A tumor-immune interaction model for hepatocellular carcinoma based on measured lymphocyte counts in patients undergoing radiotherapy

Understanding the impact of radiation-induced lymphopenia: Preclinical and clinical research perspectives

Immunotherapy has revolutionized the field of cancer treatment, changing the standard of care to the use of immune checkpoint inhibitors. Radiotherapy can boost anti-tumour immune responses by changing the tumour microenvironment, but it also can cause radiotherapy-induced lymphopenia (RIL), a decrease in circulating lymphocyte counts. RIL has been associated with lower survival in patients undergoing radiotherapy, and new studies have suggested that it can also affect immunotherapy outcome. To study RIL's effects and to explore mitigation treatment strategies, preclinical models closely mimicking the clinical situation are needed. State-of-the-art image-guided small animal irradiators now offer the possibility to target specific organs in small animals to induce RIL, aiding research on its molecular mechanisms and prevention. This review covers the relationship between radiotherapy and RIL, its impact on patient survival, and future directions to generate models to investigate and prevent RIL.

Lymphocyte radiosensitivity: An extension to the linear-quadratic model?

BACKGROUND AND PURPOSE

The linear-quadratic (LQ) model has been pivotal for evaluating the effects of radiation on cells, but it is primarily characterized by linear responses, which has exhibited limitations when applied to lymphocyte data. The present research aims to address these limitations and to explore an alternative model extended from the conventional LQ model.

MATERIALS AND METHODS

Literature providing lymphocyte counts from assays investigating apoptosis and survival after in vitro irradiation was selected. To address the nonlinearity in lymphocyte responses to radiation, we developed a saturation model characterized by a negative exponential relationship between radiation dose and cellular response. We compared the performance of this saturation model against that of conventional models, including the LQ model and its variants (linear model LM and linear-quadratic-cubic model LQC), as well as the repair-misrepair (RMR) model. The models were evaluated based on prediction-residual plots, residual standard errors, and the Akaike information criterion (AIC). We applied the saturation model to two additional datasets: (1) a dataset from the existing literature that assessed stimulated and unstimulated human lymphocytes exposed to gamma irradiation in vitro and (2) a novel dataset involving T lymphocytes from rodent spleens after exposure to various radiation types (X-rays and protons).

RESULTS

The literature (n = 15 out of 2342) showed that lymphocyte apoptosis varies with dose, time and experimental conditions. The saturation model had a lower AIC of 718 compared to the LM, LQ, LQC and RMR models (AIC of 728, 720, 720 and 734, respectively). The saturation model had a lower residual error and more consistent error distribution. Integrating time as a covariate, the saturation model also had a better AIC for demonstrating time-dependent variations in lymphocyte responses after irradiation. For datasets involving unstimulated lymphocytes before irradiation, the saturation model provided a more accurate fit than did the LM, LQ, and RMR models. In these cases, the fit of the saturation model was comparable to that of the LQC model but offered an advantage when extrapolating to higher doses, where the LQC model might underestimate survival. For stimulated lymphocytes, which are radioresistant, all the models approximated the LM. Both the LQ and saturation models indicated greater radiosensitivity to protons in vitro.

CONCLUSION

The new "saturation model" performed better than the LQ model in quantifying lymphocyte apoptosis and survival, estimating time dependency and assessing the role of radiation modalities or lymphocyte stimulation. Further experiments are warranted to experimentally explore the validity of the saturation model as a promising alternative in the clinical setting.

Early lymphocyte levels and low doses radiation exposure of lung predict lymphopenia in radiotherapy for lung cancer

Introduction

Radiation induced lymphopenia (RIL) deteriorate survival and diminishes the benefit of immune checkpoint inhibitors in combined treatment of lung cancer. Given the inconsistent data across various studies on the predictors of RIL, we aim to methodically elucidate these predictors and formulate a practical guide for clinicians.

Methods

We conducted observational cohort study in four tertiary cancer centers. Patients with non-small cell lung cancer and small cell lung cancer, without lymphopenia grade >1, who underwent standalone radiotherapy (RT) in minimum 15 fractions were eligible. Dose-volume parameters of structures and clinical factors were comprehensively analyzed using various predictors selection methods and statistical models (Linear Regressors, Elastic Net, Bayesian Regressors, Huber Regression, regression based on k-nearest neighbors, Gaussian Process Regressor, Decision Tree Regressor, Random Forest Regressor, eXtreme Gradient Boosting, Automated Machine Learning) and were ranked to predict lymphocytes count nadir (alc_nadir).

