Bioinformatics pipeline for personalized diagnostics of transplant rejection

Summary
Transplantation is the most effective treatment for kidney failure, providing improved survival and quality of
life. In the US, ~20,000-25,000 kidney transplants are performed in the US per year. Roughly ~90,000
individuals are waitlisted to receive a kidney allograft with average waiting time ~5 years. Many individuals die
before receiving the transplant. Those patients receiving a transplant are required to be on life-long,
maintenance immunosuppression (mIS) with calcineurin inhibitors (CNI), such as tacrolimus, to prevent
rejection. Unfortunately, rejection still remains the #1 cause of death-censored graft loss. Potential treatment
for graft failure is dialysis ($126K/year)1 or another transplant (~$500K) if another compatible kidney is
available. Overall, in the US the annual cost of kidney transplant failure is $1.3B with ~ half covered by
Medicare2. Thus, even a minor decrease in graft loss carries a potential for enormous human and economic
benefit.
Our preliminary and published data show that the scRNA/VDJ analysis of kidney biopsies and potentially
urine samples can identify the alloreactive expanded cytotoxic T cell clones (CD8exp) that are likely causing
rejection. We also showed that the analysis of gene expression in these clones can identify targets for anti-
rejection therapy.
The aim of the project is to develop a diagnostic test based on scRNA/VDJ analysis that will help
physicians select personalized anti-rejection therapy based on the phenotype of the cells causing rejection. In
this phase I project, we aim to (i) develop an automated computational pipelines that will process the data and
produce a report for treating physician and (ii) to demonstrate the feasibility of completing the test within
clinically reasonable time-frame. We will also collect preliminary data that will help justify a larger phase II
study that will demonstrate the clinical benefit of the test.

Grant Number: 1R41AI189162-01
NIH Institute/Center: NIH
Principal Investigator: Artem Barski