Correction: B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma

BackgroundThe macrotrabecular-massive (MTM) is a special subtype of hepatocellular carcinoma (HCC), which has commonly a dismal prognosis. This study aimed to develop a multitask deep learning radiomics (MDLR) model for predicting MTM and HCC patients’ prognosis after hepatic arterial infusion chemotherapy (HAIC).MethodsFrom June 2018 to March 2020, 158 eligible patients with HCC who underwent surgery were retrospectively enrolled in MTM related cohorts, and 752 HCC patients who underwent HAIC were included in HAIC related cohorts during the same period. DLR features were extracted from dual-phase (arterial phase and venous phase) contrast-enhanced computed tomography (CECT) of the entire liver region. Then, an MDLR model was used for the simultaneous prediction of the MTM subtype and patient prognosis after HAIC. The MDLR model for prognostic risk stratification incorporated DLR signatures, clinical variables and MTM subtype.FindingsThe predictive performance of the DLR model for the MTM subtype was 0.968 in the training cohort [TC], 0.912 in the internal test cohort [ITC] and 0.773 in the external test cohort [ETC], respectively. Multivariable analysis identified portal vein tumor thrombus (PVTT) (p = 0.012), HAIC response (p < 0.001), HAIC sessions (p < 0.001) and MTM subtype (p < 0.001) as indicators of poor prognosis. After incorporating DLR signatures, the MDLR model yielded the best performance among all models (AUC, 0.855 in the TC, 0.805 in the ITC and 0.792 in the ETC). With these variables, the MDLR model provided two risk strata for overall survival (OS) in the TC: low risk (5-year OS, 44.9%) and high risk (5-year OS, 4.9%).InterpretationA tool based on MDLR was developed to consider that the MTM is an important prognosis factor for HCC patients. MDLR showed outstanding performance for the prognostic risk stratification of HCC patients who underwent HAIC and may help physicians with therapeutic decision making and surveillance strategy selection in clinical practice.

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.

Imaging features of histological subtypes of hepatocellular carcinoma: Implication for LI-RADS

ObjectiveTo evaluate the potential of dynamic contrast-enhanced ultrasound (CEUS) quantitative parameters in preoperative prediction of macrotrabecular-massive (MTM) subtype and high Ki-67 pattern in hepatocellular carcinoma (HCC) patients.Materials and methodsThis study included a retrospective primary cohort and a multicenter prospective validation cohort comprising HCC patients who underwent surgical resection and preoperative CEUS between January 2023 and April 2024. The Clinic-CEUS model was established by combining clinical data and CEUS features, while the Clinic-Q-CEUS model was constructed by combining clinical data and CEUS features with matched quantitative parameters. Model performance was tested with the area under the receiver operating characteristic curve (AUC) in the validation cohort.ResultsA total of 170 patients (mean age, 61 years ± 11 [SD]; 130 men; primary cohort, n = 118; validation cohort, n = 52) were included. The Clinic-Q-CEUS model better predicted MTM subtype and high Ki-67 pattern than the Clinic-CEUS model (AUC, 0.860 vs. 0.753, p = 0.027 and AUC, 0.836 vs. 0.738, p = 0.036) in the primary cohort, with similar performance in the validation cohort (AUC, 0.868 vs. 0.693, p = 0.046 and AUC, 0.787 vs. 0.610, p = 0.018).ConclusionsDynamic CEUS quantification analysis could be used as an effective adjunct tool for preoperative identification of MTM subtype and high Ki-67 pattern in HCC patients.Critical relevance statementDynamic contrast-enhanced ultrasound (CEUS) quantitative parameters can help radiologists more accurately identify aggressive macrotrabecular-massive (MTM) subtype and high Ki-67 pattern in HCC patients preoperatively, which provides useful information for subsequent treatment planning.Key PointsMacrotrabecular-massive (MTM) subtype and high Ki-67 pattern in HCC affect prognosis, but diagnosis relies on invasive histopathology.A clinical-Q-CEUS model performed well in preoperative predicting aggressive HCC subtypes.Quantitative parameters of dynamic CEUS can provide valuable information to help accurately identify aggressive HCC subtypes.Graphical

