Abstract
Introduction:
Immune checkpoint inhibitor-associated acute interstitial nephritis presents significant clinical challenges. There are no reliable non-invasive biomarkers and kidney biopsy remains the gold standard for diagnosis. Prior studies have shown that urinary C-X-C-motif ligand 9 (CXCL9) is upregulated in patients with acute interstitial nephritis. However, its utility, specifically in patients with cancer treated with immune checkpoint inhibitors, is not well-understood.
Methods:
We used proteomics followed by sandwich immunoassay to analyze urinary proteins among a multicenter cohort of prospectively enrolled participants with and without immune checkpoint inhibitor-associated acute interstitial nephritis.
Results:
Among 79 participants receiving immune checkpoint inhibitors, proteomics identified urine CXCL9 as the top-performing urinary biomarker differentiating 38 patients with biopsy-proven acute interstitial nephritis from other forms of acute kidney injury. We validated these results using immunoassay in an expanded cohort of 116 patients, observing higher CXCL9 levels in immune checkpoint inhibitor-associated acute interstitial nephritis compared to several control groups. Urinary CXCL9 was strongly associated with immune checkpoint inhibitor-associated acute interstitial nephritis, with a receiver operating characteristic curve of 0.84, inter quartile range [0.74, 0.93] when compared to other forms of acute kidney injury, and an even higher discrimination when compared with all control groups (0.90, [0.83–0.96]).
Conclusions:
Urinary CXCL9 demonstrated high discrimination for differentiating acute interstitial nephritis from other forms of acute kidney injury in participants on immune checkpoint inhibitor therapy. Our findings demonstrate the significant potential of this biomarker for non-invasive diagnosis of immune checkpoint inhibitor-associated acute interstitial nephritis.
Keywords: immune checkpoint inhibitor, nephritis, biomarker, chemokine, interstitial nephritis, onconephrology
Lay Summary
Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment; however, ICI-induced activation of the immune system is associated with ICI-associated acute kidney injury (AKI), which most commonly presents as acute interstitial nephritis (ICI-AIN) on kidney histopathology. There is a critical need to identify non-invasive markers to distinguish ICI-AIN from other causes of AKI, as making this distinction has important treatment implications. Currently, kidney biopsy is the only way to reliably make this distinction. In this multicenter study, using prospectively-collected urine samples from ICI-treated patients with biopsy-proven ICI-AIN, non-AIN-AKI, and no AKI, we tested a panel of immune proteins using highly multiplexed proteomics and confirmed the findings using immunoassay. We found that CXCL9 reliably differentiated ICI-AIN from controls with high accuracy. The future development of clinically available assays for detecting CXCL9 may aid in the rapid diagnosis of ICI-AIN, thereby improving care of patients who experience AKI while on ICI therapy.
INTRODUCTION
Immune checkpoint inhibitor (ICI) therapy has revolutionized the care of patients with cancer; however, in the first year following treatment initiation, 15%‒25% of patients on ICIs develop acute kidney injury (ICI-AKI) from a variety of etiologies, including ICI-associated acute interstitial nephritis (ICI-AIN), acute tubular injury (ATI) from sepsis and nephrotoxins, prerenal azotemia, and glomerular pathologies.1,2 While the treatment of ICI-AIN requires holding ICIs and initiating corticosteroids, the management of ICI-AKI from other causes focuses on supportive care. A missed ICI-AIN diagnosis has the potential to lead to permanent kidney damage, whereas an incorrect diagnosis of ICI-AIN may lead to holding ICIs and the inappropriate use of corticosteroids, which may lead to progression of the underlying malignancy and inferior cancer-related outcomes.3 Differentiating ICI-AIN from other causes of ICI-AKI, particularly ATI, remains challenging because clinical features, urine microscopy, laboratory testing, and conventional imaging are neither sensitive nor specific for the diagnosis of ICI-AIN.1,4 Kidney biopsy is the gold-standard, but may delay care and cause complications in patients with cancer.5,6 As such, many ICI-AIN cases are diagnosed retrospectively based on renal recovery in response to ICI cessation and empiric corticosteroids.7,8 Thus, the identification of accurate, noninvasive diagnostic biomarkers for ICI-AIN remains an important unmet need. Using urine samples from patients with cancer who had biopsy-proven ICI-AIN, non-AIN-AKI, and no AKI, we identified a urine protein biomarker for ICI-AIN via a targeted proteomic analysis of immune proteins, and confirmed our findings using sandwich immunoassay.
