that uses real data to determine the quality of a workflow can be based on the inherent biological relationships in the data itself. To our knowledge, a general software framework that performs real data-driven evaluation of bioinformatics workflows does not exist.
The Evaluation and Utility of workFLOW (EUFLOW) decision-theoretic framework, developed and tested on gene expression data, enables users of bioinformatics workflows to evaluate alternative workflow paths using inherent biological relationships. EUFLOW is implemented as an R package to enable users to evaluate workflow data. EUFLOW is a framework which also permits user-guided utility and loss functions, which enables the type of analysis to be considered in the workflow path decision. This framework was originally developed to address the quality of identifier mapping services between UNIPROT accessions and Affymetrix probesets to facilitate integrated analysis1. An extension to this framework evaluates Affymetrix probeset filtering methods on real data from endometrial cancer and TCGA ovarian serous carcinoma samples.2 Further evaluation of RNASeq workflow paths demonstrates generalizability of the EUFLOW framework. Three separate evaluations are performed including: 1) identifier filtering of features with biological attributes, 2) threshold selection parameter choice for low gene count features, and 3) commonly utilized RNASeq data workflow paths on The Cancer Genome Atlas data.
The EUFLOW decision-theoretic framework developed and tested in my dissertation enables users of bioinformatics workflows to evaluate alternative workflow paths guided by inherent biological relationships and user utility.
