R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement.

BACKGROUND: 2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. PURPOSE: The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. METHODS: The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz. RESULTS: A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453  and 0.0361 mm, respectively, which is better than other research methods. CONCLUSION: The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

Effects of Narrative Exposure Therapy for Treating Depressive and Anxious Disorders: A Systematic Review and Meta-Analysis.

OBJECTIVE: Narrative exposure therapy (NET) has been used in various contexts for the treatment of the effects of trauma, with promising results in clinical trials. However, its effects on anxiety and depression are still unclear. The present study is a systematic review and meta-analysis of the effects of NET on depression and anxiety. METHODS: The Embase, Cumulative Index of Nursing and Allied Health Literature, PubMed, Web of Science core collection, Cochrane Library, Chinese National Knowledge Infrastructure, Chinese Biomedical Database, and Wangfang databases were searched from the earliest records to March 2023. Two researchers independently screened the literature, extracted data, evaluated the risk of bias, and cross-checked the data. Meta-analysis was performed using the program RevMan 5.3. RESULTS: Eleven randomized controlled trials with a total of 754 participants were included in the study. The results showed that NET reduced both the depression (standard mean difference [SMD]=-0.51, 95% confidence interval [CI] -0.73--0.29, p<0.00001) and anxiety (SMD=-0.65, 95% CI -1.13--0.18, p=0.007) scores of the patients. Furthermore, NET was found to alleviate negative emotions associated with guilt (mean difference [MD]=-3.60, 95% CI -5.52--1.68, p=0.0005) and negative change (MD=-5.80, 95% CI -9.76--1.83, p=0.004). CONCLUSION: This analysis showed that NET can alleviate depression and anxiety. It may thus be used in clinical settings to alleviate patients' negative feelings and aid their overall recovery.

Construction of performance management system for pharmacy department of tertiary public hospitals under new situation / 中国药房

OBJECTIVE To provide reference for improving the work efficiency of staff and promoting the discipline construction of pharmacy department. METHODS By analyzing the current situation of performance management in the pharmacy department of our hospital， the key successful factors were sorted out， strategic decoding was carried out and key performance indicators were extracted. The quarterly and annual performance appraisal forms were formulated for the departments of pharmacy warehouse， outpatient pharmacy， ward pharmacy， clinical pharmacy department， prescription examination center， laboratory and other departments； the performance management information platform was built. The work efficiency and output of each department were compared half a year before and after the implementation of the performance management plan. RESULTS After the implementation of the program， the average queuing time for drug collection in the outpatient department was shortened from 5 minutes to 3 minutes， the average number of dispensing infusion bags per hour in the pharmacy intravenous admixture services increased from 50 bags to 60 bags， and antibacterial use density of the hospital decreased from 42.7 DDD（defined daily doses） to 40.2 DDD. The number of academic papers published had increased from 8 to 10， and the satisfaction of clinical departments with ward pharmacies increased from 85% to 95%. CONCLUSIONS The performance management system has been successfully established in pharmacy department of our hospital， which can improve the enthusiasm of pharmacists， reflect the value of pharmaceutical care， and promote the discipline construction of pharmacy.

Activation of Gossypium hirsutum ACS6 Facilitates Fiber Development by Improving Sucrose Metabolism and Transport.

Cotton fiber yield depends on the density of fiber cell initials that form on the ovule epidermis. Fiber initiation is triggered by MYB-MIXTA-like transcription factors (GhMMLs) and requires a sucrose supply. Ethylene or its precursor ACC (1-aminocyclopropane-1-carboxylic acid) is suggested to affect fiber yield. The Gossypium hirsutum (L.) genome contains 35 ACS genes (GhACS) encoding ACC synthases. Here, we explored the role of a GhACS family member in the regulation of fiber initiation. Expression analyses showed that the GhACS6.3 gene pair was specifically expressed in the ovules during fiber initiation (3 days before anthesis to 5 days post anthesis, -3 to 5 DPA), especially at -3 DPA, whereas other GhACS genes were expressed at very low or undetectable levels. The expression profile of GhACS6.3 during fiber initial development was confirmed by qRT-PCR analysis. Transgenic lines overexpressing GhACS6.3 (GhACS6.3-OE) showed increased ACC accumulation in ovules, which promoted the formation of fiber initials and fiber yield components. This was accompanied by increased transcript levels of GhMML3 and increased transcript levels of genes encoding sucrose transporters and sucrose synthase. These findings imply that GhACS6.3 activation is required for fiber initial development. Our results lay the foundation for further research on increasing cotton fiber production.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.

