The Optimal Radiation Dose to Induce Robust Systemic Anti-Tumor Immunity

Spatially fractionated GRID radiation potentiates immune-mediated tumor control

BACKGROUND

Tumor-immune interactions shape a developing tumor and its tumor immune microenvironment (TIME) resulting in either well-infiltrated, immunologically inflamed tumor beds, or immune deserts with low levels of infiltration. The pre-treatment immune make-up of the TIME is associated with treatment outcome; immunologically inflamed tumors generally exhibit better responses to radio- and immunotherapy than non-inflamed tumors. However, radiotherapy is known to induce opposing immunological consequences, resulting in both immunostimulatory and inhibitory responses. In fact, it is thought that the radiation-induced tumoricidal immune response is curtailed by subsequent applications of radiation. It is thus conceivable that spatially fractionated radiotherapy (SFRT), administered through GRID blocks (SFRT-GRID) or lattice radiotherapy to create areas of low or high dose exposure, may create protective reservoirs of the tumor immune microenvironment, thereby preserving anti-tumor immune responses that are pivotal for radiation success.

METHODS

We have developed an agent-based model (ABM) of tumor-immune interactions to investigate the immunological consequences and clinical outcomes after 2 Gy × 35 whole tumor radiation therapy (WTRT) and SFRT-GRID. The ABM is conceptually calibrated such that untreated tumors escape immune surveillance and grow to clinical detection. Individual ABM simulations are initialized from four distinct multiplex immunohistochemistry (mIHC) slides, and immune related parameter rates are generated using Latin Hypercube Sampling.

RESULTS

In silico simulations suggest that radiation-induced cancer cell death alone is insufficient to clear a tumor with WTRT. However, explicit consideration of radiation-induced anti-tumor immunity synergizes with radiation cytotoxicity to eradicate tumors. Similarly, SFRT-GRID is successful with radiation-induced anti-tumor immunity, and, for some pre-treatment TIME compositions and modeling parameters, SFRT-GRID might be superior to WTRT in providing tumor control.

CONCLUSION

This study demonstrates the pivotal role of the radiation-induced anti-tumor immunity. Prolonged fractionated treatment schedules may counteract early immune recruitment, which may be protected by SFRT-facilitated immune reservoirs. Different biological responses and treatment outcomes are observed based on pre-treatment TIME composition and model parameters. A rigorous analysis and model calibration for different tumor types and immune infiltration states is required before any conclusions can be drawn for clinical translation.

Modelling radiobiology

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy-from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic- and patient-level determinants of response. Finally, some of the major challenges in this field and potential avenues for future improvements are also discussed.

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.

Combination of proton- or X-irradiation with anti-PDL1 immunotherapy in two murine oral cancers

Combining radiation therapy with immunotherapy is a strategy to improve both treatments. The purpose of this study was to compare responses for two syngeneic head and neck cancer (HNC) tumor models in mice following X-ray or proton irradiation with or without immune checkpoint inhibition (ICI). MOC1 (immunogenic) and MOC2 (less immunogenic) tumors were inoculated in the right hind leg of each mouse (C57BL/6J, n = 398). Mice were injected with anti-PDL1 (10 mg/kg, twice weekly for 2 weeks), and tumors were treated with single-dose irradiation (5-30 Gy) with X-rays or protons. MOC2 tumors grew faster and were more radioresistant than MOC1 tumors, and all mice with MOC2 tumors developed metastases. Irradiation reduced the tumor volume in a dose-dependent manner. ICI alone reduced the tumor volume for MOC1 with 20% compared to controls, while no reduction was seen for MOC2. For MOC1, there was a clear treatment synergy when combining irradiation with ICI for radiation doses above 5 Gy and there was a tendency for X-rays being slightly more biologically effective compared to protons. For MOC2, there was a tendency of protons being more effective than X-rays, but both radiation types showed a small synergy when combined with ICI. Although the responses and magnitudes of the therapeutic effect varied, the optimal radiation dose for maximal synergy appeared to be in the order of 10-15 Gy, regardless of tumor model.

Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy

Introduction

Cancer combination treatments involving immunotherapies with targeted radiation therapy are at the forefront of treating cancers. However, dosing and scheduling of these therapies pose a challenge. Mathematical models provide a unique way of optimizing these therapies.

Methods

Using a preclinical model of multiple myeloma as an example, we demonstrate the capability of a mathematical model to combine these therapies to achieve maximum response, defined as delay in tumor growth. Data from mice studies with targeted radionuclide therapy (TRT) and chimeric antigen receptor (CAR)-T cell monotherapies and combinations with different intervals between them was used to calibrate mathematical model parameters. The dependence of progression-free survival (PFS), overall survival (OS), and the time to minimum tumor burden on dosing and scheduling was evaluated. Different dosing and scheduling schemes were evaluated to maximize the PFS and optimize timings of TRT and CAR-T cell therapies.

Results

Therapy intervals that were too close or too far apart are shown to be detrimental to the therapeutic efficacy, as TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing while the therapies being too far apart result in tumor regrowth, negatively impacting tumor control and survival. We show that splitting a dose of TRT or CAR-T cells when administered in combination is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy.

