Cirrhosis is a high-risk state for hepatocellular carcinoma (HCC) development and represents an opportunity to prevent cancer. In the precancerous state of cirrhosis, there is an accumulation of neoantigens that may be specifically targetable through immunotherapy. We asked whether immune checkpoint inhibition could prevent tumorigenesis in a mouse model of diethylnitrosamine and carbon tetrachloride–induced HCC. We found that initiation of anti-PD-1 therapy prior to tumorigenesis could prevent up to 46% of liver tumors. This significant reduction in tumor burden was accompanied by infiltration of CD4+ Th cells and CD8+ cytotoxic T cells into the liver parenchyma. Importantly, anti-PD-1 therapy did not exacerbate liver dysfunction or worsen overall health in this liver disease model. Given the safety and preservation of quality of life observed with long-term immunotherapy use, an immunotherapy chemoprevention strategy is likely associated with a low risk-to-benefit ratio and high value care in select patients. These results encourage a prevention trial in cirrhotic patients with the highest risk of developing HCC.
See related Spotlight by Mohammed et al., p. 897
Primary liver cancer represents a major public health problem, with greater than 850,000 cases diagnosed each year (1). Among primary liver cancers, hepatocellular carcinoma (HCC) represents approximately 90% of all cases. HCC most often arises in the context of longstanding, progressive liver disease caused by chronic hepatitis B (HBV) infection, chronic hepatitis C (HCV) infection, nonalcoholic steatohepatitis (NASH), or alcoholic liver disease. Over decades, these risk factors can lead to cirrhosis in a subset of patients, and HCC then develops in 5%–30% of patients with cirrhosis (2). Once established in a cirrhotic liver, HCC is difficult to treat (5-year survival of 18%), with an incidence-to-mortality ratio approaching 1 (2). Moreover, the majority of patients with HCC present with advanced stage disease that cannot be cured (2).
Cirrhosis represents a premalignant state, and the decades-long latency of progression from cirrhosis to HCC offers a large window for intervention. Therefore, cancer prevention could be the most effective approach for reducing liver cancer burden and mortality, especially if creative and novel strategies to select the highest risk patients from the pool of patients with cirrhosis can be devised (2). Recent work by Hoshida and colleagues has identified a gene expression signature to predict HCC risk in patients with cirrhosis (3, 4), while others have developed risk models using clinical biomarkers to estimate HCC risk (5, 6). These tools could be useful in the clinical setting to identify the highest risk patients that would benefit most from prevention. To date, the only effective chemoprevention strategies proven for HCC are antivirals for viral hepatitis–associated HCC, which is not relevant to a growing proportion of patients with cirrhosis caused by nonviral etiologies (7).
Immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer. Antibody-mediated blockade of the programmed death-1 receptor (PD-1), its ligand programmed death ligand-1 (PD-L1), and cytotoxic T-lymphocyte antigen-4 (CTLA-4) have demonstrated improved clinical outcomes when administered as first- or second-line therapy in renal cell carcinoma, squamous cell lung carcinoma, advanced urothelial carcinoma, and advanced melanoma (8–12). Recent studies have shown that anti-PD-1 therapy can be effective for a subset of patients with advanced HCC. CheckMate 040, a phase I/II clinical trial using nivolumab in advanced HCC, showed an objective response rate of 20% with a manageable safety profile (8). Similarly, KEYNOTE 224 was a phase II clinical trial using pembrolizumab in patients with previously treated HCC and found an objective response rate of 18% (13). In addition to these anticancer effects, ICIs are generally well-tolerated and safe. Importantly, ICIs are associated with durable effects and have demonstrated improved patient outcomes years after initiation of treatment in melanoma, renal cancer, and lung cancer (14). Yet, no studies have investigated the use of immune checkpoint inhibition to prevent cancer in patients with high-risk cirrhosis.
Tumor-specific gene mutations result in novel amino acid sequences that can generate novel peptides, or neoantigens, which can be presented to CD8+ T cells on MHC class I molecules (15, 16). Cells presenting these nonself neoantigens are eliminated by the immune system, and neoantigen burden is correlated with immune cytolytic activity (17). Nonetheless, many predicted neoantigens are not depleted in untreated tumors (18), suggesting that immune evasion mechanisms are present throughout tumorigenesis. In addition, this indicates neoantigens alone are generally not sufficient for cancer immune rejection and provides a rationale for the use of ICIs, even in the earliest stages of cancer development.
