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Abstract Details
HepatoScore-14: Measures of Biological Heterogeneity Significantly Improve Prediction of Hepatocellular Carcinoma Risk
Hepatology. 2021 Jun;73(6):2278-2292. doi: 10.1002/hep.31555. Epub 2021 Jun 15.
Jeffrey S Morris1, Manal M Hassan2, Ye Emma Zohner3, Zeya Wang34, Lianchun Xiao5, Asif Rashid6, Abedul Haque7, Reham Abdel-Wahab8, Yehia I Mohamed8, Karri L Ballard9, Robert A Wolff8, Bhawana George7, Liang Li5, Genevera Allen310, Michael Weylandt3, Donghui Li8, Wenyi Wang4, Kanwal Raghav8, James Yao8, Hesham M Amin7, Ahmed Omar Kaseb8
Author information
1Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
2Department of Epidemiology, the University of Texas MD Anderson Cancer Center, Houston, TX.
3Department of Statistics, Rice University, Houston, TX.
4Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.
5Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
6Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.
7Department of Hematopathology, the University of Texas MD Anderson Cancer Center, Houston, TX.
8Department of Gastrointestinal Medical Oncology, the University of Texas MD Anderson Cancer Center, Houston, TX.
9Myriad RBM Inc., Austin, TX.
10Department of Computer Science, Rice University, Houston and Jan and Dan Duncan Neurological Institute, Baylor College of Medicine, Houston, TX.
Abstract
Background and aims: Therapeutic, clinical trial entry and stratification decisions for hepatocellular carcinoma (HCC) are made based on prognostic assessments, using clinical staging systems based on small numbers of empirically selected variables that insufficiently account for differences in biological characteristics of individual patients' disease.
Approach and results: We propose an approach for constructing risk scores from circulating biomarkers that produce a global biological characterization of individual patient's disease. Plasma samples were collected prospectively from 767 patients with HCC and 200 controls, and 317 proteins were quantified in a Clinical Laboratory Improvement Amendments-certified biomarker testing laboratory. We constructed a circulating biomarker aberration score for each patient, a score between 0 and 1 that measures the degree of aberration of his or her biomarker panel relative to normal, which we call HepatoScore. We used log-rank tests to assess its ability to substratify patients within existing staging systems/prognostic factors. To enhance clinical application, we constructed a single-sample score, HepatoScore-14, which requires only a subset of 14 representative proteins encompassing the global biological effects. Patients with HCC were split into three distinct groups (low, medium, and high HepatoScore) with vastly different prognoses (medial overall survival 38.2/18.3/7.1 months; P < 0.0001). Furthermore, HepatoScore accurately substratified patients within levels of existing prognostic factors and staging systems (P < 0.0001 for nearly all), providing substantial and sometimes dramatic refinement of expected patient outcomes with strong therapeutic implications. These results were recapitulated by HepatoScore-14, rigorously validated in repeated training/test splits, concordant across Myriad RBM (Austin, TX) and enzyme-linked immunosorbent assay kits, and established as an independent prognostic factor.
Conclusions: HepatoScore-14 augments existing HCC staging systems, dramatically refining patient prognostic assessments and therapeutic decision making and enrollment in clinical trials. The underlying strategy provides a global biological characterization of disease, and can be applied broadly to other disease settings and biological media.