Discussion

Mathematical models are crucial tools for optimizing the delivery of cancer combination therapy regimens with application along the lines of achieving cure, maximizing survival or minimizing toxicity.

Chemotherapy-free radiotherapy combined with immune checkpoint inhibitors: a new regimen for locally advanced non-small cell lung cancer?

Maintenance immunotherapy after concurrent chemoradiotherapy remains the standard therapeutic approach in patients with unresectable locally advanced non-small cell lung cancer (LA-NSCLC). The efficacy of pembrolizumab without chemotherapy in stage IV NSCLC has incited interest in similar approaches for LA-NSCLC. Several recent investigations involving the synergistic potential of immunotherapy combined with radiotherapy (iRT) have generated encouraging results. This review discusses the existing studies and prospective directions of chemotherapy-free iRT strategies in unresectable LA-NSCLC. Although the initial findings of chemotherapy-free iRT strategies have shown promising efficacy, we must consider the methodologic limitations of current studies and the myriad of challenges that accompany the implementation of chemotherapy-free iRT. These challenges include determining the optimal dose and fractionation, precise target volume delineation, and identification of additional suitable patient cohorts. Furthermore, the feasibility of chemotherapy-free iRT as a novel treatment modality for select patients with LA-NSCLC is contingent upon validation through randomized phase III trials.

Optimal dosage protocols for mathematical models of synergy of chemo- and immunotherapy

The release of tumor antigens during traditional cancer treatments such as radio- or chemotherapy leads to a stimulation of the immune response which provides synergistic effects these treatments have when combined with immunotherapies. A low-dimensional mathematical model is formulated which, depending on the values of its parameters, encompasses the 3 E's (elimination, equilibrium, escape) of tumor immune system interactions. For the escape situation, optimal control problems are formulated which aim to revert the process to the equilibrium scenario. Some numerical results are included.

Simulating tumor volume dynamics in response to radiotherapy: Implications of model selection

From the beginning of the usage of radiotherapy (RT) for cancer treatment, mathematical modeling has been integral to understanding radiobiology and for designing treatment approaches and schedules. There has been extensive modeling of response to RT with the inclusion of various degrees of biological complexity. In this study, we compare three models of tumor volume dynamics: (1) exponential growth with RT directly reducing tumor volume, (2) logistic growth with direct tumor volume reduction, and (3) logistic growth with RT reducing the tumor carrying capacity with the objective of understanding the implications of model selection and informing the process of model calibration and parameterization. For all three models, we: examined the rates of change in tumor volume during and RT treatment course; performed parameter sensitivity and identifiability analyses; and investigated the impact of the parameter sensitivity on the tumor volume trajectories. In examining the tumor volume dynamics trends, we coined a new metric - the point of maximum reduction of tumor volume (MRV) - to quantify the magnitude and timing of the expected largest impact of RT during a treatment course. We found distinct timing differences in MRV, dependent on model selection. The parameter identifiability and sensitivity analyses revealed the interdependence of the different model parameters and that it is only possible to independently identify tumor growth and radiation response parameters if the underlying tumor growth rate is sufficiently large. Ultimately, the results of these analyses help us to better understand the implications of model selection while simultaneously generating falsifiable hypotheses about MRV timing that can be tested on longitudinal measurements of tumor volume from pre-clinical or clinical data with high acquisition frequency. Although, our study only compares three particular models, the results demonstrate that caution is necessary in selecting models of response to RT, given the artifacts imposed by each model.

Radiotherapy with or without immunotherapy in metastatic melanoma: efficacy and tolerability

INTRODUCTION

Radiotherapy (RT) is primarily considered as a palliative treatment in patients with metastatic melanoma. However, observations suggest that when RT is combined with immune checkpoint inhibitors (ICI), it can induce an immune response leading to an anti-tumoral effect also distant from the irradiated area - a phenomenon called 'abscopal effect'. The frequency and circumstances of abscopal effect among metastatic melanoma patients remains uncertain and further research is necessary.

MATERIAL AND METHOD

This retrospective study included all metastatic melanoma patients who received non-stereotactic RT in Stockholm, Sweden in 2015-2020. Patients were grouped depending on if RT was given at start of ICI (RT + ICI(start)), at ICI progression (RT + ICI(salvage)) or without ICI (RT(only)). Response rates in irradiated (RR(irradiated)) and overall response rates in non-irradiated (ORR(non-irradiated)) metastases were evaluated together with survival and toxicity in each cohort.

RESULTS

In the RT + ICI(start) (n = 47), RT + ICI(salvage) (n = 41) and RT(only) (n = 55) cohorts, RR(irradiated) was 70.7%, 67.5% and 43.1% (p = 0.018) while the ORR(non-irradiated) was 36.1%, 14.8% and 0.0% (p = 0.003), and the median overall survival was 18.2, 15.0 and 7.2 months, respectively (p = 0.014). Local response to RT was in all cohorts associated with longer survival (p < 0.001). The frequency of grade ≥3 immune-related adverse events was 17.0% and 19.5% in the RT + ICI(start) and RT + ICI(salvage) cohorts. No increased frequency of RT-related adverse events was seen in the RT + ICI cohorts, compared to the RT(only) cohort.

