Predicting acute rejection following kidney transplantation by microarray analysis of the T cell transcriptome
Lead Research Organisation:
University of Cambridge
Department Name: Cambridge Institute for Medical Research
Abstract
During the time of kidney transplantation many changes in the immune system occur. Often these are helpful and allow the body to accept the new organ, however, changes in immune function can also lead to kidney rejection. Detecting these changes after transplantation is often difficult and usually involves taking a biopsy from the kidney to analyse. The trigger to obtain a biopsy is usually a rise in blood creatinine level by which time some irreversible kidney damage may have occurred. Depending on the biopsy results, a change in medication may be beneficial.
I am assessing whether the information obtained from a biopsy can be predicted by analysing the cells from a simple blood test pre-transplant.
I will be working closely with experts in immunology, genetics and bioinformatics to assist with the various aspects of this study.
If a blood test can predict rejection then medication can be individually tailored so that potential side-effects can be minimised in patients at low-risk of rejection and appropriate increases in medication made for patients where the risk of rejection is higher. This should prevent clinical rejection episodes where kidney function is affected and which, ultimately, reduce the survival of the transplant.
I am assessing whether the information obtained from a biopsy can be predicted by analysing the cells from a simple blood test pre-transplant.
I will be working closely with experts in immunology, genetics and bioinformatics to assist with the various aspects of this study.
If a blood test can predict rejection then medication can be individually tailored so that potential side-effects can be minimised in patients at low-risk of rejection and appropriate increases in medication made for patients where the risk of rejection is higher. This should prevent clinical rejection episodes where kidney function is affected and which, ultimately, reduce the survival of the transplant.
Technical Summary
Acute rejection remains a significant problem post-renal transplantation. Even a single acute rejection episode has a strong negative correlation with long-term graft outcome. Currently there is no method of predicting acute rejection before it becomes clinically apparent.
Microarray analysis has led to the development of biomarkers predictive of clinical outcome in a number of settings. We are proposing to use microarray analysis of purified leucocyte subsets to look prospectively for signatures which can predict acute rejection.
We have recently shown that patients with autoimmune disease can be subdivided on the basis of CD8 T cell gene expression into two groups with very different clinical prognoses (see Appendix 1).
Interestingly, the same subgroups exist in normal controls suggesting they may play a more general role in response to infection, vaccination and possibly transplantation. Given the pivotal role T cells have as mediators of transplant rejection, we plan to determine if similar subgroups exist in transplant recipients and whether they predict acute rejection.
Blood samples will be obtained from transplant recipients - one immediately prior to transplantation (D0) and one 3 months post-transplant (D90). Blood will be subject to FACS analysis to measure leucocyte subset populations and the remaining blood will be processed to isolate separate cell populations for microarray analysis. Throughout the study, a comprehensive record of clinical data will be documented.
The following analyses will be performed to address the aims of the study:
1. Microarray expression data from T cell samples will be subjected to supervised hierarchical clustering using the genes that best define subgroups of patients in autoimmune disease to determine whether similar subgroups exist in transplant recipients. Subgroups identified by this analysis will be analysed to look for correlations with acute rejection (AR) and other clinical parameters.
2. Cell subset data will be subjected to unsupervised analysis to identify transcriptional changes that might then be associated with AR. Genes identified will be further examined for functional significance.
3. Gene expression changes associated with End-Stage Renal Failure and contributing to the increased incidence of infection in this population will be sought.
The scientific opportunities of this research include the combined application of techniques rigorously optimised within the host laboratory together with bioinformatics approaches to identify cell-specific gene expression changes predictive of acute rejection. The medical application of such a discovery would be more individualised therapy post-transplantation.
Microarray analysis has led to the development of biomarkers predictive of clinical outcome in a number of settings. We are proposing to use microarray analysis of purified leucocyte subsets to look prospectively for signatures which can predict acute rejection.
We have recently shown that patients with autoimmune disease can be subdivided on the basis of CD8 T cell gene expression into two groups with very different clinical prognoses (see Appendix 1).
Interestingly, the same subgroups exist in normal controls suggesting they may play a more general role in response to infection, vaccination and possibly transplantation. Given the pivotal role T cells have as mediators of transplant rejection, we plan to determine if similar subgroups exist in transplant recipients and whether they predict acute rejection.
Blood samples will be obtained from transplant recipients - one immediately prior to transplantation (D0) and one 3 months post-transplant (D90). Blood will be subject to FACS analysis to measure leucocyte subset populations and the remaining blood will be processed to isolate separate cell populations for microarray analysis. Throughout the study, a comprehensive record of clinical data will be documented.
The following analyses will be performed to address the aims of the study:
1. Microarray expression data from T cell samples will be subjected to supervised hierarchical clustering using the genes that best define subgroups of patients in autoimmune disease to determine whether similar subgroups exist in transplant recipients. Subgroups identified by this analysis will be analysed to look for correlations with acute rejection (AR) and other clinical parameters.
2. Cell subset data will be subjected to unsupervised analysis to identify transcriptional changes that might then be associated with AR. Genes identified will be further examined for functional significance.
3. Gene expression changes associated with End-Stage Renal Failure and contributing to the increased incidence of infection in this population will be sought.
The scientific opportunities of this research include the combined application of techniques rigorously optimised within the host laboratory together with bioinformatics approaches to identify cell-specific gene expression changes predictive of acute rejection. The medical application of such a discovery would be more individualised therapy post-transplantation.