Antinuclear antibodies may predict the development of immune-related adverse events in asymptomatic patients treated with immune checkpoint inhibitors: results from a single-center cohort.

We aim at investigating the association between subclinical autoimmunity and immune-related adverse events (irAEs) in a cohort of patients treated by immune checkpoint inhibitors for solid metastatic cancer. In the context of an oncology/rheumatology outpatient clinic, we evaluated patients treated with anti-PD-1 or anti-PD-L1. Before treatment, each patient underwent a physical evaluation and a blood sample to identify the presence of a set of autoantibodies. Indeed, all the patients were followed during treatment to identify irAEs and to assess the association with autoantibodies. Fifty-one patients (M/F 16/35; median age 70 years, IQR 16.5) were evaluated; 34.8% of patients showed ANA positivity, 6.5% ENA positivity (anti-SSA), 4.3% Ratest positivity, and 2.1% (one patient) ACPA positivity. During a median period of 21 months (IQR 38.75), 39.2% of patients developed irAEs. Musculo-skeletal manifestations, in particular arthritis, were the most frequent. We found a significant association between the positivity for ANA and the development of irAES (p = 0.03, RR 2.01, 95% CI 1.03-3.92). Furthermore, the progression-free survival was significantly longer in patients developing irAEs compared to those who are not experiencing these events (p = 0.007). This study underlines the potential role of ANA positivity as a predictive biomarker for the development of irAEs.

Application of Machine Learning Models in Systemic Lupus Erythematosus.

Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario.

Autophagy Hijacking in PBMC From COVID-19 Patients Results in Lymphopenia.

Autophagy is a homeostatic process responsible for the self-digestion of intracellular components and antimicrobial defense by inducing the degradation of pathogens into autophagolysosomes. Recent findings suggest an involvement of this process in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the role of autophagy in the immunological mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis remains largely unexplored. This study reveals the presence of autophagy defects in peripheral immune cells from COVID-19 patients. The impairment of the autophagy process resulted in a higher percentage of lymphocytes undergoing apoptosis in COVID-19 patients. Moreover, the inverse correlation between autophagy markers levels and peripheral lymphocyte counts in COVID-19 patients confirms how a defect in autophagy might contribute to lymphopenia, causing a reduction in the activation of viral defense. These results provided intriguing data that could help in understanding the cellular underlying mechanisms in COVID-19 infection, especially in severe forms.

Autophagy Modulation in Lymphocytes From COVID-19 Patients: New Therapeutic Target in SARS-COV-2 Infection.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the novel coronavirus, causing coronavirus disease 2019 (COVID-19). During virus infection, several pro-inflammatory cytokines are produced, leading to the "cytokine storm." Among these, interleukin (IL)-6, tumor necrosis factor-α (TNF-α), and IL-1ß seem to have a central role in the progression and exacerbation of the disease, leading to the recruitment of immune cells to infection sites. Autophagy is an evolutionarily conserved lysosomal degradation pathway involved in different aspects of lymphocytes functionality. The involvement of IL-6, TNF-α, and IL-1ß in autophagy modulation has recently been demonstrated. Moreover, preliminary studies showed that SARS-CoV-2 could infect lymphocytes, playing a role in the modulation of autophagy. Several anti-rheumatic drugs, now proposed for the treatment of COVID-19, could modulate autophagy in lymphocytes, highlighting the therapeutic potential of targeting autophagy in SARS-CoV-2 infection.