Radiogenomics: bridging the gap between imaging and genomics for precision oncology.

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients.

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.

Adenosine triggers early astrocyte reactivity that provokes microglial responses and drives the pathogenesis of sepsis-associated encephalopathy in mice.

Molecular pathways mediating systemic inflammation entering the brain parenchyma to induce sepsis-associated encephalopathy (SAE) remain elusive. Here, we report that in mice during the first 6 hours of peripheral lipopolysaccharide (LPS)-evoked systemic inflammation (6 hpi), the plasma level of adenosine quickly increased and enhanced the tone of central extracellular adenosine which then provoked neuroinflammation by triggering early astrocyte reactivity. Specific ablation of astrocytic Gi protein-coupled A1 adenosine receptors (A1ARs) prevented this early reactivity and reduced the levels of inflammatory factors (e.g., CCL2, CCL5, and CXCL1) in astrocytes, thereby alleviating microglial reaction, ameliorating blood-brain barrier disruption, peripheral immune cell infiltration, neuronal dysfunction, and depression-like behaviour in the mice. Chemogenetic stimulation of Gi signaling in A1AR-deficent astrocytes at 2 and 4 hpi of LPS injection could restore neuroinflammation and depression-like behaviour, highlighting astrocytes rather than microglia as early drivers of neuroinflammation. Our results identify early astrocyte reactivity towards peripheral and central levels of adenosine as an important pathway driving SAE and highlight the potential of targeting A1ARs for therapeutic intervention.

Immunotherapy for recurrent or metastatic nasopharyngeal carcinoma.

Immunotherapy, particularly immune checkpoint inhibitors (ICIs), such as anti-programmed death 1/programmed death-ligand 1 (PD-1/PD-L1) therapy, has emerged as a pivotal treatment modality for solid tumors, including recurrent or metastatic nasopharyngeal carcinoma (R/M-NPC). Despite the advancements in the utilization of ICIs, there is still room for further improving patient outcomes. Another promising approach to immunotherapy for R/M-NPC involves adoptive cell therapy (ACT), which aims to stimulate systemic anti-tumor immunity. However, individual agent therapies targeting dendritic cells (DCs) appear to still be in the clinical trial phase. This current review underscores the potential of immunotherapy as a valuable adjunct to the treatment paradigm for R/M-NPC patients. Further research is warranted to enhance the efficacy of immunotherapy through the implementation of strategies such as combination therapies and overcoming immune suppression. Additionally, the development of a biomarker-based scoring system is essential for identifying suitable candidates for precision immunotherapy.

Peroxiredoxin 3 has a crucial role in the macrophage polarization by regulating mitochondrial homeostasis.

Acute lung injury (ALI) is one of the life-threatening complications of sepsis, and macrophage polarization plays a crucial role in the sepsis-associated ALI. However, the regulatory mechanisms of macrophage polarization in ALI and in the development of inflammation are largely unknown. In this study, we demonstrated that macrophage polarization occurs in sepsis-associated ALI and is accompanied by mitochondrial dysfunction and inflammation, and a decrease of PRDX3 promotes the initiation of macrophage polarization and mitochondrial dysfunction. Mechanistically, PRDX3 overexpression promotes M1 macrophages to differentiate into M2 macrophages, and enhances mitochondrial functional recovery after injury by reducing the level of glycolysis and increasing TCA cycle activity. In conclusion, we identified PRDX3 as a critical hub integrating oxidative stress, inflammation, and metabolic reprogramming in macrophage polarization. The findings illustrate an adaptive mechanism underlying the link between macrophage polarization and sepsis-associated ALI.

AHR signaling pathway mediates mitochondrial oxidative phosphorylation which leads to cytarabine resistance.

The aryl hydrocarbon receptor (AHR) has been identified as a significant driver of tumorigenesis. However, its clinical significance in acute myeloid leukemia (AML) remains largely unclear. In this study, RNA-Seq data from AML patients (bone marrow samples from 173 newly diagnosed AML patients) obtained from the TCGA database, and normal human RNA-Seq data (bone marrow samples from 70 healthy individuals) obtained from the GTEX database are downloaded for external validation and complementarity. The data analysis reveals that the AHR signaling pathway is activated in AML patients. Furthermore, there is a correlation between the expressions of AHR and mitochondrial oxidative phosphorylation genes. In vitro experiments show that enhancing AHR expression in AML cells increases mitochondrial oxidative phosphorylation and induces resistance to cytarabine. Conversely, reducing AHR expression in AML cells decreases cytarabine resistance. These findings deepen our understanding of the AHR signaling pathway's involvement in AML.

CorrDiff: Corrective Diffusion Model for Accurate MRI Brain Tumor Segmentation.

Accurate segmentation of brain tumors in MRI images is imperative for precise clinical diagnosis and treatment. However, existing medical image segmentation methods exhibit errors, which can be categorized into two types: random errors and systematic errors. Random errors, arising from various unpredictable effects, pose challenges in terms of detection and correction. Conversely, systematic errors, attributable to systematic effects, can be effectively addressed through machine learning techniques. In this paper, we propose a corrective diffusion model for accurate MRI brain tumor segmentation by correcting systematic errors. This marks the first application of the diffusion model for correcting systematic segmentation errors. Additionally, we introduce the Vector Quantized Variational Autoencoder (VQ-VAE) to compress the original data into a discrete coding codebook. This not only reduces the dimensionality of the training data but also enhances the stability of the correction diffusion model. Furthermore, we propose the Multi-Fusion Attention Mechanism, which can effectively enhances the segmentation performance of brain tumor images, and enhance the flexibility and reliability of the corrective diffusion model. Our model is evaluated on the BRATS2019, BRATS2020, and Jun Cheng datasets. Experimental results demonstrate the effectiveness of our model over state-of-the-art methods in brain tumor segmentation.

Discrete residual diffusion model for high-resolution prostate MRI synthesis.

Objective.High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems of existing models, called discrete residual diffusion model (DR-DM).Approach.First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables with increasing noise. The backward process learns the conditional transit distribution and gradually match the target data distribution. By optimizing a variant of the variational lower bound, training diffusion models effectively address the issue of mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging and improve the fidelity of reconstructed images.Main results.Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image super-resolution tasks and compared to five state-of-the-art methods.Significance.Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves the signal-to-noise ratio of MRI for prostate cancer, resulting in greater clarity and improved diagnostic accuracy for patients.

Enhancing gland segmentation in colon histology images using an instance-aware diffusion model.

In pathological image analysis, determination of gland morphology in histology images of the colon is essential to determine the grade of colon cancer. However, manual segmentation of glands is extremely challenging and there is a need to develop automatic methods for segmenting gland instances. Recently, due to the powerful noise-to-image denoising pipeline, the diffusion model has become one of the hot spots in computer vision research and has been explored in the field of image segmentation. In this paper, we propose an instance segmentation method based on the diffusion model that can perform automatic gland instance segmentation. Firstly, we model the instance segmentation process for colon histology images as a denoising process based on a diffusion model. Secondly, to recover details lost during denoising, we use Instance Aware Filters and multi-scale Mask Branch to construct global mask instead of predicting only local masks. Thirdly, to improve the distinction between the object and the background, we apply Conditional Encoding to enhance the intermediate features with the original image encoding. To objectively validate the proposed method, we compared several state-of-the-art deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset (165 images), the Colorectal Adenocarcinoma Glands (CRAG) dataset (213 images) and the RINGS dataset (1500 images). Our proposed method obtains significantly improved results for CRAG (Object F1 0.853 ± 0.054, Object Dice 0.906 ± 0.043), GlaS Test A (Object F1 0.941 ± 0.039, Object Dice 0.939 ± 0.060), GlaS Test B (Object F1 0.893 ± 0.073, Object Dice 0.889 ± 0.069), and RINGS dataset (Precision 0.893 ± 0.096, Dice 0.904 ± 0.091). The experimental results show that our method significantly improves the segmentation accuracy, and the experiment results demonstrate the efficacy of the method.

GBP2 upregulated in LPS-stimulated macrophages-derived exosomes accelerates septic lung injury by activating epithelial cell NLRP3 signaling.

Macrophages infiltration is a crucial factor causing Sepsis-associated acute lung injury (ALI). Accumulating evidence suggests macrophages-alveolar epithelial cells communication is proven to be critical in ALI. However, little is known regarding how activated macrophages regulated sepsis-associated ALI. To explore the role of macrophages-alveolar epithelial cells communication in the ALI process, our data revealed that Lipopolysaccharides-induced macrophages-derived exosomes (L-Exo) induced sepsis-associated ALI and caused alveolar epithelial cells damage. Moreover, Guanylate-binding protein 2 (GBP2) was significantly upregulated in L-Exo, and NLRP3 inflammasomes was the direct target of GBP2. Further experimentation showed that GBP2 inhibition in vitro and in vivo reserves L-Exo effects, while GBP2 overexpression in vitro and in vivo promotes L-Exo effects. These results demonstrated that L-Exo contains excessive GBP2 and promotes inflammation through targeting NLRP3 inflammasomes, which induced alveolar epithelial cells dysfunction and pyroptosis. These findings demonstrate that L-Exo exerted a deleterious effect on ALI by regulating the GBP2/NLRP3 axis, which might provide new insight on ALI prevention and treatment.

