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Considerations for combined immune checkpoint modulation and radiation treatment.

ABSTRACT: Recent advances indicate that new therapeutic strategies for the treatment of malignancies will be realized from combined radiation treatment and immune checkpoint modulation. Numerous biophysical properties must be considered for effective biologic development, including affinity, selectivity, oligomeric state and valency. High-resolution structural characterization contributes to our understanding of these properties and can lead to the realization of proteins with unique in vitro activities and novel in vivo therapeutic functions. In this article we focus on the importance of these factors for new potential biologics and consider these in the context of combination therapies with physical modalities, including radiation therapy. In particular, we examine the consequences of altered avidities and subset-specific ligand density on the rational modification of biological function in the immunoglobulin and tumor necrosis factor superfamilies and for new optimized combination therapies.

SUBMITTER: Almo SC

PROVIDER: S-EPMC4163026 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Considerations for combined immune checkpoint modulation and radiation treatment.

Almo Steven C SC Guha Chandan C

Radiation research 20140708 2

Recent advances indicate that new therapeutic strategies for the treatment of malignancies will be realized from combined radiation treatment and immune checkpoint modulation. Numerous biophysical properties must be considered for effective biologic development, including affinity, selectivity, oligomeric state and valency. High-resolution structural characterization contributes to our understanding of these properties and can lead to the realization of proteins with unique in vitro activities a ...[more]

PMID: 25003312

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Project description:PurposeCombined radiotherapy (RT) and immune checkpoint-inhibitor (ICI) therapy can act synergistically to enhance tumor response beyond what either treatment can achieve alone. Alongside the revolutionary impact of ICIs on cancer therapy, life-threatening potential side effects, such as checkpoint-inhibitor-induced (CIP) pneumonitis, remain underreported and unpredictable. In this preclinical study, we hypothesized that routinely collected data such as imaging, blood counts, and blood cytokine levels can be utilized to build a model that predicts lung inflammation associated with combined RT/ICI therapy.Materials and methodsThis proof-of-concept investigational work was performed on Lewis lung carcinoma in a syngeneic murine model. Nineteen mice were used, four as untreated controls and the rest subjected to RT/ICI therapy. Tumors were implanted subcutaneously in both flanks and upon reaching volumes of ~200 mm3 the animals were imaged with both CT and MRI and blood was collected. Quantitative radiomics features were extracted from imaging of both lungs. The animals then received RT to the right flank tumor only with a regimen of three 8 Gy fractions (one fraction per day over 3 days) with PD-1 inhibitor administration delivered intraperitoneally after each daily RT fraction. Tumor volume evolution was followed until tumors reached the maximum size allowed by the Institutional Animal Care and Use Committee (IACUC). The animals were sacrificed, and lung tissues harvested for immunohistochemistry evaluation. Tissue biomarkers of lung inflammation (CD45) were tallied, and binary logistic regression analyses were performed to create models predictive of lung inflammation, incorporating pretreatment CT/MRI radiomics, blood counts, and blood cytokines.ResultsThe treated animal cohort was dichotomized by the median value of CD45 infiltration in the lungs. Four pretreatment radiomics features (3 CT features and 1 MRI feature) together with pre-treatment neutrophil-to-lymphocyte (NLR) ratio and pre-treatment granulocyte-macrophage colony-stimulating factor (GM-CSF) level correlated with dichotomized CD45 infiltration. Predictive models were created by combining radiomics with NLR and GM-CSF. Receiver operating characteristic (ROC) analyses of two-fold internal cross-validation indicated that the predictive model incorporating MR radiomics had an average area under the curve (AUC) of 0.834, while the model incorporating CT radiomics had an AUC of 0.787.ConclusionsModel building using quantitative imaging data, blood counts, and blood cytokines resulted in lung inflammation prediction models justifying the study hypothesis. The models yielded very-good-to-excellent AUCs of more than 0.78 on internal cross-validation analyses.

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Considerations for combined immune checkpoint modulation and radiation treatment.

Publications

Considerations for combined immune checkpoint modulation and radiation treatment.

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