Serious immune-related upper gastrointestinal toxicity of immune checkpoint inhibitors: a multicenter case series.

BACKGROUND: Immune checkpoint inhibitors (ICI) improve the prognosis of many cancers but cause immune-related adverse events (IrAEs). Limited data are available on upper gastrointestinal (UGI) IrAEs. We describe the clinical characteristics, prognosis, and efficacy of medical therapy in patients with UGI IrAEs. METHODS: This is a retrospective, multicenter cohort study of patients with UGI symptoms and moderate to severe endoscopic UGI lesions, occurring after ICI. Efficacy of induction medical therapy and at the most recent follow-up was assessed. RESULTS: Forty patients were included; of these, 34 (85%) received anti-PD(L)1, either alone (n = 24) or combined with anti CTLA-4 (n = 10). Eighteen patients (45%) had concomitant enterocolitis. All patients had severe endoscopic lesions (erosions, ulcerations, hemorrhage, or necrotic lesions). Three patients who received an inefficient initial medical treatment had a complicated course: One patient died of enterocolitis, one had a pneumomediastinum, and one developed an ulcerated stricture of the pylorus. Thirty-five patients (88%) were treated with corticosteroids; 28 patients (80%) responded, and 20 (57%) reached clinical remission. Eight patients were treated with infliximab, and six responded (75%). After a median follow-up of 11 months, 36 patients (90%) were in corticosteroid-free clinical remission for their UGI symptoms. Endoscopic lesions persisted in 68% of patients. CONCLUSIONS: ICI cause severe UGI IrAEs, which are associated with enterocolitis in approximately half of the patients. Most patients with UGI IrAEs respond to corticosteroids or infliximab. These data support the recommendation to treat these patients without delay and in the same way as those with enterocolitis.

Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.

OBJECTIVE: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools. DESIGN: We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes. RESULTS: Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated 'DGMuneS', outperformed Immunoscore when used in estimating patients' prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk. CONCLUSION: These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients' prognosis.