Northwestern scientists found that the genetic distance between kidney donors and recipients could be used to more accurately predict transplant success compared to the current system, publishing their findings in the Proceedings of the National Academy of Sciences.
The current organ allocation formula takes into account a variety of factors, but a better picture of long-term success could be generated by measuring differences between the donor and recipient’s human leukocyte antigen (HLA) genotypes, according to Anat Roitberg-Tambur, DMD, PhD, research professor of Surgery in the Division of Organ Transplantation and co-author of the study.
“The degree of matching between donor and recipient should likely receive higher weight in organ allocation algorithms,” Roitberg-Tambur said.
Predicting whether or not a transplanted organ will be accepted by the recipient’s immune system — termed graft survival — is an important determinant when matching organ donors and recipients. The current Kidney Donor Profile Index (KDPI) includes factors from both the donor and recipient sides of the procedure, such as donor organ quality and the presence of problematic HLA antibodies in the recipient at the time of transplant, which can make graft survival less likely.
However, comparing HLA genotypes between both parties receives very low weight in the current algorithm, according to Roitberg-Tambur.
“A consequence is frequent new generation of HLA antibodies against the donor after transplantation, leading to increased rate of rejection episodes and eventual graft loss,” she said.
Recent studies have investigated the viability of using HLA similarity to estimate graft survival, as closer genetic matching of donors and recipients would strengthen graft survival and reduce the need for immunosuppression drugs that are known to increase the risk of infections disease or cancer for transplant recipients.
In the current study, investigators described a new approach, employing recipient age, organ KDPI and a rudimentary degree of HLA match between the donor and recipient. Using a database of over 21,000 transplants from the National Kidney Registry, Roitberg-Tambur and her colleagues confirmed that the new algorithm predicted more graft loss as donor age increased, and demonstrated an increased significance of HLA mismatching over other factors in promoting graft survival — a finding consistent with the body of peer-reviewed evidence on transplant rejection, according to the authors.
Notably, the new algorithm considered HLA distance using a non-parametric risk estimate — in other words, an estimate that changes over time, something the current KDPI approach lacks.
“Importantly, we demonstrated that the factors considered in our model exhibit different relative contributions over time, with the degree of HLA mismatch being a strong predictor of 5-year graft loss,” Roitberg-Tambur said. “It is well documented that the generation of de-novo donor-specific antibodies plays a significant role in graft rejection, which fits with our finding showing a cumulative rather than time-invariant role in graft loss.”
In addition, the investigators warned against using statistic inferences to shore up limited HLA data. Previous studies have attempted to use population-level data to fill in the gaps when HLA data is incomplete, but the current study found that these methods did not improve prediction of transplant outcome.
Charles Manski, PhD, Board of Trustees Professor in Economics in the Weinberg College of Arts and Sciences, was the lead author of the study.
