Oncology meets immunology: the cancer-immunity cycle. Int Immunol.
Effector and memory T-cell differentiation: implications for vaccine development. Nat Rev Immunol. MHC class I-restricted epitope spreading in the context of tumor rejection following vaccination with a single immunodominant CTL epitope. Eur J Immunol. Possible approaches to using computational biology to studying chronic renal allograft injury.
We also need detailed long-term data beyond what is currently available. Graft survival at 5 years is just the beginning. What happens between 5 and 10 years? We need to model these later time points and this will require data that are rarely captured. In most disease groups, there are many phenotypes and small numbers of patients in each phenotype. Thus, in order to study sufficient numbers of patients, these studies will need to be multicenter and very collaborative and we may need to combine data from many different databases.
Immunologic Signatures Laboratory
Finally, studying a complex biologic process, such as chronic injury, probably will require a change in mindset among researchers. We tend to strive to make model systems as simple as possible with as few variables. While this makes for good science, it may be an inadequate approach to studying chronic injury. The application of computational biology to transplantation seems to be a natural progression of both fields. The interaction between mathematicians and transplant biologists will likely lead to novel new interpretations of phenomena and new understanding of the mechanisms of chronic injury.
Richard Borrows declares no conflict of interest. Long-term renal allograft survival in the United States: a critical reappraisal. Am J Transplant 11 — Identifying specific causes of kidney allograft loss.
Am J Transplant 9 — Through a glass darkly: seeking clarity in preventing late kidney transplant failure. J Am Soc Nephrol 26 —9. Google Scholar. Calculating life years from transplant LYFT : methods for kidney and kidney-pancreas candidates. Am J Transplant 8 — Predicting 5-year risk of kidney transplant failure: a prediction instrument using data available at 1 year posttransplantation.
Am J Kidney Dis 4 — Predicting subsequent decline in kidney allograft function from early surveillance biopsies. Am J Transplant 5 — Predicting 5-year risk of kidney transplant failure: does histology add to clinical parameters available at 1 year post-transplantation?
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Am J Transplant 15 Endothelial gene expression in kidney transplants with alloantibody indicates antibody-mediated damage despite lack of C4d staining. Fibrosis with inflammation at one year predicts transplant functional decline. J Am Soc Nephrol 21 — Identification of a B cell signature associated with renal transplant tolerance in humans. J Clin Invest — A common gene signature across multiple studies relate to biomarkers and functional regulation in tolerant to renal allograft.
Kidney Int 87 — Molecular classifiers for acute kidney transplant rejection in peripheral blood by whole genome gene expression profiling. Am J Transplant 14 — Genomic profiles and predictors of early allograft dysfunction after human liver transplantation. Am J Transplant 15 — Molecular microscope strategy to improve risk stratification in early antibody-mediated kidney allograft rejection.
J Am Soc Nephrol 25 — A three-gene assay for monitoring immune quiescence in kidney transplantation. J Am Soc Nephrol PLoS Med 11 :e MicroRNA expression profiles predictive of human renal allograft status. Circulating micoRNAs associate with diabetic nephropathy and systemic microvascular damage and normalize after simultaneous pancreas-kidney transplantation.
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Subpart H. Ioannidis JPA. Biomarkers failures. Clin Chem 59 —4. Msr1 MSR1 macrophage scavenger receptor 1 [Source Stat2 STAT2 signal transducer and activator of trans Stat6 STAT6 signal transducer and activator of trans Copyright c Broad Institute, Inc. All rights reserved.