ESM1 as a Marker of Macrotrabecular-Massive Hepatocellular Carcinoma

Hepatocellular Carcinoma: Role of Pathology in the Era of Precision Medicine

Hepatocellular carcinoma (HCC) is a morphologically heterogeneous tumor with variable architectural growth patterns and several distinct histologic subtypes. Large-scale attempts have been made over the past decade to identify targetable genomic alterations in HCC; however, its translation into clinical personalized care remains a challenge to precision oncology. The role of pathology is no longer limited to confirmation of diagnosis when radiologic features are atypical. Pathology is now in a position to predict the underlying molecular alteration, prognosis, and behavior of HCC. This review outlines various aspects of histopathologic diagnosis and role of pathology in cutting-edge diagnostics of HCC.

BAP1 mutations define a homogeneous subgroup of hepatocellular carcinoma with fibrolamellar-like features and activated PKA

BACKGROUND & AIMS

DNAJB1-PRKACA fusion is a specific driver event in fibrolamellar carcinoma (FLC), a rare subtype of hepatocellular carcinoma (HCC) that occurs in adolescents and young adults. In older patients, molecular determinants of HCC with mixed histological features of HCC and FLC (mixed-FLC/HCC) remain to be discovered.

METHODS

A series of 151 liver tumors including 126 HCC, 15 FLC, and 10 mixed-FLC/HCC were analyzed by RNAseq and whole-genome- or whole-exome sequencing. Western blots were performed to validate genomic discoveries. Results were validated using the TCGA database.

RESULTS

Most of the mixed-FLC/HCC RNAseq clustered in a robust subgroup of 17 tumors, which all had mutations or translocations inactivating BAP1, the gene encoding BRCA1-associated protein-1. Like FLC, BAP1-HCC were significantly enriched in females, patients with a lack of chronic liver disease, and fibrotic tumors compared to non-BAP1 HCC. However, patients were older and had a poorer prognosis than those with FLC. BAP1 tumors were immune hot, showed progenitor features and did not show DNAJB1-PRKACA fusion, while almost none of these tumors had mutations in CTNNB1, TP53 and TERT promoter. In contrast, 80% of the BAP1 tumors showed a chromosome gain of PRKACA at 19p13, combined with a loss of PRKAR2A (coding for the inhibitory regulatory subunit of PKA) at 3p21, leading to a high PRKACA/PRKAR2A ratio at the mRNA and protein levels.

CONCLUSION

We have characterized a subgroup of BAP1-driven HCC with fibrolamellar-like features and a dysregulation of the PKA pathway, which could be at the root of the clinical and histological similarities between BAP1 tumors and DNAJB1-PRKACA FLCs.

LAY SUMMARY

Herein, we have defined a homogeneous subgroup of hepatocellular carcinomas in which the BAP1 gene is inactivated. This leads to the development of cancers with features similar to those of fibrolamellar carcinoma. These tumors more frequently develop in females without chronic liver disease or cirrhosis. The presence of PKA activation and T cell infiltrates suggest that these tumors could be treated with PKA inhibitors or immunomodulators.

Multiphase Liver MRI for Identifying the Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma

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.

Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning

OBJECTIVE

The aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinoma (HCC) from adjacent normal tissue and predict the prognosis of HCC patients after surgical resection.

METHODS

A fully automated pipeline was constructed using computational approaches to analyze the quantitative features of histopathological slides of HCC patients, in which the features were extracted from the hematoxylin and eosin (H&E)-stained whole-slide images of HCC patients from The Cancer Genome Atlas and tissue microarray images from West China Hospital. The extracted features were used to train the statistical models that classify tissue slides and predict patients' survival outcomes by machine-learning methods.

RESULTS

A total of 1733 quantitative image features were extracted from each histopathological slide. The diagnostic classifier based on 31 features was able to successfully distinguish HCC from adjacent normal tissues in both the test [area under the receiver operating characteristic curve (AUC) 0.988] and external validation sets (AUC 0.886). The random-forest prognostic model using 46 features was able to significantly stratify patients in each set into longer- or shorter-term survival groups according to their assigned risk scores. Moreover, the prognostic model we constructed showed comparable predicting accuracy as TNM staging systems in predicting patients' survival at different time points after surgery.

CONCLUSIONS

Our findings suggest that machine-learning models derived from image features can assist clinicians in HCC diagnosis and its prognosis prediction after hepatectomy.