To investigate the value of quantitative parameters derived from gadobenate dimeglumine-enhanced magnetic resonance imaging (MRI) for predicting molecular subtype of hepatocellular carcinoma (HCC) and overall survival. This multicenter retrospective study included 218 solitary HCC patients who underwent gadobenate dimeglumine-enhanced MRI. All HCC lesions were resected and pathologically confirmed. The lesion-to-liver contrast enhancement ratio (LLCER) and lesion-to-liver contrast (LLC) were measured in the hepatobiliary phase. Potential risk factors for proliferative HCC were assessed by logistic regression. The ability of LLCER and LLC to predict proliferative HCC was assessed by the receiver operating characteristic (ROC) curve. Prognostic factors were evaluated using the Cox proportional hazards regression model for survival outcomes. LLCER was an independent predictor of proliferative HCC (odds ratio, 0.015; 95% confidence interval [CI], 0.008-0.022; p < 0.001). The area under the ROC curve was 0.812 (95% CI, 0.748-0.877), higher than that of LLC, alpha-fetoprotein > 100 ng/ml, satellite nodules, and rim arterial phase hyperenhancement (all p ≤ 0.001). HCC patients with LLCER < -4.59% had a significantly higher incidence of proliferative HCC than those with the LLCER ≥ -4.59%. During the follow-up period, LLCER was an independent predictor of overall survival (hazard ratio, 0.070; 95% CI, 0.015-0.324; p = 0.001) in HCC patients. Gadobenate dimeglumine-enhanced quantitative parameter in the hepatobiliary phase can predict the proliferative subtype of solitary HCC with a moderately high accuracy. Quantitative information from gadobenate dimeglumine-enhanced MRI can provide crucial information on hepatocellular carcinoma subtypes. It might be valuable to design novel therapeutic strategies, such as targeted therapies or immunotherapy. • The lesion-to-liver contrast enhancement ratio (LLCER) is an independent predictor of proliferative hepatocellular carcinoma (HCC). • The ability of LLCER to predict proliferative HCC outperformed lesion-to-liver contrast, alpha-fetoprotein > 100 ng/ml, satellite nodules, and rim arterial phase hyperenhancement. • HCC patients with LLCER < -4.59% had a significantly higher incidence of proliferative HCC than those with the LLCER ≥ -4.59%.

Chromophobe hepatocellular carcinoma with abrupt anaplasia: a proposal for a new subtype of hepatocellular carcinoma with unique morphological and molecular features

Metabolic reprogramming plays an important role in tumorigenesis. However, the metabolic types of different tumors are diverse and lack in-depth study. Here, through analysis of big databases and clinical samples, we identified a carbamoyl phosphate synthetase 1 (CPS1)-deficient hepatocellular carcinoma (HCC) subtype, explored tumorigenesis mechanism of this HCC subtype, and aimed to investigate metabolic reprogramming as a target for HCC prevention. A pan-cancer study involving differentially expressed metabolic genes of 7,764 tumor samples in 16 cancer types provided by The Cancer Genome Atlas (TCGA) demonstrated that urea cycle (UC) was liver-specific and was down-regulated in HCC. A large-scale gene expression data analysis including 2,596 HCC cases in 7 HCC cohorts from Database of HCC Expression Atlas and 17,444 HCC cases from in-house hepatectomy cohort identified a specific CPS1-deficent HCC subtype with poor clinical prognosis. In vitro and in vivo validation confirmed the crucial role of CPS1 in HCC. Liquid chromatography-mass spectrometry assay and Seahorse analysis revealed that UC disorder (UCD) led to the deceleration of the tricarboxylic acid cycle, whereas excess ammonia caused by CPS1 deficiency activated fatty acid oxidation (FAO) through phosphorylated adenosine monophosphate-activated protein kinase. Mechanistically, FAO provided sufficient ATP for cell proliferation and enhanced chemoresistance of HCC cells by activating forkhead box protein M1. Subcutaneous xenograft tumor models and patient-derived organoids were employed to identify that blocking FAO by etomoxir may provide therapeutic benefit to HCC patients with CPS1 deficiency. In conclusion, our results prove a direct link between UCD and cancer stemness in HCC, define a CPS1-deficient HCC subtype through big-data mining, and provide insights for therapeutics for this type of HCC through targeting FAO.