It is widely accepted that high mutation and neoantigen loads are associated with greater ICI efficacy in multiple cancer types (19–21). In addition, loss of neoantigen presentation results in acquired immunotherapy resistance, further demonstrating that neoantigens are required for ICI response (22). Thus, if present, neoantigens in early preneoplastic cells would suggest that subclinical disease may be amenable to treatment with immune checkpoint inhibition. In fact, recent evidence from a study of lung adenocarcinomas demonstrated that cancers with subclonal neoantigens (present in a subset of cells) are less likely to respond to immunotherapies (23). In contrast, clonal neoantigens (present in all cells) are more likely to elicit a successful immunotherapy response. This suggests that eradicating malignant cells at earlier stages of cancer development may not only be efficacious, but would also prevent further genomic evolution and subclonal outgrowth of neoantigens leading to acquired immunotherapy resistance. However, whether neoantigens play any role in the precancerous state of liver cirrhosis is not known.
Recent work from our laboratory and others have shown that fibrotic livers carry somatic mutations and that mutational burden correlates with fibrosis stage (24, 25). Because it is unclear whether these mutations generate neoantigens and an immune response, we examined genomic datasets generated from human cirrhosis samples in our laboratory (24) and The Cancer Genome Atlas data from human patients with HCC (26) to assess neoantigen load. Then, we hypothesized that immune checkpoint inhibition in the precancerous, fibrotic liver might enhance T-cell–mediated elimination of highly mutated, preneoplastic or transformed clones bearing neoantigens, preempting tumorigenesis. We used the diethylnitrosamine (DEN) and carbon tetrachloride (CCl4) mouse model of mutagenesis, liver damage, and carcinogenesis to test whether anti-PD-1 therapy could thus prevent HCC development.
Materials and Methods
Mutation and neoantigen calling
For mouse samples, exome-sequencing reads were aligned to the GRCm38 genome by BWA-MEM (27). For human samples, exome-sequencing reads were aligned to the GRCh38 genome by BWA-MEM (27). Picard was used to add read group information and Sambamba was used to perform base quality score recalibration and local realignment around indels. MuTect (28), VarScan (29), Shimmer (30), SpeedSeq (31), Manta (32), and Strelka2 (33) were used to call SNPs and indels. A mutation called by any three or >3 of these algorithms was retained. Annovar was used to annotate SNPs and indels (34). All SNPs and indels were combined and kept if there were ≥7 total (wild-type and variant) reads in the blood sample and ≥3 variant reads in the liver sample. Somatic mutations and germline mutations were annotated according to the variant allele frequencies (VAF) in the liver and normal blood samples.
We used the QBRC pipeline for neoantigen calling (35). We started neoantigen analysis with somatic mutations called by the QBRC mutation calling pipeline. We used only frameshift, nonframeshift, missense, and stoploss mutations that were predicted to lead to protein coding changes. We kept only somatic mutations whose VAFs were <0.02 (2%) in the blood sample and >0.05 (5%) in the liver samples. For H2-K and H2-D, we predicted the neoantigens of 8–13 amino acids in length. Putative neoantigens with amino acid sequences exactly matching known mouse protein sequences were filtered out. For MHC bindings, IEDB recommended mode (http://tools.iedb.org/main/) was used for prediction of binding affinities. Neoantigens were kept only if the predicted ranks of binding affinities were <2% when compared with an atlas of wild-type peptides. Liver RNA-sequencing (RNA-seq) data were aligned to the GRm38 reference genome using the STAR aligner (36). FeatureCounts was used to summarize gene expression levels (37). Neoantigens whose corresponding mutations were in genes with expression level <1 RPKM in either the specific exon or the whole transcript were filtered out.