Results

Two hundred thirty eight patients (stage I-3.4%, II-17.6%, III-75.2%, IV-3.8%) who underwent RT to median dose of 60 Gy were analyzed. Median alc_nadir was 0.68K/mm3. The 60 feature sets were evaluated in 600 models (RMSE 0.27-0.41K/mm³). The most important features were baseline lymphocyte count (alc_1), mean lung_dose, lung v05, lung v10, heart v05 and effective dose to immune cells (edic). In patients with alc_1 ≤ 2.005K/mm3, median alc_nadir predictions were 0.54K/mm3 for lung_v05p > 51.8% and 0.76K/mm3 for lung_v05p ≤ 51.8%. Lymphopenia was rare in patients with alc_1 > 2.005K/mm3.

Discussion

RIL was most severe in patients with low early lymphocyte counts, primarily triggered by low RT doses in the heart and lungs.

Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.

Modeling the Synergistic Impact of Yttrium 90 Radioembolization and Immune Checkpoint Inhibitors on Hepatocellular Carcinoma

The impact of yttrium 90 radioembolization (Y90-RE) in combination with immune checkpoint inhibitors (ICIs) has recently gained attention. However, it is unclear how sequencing and dosage affect therapeutic efficacy. The purpose of this study was to develop a mathematical model to simulate the synergistic effects of Y90-RE and ICI combination therapy and find the optimal treatment sequences and dosages. We generated a hypothetical patient cohort and conducted simulations to apply different treatments to the same patient. The compartment of models is described with ordinary differential equations (ODEs), which represent targeted tumors, non-targeted tumors, and lymphocytes. We considered Y90-RE as a local treatment and ICIs as a systemic treatment. The model simulations show that Y90-RE and ICIs administered simultaneously yield greater benefits than subsequent sequential therapy. In addition, applying Y90-RE before ICIs has more benefits than applying ICIs before Y90-RE. Moreover, we also observed that the median PFS increased up to 31~36 months, and the DM rates at 3 years decreased up to 36~48% as the dosage of the two drugs increased (p < 0.05). The proposed model predicts a significant benefit of Y90-RE with ICIs from the results of the reduced irradiated tumor burden and the associated immune activation and suppression. Our model is expected to help optimize complex strategies and predict the efficacy of clinical trials for HCC patients.

Predicting Severity of Radiation Induced Lymphopenia in Individual Proton Therapy Patients for Varying Dose Rate and Fractionation Using Dynamic 4-Dimensional Blood Flow Simulations

PURPOSE

Radiation-induced lymphopenia has gained attention recently as the result of its correlation with survival in a range of indications, particularly when combining radiation therapy (RT) with immunotherapy. The purpose of this study is to use a dynamic blood circulation model combined with observed lymphocyte depletion in patients to derive the in vivo radiosensitivity of circulating lymphocytes and study the effect of RT delivery parameters.

METHODS AND MATERIALS

We assembled a cohort of 17 patients with hepatocellular carcinoma treated with proton RT alone in 15 fractions (fx) using conventional dose rates (beam-on time [BOT], 120 seconds) for whom weekly absolute lymphocyte counts (ALCs) during RT and follow-up were available. We used HEDOS, a time-dependent, whole-body, blood flow computational framework, in combination with explicit liver blood flow modeling, to calculate the dose volume histograms for circulating lymphocytes for changing BOTs (1 second-300 seconds) and fractionations (5 fx, 15 fx). From this, we used the linear cell survival model and an exponential model to determine patient-specific lymphocyte radiation sensitivity, α, and recovery, σ, respectively.

RESULTS

The in vivo-derived patient-specific α had a median of 0.65 Gy-1 (range, 0.30-1.38). Decreasing BOT to 1 second led to an increased average end-of-treatment ALC of 27.5%, increasing to 60.3% when combined with the 5-fx regimen. Decreasing to 5 fx at the conventional dose rate led to an increase of 17.0% on average. The benefit of both increasing dose rate and reducing the number of fractions was patient specificࣧpatients with highly sensitive lymphocytes benefited most from decreasing BOT, whereas patients with slow lymphocyte recovery benefited most from the shorter fractionation regimen.

CONCLUSIONS

We observed that increasing dose rate at the same fractionation reduced ALC depletion more significantly than reducing the number of fractions. High-dose-rates led to an increased sparing of lymphocytes when shortening the fractionation regimen, particularly for patients with radiosensitive lymphocytes at elevated risk.