CONCLUSION

This retrospective study showed that melanoma patients receiving RT in combination with ICI had a superior antitumoral response in both irradiated and non-irradiated lesions as compared to patients receiving only RT. Additionally, a subgroup of patients receiving RT when progressing on ICI experienced tumor regression also in non-irradiated areas.

Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems

Theoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation that enables rigorous analysis of these models. However, spatial cumulant models (SCMs), which have arisen from theoretical ecology, describe population dynamics generated by a specific family of IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved population models formulated by a system of differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances). We exemplify how SCMs can be used in mathematical oncology by modelling theoretical cancer cell populations comprising interacting growth factor-producing and non-producing cells. To formulate model equations, we use computational tools that enable the generation of STPPs, SCMs and mean-field population models (MFPMs) from user-defined model descriptions (Cornell et al. Nat Commun 10:4716, 2019). To calculate and compare STPP, SCM and MFPM-generated summary statistics, we develop an application-agnostic computational pipeline. Our results demonstrate that SCMs can capture STPP-generated population density dynamics, even when MFPMs fail to do so. From both MFPM and SCM equations, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treatment strategies in STPP-generated cell populations, our results demonstrate that SCM-informed strategies outperform MFPM-informed strategies in terms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions, and can be used to describe and perturb STPP-generated cell population dynamics. We, therefore, argue that SCMs can be used to increase IBMs' applicability in cancer research.

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.

Clonogenicity-based radioresistance determines the expression of immune suppressive immune checkpoint molecules after hypofractionated irradiation of MDA-MB-231 triple-negative breast cancer cells

Only a subset of patients with triple-negative breast cancer (TNBC) benefits from a combination of radio- (RT) and immunotherapy. Therefore, we aimed to examine the impact of radioresistance and brain metastasizing potential on the immunological phenotype of TNBC cells following hypofractionated RT by analyzing cell death, immune checkpoint molecule (ICM) expression and activation of human monocyte-derived dendritic cells (DCs). MDA-MB-231 triple-negative breast cancer tumor cells were used as model system. Apoptosis was the dominant cell death form of brain metastasizing tumor cells, while Hsp70 release was generally significantly increased following RT and went along with necrosis induction. The ICMs PD-L1, PD-L2, HVEM, ICOS-L, CD137-L and OX40-L were found on the tumor cell surfaces and were significantly upregulated by RT with 5 x 5.2 Gy. Strikingly, the expression of immune suppressive ICMs was significantly higher on radioresistant clones compared to their respective non-radioresistant ones. Although hypofractionated RT led to significant cell death induction and release of Hsp70 in all tumor cell lines, human monocyte-derived DCs were not activated after co-incubation with RT-treated tumor cells. We conclude that radioresistance is a potent driver of immune suppressive ICM expression on the surface of TNBC MDA-MB-231 cells. This mechanism is generally known to predominantly influence the effector phase, rather than the priming phase, of anti-tumor immune responses.

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.

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.

Stereotactic Body Radiotherapy Immunological Planning-A Review With a Proposed Theoretical Model

In the stereotactic body radiotherapy (SBRT) and immunotherapy era, we are moving toward an “immunological radiation plan”, i.e., radiation scheduling with abscopal effect as a vital endpoint as well. The literature review of part A enumerates the advantages of the intermediate dose of SBRT 6–10 Gy per fraction, appropriate use of dose painting, proper timing with immunotherapy, and the potential of immunoadjuvants to maximize cell kill in the irradiated lesions, found to have improved the abscopal effects. Part B summarizes part A, primarily the findings of animal trials, forming the basis of the tenets of the proposed model given in part C to realize the true abscopal potential of the SBRT tumor cell kill of the index lesions. Part C proposes a theoretical model highlighting tumor vasculature integrity as the central theme for converting “abscopal effect by chance” to “abscopal effect by design” using a harmonized combinatorial approach. The proposed model principally deals with the use of SBRT in strategizing increased cell kill in irradiated index tumors along with immunomodulators as a basis for improving the consistency of the abscopal effect. Included is the possible role of integrating immunotherapy just after SBRT, “cyclical” antiangiogenics, and immunoadjuvants/immune metabolites as abscopal effect enhancers of SBRT tumor cell kill. The proposed model suggests convergence research in adopting existing numerous SBRT abscopal enhancing strategies around the central point of sustained vascular integrity to develop decisive clinical trial protocols in the future.

