Re-engineering the Immune Response to Metastatic Cancer: Antibody-Recruiting Small Molecules Targeting the Urokinase Receptor.

Developing selective strategies to treat metastatic cancers remains a significant challenge. Herein, we report the first antibody-recruiting small molecule (ARM) that is capable of recognizing the urokinase-type plasminogen activator receptor (uPAR), a uniquely overexpressed cancer cell-surface marker, and facilitating the immune-mediated destruction of cancer cells. A co-crystal structure of the ARM-U2/uPAR complex was obtained, representing the first crystal structure of uPAR complexed with a non-peptide ligand. Finally, we demonstrated that ARM-U2 substantially suppresses tumor growth in vivo with no evidence of weight loss, unlike the standard-of-care agent doxorubicin. This work underscores the promise of antibody-recruiting molecules as immunotherapeutics for treating cancer.

Chemically synthesized molecules with the targeting and effector functions of antibodies.

This article reports the design, synthesis, and evaluation of a novel class of molecules of intermediate size (approximately 7000 Da), which possess both the targeting and effector functions of antibodies. These compounds­called synthetic antibody mimics targeting prostate cancer (SyAM-Ps)­bind simultaneously to prostate-specific membrane antigen and Fc gamma receptor I, thus eliciting highly selective cancer cell phagocytosis. SyAMs have the potential to combine the advantages of both small-molecule and biologic therapies, and may address many drawbacks associated with available treatments for cancer and other diseases.

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency. This study includes a real-world cohort of 394 patients who each received radiation therapy (mainly for lung cancer). Segmentation of the esophagus was performed by 8 physicians as part of routine clinical care. We evaluated manual segmentation by comparing the length and edge roughness of segmentations among physicians to analyze inconsistencies. We trained eight multiple- and individual-physician segmentation models in total, based on U-Net architectures and residual backbones. We used the volumetric Dice coefficient to measure the performance for each model. We proposed a metric, edge roughness, to quantify the shift of segmentation among adjacent slices by calculating the curvature of edges of the 2D sagittal- and coronal-view projections. The auto-segmentation model trained on multiple physicians (MD1-7) achieved the highest mean Dice of 73.7 ± 14.8%. The individual-physician model (MD7) with the highest edge roughness (mean ± SD: 0.106 ± 0.016) demonstrated significantly lower volumetric Dice for test cases compared with other individual models (MD7: 58.5 ± 15.8%, MD6: 67.1 ± 16.8%, p < 0.001). A multiple-physician model trained after removing the MD7 data resulted in fewer outliers (e.g., Dice ≤ 40%: 4 cases for MD1-6, 7 cases for MD1-7, Ntotal = 394). While we initially detected this pattern in a single clinician, we validated the edge roughness metric across the entire dataset. The model trained with the lowest-quantile edge roughness (MDER-Q1, Ntrain = 62) achieved significantly higher Dice (Ntest = 270) than the model trained with the highest-quantile ones (MDER-Q4, Ntrain = 62) (MDER-Q1: 67.8 ± 14.8%, MDER-Q4: 62.8 ± 15.7%, p < 0.001). This study demonstrates that there is significant variation in style and quality in manual segmentations in clinical care, and that training AI auto-segmentation algorithms from real-world, clinical datasets may result in unexpectedly under-performing algorithms with the inclusion of outliers. Importantly, this study provides a novel evaluation metric, edge roughness, to quantify physician variation in segmentation which will allow developers to filter clinical training data to optimize model performance.

Lung sparing in MR-guided non-adaptive SBRT treatment of peripheral lung tumors.