A Mathematical Model for Predicting Patient Responses to Combined Radiotherapy with CTLA-4 Immune Checkpoint Inhibitors

The purpose of this study was to develop a cell-cell interaction model that could predict a tumor's response to radiotherapy (RT) combined with CTLA-4 immune checkpoint inhibition (ICI) in patients with hepatocellular carcinoma (HCC). The previously developed model was extended by adding a new term representing tremelimumab, an inhibitor of CTLA-4. The distribution of the new immune activation term was derived from the results of a clinical trial for tremelimumab monotherapy (NCT01008358). The proposed model successfully reproduced longitudinal tumor diameter changes in HCC patients treated with tremelimumab (complete response = 0%, partial response = 17.6%, stable disease = 58.8%, and progressive disease = 23.6%). For the non-irradiated tumor control group, adding ICI to RT increased the clinical benefit rate from 8% to 32%. The simulation predicts that it is beneficial to start CTLA-4 blockade before RT in terms of treatment sequences. We developed a mathematical model that can predict the response of patients to the combined CTLA-4 blockade with radiation therapy. We anticipate that the developed model will be helpful for designing clinical trials with the ultimate aim of maximizing the efficacy of ICI-RT combination therapy.

The current understanding of the immune landscape relative to radiotherapy across tumor types

Radiotherapy is part of the standard of care treatment for a great majority of cancer patients. As a result of radiation, both tumor cells and the environment around them are affected directly by radiation, which mainly primes but also might limit the immune response. Multiple immune factors play a role in cancer progression and response to radiotherapy, including the immune tumor microenvironment and systemic immunity referred to as the immune landscape. A heterogeneous tumor microenvironment and the varying patient characteristics complicate the dynamic relationship between radiotherapy and this immune landscape. In this review, we will present the current overview of the immunological landscape in relation to radiotherapy in order to provide insight and encourage research to further improve cancer treatment. An investigation into the impact of radiation therapy on the immune landscape showed in several cancers a common pattern of immunological responses after radiation. Radiation leads to an upsurge in infiltrating T lymphocytes and the expression of programmed death ligand 1 (PD-L1) which can hint at a benefit for the patient when combined with immunotherapy. In spite of this, lymphopenia in the tumor microenvironment of 'cold' tumors or caused by radiation is considered to be an important obstacle to the patient's survival. In several cancers, a rise in the immunosuppressive populations is seen after radiation, mainly pro-tumoral M2 macrophages and myeloid-derived suppressor cells (MDSCs). As a final point, we will highlight how the radiation parameters themselves can influence the immune system and, therefore, be exploited to the advantage of the patient.

A Biomathematical Model of Tumor Response to Radioimmunotherapy With αPDL1 and αCTLA4

There is evidence of synergy between radiotherapy and immunotherapy. Radiotherapy can increase liberation of tumor antigens, causing activation of antitumor T-cells. This effect can be boosted with immunotherapy. Radioimmunotherapy has potential to increase tumor control rates. Biomathematical models of response to radioimmunotherapy may help on understanding of the mechanisms affecting response, and assist clinicians on the design of optimal treatment strategies. In this work we present a biomathematical model of tumor response to radioimmunotherapy. The model uses the linear-quadratic response of tumor cells to radiation (or variation of it), and builds on previous developments to include the radiation-induced immune effect. We have focused this study on the combined effect of radiotherapy and αPDL1/ αCTLA4 therapies. The model can fit preclinical data of volume dynamics and control obtained with different dose fractionations and αPDL1/ αCTLA4. A biomathematical study of optimal combination strategies suggests that a good understanding of the involved biological delays, the biokinetics of the immunotherapy drug, and the interplay between them, may be of paramount importance to design optimal radioimmunotherapy schedules. Biomathematical models like the one we present can help to interpret experimental data on the synergy between radiotherapy and immunotherapy, and to assist in the design of more effective treatments.

Implications of recent neoadjuvant clinical trials on the future practice of radiotherapy in locally advanced rectal cancer

Over the last two decades, the standard treatment for locally advanced rectal cancer (LARC) has been neoadjuvant chemoradiotherapy plus total mesorectal excision followed by adjuvant chemotherapy. Total neoadjuvant treatment (TNT) and immunotherapy are two major issues in the treatment of LARC. In the two latest phase III randomized controlled trials (RAPIDO and PRODIGE23), the TNT approach achieved higher rates of pathologic complete response and distant metastasis-free survival than conventional chemoradiotherapy. Phase I/II clinical trials have reported promising response rates to neoadjuvant (chemo)-radiotherapy combined with immunotherapy. Accordingly, the treatment paradigm for LARC is shifting toward methods that increase the oncologic outcomes and organ preservation rate. However, despite the progress of these combined modality treatment strategies for LARC, the radiotherapy details in clinical trials have not changed significantly. To guide future radiotherapy for LARC with clinical and radiobiological evidence, this study reviewed recent neoadjuvant clinical trials evaluating TNT and immunotherapy from a radiation oncologist’s perspective.