Mice and the diethylnitrosamine + CCl4 model
Wild-type male and female C3H/HeJ mice were obtained from the Jackson Laboratory and bred to generate 42 male pups all around the same age. This strain was chosen for its well-known susceptibility to DEN-induced tumors (38). Diethylnitrosamine (N0756, Sigma-Aldrich) was dissolved in 0.9% NaCl and injected intraperitoneally at 2 weeks of age at a dose of 25 mg/kg to induce hepatocyte mutagenesis. At weaning age (P21), the mice were randomly assigned to either IgG control or anti-PD-1 groups. To accelerate tumorigenesis and introduce low levels of cell death and fibrosis, we also gave once weekly intraperitoneal injections of CCl4 starting at 6 weeks of age. CCl4 (289116, Sigma-Aldrich) was dissolved in corn oil (4%) and injected intraperitoneally. In vivo grade monoclonal anti-PD-1 antibody (clone RMP1-14) and isotype control antibody (clone 2A3) were obtained from BioXCell. Mice were injected intraperitoneally with 175 μg of antibody dissolved in 100 μL dilution buffer every other week starting at 10 weeks of age until 20 weeks of age for a total of six doses. When mice were sacrificed, livers were weighed and pictures of the front and back of livers were taken. For quantification of surface tumors, these pictures were randomly assigned numbers by a third party who created a decoding key for group analysis after quantification.
Mice were weighed weekly from 6 weeks of age until the end of the experiment to ensure that the antibody treatment was not affecting overall animal health in either group. Blood was collected from all mice every 4 weeks from 6 weeks of age until the end of the experiment for assessment of liver function through plasma levels of aspartate transaminase (AST), alanine transaminase (ALT), and total bilirubin (Tbil).
Multiplex IHC for immune cells
Livers collected from animals sacrificed at 10, 21, and 23 weeks of age were fixed in 4% paraformaldehyde overnight, followed by dehydration in 50% EtOH. Livers were then embedded in paraffin and sectioned by the UT Southwestern Histopathology Core.
IHC was performed as described previously (39). Briefly, slides were warmed in a 60°C oven for 10 minutes followed by deparaffinization and rehydration. Before antigen retrieval, slides were fixed in 10% neutral buffered formalin for 30 minutes followed by a PBS wash. Antigen retrieval was performed in antigen retrieval buffer [10 mmol/L Tris-HCl, 1 mmol/L EDTA with 10% glycerol (pH 9)] at 110°C for 18 minutes (∼4–5 ψ). Slides were then allowed to be cooled down to room temperature and were washed once with PBS. Tissue sections were blocked with 2.5% Goat Serum (Vector Laboratories, S-1012) for 30 minutes followed by incubation with primary antibodies overnight: CD3 (1:2000; Thermo Fisher Scientific, PA1-29547), CD4 (1:2000; Abcam, ab183685), and CD8 (1:4000; Cell Signaling Technology, 98941). Slides were washed three times for 5 minutes in PBST containing 0.05% Tween20 and 2 mmol/L EDTA and incubated with horseradish peroxidase–conjugated Secondary Antibody (ImmPRESS; Vector Laboratories, MP-7401) for 30 minutes on a shaker. Slides were then washed three times for 5 minutes in PBST. For developing the fluorescence signal, TSA Detection System (PerkinElmer) was used. We used OPAL 520 to stain CD3, OPAL 570 to stain CD4, and OPAL 690 to stain CD8. Multiplex staining was performed by stripping the previous antibody in 10 mmol/L citrate buffer (pH 6.2) plus 10% glycerol at 110°C for 2 minutes before probing with the next primary antibody. Slides were counter stained with DAPI and then coverslipped using ProLong Gold Mount (no. P36931; Life Technologies).
Imaging and image segmentation
Following immunostaining, full liver sections were imaged and scanned using a 20X AxioScan scanning microscope and Zen microscope software, available through the UT Southwestern Whole Brain Microscopy Core Facility. The following channels were used to acquire images: DAPI, AF488 (for CD3), AF555 (for CD4), and AF660 (for CD8). Note that AlexFluor channels were used, as the AxioScan scanning microscope and Zen software did not specifically have channels for acquisition of OPAL dyes. This image scanning allowed for a complete, unbiased, and quantifiable characterization of the immune cell population within the entire tissue section, rather than the restricted analysis of a selected frame or area of an image.
To quantify cells of interest, image segmentation was performed using custom-written Fiji and Excel macros, by performing the following steps: (i) the CD3 channel was used to threshold and select regions of interest (ROI), with signal intensity, cell shape, and size taken into account. (ii) The maximum intensity within each of these ROIs was measured. (iii) A small, 2-μm-thick band was drawn around each ROI and mean intensity within this “background” was measured. (iv) The ratio of max ROI intensity over mean “background” intensity was calculated, and only ROIs with ratio >1.7 were considered as CD3-positive cells. (v) The maximum ROI intensities and “background” intensities of these CD3-positive cells were also measured in the CD4 and CD8 channels, respectively, and the same ratio of maximum ROI intensity over mean “background” (i.e., >1.7) was applied. This procedure allowed us to identify cells that were CD3+ CD4+ and CD3+ CD8+ in each image. To account for the different sizes of each section stained and imaged, total surface area of each section was measured. To this aim, the brightness threshold for the CD3 channel was lowered to include the background staining of the entire tissue to create an ROI encompassing the entire section, followed by measurement of the total surface area. This value was used to calculate cell density expressed as cells/mm2.
