Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison.

BACKGROUND: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. METHODS: The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. RESULTS: We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. CONCLUSIONS: NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

Age-related changes after intracerebral hemorrhage: a comparative proteomics analysis of perihematomal tissue.

The risk factors and causes of intracerebral hemorrhage (ICH) and the degree of functional recovery after ICH are distinct between young and elderly patients. The increasing incidence of ICH in young adults has become a concern; however, research on the molecules and pathways involved ICH in subjects of different ages is lacking. In this study, tandem mass tag (TMT)-based proteomics was utilized to examine the protein expression profiles of perihematomal tissue from young and aged mice 24 h after collagenase-induced ICH. Among the 5,129 quantified proteins, ICH induced 108 and 143 differentially expressed proteins (DEPs) in young and aged mice, respectively; specifically, there were 54 common DEPs, 54 unique DEPs in young mice and 89 unique DEPs in aged mice. In contrast, aging altered the expression of 58 proteins in the brain, resulting in 39 upregulated DEPs and 19 downregulated DEPs. Bioinformatics analysis indicated that ICH activated different proteins in complement pathways, coagulation cascades, the acute phase response, and the iron homeostasis signaling pathway in mice of both age groups. Protein-protein interaction (PPI) analysis and ingenuity pathway analysis (IPA) demonstrated that the unique DEPs in the young and aged mice were related to lipid metabolism and carbohydrate metabolism, respectively. Deeper paired-comparison analysis demonstrated that apolipoprotein M exhibited the most significant change in expression as a result of both aging and ICH. These results help illustrate age-related protein expression changes in the acute phase of ICH.

MoViT: Memorizing Vision Transformers for Medical Image Analysis.

The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to their complementary benefits. However, compared with CNNs, transformers require considerably more training data, due to a larger number of parameters and an absence of inductive bias. The need for increasingly large datasets continues to be problematic, particularly in the context of medical imaging, where both annotation efforts and data protection result in limited data availability. In this work, inspired by the human decision-making process of correlating new "evidence" with previously memorized "experience", we propose a Memorizing Vision Transformer (MoViT) to alleviate the need for large-scale datasets to successfully train and deploy transformer-based architectures. MoViT leverages an external memory structure to cache history attention snapshots during the training stage. To prevent overfitting, we incorporate an innovative memory update scheme, attention temporal moving average, to update the stored external memories with the historical moving average. For inference speedup, we design a prototypical attention learning method to distill the external memory into smaller representative subsets. We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available. More importantly, MoViT can reach a competitive performance of ViT with only 3.0% of the training data. In conclusion, MoViT provides a simple plug-in for transformer architectures which may contribute to reducing the training data needed to achieve acceptable models for a broad range of medical image analysis tasks.

Atrial lead perforation early after device implantation-a case report and literature review.

We report three patients with screw-in lead perforation in the right atrial free wall not long after device implantation. All the patients complained of intermittent stabbing chest pain associated with deep breathing during the implantation. The "dry" epicardial puncture was utilized to avoid hemopericardium during lead extraction in the first case. The atrial electrode was repositioned in all cases and replaced by a new passive fixation lead in two patients with resolution of the pneumothorax or pericardial effusion. A literature review of 50 reported cases of atrial lead perforation was added to the findings in our case report.

Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities.

BACKGROUND: Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based methods, suffer from drawbacks such as long training times, high model complexity, and poor scalability. METHOD: This paper proposes IMS2Trans, a novel lightweight scalable Swin Transformer network by utilizing a single encoder to extract latent feature maps from all available modalities. This unified feature extraction process enables efficient information sharing and fusion among the modalities, resulting in efficiency without compromising segmentation performance even in the presence of missing modalities. RESULTS: Two datasets, BraTS 2018 and BraTS 2020, containing incomplete modalities for brain tumor segmentation are evaluated against popular benchmarks. On the BraTS 2018 dataset, our model achieved higher average Dice similarity coefficient (DSC) scores for the whole tumor, tumor core, and enhancing tumor regions (86.57, 75.67, and 58.28, respectively), in comparison with a state-of-the-art model, i.e. mmFormer (86.45, 75.51, and 57.79, respectively). Similarly, on the BraTS 2020 dataset, our model scored higher DSC scores in these three brain tumor regions (87.33, 79.09, and 62.11, respectively) compared to mmFormer (86.17, 78.34, and 60.36, respectively). We also conducted a Wilcoxon test on the experimental results, and the generated p-value confirmed that our model's performance was statistically significant. Moreover, our model exhibits significantly reduced complexity with only 4.47 M parameters, 121.89G FLOPs, and a model size of 77.13 MB, whereas mmFormer comprises 34.96 M parameters, 265.79 G FLOPs, and a model size of 559.74 MB. These indicate our model, being light-weighted with significantly reduced parameters, is still able to achieve better performance than a state-of-the-art model. CONCLUSION: By leveraging a single encoder for processing the available modalities, IMS2Trans offers notable scalability advantages over methods that rely on multiple encoders. This streamlined approach eliminates the need for maintaining separate encoders for each modality, resulting in a lightweight and scalable network architecture. The source code of IMS2Trans and the associated weights are both publicly available at https://github.com/hudscomdz/IMS2Trans.

Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we proposed a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduced a lightweight superpixel-based graph neural network (GNN) and broke new ground by introducing superpixel metric learning to segment nucleus and cytoplasm. Remarkably, our proposed segmentation model superpixel metric graph neural network (SMGNN) achieved state of the art segmentation performance while utilizing at most 10000$ \times $ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes showed satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories. Our code is publicly available at https://github.com/jyh6681/SPXL-GNN.

RandStainNA++: Enhance Random Stain Augmentation and Normalization through Foreground and Background Differentiation.

The wide prevalence of staining variations in digital pathology presents a significant obstacle, often undermining the effectiveness of diagnosis and analysis. The current strategies to counteract this issue primarily revolve around Stain Normalization (SN) and Stain Augmentation (SA). Nonetheless, these methodologies come with inherent limitations. They struggle to adapt to the vast array of staining styles, tend to presuppose linear associations between color spaces, and often lead to unrealistic color transformations. In response to these challenges, we introduce RandStainNA++, a novel method seamlessly integrating SN and SA. This method exploits the versatility of random SN and SA within randomly selected color spaces, effectively managing variations for the foreground and background independently. By refining the transformations of staining styles for the foreground and background within a realistic scope, this strategy promotes the generation of more practical staining transformations during the training phase. Further enhancing our approach, we propose a unique self-distillation method. This technique incorporates prior knowledge of stain variation, substantially augmenting the generalization capability of the network. The striking results yield that, compared to conventional classification models, our method boosts performance by a significant margin of 16-25%. Furthermore, when juxtaposed with baseline segmentation models, the Dice score registers an increase of 0.06. The codes are available at https://github.com/wagnchogn/RandStainNA-plusplus.

SERPINA3: A novel inflammatory biomarker associated with cerebral small vessel disease burden in ischemic stroke.

BACKGROUND AND OBJECTIVE: Inflammation has emerged as a prominent risk factor for cerebral small vessel disease (CSVD). However, the specific association between various inflammatory biomarkers and the development of CSVD remains unclear. Serine proteinase inhibitor A3 (SERPINA3), Matrix metalloproteinase-9 (MMP-9), Tissue inhibitor metalloproteinase-1 (TIMP-1), Monocyte Chemoattractant Protein-1 (MCP-1) are several inflammatory biomarkers that are potentially involved in the development of CSVD. In this present study, we aimed to investigate the relationship between candidate molecules and CSVD features. METHOD: The concentration of each biomarker was measured in 79 acute ischemic stroke patients admitted within 72 h after symptom onset. The associations between blood levels of inflammatory markers and CSVD score were investigated, as well as each CSVD feature, including white matter hyperintensities (WMH), lacunes, and enlarged perivascular spaces (EPVS). RESULTS: The mean age was 69.0 ± 11.8 years, and 65.8% of participants were male. Higher SERPINA3 level (>78.90 ng/mL) was significantly associated with larger WMH volume and higher scores on Fazekas's scale in all three models. Multiple regression analyses revealed the linear association between absolute WMH burden and SERPINA3 level, especially in model 3 (ß = 0.14; 95% confidence interval [CI], 0.04-0.24 ; p = 0.008 ). Restricted cubic spline regression demonstrated a dose-response relationship between SERPINA3 level and larger WMH volume (p nonlineariy = 0.0366 and 0.0378 in model 2 and mode 3, respectively). Using a receiving operating characteristic (ROC) curve, plasma SERPINA3 level of 64.15 ng/mL distinguished WMH >7.8 mL with the highest sensitivity and specificity (75.92% and 60%, respectively, area under curve [AUC] = 0.668, p = 0.0102). No statistically significant relationship has been found between other candidate biomarkers and CSVD features. CONCLUSION: In summary, among four inflammatory biomarkers that we investigated, SERPINA3 level at baseline was associated with WMH severity, which revealed a novel biomarker for CSVD and validated its relationship with inflammation and endothelial dysfunction.

