Informing Decisions in Liver Cancer: Use Biology to Better Characterize the Disease

Informing Decisions in Liver Cancer: Use Biology to Better Characterize the Disease

September 2020

Hepatocellular carcinoma (HCC), or liver cancer, is the fifth-most-common cancer worldwide. With an estimated five-year survival rate of less than 10 percent, it is the United States’ fastest growing cause of cancer-related death in men. These dire statistics hail, in part, from the lack of validated biomarkers that can characterize the biological features of an individual’s cancer and thus inform therapeutic decisions. However a novel method, reported recently in the journal Hepatology, stands poised to change that.

“Instead of using the conventional approach to describe a patient’s status—selecting a subset of clinical factors and biomarkers associated with patient survival or other clinical factors—we developed a novel blood biomarker score. The liver sheds numerous proteins and cytokines into the blood, so blood plasma can contain biomarkers that reflect specific biological characteristics of a liver tumor,” says lead author Jeffrey Morris, PhD. “And we believe a similar strategy can also provide informative signatures and aid precision therapeutics for other cancers and diseases such as kidney disease or even COVID-19.

“Although the HepatoScore is based on only 14 blood biomarkers and contains no clinical information about the patients, its prognostic stratification rivals existing clinical staging systems. And when we combine it with conventional systems, it is especially valuable: it reflects an improved prognosis for some patients and forecasts an otherwise unexpected decline for others.” 

All of the study’s 766 patient participants had a pathologically or radiologically confirmed diagnosis of HCC and were U.S. residents; none had other types of primary liver cancer, unknown primary tumors, or concurrent or past history of cancer at another organ site. Two hundred healthy controls were included.

Several of the existing staging systems for HCC examine individual tumor parameters and liver markers, and include the necessary factors to assess the severity of underlying cirrhosis. However, although they are the standard for clinical practice and for stratifying patient groups in clinical trials, these systems are limited: They look at just a few clinical parameters. Only one biomarker—AFP, a specific glycoprotein that has been the most practical and widely used for HCC diagnosis—is tumor-related. “These systems do not capture the biological variation among patients that is a hallmark of cancer; within any given level, significant, unexplained differences remain,” says Dr. Morris.

To explain these differences, the research team, including collaborators from the University of Texas M.D. Anderson Cancer Center, developed HepatoScore.  HepatoScore provides a measure of disease severity based on a biological signature that's obtained from a patient’s blood sample using a novel computational strategy. The team has shown that HepatoScore substantially refines a clinician’s assessment of how severe a patient’s individual cancer is.  In one of many striking examples, they showed that metastatic HCC patients with low HepatoScores in fact have a substantially better prognosis than non-metastatic HCC patients with high ones.

The researchers believe that clinicians should compute a HepatoScore for all HCC patients. They can use it to select appropriate subsets of patients for clinical trials and as a stratification factor to ensure that the two arms of randomized clinical trials are balanced in terms of disease severity, an important consideration. “While an improved assessment of disease severity could be used to give patients a more accurate estimate of their expected, or median, survival, I think the most important application is to inform the physician about what treatments they should consider,” Dr. Morris adds.

In order to deploy HepatoScore-14 clinically, the researchers say, they need a validated commercial assay for measuring the proteins; this would pair with their computational algorithm to produce the score. They are currently assessing potential partners who might be able to produce such an assay to scale.

Authors: 

Jeffrey Morris, Manal M. Hassan, Ye Emma Zohner, Zeya Wang, Lianchun Xiao, Asif Rashid, Abedul Haque, Reham Abdel‐Wahab, Yehia I. Mohamed, Karri L. Ballard, Robert A. Wolff, Bhawana George, Liang Li, Genevera Allen, Michael Weylandt, Donghui Li, Wenyi Wang, Kanwal Raghav, James Yao, Hesham M. Amin, Ahmed Omar Kaseb

Read the study in the journal Hepatology.

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