A Comprehensive Evaluation of ZrC Nanoparticle in Combined Photothermal and Radiation Therapy for Treatment of Triple-Negative Breast Cancer

Because of the difficulty in treating triple-negative breast cancer (TNBC), the search for treatments has never stopped. Treatment opinions remain limited for triple-negative breast cancer (TNBC). The current treatment approach of using photothermal therapy (PTT) is often imprecise and has limited penetration below the surface of the skin. On the other hand, radiation therapy (RT) has its unavoidable disadvantages, such as side effects or ineffectiveness against hypoxic tumor microenvironment (TME). In this study, we proposed the use of ZrC nanoparticles in conjunction with RT/PTT to enhance antitumor and antimetastatic effect. We modified the ZrC nanoparticle with bovine serum albumin (BSA) and folic acid (FA), sizing desirable about 100nm. The photothermal conversion efficiency was calculated to be 40.51% and sensitizer enhancement ration (SER) was 1.8. With addition of ZrC NPs, more DNA were damaged in γ-H2AX and more ROS were detected with immunofluorescence. In vitro and vivo, the combined therapy with ZrC NPS showed the best effect of tumor cell inhibition and safety. Our results provide evidence that the combination of ZrC NPs, PT, and RT is effective in of TNBC, making it a great potential application for cancer therapy in clinic.

Immune Checkpoints Inhibitors and SRS/SBRT Synergy in Metastatic Non-Small-Cell Lung Cancer and Melanoma: A Systematic Review

Background: Several immunotherapy (IT) agents are FDA approved for treatment of melanoma and non-small-cell lung cancer (NSCLC). The addition of stereotactic radiosurgery (SRS) or stereotactic body radiation therapy (SBRT) to immunotherapy looks promising. A systematic review was conducted to evaluate the possible synergistic effects of immune checkpoints inhibitors (ICIs) and stereotactic radiation therapy in melanoma and NSCLC. Materials and methods: Pubmed databases from January 2010 to December 2020 were reviewed to identify English language studies reporting control of local and abscopal effect of the combination of ICI-SBRT/SRS in metastatic NSCLC and melanoma cancer. The inclusion criteria were followed according to PICO criteria. Results: Thirty-nine articles were included of the 2141 initial results. The reported rates for local control were 16.5–100% and 40–94% in brain and extracerebral metastases, respectively. Distant/abscopal response rates were 1–45% in extracerebral metastases. Abscopal effect could not be evaluated in brain metastases because it was not reported in studies. Treatments were well tolerated with few grade 4 toxicities and no grade 5. Conclusions: The combined treatment of ICI-SBRT/SRS achieves high local control and non-negligible abscopal response in patients with extracerebral metastases, with its benefit in cerebral metastases being more controversial. Clinical trials are needed to better characterize the potential synergism.

Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability

Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.

Angiogenesis and immune checkpoint dual blockade in combination with radiotherapy for treatment of solid cancers: opportunities and challenges

Several immune checkpoint blockades (ICBs) capable of overcoming the immunosuppressive roles of the tumor immune microenvironment have been approved by the US Food and Drug Administration as front-line treatments of various tumor types. However, due to the considerable heterogeneity of solid tumor cells, inhibiting one target will only influence a portion of the tumor cells. One way to enhance the tumor-killing efficiency is to develop a multiagent therapeutic strategy targeting different aspects of tumor biology and the microenvironment to provide the maximal clinical benefit for patients with late-stage disease. One such strategy is the administration of anti-PD1, an ICB, in combination with the humanized monoclonal antibody bevacizumab, an anti-angiogenic therapy, to patients with recurrent/metastatic malignancies, including hepatocellular carcinoma, metastatic renal cell carcinoma, non-small cell lung cancer, and uterine cancer. Radiotherapy (RT), a critical component of solid cancer management, has the capacity to prime the immune system for an adaptive antitumor response. Here, we present an overview of the most recent published data in preclinical and clinical studies elucidating that RT could further potentiate the antitumor effects of immune checkpoint and angiogenesis dual blockade. In addition, we explore opportunities of triple combinational treatment, as well as discuss the challenges of validating biomarkers and the management of associated toxicity.

Hypofractionated stereotactic re-irradiation with pembrolizumab and bevacizumab in patients with recurrent high-grade gliomas: results from a phase I study

BACKGROUND

Radiotherapy may synergize with programmed cell death 1 (PD1)/PD1 ligand (PD-L1) blockade. The purpose of this study was to determine the recommended phase II dose, safety/tolerability, and preliminary efficacy of combining pembrolizumab, an anti-PD1 monoclonal antibody, with hypofractionated stereotactic irradiation (HFSRT) and bevacizumab in patients with recurrent high-grade gliomas (HGGs).

METHODS

Eligible subjects with recurrent glioblastoma or anaplastic astrocytoma were treated with pembrolizumab (100 or 200 mg based on dose level Q3W) concurrently with HFSRT (30 Gy in 5 fractions) and bevacizumab 10 mg/kg Q2W.

RESULTS

Thirty-two patients were enrolled (bevacizumab-naïve, n = 24; bevacizumab-resistant, n = 8). The most common treatment-related adverse events (TRAEs) were proteinuria (40.6%), fatigue (25%), increased alanine aminotransferase (25%), and hypertension (25%). TRAEs leading to discontinuation occurred in 1 patient who experienced a grade 3 elevation of aspartate aminotransferase. In the bevacizumab-naïve cohort, 20 patients (83%) had a complete response or partial response. The median overall survival (OS) and progression-free survival (PFS) were 13.45 months (95% CI: 9.46-18.46) and 7.92 months (95% CI: 6.31-12.45), respectively. In the bevacizumab-resistant cohort, PR was achieved in 5 patients (62%). Median OS was 9.3 months (95% CI: 8.97-18.86) with a median PFS of 6.54 months (95% CI: 5.95-18.86). The majority of patients (n = 20/26; 77%) had tumor-cell/tumor-microenvironment PD-L1 expression <1%.