Stroke depression: a concept with clinical applicability.

Stroke is a common neurological condition and among the leading causes of death and disability worldwide. Depression is both a risk factor for and complication of stroke, and the two conditions may have a complex reciprocal relationship over time. However, the secondary effects of depression on stroke are often overlooked, resulting in increased morbidity and mortality. In the previous concept of 'poststroke depression', stroke and depression were considered as two independent diseases. It often delays the diagnosis and treatment of patients. The concept 'stroke depression' proposed in this article will emphasise more the necessity of aggressive treatment of depression in the overall management of stroke, thus to reduce the incidence of stroke and in the meantime, improve the prognosis of stroke. Hopefully, it will lead us into a new era of acute stroke intervention.

A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification.

Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network. Specifically, a semi-supervised framework based on knowledge distillation is first developed to pre-train a patch encoder for extracting disease-relevant features. Then, a hierarchical Graph V-Net is designed to construct a hierarchical graph representation from neighboring/similar individual patches for coarse-to-fine classification, where each graph node (corresponding to one patch) is attached with extracted disease-relevant features and its target label during training is the average label of all pixels in the corresponding patch. To evaluate the performance of our proposed hierarchical Graph V-Net, we collect a large WSI dataset of 560 WSIs, with 30 labeled WSIs from the BACH dataset (through our further refinement), 30 labeled WSIs and 500 unlabeled WSIs from Yunnan Cancer Hospital. Those 500 unlabeled WSIs are employed for patch-level pre-training to improve feature representation, while 60 labeled WSIs are used to train and test our proposed hierarchical Graph V-Net. Both comparative assessment and ablation studies demonstrate the superiority of our proposed hierarchical Graph V-Net over state-of-the-art methods in classifying breast cancer from WSIs. The source code and our annotations for the BACH dataset have been released at https://github.com/lyhkevin/Graph-V-Net.

Multi-time point metabolomics reveals key metabolic features from the ultra-early stage of intracerebral hemorrhage in mice.

Despite decades of intensive research, there are still very limited options for the effective treatment of intracerebral hemorrhage (ICH). Recently, mounting evidence has indicated that the ultra-early stage (<3 h), serving as the primary phase of ICH, plays a pivotal role and may even surpass other stages in terms of its significance. Therefore, uncovering the metabolic alterations induced by ICH in the ultra-early stage is of crucial importance. To investigate this, the collagenase ICH mouse model was employed in this study. ICH or sham-operated mice were euthanized at the ultra-early stage of 3 h and the acute stage of 24 h and 72 h after the operation. Then, the metabolic changes in the perihematomal tissues were detected by liquid chromatography coupled with tandem mass spectrometry. In total, alterations in the levels of 465 metabolites were detected. A total of 136 metabolites were significantly changed at 3 h. At 24 h and 72 h, the amounts were 132 and 126, respectively. Additionally, the key corresponding metabolic pathways for these time points were analyzed through KEGG. To gather additional information, quantitative real-time transcription polymerase chain reaction, enzyme-linked immunosorbent assay and Western blots were performed to validate the metabolic changes. Overall, ICH significantly alters important physiological functions such as cysteine metabolism, purine metabolism, synaptic alterations, the synaptic vesicle cycle, and the ATP-binding cassette transporter system. These might be the key pathologic mechanisms of the ultra-early stage induced by ICH.

Development and validation of a copper-related gene prognostic signature in hepatocellular carcinoma.

