B cells are associated with survival and immunotherapy response in sarcoma.

Soft-tissue sarcomas represent a heterogeneous group of cancer, with more than 50 histological subtypes1,2. The clinical presentation of patients with different subtypes is often atypical, and responses to therapies such as immune checkpoint blockade vary widely3,4. To explain this clinical variability, here we study gene expression profiles in 608 tumours across subtypes of soft-tissue sarcoma. We establish an immune-based classification on the basis of the composition of the tumour microenvironment and identify five distinct phenotypes: immune-low (A and B), immune-high (D and E), and highly vascularized (C) groups. In situ analysis of an independent validation cohort shows that class E was characterized by the presence of tertiary lymphoid structures that contain T cells and follicular dendritic cells and are particularly rich in B cells. B cells are the strongest prognostic factor even in the context of high or low CD8+ T cells and cytotoxic contents. The class-E group demonstrated improved survival and a high response rate to PD1 blockade with pembrolizumab in a phase 2 clinical trial. Together, this work confirms the immune subtypes in patients with soft-tissue sarcoma, and unravels the potential of B-cell-rich tertiary lymphoid structures to guide clinical decision-making and treatments, which could have broader applications in other diseases.

Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images.

Liver resection is one of the best treatments for small hepatocellular carcinoma (HCC), but post-resection recurrence is frequent. Biotherapies have emerged as an efficient adjuvant treatment, making the identification of patients at high risk of recurrence critical. Microvascular invasion (mVI), poor differentiation, pejorative macrotrabecular architectures, and vessels encapsulating tumor clusters architectures are the most accurate histologic predictors of recurrence, but their evaluation is time-consuming and imperfect. A supervised deep learning-based approach with ResNet34 on 680 whole slide images (WSIs) from 107 liver resection specimens allowed us to build an algorithm for the identification and quantification of these pejorative architectures. This model achieved an accuracy of 0.864 at patch level and 0.823 at WSI level. To assess its robustness, it was validated on an external cohort of 29 HCCs from another hospital, with an accuracy of 0.787 at WSI level, affirming its generalization capabilities. Moreover, the largest connected areas of the pejorative architectures extracted from the model were positively correlated to the presence of mVI and the number of tumor emboli. These results suggest that the identification of pejorative architectures could be an efficient surrogate of mVI and have a strong predictive value for the risk of recurrence. This study is the first step in the construction of a composite predictive algorithm for early post-resection recurrence of HCC, including artificial intelligence-based features.

Integration of new technologies in the multidisciplinary approach to primary liver tumours: The next-generation tumour board.

Primary liver tumours, including benign liver tumours, hepatocellular carcinoma and cholangiocarcinoma, present a multifaceted challenge, necessitating a collaborative approach, as evidenced by the role of the multidisciplinary tumour board (MDTB). The approach to managing primary liver tumours gathers specialized teams, including surgeons, radiologists, oncologists, pathologists, hepatologists, and radiation oncologists, to propose individualized treatment plans. The evolving landscape of primary liver cancer treatment introduces complexities, particularly with the expanding array of systemic and locoregional therapies, alongside the potential integration of molecular biology and artificial intelligence (AI) into MDTB in the future. Precision medicine demands collaboration across disciplines, challenging traditional frameworks. The next decade anticipates the convergence of AI, molecular biology, pathology, and advanced imaging, requiring adaptability in MDTB structure to incorporate these cutting-edge technologies. Navigating this evolution also requires a focus on enhancing basic, translational, and clinical research, as well as boosting clinical trials through an upgraded use of MDTBs as hubs for scientific collaboration and raising literacy about AI and new technologies. In this review, we will delineate the current unmet needs in the clinical management of primary liver cancers, discuss our perspective on the future role of MDTBs in primary liver cancers ("next generation" MDTBs), and unravel the potential power and limitations of novel technologies that may shape the multidisciplinary care landscape for primary liver cancers in the coming decade.

Association of LI-RADS and Histopathologic Features with Survival in Patients with Solitary Resected Hepatocellular Carcinoma.

