Dataset Information

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

ABSTRACT: BACKGROUND:Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. METHODS:A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. RESULTS:The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. CONCLUSIONS:This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

SUBMITTER: Kosinsky Y

PROVIDER: S-EPMC5830328 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

Kosinsky Yuri Y Dovedi Simon J SJ Peskov Kirill K Voronova Veronika V Chu Lulu L Tomkinson Helen H Al-Huniti Nidal N Stanski Donald R DR Helmlinger Gabriel G

Journal for immunotherapy of cancer 20180227 1

<h4>Background</h4>Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radia ...[more]

PMID: 29486799

Similar Datasets

Project description:Simple Summary Although immune checkpoint blockade has yielded unprecedented and durable responses in cancer patients, the efficacy of this treatment remains limited. Radiation therapy can induce immunogenic cell death that contributes to the local efficacy of irradiation. However, radiation-induced systemic responses are scarce. Studies combining radiation with checkpoint inhibitors suggest a synergistic potential of this strategy. In this review, we focused on parameters that can be optimized to enhance the anti-tumor immune response that results from this association, in order to achieve data on dose, fractionation, target volume, lymph nodes sparing, radiation particles, and other immunomodulatory agents. These factors should be considered in future trials for better clinical outcomes. To this end, we discussed the main preclinical and clinical data available to optimize the efficacy of the treatment combination. Abstract Immune checkpoint inhibitors have been associated with long-term complete responses leading to improved overall survival in several cancer types. However, these novel immunotherapies are only effective in a small proportion of patients, and therapeutic resistance represents a major limitation in clinical practice. As with chemotherapy, there is substantial evidence that radiation therapy promotes anti-tumor immune responses that can enhance systemic responses to immune checkpoint inhibitors. In this review, we discuss the main preclinical and clinical evidence on strategies that can lead to an enhanced response to PD-1/PD-L1 blockade in combination with radiation therapy. We focused on central issues in optimizing radiation therapy, such as the optimal dose and fractionation for improving the therapeutic ratio, as well as the impact on immune and clinical responses of dose rate, target volume, lymph nodes irradiation, and type of radiation particle. We explored the addition of a third immunomodulatory agent to the combination such as other checkpoint inhibitors, chemotherapy, and treatment targeting the tumor microenvironment components. The strategies described in this review provide a lead for future clinical trials.

Project description:Background: Effective radiation treatment (RT) for recurrent nasopharyngeal cancers (NPC), featuring an intrinsic hypoxic sub-volume, remains a clinical challenge. Lack of disease‐specific in-vitro models, together with difficulties in establishing patient derived xenograft (PDX) models, have further hindered development of personalized therapeutic options. Methods: Here in, we established two NPC organoid lines from recurrent NPC PDX models and further characterized and compared these models with original patient tumors using RNA sequencing analysis. Organoids were cultured in hypoxic conditions to examine the effects of hypoxia and radioresistance. These models were then utilized to determine the radiobiological parameters, such as α/β ratio and oxygen enhancement ratio (OER), characteristic to radiosensitive normoxic and radioresistant hypoxic NPC, using simple dose-survival data analytic tools. The results were further validated in-vitro and in-vivo, to determine the optimal boost dose and fractionation regimen required to achieve effective NPC tumor regression. Results: Despite the differences in tumor microenvironment due to the lack of human stroma, RNA sequencing analysis revealed good correlation of NPC PDX and organoid models with patient tumors. Additionally, the established models also mimicked inter-tumoral heterogeneity. Hypoxic NPC organoids were highly radioresistant and had high α/β ratio compared to its normoxic counterparts. In-vitro and in-vivo fractionation studies showed that hypoxic NPC was less sensitive to RT fractionation scheme and required a large bolus dose or 1.4 times of the fractionated dose that was effective against normoxic cells in order to compensate for oxygen deficiency. Conclusion: This study is the first direct experimental evidence to predict optimal RT boost dose required to cause sufficient damage to recurrent hypoxic NPC tumor cells, which can be further used to develop dose-painting algorithms in clinical practice.

Project description:The local Fourier-space relation between diffracted intensity I, diffraction wavevector q and dose D, , is key to probing and understanding radiation damage by X-rays and energetic particles in both diffraction and imaging experiments. The models used in protein crystallography for the last 50 years provide good fits to experimental I(q) versus nominal dose data, but have unclear physical significance. More recently, a fit to diffraction and imaging experiments suggested that the maximum tolerable dose varies as q -1 or linearly with resolution. Here, it is shown that crystallographic data have been strongly perturbed by the effects of spatially nonuniform crystal irradiation and diffraction during data collection. Reanalysis shows that these data are consistent with a purely exponential local dose dependence, = I 0(q)exp[-D/D e(q)], where D e(q) ? q ? with ? ? 1.7. A physics-based model for radiation damage, in which damage events occurring at random locations within a sample each cause energy deposition and blurring of the electron density within a small volume, predicts this exponential variation with dose for all q values and a decay exponent ? ? 2 in two and three dimensions, roughly consistent with both diffraction and imaging experiments over more than two orders of magnitude in resolution. The B-factor model used to account for radiation damage in crystallographic scaling programs is consistent with ? = 2, but may not accurately capture the dose dependencies of structure factors under typical nonuniform illumination conditions. The strong q dependence of radiation-induced diffraction decays implies that the previously proposed 20-30?MGy dose limit for protein crystallography should be replaced by a resolution-dependent dose limit that, for atomic resolution data sets, will be much smaller. The results suggest that the physics underlying basic experimental trends in radiation damage at T ? 100?K is straightforward and universal. Deviations of the local I(q, D) from strictly exponential behavior may provide mechanistic insights, especially into the radiation-damage processes responsible for the greatly increased radiation sensitivity observed at T ? 300?K.

Project description:PURPOSE:This study examined tumor growth delay resulting from partial irradiation in preclinical mouse models. METHODS AND MATERIALS:We investigated 67NR murine orthotopic breast tumors in both immunocompetent and nude mice. Treatment was delivered to 50% or 100% of the tumor using a 2 × 2 cm collimator on a microirradiator. Radiation response was modulated by treatment with anti-CD8 and anti-intercellular adhesion molecule (anti-ICAM) antibodies. Similar experiments were performed using the less immunogenic Lewis lung carcinoma mouse model. Tumor growth delay and ?-H2AX phosphorylation were measured, and immune response was assessed by immunofluorescence and flow cytometry at 1 and 7 days after radiation therapy. Tumor expression of cellular adhesion molecules was also measured at different times after radiation therapy. RESULTS:Partial irradiation led to tumor responses similar to those of fully exposed tumors in immunocompetent mice, but not in nude mice. After a single dose of 10 Gy, infiltration of CD8+ T cells was observed along with increased expression of ICAM. The response to 10 Gy in hemi-irradiated tumors was abrogated by treatment with either anti-CD8 or anti-ICAM antibodies. Similar responses were obtained in the less immunogenic Lewis lung carcinoma mouse model delivering 15 Gy to half the tumor volume. Treatment with FTY720, a compound that inhibits T-cell egress from lymph nodes, did not affect tumor response at the time of CD8+ T cells infiltration in the nonirradiated area of the tumor. This result indicated that the most likely source of these cells is the irradiated portion of the hemi-irradiated tumors. In addition, a significant abscopal effect was observed after partial irradiation with a single dose of 10 Gy in the 67NR model. CONCLUSIONS:In these models, radiation controls tumor growth both directly through cell killing and indirectly through immune activation. This outcome raises the possibility that this effect could be induced in the clinic.

Dataset Information

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

Publications

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets