BackgroundPatients with cancer who are infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are more likely to develop severe illness and die compared with those without cancer. The impact of immune checkpoint inhibition (ICI) on the severity of COVID-19 illness is unknown. The aim of this study was to investigate whether ICI confers an additional risk for severe COVID-19 in patients with cancer.MethodsWe analyzed data from 110 patients with laboratory-confirmed SARS-CoV-2 while on treatment with ICI without chemotherapy in 19 hospitals in North America, Europe and Australia. The primary objective was to describe the clinical course and to identify factors associated with hospital and intensive care (ICU) admission and mortality.FindingsThirty-five (32%) patients were admitted to hospital and 18 (16%) died. All patients who died had advanced cancer, and only four were admitted to ICU. COVID-19 was the primary cause of death in 8 (7%) patients. Factors independently associated with an increased risk for hospital admission were ECOG ≥2 (OR 39.25, 95% CI 4.17 to 369.2, p=0.0013), treatment with combination ICI (OR 5.68, 95% CI 1.58 to 20.36, p=0.0273) and presence of COVID-19 symptoms (OR 5.30, 95% CI 1.57 to 17.89, p=0.0073). Seventy-six (73%) patients interrupted ICI due to SARS-CoV-2 infection, 43 (57%) of whom had resumed at data cut-off.InterpretationCOVID-19–related mortality in the ICI-treated population does not appear to be higher than previously published mortality rates for patients with cancer. Inpatient mortality of patients with cancer treated with ICI was high in comparison with previously reported rates for hospitalized patients with cancer and was due to COVID-19 in almost half of the cases. We identified factors associated with adverse outcomes in ICI-treated patients with COVID-19.

Immune checkpoint inhibitors have improved survival in numerous advanced malignancies, but are associated with a number of immune-related adverse events, including endocrinopathies. Endogenous Cushing's syndrome (CS) is a rare disorder resulting from exposure to high levels of circulating cortisol. CS can be caused either by adrenal cortex tumors or hyperplasia or by pituitary or extra-pituitary tumors over-secreting ACTH (known as ACTH-dependent CS). We report the first case of transient ACTH-dependent CS, which appeared after combined ipilimumab and nivolumab therapy. Our patient presented typical clinical features of CS after three infusions of combined therapy, high serum and daily urinary free cortisol, and high serum ACTH levels. Pituitary MRI showed an enlargement of the pituitary gland suggesting ACTH secretion of pituitary origin, which was confirmed by inferior petrosal sinus sampling. The pituitary findings were preceded by thyroiditis. The evolution was characterized by spontaneous CS regression and subsequent appearance of severe corticotroph deficiency consistent with destructive hypophysitis. Immunotherapy is a novel cause of CS.

Purpose: Less than 50% of patients with melanoma respond to anti–programmed cell death protein 1 (anti–PD-1), and this treatment can induce severe toxicity. Predictive markers are thus needed to improve the benefit/risk ratio of immune checkpoint inhibitors (ICI). Baseline tumor parameters such as programmed death ligand 1 (PD-L1) expression, CD8+ T-cell infiltration, mutational burden, and various transcriptomic signatures are associated with response to ICI, but their predictive values are not sufficient. Interaction between PD-1 and its main ligand, PD-L1, appears as a valuable target of anti–PD-1 therapy. Thus, instead of looking at PD-L1 expression only, we evaluated the predictive value of the proximity between PD-1 and its neighboring PD-L1 molecules in terms of response to anti–PD-1 therapy. Experimental Design: PD-1/PD-L1 proximity was assessed by proximity ligation assay (PLA) on 137 samples from two cohorts (exploratory n = 66 and validation n = 71) of samples from patients with melanoma treated with anti–PD-1±anti–CTLA-4. Additional predictive biomarkers, such as PD-L1 expression (MELscore), CD8+ cells density, and NanoString RNA signature, were also evaluated. Results: A PD-1/PD-L1 PLA model was developed to predict tumor response in an exploratory cohort and further evaluated in an independent validation cohort. This score showed higher predictive ability (AUC = 0.85 and 0.79 in the two cohorts, respectively) for PD-1/PD-L1 PLA as compared with other parameters (AUC = 0.71–0.77). Progression-free and overall survival were significantly longer in patients with high PLA values (P = 0.00019 and P < 0.0001, respectively). Conclusions: The proximity between PD-1 and PD-L1, easily assessed by this PLA on one formalin-fixed paraffin-embedded section, appears as a new biomarker of anti–PD-1 efficacy.