Urine proteomics defines an immune checkpoint-associated nephritis signature - Cassian Yee; Jamie S Lin; James P Long; Shailbala Singh; Yanlan Dong (Journal for ImmunoTherapy of Cancer)
Immune checkpoint inhibitor (ICI) therapy is a cornerstone treatment for many cancers, but it can induce severe immunotoxicity, including acute interstitial nephritis (AIN). Currently, kidney biopsy is required to differentiate ICI-AIN from other causes of acute kidney injury (AKI). However, this invasive approach can lead to morbidity, delayed glucocorticoid treatment for patients with AIN, and unnecessarily prolonged suspension of ICI therapy in non-AIN patients. Delayed or incorrect diagnosis of ICI-AIN is particularly detrimental, as over 50% of patients are at risk of permanent renal damage. Thus, there is an urgent need for non-invasive biomarkers that can rapidly and accurately distinguish ICI-AIN from other causes of AKI.The urine and plasma proteome contain actively secreted proteins that provide real-time insights into dynamic physiological processes. However, identification of effective biomarkers of disease using established technologies such as proximity ligation assays (PLA) and bead-based immunoassays is challenging due to their limited sensitivity and loss of precision in multiplex analysis.To address this, we employed cutting-edge NUcleic acid Linked Immuno-Sandwich Assay (NULISA) technology to measure protein expression in urine and plasma samples from AKI patients undergoing ICI therapy. NULISA offers 10,000-fold greater precision than PLA, enabling quantification of over 200 inflammatory proteins with unprecedented precision. Our analysis revealed that urine was more sensitive and specific than plasma in distinguishing ICI-AIN from non-AIN cases. Pathway analyses highlighted the involvement of JAK-STAT and tumor necrosis factor (TNF) signaling in ICI-AIN pathogenesis. We identified several novel urine biomarkers, including IL-5, Fas, TNFSF4, CD274, IL-20, TNFSF15, TSLP, TREM1 and CCL1 while confirming previously reported markers such as CXCL9 and TNF-α. Using statistical and machine learning methods, we constructed a novel urine biomarker signature—IL-5+Fas—that achieved an area under the curve of 0.94 for diagnosing ICI-AIN.By leveraging high-sensitivity proteomics, we developed a non-invasive strategy for diagnosing ICI-AIN. This approach will enable earlier intervention to mitigate immunotoxicity, preservation of antitumor efficacy of ICI therapy in non-AIN patients, and safe rechallenge of ICI therapy in patients previously treated for ICI-AIN.