Background The recently described "macrotrabecular-massive" (MTM) histologic subtype of hepatocellular carcinoma (HCC) (MTM-HCC) represents an aggressive form of HCC and is associated with poor survival. Purpose To investigate whether preoperative MRI can help identify MTM-HCCs in patients with HCC. Materials and Methods This retrospective study included patients with HCC treated with surgical resection between January 2008 and February 2018 and who underwent preoperative multiphase contrast material-enhanced MRI. Least absolute shrinkage and selection operator (LASSO)-penalized and multivariable logistic regression analyses were performed to identify clinical, biologic, and imaging features associated with the MTM-HCC subtype. Early recurrence (within 2 years) and overall recurrence were evaluated by using Kaplan-Meier analysis. Multivariable Cox regression analysis was performed to determine predictors of early and overall recurrence. Results One hundred fifty-two patients (median age, 64 years; interquartile range, 56-72 years; 126 men) with 152 HCCs were evaluated. Twenty-six of the 152 HCCs (17%) were MTM-HCCs. LASSO-penalized logistic regression analysis identified substantial necrosis, high serum α-fetoprotein (AFP) level (>100 ng/mL), and Barcelona Clinic Liver Cancer (BCLC) stage B or C as independent features associated with MTM-HCCs. At multivariable analysis, substantial necrosis (odds ratio = 32; 95% confidence interval [CI] = 8.9, 114; P < .001), high serum AFP level (odds ratio = 4.4; 95% CI = 1.3, 16; P = .02), and BCLC stage B or C (odds ratio = 4.2; 95% CI = 1.2, 15; P = .03) were independent predictors of MTM-HCC subtype. Substantial necrosis helped identify 65% (17 of 26; 95% CI: 44%, 83%) of MTM-HCCs (sensitivity) with a specificity of 93% (117 of 126; 95% CI: 87%, 97%). In adjusted models, only the presence of satellite nodules was independently associated with both early (hazard ratio = 3.7; 95% CI: 1.5, 9.4; P = .006) and overall (hazard ratio = 3.0; 95% CI: 1.3, 7.2; P = .01) tumor recurrence. Conclusion At multiphase contrast-enhanced MRI, substantial necrosis helped identify macrotrabecular-massive hepatocellular carcinoma subtype with high specificity. © RSNA, 2020.

Transcriptional regulators in hepatocarcinogenesis – Key integrators of malignant transformation

Background &amp; Aims: While many studies revealed transcriptomic subtypes of hepatocellular carcinoma (HCC), they are not translated to the clinic yet due to lack of consensus. We aim to examine consensus of transcriptomic subtypes and uncover their clinical significance. Methods: We integrated 15 previously established transcriptomic signatures for HCC to uncover consensus subtypes. We also developed and validated a robust predictor of consensus subtype with 100 genes (PICS100). Informatics and statistics approaches were applied to find clinical relevant association of genomic features. Patient derived xenograft (PDX) models were used for testing hypothesis from analysis of transcriptomic data. Results: We identified 5 clinically and molecularly distinct consensus subtypes. STM (STeM) is characterized by high stem cell features, vascular invasion, and poor prognosis. CIN (Chromosomal INstability) has moderate stem cell features but high genomic instability and low immune activity. IMH (IMmune High) is characterized by high immune activity. BCM (Beta-Catenin with high Male predominance) is characterized by prominent β-catenin activation, low miRNA expression, hypomethylation, and high sensitivity to sorafenib. DLP (Differentiated and Low Proliferation) is differentiated with high HNF4A activity. We also identified potential serum biomarkers that can stratify patients into 5 subtypes. Conclusions: Five HCC subtypes are associated with potential response to treatments and highly conserved in pre-clinical models, providing a framework for selecting the most appropriate models for preclinical studies of new drugs and potentially for future clinical trials. Citation Format: Ju-Seog Lee, Sun Young Yim, Sung Hwan Lee, Yun Seong Jeong. Consensus subtypes of hepatocellular carcinoma associated with clinical outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 768.

