Cytosolic double-stranded DNA surveillance by cyclic GMP-AMP synthase (cGAS)–Stimulator of Interferon Genes (STING) signaling triggers cellular senescence, autophagy, biased mRNA translation, and interferon-mediated immune responses. However, detailed mechanisms and physiological relevance of STING-induced senescence are not fully understood. Here, we unexpectedly found that interferon regulatory factor 3 (IRF3), activated during innate DNA sensing, forms substantial endogenous complexes in the nucleus with retinoblastoma (RB), a key cell cycle regulator. The IRF3-RB interaction attenuates cyclin-dependent kinase 4/6 (CDK4/6)–mediated RB hyperphosphorylation that mobilizes RB to deactivate E2 family (E2F) transcription factors, thereby driving cells into senescence. STING-IRF3-RB signaling plays a notable role in hepatic stellate cells (HSCs) within various murine models, pushing activated HSCs toward senescence. Accordingly, IRF3 global knockout or conditional deletion in HSCs aggravated liver fibrosis, a process mitigated by the CDK4/6 inhibitor. These findings underscore a straightforward yet vital mechanism of cGAS-STING signaling in inducing cellular senescence and unveil its unexpected biology in limiting liver fibrosis.

Background: Whole brain radiotherapy (WBRT) can impair patients’ cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we proposed a credible approach of automatic hippocampal delineation based on convolutional neural networks. Methods: Referring to the hippocampus contouring atlas proposed by RTOG 0933, we manually delineated (MD) the hippocampus on the MRI data sets (3-dimensional T1-weighted with slice thickness of 1 mm, n = 175), which were used to construct a three-dimensional convolutional neural network aiming for the hippocampus automatic delineation (AD). The performance of this AD tool was tested on three cohorts: (a) 3D T1 MRI with 1-mm slice thickness (n = 30); (b) non-3D T1-weighted MRI with 3-mm slice thickness (n = 19); (c) non-3D T1-weighted MRI with 1-mm slice thickness (n = 11). All MRIs confirmed with normal hippocampus has not been violated by any disease. Virtual radiation plans were created for AD and MD hippocampi in cohort c to evaluate the clinical feasibility of the artificial intelligence approach. Statistical analyses were performed using SPSS version 23. P < 0.05 was considered significant. Results: The Dice similarity coefficient (DSC) and Average Hausdorff Distance (AVD) between the AD and MD hippocampi are 0.86 ± 0.028 and 0.18 ± 0.050 cm in cohort a, 0.76 ± 0.035 and 0.31 ± 0.064 cm in cohort b, 0.80 ± 0.015 and 0.24 ± 0.021 cm in cohort c, respectively. The DSC and AVD in cohort a were better than those in cohorts b and c (P < 0.01). There is no significant difference between the radiotherapy plans generated using the AD and MD hippocampi. Conclusion: The AD of the hippocampus based on a deep learning algorithm showed satisfying results, which could have a positive impact on improving delineation accuracy and reducing work load.

Purpose: Tifcemalimab is a recombinant humanized IgG4k monoclonal antibody targeting B- and T-lymphocyte attenuator. Co-blockade of B- and T-lymphocyte attenuator and programmed death-1 pathways improved outcomes in nonclinical models. This phase I/II trial evaluated the safety and preliminary efficacy of tifcemalimab plus toripalimab in advanced lung cancer. Patients and Methods: Eligible patients with pathologically confirmed advanced non–small cell lung cancer (NSCLC) without sensitive EGFR variation and anaplastic lymphoma kinase fusion who failed standard treatment including one PD-1/PD-L1 inhibitor or those with refractory extensive-stage small cell lung cancer (SCLC) received tifcemalimab (200 mg) and toripalimab (240 mg) every 3 weeks intravenously until disease progression or intolerable toxicity. The Simon two-stage optimal design was used in the expansion part. The primary endpoints included safety and objective response rate (ORR) per RECIST version 1.1. Results: Twenty-four patients with NSCLC and 43 with SCLC were enrolled (median age of all patients, 60.0 years). All patients with NSCLC and 14 (32.6%) with SCLC had received previous immunotherapy. Fifty-five (82.1%) patients experienced treatment-related adverse events, and five (7.5%) patients reported grade ≥3 immune-related adverse events. For NSCLC, the ORR was 4.3%, and disease control rate was 47.8%. The median progression-free survival and overall survival were 1.5 and 18.9 months, respectively. For SCLC, the ORR and disease control rate were 35.0% and 55.0%, respectively. The median duration of response, progression-free survival, and overall survival were 5.7, 2.8, and 12.3 months, respectively. Conclusions: Tifcemalimab plus toripalimab showed promising antitumor activities with acceptable safety, especially in advanced refractory SCLC.