Background Both Liver Imaging Reporting and Data System (LI-RADS) and histopathologic features provide prognostic information in patients with hepatocellular carcinoma (HCC), but whether LI-RADS is independently associated with survival is uncertain. Purpose To assess the association of LI-RADS categories and features with survival outcomes in patients with solitary resected HCC. Materials and Methods This retrospective study included patients with solitary resected HCC from three institutions examined with preoperative contrast-enhanced CT and/or MRI between January 2008 and December 2019. Three independent readers evaluated the LI-RADS version 2018 categories and features. Histopathologic features including World Health Organization tumor grade, microvascular and macrovascular invasion, satellite nodules, and tumor capsule were recorded. Overall survival and disease-free survival were assessed with Cox regression models. Marginal effects of nontargetoid features on survival were estimated using propensity score matching. Results A total of 360 patients (median age, 64 years [IQR, 56-70 years]; 280 male patients) were included. At CT and MRI, the LI-RADS LR-M category was associated with increased risk of recurrence (CT: hazard ratio [HR] = 1.83 [95% CI: 1.26, 2.66], P = .001; MRI: HR = 2.22 [95% CI: 1.56, 3.16], P < .001) and death (CT: HR = 2.47 [95% CI: 1.72, 3.55], P < .001; MRI: HR = 1.80 [95% CI: 1.32, 2.46], P < .001) independently of histopathologic features. The presence of at least one nontargetoid feature was associated with an increased risk of recurrence (CT: HR = 1.80 [95% CI: 1.36, 2.38], P < .001; MRI: HR = 1.93 [95% CI: 1.81, 2.06], P < .001) and death (CT: HR = 1.51 [95% CI: 1.10, 2.07], P < .010) independently of histopathologic features. In matched samples, recurrence was associated with the presence of at least one nontargetoid feature at CT (HR = 2.06 [95% CI: 1.15, 3.66]; P = .02) or MRI (HR = 1.79 [95% CI: 1.01, 3.20]; P = .048). Conclusion In patients with solitary resected HCC, LR-M category and nontargetoid features were negatively associated with survival independently of histopathologic characteristics. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kartalis and Grigoriadis in this issue.

Body weight changes and duration of estrogen exposure modulate the evolution of hepatocellular adenomas after contraception discontinuation.

BACKGROUND AND AIMS: The natural history of hepatocellular adenomas (HCAs) remains to be better described, especially in nonresected patients. We aim to identify the predictive factors of HCA evolution after estrogen-based contraception discontinuation. APPROACH AND RESULTS: We retrospectively included patients with a histological diagnosis of HCA from three centers. Clinical, radiological, and pathological data were collected to identify predictive factors of radiological evolution per Response Evaluation Criteria in Solid Tumors, version 1.1, and occurrence of complications (bleeding, malignant transformation). We built a score using variables that modulate estrogen levels: body mass index and duration of estrogen-based contraception. An external cohort was used to validate this score. 183 patients were included in the cohort, including 161 women (89%) using estrogen-based contraception for a median of 12 years. Thirty percent of patients had at least one HNF1A -inactivated HCA, 45.5% at least one inflammatory HCA, and 11% at least one HCA with activation of ß-catenin (bHCA). Twenty-one symptomatic bleedings (11%) and eleven malignant transformations (6%) occurred. Ages < 37 years old ( p = 0.004) and HCA > 5 cm at imaging were independently associated with symptomatic bleeding ( p = 0.003), whereas a bHCA was associated with malignant transformation ( p < 0.001). After a median follow-up of 5 years, radiological regression was observed in 31%, stabilization in 47%, and progression in 22% of patients. Weight loss was associated with regression ( p < 0.0001) and weight gain with progression ( p = 0.02). The estrogen exposure score predicted radiological regression (odds ratio, 2.33; confidence interval 95%, 1.29-4.19; p = 0.005) with a linear relationship between the rate of estrogen exposure and the probability of regression. This result was confirmed in an external cohort of 72 female patients ( p = 0.003). CONCLUSION: Weight variation is strongly associated with radiological evolution after oral contraception discontinuation. A score of estrogen exposure, easily assessable in clinical practice at diagnosis, predicts regression of HCA.

Differential diagnosis of small hepatocellular nodules in cirrhosis: surrogate histological criteria of TERT promoter mutations.

