Efficacy and safety of mycophenolate mofetil and tacrolimus as second-line therapy for patients with autoimmune hepatitis

Background: Predniso(lo)ne, alone or in combination with azathioprine, is the standard of care (SOC) therapy for autoimmune hepatitis (AIH). However, the SOC therapy is poorly tolerated or does not control disease activity in up to 20% of patients. We assessed the efficacy of mycophenolate mofetil (MMF) and tacrolimus as second-line therapy for patients with AIH. Patients and methods: We performed a retrospective study of data (from 19 centres in Europe, the United States, Canada, and China) from 201 patients with AIH who received second-line therapy (121 received MMF and 80 received tacrolimus), for a median of 62 months (range, 6–190 months). Patients were categorized according to their response to SOC. Patients in group 1 (n=108) had a complete response to the SOC, but were switched to second line therapy due to side effects of predniso(lo)ne or azathioprine, whereas patients in group 2 (n=93) had not responded to SOC. Results: There was no significant difference in the proportion of patients with a complete response to MMF (69.4%) vs tacrolimus (72.5%) (P=.639). In group 1, MMF and tacrolimus maintained a biochemical remission in 91.9% and 94.1% of patients, respectively (P=.682). Significantly more group 2 patients given tacrolimus compared to MMF had a complete response (56.5 % vs. 34%, P=.029) There were similar proportions of liver-related deaths or liver transplantation among patients given MMF (13.2%) vs tacrolimus (10.3%) (log-rank, P=.472). Ten patients receiving MMF (8.3%) and 10 patients receiving tacrolimus (12.5%) developed side effects that required therapy withdrawal. Conclusions: Long-term therapy with MMF or tacrolimus was generally well tolerated by patients with AIH. The agents were equally effective in previous complete responders who did not tolerate SOC therapy. Tacrolimus led to a complete response in a greater proportion of previous non-responder patients compared to MMF

Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study

Background Clinical benefits of atezolizumab plus bevacizumab (atezolizumab–bevacizumab) are observed only in a subset of patients with hepatocellular carcinoma and the development of biomarkers is needed to improve therapeutic strategies. The atezolizumab–bevacizumab response signature (ABRS), assessed by molecular biology profiling techniques, has been shown to be associated with progression-free survival after treatment initiation. The primary objective of our study was to develop an artificial intelligence (AI) model able to estimate ABRS expression directly from histological slides, and to evaluate if model predictions were associated with progression-free survival. Methods In this multicentre retrospective study, we developed a model (ABRS-prediction; ABRS-P), which was derived from the previously published clustering-constrained attention multiple instance learning (or CLAM) pipeline. We trained the model fit for regression analysis using a multicentre dataset from The Cancer Genome Atlas (patients treated by surgical resection, n=336). The ABRS-P model was externally validated on two independent series of samples from patients with hepatocellular carcinoma (a surgical resection series, n=225; and a biopsy series, n=157). The predictive value of the model was further tested in a series of biopsy samples from a multicentre cohort of patients with hepatocellular carcinoma treated with atezolizumab–bevacizumab (n=122). All samples in the study were from adults (aged ≥18 years). The validation sets were sampled between Jan 1, 2008, to Jan 1, 2023. For the multicentre validation set, the primary objective was to assess the association of high versus low ABRS-P values, defined relative to cross-validation median split thresholds in the first biopsy series, with progression-free survival after treatment initiation. Additionally, we performed spatial transcriptomics and matched prediction heatmaps with in situ expression profiles. Findings Of the 840 patients sampled, 641 (76%) were male and 199 (24%) were female. Across the development and validation datasets, hepatocellular carcinoma risk factors included alcohol intake, hepatitis B and C virus infections, and non-alcoholic steatohepatitis. Using cross-validation in the development series, the mean Pearson’s correlation between ABRS-P values and ABRS score (mean expression of ABRS genes) was 0·62 (SD 0·09; mean p<0·0001, SD<0·0001). The ABRS-P generalised well on the external validation series (surgical resection series, r=0·60 [95% CI 0·51–0·68], p<0·0001; biopsy series, r=0·53 [0·40–0·63], p<0·0001). In the 122 patients treated with atezolizumab–bevacizumab, those with ABRS-P-high tumours (n=74) showed significantly longer median progression-free survival than those with ABRS-P-low tumours (n=48) after treatment initiation (12 months [95% CI 7–not reached] vs 7 months [4–9]; p=0·014). Spatial transcriptomics showed significantly higher ABRS score, along with upregulation of various other immune effectors, in tumour areas with high ABRS-P values versus areas with low ABRS-P values. Interpretation Our study indicates that AI applied on hepatocellular carcinoma digital slides is able to serve as a biomarker for progression-free survival in patients treated with atezolizumab–bevacizumab. This approach could be used in the development of inexpensive and fast biomarkers for targeted therapies. The combination of AI heatmaps with spatial transcriptomics provides insight on the molecular features associated with predictions. This methodology could be applied to other cancers or diseases and improve understanding of the biological mechanisms that drive responses to treatments