Strategies for improving early detection, diagnosis and treatment of liver cancer

Division Of Basic Sciences - Nci

Investigators

Linked publications, trials & patents

Abstract

Primary liver cancer (PLC) is a rising cause of cancer deaths in the US. Although immunotherapy with immune checkpoint inhibitors (ICIs) induces a potent response in a subset of patients, response rates vary among individuals. Predicting which patients will respond to ICIs is of great interest in the field. We initiated the NCI-CLARITY study (National Cancer Institute Cancers of the Liver: Accelerating Research of Immunotherapy by a Transdisciplinary Network), a multisite prospective study of PLC clinical trial data and accompanying biospecimens, focused on exploring the underlying mechanisms that determine immunotherapy responses. We previously reported on a parallel retrospective arm of NCI-CLARITY, entailing molecular classification of 230 primary HCC (hepatocellular carcinoma) and BTC (biliary tract cancer) tumors (mainly iCCA (intrahepatic cholangiocarcinoma)) and adjacent non-tumor tissues, largely stored as FFPE (formalin-fixed, paraffin-embedded) specimens attained before and following ICI treatment. We molecular features of tumors linked to treatment responses. To extend this study further, We have utilized the phage display immunoprecipitation sequencing approach to determine serological responses to the human virome as potential functional biomarkers for risk stratification and early onset of liver cancer. Recently, we conducted a proof-of-principle study by performing serological profiling of the viral infection history in 899 individuals from an NCI-UMD case-control study using a synthetic human virome, VirScan. We developed a viral exposure signature and validated the results in a longitudinal cohort with 173 at-risk patients who had long-term follow-up for HCC development. Our viral exposure signature was significantly associated with HCC status among at-risk individuals in the validation cohort (area under the curve: 0.91 [95% CI 0.87-0.96] at baseline and 0.98 [95% CI 0.97-1] at diagnosis). The signature identified cancer patients prior to a clinical diagnosis and was superior to alpha-fetoprotein. This study allows us to establish a viral exposure signature that can predict HCC among at-risk patients prior to a clinical diagnosis, which may be useful in HCC surveillance. To extend the above findings, we established the CASCADE algorithm to classify tumors in the lineage-ecological space using sincle cell transcriptome data and further monitor tumor evolution in response to treatment. In addition, we have also evaluated the pan-serological profiles of Thai HCC and iCCA compared to several diseased and non-diseased control populations to identify risk factors and biomarkers of liver cancer as a part of TIGER-LC consortium studies. We used phage immunoprecipitation sequencing, an anti-viral antibody screening method using a synthetic-phage-displayed human virome epitope library, to screen patient serum samples for exposure to over 1,280 strains of pathogenic and non-pathogenic viruses. Using machine learning methods to develop an HCC or iCCA viral score, we discovered that both viral scores were positively associated with several liver function markers in two separate at-risk populations independent of viral hepatitis status. The HCC score predicted all-cause mortality over eight years in patients with chronic liver disease at risk of HCC, while the viral hepatitis status was not predictive of survival. These results suggest that non-hepatitis viral infections may contribute to HCC and iCCA development and could be biomarkers in at-risk populations. We are conducting several prospective cohorts for the development of HCC and iCCA and plan to validate the identify viral scores to predict risks of HCC and iCCA.