Are charged particles a good match for combination with immunotherapy? Current knowledge and perspectives

Charged particle radiotherapy, mainly using protons and carbon ions, provides physical characteristics allowing for a volume conformal irradiation and a reduction of the integral dose to normal tissue. Carbon ion therapy additionally features an increased biological effectiveness resulting in peculiar molecular effects. Immunotherapy, mostly performed with immune checkpoint inhibitors, is nowadays considered a pillar in cancer therapy. Based on the advantageous features of charged particle radiotherapy, we review pre-clinical evidence revealing a strong potential of its combination with immunotherapy. We argue that the combination therapy deserves further investigation with the aim of translation in clinics, where a few studies have been set up already.

Lymphocyte sparing normal tissue effects in the clinic (LymphoTEC): A systematic review of dose constraint considerations to mitigate radiation-related lymphopenia in the era of immunotherapy

BACKGROUND

Radiation-related lymphopenia has been associated with suboptimal tumor control rates leading to inferior survival outcomes. To date, no standardized dose constraints are available to limit radiation dose to resident and circulating lymphocyte populations. We undertook this systemic review of the literature to provide a synopsis of the dosimetric predictors of radiation-related lymphopenia in solid malignancies.

METHODOLOGY

A systematic literature review of PubMed (National Institutes of Health), Cochrane Central (Cochrane collaboration), and Google Scholar was conducted with the following keywords: "radiation", "lymphopenia", "cancer", "dosimetric predictors" with an inclusion deadline of May 31, 2022. Studies that met prespecified inclusion criteria were designated either Good, Fair, or Poor Quality based on the Newcastle-Ottawa quality assessment. The dosimetric parameters derived from Good Quality studies were tabulated as LymphoTEC dose constraints. Dosimetric parameters derived from Fair and Poor-quality studies were grouped as optional.

RESULTS

An initial systematic search of the literature yielded 1,632 articles. After screening, a total of 48 studies met inclusion criteria and were divided into the following categories: central nervous system (CNS, 6), thoracic (11), gastrointestinal (26), gynecologic (2), head and neck, breast, and genitourinary (one each) cancers. Lung mean dose, heart mean dose, brain V25, spleen mean dose, estimated dose to immune cells, and bone marrow V10 were among the strongest predictors for severe lymphopenia related to radiotherapy.

CONCLUSION

Optimizing the delivery of radiation therapy to limit dose to lymphocyte-rich structures may curb the negative oncologic impact of lymphocyte depletion. The dose constraints described herein may be considered for prospective validation and future use in clinical trials to limit risk of radiation-related lymphopenia and possibly improve cancer-associated outcomes.

Prediction and clinical impact of delayed lymphopenia after chemoradiotherapy in locally advanced non-small cell lung cancer

Introduction

The dosimetric factors of radiotherapy have an acute impact on the host immune system during chemoradiotherapy (CRT) in locally advanced non-small cell lung cancer (NSCLC). However, even after CRT, a substantial number of patients remain immunosuppressed with delayed lymphopenia. Therefore, we aimed to evaluate clinical and dose-volumetric predictors of delayed lymphopenia after CRT in locally advanced NSCLC.

Materials and methods

We retrospectively reviewed 272 patients with locally advanced NSCLC who received definitive CRT from January 2012 to August 2020. Differential blood count data, including serum albumin values, were obtained at baseline, during and at first follow up after CRT. Acute and delayed lymphopenia events were defined as grade III/IV lymphopenia developed during or 4-12 weeks after CRT completion, which accounted for 84% and 10% of cases, respectively. Dose-volume histogram parameters for planned target volume, whole body, heart, lung, great vessels, spleen, esophagus and thoracic vertebral bodies were evaluated.

Results

Multivariate analysis revealed that patients with delayed lymphopenia were associated with inferior overall survival (HR 2.53, P = 0.001) and progression-free survival (HR 1.98, P = 0.006). However, there was no significant survival difference between groups stratified by acute lymphopenia. On multivariable logistic regression models, lung V5, baseline ALC, during-CRT ALC, and albumin nadir were significant predictors for delayed lymphopenia. Furthermore, the nomogram for delayed lymphopenia based on these variables had good discrimination (area under the curve, 0.905).

Conclusions

In this study, we investigated the prognostic significance of delayed lymphopenia and identified clinico-dosimetric parameters to predict delayed lymphopenia.