Mutation analysis of diethylnitrosamine + CCl4 tumors
At the 23-week sacrifice timepoint, tumors were carefully and individually macrodissected from nontumor liver tissue using a disposable scalpel and scissors. Tumors were collected into individual 1.5 mL microtubes, and then immediately frozen in dry ice. Genomic DNA was extracted and purified from 17 tumors from 13 IgG control–treated animals and from 17 tumors from 14 anti-PD-1–treated animals using the Invitrogen PureLink Genomic DNA Mini Kit (catalog no. #K182001). Then, PCR primers flanking known, recurrent, point mutations in Hras, Egfr, and Braf in DEN-induced tumors were designed. The primers were synthesized by Integrated DNA Technologies. PCRs were performed on genomic DNA obtained from the tumors as mentioned above, and then PCR products were run in 2% agarose gels. Then, the PCR products were isolated after visualization on a UV transilluminator. PCR products were extracted and purified using the Qiagen QIAquick Gel Extraction Kit (catalog no., #28704). PCR products were sent to Genewiz for Sanger sequencing.
PCR primers used are as follows:
mHras-FWD: atccatcagggtatgagaggtg; mHras-REV: gcatgactgtgtccaggacatt; expected product size, 403 bp.
mEgfr-FWD: ggtgtttctgactatcctgg; mEgfr-REV: tgaggactgttgggtgaaagg; expected product size, 343 bp.
mBraf-FWD: cagaggacatacgaatctctg; mBraf-REV: gcccttcagtgtatttctcgt; expected product size, 353 bp.
Variation is indicated using SEM and presented as mean ± SEM. Unless otherwise stated in the figure legends, two-tailed Student t tests (two-sample equal variance) were used to test the significance of differences between two groups. Statistical significance is displayed as: *, P < 0.05; **, P < 0.01; ***, P < 0.001.
All mice were handled in accordance with, and with the approval of the Institutional Animal Care and Use Committee at UT Southwestern (Dallas, TX). Wild-type male and female C3h/HeJ mice were obtained from the Jackson Laboratory to breed the experimental cohorts.
The cirrhotic liver harbors many neoantigens and may respond to immunotherapy
To assess the ability of neoantigens to generate a robust immune response, we measured neoantigen load in cirrhosis and HCC samples. Briefly, whole-exome sequencing reads from human cirrhosis or HCC and paired blood samples were used to call mutations. Then, somatic mutations (including frameshift, nonframeshift, missense, and stoploss mutations) predicted to lead to changes in amino acid sequence were used to call neoantigens. Only those neoantigens with >5% VAF in the liver/tumor samples and that ranked in the top 2% of MHC-binding affinities were kept. Finally, using RNA-seq data from the same samples, only neoantigens with >1 RPKM were kept.
First, we found that mutations in liver cirrhosis lead to neoantigen production and presentation (Fig. 1A). Given that fibrotic livers harbor many more mutations than nonfibrotic livers (24, 25), it is likely that there are also more neoantigens in liver cirrhosis as compared with nonfibrotic liver. Indeed, we found that neoantigen load tends to increase with worsened fibrosis stage (Supplementary Fig. S1), which is consistent with our previous finding that mutational burden is highly associated with fibrosis stage (24). It is also known that higher fibrosis stage is associated with an increased risk of developing HCC (40, 41). Taken together, the cirrhotic liver likely harbors neoantigen-presenting malignant clones in the early stages of cancer development that would therapeutically respond to immunotherapy. As might be expected on the basis of this assumption, we found that the average neoantigen load (Fig. 1B and C) increases significantly from cirrhosis to clinically detectable HCC.
These findings confirm that neoantigens are present in liver cirrhosis, thus making immune checkpoint inhibition a viable preventive strategy. Thus, we hypothesized that in the precancer state of progressive liver disease and cirrhosis, immune checkpoint blockade would be effective in eliminating malignant clones, preempting tumorigenesis. On the other hand, the presence of neoantigens throughout the cirrhotic liver also introduces the possibility of widespread hepatocyte death with immunotherapy, demanding a careful examination of both efficacy and safety.
