In silico trials of combination immuno-radiation for unresectable hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is rising in incidence and is poised to be one of the leading causes of cancer-related deaths worldwide (1,2). While ablation, resection, and liver transplant are potentially curative therapies, at least 60% of patients are not surgical candidates. In patients with locally advanced unresectable disease, immunotherapy has become the standard of care (3-6). There are multiple on-going clinical trials evaluating HCC response to various immune checkpoint inhibitors (ICIs), either monotherapy or combination therapy, as well as tyrosine kinase inhibitor (TKI) therapy, often in combination with ICI. Many studies have shown varying overall response rates (ORR) from less than 12% to greater than 35%, depending on the type of monotherapy or combination therapy with ICIs and TKIs (3,5,7,8). Although ICIs show improved efficacy in ORR in patients with advanced stage HCC, further enhancing the effectiveness of ICI therapy has been an emerging area of clinical research in the management of patients with advanced stage HCC.

External beam radiotherapy (EBRT) has also been shown to be an effective treatment for unresectable locally advanced HCC in patients who are not candidates for ablation, with acceptable safety profile and excellent local control (9). Numerous studies have demonstrated excellent local control with the combination of EBRT with other locoregional therapies such as transarterial chemoembolization or thermal ablation in intermediate and advanced stage HCC (10). However, in patients with advanced stage HCC, particularly those with extrahepatic disease, combination therapies with systemic agents (ICIs or TKIs) plus EBRT may improve ORR and progression-free survival (PFS). Recently, a randomized trial of stereotactic body radiation vs. sorafenib (RTOG 1112) showed an overall survival benefit to radiotherapy (RT) compared to sorafenib (ASTRO 2022). There is emerging interest in harnessing the combination of immunotherapy and radiation in pursuit of the abscopal effect (11). There have already been published phase I trials on combination immunotherapy and RT for HCC with promising results (12,13). Given the low ORR with systemic therapy alone and the low rates of PFS seen in patients treated with EBRT alone, there is an unmet need for combination therapies which can provide excellent local control (EBRT) in combination with systemic control (ICI therapy) in patients with advanced stage HCC.

Emerging evidence on the role of effector T-cell efficacy on stimulating the immune system to promote tumor cell destruction has been the mainstay of ICI therapy. Numerous pre-clinical trials have shown the synergistic interaction between ICI therapy and radiation therapy (14). Radiation-induced cell death exposes tumor-specific antigens to the immune system, which sparks a cytokine cascade resulting in activation of normally suppressed tumor-specific T-lymphocytes, thus facilitating activated T-lymphocyte recruitment to the tumor (14). Thus, the combination of radiation and ICI therapy allows for further enhancement of anti-tumor effects.

The current in silico trial postulates on modeling the mechanism behind the interaction of durvalumab monotherapy and radiation in treatment of HCC. While recent studies combine durvalumab and tremelimumab, there is evidence that single agent durvalumab has some activity against HCC and that radiation may potentiate its effects (4,15,16). In the proposed mathematical model, the authors utilize a cell compartment approach as described by ordinary differential equations and represent irradiated and nonirradiated tumor cells and lymphocytes. The model simulates radiation kill via the linear quadratic model, while the effects of immunotherapy (immune-check point inhibition) is modeled via an immune activation term that is based on tumor size changes. In order to encompass observable information from clinical data, the design of the model has been restricted to phenomenological information rather than mechanistic underpinning, which may still be valuable for clinical trial design but may limit the ability to make radiobiological inferences. The current model incorporates baseline immune features such as lymphocyte count, radiation fraction size, and sequencing of immunotherapy and radiation. The mathematical model attempts to provide a framework for designing trials with this combination therapy and for evaluating objective response.

Given the heterogeneity of radiation treatment parameters in everyday clinical practice, such as radiation dose, fractionation, sequencing and patient selection, the current mathematical model can incorporate these differences to guide treatment combinations. The current mathematical model adjusts for the aforementioned RT parameters that we can control, not the underlying biological heterogeneity in patient population, in order to best predict optimal treatment outcomes. The current study compares PFS in patients with ICI and differing percentage of RT volumes to ICI alone, as well as differing times of ICI therapy with RT (concomitant versus RT with ICI treatment break).

The current mathematical model predicts that the response rates based on PFS were maximized when ICI-RT combination regimen was with an irradiated tumor fraction of 90% as opposed to 50%. Furthermore, the mathematical model suggests improved response rates when durvalumab and EBRT were simultaneously given, and decreasing efficacy with an ICI and radiation gap, results which are concordant with the PACIFIC trial evaluating durvalumab with chemo-radiation in lung cancer (17,18). Finally, the mathematical model predicts that baseline lymphocyte counts strongly predict outcomes, such that patients with higher baseline lymphocyte counts and lower tumor burden have better response rates. These results are corroborated by other studies evaluating lymphocyte count in non-small cell lung cancer and tumor burden to ICI monotherapy response (19,20).

There has been interest in applying more data driven techniques with artificial intelligence (AI) to learn directly from clinical data and subsequently optimize outcome prediction and decision making. Examples of such approaches are using quantitative image analysis (radiomics), for instance (21,22). However, it is recognized that AI methods may require large datasets and the combination with mathematical modeling may alleviate such requirements in outcome modeling of cancer response in general and radio-immunotherapy in particular (23).

In conclusion, the proposed mathematical model provides a framework to identify optimal ICI + RT combinations for advanced stage HCC treatment to maximize treatment efficacy, while accounting for patient heterogeneity. While their results are concordant with literature evaluating ICI + RT in other tumor subtypes, further studies with more clinical data and larger patient sample sizes are needed to understand the true predictive nature of this mathematical model.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article did not undergo external peer review.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-2906/coif). IEN receives funds from NIH and DOD, and serves as the Deputy Editor of Journal of Medical Physics. DO receives research Funding from Astra Zeneca and Varian and Honorarium from Up to Date. The other author has no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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