Proteomics for Biomarker Discovery for Diagnosis and Prognosis of Kidney Transplantation Rejection

Abstract

Renal transplantation is currently the treatment of choice for end-stage kidney disease, enabling a quality of life superior to dialysis. Despite this, all transplanted patients are at risk of allograft rejection processes. The gold-standard diagnosis of graft rejection, based on histological analysis of kidney biopsy, is prone to sampling errors and carries high costs and risks associated with such invasive procedures. Furthermore, the routine clinical monitoring, based on urine volume, proteinuria, and serum creatinine, usually only detects alterations after graft histologic damage and does not differentiate between the diverse etiologies. Therefore, there is an urgent need for new biomarkers enabling to predict, with high sensitivity and specificity, the rejection processes and the underlying mechanisms obtained from minimally invasive procedures to be implemented in routine clinical surveillance. These new biomarkers should also detect the rejection processes as early as possible, ideally before the78 clinical outputs, while enabling balanced immunotherapy in order to minimize rejections and reducing the high toxicities associated with these drugs. Proteomics of biofluids, collected through non-invasive or minimally invasive analysis, e.g., blood or urine, present inherent characteristics that may provide biomarker candidates. The current manuscript reviews biofluids proteomics toward biomarkers discovery that specifically identify subclinical, acute, and chronic immune rejection processes while allowing for the discrimination between cell-mediated or antibody-mediated processes. In time, these biomarkers will lead to patient risk stratification, monitoring, and personalized and more efficient immunotherapies toward higher graft survival and patient quality of life.

Screening of novel biomarkers for acute kidney transplant rejection using DIA‐MS based proteomics

“…In our previous study, the serum proteomic profiling of prostate cancer and benign prostatic hyperplasia patients was analyzed using DIA‐MS, our results showed osteopontin and ceruloplasmin could be effective biomarkers to differentiate aggressive prostate cancer from nonaggressive and benign prostatic condition, which indicated that DIA‐MS has great potential in novel biomarker screening [30]. Proteomics has been applied to screen biomarkers of acute rejection in several studies [11–15]. However, these studies rarely attempt to differentiate high‐risk from low‐risk conditions.…”

“…TCMR can be determined through biopsy in the early stage of rejection while late‐stage biopsy usually presents as ABMR. Studies have showed that the adjustment of immunosuppressive agent has a positive impact on the TCMR treatment [13].…”

“…Researchers have studied potential biomarkers to predict the development of acute rejection for many years and proteomics has been widely used in the study of clinical biomarkers. In the field of transplantation medicine, proteomics based on mass spectrometry also provides a powerful screening tool for rejection‐related biomarkers [13–15]. In recent years, researchers have proposed a nonlabeled quantitative technology based on data independent acquisition (DIA).…”

Biomarker-Development Proteomics in Kidney Transplantation: An Updated Review

“…Despite improvements to immunosuppressive agents that have significantly reduced rejection events, kidney transplant patients are still at risk of allograft rejection (AR) [ 49 ]. Immune graft rejection can be clinically classified as hyperacute, acute, and chronic rejection [ 50 ]. Hyperacute rejection is now quite rare and is caused by antibodies pre-existing in the patient’s bloodstream against antigens on the allograft; it occurs within a few minutes to a few hours after surgery.…”

“…In a prospective, multicenter, phase III diagnostic study enrolling 624 KTRs from different European countries, the use of a 14-peptide panel (previously validated in a small cohort) on urine samples taken immediately before graft biopsy diagnosing TCMR did not predict borderline TCMR and was poorly predictive of acute TCMR, as the sensitivity of the model was 0.66, specificity 0.47, and AUC 0.60 [ 59 ]. These results very likely reflect the limitations associated with the population used to build the predictive model [ 50 ].…”