PD-1 blockade prior to tumorigenesis prevents mutagen-induced HCC development
To induce liver tumorigenesis and test preventive immunotherapy, we utilized the well-characterized model of DEN + CCl4–induced HCC (42). DEN is metabolized by hepatocytes to generate free radicals that cause DNA damage and lead to the production of reactive oxygen species (43). We reasoned that such widespread damage to DNA would closely model the mutational burden seen in human cirrhosis and HCC (44). Previous work from our laboratory and others has characterized the mutational landscape in DEN-induced tumors (45, 46), and our studies found recurrent mutations in several oncogenes and tumor suppressors (46). However, DEN used alone as a single agent does not recapitulate the chronic damage and liver fibrosis associated with HCC tumorigenesis in patients. Repeated injections of CCl4 are widely used to model liver damage and fibrosis. CCl4 is metabolized by CYP2E1 enzymes in pericentral hepatocytes to generate the trichloromethyl radical, which results in centrilobular hepatocyte necrosis, activation of Kupffer cells and an inflammatory response, and activation of hepatic stellate cells (42, 47–49), causing liver fibrosis similar to that observed in patients with cirrhosis. Thus, we combined these two agents to model malignant transformation in a background of chronic liver damage and fibrosis.
We induced cancers using DEN + CCl4 in C3h/HeJ strain mice, which are prone to developing liver cancers (Fig. 2A). At 10 weeks of age, 4 animals were sacrificed prior to receiving any antibody treatment, and there were no macroscopic or microscopic tumors (Fig. 2B; Supplementary Fig. S2). This was important as we sought to test the efficacy of immune checkpoint inhibition in preventing tumor formation, rather than in therapeutically treating existing tumors. Then, starting at 10 weeks of age, mice were given intraperitoneal injections of either IgG control antibody or anti-PD-1 antibody every other week until 20 weeks of age, for a total of six doses (Fig. 2A).
At 23 weeks of age, most remaining mice were sacrificed for liver collection and analysis of tumor burden. Anti-PD-1 animals (n = 14) had decreased tumor burden compared with IgG control animals (n = 13) as assessed by liver to body weight ratios, as well as by surface tumor number. IgG control–treated livers weighed on average 6.3% ± 0.9% of the animal's body weight and anti-PD-1–treated livers weighed on average 5.7% ± 0.7% of body weight (P = 0.0603), consistent with decreased tumor burden in the anti-PD-1 group (Fig. 2C). Furthermore, IgG control–treated animals had an average of 46 ± 14.92 surface tumors and anti-PD-1–treated animals had an average of 25 ± 16.70 surface tumors (P = 0.0025; Fig. 2C–E). This showed that the anti-PD-1 treatment had a significant effect in preventing the development of 46% of tumors. Notably, 4 animals in the anti-PD-1–treated group had less than 10 surface tumors, while no animal had less than 27 surface tumors in the IgG control–treated group. Only 2 animals (15.4%) in the anti-PD-1–treated group had more than the IgG control average of 46 surface tumors (Fig. 2F and G). It should be noted that 3 animals in each group were kept alive until around 39 weeks of age to determine whether anti-PD-1 injections affected survival, but because no mice in either group died, all 6 mice were euthanized.
Because serious drug toxicity may offset the benefit of chemoprevention strategies for patients with significant comorbidities, including cirrhosis, we examined metrics of toxicity. Average weights of mice were the same between IgG control–treated and anti-PD-1–treated groups for the duration of the experiment (Fig. 3A). AST levels were similar between groups in early timepoints, but at 14, 18, and 22 weeks of age, AST was lower in anti-PD-1–treated mice compared with IgG control–treated mice (Fig. 3B). ALT and Tbil levels were similar between groups for the duration of the experiment (Fig. 3C and D). These results showed that immune checkpoint blockade did not cause major adverse effects on overall health or liver function, even in the setting of established liver damage.