METHODS
We used proteomics followed by sandwich immunoassay to analyze and compare proteins in the urine of prospectively enrolled ICI-treated participants from 3 centers who had biopsy-proven ICI-AIN, non-AIN-AKI, and no AKI, as well as non-ICI treated cancer patients without AKI (see Supplemental Methods for additional details).
RESULTS
We included 116 participants from 3 U.S. cancer centers (Brigham and Women’s Hospital [BWH]/Dana-Farber Cancer Institute [DFCI], Massachusetts General Hospital [MGH], and Smilow Cancer Center at Yale-New Haven Hospital [YNHH], Supplementary Figure S1). Of these, 38 had biopsy-confirmed ICI-AIN, 36 had non-AIN-AKI (including 18 with biopsy confirmation), 25 were ICI-treated cancer patients without AKI, and 17 had cancer but were treated with other anti-cancer agents. Patient characteristics of the expanded cohort are shown in Table 1.
Table 1.
Baseline Characteristics
| Participant Characteristics | ICI-AIN (n = 38) | Non-AIN-AKI (n = 36) | ICI-treated without AKI (n = 25) | Non-ICI treated patients with cancer (n = 17) |
|---|---|---|---|---|
| Age at collection, years, median (IQR) | 66 (59–74) | 68 (64–74) | 69 (62–77) | 64 (56–71) |
| Male, n (%) | 23 (82.1) | 14 (51.9) | 14 (56.0) | 6 (35.3) |
| Race, n (%) | ||||
| White | 31 (81.6) | 24 (88.9) | 25 (100) | 17 (100) |
| Asian | 3 (7.9) | 0 (0) | 0 (0) | 0 (0) |
| Black | 2 (5.3) | 2 (7.4) | 0 (0) | 0 (0) |
| Other/Unknown | 2 (5.3) | 1 (3.7) | 0 (0) | 0 (0) |
| Comorbidities, n (%) | ||||
| Hypertension | 25 (65.8) | 16 (59.3) | 12 (48.0) | 8 (47.1) |
| Diabetes | 7 (18.4) | 9 (33.3) | 2 (8.0) | 0 (0) |
| CHF | 1 (2.6) | 2 (7.4) | 0 (0) | 1 (5.9) |
| Cirrhosis | 0 (0) | 2 (7.4) | 0 (0) | 1 (5.9) |
| Body mass index, median (IQR) | 26.7 (24.1–31.4) | 29.1 (24.9–33.3) | 25.6 (23.0–29.1) | 24.7 (23.4–32.8) |
| Baseline SCr prior to receiving therapy, mg/dl, median (IQR) | 0.89 (0.76–1.10) | 1.14 (0.95–1.26) | 0.88 (0.81–0.99) | 0.88 (0.81–1.03) |
| Baseline eGFR (ml/min per 1.73 m2), median (IQR) | 89 (73–98) | 63 (51–78) | 87 (74–95) | 81 (71–94) |
| eGFR Categories, n (%) | ||||
| ≥90 | 19 (50.0) | 5 (18.5) | 9 (36.0) | 6 (35.3) |
| 60–89 | 13 (34.2) | 9 (33.3) | 13 (52.0) | 8 (47.1) |
| 45–59 | 1 (2.6) | 9 (33.3) | 2 (8.0) | 3 (17.6) |
| <45 | 5 (13.2) | 4 (14.8) | 1 (4.0) | 0 (0) |
| Lab values at urine collection | ||||
| Serum creatinine, mg/dl, median (IQR) | 2.09 (1.57–2.50) | 1.67 (1.45–2.07) | 0.87 (0.75–0.95) | 0.88 (0.71–1.03) |
| Blood urea nitrogen, mg/dl, median (IQR) | 33 (23–49) | 32 (22–45) | 16 (15–19) | 11 (15–25) |
| Urine dipstick protein, >2+, median (IQR) | 6 (15.8) | 5 (18.5) | 0 (0) | 0 (0) |
| Leukocyturia >5 WBCs or ≥2+ (%) | 11 (28.9) | 4 (11.1) | 0 (0) | 0 (0) |
| Extrarenal irAE, n (%) | 7 (18.4) | 5 (18.5) | 3 (12.0) | 0 (0) |
| Malignancy, n (%) | ||||
| Lung | 12 (31.6) | 6 (22.2) | 6 (24.0) | 3 (17.6) |
| Bladder/Urothelial | 5 (13.2) | 1 (3.7) | 0 (0) | 4 (23.5) |
| Head/Neck | 2 (5.3) | 2 (7.4) | 1 (4.0) | 2 (22.2) |
| Melanoma | 8 (21.1) | 3 (11.1) | 17 (68.0) | 0 (0) |
| Other | 11 (28.9) | 15 (55.6) | 1 (4.0) | 4 (23.5) |
| PPI, n (%) | 19 (50.0) | 11 (40.7) | 7 (28.0) | 7 (41.2) |
| Concomitant nephrotoxic chemotherapy, n (%) | ||||
| Cisplatin | 2 (5.3) | 3 (11.1) | 0 (0) | 9 (52.9) |
| VEGFi/TKI | 0 (0) | 3 (11.1) | 0 (0) | 8 (47.1) |
| Pemetrexed | 3 (7.9) | 5 (18.5) | 4 (16.0) | 3 (17.6) |
| Carboplatin | 5 (13.2) | 3 (11.1) | 2 (8.0) | 0 (0) |
| Other | 2 (5.3) | 3 (11.1) | 1 (4.0) | 0 (0) |
| ICI Class, n (%) | ||||
| Anti-CTLA-4 | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
