The schedule of ATR inhibitor AZD6738 can potentiate or abolish antitumor immune responses to radiotherapy.

Inhibitors of the DNA damage signaling kinase ATR increase tumor cell killing by chemotherapies that target DNA replication forks but also kill rapidly proliferating immune cells including activated T cells. Nevertheless, ATR inhibitor (ATRi) and radiotherapy (RT) can be combined to generate CD8+ T cell-dependent antitumor responses in mouse models. To determine the optimal schedule of ATRi and RT, we determined the impact of short-course versus prolonged daily treatment with AZD6738 (ATRi) on responses to RT (days 1-2). Short-course ATRi (days 1-3) plus RT caused expansion of tumor antigen-specific, effector CD8+ T cells in the tumor-draining lymph node (DLN) at 1 week after RT. This was preceded by acute decreases in proliferating tumor-infiltrating and peripheral T cells and a rapid proliferative rebound after ATRi cessation, increased inflammatory signaling (IFN-ß, chemokines, particularly CXCL10) in tumors, and an accumulation of inflammatory cells in the DLN. In contrast, prolonged ATRi (days 1-9) prevented the expansion of tumor antigen-specific, effector CD8+ T cells in the DLN, and entirely abolished the therapeutic benefit of short-course ATRi with RT and anti-PD-L1. Our data argue that ATRi cessation is essential to allow CD8+ T cell responses to both RT and immune checkpoint inhibitors.

A novel approach to infectious disease control and radiotherapy risk management.

BACKGROUND: Infectious disease outbreaks have always presented challenges to the operation of healthcare systems. In particular, the treatment of cancer patients within Radiation Oncology often cannot be delayed or compromised due to infection control measures. Therefore, there is a need for a strategic approach to simultaneously managing infection control and radiotherapy risks. PURPOSE: To develop a systematic risk management method that uses mathematical models to design mitigation efforts for control of an infectious disease outbreak, while ensuring safe delivery of radiotherapy. METHODS: A two-stage failure mode and effect analysis (FMEA) approach is proposed to modify radiotherapy workflow during an infectious disease outbreak. In stage 1, an Infection Control FMEA (IC-FMEA) is conducted, where risks are evaluated based on environmental parameters, clinical interactions, and modeling of infection risk. occupancy risk index (ORI) is defined as a metric for infection transmission risk level in each room, based on the degree of occupancy. ORI, in combination with ventilation rate per person (Rp ), is used to provide a broad infection risk assessment of workspaces. For detailed IC-FMEA of clinical processes, infection control failure mode (ICFM) is defined to be any instance of disease transmission within the clinic. Infection risk priority number (IRPN) has been formulated as a function of time, distance, and degree of protective measures. Infection control measures are then systematically integrated into the workflow. Since the workflow is perturbed by infection control measures, there is a possibility of introducing new radiotherapy failure modes or increased likelihood of existing failure modes. Therefore, in stage 2, a conventional radiotherapy FMEA (RT-FMEA) should be performed on the adjusted workflow. RESULTS: The COVID-19 pandemic was used to illustrate stage 1 IC-FMEA. ORI and Rp values were calculated for various workspaces within a clinic. A deep inspiration breath hold (DIBH) CT simulation was used as an example to demonstrate detailed IC-FMEA with ICFM identification and IRPN evaluation. A total of 90 ICFMs were identified in the DIBH simulation process. The calculated IRPN values were found to be progressively decreasing for workflows with minimal, moderate, and enhanced levels of protective measures. CONCLUSION: The framework developed in this work provides tools for radiotherapy clinics to systematically assess risk and adjust workflows during the evolving circumstances of any infectious disease outbreak.

Technical and dosimetric implications of respiratory induced density variations in a heterogeneous lung phantom.

BACKGROUND: Stereotactic Body Radiotherapy (SBRT) is an ablative dose delivery technique which requires the highest levels of precision and accuracy. Modeling dose to a lung treatment volume has remained a complex and challenging endeavor due to target motion and the low density of the surrounding media. When coupled together, these factors give rise to pulmonary induced tissue heterogeneities which can lead to inaccuracies in dose computation. This investigation aims to determine which combination of imaging techniques and computational algorithms best compensates for time dependent lung target displacements. METHODS: A Quasar phantom was employed to simulate respiratory motion for target ranges up to 3 cm. 4DCT imaging was used to generate Average Intensity Projection (AIP), Free Breathing (FB), and Maximum Intensity Projection (MIP) image sets. In addition, we introduce and compare a fourth dataset for dose computation based on a novel phase weighted density (PWD) technique. All plans were created using Eclipse version 13.6 treatment planning system and calculated using the Analytical Anisotropic Algorithm and Acuros XB. Dose delivery was performed using Truebeam STx linear accelerator where radiochromic film measurements were accessed using gamma analysis to compare planned versus delivered dose. RESULTS: In the most extreme case scenario, the mean CT difference between FB and MIP datasets was found to be greater than 200 HU. The near maximum dose discrepancies between AAA and AXB algorithms were determined to be marginal (< 2.2%), with a greater variability occurring within the near minimum dose regime (< 7%). Radiochromatic film verification demonstrated all AIP and FB based computations exceeded 98% passing rates under conventional radiotherapy tolerances (gamma 3%, 3 mm). Under more stringent SBRT tolerances (gamma 3%, 1 mm), the AIP and FB based treatment plans exhibited higher pass rates (> 85%) when compared to MIP and PWD (< 85%) for AAA computations. For AXB, however, the delivery accuracy for all datasets were greater than 85% (gamma 3%,1 mm), with a corresponding reduction in overall lung irradiation. CONCLUSIONS: Despite the substantial density variations between computational datasets over an extensive range of target movement, the dose difference between CT datasets is small and could not be quantified with ion chamber. Radiochromatic film analysis suggests the optimal CT dataset is dependent on the dose algorithm used for evaluation. With AAA, AIP and FB resulted in the best conformance between measured versus calculated dose for target motion ranging up to 3 cm under both conventional and SBRT tolerance criteria. With AXB, pass rates improved for all datasets with the PWD technique demonstrating slightly better conformity over AIP and FB based computations (gamma 3%, 1 mm). As verified in previous studies, our results confirm a clear advantage in delivery accuracy along with a relative decrease in calculated dose to the lung when using Acuros XB over AAA.