CONCLUSIONS

The combination of HFSRT with pembrolizumab and bevacizumab in patients with recurrent HGG is generally safe and well tolerated. These findings merit further investigation of HFSRT with immunotherapy in HGGs.

Modeling Radioimmune Response-Current Status and Perspectives

The combination of immune therapy with radiation offers an exciting and promising treatment modality in cancer therapy. It has been hypothesized that radiation induces damage signals within the tumor, making it more detectable for the immune system. In combination with inhibiting immune checkpoints an effective anti-tumor immune response may be established. This inversion from tumor immune evasion raises numerous questions to be solved to support an effective clinical implementation: These include the optimum immune drug and radiation dose time courses, the amount of damage and associated doses required to stimulate an immune response, and the impact of lymphocyte status and dynamics. Biophysical modeling can offer unique insights, providing quantitative information addressing these factors and highlighting mechanisms of action. In this work we review the existing modeling approaches of combined ‘radioimmune’ response, as well as associated fields of study. We propose modeling attempts that appear relevant for an effective and predictive model. We emphasize the importance of the time course of drug and dose delivery in view to the time course of the triggered biological processes. Special attention is also paid to the dose distribution to circulating blood lymphocytes and the effect this has on immune competence.

Radiotherapy as an immune checkpoint blockade combination strategy for hepatocellular carcinoma

In the immune oncology era, the clinical efficacy of immune checkpoint inhibitors (ICIs) against most solid cancers is well known. In hepatocellular carcinoma, the recent success of combination therapy with targeting agents has accelerated the search for novel combination strategies. Radiotherapy (RT), an attractive modality, can be combined with ICIs, which act as strong modulators of the tumor immune microenvironment. Herein, we discuss immune modulation caused by radiation and the current trials of RT–ICI combination treatment as well as future perspectives.

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Research Progress and Existing Problems for Abscopal Effect

Radiation therapy plays a vital role in the treatment of tumours. In particular, the occurrence of the “abscopal effect” brings about a favourable turn for the treatment of patients with advanced metastatic malignant tumours. Because of the abscopal effect, non-irradiated areas are also treated. However, the abscopal effect occurs by chance, not through seeking. Although the abscopal effect has been studied enthusiastically, the desired result does not appear to be achieved. Moreover, its combination with immunotherapy appears to be overwhelming. There is an opinion that abscopal effect is difficult to achieve by irradiation of a single tumour, and irradiation of multiple or total lesions is advocated to increase the possibility of obtaining clinically meaningful outcomes. Obviously, there are still questions about the mechanism, condition and possibility underlying the occurrence of the abscopal effect. Can the abscopal effect truly change the future treatment strategy as the researchers expect? What are the current problems? This article reviewed the research in recent years to explore the progress and controversy surrounding the abscopal effect of radiation therapy.

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

PURPOSE

The impact of radiation therapy on the immune system has recently gained attention particularly when delivered in combination with immunotherapy. However, it is unclear how different treatment fractionation regimens influence the interaction between the immune system and radiation. The goal of this work was to develop a mathematical model that quantifies both the immune stimulating as well as the immunosuppressive effects of radiotherapy and simulates the effects of different fractionation regimens based on patient data.

METHODS AND MATERIALS

The framework describes the temporal evolution of tumor cells, lymphocytes, and inactivated dying tumor cells releasing antigens during radiation therapy, specifically modeling how recruited lymphocytes inhibit tumor progression. The parameters of the model were partly taken from the literature and in part extracted from blood samples (circulating lymphocytes: CLs) collected from hepatocellular carcinoma patients undergoing radiotherapy and their outcomes. The dose volume histograms to circulating lymphocytes were calculated with a probability-based model.

RESULTS

Based on the fitted parameters, the model enabled a study into the depletion and recovery of CLs in patients as a function of fractionation regimen. Our results quantify the ability of short fractionation regimens to lead to shorter periods of lymphocyte depletion and predict faster recovery after the end of treatment. The model shows that treatment breaks between fractions can prolong the period of lymphocyte depletion and should be avoided.

CONCLUSIONS

This study introduces a mathematical model for tumor-immune interactions using clinically extracted radiotherapy patient data, which can be applied to design trials aimed at minimizing lymphocyte depleting effects in radiation therapy.

Immunologic Consequences of Sequencing Cancer Radiotherapy and Surgery

PURPOSE

Early-stage cancers are routinely treated with surgery followed by radiotherapy (SR). Radiotherapy before surgery (RS) has been widely ignored for some cancers. We evaluate overall survival (OS) and disease-free survival (DFS) with SR and RS for different cancer types and simulate the plausibility of RS- and SR-induced antitumor immunity contributing to outcomes.