| Anti-PD-1 | 27 (71.1) | 15 (55.6) | 19 (76.0) | 0 (0) |
| Anti-PD-L1 | 4 (10.5) | 6 (22.2) | 1 (4.0) | 0 (0) |
| Combination Immunotherapy | 7 (18.4) | 6 (22.2) | 5 (20.0) | 0 (0) |
Estimated glomerular filtration rate (eGFR) was determined using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Of the 36 cases of non-AIN ICI-AKI, 18 had biopsy-confirmed ATI, 16 were clinically adjudicated pre-renal/ATI, and 2 had urinary tract obstruction. Missing values for dipstick proteinuria included 3 participants with non-AIN ICI-AKI, 8 participants with ICI-AIN, and 14 ICI non-AKI control participants for whom urinalyses were unavailable. Combination immunotherapy included either anti-CTLA-4 and anti-PD-1/PD-L1 combination, or anti-PD-1 and anti-LAG 3 combination therapy. Among ICI-treated patients without AKI, the median time from first dose of ICI to sample collection was 10 weeks (IQR 8–27).
Abbreviations: AIN, acute interstitial nephritis; AKI, acute kidney injury; CHF, congestive heart failure; CTLA-4, cytotoxic T-lymphocyte associated protein-4; ICI, immune checkpoint inhibitor; IQR, interquartile range; eGFR, estimated glomerular filtration rate; irAE, immune-related adverse events; PD-1, programmed cell death-1; PDL1, programmed cell death-ligand 1; PPI, proton pump inhibitor; SCr, serum creatinine; VEGFi/TKI, vascular endothelial growth factor inhibitor/tyrosine kinase inhibitor; WBC, white blood cell
We performed highly multiplexed urine proteomics using two panels, the (Olink Target 96 Immuno-Oncology Panel and Target 96 Inflammation Panel,) in a subset of 79 participants (Supplementary Tables S1–S4). Of the 140 proteins analyzed, 6 proteins had false discovery rates of <0.05 (Benjamini-Hochberg Q value) (Figure 1a, Supplementary Figures S2–S3, Supplementary Table S5). Among these, the interferon-γ-induced chemokines, particularly CXC-motif ligand (CXCL) 9 had the strongest association with ICI-AIN (Figure 1b, all P < 0.001). CXCL9 was >10-fold higher in participants with ICI-AIN compared to non-AIN-AKI and patients without AKI (Figure 1b). We used a modified sandwich electrochemiluminescence immunoassay to quantify CXCL9 in 99 ICI-treated patients and 17 patients with cancer receiving non-ICI anti-neoplastic therapies. We noted a strong correlation between CXCL9 measured by urine proteomics and by immunoassay (correlation coefficient = 0.94) (Supplementary Figure S4). Median urine CXCL9 levels were higher in those with biopsy-proven ICI-AIN (urine CXCL9:creatinine, 1494 [interquartile range (IQR) 385, 4570] ng/g) than in those with non-AIN-AKI (61 [IQR 19, 220]), in ICI-treated patients without AKI (11 [IQR 4, 23]) and in non-ICI-treated patients with cancer (9 [IQR 3, 15]) (Figure 1c). Clinically-adjudicated patients with non-AIN-AKI (Supplementary Table S6) had similar median urine CXCL9 levels (41 [IQR 9,171]) compared to patients with biopsy-proven non-AIN-AKI (74 [IQR 42,427]). When comparing ICI-AIN to non-AIN AKI, urine CXCL9 had an AUC of 0.84 (0.74, 0.93) (Figure 1d). In a sensitivity analysis, comparing ICI-AIN to all controls, we observed an AUC of 0.90 (0.83, 0.96) (Supplementary Figure S5). Similar results were obtained when we used CXCL9 values without indexing to urine creatinine (Supplementary Figure S4b). The optimal cutoff using the Youden method in this cohort was 269.5 ng/g which showed a sensitivity and specificity of 82% and 85%, respectively (Supplementary Table S7).9
Figure 1. Urinary CXCL9 is upregulated among participants with ICI-AIN.