AIMS: The differential diagnosis of small hepatocellular nodules in cirrhosis between dysplastic nodules and hepatocellular carcinoma (HCC) remains challenging on biopsy. As TERT promoter (pTERT) mutations may indicate the nodules already engaged in the malignant process, the aim of this study was to identify histological criteria associated with pTERT mutations by detecting these mutations by ddPCR in small formalin-fixed paraffin-embedded (FFPE) hepatocellular nodules arising in cirrhosis. METHODS AND RESULTS: We built a bicentric cohort data set of 339 hepatocellular nodules < 2 cm from cirrhotic samples, divided into a test cohort of 299 resected samples and a validation cohort of 40 biopsies. Pathological review, based on the evaluation of 14 histological criteria, classified all nodules. pTERT mutations were identified by ddPCR in FFPE samples. Among the 339 nodules, ddPCR revealed pTERT mutations in 105 cases (31%), including 90 and 15 cases in the test and validation cohorts, respectively. On multivariate analysis, three histological criteria were associated with pTERT mutations in the test cohort: increased cell density (P = 0.003), stromal invasion (P = 0.036) and plate-thickening anomalies (P < 0.001). With the combination of at least two of these major criteria, the AUC for predicting pTERT mutations was 0.84 in the test cohort (sensitivity: 86%, specificity: 83%) and 0.81 in the validation cohort (sensitivity: 87%, specificity: 76%). CONCLUSIONS: We identified three histological criteria as surrogate markers of pTERT mutations that may be used in routine biopsy to more clearly classify small hepatocellular nodules arising in cirrhosis.

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. Finally, 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 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. 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. FUNDING: 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.

RSK2 inactivation cooperates with AXIN1 inactivation or ß-catenin activation to promote hepatocarcinogenesis.

BACKGROUND & AIMS: Recurrent somatic mutations of the RPS6KA3 gene encoding for the serine/threonine kinase RSK2 were identified in hepatocellular carcinomas (HCCs), suggesting its tumour-suppressive function. Our goal was to demonstrate the tumour suppressor role of RSK2 in the liver and investigate the functional consequences of its inactivation. METHODS: We analysed a series of 1,151 human HCCs for RSK2 mutations and 20 other driver genetic alterations. We then modelled RSK2 inactivation in mice in various mutational contexts recapitulating or not those naturally found in human HCC, using transgenic mice and liver-specific carcinogens. These models were monitored for liver tumour appearance and subjected to phenotypic and transcriptomic analyses. Functional consequences of RSK2 rescue were also investigated in a human RSK2-deficient HCC cell line. RESULTS: RSK2-inactivating mutations are specific to human HCC and frequently co-occur with AXIN1-inactivating or ß-catenin-activating mutations. Modelling of these co-occurrences in mice showed a cooperative effect in promoting liver tumours with transcriptomic profiles recapitulating those of human HCCs. By contrast, there was no cooperation in liver tumour induction between RSK2 loss and BRAF-activating mutations chemically induced by diethylnitrosamine. In human liver cancer cells, we also showed that RSK2 inactivation confers some dependency to the activation of RAS/MAPK signalling that can be targeted by MEK inhibitors. CONCLUSIONS: Our study demonstrates the tumour suppressor role of RSK2 and its specific synergistic effect in hepatocarcinogenesis when its loss of function is specifically combined with AXIN1 inactivation or ß-catenin activation. Furthermore, we identified the RAS/MAPK pathway as a potential therapeutic target for RSK2-inactivated liver tumours. IMPACT AND IMPLICATIONS: This study demonstrated the tumour suppressor role of RSK2 in the liver and showed that its inactivation specifically synergises with AXIN1 inactivation or ß-catenin activation to promote the development of HCC with similar transcriptomic profiles as found in humans. Furthermore, this study highlights that activation of the RAS/MAPK pathway is one of the key signalling pathways mediating the oncogenic effect of RSK2 inactivation that can be targeted with already available anti-MEK therapies.

ERS: A simple scoring system to predict early recurrence after surgical resection for hepatocellular carcinoma.

