Objective: Our aim was to determine independent risk factors of clinical bleeding of hepatocellular adenoma (HCA) to define a better management strategy. Summary Background Data: HCA is a rare benign liver tumor with severe complications: malignant transformation that is rare (5%–8%) and more often, hemorrhage (20%–27%). To date, only size > 5 cm and histological subtype (possibly sonic hedgehog) are associated with bleeding, but these criteria are not clearly established. Methods: We retrospectively collected data from a cohort of 268 patients with HCA managed in our tertiary center, from 1984 to 2020 and focused on clinical bleeding. Hemorrhage was considered severe when it required intensive care and moderate when bleeding symptoms required a hospitalization. We included 261 patients, of whom 130 (49.8%) had multiple HCAs or liver adenomatosis. All surgical specimen and liver biopsy were reviewed by an experienced liver pathologist and reclassified in the light of the current immunohistochemistry. Mean duration of follow-up was 93.3 months (range 1–363). We analyzed type, frequency, consequences of bleeding, and risk factors among clinical data and HCA characteristics. Results: Eighty-three HCA (31.8%) were hemorrhagic. There were 4 pregnant women with 1 newborn death. One patient died before treatment. Surgery was performed in 78 (94.0%) patients. Mortality was nil and severe complications occurred in 11.5%. Multivariate analysis identified size (OR 1.02 [1.01–1.02], P < 0.001), shHCA (OR 21.02 [5.05–87.52], P < 0.001), b-catenin mutation on exon 7/8 (OR 6.47 [1.78–23.55], P = 0.0046), chronic alcohol consumption (OR 9.16 [2.47–34.01], P < 0.001) as independent risk factors of clinical bleeding. Conclusions: This series, focused on the hemorrhagic risk of HCA, shows that size, but rather more molecular subtype is determinant in the natural history of HCA.

BaCKgRoUND aND aIMS:Through an exploratory proteomic approach based on typical hepatocellular adenomas (HCAs), we previously identified a diagnostic biomarker for a distinctive subtype of HCA with high risk of bleeding, already validated on a multicenter cohort. We hypothesized that the whole protein expression deregulation profile could deliver much more informative data for tumor characterization. Therefore, we pursued our analysis with the characterization of HCA proteomic profiles, evaluating their correspondence with the established genotype/phenotype classification and assessing whether they could provide added diagnosis and prognosis values.appRoaCH aND ReSUltS: From a collection of 260 cases, we selected 52 typical cases of all different subgroups on which we built a reference HCA proteomics database. Combining laser microdissection and mass-spectrometry-based proteomic analysis, we compared the relative protein abundances between tumoral (T) and nontumoral (NT) liver tissues from each patient and we defined a specific proteomic profile of each of the HCA subgroups. Next, we built a matching algorithm comparing the proteomic profile extracted from a patient with our reference HCA database. Proteomic profiles allowed HCA classification and made diagnosis possible, even for complex cases with immunohistological or genomic analysis that did not lead to a formal conclusion.Despite a well-established pathomolecular classification, clinical practices have not substantially changed and the HCA management link to the assessment of the malignant transformation risk remains delicate for many surgeons. That is why we also identified and validated a proteomic profile that would directly evaluate malignant transformation risk regardless of HCA subtype. CoNClUSIoNS:This work proposes a proteomic-based machine learning tool, operational on fixed biopsies, that can improve diagnosis and prognosis and therefore patient management for HCAs. (Hepatology 2021;74:1595-1610.

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