Objective: This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC). Methods: A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC). Results: The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039). Conclusion: The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Unsupervised registration for liver CT-MR images based on the multiscale integrated spatial-weight module and dual similarity guidance.

PURPOSE: Multimodal registration is a key task in medical image analysis. Due to the large differences of multimodal images in intensity scale and texture pattern, it is a great challenge to design distinctive similarity metrics to guide deep learning-based multimodal image registration. Besides, since the limitation of the small receptive field, existing deep learning-based methods are mainly suitable for small deformation, but helpless for large deformation. To address the above issues, we present an unsupervised multimodal image registration method based on the multiscale integrated spatial-weight module and dual similarity guidance. METHODS: In this method, a U-shape network with our multiscale integrated spatial-weight module is embedded into a multi-resolution image registration architecture to achieve end-to-end large deformation registration, where the spatial-weight module can effectively highlight the regions with large deformation and aggregate discriminative features, and the multi-resolution architecture further helps to solve the optimization problem of the network in a coarse-to-fine pattern. Furthermore, we introduce a special loss function based on dual similarity, which represents both global gray-scale similarity and local feature similarity, to optimize the unsupervised multimodal registration network. RESULTS: We verified the effectiveness of the proposed method on liver CT-MR images. Experimental results indicate that the proposed method achieves the optimal DSC value and TRE value of 92.70 ± 1.75(%) and 6.52 ± 2.94(mm), compared with other state-of-the-art registration algorithms. CONCLUSION: The proposed method can accurately estimate the large deformation field by aggregating multiscale features, and achieve higher registration accuracy and fast registration speed. Comparative experiments also demonstrate the effectiveness and generalization ability of the algorithm.

Implicit Neural Representations With Structured Latent Codes for Human Body Modeling.

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. Furthermore, we combine Neural Body with implicit surface models to improve the learned geometry. To evaluate our approach, we perform experiments on both synthetic and real-world data, which show that our approach outperforms prior works by a large margin on novel view synthesis and 3D reconstruction. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.

Discriminating normal and inflammatory mice models by viscosity changes with a two-photon fluorescent probe.

Studies have found that the intracellular viscosity changes have close relationship with many diseases, therefore design and synthesis of fluorescent probe for testing intracellular viscosity is of great significance to the development of clinical. Herein, we developed a new two-photon near infrared probe (HCT) for viscosity imaging to discriminate normal and inflammatory models. Experimental results displayed that HCT has great sensitivity for the detection of viscosity, and based on the excellent performance of its photostability and lower cytotoxicity, HCT was successfully utilized for single-photon/ two-photon fluorescence imaging of the viscosity in living cells. More importantly, we employ HCT to further showcase in living tissues. Additionally, HCT could be used to discriminate between normal and inflamed mice, heralding its practical application in biomedical aspects.

The predictive value of eGFR combined with BNP detection in acute kidney injury after acute myocardial infarction.

Objective: The combined detection of eGFR and BNP may provide some value in predicting the occurrence of AKI after AMI, and the study is designed to propose and validate this hypothesis. Methods: In retrospective research, AMI patients hospitalized at Weifang People's Hospital from January to December 2020 were included. Whether AKI occurred within a week of admission, patients were divided into two groups. Clinical data from two groups of patients were collected, and the Logistic regression model analysed the risk factors for AKI after AMI. The association between eGFR and BNP was analysed using Pearson linear correlation. The predictive value of eGFR and BNP alone and combined detection on AKI after AMI was analysed using the receiver operating characteristic (ROC) curve. Results: Multivariate logistic regression showed that eGFR, BNP, HDLC, UA, and K ions were AKI risk factors (P < 0.05). The eGFR correlates negatively with BNP (R = -0.324, P < 0.05). The area under the curve (AUC) of eGFR and BNP alone and combined prediction for post-AMI AKI were 0.793, 0.826, and 0.831, respectively. Conclusion: The combined detection of eGFR and BNP has a high predictive value for AKI development in AMI patients.