CD4+ T-cell and CD8+ T-cell tissue infiltration was associated with tumor prevention
The PD-1/PD-L1 interaction between naïve T cells and antigen-presenting cells results in decreased T-cell proliferation, survival, and IL-2 production (50, 51). We hypothesized that treatment with anti-PD-1 antibody would result in an increased T-cell response in the normal liver parenchyma. However, we wanted to determine whether this would be mediated through CD3+ CD4+ Th cells or CD3+ CD8+ cytotoxic T cells, or both. CD4+ T cells interact directly through their T-cell receptor with antigen-presenting cells, such as dendritic cells, and respond appropriately, either by the production of cytokines for recruitment of other immune cells or diminution of the immune response. CD8+ T-cell receptors bind to MHC class I complex molecules on all cell types, and if presented with peptides recognized as foreign, induce cell death through molecules such as perforin or granzyme B (51). It is known that both tumor-infiltrating CD4+ memory T cells and CD8+ cytotoxic T cells are correlated with increased survival in many different cancer types (52).
We performed multiplex IHC for CD3, CD4, and CD8, and imaged whole liver sections (Fig. 4A and B; Supplementary Fig. S3A–S3D). An image segmentation and analysis pipeline (Supplementary Fig. S4) was devised to quantify CD3+ cells, CD3+ CD4+ T cells, and CD3+ CD8+ T cells in IgG control and anti-PD-1 livers. We analyzed T-cell infiltration into the whole-liver parenchyma, rather than in tumors alone, which is more important for prevention in high risk, precancer states via upregulated immune surveillance of malignant clones. The examination of tumor-infiltrating T cells was also precluded by the sparseness of tumors in anti-PD-1–treated animals.
At 21 weeks of age, we randomly selected and sacrificed 2 IgG control animals and 2 anti-PD-1 animals to examine immune cells immediately after the final doses of antibody injections. We found that these particular animals had similar tumor burden, average of 34 and 37 tumors, respectively (Supplementary Fig. S5A–S5C), suggesting that the 2 anti-PD-1 animals randomly selected did not exhibit a preventive response. We then found that there was also no difference in CD3+ cells/mm2, CD3+ CD4+ cells/mm2, or CD3+ CD8+ cells/mm2 in the livers of these animals (Supplementary Fig. S5D–S5F).
To clearly determine whether successful tumor prevention was associated with increased T-cell infiltration, we then selected and analyzed two sections from two different liver lobes of 3 control animals (average 64 surface tumors) and 4 anti-PD-1 animals (average four surface tumors) sacrificed at 23 weeks for the presence of immune cells (Fig. 4C). The total area analyzed for IgG control liver sections was 225.43 mm2 and the total area analyzed for anti-PD-1 liver sections was 281.82 mm2. There was a significant increase in CD3+ cells/mm2 (Fig. 4D), CD3+ CD4+ cells/mm2 (Fig. 4E), and CD3+ CD8+ cells/mm2 (Fig. 4F) in the livers of the anti-PD-1 animals. This suggests that anti-PD-1 therapy increased the adaptive immune response and, specifically, increased infiltration of both CD4+ T cells and CD8+ T cells into premalignant liver tissues. However, we did not examine the specific CD4+ T-cell subsets (Th1, Th2, Th9, Th17, Th22, regulatory T cells, and Tfh) that are enriched in anti-PD-1 livers.
Together, these data suggest that upregulation of CD4+ T cells and CD8+ T cells does indeed correlate with the tumor preventive response.
Response to preventive PD-1 blockade is not dependent on specific recurrent mutations
A previous study showed that liver tumorigenesis driven by oncogenic mutant Nras causes senescence and elimination of malignant clones (53). Another group showed that oncogenic mutant Ctnnb1 in liver tumors imparts resistance to immune checkpoint inhibition (54). To understand whether such mutation-specific mechanisms were at play in determining the response or nonresponse to anti-PD-1 therapy in our mouse model, we examined oncogenic mutations in these tumors. Previous work has shown that A182 in Hras, T760 in Egfr, and T1910 in Braf are frequently and recurrently mutated in DEN-induced tumors (45, 46). One possibility is the selective elimination of clones carrying these oncogenic driver mutations. If this were the case, the proportion of remaining tumors carrying these oncogenic mutations would be lower in the anti-PD-1 group compared with the control group.