Modeling of radiation effects to immune system: a review

Cancer metastasis is the major cause of cancer mortality and accounts for about 90% of cancer death. Although radiation therapy has been considered to reduce the localized cancer burden, emerging evidence that radiation can potentially turn tumors into an in situ vaccine has raised significant interest in combining radiation with immunotherapy. However, the combination approach might be limited by the radiation-induced immunosuppression. Assessment of radiation effects on the immune system at the patient level is critical to maximize the systemic antitumor response of radiation. In this review, we summarize the developed solutions in three different categories for systemic radiation therapy: blood dose, radiation-induced lymphopenia, and tumor control. Furthermore, we address how they could be combined to optimize radiotherapy regimens and maximize their synergy with immunotherapy.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system

Radiotherapy is a primary therapeutic modality widely utilized with curative intent. Traditionally tumor response was hypothesized to be due to high levels of cell death induced by irreparable DNA damage. However, the immunomodulatory aspect of radiation is now widely accepted. As such, interest into the combination of radiotherapy and immunotherapy is increasing, the synergy of which has the potential to improve tumor regression beyond that observed after either treatment alone. However, questions regarding the timing (sequential vs concurrent) and dose fractionation (hyper-, standard-, or hypo-fractionation) that result in improved anti-tumor immune responses, and thus potentially enhanced tumor inhibition, remain. Here we discuss the biological response to radiotherapy and its immunomodulatory properties before giving an overview of pre-clinical data and clinical trials concerned with answering these questions. Finally, we review published mathematical models of the impact of radiotherapy on tumor-immune interactions. Ranging from considering the impact of properties of the tumor microenvironment on the induction of anti-tumor responses, to the impact of choice of radiation site in the setting of metastatic disease, these models all have an underlying feature in common: the push towards personalized therapy.

Dynamics of an Antitumour Model with Pulsed Radioimmunotherapy

In this paper, an antitumour model for characterising radiotherapy and immunotherapy processes at different fixed times is proposed. The global attractiveness of the positive periodic solution for each corresponding subsystem is proved with the integral inequality technique. Then, based on the differentiability of the solutions with respect to the initial values, the eigenvalues of the Jacobian matrix at a fixed point corresponding to the tumour-free periodic solution are determined, resulting in a sufficient condition for local stability. The solutions to the ordinary differential equations are compared, the threshold condition for the global attractiveness of the tumour-free periodic solution is provided in terms of an indicator R 0, and the permanence of a system with at least one tumour-present periodic solution is investigated. Furthermore, the effects of the death rate, effector cell injection dosage, therapeutic period, and effector cell activation rate on indicator R 0 are determined through numerical simulations, and the results indicate that radioimmunotherapy is more effective than either radiotherapy or immunotherapy alone.

Association of neutrophil-to-lymphocyte ratio, radiotherapy fractionation/technique, and risk of development of distant metastasis among patients with locally advanced rectal cancer

BACKGROUND

We investigated the prognostic impact of the neutrophil-to-lymphocyte ratio (NLR) in patients with locally advanced rectal cancer (LARC) and whether modifiable factors in radiotherapy (RT) influenced the NLR.

METHODS

Data of 1386 patients who were treated with neoadjuvant RT and concurrent or sequential chemotherapy for LARC between 2006 and 2019 were evaluated. Most patients (97.8%) were treated with long-course RT (LCRT; 50-50.4 Gy in 25-28 fractions) using three-dimensional conformal radiotherapy (3D-CRT) (n = 851) or helical tomotherapy (n = 504), and 30 patients underwent short-course RT (SCRT; 25 Gy in 5 fractions, followed by XELOX administration for 6 weeks). Absolute neutrophil and lymphocyte counts were obtained at initial diagnosis, before and during the preoperative RT course, and after preoperative concurrent chemoradiotherapy. The primary endpoint was distant metastasis-free survival (DMFS).

RESULTS

The median follow-up time was 61.3 (4.1-173.7) months; the 5-year DMFS was 80.1% and was significantly associated with the NLR after RT but not before. A post-RT NLR ≥ 4 independently correlated with worse DMFS (hazard ratio, 1.42; 95% confidence interval, 1.12-1.80), along with higher ypT and ypN stages. Post-RT NLR (≥ 4) more frequently increased following LCRT (vs. SCRT, odds ratio [OR] 2.77, p = 0.012) or helical tomotherapy (vs. 3D-CRT, OR 1.29, p < 0.001).

CONCLUSIONS

Increased NLR after neoadjuvant RT is associated with increased distant metastasis risk and poor survival outcome in patients with LARC. Moreover, high NLR following RT is directly related to RT fractionation, delivery modality, and tumor characteristics. These results are hypothesis-generating only, and confirmatory studies are required.