Introduction: Reliable biomarkers are in need to predict the prognosis of hepatocellular carcinoma (HCC). Whilst recent evidence has established the critical role of copper homeostasis in tumor growth and progression, no previous studies have dealt with the copper-related genes (CRGs) signature with prognostic potential in HCC. Methods: To develop and validate a CRGs prognostic signature for HCC, we retrospectively included 353 and 142 patients as the development and validation cohort, respectively. Copper-related Prognostic Signature (Copper-PSHC) was developed using differentially expressed CRGs with prognostic value. The hazard ratio (HR) and the area under the time-dependent receiver operating characteristic curve (AUC) during 3-year follow-up were utilized to evaluate the performance. Additionally, the Copper-PSHC was combined with age, sex, and cancer stage to construct a Copper-clinical-related Prognostic Signature (Copper-CPSHC), by multivariate Cox regression. We further explored the underlying mechanism of Copper-PSHC by analyzing the somatic mutation, functional enrichment, and tumor microenvironment. Potential drugs for the high-risk group were screened. Results: The Copper-PSHC was constructed with nine CRGs. Patients in the high-risk group demonstrated a significantly reduced overall survival (OS) (adjusted HR, 2.65 [95% CI, 1.83-3.84] and 3.30, [95% CI, 1.27-8.60] in the development and validation cohort, respectively). The Copper-PSHC achieved a 3-year AUC of 0.74 [95% CI, 0.67-0.82] and 0.71 [95% CI, 0.56-0.86] for OS in the development and validation cohort, respectively. Copper-CPSHC yield a 3-year AUC of 0.73 [95% CI, 0.66-0.80] and 0.72 [95% CI, 0.56-0.87] for OS in the development and validation cohort, respectively. Higher tumor mutation burden, downregulated metabolic processes, hypoxia status and infiltrated stroma cells were found for the high-risk group. Six small molecular drugs were screened for the treatment of the high-risk group. Conclusion: Copper-PSHC services as a promising tool to identify HCC with poor prognosis and to improve disease outcomes by providing potential clinical decision support in treatment.

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas.

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting. In this study, we evaluated and compared the diagnostic performances of APTw MRI with several non-contrast-enhanced MRI sequences, such as diffusion-weighted imaging, susceptibility-weighted imaging, and pseudo-continuous arterial spin labeling. Thirty-nine scans from 28 glioma patients were obtained on a 3 T MRI scanner. A histogram analysis approach was employed to extract parameters from each tumor area. Statistically significant parameters (P < 0.05) were selected to train multivariate logistic regression models to evaluate the performance of MRI sequences. Multiple histogram parameters, particularly from APTw and pseudo-continuous arterial spin labeling images, demonstrated significant differences between treatment effect and recurrent tumor. The regression model trained on the combination of all significant histogram parameters achieved the best result (area under the curve = 0.89). We found that APTw images added value to other advanced MR images for the differentiation of treatment effect and tumor recurrence.

Neutrophil percentage to albumin ratio is associated with stroke-associated pneumonia and poor outcome in patients with spontaneous intracerebral hemorrhage.

Background: The purpose of this study was to investigate the diagnostic performance of the neutrophil percentage-to-albumin ratio (NPAR) for predicting stroke-associated pneumonia (SAP) and functional outcome in patients with intracerebral hemorrhage (ICH). Methods: We analyzed our prospective database of consecutive ICH patients who were admitted to the First Affiliated Hospital of Chongqing Medical University from January 2016 to September 2021. We included subjects with a baseline computed tomography available and a complete NPAR count performed within 6h of onset. The patients' demographic and radiological characteristics were analyzed. Good outcome was defined as a modifed Rankin Scale score of 0-3 at 90 days. Poor outcome was defined as a modifed Rankin Scale score of 4-6 at 90 days. Multivariable logistic regression models were used to investigate the association between NPAR, SAP, and functional outcome. Receiver operating characteristic (ROC) curve analysis was conducted to identify the optimal cutoff of NPAR to discriminate between good and poor outcomes in ICH patients. Results: A total of 918 patients with ICH confirmed by non-contrast computed tomography were included. Of those, 316 (34.4%) had SAP, and 258 (28.1%) had poor outcomes. Multivariate regression analysis showed that higher NPAR on admission was an independent predictor of SAP (adjusted odds ratio: 2.45; 95% confidence interval, 1.56-3.84; P<0.001) and was associated with increased risk of poor outcome (adjusted odd ratio:1.72; 95% confidence interval, 1.03-2.90; P=0.040) in patients with ICH. In ROC analysis, an NPAR of 2 was identified as the optimal cutoff value to discriminate between good and poor functional outcomes. Conclusion: Higher NPAR is independently associated with SAP and poor functional outcome in patients with ICH. Our findings suggest that early prediction of SAP is feasible by using a simple biomarker NPAR.