We genotyped these mutations within 34 IgG control- or anti-PD-1–treated tumors. In the Hras gene, 24% of IgG control tumors had A182T or A182G mutations, 29% had a C181A mutation, and 47% were wild-type. On the other hand, 12% of anti-PD-1 tumors had A182T or A182G mutations, 35% had C181A mutations, and 53% were wild-type (Fig. 5A). In Egfr, 6% of IgG control tumors had a T760A mutation and 94% were wild-type. 12% of anti-PD-1 tumors had a T760A mutation and 88% were wild-type (Fig. 5B). In Braf, 41% of IgG control tumors had a T1910A mutation and 59% were wild-type. On the other hand, 29% of anti-PD-1 tumors had a T1910A mutation and 71% were wild-type (Fig. 5C). While there may have been a trend toward reduced A182T/G mutations in Hras and T1910A mutations in Braf in the anti-PD-1–treated animals, there were no statistically significant differences in tumor genotyped for the point mutations in any of the three genes assessed. Our results indicate that the response to preventive immunotherapy is not dependent on specific recurrent oncogenic mutations, and thus, this strategy could be applicable to the broader cirrhosis population.
The incidence of HCC in the United States is expected to increase as the rates of obesity and NASH rapidly rise (2). Resection and liver transplantation are reserved for relatively healthy surgical candidates with limited tumor burden, while systemic therapies for advanced HCC extend life by only a few months (55–58). Regardless of etiology, chronic liver diseases eventually converge onto end-stage cirrhosis before a subset of these patients progress to HCC. Because this decades-long process of liver disease progression offers a window of opportunity to preempt HCC tumorigenesis in a high-risk population, we endeavored to explore the use of immune checkpoint inhibition in the preventive setting.
There are several strategic advantages of utilizing immunotherapy as chemoprevention. Pharmacokinetic studies of checkpoint inhibitors demonstrate a prolonged half-life (up to 27 days) and little evidence of a correlation between drug efficacy and exposure (59). This suggests that for the purpose of chemoprevention in patients suspected to have limited cancer burden, such as those with cirrhosis but without clinically detectable HCC, less frequent drug dosing may still be effective. The effectiveness of less frequent dosing strategies with immunotherapies is supported by recent findings in patients with melanoma where a single neoadjuvant dose of an ICI was demonstrated to be sufficient to induce complete pathologic responses (60). Nevertheless, as hepatocytes in the cirrhotic liver will continuously accumulate mutations over time, an intermittent dosing rather than a single-dose immunotherapy regimen is likely to be more effective in chemoprevention. In addition, immunotherapies are well-tolerated in many patients, including those with significant comorbidities and with a performance status that would preclude other anticancer therapies (61). Importantly, long-term use of ICIs for up to 5 years in multiple cancer types have not uncovered new safety signals (14).
Despite the favorable safety profile of ICIs, it is still worth discussing selection of patients at the highest risk of developing HCC, given that only 5%–30% (2) of patients with cirrhosis go on to develop HCC. Current surveillance protocol is centered around imaging studies, and nodules that are detected are scored using the Liver Imaging Reporting and Data System (LI-RADS) on a scale from LR-1 to LR-5 (62). LI-RADS-3 nodules are defined as “intermediate probability of malignancy” and LI-RADS-4 nodules are “probably HCC” (62). Interestingly, one group has identified and validated an HCC risk gene expression signature for hepatitis B/C infection, alcohol, and NASH cirrhosis (3, 4). This signature can be used to predict cancer risk within the carcinogenic field in patients with cirrhosis. In particular, the “poor prognosis” signature was highly associated with progression to advanced cirrhosis, development of HCC, and survival (3). Another group has developed HCC risk models for patients with HCV who have undergone antiviral treatment (5), as well as for patients with alcoholic liver disease and nonalcoholic fatty liver disease cirrhosis (6). These models take various clinical biomarkers into account, such as age, gender, race, serum aminotransferase levels, platelet count, and others, to stratify patients according to estimated annual HCC risk into low, (<1% risk/year), medium (1%–3% risk/year), and high risk (>3% risk/year). These novel tools, in parallel or in combination with the LI-RADS score, could be invaluable in the clinical setting to select patients most likely to benefit from preventive immunotherapy, while minimizing overall healthcare costs and unnecessary exposure to potential toxicity and side-effects. It is worthwhile to note that existing guidelines support the use of HCC screening for patients with cirrhosis or other risk factors for HCC, but no prospective data has ever proven an improvement in survival with biochemical and imaging surveillance (63). This may be related to the fact that many patients with cirrhosis who develop clinically detectable HCC do not or cannot undergo curative regional or surgical interventions. The ambiguous utility of current HCC screening strategies further reduces the relative risk and increases the potential benefit of an immunotherapy chemoprevention strategy in at-risk persons.
