Detecting Liver Cancer Using Cell-Free DNA Fragmentomes.

Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome analyses to evaluate 724 individuals from the United States, the European Union, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multifeature fragmentome data, the sensitivity for detecting cancer was 88% in an average-risk population at 98% specificity and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for noninvasive cancer detection. SIGNIFICANCE: There is a great need for accessible and sensitive screening approaches for HCC worldwide. We have developed an approach for examining genome-wide cfDNA fragmentation features to provide a high-performing and cost-effective approach for liver cancer detection. See related commentary Rolfo and Russo, p. 532. This article is highlighted in the In This Issue feature, p. 517.

Magnetic Resonance Imaging Predictors of Hepatocellular Carcinoma Progression and Dropout in Patients in Liver Transplantation Waiting List.

With the rising incidence of hepatocellular carcinoma (HCC), more patients are now eligible for liver transplantation. Consequently, HCC progression and dropout from the waiting list are also anticipated to rise. We developed a predictive model based on radiographic features and alpha-fetoprotein to identify high-risk patients. Methods: This is a case-cohort retrospective study of 76 patients with HCC who were listed for liver transplantation with subsequent liver transplantation or delisting due to HCC progression. We analyzed imaging-based predictive variables including tumor margin (well- versus ill-defined), capsule bulging lesions, volumetric analysis and distance to portal vein, tumor numbers, and tumor diameter. Volumetric analysis of the index lesions was used to quantify index tumor total volume and volumetric enhancement, whereas logistic regression and receiver operating characteristic curve (ROC) analyses were used to predict the main outcome of disease progression. Results: In univariate analyses, the following baseline variables were significantly associated with disease progression: size and number of lesions, sum of lesion diameters, lesions bulging the capsule, and total and venous-enhancing (viable) tumor volumes. Based on multivariable analyses, a risk model including lesion numbers and diameter, capsule bulging, tumor margin (infiltrative versus well-defined), and alpha-fetoprotein was developed to predict HCC progression and dropout. The model has an area under the ROC of 82%, which was significantly higher than Milan criteria that has an area under the ROC of 67%. Conclusions: Our model has a high predictive test for patient dropout due to HCC progression. This model can identify high-risk patients who may benefit from more aggressive HCC treatment early after diagnosis to prevent dropout due to such disease progression.