Background &amp; aims: While many studies revealed genomic subtypes of hepatocellular carcinoma (HCC), they are not translated to the clinic yet due to lack of consensus. We aim to examine consensus of genomic subtypes and uncover their clinical significance. Methods: We integrated 15 previously established genomic signatures for HCC to uncover consensus genomic subtypes. We also developed and validated a robust predictor of consensus subtype with 100 genes (PICS100). Informatics and statistics approaches were applied to find clinical relevant association of genomic features. Patient derived xenograft (PDX) models were used for testing hypothesis from analysis of genomic data. Results: We identified 5 clinically and molecularly distinct consensus subtypes. STM (STeM) is characterized by high stem cell features, vascular invasion, and poor prognosis. CIN (Chromosomal INstability) has moderate stem cell features but high genomic instability and low immune activity. IMH (IMmune High) is characterized by high immune activity. BCM (Beta-Catenin with high Male predominance) is characterized by prominent β-catenin activation, low miRNA expression, hypomethylation, and high sensitivity to sorafenib. DLP (Differentiated and Low Proliferation) is differentiated with high HNF4A activity. We also identified potential serum biomarkers that can stratify patients into 5 subtypes. Because these subtypes are highly associated with currently available treatments, our findings may provide the foundation for rationalized biomarker-based clinical trials. Conclusions: Five HCC subtypes are highly associated with response to standard and experimental treatments and highly conserved in pre-clinical models, providing a framework for selecting the most appropriate models for preclinical studies and rationalized clinical trials of new drugs. Citation Format: Sung Hwan Lee, Ju-Seog Lee. Consensus subtypes of hepatocellular carcinoma associated with clinical outcomes, response to therapies, and multiple biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 480.

Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm.

Background: Recent studies highlighted the functional role of protein arginine methyltransferases (PRMTs) catalyzing the methylation of protein arginine in malignant progression of various tumors. Stratification the subtypes of hepatocellular carcinoma (HCC) is fundamental for exploring effective treatment strategies. Here, we aim to conduct a comprehensive analysis of PRMTs with bioinformatic tools to identify novel biomarkers for HCC subtypes classification and prognosis prediction, which may be potential ideal targets for therapeutic intervention. Methods: The expression profiling of PRMTs in HCC tissues was evaluated based on the data of TCGA-LIHC cohort, and further validated in HCC TMA cohort and HCC cell lines. HCC was systematically classified based on PRMT family related genes. Subsequently, the differentially expressed genes (DEGs) between molecular subtypes were identified, and prognostic risk model were constructed using least absolute shrinkage and selection operator (LASSO) and Cox regression analysis to evaluate the prognosis, gene mutation, clinical features, immunophenotype, immunotherapeutic effect and antineoplastic drug sensitivity of HCC. Results: PRMTs expression was markedly altered both in HCC tissues and HCC cell lines. Three molecular subtypes with distinct immunophenotype were generated. 11 PRMT-related genes were enrolled to establish prognostic model, which presented with high accuracy in predicting the prognosis of two risk groups in the training, validation, and immunotherapy cohort, respectively. Additionally, the two risk groups showed significant difference in immunotherapeutic efficacy. Further, the sensitivity of 72 anticancer drugs was identified using prognostic risk model. Conclusion: In summary, our findings stratified HCC into three subtypes based on the PRMT-related genes. The prognostic model established in this work provide novel insights into the exploration of related therapeutic approaches in treating HCC.