We chose to use the DEN + CCl4 mouse model of fibrosis and HCC (64–66). This is the best-suited model for a preventive preclinical trial because it recapitulates both the high-mutational burden of human HCC and the chronic damage that ultimately leads to cirrhosis, which is seen in 80%–90% of patients with HCC (2, 67). Yet, while DEN tumors meet pathologic criteria for HCC (42, 45), a significant limitation of this model is that on the molecular level, the most frequently recurrent driver mutations (activating mutations in Hras, Egfr, and Braf) are rarely observed in human HCC (26). Other commonly used mouse models of cancer, which rely on various combinations of tumor suppressor deletion (such as Tp53) and oncogene activation (such as Ctnnb1 and Myc; refs. 54, 68–70), more accurately mirror the genetics of human HCC. However, they do not recapitulate the evolutionary course of tumorigenesis from mutant clone to HCC; rather, they reproduce the genetic end state of what is likely a complex, dynamic process. In addition, genetically engineered mouse (GEM) models of HCC do not replicate the surrounding tissue damage and organ dysfunction context from which most HCCs arise. Establishing this tissue context is critical for testing tumor prevention in the premalignant field. This is also important because we sought to characterize the toxicity of our preventive intervention in the presence of chronic liver damage. Finally, given the key role of neoantigen presentation in immune surveillance (15, 16, 21), we felt that it is important to use a model with widespread mutagenesis and chronic liver damage (24, 25). Overall, we believe our model has advantages and disadvantages for testing preventive immunotherapy, and it is clear that additional GEM models of HCCs need to be examined with this regimen.
In our experiment, we show that antibody-mediated blockade of the PD-1 and PD-L1 interaction can prevent a significant fraction (46% average reduction) of HCCs. Interestingly, there was a variation in response, in that some animals saw moderate reduction and other animals exhibited almost no tumor burden. Further work to understand the mechanisms of resistance and the relative contribution of tumor cell intrinsic and immune-specific properties to this phenotype could help to uncover the source of this variation. Nevertheless, this model and treatment regimen can now be utilized as a platform to test other drugs or therapies for use in combination with PD-1 blockade.
Overall, the risk reduction we observed in the mouse model certainly makes a trial in select patients with high-risk cirrhosis worth exploring. However, one point for consideration is that immune checkpoint blockade is well-known to have side-effects, most notably autoimmunity. The risk of adding more side-effects to the long list of medical problems seen in patients with end-stage liver disease must be considered carefully. Our results would predict that immunotherapy, even in the preventive setting, would not exacerbate liver dysfunction, an observation that is consistent with the profile of adverse events observed in clinical trials of PD-1 blockade in patients with HCC (8, 13). HCC will increasingly assert itself as a public health challenge, especially as the prevalence of metabolic syndrome increases worldwide. However, as in many medical disciplines, preventive, rather than ex post facto, treatment may more effectively relieve the morbidity and mortality of HCC.
Disclosure of Potential Conflicts of Interest
H. Zhu reports grants from SU2C/AACR, CPRIT, and NIH/NCI during the conduct of the study, and grants and personal fees from Twentyeight-Seven Therapeutics (consulting fees and research support) outside the submitted work. No potential conflicts of interest were disclosed by the other authors.
A.S. Chung: Conceptualization, formal analysis, validation, investigation, methodology, writing-original draft, writing-review and editing. M. Mettlen: Software, formal analysis, methodology, writing-review and editing. D. Ganguly: Investigation, methodology, writing-original draft. T. Lu: Data curation, formal analysis, investigation, methodology, writing-review and editing. T. Wang: Conceptualization, supervision, funding acquisition. R.A. Brekken: Supervision, writing-review and editing. D. Hsiehchen: Conceptualization, writing-original draft. H. Zhu: Conceptualization, formal analysis, supervision, funding acquisition, writing-review and editing.
We would like to thank Helen Hobbs and Teresa Eversole for contributing human liver samples; Sam Wang, Yujin Hoshida, and Amit Singal for comments on the article; John Shelton for histopathology; and the CRI Sequencing Core (Jian Xu and Xin Liu) and Admera Health (Yun Zhao) for genomics. T. Wang was supported by CPRIT (RP190208). H. Zhu was supported by the Pollack Foundation, NCI R01 CA251928, a UT Southwestern Simmons Comprehensive Cancer Center Pilot Award, and a Stand Up To Cancer Innovative Research grant (SU2C-AACR-IRG 10-16).
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