Mathematical Modeling to Simulate the Effect of Adding Radiation Therapy to Immunotherapy and Application to Hepatocellular Carcinoma

PURPOSE

To develop a comprehensive framework to simulate the response to immune checkpoint inhibitors (ICIs) in combination with radiation therapy (RT) and to apply the framework for investigating ICI-RT combination regimen in patients with hepatocellular carcinoma (HCC).

METHODS AND MATERIALS

The mechanistic mathematical model is based on dynamic biological interactions between the immune system and the tumor using input data from patient blood samples and outcomes of clinical trials. The cell compartments are described by ordinary differential equations and represent irradiated and nonirradiated tumor cells and lymphocytes. The effect of ICI is modeled using an immune activation term that is based on tumor size changes observed in a phase 1/2 clinical trial for HCC. Simulated combination regimen are based on ongoing ICI-RT trials.

RESULTS

The proposed framework successfully describes tumor volume trajectories observed in early-stage clinical trials of durvalumab monotherapy in patients with HCC. For ICI-RT treatment regimen the irradiated tumor fraction is the most important parameter for the efficacy. For 90% of the tumor cells being irradiated, adding RT to ICI yields an increase in clinical benefit from 33% to 71% in nonirradiated tumor sites. The model agrees with clinical data showing an association of outcome with initial tumor volume and lymphocyte counts. We demonstrate model application in clinical trial design to predict progression-free survival curves, showing that the cohort size to show significant improvement heavily depends on the irradiated tumor fraction.

CONCLUSIONS

We present a framework extending radiation cell kill models to include circulating lymphocytes and the effect of ICIs and enable simulation of combination strategies. The simulations predict that a significant amount of the benefit from RT in combination with ICI stems from the reduction in irradiated tumor burden and associated immune suppression. This aspect needs to be included in the interpretation of outcomes and the design of novel combination trials.

Lymphocyte dynamics during and after chemo-radiation correlate to dose and outcome in stage III NSCLC patients undergoing maintenance immunotherapy

PURPOSE

We investigated the dynamics of lymphocyte depletion and recovery during and after definitive concurrent chemoradiotherapy (CCRT), dose to which structures is correlated to them, and how they affect the prognosis of stage III non-small cell lung cancer (NSCLC) patients undergoing maintenance immunotherapy.

METHODS AND MATERIALS

In this retrospective study, absolute lymphocyte counts (ALC) of 66 patients were obtained before, during, and after CCRT. Persistent lymphopenia was defined as ALC < 500/μL at 3 months after CCRT. The impact of regional dose on lymphocyte depletion and recovery was investigated using voxel-based analysis (VBA).

RESULTS

Most patients (n = 65) experienced lymphopenia during CCRT: 39 patients (59.0%) had grade (G) 3+ lymphopenia. Fifty-nine patients (89.3%) recovered from treatment-related lymphopenia at 3 months after CCRT, whereas 7 (10.6%) showed persistent lymphopenia. Patient characteristics associated with persistent lymphopenia were older age and ALC before and during treatment. In multivariable Cox regression analysis, recovery from lymphopenia was identified as a significant prognostic factor for Progression Free Survival (HR 0.35, 95% CI 0.13-0.93, p = 0.034) and Overall Survival (HR 0.24, 95% CI 0.08-0.68, p = 0.007). Voxel-based analysis showed strong correlation of dose to the upper mediastinum with lymphopenia at the end of CCRT, but not at 3 months after CCRT.

CONCLUSION

Recovery from lymphopenia is strongly correlated to improved survival of patients undergoing CCRT and adjuvant immunotherapy, and is correlated to lymphocyte counts pre- and post-CCRT. VBA reveals high correlation of dose to large vessels to lymphopenia at the end of CCRT. Therefore, efforts should be made not only for preventing lymphocyte depletion during CCRT but also for helping lymphocyte recovery after CCRT.

