Tod M. Klingler

Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling

Rejection diagnosis by endomyocardial biopsy (EMB) is invasive, expensive and variable. We investigated gene expression profiling of peripheral blood mononuclear cells (PBMC) to discriminate ISHLT grade 0 rejection (quiescence) from moderate/severe rejection (ISHLT ≥3A). Patients were followed prospectively with blood sampling at post‐transplant visits. Biopsies were graded by ISHLT criteria locally and by three independent pathologists blinded to clinical data. Known alloimmune pathways and leukocyte microarrays identified 252 candidate genes for which real‐time PCR assays were developed. An 11 gene real‐time PCR test was derived from a training set (n = 145 samples, 107 patients) using linear discriminant analysis (LDA), converted into a score (0–40), and validated prospectively in an independent set (n = 63 samples, 63 patients). The test distinguished biopsy‐defined moderate/severe rejection from quiescence (p = 0.0018) in the validation set, and had agreement of 84% (95% CI 66% C94%) with grade ISHLT ≥3A rejection. Patients >1 year post‐transplant with scores below 30 (approximately 68% of the study population) are very unlikely to have grade ≥3A rejection (NPV = 99.6%). Gene expression testing can detect absence of moderate/severe rejection, thus avoiding biopsy in certain clinical settings. Additional clinical experience is needed to establish the role of molecular testing for clinical event prediction and immunosuppression management.

Clinical Implications and Longitudinal Alteration of Peripheral Blood Transcriptional Signals Indicative of Future Cardiac Allograft Rejection

BACKGROUND We have previously demonstrated that a peripheral blood transcriptional profile using 11 distinct genes predicts onset of cardiac allograft rejection weeks to months prior to the actual event. METHODS In this analysis, we ascertained the performance of this transcriptional algorithm in a Bayesian representative population: 28 cardiac transplant recipients who progressed to moderate to severe rejection; 53 who progressed to mild rejection; and 46 who remained rejection-free. Furthermore, we characterized longitudinal alterations in the transcriptional gene expression profile before, during and after recovery from rejection. RESULTS In this patient cohort, we found that a gene expression score (range 0 to 40) of or =3A) rejection; 16 of 53 (30%) from the intermediate group (those who progressed to ISHLT Grade 1B or 2) and 13 of 46 (28%) controls (who remained Grade 0 or 1A) had scores < or =20. A gene score of > or =30 was associated with progression to moderate to severe rejection in 58% of cases. These two extreme scores (< or =20 or > or =30) represented 44% of the cardiac transplant population within 6 months post-transplant. In addition, longitudinal gene expression analysis demonstrated that baseline scores were significantly higher for those who went on to reject, remained high during an episode of rejection, and dropped post-treatment for rejection (p < 0.01). CONCLUSIONS The use of gene expression profiling early after transplantation allows for separation into low-, intermediate- or high-risk categories for future rejection, permitting development of discrete surveillance strategies.

Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation

Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information‐theoretic, reverse‐engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B‐cells (NFκB), Fas (TNFRSF6)‐associated via death domain (FADD) and c‐AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first‐neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P < 0.001, Fisher’s exact test). Our results confirm the accuracy of ARACNe to reconstruct the PBMC transcriptional network and show the utility of systems biological approaches to identify possible molecular targets and biomarkers.

Low Variability of Intraindividual Longitudinal Leukocyte Gene Expression Profiling Cardiac Allograft Rejection Scores

with ET-Kyoto solution before pancreas preservation significantly improved islet yield from both deceased donor and autologous live pancreata (4, 9). Both collagenase infusion and DI require a pancreatic duct, therefore, an opened main pancreatic duct as created by the Puestow procedure is a major problem. To overcome this issue, we reconstructed the pancreatic duct and jejunum conduit using autosuture. This simple procedure made it possible for us to reconstruct a watertight conduit for collagenase infusion and DI. This is the first report to show that the reconstruction of the pancreatic duct resulted in successful islet isolation and, in turn, insulin independence. In addition, duct reconstruction could be applicable to posttraumatic pancreatectomy cases in which a laceration or damage to the duct could be present (10). Bacteria contamination is a concern using this technique. In this case, bacteremia was not observed; however, we recommend careful observation for bacteremia. Even if pancreatitis is in its advanced phase, TP with AIT could effectively reduce abdominal pain and maintain excellent glycemic control as long as patients receive a sufficient quantity of islets (11). This reconstruction method should be useful to promote TP with AIT after Puestow procedure for patients who have advanced pancreatitis.