BACKGROUND: Surgical resection (SR) is a potentially curative treatment of hepatocellular carcinoma (HCC) hampered by high rates of recurrence. New drugs are tested in the adjuvant setting, but standardised risk stratification tools of HCC recurrence are lacking. OBJECTIVES: To develop and validate a simple scoring system to predict 2-year recurrence after SR for HCC. METHODS: 2359 treatment-naïve patients who underwent SR for HCC in 17 centres in Europe and Asia between 2004 and 2017 were divided into a development (DS; n = 1558) and validation set (VS; n = 801) by random sampling of participating centres. The Early Recurrence Score (ERS) was generated using variables associated with 2-year recurrence in the DS and validated in the VS. RESULTS: Variables associated with 2-year recurrence in the DS were (with associated points) alpha-fetoprotein (<10 ng/mL:0; 10-100: 2; >100: 3), size of largest nodule (≥40 mm: 1), multifocality (yes: 2), satellite nodules (yes: 2), vascular invasion (yes: 1) and surgical margin (positive R1: 2). The sum of points provided a score ranging from 0 to 11, allowing stratification into four levels of 2-year recurrence risk (Wolbers' C-indices 66.8% DS and 68.4% VS), with excellent calibration according to risk categories. Wolber's and Harrell's C-indices apparent values were systematically higher for ERS when compared to Early Recurrence After Surgery for Liver tumour post-operative model to predict time to early recurrence or recurrence-free survival. CONCLUSIONS: ERS is a user-friendly staging system identifying four levels of early recurrence risk after SR and a robust tool to design personalised surveillance strategies and adjuvant therapy trials.

MRI features associated with HCC histologic subtypes: a western American and European bicenter study.

OBJECTIVES: To evaluate if preoperative MRI can predict the most frequent HCC subtypes in North American and European patients treated with surgical resection. METHODS: A total of 119 HCCs in 97 patients were included in the North American group and 191 HCCs in 176 patients were included in the European group. Lesion subtyping was based on morphologic features and immuno-histopathological analysis. Two radiologists reviewed preoperative MRI and evaluated the presence of imaging features including LI-RADS major and ancillary features to identify clinical, biologic, and imaging features associated with the main HCC subtypes. RESULTS: Sixty-four percent of HCCs were conventional. The most frequent subtypes were macrotrabecular-massive (MTM-15%) and steatohepatitic (13%). Necrosis (OR = 3.32; 95% CI: 1.39, 7.89; p = .0064) and observation size (OR = 1.011; 95% CI: 1.0022, 1.019; p = .014) were independent predictors of MTM-HCC. Fat in mass (OR = 15.07; 95% CI: 6.57, 34.57; p < .0001), tumor size (OR = 0.97; 95% CI: 0.96, 0.99; p = .0037), and absence of chronic HCV infection (OR = 0.24; 95% CI: 0.084, 0.67; p = .0068) were independent predictors of steatohepatitic HCC. Independent predictors of conventional HCCs were viral C hepatitis (OR = 3.20; 95% CI: 1.62, 6.34; p = .0008), absence of fat (OR = 0.25; 95% CI: 0.12, 0.52; p = .0002), absence of tumor in vein (OR = 0.34; 95% CI: 0.13, 0.84; p = .020), and higher tumor-to-liver ADC ratio (OR = 1.96; 95% CI: 1.14, 3.35; p = .014) CONCLUSION: MRI is useful in predicting the most frequent HCC subtypes even in cohorts with different distributions of liver disease etiologies and tumor subtypes which might have future treatment and management implications. KEY POINTS: • Representation of both liver disease etiologies and HCC subtypes differed between the North American and European cohorts of patients. • Retrospective two-center study showed that liver MRI is useful in predicting the most frequent HCC subtypes.

Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study.