MATERIALS AND METHODS

We analyzed a SEER data set of early-stage cancers treated with SR or RS. OS and DFS were calculated for cancers with sufficient numbers for statistical power (cancers of lung and bronchus, esophagus, rectum, cervix uteri, corpus uteri, and breast). We simulated the immunologic consequences of SR, RS, and radiotherapy alone in a mathematical model of tumor-immune interactions.

RESULTS

RS improved OS for cancers with low 20-year survival rates (lung: hazard ratio [HR], 0.88; P = .046) and improved DFS for cancers with higher survival (breast: HR = 0.64; P < .001). For rectal cancer, with intermediate 20-year survival, RS improved both OS (HR = 0.89; P = .006) and DFS (HR = 0.86; P = .04). Model simulations suggested that RS could increase OS by eliminating cancer for a broader range of model parameters and radiotherapy-induced antitumor immunity compared with SR for selected parameter combinations. This could create an immune memory that may explain increased DFS after RS for certain cancers.

CONCLUSION

Study results suggest plausibility that radiation to the bulk of the tumor could induce a more robust immune response and better harness the synergy of radiotherapy and antitumor immunity than postsurgical radiation to the tumor bed. This exploratory study provides motivation for prospective evaluation of immune activation of RS versus SR in controlled clinical studies.

Pure abscopal effect of radiotherapy in a salivary gland carcinoma: Case report, literature review, and a search for new approaches

We report the case of an 84-year-old woman with poorly differentiated non-small cell carcinoma of the right parotid who presented with headache, was found to have a primary right parotid gland cancer as well as metastatic disease, and underwent palliative radiotherapy to the primary site. The patient received no chemotherapy or immunotherapy, but both the primary site and several non-irradiated foci in the lungs regressed or completely resolved. The patient remained free of disease for about one year before progression. The case is a rare instance of abscopal regression of metastatic disease in the absence of pharmacologic immunomodulation. A literature review surveys the history of the abscopal effect of radiation therapy, attempts to understand the mechanisms of its successes and failures, and points to new approaches that can inform and improve the outcomes of radioimmunotherapy.

Spatially fractionated radiotherapy (SFRT) targeting the hypoxic tumor segment for the intentional induction of non-targeted effects: An in silico study to exploit a new treatment paradigm

Introduction

The possibility of intentionally triggering non targeted effects (NTEs) using spatially fractionated radiotherapy (SFRT) alone or combined with immunotherapy is an intriguing and fascinating area of research. Among different techniques for SFRT, stereotactic body radiotherapy targeting exclusively the central hypoxic segment of bulky tumors, (SBRT-PATHY) might trigger immunogenic cell death more efficiently. This in silico study aims to identify the best possible dosimetric trade-off for prescribing SFRT with volumetric modulated arc (VMAT) based stereotactic radiotherapy (SRT).

Material and methods

Eight spherical volumes defined "Gross Tumor Volumes" (GTVs) were generated with diameters of 3-10 cm (with incremental steps of 1 cm), simulating tumor lesions. The inner third part of each GTV (GTV_central) was selected to simulate the central hypoxic area and a ring structure was derived around it to simulate the tumor periphery (GTV_peripheral). Volumetric modulated arc radiation treatment (VMAT) plans were calculated to deliver a single fraction of 10 Gy to each GTV_central with different dose prescription methods: target mean and isodose driven (40, 50, 60, 70, 80 and 90%).The volume of GTV_peripheral receiving less than 2 Gy was recorded as dosimetric performance indicator.

Results

56 possible dosimetric scenarios were analyzed. The largest percentage of GTV_peripheral spared from the dose of 2 Gy was achieved with dose prescription methods to the 70% isodose line for lesions smaller than 6 cm (range 42.9-48.4%) and to the target mean for larger ones (range 52.9-64.5%).

Conclusions

Optimizing the dose prescription method may reduce the dose to tumor periphery in VMAT-based SFRT, thus potentially sparing tumor infiltrating immune cells. The optimal method may vary according to the size of the lesion. This should be taken into account when designing prospective trials using SFRT.

A minimal modeling framework of radiation and immune system synergy to assist radiotherapy planning

Recent evidence indicates the ability of radiotherapy to induce local and systemic tumor-specific immune responses as a result of immunogenic cell death. However, fractionation regimes routinely used in clinical practice typically ignore the synergy between radiation and the immune system, and instead attempt to completely eradicate tumors by the direct lethal effect of radiation on cancer cells. This paradigm is expected to change in the near future due to the potential benefits of considering radiation-induced antitumor immunity during treatment planning. Towards this goal, we propose a minimal modeling framework based on key aspects of the tumor-immune system interplay to simulate the effects of radiation on tumors and the immunological consequences of radiotherapy. The impacts of tumor-associated vasculature and intratumoral oxygen-mediated heterogeneity on treatment outcomes are ininvestigated. The model provides estimates of the minimum radiation doses required for tumor eradication given a certain number of treatment fractions. Moreover, estimates of treatment duration for disease control given predetermined fractional radiation doses can be also obtained. Although theoretical in nature, this study motivates the development and establishment of immune-based decision-support tools in radiotherapy planning.