Objective: Accurate delineation of the hippocampal region via magnetic resonance imaging (MRI) is crucial for the prevention and early diagnosis of neurosystemic diseases. Determining how to accurately and quickly delineate the hippocampus from MRI results has become a serious issue. In this study, a pixel-level semantic segmentation method using 3D-UNet is proposed to realize the automatic segmentation of the brain hippocampus from MRI results. Methods: Two hundred three-dimensional T1-weighted (3D-T1) nongadolinium contrast-enhanced magnetic resonance (MR) images were acquired at Hangzhou Cancer Hospital from June 2020 to December 2022. These samples were divided into two groups, containing 175 and 25 samples. In the first group, 145 cases were used to train the hippocampus segmentation model, and the remaining 30 cases were used to fine-tune the hyperparameters of the model. Images for twenty-five patients in the second group were used as the test set to evaluate the performance of the model. The training set of images was processed via rotation, scaling, grey value augmentation and transformation with a smooth dense deformation field for both image data and ground truth labels. A filling technique was introduced into the segmentation network to establish the hippocampus segmentation model. In addition, the performance of models established with the original network, such as VNet, SegResNet, UNetR and 3D-UNet, was compared with that of models constructed by combining the filling technique with the original segmentation network. Results: The results showed that the performance of the segmentation model improved after the filling technique was introduced. Specifically, when the filling technique was introduced into VNet, SegResNet, 3D-UNet and UNetR, the segmentation performance of the models trained with an input image size of 48 × 48 × 48 improved. Among them, the 3D-UNet-based model with the filling technique achieved the best performance, with a Dice score (Dice score) of 0.7989 ± 0.0398 and a mean intersection over union (mIoU) of 0.6669 ± 0.0540, which were greater than those of the original 3D-UNet-based model. In addition, the oversegmentation ratio (OSR), average surface distance (ASD) and Hausdorff distance (HD) were 0.0666 ± 0.0351, 0.5733 ± 0.1018 and 5.1235 ± 1.4397, respectively, which were better than those of the other models. In addition, when the size of the input image was set to 48 × 48 × 48, 64 × 64 × 64 and 96 × 96 × 96, the model performance gradually improved, and the Dice scores of the proposed model reached 0.7989 ± 0.0398, 0.8371 ± 0.0254 and 0.8674 ± 0.0257, respectively. In addition, the mIoUs reached 0.6669 ± 0.0540, 0.7207 ± 0.0370 and 0.7668 ± 0.0392, respectively. Conclusion: The proposed hippocampus segmentation model constructed by introducing the filling technique into a segmentation network performed better than models built solely on the original network and can improve the efficiency of diagnostic analysis.

Little is known about the efficacy of alectinib for papillary thyroid cancer with STRN-ALK fusion. A 64-year-old female presented with metastatic papillary thyroid cancer, widespread to lungs, mediastinal lymph nodes and brain 20 years after surgery. Disease progression still occurred after radioactive iodine therapy, chemotherapy, and radiotherapy. Tissue obtained from left cervical lymph node confirmed metastatic papillary thyroid cancer. Molecular profiling from re-biopsy tissue identified an STRN-ALK fusion rearrangement. After multidisciplinary discussion, alectinib was administered to the patient. Treatment was well tolerated, and follow-up images confirmed a partial response. ALK occurs rarely, with limited data suggesting the efficacy of ALK inhibitors in thyroid cancer. We presented the first case of a patient with PTC and STRN-ALK fusion to be treat effectively with alectinib.