Person:

Abdi, Reza

With a steady increase in its incidence and lack of curative treatment, type 1 diabetes (T1D) has emerged as a major health problem worldwide. To design novel effective therapies, there is a pressing need to identify regulatory targets controlling the balance of autoreactive to regulatory-T-cells (Tregs). We previously showed that the inhibition of the γ-subunit of the Phosphoinositide-3-kinase (PI3K), significantly suppress autoimmune-diabetes. To further delineate the mechanisms and the selectivity of specific immune modulation by PI3Kγ-inhibition, we developed a new NOD mouse model of T1D lacking the γ-subunit of PI3K. Strikingly, the loss of PI3Kγ protected 92% of the NOD-mice from developing spontaneous diabetes. The NOD.PI3Kγ-/- mice are protected from insulitis secondary to a defect in CD4 and CD8 autoreactive-T-cells activation and survival. In addition, PI3Kγ-deficiency promoted Treg generation in-vitro and in-vivo. Furthermore, PI3Kγ-inhibitor (AS605240) inhibited proliferation and cytokine production of a human CD4+ T-cell clone specific for GAD555-567 peptide that was isolated from a patient with T1D. These studies demonstrate the key role of the PI3Kγ pathway in regulating autoimmune-diabetes and provide rationales for future devise of anti- PI3Kγ therapy in T1D.

Allogeneic hematopoietic cell transplantation (HCT) is the treatment of choice for a variety of hematologic malignancies and disorders. Unfortunately, acute graft-versus-host disease (GVHD) is a frequent complication of HCT. While substantial research has identified clinical, genetic and proteomic risk factors for acute GVHD, few studies have sought to develop risk prediction tools that quantify absolute risk. Such tools would be useful for: optimizing donor selection; guiding GVHD prophylaxis, post-transplant treatment and monitoring strategies; and, recruitment of patients into clinical trials. Using data on 9,651 patients who underwent first allogeneic HLA-identical sibling or unrelated donor HCT between 01/1999-12/2011 for treatment of a hematologic malignancy, we developed and evaluated a suite of risk prediction tools for: (i) acute GVHD within 100 days post-transplant and (ii) a composite endpoint of acute GVHD or death within 100 days post-transplant. We considered two sets of inputs: (i) clinical factors that are typically readily-available, included as main effects; and, (ii) main effects combined with a selection of a priori specified two-way interactions. To build the prediction tools we used the super learner, a recently developed ensemble learning statistical framework that combines results from multiple other algorithms/methods to construct a single, optimal prediction tool. Across the final super learner prediction tools, the area-under-the curve (AUC) ranged from 0.613–0.640. Improving the performance of risk prediction tools will likely require extension beyond clinical factors to include biological variables such as genetic and proteomic biomarkers, although the measurement of these factors may currently not be practical in standard clinical settings.