A) Volcano plot demonstrating associations of proximity extension assay-based measurement of urine proteins with ICI-AIN diagnosis. Proteins with Q values <0.05 using the Benjamini-Hochberg procedure are highlighted in red; B) Urinary CXCL9, CXCL10, and CXCL11 levels as measured by urine proteomics in ICI-AIN, non-AIN-AKI, and ICI-treated participants without AKI. Measurements are reported in NPX units which are log2 based. Boxes represent interquartile range and horizontal lines within boxes represent the median. Trend test p-value < 0.001 for ICI-AIN; C) CXCL9 quantification by modified sandwich electrochemiluminescence immunoassay in ICI-AIN, non-AIN-AKI, ICI-treated participants without AKI, and non-ICI-treated controls with cancer indexed to urine creatinine; D) Area under receiver operating characteristics curve of urine CXCL9 for AIN diagnosis.
DISCUSSION
In a multicenter cohort of participants treated with ICIs, we used proteomics with measurement of 140 proteins in the urine to show that interferon-γ-induced chemokines, specifically CXCL9, CXCL10 and CXCL11, most reliably differentiated ICI-AIN from non-AIN AKI and ICI-treated controls. We confirmed this finding for CXCL9 using sandwich immunoassay, and we found that this biomarker provided high discrimination, sensitivity, and specificity for ICI-AIN.
CXCL9/10/11 are induced by interferon-γ and promote lymphocyte recruitment at sites of inflammation by binding to the CXCR3 receptor. In the non-ICI setting, we previously showed that urine CXCL9 differentiates AIN from other causes of AKI, correlates with the severity of histological features of AIN, and likely originates in the kidneys.10 However, less is understood about urine CXCL9 exclusively among ICI-treated patients with cancer, who are expected to have higher level of inflammatory markers even in the absence of AIN. Here, we show that CXCL9 is indeed strongly associated with ICI-AIN, and that it showed high discrimination for differentiating ICI-AIN from non-AIN AKI and even higher when compared with all controls. The optimal cutoff in ICI setting for AIN diagnosis was higher than the optimal cut-off in non-ICI setting, indicating the need to re-establish cut-offs for AIN biomarkers, specifically in the ICI setting.
Other groups have also reported higher expression of interferon-γ-induced chemokines in kidney biopsy specimens of ICI-treated patients with ICI-AIN compared with controls.11–13 One recent, single-center study of 22 patients with ICI-AIN and 27 with non-AIN-AKI also revealed CXCL9 as a top performing marker, along with interleukin-5 and Fas. However, beyond the initial proteomics assay, these results were not investigated in additional groups of control patients, including those taking ICIs with stable kidney function or in patients taking other anti-neoplastic therapies.13 Another recent study showed that a rise in interferon-γ-induced chemokines occurs within 1–2 weeks after starting ICI therapy among patients who later experience irAEs, including AIN.14 Taken together, these findings suggest that CXCL9 could be implemented to diagnose ICI-AIN and potentially obviate the need for kidney biopsy in a subset of patients; however, the diagnostic cut-off for AIN may need to be higher in ICI-treated patients, thereby increasing specificity as well.15 In contrast to our prior work, where we did not find elevated CXCL10/11 in cases of AIN compared to controls, our findings of higher levels of CXCL10/11 suggest that that patients with ICI-AIN may respond to interferon-γ-targeted treatments such as JAK/STAT inhibition, although the potential risk of cancer progression would need to be considered.