Hepatocellular carcinoma (HCC) is the most prevalent primary cancer and a highly aggressive liver malignancy. Liver cancer cells reprogram their metabolic pathways to meet their needs for rapid proliferation and generation of the biomass for tumor growth. The accelerated aerobic glycolysis in tumor has been considered one of the hallmarks that distinguish cancer cells from normal cells. In the present study, liver hepatocellular cancer dataset of the Cancer Genome Atlas (LIHC TCGA) and datasets from Gene Expression Omnibus (GEO) were used to investigate the alterations in expressions of the genes involved in the glucose metabolic pathways as well as their association with the patient clinical stage and survival in liver cancer. In this study, 97 genes including glucose and lactate transporters, the enzymes involved in glucose metabolism (glycolysis, gluconeogenesis, regulation of glucose metabolism, tricarboxylic acid cycle (TCA), pentose phosphate pathway), and glycogen metabolism (glycogen synthesis, glycogen degradation, regulation of glycogen metabolism) were included. The results showed that the expressions of around 50% of genes involved in the glucose metabolic pathway are altered in liver tumors compared to the corresponding normal tissues (p-values<0.005 and fold change≥1.5). More importantly, the differentially expressed genes are associated with advanced AJCC (American Joint Committee on Cancer) clinical stage (p-values<0.05), and reduced overall survival (OS) and recurrence-free survival (RFS) as determined by multivariate Cox proportional hazard models (p-values<0.05). Unexpectedly but interestingly, supervised clustering with the differentially expressed genes of the LIHC TCGA data identified a subgroup of patients with worse OS compared to those patients that clustered with the normal samples (p-value=0.00015, and 5-year median survival time (months) is 25.50 with 95% CI (20.90, 42.37) vs. 47.43 with 95% CI (40.33, NA)). This group of patients had decreased activation (p-values<0.0001) of FXR/RXR and LXR/RXR involved in lipid, cholesterol and glucose metabolism, but had increased atherosclerosis and leukocyte extravasation signaling (p-values<0.0001) activated during inflammation. This indicates those patients had decreased metabolism and increased inflammation which may contribute to the worse OS. Collectively, this study indicates that expressions of the glucose metabolic genes could be used as potential prognostic markers as well as therapeutic targets for liver cancer.Citation Format: Xiaoli Zhang, Kalpana Ghoshal, Lang Li. Identification of a subtype of hepatocellular carcinoma with poor prognosis based on expression of genes within the glucose metabolic pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4938.

BackgroundThe molecular subtypes of endometrial carcinoma are significantly correlated with survival outcomes and can guide surgical methods and postoperative adjuvant therapy. Among them, the TP53mut subtype has the worst prognosis and can only be determined by detection after surgery. Therefore, identifying preoperative noninvasive clinical parameters for early prediction of the TP53mut subtype would provide important guidance in choosing the appropriate surgical method and early warning for clinicians. Our study aimed to establish a model for the early prediction of the TP53mut subtype by using preoperative noninvasive parameters of endometrial cancer and screen out potential TP53mut patients.MethodsInformation and pathological specimens of 376 patients who underwent surgery for FIGO stage I-IV endometrial cancer in the Department of Gynecology, Peking University Cancer Hospital, from June 2011 to July 2020 were collected, and 178 cases were finally included in the study as the training dataset (part A). Thirty-six cases from January 2022 to March 2023 were collected as the validation dataset (part B). Molecular subtyping was performed using a one-stop next-generation sequencing (NGS) approach. Compared with the TP53mut subtype, the POLE EDM, MSI-H and TP53 wild-type subtypes were defined as non-TP53mut subtypes. Univariate Cox regression analysis and multivariate logistic analysis were performed to determine the preoperative clinical parameters associated with the TP53mut subtype. A nomogram prediction model was established using preoperative noninvasive parameters, and its efficacy in predicting TP53mut subtype and survival outcomes was verified.ResultsThe TP53mut subtype was identified in 12.4% of the part A and 13.9% of the part B. Multivariate logistic regression analysis showed that HDL-C/LDL-C level, CA125 level, and cervical or lower uterine involvement were independent influencing factors associated with the TP53mut subtype (p = 0.016, 0.047, <0.001). A TP53mut prognostic model (TPMM) was constructed based on the factors identified in the multivariate analysis, namely, TPMM = -1.385 × HDL-C/LDL-C + 1.068 × CA125 + 1.89 × CI or LUI, with an AUC = 0.768 (95% CI, 0.642 to 0.893) in the part A. The AUC of TPMM for predicting TP53mut subtype in the part B was 0.781(95% CI, 0.581 to 0.980). The progression-free survival (PFS) and overall survival (OS) of patients with the TP53mut subtype were significantly worse than those of patients with the non-TP53mut subtype, as predicted by the model in the part A.ConclusionsTP53mut prediction model (TPMM) had good diagnostic accuracy, and survival analysis showed the model can identify patients with different prognostic risk.