Gene Expression Signatures of Peripheral Blood Mononuclear Cells during the Early Post-Transplant Period in Patients Developing Cardiac Allograft Vasculopathy

Background. Cardiac allograft vasculopathy (CAV) is a major cause of graft loss and death after heart transplantation. Currently, no diagnostic methods are available during the early post-transplant period to accurately identify patients at risk of CAV. We hypothesized that PBMC gene expression profiles (GEP) can identify patients at risk of CAV. Methods. We retrospectively analyzed a limited set of whole-genome PBMC microarrays from 10 post-transplant patients who did (n = 3) or did not (n = 7) develop advanced grade CAV during their long-term follow-up. We used significance analysis of microarrays to identify differentially expressed genes and High-Throughput GoMiner to assess gene ontology (GO) categories. We corroborated our findings by retrospective analysis of PBMC real-time PCR data from 33 patients. Results. Over 300 genes were differentially expressed (FDR < 5%), and 18 GO-categories including “macrophage activation”, “Interleukin-6 pathway”, “NF-KappaB cascade”, and “response to virus” were enriched by these genes (FDR < 5%). Out of 8 transcripts available for RT-PCR analysis, we confirmed 6 transcripts (75.0%) including FPRL1, S100A9, CXCL10, PRO1073, and MMP9 (P < .05). Conclusion. Our pilot data suggest that GEP of PBMC may become a valuable tool in the evaluation of patients at risk of CAV. Larger prospectively designed studies are needed to corroborate our hypothesis.

GTPase activating proteins

Small GTPases such as ras p21 have low intrinsic GTPase activity and depend on GTPase activating proteins (GAPs) to convent their active GTP-bound forms to their inactive GDP-bound counterparts (Bollag and McCormick 1991b). GAPs therefore appear to be major negative regulators of these GTPases (Fig. 1). In addition, we and others have proposed the possibility that GAP-mediated down-regulation may be coupled to signal output so that GAPs comprise part of small GTPase effector systems (Adari et al. 1988; Cales et al. 1988; McCormick 1989; Hall 1990). This proposal is based mainly on the fact that GAPs interact only with the GTP-bound forms of ras p21 proteins (a criterion for a ras effector), and that GAPs bind ras p21 proteins at or near the so-called effector-binding site. The hypothesis has gained support from the recent observation that certain effectors of heterotrimeric G-proteins are also GAPs for these proteins (Berstein et al. 1992; Arshavsky et al. 1992), but remains controversial for small GTPases such as ras p21. In addition to the major issue of a possible role in effector functions for GAPs, a number of fundamental questions relating to their function have yet to be solved. For example, we have very little idea how, at the molecular level, GAPs stimulate GTPase activity or how GAP activities are regulated in cells. In this chapter, we will discuss known properties of GAPs for ras p21 and related proteins and speculate about their possible functions.

Abstract 4870: GenePool: A cloud-based technology for rapidly data mining large-scale, patient-derived cancer genomic cohorts including The Cancer Genome Atlas

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA GenePool is a cloud-based system for the secure storage, management, analysis, visualization and interpretation of large-scale human genomics data. The GenePool platform enables rapid interrogation of massive amounts of data generated by current laboratory methods and was designed to meet the needs of scientists engaged in cancer biomarker discovery and characterization. The software solution provides users with an intuitive interface, an easy way to store and select genomics datasets according to sample-associated metadata for analysis, and an automated system for performing routine, well-characterized genomics workflows. Results are presented with multiple options for annotation, sorting and data export, with a visualization tool that facilitates browsing of genomic data for biomarker identification. We will demonstrate how GenePool removes the data download, management, and computing burdens faced by researchers interested in working with large amounts of patient-derived cancer genomics exemplified by The Cancer Genome Atlas. To date, TCGA has completed molecular characterization of more than 9,000 patient samples from a target of 17,000, and will have released an estimated 2.5 petabytes of data at project completion. We will present case studies using the >25 cancer cohorts worth of RNA-Seq, miRNA-Seq, Exome Sequencing, Protein Expression, Copy Number, and DNA Methylation data (provided to the community through the TCGA Data Portal) and made readily available for sample selection and analysis within GenePool. As validation of the GenePool platform, we will present results obtained within minutes that validate knowledge reported in the literature over the course of decades. We will then present novel findings for less understood cancer indications. Note: This abstract was not presented at the meeting. Citation Format: Sandeep Sanga, Antoaneta Vladimirova, Richard D. Goold, Tod M. Klingler. GenePool: A cloud-based technology for rapidly data mining large-scale, patient-derived cancer genomic cohorts including The Cancer Genome Atlas. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4870. doi:10.1158/1538-7445.AM2015-4870