A dynamic blood flow model to compute absorbed dose to circulating blood and lymphocytes in liver external beam radiotherapy

We have developed a novel 4D dynamic liver blood flow model, capable of accurate dose estimation to circulating blood cells during liver-directed external beam radiotherapy, accounting for blood recirculation and radiation delivery time structure. Adult male and adult female liver computational phantoms with detailed vascular trees were developed to include the hepatic arterial, hepatic portal venous, and hepatic venous trees. A discrete time Markov Chain approach was applied to determine the spatiotemporal distribution of 105blood particles (BP) in the human body based on reference values for cardiac output and organ blood volumes. For BPs entering the liver, an explicit Monte Carlo simulation was implemented to track their propagation along ∼2000 distinct vascular pathways through the liver. The model tracks accumulated absorbed dose from time-dependent radiation fields with a 0.1 s time resolution. The computational model was then evaluated for 3 male and 3 female patients receiving photon (VMAT and IMRT) and proton (passive SOBP and active PBS) treatments. The dosimetric impact of treatment modality, delivery time, and fractionation on circulating blood cells was investigated and quantified using the mean dose (μdose,b),V>0Gy,V>0.125Gy,andD2%. Average reductions inμdose,b,V>0Gy,V>0.125GyandD2%of 45%, 6%, 53%, 19% respectively, were observed for proton treatments as compared to photon treatments. Our simulation also showed thatV>0Gy,V>0.125Gy, andD2%were highly sensitive to the beam-on time. BothV>0GyandV>0.125Gyincreased with beam-on time, whereasD2%decreased with increasing beam-on time, demonstrating the tradeoff between low dose to a large fraction of blood cells and high dose to a small fraction of blood cells. Consequently, proton treatments are not necessarily advantageous in terms of dose to the blood simply based on integral dose considerations. Instead, both integral dose and beam-on time can substantially impact relevant dosimetric indices.

Leveraging Blood-Based Diagnostics to Predict Tumor Biology and Extend the Application and Personalization of Radiotherapy in Liver Cancers

While the incidence of primary liver cancers has been increasing worldwide over the last few decades, the mortality has remained consistently high. Most patients present with underlying liver disease and have limited treatment options. In recent years, radiotherapy has emerged as a promising approach for some patients; however, the risk of radiation induced liver disease (RILD) remains a limiting factor for some patients. Thus, the discovery and validation of biomarkers to measure treatment response and toxicity is critical to make progress in personalizing radiotherapy for liver cancers. While tissue biomarkers are optimal, hepatocellular carcinoma (HCC) is typically diagnosed radiographically, making tumor tissue not readily available. Alternatively, blood-based diagnostics may be a more practical option as blood draws are minimally invasive, widely availability and may be performed serially during treatment. Possible blood-based diagnostics include indocyanine green test, plasma or serum levels of HGF or cytokines, circulating blood cells and genomic biomarkers. The albumin–bilirubin (ALBI) score incorporates albumin and bilirubin to subdivide patients with well-compensated underlying liver dysfunction (Child–Pugh score A) into two distinct groups. This review provides an overview of the current knowledge on circulating biomarkers and blood-based scores in patients with malignant liver disease undergoing radiotherapy and outlines potential future directions.

Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy

PURPOSE

To stratify patients and aid clinical decision making, we developed machine learning models to predict treatment failure and radiation-induced toxicities after radiotherapy (RT) in patients with hepatocellular carcinoma across institutions.

MATERIALS AND METHODS

The models were developed using linear and nonlinear algorithms, predicting survival, nonlocal failure, radiation-induced liver disease, and lymphopenia from baseline patient and treatment parameters. The models were trained on 207 patients from Massachusetts General Hospital. Performance was quantified using Harrell's c-index, area under the curve (AUC), and accuracy in high-risk populations. Models' structures were optimized in a nested cross-validation approach to prevent overfitting. A study analysis plan was registered before external validation using 143 patients from MD Anderson Cancer Center. Clinical utility was assessed using net-benefit analysis.

RESULTS

The survival model stratified high-risk versus low-risk patients well in the external validation cohort (c-index = 0.75), better than existing risk scores. Predictions of 1-year survival and nonlocal failure were excellent (external AUC = 0.74 and 0.80, respectively), especially in the high-risk group (accuracy > 90%). Cause-of-death analysis showed differential modes of treatment failure in these cohorts and indicated that these models could be used to stratify RT patients for liver-sparing treatment regimen or combination approaches with systemic agents. Predictions of liver disease and lymphopenia were good but less robust (external AUC = 0.68 and 0.7, respectively), suggesting the need for more comprehensive consideration of dosimetry and better predictive biomarkers. The liver disease model showed excellent accuracy in the high-risk group (92%) and revealed possible interactions of platelet count with initial liver function.

CONCLUSION

Machine learning approaches can provide reliable outcome predictions in patients with hepatocellular carcinoma after RT in diverse cohorts across institutions. The excellent performance, particularly in high-risk patients, suggests novel strategies for patient stratification and treatment selection.