Research progress of accelerated liver regeneration induced by associating liver partition and portal vein ligation for staged hepatectomy / 中华肝胆外科杂志

Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) can induce accelerated regeneration of future liver remnant (FLR) and provide the opportunity of radical resection for previously inoperable patients with liver cancer, which has been considered to be one of the most important breakthroughs in liver surgery during the 21st century. It is of great significance to fully understand the mechanism of accelerated liver regeneration induced by ALPPS. This article comprehensively reviews the research progress in this field during the past 10 years.

[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.

Grim-19 deficiency promotes decidual macrophage autophagy in recurrent spontaneous abortion.

Dysregulation of decidual macrophages leads to the occurrence of recurrent spontaneous abortion (RSA). However, the role of macrophages in RSA occurrence remains unclear. In this study, we found that the expression of Grim-19 was decreased, and the expression of autophagy related proteins Beclin1, LC3B II/I and BNIP3 was markedly upregulated in decidual macrophages of RSA patients compared with the normal pregnancy group. Furthermore, we demonstrated that downregulation of GRIM-19 increased the expression of autophagy related proteins Beclin1, LC3B II/I, BNIP3 and the proinflammatory cytokines IL1B, IL6 and TNFa in uterine mononuclear cells of GRIM-19+/- mice. The proportion of CD45+CD11b+F4/80+LC3B+ cells in GRIM-19+/- mouse uteri was significantly higher than that in WT mouse uteri. In addition, we confirmed that inhibition of Grim-19 by siRNA enhanced the expression of autophagy related proteins in RAW264.7 cells and THP-1 cells. More importantly, downregulation of Grim-19 in RAW264.7 cells promoted the release of proinflammatory cytokines and promoted phagocytic activity, which could be reversed by autophagy blockade. For THP-1-derived macrophages, the results of RNA-seq suggested that Grim-19 mainly modulates immune and inflammatory-related pathways, leading to cytokine production, and thus contributing to inflammation. Therefore, our data reveal that Grim-19 deficiency influences macrophage function, characterized by enhanced proinflammatory cytokines and phagocytic activity, and this might be regulated by autophagy. This may represent a novel mechanism for the occurrence of RSA.

A coarse-to-fine cascade deep learning neural network for segmenting cerebral aneurysms in time-of-flight magnetic resonance angiography.

BACKGROUND: Accurate segmentation of unruptured cerebral aneurysms (UCAs) is essential to treatment planning and rupture risk assessment. Currently, three-dimensional time-of-flight magnetic resonance angiography (3D TOF-MRA) has been the most commonly used method for screening aneurysms due to its noninvasiveness. The methods based on deep learning technologies can assist radiologists in achieving accurate and reliable analysis of the size and shape of aneurysms, which may be helpful in rupture risk prediction models. However, the existing methods did not accomplish accurate segmentation of cerebral aneurysms in 3D TOF-MRA. METHODS: This paper proposed a CCDU-Net for segmenting UCAs of 3D TOF-MRA images. The CCDU-Net was a cascade of a convolutional neural network for coarse segmentation and the proposed DU-Net for fine segmentation. Especially, the dual-channel inputs of DU-Net were composed of the vessel image and its contour image which can augment the vascular morphological information. Furthermore, a newly designed weighted loss function was used in the training process of DU-Net to promote the segmentation performance. RESULTS: A total of 270 patients with UCAs were enrolled in this study. The images were divided into the training (N = 174), validation (N = 43), and testing (N = 53) cohorts. The CCDU-Net achieved a dice similarity coefficient (DSC) of 0.616 ± 0.167, Hausdorff distance (HD) of 5.686 ± 7.020 mm, and volumetric similarity (VS) of 0.752 ± 0.226 in the testing cohort. Compared with the existing best method, the DSC and VS increased by 18% and 5%, respectively, while the HD decreased by one-tenth. CONCLUSIONS: We proposed a CCDU-Net for segmenting UCAs in 3D TOF-MRA, and the obtained results show that the proposed method outperformed other existing methods.

Lenalidomide bypasses CD28 co-stimulation to reinstate PD-1 immunotherapy by activating Notch signaling.

