Clinicopathological indices to predict hepatocellular carcinoma molecular classification

Abstract

Background & aims: Hepatocellular carcinoma (HCC) is the second most lethal cancer caused by lack of effective therapies. Although promising, HCC molecular classification, which enriches potential responders to specific therapies, has not yet been assessed in clinical trials of anti-HCC drugs. We aimed to overcome these challenges by developing clinicopathological surrogate indices of HCC molecular classification.

Methods: Hepatocellular carcinoma classification defined in our previous transcriptome meta-analysis (S1, S2 and S3 subclasses) was implemented in an FDA-approved diagnostic platform (Elements assay, NanoString). Ninety-six HCC tumours (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumours (validation set). Molecular deregulations associated with the histopathological features were determined by pathway analysis. Sample sizes for HCC clinical trials enriched with specific molecular subclasses were determined.

Results: Hepatocellular carcinoma subclass-predictive indices were steatohepatitic (SH)-HCC variant and immune cell infiltrate for S1 subclass, macrotrabecular/compact pattern, lack of pseudoglandular pattern, and high serum alpha-foetoprotein (>400 ng/ml) for S2 subclass, and microtrabecular pattern, lack of SH-HCC and clear cell variants, and lower histological grade for S3 subclass. Macrotrabecular/compact pattern, a predictor of S2 subclass, was associated with the activation of therapeutically targetable oncogene YAP and stemness markers EPCAM/KRT19. BMP4 was associated with pseudoglandular pattern. Subclass-predictive indices-based patient enrichment reduced clinical trial sample sizes from 121, 184 and 53 to 30, 43 and 22 for S1, S2 and S3 subclass-targeting therapies respectively.

Conclusions: Hepatocellular carcinoma molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.

Keywords: clinical diagnostic; gene expression; histopathology; molecular subclass; predictive index.

Figure 1

A. Histopathological features of HCC tumors. Architectural patterns and cytological variants analyzed for correlation with HCC molecular subclasses. B. HCC molecular subclasses and clinicopathological features in the training (left) and validation (right) sets. Black bars indicate positivity of the feature. HCC tumors with high immune cell infiltrate (score of 2) is shown by black bars. Red, blue and yellow colors in the horizontal bars above the heatmaps indicate S1, S2 and S3 tumors, respectively. Red and blue colors in the heatmaps indicate high and low gene expression, respectively.