Abscopal effect after palliative five-fraction radiation therapy on bone and lymph node metastases from luminal B breast cancer: a case report and clinical implications for palliative radiation therapy

The abscopal effect is a phenomenon in which radiation therapy results in the regression of metastatic lesions at a distance from the irradiated lesions. Here, we have described a 37-year-old woman with advanced luminal B breast cancer who presented with severe pain at multiple sites. Multiple bone, lymph node, and lung metastases were found on computed tomography (CT). She refused to receive any systemic therapy, but she agreed to receive palliative radiotherapy (RT). Multi-site RT (25 or 30 Gy in 5 fractions) was performed for pain palliation. The pain was completely relieved after RT. Furthermore, the pulmonary CT after 3 months of RT showed a dramatic regression of the previous multiple lung metastases. This is the case report demonstrating the abscopal effect in South Korea.

Modelling treatment-response rates

The time course of tumour responses to immunotherapies can be mathematically predicted on the basis of tumour-growth rates, the rates of immune activation and of tumour–immune-cell interactions, and the efficacy of immune-mediated tumour killing.

Radiation-Associated Lymphopenia and Outcomes of Patients with Unresectable Hepatocellular Carcinoma Treated with Radiotherapy

Background

The immune system plays a crucial role in cancer surveillance. Previous studies have shown that lymphopenia associated with radiotherapy (RT) portends a poor prognosis. We sought to differentiate the effects of proton and photon RT on changes in absolute lymphocyte count (ALC) for patients with hepatocellular carcinoma (HCC).

Patients and Methods

Patients with HCC treated with definitive RT from 2006 to 2016 were studied. Serial ALCs were graded according to CTCAE v4.0. Overall survival (OS), disease-free survival, and distant metastasis-free survival were analyzed using the Kaplan-Meier method. Univariable and multivariable Cox-proportional hazards analyses were used to identify predictors of OS. A cohort analysis matched for treatment volume was performed to investigate differences in ALC dynamics between photon and proton therapy.

Results

Of 143 patients identified, the median age was 66 (range, 19-90) years. The treatment modality was photon in 103 (72%) and proton in 40 (28%). Median follow-up was 17 months (95% confidence interval, 13-25 months). The median time to ALC nadir after initiation of RT was 17 days with a median relative decrease of 67%. Those who received proton RT had a higher median ALC nadir (0.41 vs 0.32 k/µL, p=0.002) and longer median OS (33 vs 13 months, p=0.002) than those who received photon RT. Matched cohort analyses revealed a larger low-dose liver volume in the photon group, which correlated with lower ALC. On multivariable Cox analysis, Grade 3 or higher lymphopenia prior to or after RT, portal venous tumor thrombus, larger planning target volumes, Child-Pugh (CP) Class B, and increased CP score after RT were associated with a higher risk of death, whereas the use of proton therapy was associated with lower risk.

Conclusion

Grade 3 or higher lymphopenia may be associated with poorer outcomes in patients receiving RT for HCC. Protons may mitigate lymphopenia compared with photons, potentially due to reduced dose exposure of sites of lymphopoiesis.

Hematological toxicity in patients with solid malignant tumors treated with radiation - Temporal analysis, dose response and impact on survival

PURPOSE

To describe the kinetics of the peripheral blood components after radiotherapy, to examine radiation exposure vs. End-of-Radiation-Therapy (EoRT) counts and to associate the EoRT lymphocyte count with death and cancer treatment failure.

MATERIALS AND METHODS

Cohort study of patients who received curative intent radiotherapy for solid tumor diagnoses from 2009-2016 at Rigshospitalet, Copenhagen and had available 3D radiation exposure data. We illustrated peripheral blood count kinetics within 12 months before and after radiotherapy start and analyzed the impact of the irradiated body volume. We investigated overall survival and cancer treatment failure according to EoRT lymphopenia using Cox regression analyses.

RESULTS

We analyzed 4055 patients with both pre-treatment and EoRT platelet counts and 2318 patients who also had neutrophil and lymphocyte counts. Only the lymphocyte decline after radiotherapy start was clinically relevant and remained low one year after radiotherapy. The higher the volume of the body exposed to radiation, the lower the EoRT blood counts. Female gender (p < 0.001), number of fractions (p = 0.010), dose-volume (p < 0.001) and concomitant use of chemotherapy, particularly the platinum compounds (p < 0.001) were independently associated with a lower EoRT lymphocyte count. Patients with head and neck cancer had the lowest EoRT lymphocyte count. Patients with lymphopenia had a higher risk of death in the year after radiotherapy, compared with patients with no lymphopenia.

CONCLUSION

Radiation schemes with fewer fractions and radiation techniques allowing reduction of the volume of the body exposed to radiation could be expected to better preserve patients' immune function.

Data Driven Mathematical Model of Colon Cancer Progression

Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.