Programmed cell death protein 1 (PD-1) checkpoint blockade therapy requires the CD28 co-stimulatory receptor for CD8+ T cell expansion and cytotoxicity. However, CD28 expression is frequently lost in exhausted T cells and during immune senescence, limiting the clinical benefits of PD-1 immunotherapy in individuals with cancer. Here, using a cereblon knockin mouse model that regains in vivo T cell response to lenalidomide, an immunomodulatory imide drug, we show that lenalidomide reinstates the anti-tumor activity of CD28-deficient CD8+ T cells after PD-1 blockade. Lenalidomide redirects the CRL4Crbn ubiquitin ligase to degrade Ikzf1 and Ikzf3 in T cells and unleashes paracrine interleukin-2 (IL-2) and intracellular Notch signaling, which collectively bypass the CD28 requirement for activation of intratumoral CD8+ T cells and inhibition of tumor growth by PD-1 blockade. Our results suggest that PD-1 immunotherapy can benefit from a lenalidomide combination when treating solid tumors infiltrated with abundant CD28- T cells.

Combination of UNet++ and ResNeSt to classify chronic inflammation of the choledochal cystic wall in patients with pancreaticobiliary maljunction.

OBJECTIVES: The aim of this study was to establish an automatic classification model for chronic inflammation of the choledoch wall using deep learning with CT images in patients with pancreaticobiliary maljunction (PBM). METHODS: CT images were obtained from 76 PBM patients, including 61 cases assigned to the training set and 15 cases assigned to the testing set. The region of interest (ROI) containing the choledochal lesion was extracted and segmented using the UNet++ network. The degree of severity of inflammation in the choledochal wall was initially classified using the ResNeSt network. The final classification result was determined per decision rules. Grad-CAM was used to explain the association between the classification basis of the network and clinical diagnosis. RESULTS: Segmentation of the lesion on the common bile duct wall was roughly obtained with the UNet++ segmentation model and the average value of Dice coefficient of the segmentation model in the testing set was 0.839 ± 0.150, which was verified through fivefold cross-validation. Inflammation was initially classified with ResNeSt18, which resulted in accuracy = 0.756, sensitivity = 0.611, specificity = 0.852, precision = 0.733, and area under curve (AUC) = 0.711. The final classification sensitivity was 0.8. Grad-CAM revealed similar distribution of inflammation of the choledochal wall and verified the inflammation classification. CONCLUSIONS: By combining the UNet++ network and the ResNeSt network, we achieved automatic classification of chronic inflammation of the choledoch in PBM patients and verified the robustness through cross-validation performed five times. This study provided an important basis for classification of inflammation severity of the choledoch in PBM patients. ADVANCES IN KNOWLEDGE: We combined the UNet++ network and the ResNeSt network to achieve automatic classification of chronic inflammation of the choledoch in PBM. These results provided an important basis for classification of choledochal inflammation in PBM and for surgical therapy.

Simulating Fractures With Bonded Discrete Element Method.

Along with motion and deformation, fracture is a fundamental behaviour for solid materials, playing a critical role in physically-based animation. Many simulation methods including both continuum and discrete approaches have been used by the graphics community to animate fractures for various materials. However, compared with motion and deformation, fracture remains a challenging task for simulation, because the material's geometry, topology and mechanical states all undergo continuous (and sometimes chaotic) changes as fragmentation develops. Recognizing the discontinuous nature of fragmentation, we propose a discrete approach, namely the Bonded Discrete Element Method (BDEM), for fracture simulation. The research of BDEM in engineering has been growing rapidly in recent years, while its potential in graphics has not been explored. We also introduce several novel changes to BDEM to make it more suitable for animation design. Compared with other fracture simulation methods, the BDEM has some attractive benefits, e.g., efficient handling of multiple fractures, simple formulation and implementation, and good scaling consistency. But it also has some critical weaknesses, e.g., high computational cost, which demand further research. A number of examples are presented to demonstrate the pros and cons, which are then highlighted in the conclusion and discussion.

Distinguishing normal and inflammatory models by viscosity changes with sensitively mitochondrial-trackable fluorescent probe.

Biological microenvironment plays a momentous role in the regulation of various vital activities, and its abnormal changes are often closely related to some diseases. Viscosity, as an indispensable part of microenvironment parameters, has always been one of the research hotspots of investigators. Herein, we constructed a new red-emitting fluorescent probe (HVM) to identify the abnormal situation of mitochondria through viscosity changes in the biological microenvironment. Interestingly, HVM has excellent optical properties such as large stokes shift (160 nm), viscosity sensitivity (195-fold), high photostability, and biochemical properties with low cytotoxicity and excellent biocompatibility. For these reasons, the novel probe could successfully be used to identify the normal and inflammatory models via viscosity changes in biological experiments. Therefore, we provided a convenient synthetic route to obtain viscosity sensor HVM with excellent application properties.

