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Page 8 of 13 Kwee et al. Hepatoma Res 2021;7:8 I http://dx.doi.org/10.20517/2394-5079.2020.124
[46]
high likelihood of being CTNNB1 mutated . A recent integrative analysis of DNA methylation and
gene expression revealed another sub-type of HCC that showed enrichment for CTNNB1 mutations
[65]
and signatures of Wnt activation but lacked signs of immune-activation . This tumor sub-type showed
significant overlap with both the Hoshida S3 class and the Chiang CTNNB1-activated class that were
characterized by high tumor FCH uptake in our studies. The Boyault G5 and G6 classes, among the earliest
[63]
to be associated with CTNNB1 mutations , have also been recently implicated with poorly immunogenic
sub-types [10,65] . This high degree of transcriptomic overlap forms an intriguing link between tumor FCH
[66]
avidity and poor anti-tumor immunity. Furthermore, a TIMER (Tumor Immune Estimation Resource ,
accessed via timer.cistrome.org) based analysis of the tumor expression profiles revealed significantly
higher estimated densities of monocytic, CD8+, and dendritic cells among the tumors that displayed low
FCH uptake [Figure 4], suggesting that poor FCH avidity was associated with immune cell infiltration.
While collectively these results support associations between Wnt/b-catenin activation, lipid metabolism,
and tumor immune-evasion in HCC, it remains to be tested in clinical trials whether molecular imaging
biomarkers such as those derived from FCH PET/CT can serve as reliable predictors of immunotherapy
response for HCC.

PERSPECTIVES ON THE CLINICAL FUTURE OF IMMUNOTHERAPY BIOMARKERS IN HCC
In 2019, the KEYNOTE-240 phase III trial was reported to have failed in achieving its pre-determined
[6]
statistical endpoints for survival . However, durations of clinical response in the trial ranged from 1.5 months
to 23.6 months, and the risk of death overall was reduced by 22% (HR = 0.781, 95%CI: 0.611-0.998, P =
0.0238). The implication of these results is that some patients will benefit substantially from these agents,
but the benefits will be thinly spread across too many patients in the absence of a robust predictive
biomarker that can be used to refine patient selection. Because anti-PD1 agents can lead to prolonged
disease control in those who do respond, a predictive biomarker of treatment resistance/response could
have substantial value in both the clinical and research domains. For clinical trials, a reliable predictive
biomarker may substantially reduce the study sample size required and increase the statistical power for
[67]
an a-priori treatment effect size . Because immune-related adverse events to ICI therapy are non-trivial,
bringing such a biomarker to the clinical practice space would help guide patients with vulnerable tumors
to appropriate therapy while protecting those who are unlikely to respond from the hazards of futile
treatment and its side effects. From a healthcare economics standpoint, a predictive biomarker would help
to enhance the value proposition of ICI treatment by reducing costs associated with wasted treatments and
unhalted disease progression.

However, robust biomarkers, detectors, predictors, and other classifiers that are singular in nature are rare
in the field of cancer. There are multiple biological and statistical reasons for why an integrative biomarker
would perform better than a single or narrowly targeted set of biomarkers [35,65,68-70] . An understanding of
how different non-convergent molecular pathways and phenotypes can shape tumor immunity in HCC
may support multimarker integration as an approach to predicting immunotherapeutic response [10,35,46,65,70] .
There is also a growing number of statistical learning and machine learning based approaches to
building, integrating, and evaluating multi-biomarker classifiers [68,71,72] , although the optimal method for
assigning significance to any incremental gains in biomarker classification performance has been a topic
of debate [73,74] . The tools for integrative biomarker design and analysis have also become research tools
to elucidate the biologic origin and functional significance of different biomarkers with the potential of
shedding more light on their clinical and biological importance.

The contemporary clinical approach to the diagnosis of HCC has evolved into something rather unique
among solid tumors. Diagnostic algorithms for HCC, such as those based on the National Comprehensive
[75]
Cancer Networks (NCCN) guidelines , allow for the diagnosis of HCC to be predicated on the results
of radiographic testing in appropriately selected patients. In those patients with cirrhosis or chronic liver