DNA-based scoring system uses many genes to predict long-term success of transplantation.
A new scoring system that compares the genetic matching of kidney donors and recipients could help improve predictions of transplant success, according to a study published in PLOS Computational Biology.
Doctors often match a kidney donor to a recipient by detecting differences in DNA sequences at a few specific locations in the genomes of transplant recipients and their organ donor. The fewer the differences between a donor and a recipient in that region, the better the chances of good kidney function after the transplant. However, 40 to 50 percent of kidney transplants still fail within 10 years, indicating that other parts of the genome may impact long-term success.
To investigate broader genetic impact on kidney transplants, Dr. Laurent Mesnard and colleagues collected DNA data for a large number of genes from 53 pairs of kidney donors and recipients. Together with the lab of study co-senior author Dr. Fabien Campagne, the investigators developed a computational method that assigned a score to each donor/recipient pair based on mismatches in their DNA sequences.
After transplantation surgery, the researchers followed each donor/recipient pair for several years to see how well their mismatch score predicted kidney function. They found that the score significantly predicted the ability of the transplanted kidneys to properly filter blood.
“Future studies will be able to build on this new concept to confirm the initial observations,” Dr. Campagne says. “They may lead to using this new concept in the clinic to optimize the matching of donor and recipients before transplantation.”
“There is a striking shortage of kidneys for transplantation worldwide, and a major contributor to the shortage are patients with a failed kidney transplant who return to the transplant wait list, ” says study co-senior author Dr. Manikkam Suthanthiran. “Should our novel scoring system be validated in future clinical trials, a real opportunity could emerge for minimizing the disparity between organ supply and demand.”
The study was developed at Weill Cornell Medicine, New York, as a collaboration between the Campagne laboratory in the Department of Physiology and Biophysics and the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, and the Suthanthiran laboratory in the Departments of Medicine and Transplantation Medicine, with contributions from colleagues at Hopital Tenon, Paris, France and Northwestern University Feinberg School of Medicine, Chicago.
The authors received no specific funding for this work.
LM, TM, MS and FC disclose that they are named inventors in a filed international patent application entitled “A METHOD TO MATCH ORGAN DONORS TO RECIPIENTS FOR TRANSPLANTATION”.
Article: Exome Sequencing and Prediction of Long-Term Kidney Allograft Function, Mesnard L, Muthukumar T, Burbach M, Li C, Shang H, Dadhania D, et al., PLOS Computational Biology, doi:10.1371/journal.pcbi.1005088, published 29 September 2016.