MO363CHECKPOINT-INHIBITOR-ASSOCIATED ACUTE KIDNEY INJURY AND MORTALITY: AN OBSERVATIONAL STUDY

Background and Aims

Immune checkpoint inhibitors, approved for the treatment of various types of cancer, are known to cause a unique spectrum of autoimmune-related side effects, including acute kidney injury (AKI). The aim of this study was to describe the incidence, risk factors, renal outcomes, and mortality of AKI in patients receiving checkpoint inhibitors.

Method

Patients receiving checkpoint inhibitors between January 2013 and May 2020 were identified using the Utrecht Patient Oriented Database. AKI was defined as an increase in creatinine of ≥1.5 times the baseline value. Cox proportional hazard regression analysis was used to assess risk factors for AKI and to evaluate the relationship between AKI and mortality. Persistent kidney injury was diagnosed in AKI patients with a final creatinine measurement of >1.3 times the baseline value.

Results

Out of 676 patients receiving checkpoint inhibitors, AKI occurred in 96 (14.2%) patients. Chart review showed that AKI was checkpoint inhibitor-associated in 32 (4.7%) patients. Baseline variables associated with AKI were a primary gynecological malignancy (HR 3.91, 95% CI 1.55 to 9.85), treatment with checkpoint inhibitor ipilimumab (HR 2.31, 95% CI 1.03 to 5.20), and pre-existent use of a diuretic (HR 2.61, 95% CI 1.21 to 5.60), an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker (HR 2.49, 95% CI 1.10 to 5.60), and a proton pump inhibitor (HR 1.69, 95% CI 1.04 to 2.75). In 35.4% of the patients who had developed AKI, persistent kidney dysfunction was observed at the end of follow-up. Patients who developed AKI had a 2.13-fold (95% CI 1.58 to 2.87) increased mortality risk compared to patients who did not develop AKI. Mortality risk was not increased by checkpoint inhibitor-associated AKI (HR 1.11, 95% CI 0.64 to 1.92), but only by AKI related to other causes (HR 2.87, 95% CI 2.04 to 4.04).

Conclusion

Patients receiving checkpoint inhibitors frequently develop AKI, however, checkpoint inhibitor-associated AKI does not seem to increase mortality.

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis

OBJECTIVE

To predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment.

METHODS

Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study.

RESULTS

Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models.

CONCLUSION

Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment.

Identification of self-epitopes recognized by T cells in rheumatoid arthritis demonstrates matrix metalloproteinases as a novel T cell target

OBJECTIVE

To identify novel arthritis-associated and/or cartilage-specific self-epitopes recognized by T cells in patients with rheumatoid arthritis (RA).

METHODS

Human analogs of several self-epitopes recognized in the rat adjuvant arthritis (AA) model (n = 13) were tested for T cell recognition in patients with RA and healthy controls. Recognition was assessed by proliferative activity of peripheral blood mononuclear cells (PBMC). In addition, cytokine production was determined.

RESULTS

Six out of the 13 peptides recognized during AA were also recognized by more than 20% of the RA patients, in contrast to only one out of the 16 control peptides that were not recognized during AA. The highest proliferative responses were to matrix metalloproteinase (MMP)-derived peptides. The response to a MMP-1 epitope was significantly higher in RA patients than in healthy controls. Moreover, this MMP-1 epitope increased interleukin 4 (IL-4) production of RA PBMC and decreased IL-4 production by control PBMC. The proliferative response to a MMP-3 epitope was similar in RA patients and controls; however, the MMP-3 epitope increased IL-4, and concomitantly IL-1beta and tumor necrosis factor-a production of RA PBMC, whereas these cytokines were unaffected in control PBMC.

CONCLUSION

This study shows the presence of immune reactions to MMP-derived T cell epitopes that are associated with RA, suggesting a novel role of MMP in RA.

Blood-induced joint damage: longterm effects in vitro and in vivo

OBJECTIVE

We previously showed that 4-day in vitro exposure of human cartilage to blood, as well as a single experimental joint bleeding in dogs, resulted in a disturbed cartilage matrix turnover lasting at least 2 weeks. We now evaluate the longterm outcome of the adverse in vitro and in vivo effects of blood on cartilage matrix turnover.

METHODS

Human and canine articular cartilage tissue was cultured in the presence of homologous whole blood during 4 days. The in vitro cartilage matrix turnover was analyzed directly after blood exposure or following culture for additional periods of 2, 5, and 10 weeks in the absence of blood. The in vivo longterm effects were determined by injecting autologous blood into the right knee of 12 Beagle dogs. Six dogs were killed shortly after blood injections; the 6 remaining dogs were killed 10 weeks later. Cartilage matrix turnover and the cartilage destructive properties of the synovial tissue were analyzed.

RESULTS

Short term (4 days) in vitro exposure of human or canine cartilage to whole blood inhibited proteoglycan synthesis by more than 98% (day 4), an inhibition which lasted until week 10 (70 and 75% inhibition, respectively). Also the in vivo short term exposure of cartilage to blood induced the adverse changes in cartilage proteoglycan turnover seen shortly after exposure. However, in vivo 10 weeks after the last injection, normalization of cartilage matrix turnover was observed. Synovial inflammation was absent and no destructive activity was found.

CONCLUSION

These data show a discrepancy between the in vitro and in vivo longterm effects of blood on cartilage. A possible explanation for the in vivo recovery after experimental joint bleeding in dogs could be that the observed changes in cartilage only predispose to acute damage but that additional (e.g., mechanical) factors are needed to induce permanent joint damage.