A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis.

Supervised machine learning methods are usually used to build a custom model for disease diagnosis and auxiliary prognosis in radiomics studies. A classical machine learning pipeline involves a series of steps and multiple algorithms, which leads to a great challenge to find an appropriate combination of algorithms and an optimal hyper-parameter set for radiomics model building. We developed a freely available software package for radiomics model building. It can be used to lesion labeling, feature extraction, feature selection, classifier training and statistic result visualization. This software provides a user-friendly graphic interface and flexible IOs for radiologists and researchers to automatically develop radiomics models. Moreover, this software can extract features from corresponding lesion regions in multi-modality images, which is labeled by semi-automatic or full-automatic segmentation algorithms. It is designed in a loosely coupled architecture, programmed with Qt, VTK, and Python. In order to evaluate the availability and effectiveness of the software, we utilized it to build a CT-based radiomics model containing peritumoral features for malignancy grading of cell renal cell carcinoma. The final model got a good performance of grading study with AUC=0.848 on independent validation dataset.Clinical Relevance-the developed provides convenient and powerful toolboxes to build radiomics models for radiologists and researchers on clinical studies.

Activation of 1-Aminocyclopropane-1-Carboxylic Acid Synthases Sets Stomatal Density and Clustered Ratio on Leaf Epidermis of Arabidopsis in Response to Drought.

The adjustment of stomatal density and clustered ratio on the epidermis is the important strategy for plants to respond to drought, because the stoma-based water loss is directly related to plant growth and survival under drought conditions. But the relevant adjustment mechanism still needs to be explored. 1-Aminocyclopropane-1-carboxylate (ACC) is disclosed to promote stomatal development, while in vivo ACC levels depend on activation of ACC synthase (ACS) family members. Based on the findings of ACS expression involving in drought response and several ACS activity inhibitors reducing stomatal density and cluster in drought response, here we examined how ACS activation is involved in the establishment of stomatal density and cluster on the epidermis under drought conditions. Preliminary data indicated that activation of ACS2 and/or ACS6 (ACS2/6) increased stomatal density and clustered ratio on the Arabidopsis leaf epidermis by accumulating ACC under moderate drought, and raised the survival risk of seedlings under escalated drought. Further exploration indicated that, in Arabidopsis seedlings stressed by drought, the transcription factor SPEECHLESS (SPCH), the initiator of stomatal development, activates ACS2/6 expression and ACC production; and that ACC accumulation induces Ca2+ deficiency in stomatal lineage; this deficiency inactivates a subtilisin-like protease STOMATAL DENSITY AND DISTRIBUTION 1 (SDD1) by stabilizing the inhibition of the transcription factor GT-2 Like 1 (GTL1) on SDD1 expression, resulting in an increases of stomatal density and cluster ratio on the leaf epidermis. This work provides a novel evidence that ACS2/6 activation plays a key role in the establishment of stomatal density and cluster on the leaf epidermis of Arabidopsis in response to drought.

Tumor evolution selectively inactivates the core microRNA machinery for immune evasion.

Cancer cells acquire genetic heterogeneity to escape from immune surveillance during tumor evolution, but a systematic approach to distinguish driver from passenger mutations is lacking. Here we investigate the impact of different immune pressure on tumor clonal dynamics and immune evasion mechanism, by combining massive parallel sequencing of immune edited tumors and CRISPR library screens in syngeneic mouse tumor model and co-culture system. We find that the core microRNA (miRNA) biogenesis and targeting machinery maintains the sensitivity of cancer cells to PD-1-independent T cell-mediated cytotoxicity. Genetic inactivation of the machinery or re-introduction of ANKRD52 frequent patient mutations dampens the JAK-STAT-interferon-γ signaling and antigen presentation in cancer cells, largely by abolishing miR-155-targeted silencing of suppressor of cytokine signaling 1 (SOCS1). Expression of each miRNA machinery component strongly correlates with intratumoral T cell infiltration in nearly all human cancer types. Our data indicate that the evolutionarily conserved miRNA pathway can be exploited by cancer cells to escape from T cell-mediated elimination and immunotherapy.