JAK inhibitors and the risk of malignancy: a meta-analysis across disease indications.

OBJECTIVES: To estimate the association of Janus kinase inhibitors (JAKi) with the incidence of malignancy, compared with placebo, tumour necrosis factor (TNF)-α inhibitors (TNFi) and methotrexate. METHODS: Systematic searches of databases were performed, to December 2022, to identify phase II/III/IV randomised clinical trials (RCTs) and long-term extension (LTE) studies of JAKi (tofacitinib, baricitinib, upadacitinib, filgotinib, peficitinib) compared with placebo, TNFi or methotrexate, in adults with rheumatoid arthritis, psoriatic arthritis, psoriasis, axial spondyloarthritis, inflammatory bowel disease or atopic dermatitis. Network and pairwise meta-analyses were performed to estimate incidence rate ratios (IRRs) for malignancy between JAKi and comparators. Bias was assessed using the Cochrane Risk of Bias-2 tool. RESULTS: In 62 eligible RCTs and 16 LTE studies, there were 82 366 person-years of exposure to JAKi, 2924 to placebo, 7909 to TNFi and 1074 to methotrexate. The overall malignancy incidence rate was 1.15 per 100 person-years in RCTs, and 1.26 per 100 person-years across combined RCT and LTE data. In network meta-analyses, the incidence of all malignancies including non-melanomatous skin cancers (NMSCs) was not significantly different between JAKi and placebo (IRR 0.71; 95% CI 0.44 to 1.15) or between JAKi and methotrexate (IRR 0.77; 95% CI 0.35 to 1.68). Compared with TNFi, however, JAKi were associated with an increased incidence of malignancy (IRR 1.50; 95% CI 1.16 to 1.94). Findings were consistent when analysing NMSC only and when analysing combined RCT/LTE data. CONCLUSIONS: JAKi were associated with a higher incidence of malignancy compared with TNFi but not placebo or methotrexate. Cancers were rare events in all comparisons. PROSPERO REGISTRATION NUMBER: CRD42022362630.

COVID-19 admissions and mortality in patients with early inflammatory arthritis: results from a UK national cohort.

OBJECTIVE: To describe the risks and predictors of coronavirus disease 2019 (COVID-19) hospitalization and mortality among patients with early inflammatory arthritis (EIA), recruited to the National Early Inflammatory Arthritis Audit (NEIAA). METHODS: NEIAA is an observational cohort. We included adults with EIA from Feb 2020 to May 2021. Outcomes of interest were hospitalization and death due to COVID-19, using NHS Digital linkage. Cox proportional hazards were used to calculate hazard ratios for outcomes according to initial treatment strategy, with adjustment for confounders. RESULTS: From 14 127 patients with EIA, there were 143 hospitalizations and 47 deaths due to COVID-19, with incidence rates per 100 person-years of 0.93 (95% CI 0.79, 1.10) for hospitalization and 0.30 (95% CI 0.23, 0.40) for death. Increasing age, male gender, comorbidities and ex-smoking were associated with increased risk of worse COVID-19 outcomes. Higher baseline DAS28 was not associated with COVID-19 admissions [confounder adjusted hazard ratio (aHR) 1.10; 95% CI 0.97, 1.24] or mortality (aHR 1.11; 95% CI 0.90, 1.37). Seropositivity was not associated with either outcome. Higher symptom burden on patient-reported measures predicted worse COVID-19 outcomes. In unadjusted models, CS associated with COVID-19 death (HR 2.29; 95% CI 1.02, 5.13), and SSZ monotherapy associated with COVID-19 admission (HR 1.92; 95% CI 1.04, 3.56). In adjusted models, associations for CS and SSZ were not statistically significant. CONCLUSION: Patient characteristics have stronger associations with COVID-19 than the initial treatment strategy in patients with EIA. An important limitation is that we have not looked at treatment changes over time.

Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours.

AIMS: To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphocytes (TILs) in tissue samples of testicular germ cell tumours and to assess whether the TIL counts correlate with relapse status of the patient. METHODS: TILs were manually annotated in 259 tumour regions from 28 whole-slide images (WSIs) of H&E-stained tissue samples. A deep learning algorithm was trained on half of the regions and tested on the other half. The algorithm was further applied to larger areas of tumour WSIs from 89 patients and correlated with clinicopathological data. RESULTS: A correlation coefficient of 0.89 was achieved when comparing the algorithm with the manual TIL count in the test set of images in which TILs were present (n=47). In the WSI regions from the 89 patient samples, the median TIL density was 1009/mm2. In seminomas, none of the relapsed patients belonged to the highest TIL density tertile (>2011/mm2). TIL quantifications performed visually by three pathologists on the same tumours were not significantly associated with outcome. The average interobserver agreement between the pathologists when assigning a patient into TIL tertiles was 0.32 (Kappa test) compared with 0.35 between the algorithm and the experts, respectively. A higher TIL density was associated with a lower clinical tumour stage, seminoma histology and lack of lymphovascular invasion. CONCLUSIONS: Deep learning-based image analysis can be used for detecting TILs in testicular germ cell cancer more objectively and it has potential for use as a prognostic marker for disease relapse.