The transcriptomic G1-G6 signature of hepatocellular carcinoma in an Asian population Association of G3 with microvascular invasion

10.1097/MD.0000000000005263Medicine (United States)9547e526

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

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. 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. Finally, we performed spatial transcriptomics and matched prediction heatmaps with in situ expression profiles. 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 r=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. 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. Institut National du Cancer, Fondation ARC, China Scholarship Council, Ligue Contre le Cancer du Val de Marne, Fondation de l'Avenir, Ipsen, and Fondation Bristol Myers Squibb Pour la Recherche en Immuno-Oncologie

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

International and multicenter real-world study of sorafenib-treated patients with hepatocellular carcinoma under dialysis

Background &amp; Aims: Information on safety and efficacy of systemic treatment in patients with hepatocellular carcinoma (HCC) under dialysis are limited due to patient exclusion from clinical trials. Thus, we aimed to evaluate the rate, prevalence, tolerability, and outcome of sorafenib in this population. Methods: We report a multicenter study comprising patients from Latin America and Europe. Patients treated with sorafenib were enrolled; demographics, dose modifications, adverse events (AEs), treatment duration, and outcome of patients undergoing dialysis were recorded. Results: As of March 2018, 6156 HCC patients were treated in 44 centres and 22 patients were concomitantly under dialysis (0.36%). The median age was 65.5&nbsp;years, 40.9% had hepatitis C, 75% had Child-Pugh A, and 85% were Barcelona Clinic Liver Cancer-C. The median time to first dose modification, treatment duration and overall survival rate were 2.4&nbsp;months (interquartile ranges [IQR], 0.8-3.8), 10.8&nbsp;months (IQR, 4.5-16.9), and 17.5&nbsp;months (95% CI, 7.2-24.5), respectively. Seventeen patients required at least 1 dose modification. The main causes of first dose modification were asthenia/worsening of Eastern Cooperative Oncology Group-Performance Status and diarrhoea. At the time of death or last follow-up, four patients were still on treatment and 18 had discontinued sorafenib: 14 were due to tumour progression, 2 were sorafenib-related, and 2 were non-sorafenib-related AE. Conclusions: The outcomes observed in this cohort seem comparable to those in the non-dialysis population. Thus, to the best of our knowledge, this is the largest and most informative dataset regarding systemic treatment outcomes in HCC patients undergoing dialysis

Molecular Classification of Hepatocellular Adenoma Associates with Risk Factors, Bleeding, and Malignant Transformation

Background & Aims Hepatocellular adenomas (HCAs) are benign liver tumors that can be assigned to molecular subtypes based on inactivating mutations in hepatocyte nuclear factor 1A, activating mutations in \u3b2-catenin, or activation of inflammatory signaling pathways. We aimed to update the classification system for HCA and associate the subtypes with disease risk factors and complications. Methods We analyzed expression levels of 20 genes and sequenced exon regions of 8 genes (HNF1A, IL6ST, CTNNB1, FRK, STAT3, GNAS, JAK1, and TERT) in 607 samples of 533 HCAs from 411 patients, collected from 28 centers mainly in France from 2000 and 2014. We performed gene expression profile, RNA sequence, whole-exome and genome sequence, and immunohistochemical analyses of select samples. Molecular data were associated with risk factors, histopathology, bleeding, and malignant transformation. Results Symptomatic bleeding occurred in 14% of the patients (85% of cases were female, median age, 38 years); 7% of the nodules were borderline between HCA and hepatocellular carcinoma, and 3% of patients developed hepatocellular carcinoma from HCA. Based on molecular features, we classified HCA into 8 subgroups. One new subgroup, composed of previously unclassified HCA, represented 4% of HCAs overall and was associated with obesity and bleeding. These tumors were characterized by activation of sonic hedgehog signaling, due to focal deletions that fuse the promoter of INHBE with GLI1. Analysis of genetic heterogeneity among multiple HCAs, from different patients, revealed a molecular subtype field effect; multiple tumors had different mutations that deregulated similar pathways. Specific molecular subtypes of HCA associated with various HCA risk factors, including imbalances in estrogen or androgen hormones. Specific molecular subgroup of HCA with \u3b2-catenin and sonic hedgehog activation associated with malignant transformation and bleeding, respectively. Conclusions Using sequencing and gene expression analyses, we identified a subgroup of HCA characterized by fusion of the INHBE and GLI1 genes and activation of sonic hedgehog pathway. Molecular subtypes of HCAs associated with different patients\u2019 risk factors for HCA, disease progression, and pathology features of tumors. This classification system might be used to select treatment strategies for patients with HCA

摂津国 山口村 紙漉人足駄賃切手 銀5匁

日本銀行金融研究所所蔵藩札等資料番号：ⅢAエドb2-5-17-1科学研究費助成事業（研究成果公開促進費）で電子化を実施データベースの名称：藩札等に関する統合データベース課題番号：19HP8033利用に関するお問い合わせ：画像の転載（出版物・HP等）に際しては、日本銀行貨幣博物館への申請手続きが必要です。詳しくは貨幣博物館ホームページ（http://www.imes.boj.or.jp/cm/service/）をご覧ください