Objective.We aim to: (1) quantify the benefits of lung sparing using non-adaptive magnetic resonance guided stereotactic body radiotherapy (MRgSBRT) with advanced motion management for peripheral lung cancers compared to conventional x-ray guided SBRT (ConvSBRT); (2) establish a practical decision-making guidance metric to assist a clinician in selecting the appropriate treatment modality.Approach.Eleven patients with peripheral lung cancer who underwent breath-hold, gated MRgSBRT on an MR-guided linear accelerator (MR linac) were studied. Four-dimensional computed tomography (4DCT)-based retrospective planning using an internal target volume (ITV) was performed to simulate ConvSBRT, which were evaluated against the original MRgSBRT plans. Metrics analyzed included planning target volume (PTV) coverage, various lung metrics and the generalized equivalent unform dose (gEUD). A dosimetric predictor for achievable lung metrics was derived to assist future patient triage across modalities.Main results.PTV coverage was high (median V100% > 98%) and comparable for both modalities. MRgSBRT had significantly lower lung doses as measured by V20 (median 3.2% versus 4.2%), mean lung dose (median 3.3 Gy versus 3.8 Gy) and gEUD. Breath-hold, gated MRgSBRT resulted in an average reduction of 47% in PTV volume and an average increase of 19% in lung volume. Strong correlation existed between lung metrics and the ratio of PTV to lung volumes (RPTV/Lungs) for both modalities, indicating that RPTV/Lungsmay serve as a good predictor for achievable lung metrics without the need for pre-planning. A threshold value of RPTV/Lungs< 0.035 is suggested to achieve V20 < 10% using ConvSBRT. MRgSBRT should otherwise be considered if the threshold cannot be met.Significance.The benefits of lung sparing using MRgSBRT were quantified for peripheral lung tumors; RPTV/Lungswas found to be an effective predictor for achievable lung metrics across modalities. RPTV/Lungscan assist a clinician in selecting the appropriate modality without the need for labor-intensive pre-planning, which has significant practical benefit for a busy clinic.

A "turn-on" fluorescent sensor for methylglyoxal.

Methylglyoxal (MGO), a dicarbonyl metabolite produced by all living cells, has been associated with a number of human diseases. However, studies of the role(s) MGO plays biologically have been handicapped by a lack of direct methods for its monitoring and detection. To address this limitation, we have developed a fluorescent sensor (methyl diaminobenzene-BODIPY, or "MBo") that can detect MGO under physiological conditions. We show that MBo is selective for MGO over other biologically relevant dicarbonyls and is suitable for detecting MGO in complex environments, including that of living cells. In addition, we demonstrate MBo's utility in estimating plasma concentrations of MGO. The results reported herein have the potential to advance both clinical and basic science research and practice.

Radiotherapy Dose in Patients Receiving Immunotherapy.

There is significant rationale for combining radiation therapy (RT) and immuno-oncology (IO) agents, but the optimal radiation parameters are unknown. This review summarizes key trials in the RT and IO space with a focus on RT dose. Very low RT doses solely modulate the tumor immune microenvironment, intermediate doses both modulate the tumor immune microenvironment and kill some fraction of tumor cells, and ablative doses eliminate the majority of target tumor cells and also possess immunomodulatory effects. Ablative RT doses may have high toxicity if targets are adjacent to radiosensitive normal organs. The majority of completed trials have been conducted in the setting of metastatic disease and direct RT to a single lesion with the goal of generating systemic antitumor immunity termed the abscopal effect. Unfortunately, reliable generation of an abscopal effect has proved elusive over a range of radiation doses. Newer trials are exploring the effects of delivering RT to all or most sites of metastatic disease, with dose personalization based on the number and location of lesions. Additional directions include testing RT and IO in earlier stages of disease, sometimes in further combination with chemotherapy and surgery, where lower doses of RT may still contribute substantially to pathologic responses.

Pneumonitis in Patients Receiving Thoracic Radiotherapy and Osimertinib: A Multi-Institutional Study.

Introduction: Thoracic radiotherapy (TRT) is increasingly used in patients receiving osimertinib for advanced NSCLC, and the risk of pneumonitis is not established. We investigated the risk of pneumonitis and potential risk factors in this population. Methods: We performed a multi-institutional retrospective analysis of patients under active treatment with osimertinib who received TRT between April 2016 and July 2022 at two institutions. Clinical characteristics, including whether osimertinib was held during TRT and pneumonitis incidence and grade (Common Terminology Criteria for Adverse Events version 5.0) were documented. Logistic regression analysis was performed to identify risk factors associated with grade 2 or higher (2+) pneumonitis. Results: The median follow-up was 10.2 months (range: 1.9-53.2). Of 102 patients, 14 (13.7%) developed grade 2+ pneumonitis, with a median time to pneumonitis of 3.2 months (range: 1.5-6.3). Pneumonitis risk was not significantly increased in patients who continued osimertinib during TRT compared with patients who held osimertinib during TRT (9.1% versus 15.0%, p = 0.729). Three patients (2.9%) had grade 3 pneumonitis, none had grade 4, and two patients had grade 5 events (2.0%, diagnosed 3.2 mo and 4.4 mo post-TRT). Mean lung dose was associated with the development of grade 2+ pneumonitis in multivariate analysis (OR = 1.19, p = 0.021). Conclusions: Although the overall rate of pneumonitis in patients receiving TRT and osimertinib was relatively low, there was a small risk of severe toxicity. The mean lung dose was associated with an increased risk of developing pneumonitis. These findings inform decision-making for patients and providers.