Molecular and Cellular Functions of CTLA-4

Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) is an inhibitory receptor belonging to the CD28 immunoglobulin subfamily, expressed primarily by T-cells. Its ligands, CD80 and CD86, are typically found on the surface of antigen-presenting cells and can either bind CD28 or CTLA-4, resulting in a costimulatory or a co-inhibitory response, respectively. Because of its dampening effect, CTLA-4 is a crucial regulator of T-cell homeostasis and self-tolerance. The mechanisms by which CTLA-4 exerts its inhibitory function can be categorized as either cell-intrinsic (affects the CTLA-4 expressing T-cell) or cell-extrinsic (affects secondary cells). Research from the last decade has shown that CTLA-4 mainly acts in a cell-extrinsic manner via its competition with CD28, CTLA-4-mediated trans-endocytosis of CD80 and CD86, and its direct tolerogenic effects on the interacting cell. Nonetheless, intrinsic CTLA-4 signaling has been implicated in T-cell motility and the regulation of CTLA-4 its subcellular localization amongst others. CTLA-4 is well recognized as a key immune checkpoint and has gained significant momentum as a therapeutic target in the field of autoimmunity and cancer. In this chapter, we describe the role of costimulation in immune response induction as well as the main mechanisms by which CTLA-4 can inhibit this process.

Assessing the interactions between radiotherapy and antitumour immunity

Immunotherapy, specifically the introduction of immune checkpoint inhibitors, has transformed the treatment of cancer, enabling long-term tumour control even in individuals with advanced-stage disease. Unfortunately, only a small subset of patients show a response to currently available immunotherapies. Despite a growing consensus that combining immune checkpoint inhibitors with radiotherapy can increase response rates, this approach might be limited by the development of persistent radiation-induced immunosuppression. The ultimate goal of combining immunotherapy with radiotherapy is to induce a shift from an ineffective, pre-existing immune response to a long-lasting, therapy-induced immune response at all sites of disease. To achieve this goal and enable the adaptation and monitoring of individualized treatment approaches, assessment of the dynamic changes in the immune system at the patient level is essential. In this Review, we summarize the available clinical data, including forthcoming methods to assess the immune response to radiotherapy at the patient level, ranging from serum biomarkers to imaging techniques that enable investigation of immune cell dynamics in patients. Furthermore, we discuss modelling approaches that have been developed to predict the interaction of immunotherapy with radiotherapy, and highlight how they could be combined with biomarkers of antitumour immunity to optimize radiotherapy regimens and maximize their synergy with immunotherapy.

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors.

Mathematical Models of Cancer: When to Predict Novel Therapies, and When Not to

The number of publications on mathematical modeling of cancer is growing at an exponential rate, according to PubMed records, provided by the US National Library of Medicine and the National Institutes of Health. Seminal papers have initiated and promoted mathematical modeling of cancer and have helped define the field of mathematical oncology (Norton and Simon in J Natl Cancer Inst 58:1735-1741, 1977; Norton in Can Res 48:7067-7071, 1988; Hahnfeldt et al. in Can Res 59:4770-4775, 1999; Anderson et al. in Comput Math Methods Med 2:129-154, 2000. https://doi.org/10.1080/10273660008833042 ; Michor et al. in Nature 435:1267-1270, 2005. https://doi.org/10.1038/nature03669 ; Anderson et al. in Cell 127:905-915, 2006. https://doi.org/10.1016/j.cell.2006.09.042 ; Benzekry et al. in PLoS Comput Biol 10:e1003800, 2014. https://doi.org/10.1371/journal.pcbi.1003800 ). Following the introduction of undergraduate and graduate programs in mathematical biology, we have begun to see curricula developing with specific and exclusive focus on mathematical oncology. In 2018, 218 articles on mathematical modeling of cancer were published in various journals, including not only traditional modeling journals like the Bulletin of Mathematical Biology and the Journal of Theoretical Biology, but also publications in renowned science, biology, and cancer journals with tremendous impact in the cancer field (Cell, Cancer Research, Clinical Cancer Research, Cancer Discovery, Scientific Reports, PNAS, PLoS Biology, Nature Communications, eLife, etc). This shows the breadth of cancer models that are being developed for multiple purposes. While some models are phenomenological in nature following a bottom-up approach, other models are more top-down data-driven. Here, we discuss the emerging trend in mathematical oncology publications to predict novel, optimal, sometimes even patient-specific treatments, and propose a convention when to use a model to predict novel treatments and, probably more importantly, when not to.

Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine

Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.

Optimized fractionated radiotherapy with anti-PD-L1 and anti-TIGIT: a promising new combination

PURPOSE/OBJECTIVE

Radiotherapy (RT) induces an immunogenic antitumor response, but also some immunosuppressive barriers. It remains unclear how different fractionation protocols can modulate the immune microenvironment. Clinical studies are ongoing to evaluate immune checkpoint inhibitors (ICI) in association with RT. However, only few trials aim to optimize the RT fractionation to improve efficacy of these associations. Here we sought to characterize the effect of different fractionation protocols on immune response with a view to associating them with ICI.

MATERIALS/METHODS

Mice bearing subcutaneous CT26 colon tumors were irradiated using a SARRP device according to different radiation schemes with a same biologically effective dose. Mice were monitored for tumor growth. The radiation immune response (lymphoid, myeloid cells, lymphoid cytokines and immune checkpoint targets) was monitored by flow cytometry at different timepoints after treatment and by RNA sequencing analysis (RNAseq). The same radiation protocols were performed with and without inhibitors of immune checkpoints modulated by RT.

RESULTS

In the absence of ICI, we showed that 18x2Gy and 3x8Gy induced the longest tumor growth delay compared to 1×16.4Gy. While 3x8Gy and 1×16.4Gy induced a lymphoid response (CD8⁺ T-cells, Regulators T-cells), 18x2Gy induced a myeloid response (myeloid-derived suppressor cells, tumor-associated macrophages 2). The secretion of granzyme B by CD8⁺ T cells was increased to a greater extent with 3x8Gy. The expression of PD-L1 by tumor cells was moderately increased by RT, but most durably with 18x2Gy. T cell immunoreceptor with Ig and ITIM domains (TIGIT) expression by CD8⁺ T-cells was increased with 3x8Gy, but decreased with 18x2Gy. These results were also observed with RNAseq. RT was dramatically more effective with 3x8Gy compared to all the other treatments schemes when associated with anti-TIGIT and anti-PD-L1 (9/10 mice in complete response). The association of anti-PD-L1 and RT was also effective in the 18x2Gy group (8/12 mice in complete response).

CONCLUSION

Each fractionation scheme induced different lymphoid and myeloid responses as well as various modulations of PD-L1 and TIGIT expression. Furthermore, 3x8Gy was the most effective protocol when associated with anti-PD-L1 and anti-TIGIT. This is the first study combining RT and anti-TIGIT with promising results; further studies are warranted.

The 6th R of Radiobiology: Reactivation of Anti-Tumor Immune Response

Historically, the 4Rs and then the 5Rs of radiobiology explained the effect of radiation therapy (RT) fractionation on the treatment efficacy. These 5Rs are: Repair, Redistribution, Reoxygenation, Repopulation and, more recently, intrinsic Radiosensitivity. Advances in radiobiology have demonstrated that RT is able to modify the tumor micro environment (TME) and to induce a local and systemic (abscopal effect) immune response. Conversely, RT is able to increase some immunosuppressive barriers, which can lead to tumor radioresistance. Fractionation and dose can affect the immunomodulatory properties of RT. Here, we review how fractionation, dose and timing shape the RT-induced anti-tumor immune response and the therapeutic effect of RT. We discuss how immunomodulators targeting immune checkpoint inhibitors and the cGAS/STING (cyclic GMP-AMP Synthase/Stimulator of Interferon Genes) pathway can be successfully combined with RT. We then review current trials evaluating the RT/Immunotherapy combination efficacy and suggest new innovative associations of RT with immunotherapies currently used in clinic or in development with strategic schedule administration (fractionation, dose, and timing) to reverse immune-related radioresistance. Overall, our work will present the existing evidence supporting the claim that the reactivation of the anti-tumor immune response can be regarded as the 6th R of Radiobiology.

Illuminating the Numbers: Integrating Mathematical Models to Optimize Photomedicine Dosimetry and Combination Therapies

Cancer photomedicine offers unique mechanisms for inducing local tumor damage with the potential to stimulate local and systemic anti-tumor immunity. Optically-active nanomedicine offers these features as well as spatiotemporal control of tumor-focused drug release to realize synergistic combination therapies. Achieving quantitative dosimetry is a major challenge, and dosimetry is fundamental to photomedicine for personalizing and tailoring therapeutic regimens to specific patients and anatomical locations. The challenge of dosimetry is perhaps greater for photomedicine than many standard therapies given the complexity of light delivery and light-tissue interactions as well as the resulting photochemistry responsible for tumor damage and drug-release, in addition to the usual intricacies of therapeutic agent delivery. An emerging multidisciplinary approach in oncology utilizes mathematical and computational models to iteratively and quantitively analyze complex dosimetry, and biological response parameters. These models are parameterized by preclinical and clinical observations and then tested against previously unseen data. Such calibrated and validated models can be deployed to simulate treatment doses, protocols, and combinations that have not yet been experimentally or clinically evaluated and can provide testable optimal treatment outcomes in a practical workflow. Here, we foresee the utility of these computational approaches to guide adaptive therapy, and how mathematical models might be further developed and integrated as a novel methodology to guide precision photomedicine.

Abscopal Effect, From Myth to Reality: From Radiation Oncologists' Perspective

The abscopal effect is mediated by a systemic anti-tumor immune response and reflects the regression of non-irradiated metastatic lesions at a distance from the primary site of irradiation. This review will focus on understanding the biological rationale behind the abscopal effect of radiotherapy (RT), which has a recently renewed interest as a result of the successes achieved with immunotherapy and RT in combination. Both RT and immunotherapy are standard components of modern treatment regimens. Combination of these two modalities results in an increased response in the irradiated lesions themselves and the metastatic regions distant from the site of irradiation. We will summarize the abscopal effect of radiotherapy, in particular, the synergistic effect of RT and immunotherapy.