Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.

Introduction: Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors. Methods: We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs. Results: Logistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43). Discussion: Our machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.

Neurological applications of belzutifan in von Hippel-Lindau disease.

Von Hippel-Lindau (VHL) disease is a tumor predisposition syndrome caused by mutations in the VHL gene that presents with visceral neoplasms and growths, including clear cell renal cell carcinoma, and central nervous system manifestations, such as hemangioblastomas of the brain and spine. The pathophysiology involves dysregulation of oxygen sensing caused by the inability to degrade HIFα, leading to the overactivation of hypoxic pathways. Hemangioblastomas are the most common tumors in patients with VHL and cause significant morbidity. Until recently, there were no systemic therapies available for patients that could effectively reduce the size of these lesions. Belzutifan, the first approved HIF-2α inhibitor, has demonstrated benefit in VHL-associated tumors, with a 30% response rate in hemangioblastomas and ~30%-50% reduction in their sizes over the course of treatment. Anemia is the most prominent adverse effect, affecting 76%-90% of participants and sometimes requiring dose reduction or transfusion. Other significant adverse events include hypoxia and fatigue. Overall, belzutifan is well tolerated; however, long-term data on dosing regimens, safety, and fertility are not yet available. Belzutifan holds promise for the treatment of neurological manifestations of VHL and its utility may influence the clinical management paradigms for this patient population.