Strengths of our study include the use of biopsy-confirmed ICI-AIN cases, rigorous, prospective biosample collection, and validation of proteomics findings using immunoassay. However, our study also has some limitations. First, AIN was defined based on pathology reports. Some studies have shown poor inter-rater agreement for renal histological diagnosis and features; to mitigate the potential for misclassification, we enrolled participants with moderate-to-severe ICI-AIN rather than participants with mild or focal AIN.16,17 Second, the control participants without AKI selected for Olink had no concurrent irAEs nor did they receive concomitant nephrotoxic chemotherapies, whereas the participants with ICI-AIN and non-AIN-AKI had a wide array of malignancies, sometimes received concomitant nephrotoxic treatments, and often had extrarenal irAEs. These findings may limit generalizability, since >80% of patients will experience an irAE following treatment with ICIs; however, this also minimizes the possibility that participants might have had subclinical AIN, which may occur with concurrent irAEs.18 Additionally, the CXCL9 assay used in this study was for research purposes only rather than clinically validated assay and therefore may be susceptible to batch-to-batch variability, making it difficult to directly compare these results to prior studies. Finally, though this is the largest biomarker study focusing on ICI-AIN and including comparison against several control groups with cancer, we acknowledge that this work must be replicated in larger, multicenter cohorts.
In conclusion, we found that interferon-γ-induced chemokines CXCL9/10/11 were the top performing biomarkers of ICI-AIN using urine proteomics, with confirmation of CXCL9 by sandwich immunoassay. Ultimately, the approval of future, clinically available assays for detection of these urinary biomarkers may aid in the rapid diagnosis of ICI-AIN.
Supplementary Material
Supplementary material is available online at www.kidney-international.org.
FUNDING
This study was supported by NIDDK awards, R01DK128087 (DGM), R01DK130839 (MES), R01DK140717 (MEG and DGM), K23 DK125672 (SG), the DFCI Wong Foundational Award for Translational Research (SG), and the American Kidney Fund Clinical Scientist in Nephrology Fellow program (KM). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
CONFLICTS OF INTEREST/DISCLOSURES
SG received research funding from NIDDK K23DK125672, NIDDK R03DK141708, BTG International, Dana-Farber Cancer Institute’s Wong Foundation, Janssen, and AstraZeneca. SG is a consultant for Secretome, Proletariat Therapeutics, Alexion, MediBeacon and Mersana Therapeutics. KM is funded by the American Kidney Fund Clinical Scientist in Nephrology Fellow Program. DGM and CRP are named co-inventors on a pending patent, “Methods and Systems for Diagnosis of Acute Interstitial Nephritis.” DGM and CRP are founders of the diagnostics company Predict AIN, LLC. CRP serves as a member of the advisory board of and owns equity in RenalytixAI. DGM serves as consultant for BioHaven, Inc. HK receives research funding from Merck, Bristol-Myers Squibb, Apexigen, and Pfizer. HK receives personal fees from Iovance, Merck, Chemocentryx, Bristol-Myers Squibb, Signatero, Gigagen, GI reviewers, Pilant Therapeutics, Esai, Invox, Wherewolf, and Teva. EB serves as a consultant/advisory board member for Obsidian, Anaveon, Merck and Werewolf pharmaceuticals. Clinical trial support from Lilly, Novartis, Partners therapeutics, Genentech and BVD. MES declares research funding from Angion, Otsuka, Gilead, Cabaletta, Novartis, EMD-Serono, Roche/Genetech, Merck, and has served on scientific advisory boards or had scientific consulting agreements with Vera, Travere, Calliditas, Mallinckrodt, Novartis, Otsuka, Relay TX, and is a data safety monitoring committee member for Alpine Immune Sciences/Vertex.
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DATA STATEMENT
De-identified biomarker data may be available upon the execution of appropriate materials and data use agreements.
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Supplementary Materials
Data Availability Statement
De-identified biomarker data may be available upon the execution of appropriate materials and data use agreements.