INTRODUCTION: The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potentially useful tool to provide an additional layer of clinically relevant information, but has not been systematically assessed in GC. OBJECTIVE: We aimed to train, test and externally validate a deep learning-based classifier for GC histology subtyping using routine H&E stained tissue sections from gastric adenocarcinomas and to assess its potential prognostic utility. METHODS: We trained a binary classifier on intestinal and diffuse type GC whole slide images for a subset of the TCGA cohort (N = 166) using attention-based multiple instance learning. The ground truth of 166 GC was obtained by two expert pathologists. We deployed the model on two external GC patient cohorts, one from Europe (N = 322) and one from Japan (N = 243). We assessed classification performance using the Area Under the Receiver Operating Characteristic Curve (AUROC) and prognostic value (overall, cancer specific and disease free survival) of the DL-based classifier with uni- and multivariate Cox proportional hazard models and Kaplan-Meier curves with log-rank test statistics. RESULTS: Internal validation using the TCGA GC cohort using five-fold cross-validation achieved a mean AUROC of 0.93 ± 0.07. External validation showed that the DL-based classifier can better stratify GC patients' 5-year survival compared to pathologist-based Laurén classification for all survival endpoints, despite frequently divergent model-pathologist classifications. Univariate overall survival Hazard Ratios (HRs) of pathologist-based Laurén classification (diffuse type versus intestinal type) were 1.14 (95% Confidence Interval (CI) 0.66-1.44, p-value = 0.51) and 1.23 (95% CI 0.96-1.43, p-value = 0.09) in the Japanese and European cohorts, respectively. DL-based histology classification resulted in HR of 1.46 (95% CI 1.18-1.65, p-value < 0.005) and 1.41 (95% CI 1.20-1.57, p-value < 0.005), in the Japanese and European cohorts, respectively. In diffuse type GC (as defined by the pathologist), classifying patients using the DL diffuse and intestinal classifications provided a superior survival stratification, and demonstrated statistically significant survival stratification when combined with pathologist classification for both the Asian (overall survival log-rank test p-value < 0.005, HR 1.43 (95% CI 1.05-1.66, p-value = 0.03) and European cohorts (overall survival log-rank test p-value < 0.005, HR 1.56 (95% CI 1.16-1.76, p-value < 0.005)). CONCLUSION: Our study shows that gastric adenocarcinoma subtyping using pathologist's Laurén classification as ground truth can be performed using current state of the art DL techniques. Patient survival stratification seems to be better by DL-based histology typing compared with expert pathologist histology typing. DL-based GC histology typing has potential as an aid in subtyping. Further investigations are warranted to fully understand the underlying biological mechanisms for the improved survival stratification despite apparent imperfect classification by the DL algorithm.

Impact of COVID-19 on the management of hepatocellular carcinoma in a high-prevalence area: What's new 12 months later?

INTRODUCTION AND OBJECTIVES: The lockdown policy introduced in 2020 to minimize the spread of the COVID-19 pandemic, significantly affected the management and care of patients affected by hepatocellular carcinoma (HCC). The aim of this follow-up study was to determine the 12 months impact of the COVID-19 pandemic on the cohort of patients affected by HCC during the lockdown, within six French academic referral centers in the metropolitan area of Paris. MATERIALS AND METHODS: We performed a 12 months follow-up of the cross-sectional study cohort included in 2020 on the management of patients affected by HCC during the first six weeks of the COVID-19 pandemic (exposed), compared to the same period in 2019 (unexposed). Overall survival were compared between the groups. Predictors of mortality were analysed with Cox regression. RESULTS: From the initial cohort, 575 patients were included (n = 263 Exposed_COVID, n = 312 Unexposed_COVID). Overall and disease free survival at 12 months were 59.9 ± 3.2% vs 74.3 ± 2.5% (p<0.001) and 40.2 ± 3.5% vs 63.5 ± 3.1% (p<0.001) according to the period of exposure (Exposed_COVID vs Unexposed_COVID, respectively). Adjusted Cox regression revealed that the period of exposure (Exposed_COVID HR: 1.79, 95%CI (1.36, 2.35) p<0.001) and BCLC stage B, C and D (BCLC B HR: 1.82, 95%CI (1.07, 3.08) p = 0.027 - BCLC C HR: 1.96, 95%CI (1.14, 3.38) p = 0.015 - BCLC D HR: 3.21, 95%CI (1.76, 5.85) p<0.001) were predictors of death. CONCLUSIONS: Disruption of routine healthcare services because of the pandemic translated to reduced 1 year overall and disease-free survival among patients affected by HCC, in the metropolitan area of Paris, France.

[Development of the use of artificial intelligence in the management of chronic inflammatory bowel disease]. / Mise au point de l'utilisation de l'intelligence artificielle dans la prise en charge des maladies inflammatoires chroniques de l'intestin.