Artifact Detection and Restoration in Histology Images With Stain-Style and Structural Preservation.

The artifacts in histology images may encumber the accurate interpretation of medical information and cause misdiagnosis. Accordingly, prepending manual quality control of artifacts considerably decreases the degree of automation. To close this gap, we propose a methodical pre-processing framework to detect and restore artifacts, which minimizes their impact on downstream AI diagnostic tasks. First, the artifact recognition network AR-Classifier first differentiates common artifacts from normal tissues, e.g., tissue folds, marking dye, tattoo pigment, spot, and out-of-focus, and also catalogs artifact patches by their restorability. Then, the succeeding artifact restoration network AR-CycleGAN performs de-artifact processing where stain styles and tissue structures can be maximally retained. We construct a benchmark for performance evaluation, curated from both clinically collected WSIs and public datasets of colorectal and breast cancer. The functional structures are compared with state-of-the-art methods, and also comprehensively evaluated by multiple metrics across multiple tasks, including artifact classification, artifact restoration, downstream diagnostic tasks of tumor classification and nuclei segmentation. The proposed system allows full automation of deep learning based histology image analysis without human intervention. Moreover, the structure-independent characteristic enables its processing with various artifact subtypes. The source code and data in this research are available at https://github.com/yunboer/AR-classifier-and-AR-CycleGAN.

Mine local homogeneous representation by interaction information clustering with unsupervised learning in histopathology images.

BACKGROUND AND OBJECTIVE: The success of data-driven deep learning for histopathology images often depends on high-quality training sets and fine-grained annotations. However, as tumors are heterogeneous and annotations are expensive, unsupervised learning approaches are desirable to obtain full automation. METHODS: In this paper, an Interaction Information Clustering (IIC) method is proposed to extract locally homogeneous features in mutually exclusive clusters. Trained in an unsupervised paradigm, the framework learns invariant information from multiple spatially adjacent regions for improved classification. Additionally, an adaptive Conditional Random Field (CRF) model is incorporated to detect spatially adjacent image patches of high morphological homogeneity in an offset-constraint free manner. RESULTS: Empirically, the proposed model achieves an observable improvement of 11.4% on the downstream patch-level classification accuracy, compared with state-of-the-art unsupervised learning approaches. CONCLUSION: Furthermore, evaluated with our clinically collected histopathology whole-slide images, the proposed model shows high consistency in tissue distribution compared with well-trained supervised learning, which is of important diagnostic significance in clinical practice.

Macrophage-Derived MMP-9 and MMP-2 are Closely Related to the Rupture of the Fibrous Capsule of Hepatocellular Carcinoma Leading to Tumor Invasion.

BACKGROUND: Hepatocellular carcinoma (HCC) is an aggressive tumor with a poor clinical prognosis. Rupture of the fibrous capsule (FC) is a very important clinical phenomenon in the invasion and metastasis of HCC. FC is mainly composed of type I collagen (COL1A1). However, it is not clear what caused the FC rupture. In this study, we aimed to determine whether the rupture of FC in HCC patients was related to macrophage-derived MMP-9 and MMP-2, and their clinical diagnostic value for FC rupture. RESULTS: By performing immunohistochemical and immunofluorescence staining of ruptured FC and intact FC, the results showed that the ruptured area of FC aggregated a large number of macrophages with MMP-9 and MMP-2. Western blot analysis and Quantitative real-time PCR were used to assess the expression of MMP-9 and MMP-2 in the ruptured and relatively intact area of FC in ruptured FC patients, and the results revealed a significantly different expression of MMP-9 and MMP-2. ELISA experiments show that we could discriminate effectively between ruptured FC and intact FC by MMP-9 and MMP-2. CONCLUSIONS: Taken together, macrophage-derived MMP-9 and MMP-2 were closely related to the rupture of the FC of HCC and subsequently led to the migration and invasion of the tumor cells through the ruptured area of FC to the para cancer. It is suggested that when performing surgical resection, it is necessary to expand the range of tumor resection for patients with ruptured FC and hence reduce the possibility of recurrence and metastasis in HCC patients.

LCN2 secreted by tissue-infiltrating neutrophils induces the ferroptosis and wasting of adipose and muscle tissues in lung cancer cachexia.

