Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures

Abstract

Background & aims: Current hepatocellular carcinoma (HCC) risk scores do not reflect changes in HCC risk resulting from liver disease progression/regression over time. We aimed to develop and validate two novel prediction models using multivariate longitudinal data, with or without cell-free DNA (cfDNA) signatures.

Methods: A total of 13,728 patients from two nationwide multicenter prospective observational cohorts, the majority of whom had chronic hepatitis B, were enrolled. aMAP score, as one of the most promising HCC prediction models, was evaluated for each patient. Low-pass whole-genome sequencing was used to derive multi-modal cfDNA fragmentomics features. A longitudinal discriminant analysis algorithm was used to model longitudinal profiles of patient biomarkers and estimate the risk of HCC development.

Results: We developed and externally validated two novel HCC prediction models with a greater accuracy, termed aMAP-2 and aMAP-2 Plus scores. The aMAP-2 score, calculated with longitudinal data on the aMAP score and alpha-fetoprotein values during an up to 8-year follow-up, performed superbly in the training and external validation cohorts (AUC 0.83-0.84). The aMAP-2 score showed further improvement and accurately divided aMAP-defined high-risk patients into two groups with 5-year cumulative HCC incidences of 23.4% and 4.1%, respectively (p = 0.0065). The aMAP-2 Plus score, which incorporates cfDNA signatures (nucleosome, fragment and motif scores), optimized the prediction of HCC development, especially for patients with cirrhosis (AUC 0.85-0.89). Importantly, the stepwise approach (aMAP -> aMAP-2 -> aMAP-2 Plus) stratified patients with cirrhosis into two groups, comprising 90% and 10% of the cohort, with an annual HCC incidence of 0.8% and 12.5%, respectively (p <0.0001).

Conclusions: aMAP-2 and aMAP-2 Plus scores are highly accurate in predicting HCC. The stepwise application of aMAP scores provides an improved enrichment strategy, identifying patients at a high risk of HCC, which could effectively guide individualized HCC surveillance.

Impact and implications: In this multicenter nationwide cohort study, we developed and externally validated two novel hepatocellular carcinoma (HCC) risk prediction models (called aMAP-2 and aMAP-2 Plus scores), using longitudinal discriminant analysis algorithm and longitudinal data (i.e., aMAP and alpha-fetoprotein) with or without the addition of cell-free DNA signatures, based on 13,728 patients from 61 centers across mainland China. Our findings demonstrated that the performance of aMAP-2 and aMAP-2 Plus scores was markedly better than the original aMAP score, and any other existing HCC risk scores across all subsets, especially for patients with cirrhosis. More importantly, the stepwise application of aMAP scores (aMAP -> aMAP-2 -> aMAP-2 Plus) provides an improved enrichment strategy, identifying patients at high risk of HCC, which could effectively guide individualized HCC surveillance.

Keywords: Hepatocellular carcinoma; Longitudinal discriminant analysis; cell-free DNA signatures; prediction model.