Complexity of inflammatory bowel diseases (IBD) lies on their management and their biology. Clinics, blood and fecal samples tests, endoscopy and histology are the main tools guiding IBD treatment, but they generate a large amount of data, difficult to analyze by clinicians. Because of its capacity to analyze large number of data, artificial intelligence is currently generating enthusiasm in medicine, and this technology could be used to improve IBD management. In this review, after a short summary on IBD management and artificial intelligence, we will report pragmatic examples of artificial intelligence utilisation in IBD. Lastly, we will discuss the limitations of this technology.

Hepatitis B virus integrations promote local and distant oncogenic driver alterations in hepatocellular carcinoma.

OBJECTIVE: Infection by HBV is the main risk factor for hepatocellular carcinoma (HCC) worldwide. HBV directly drives carcinogenesis through integrations in the human genome. This study aimed to precisely characterise HBV integrations, in relation with viral and host genomics and clinical features. DESIGN: A novel pipeline was set up to perform viral capture on tumours and non-tumour liver tissues from a French cohort of 177 patients mainly of European and African origins. Clonality of each integration event was determined with the localisation, orientation and content of the integrated sequence. In three selected tumours, complex integrations were reconstructed using long-read sequencing or Bionano whole genome mapping. RESULTS: Replicating HBV DNA was more frequently detected in non-tumour tissues and associated with a higher number of non-clonal integrations. In HCC, clonal selection of HBV integrations was related to two different mechanisms involved in carcinogenesis. First, integration of viral enhancer nearby a cancer-driver gene may lead to a strong overexpression of oncogenes. Second, we identified frequent chromosome rearrangements at HBV integration sites leading to cancer-driver genes (TERT, TP53, MYC) alterations at distance. Moreover, HBV integrations have direct clinical implications as HCC with a high number of insertions develop in young patients and have a poor prognosis. CONCLUSION: Deep characterisation of HBV integrations in liver tissues highlights new HBV-associated driver mechanisms involved in hepatocarcinogenesis. HBV integrations have multiple direct oncogenic consequences that remain an important challenge for the follow-up of HBV-infected patients.

Common genetic variation in alcohol-related hepatocellular carcinoma: a case-control genome-wide association study.

BACKGROUND: Hepatocellular carcinoma is a frequent consequence of alcohol-related liver disease, with variable incidence among heavy drinkers. We did a genome-wide association study (GWAS) to identify common genetic variants for alcohol-related hepatocellular carcinoma. METHODS: We conducted a two-stage case-control GWAS in a discovery cohort of 2107 unrelated European patients with alcohol-related liver disease aged 20-92 years recruited between Oct 22, 1993, and March 12, 2017. Cases were patients with alcohol-related hepatocellular carcinoma diagnosed by imaging or histology. Controls were patients with alcohol-related liver disease without hepatocellular carcinoma. We used an additive logistic regression model adjusted for the first ten principal components to assess genetic variants associated with alcohol-related hepatocellular carcinoma. We did another analysis with adjustment for age, sex, and liver fibrosis. New candidate associations (p<1 × 10-6) and variants previously associated with alcohol-related hepatocellular carcinoma were evaluated in a validation cohort of 1933 patients with alcohol-related liver disease aged 29-92 years recruited between July 21, 1995, and May 2, 2019. We did a meta-analysis of the two case-control cohorts. FINDINGS: The discovery cohort included 775 cases and 1332 controls. Of 7 962 325 variants assessed, we identified WNT3A-WNT9A (rs708113; p=1·11 × 10-8) and found support for previously reported regions associated with alcohol-related hepatocellular carcinoma risk at TM6SF2 (rs58542926; p=6·02 × 10-10), PNPLA3 (rs738409; p=9·29 × 10-7), and HSD17B13 (rs72613567; p=2·49 × 10-4). The validation cohort included 874 cases and 1059 controls and three variants were replicated: WNT3A-WNT9A (rs708113; p=1·17 × 10-3), TM6SF2 (rs58542926; p=4·06 × 10-5), and PNPLA3 (rs738409; p=1·17 × 10-4). All three variants reached GWAS significance in the meta-analysis: WNT3A-WNT9A (odds ratio 0·73, 95% CI 0·66-0·81; p=3·93 × 10-10), TM6SF2 (1·77, 1·52-2·07; p=3·84×10-13), PNPLA3 (1·34, 1·22-1·47; p=7·30 × 10-10). Adjustment for clinical covariates yielded similar results. We observed an additive effect of at-risk alleles on alcohol-related hepatocellular carcinoma. WNT3A-WNT9A rs708113 was not associated with liver fibrosis. INTERPRETATION: WNT3A-WNT9A is a susceptibility locus for alcohol-related hepatocellular carcinoma, suggesting an early role of the Wnt-ß-catenin pathway in alcohol-related hepatocellular carcinoma carcinogenesis. FUNDING: Ligue Nationale contre le Cancer, Bpifrance, INSERM, AFEF, CARPEM, Labex OncoImmunology, and Agence Nationale de la Recherche.

Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources - including electronic health record data, imaging modalities, histopathology and molecular biomarkers - to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC.

Bi-allelic hydroxymethylbilane synthase inactivation defines a homogenous clinico-molecular subtype of hepatocellular carcinoma.

BACKGROUND & AIMS: Acute intermittent porphyria (AIP), caused by heterozygous germline mutations of the heme synthesis pathway enzyme HMBS (hydroxymethylbilane synthase), confers a high risk of hepatocellular carcinoma (HCC) development. Yet, the role of HMBS in liver tumorigenesis remains unclear. METHODS: Herein, we explore HMBS alterations in a large series of 758 HCC cases, including 4 patients with AIP. We quantify the impact of HMBS mutations on heme biosynthesis pathway intermediates and we investigate the molecular and clinical features of HMBS-mutated tumors. RESULTS: We identify recurrent bi-allelic HMBS inactivation, both in patients with AIP acquiring a second somatic HMBS mutation and in sporadic HCC with 2 somatic hits. HMBS alterations are enriched in truncating mutations, in particular in splice regions, leading to abnormal transcript structures. Bi-allelic HMBS inactivation results in a massive accumulation of its toxic substrate porphobilinogen and synergizes with CTNNB1-activating mutations, leading to the development of well-differentiated tumors with a transcriptomic signature of Wnt/ß-catenin pathway activation and a DNA methylation signature related to ageing. HMBS-inactivated HCC mostly affects females, in the absence of fibrosis and classical HCC risk factors. CONCLUSIONS: These data identify HMBS as a tumor suppressor gene whose bi-allelic inactivation defines a homogenous clinical and molecular HCC subtype. LAY SUMMARY: Heme (the precursor to hemoglobin, which plays a key role in oxygen transport around the body) synthesis occurs in the liver and involves several enzymes including hydroxymethylbilane synthase (HMBS). HMBS mutations cause acute intermittent porphyria, a disease caused by the accumulation of toxic porphyrin precursors. Herein, we show that HMBS inactivation is also involved in the development of liver cancers with distinct clinical and molecular characteristics.

Gene expression signature as a surrogate marker of microvascular invasion on routine hepatocellular carcinoma biopsies.

BACKGROUND & AIMS: Microvascular invasion (MVI), a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma (HCC), is only detectable by microscopic examination of the surgical specimen. We aimed to define a transcriptomic signature associated with MVI in HCC than can be applied to formalin-fixed paraffin-embedded (FFPE) biopsies for use in clinical practice. METHODS: To identify a gene expression signature related to MVI by using NanoString technology, we selected a set of 200 genes according to the literature and RNA-sequencing data obtained from a cohort of 150 frozen HCC samples previously published. We used 178 FFPE-archived HCC samples, including 109 surgical samples for the training set and 69 paired pre-operative biopsies for the validation set. In 14 cases of the training set, a paired biopsy was available and was also analyzed. RESULTS: We identified a 6-gene signature (ROS1, UGT2B7, FAS, ANGPTL7, GMNN, MKI67) strongly associated with MVI in the training set of FFPE surgical HCC samples, with 82% accuracy (sensitivity 82%, specificity 81%, AUC 0.82). The NanoString gene expression was highly correlated in 14 paired surgical/biopsy HCC samples (mean R: 0.97). In the validation set of 69 FFPE HCC biopsies, the 6-gene NanoString signature predicted MVI with 74% accuracy (sensitivity 73%, specificity 76%, AUC 0.74). Moreover, on multivariate analysis, the MVI signature was associated with overall survival in both sets (hazard ratio 2.29; 95% CI 1.03-5.07; p = 0.041). CONCLUSION: We defined a 6-gene signature that can accurately predict MVI in FFPE HCC biopsy samples, which is also associated with overall survival, although its survival impact must be confirmed by extensive study with further clinical data. LAY SUMMARY: Microvascular invasion, a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma, is only detectable by microscopic examination of a surgical specimen. In this study, we defined a relevant surrogate signature of microvascular invasion in hepatocellular carcinoma that may be applied in clinical practice with routine tumor biopsy and integrated into the therapeutic strategy.

Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology.

BACKGROUND & AIMS: Patients with hepatocellular carcinoma (HCC) displaying overexpression of immune gene signatures are likely to be more sensitive to immunotherapy, however, the use of such signatures in clinical settings remains challenging. We thus aimed, using artificial intelligence (AI) on whole-slide digital histological images, to develop models able to predict the activation of 6 immune gene signatures. METHODS: AI models were trained and validated in 2 different series of patients with HCC treated by surgical resection. Gene expression was investigated using RNA sequencing or NanoString technology. Three deep learning approaches were investigated: patch-based, classic MIL and CLAM. Pathological reviewing of the most predictive tissue areas was performed for all gene signatures. RESULTS: The CLAM model showed the best overall performance in the discovery series. Its best-fold areas under the receiver operating characteristic curves (AUCs) for the prediction of tumors with upregulation of the immune gene signatures ranged from 0.78 to 0.91. The different models generalized well in the validation dataset with AUCs ranging from 0.81 to 0.92. Pathological analysis of highly predictive tissue areas showed enrichment in lymphocytes, plasma cells, and neutrophils. CONCLUSION: We have developed and validated AI-based pathology models able to predict the activation of several immune and inflammatory gene signatures. Our approach also provides insights into the morphological features that impact the model predictions. This proof-of-concept study shows that AI-based pathology could represent a novel type of biomarker that will ease the translation of our biological knowledge of HCC into clinical practice. LAY SUMMARY: Immune and inflammatory gene signatures may be associated with increased sensitivity to immunotherapy in patients with advanced hepatocellular carcinoma. In the present study, the use of artificial intelligence-based pathology enabled us to predict the activation of these signatures directly from histology.

Nestin as a diagnostic and prognostic marker for combined hepatocellular-cholangiocarcinoma.

BACKGROUND & AIMS: Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) associated with a poor prognosis. Given the challenges in its identification and its clinical implications, biomarkers are critically needed. We aimed to investigate the diagnostic and prognostic value of the immunohistochemical expression of Nestin, a progenitor cell marker, in a large multicentric series of PLCs. METHODS: We collected 461 cHCC-CCA samples from 32 different clinical centers. Control cases included 368 hepatocellular carcinomas (HCCs) and 221 intrahepatic cholangiocarcinomas (iCCAs). Nestin immunohistochemistry was performed on whole tumor sections. Diagnostic and prognostic performances of Nestin expression were determined using receiver-operating characteristic curves and Cox regression modeling. RESULTS: Nestin was able to distinguish cHCC-CCA from HCC with AUCs of 0.85 and 0.86 on surgical and biopsy samples, respectively. Performance was lower for the distinction of cHCC-CCA from iCCA (AUCs of 0.59 and 0.60). Nestin, however, showed a high prognostic value, allowing identification of the subset of cHCC-CCA ("Nestin High", >30% neoplastic cells with positive staining) associated with the worst clinical outcome (shorter disease-free and overall survival) after surgical resection and liver transplantation, as well as when assessment was performed on biopsies. CONCLUSION: We show in different clinical settings that Nestin has diagnostic value and that it is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy. LAY SUMMARY: There are different types of primary liver cancers (i.e. cancers that originate in the liver). Accurately identifying a specific subtype of primary liver cancer (and determining its associated prognosis) is important as it can have a major impact on treatment allocation. Herein, we show that a protein called Nestin could be used to refine risk stratification and improve treatment allocation for patients with combined hepatocellular carcinoma, a rare but highly aggressive subtype of primary liver cancer.