BACKGROUND: Cancer cachexia is a deadly wasting syndrome that accompanies various diseases (including ~ 50% of cancers). Clinical studies have established that cachexia is not a nutritional deficiency and is linked to expression of certain proteins (e.g., interleukin-6 and C-reactive protein), but much remains unknown about this often fatal syndrome. METHODS: First, cachexia was created in experimental mouse models of lung cancer. Samples of human lung cancer were used to identify the association between the serum lipocalin 2 (LCN2) level and cachexia progression. Then, mouse models with LCN2 blockade or LCN2 overexpression were used to ascertain the role of LCN2 upon ferroptosis and cachexia. Furthermore, antibody depletion of tissue-infiltrating neutrophils (TI-Neu), as well as myeloid-specific-knockout of Lcn2, were undertaken to reveal if LCN2 secreted by TI-Neu caused cachexia. Finally, chemical inhibition of ferroptosis was conducted to illustrate the effect of ferroptosis upon tissue wasting. RESULTS: Protein expression of LCN2 was higher in the wasting adipose tissue and muscle tissues of experimental mouse models of lung cancer cachexia. Moreover, evaluation of lung cancer patients revealed an association between the serum LCN2 level and cachexia progression. Inhibition of LCN2 expression reduced cachexia symptoms significantly and inhibited tissue wasting in vivo. Strikingly, we discovered a significant increase in the number of TI-Neu in wasting tissues, and that these innate immune cells secreted high levels of LCN2. Antibody depletion of TI-Neu, as well as myeloid-specific-knockout of Lcn2, prevented ferroptosis and tissue wasting in experimental models of lung cancer cachexia. Chemical inhibition of ferroptosis alleviated tissue wasting significantly and also prolonged the survival of cachectic mice. CONCLUSIONS: Our study provides new insights into how LCN2-induced ferroptosis functionally impacts tissue wasting. We identified LCN2 as a potential target in the treatment of cancer cachexia.

Interactions between driver genes shape the signaling pathway landscape and direct hepatocellular carcinoma therapy.

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies, whose initiation and development are driven by alterations in driver genes. In this study, we identified four driver genes (TP53, PTEN, CTNNB1, and KRAS) that show a high frequency of somatic mutations or copy number variations (CNVs) in patients with HCC. Four different spontaneous HCC mouse models were constructed to screen for changes in various kinase signaling pathways. The sgTrp53 + sgPten tumor upregulated mTOR and noncanonical nuclear factor-κB signaling, which was shown to be strongly inhibited by rapamycin (an mTOR inhibitor) in vitro and in vivo. The JAK-signal transducer and activator of transcription (STAT) signaling was activated in Ctnnb1mut + sgPten tumor, the proliferation of which was strongly inhibited by napabucasin (a STAT3 inhibitor). Additionally, mTOR, cytoskeleton, and AMPK signaling were upregulated while rapamycin and ezrin inhibitors exerted potent antiproliferative effects in sgPten + KrasG12D tumor. We found that JAK-STAT, MAPK, and cytoskeleton signaling were activated in sgTrp53 + KrasG12D tumor and the combination of sorafenib and napabucasin led to the complete inhibition of tumor growth in vivo. In patients with HCC who had the same molecular classification as our mouse models, the downstream signaling pathway landscapes associated with genomic alterations were identical. Our research provides novel targeted therapeutic options for the clinical treatment of HCC, based on the presence of specific genetic alterations within the tumor.

Tumors evade immune cytotoxicity by altering the surface topology of NK cells.

The highly variable response rates to immunotherapies underscore our limited knowledge about how tumors can manipulate immune cells. Here the membrane topology of natural killer (NK) cells from patients with liver cancer showed that intratumoral NK cells have fewer membrane protrusions compared with liver NK cells outside tumors and with peripheral NK cells. Dysregulation of these protrusions prevented intratumoral NK cells from recognizing tumor cells, from forming lytic immunological synapses and from killing tumor cells. The membranes of intratumoral NK cells have altered sphingomyelin (SM) content and dysregulated serine metabolism in tumors contributed to the decrease in SM levels of intratumoral NK cells. Inhibition of SM biosynthesis in peripheral NK cells phenocopied the disrupted membrane topology and cytotoxicity of the intratumoral NK cells. Targeting sphingomyelinase confers powerful antitumor efficacy, both as a monotherapy and as a combination therapy with checkpoint blockade.