CD7 targeted "off-the-shelf" CAR-T demonstrates robust in vivo expansion and high efficacy in the treatment of patients with relapsed and refractory T cell malignancies.

T-cell acute lymphoblastic leukemia (T-ALL) represents an area of highly unmet medical needs. Once relapsed, patients have limited treatment options and poor prognosis. T-ALL antigens such as CD7 is extensively expressed in normal T cells and natural killer (NK) cells, and extending the success of CAR-T therapy to T cell malignancies was challenged by CAR-T cell fratricide, high production cost, and potential product contaminations. GC027 is an "off-the-shelf" allogeneic CD7 targeted CAR-T therapeutic product for T cell malignancies. It demonstrated superior cell expansion and antileukemia efficacy in mouse xenograft model. In our previous study, we observed promising efficacy results in the first two relapsed and refractory(R/R) T-ALL patients treated with GC027. In the expanded study, 11 out of 12 patients had rapid eradication of T-lymphoblasts and reached complete response within 1-month after GC027 infusion. GC027 cells expanded quickly beginning at infusion and reached to peak around 5-10 days after infusion. For most patients with a response(9/11), GC027 could not be detected via flow cytometry or qPCR 4 weeks after infusion. One patient had progression free survival of >3 years. With manageable toxicity profile, GC027 demonstrated superior clinical efficacy to standard chemotherapy regimens in (R/R) T cell malignancies.

Single-cell RNA-seq reveals a microenvironment and an exhaustion state of T/NK cells in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous blood cancer. Effective immunotherapies for AML are hindered by a lack of understanding of the tumor microenvironment (TME). Here, we retrieved published single-cell RNA sequencing data for 128,688 cells derived from 29 bone marrow aspirates, including 21 AML patients and eight healthy donors. We established a global tumor ecosystem including nine main cell types. Myeloid, T, and NK cells were further re-clustered and annotated. Developmental trajectory analysis indicated that exhausted CD8+ T cells might develop via tissue residual memory T cells (TRM) in the AML TME. Significantly higher expression levels of exhaustion molecules in AML TRM cells suggested that these cells were influenced by the TME and entered an exhausted state. Meanwhile, the upregulation of checkpoint molecules and downregulation of granzyme were also observed in AML NK cells, suggesting an exhaustion state. In conclusion, our comprehensive profiling of T/NK subpopulations provides deeper insights into the AML immunosuppressive ecosystem, which is critical for immunotherapies.

In the mouse cortex, oligodendrocytes regain a plastic capacity, transforming into astrocytes after acute injury.

Acute brain injuries evoke various response cascades directing the formation of the glial scar. Here, we report that acute lesions associated with hemorrhagic injuries trigger a re-programming of oligodendrocytes. Single-cell RNA sequencing highlighted a subpopulation of oligodendrocytes activating astroglial genes after acute brain injuries. By using PLP-DsRed1/GFAP-EGFP and PLP-EGFPmem/GFAP-mRFP1 transgenic mice, we visualized this population of oligodendrocytes that we termed AO cells based on their concomitant activity of astro- and oligodendroglial genes. By fate mapping using PLP- and GFAP-split Cre complementation and repeated chronic in vivo imaging with two-photon laser-scanning microscopy, we observed the conversion of oligodendrocytes into astrocytes via the AO cell stage. Such conversion was promoted by local injection of IL-6 and was diminished by IL-6 receptor-neutralizing antibody as well as by inhibiting microglial activation with minocycline. In summary, our findings highlight the plastic potential of oligodendrocytes in acute brain trauma due to microglia-derived IL-6.

A novel 5-gene prognostic signature to improve risk stratification of cytogenetically normal acute myeloid leukemia.

PURPOSE: Prognostic prediction is a challenging task in cytogenetically normal acute myeloid leukemia (CN-AML) patients. In this study, we aimed at developing a novel prognostic signature to predict and stratify the survival of CN-AML patients. METHODS: Using a training dataset (GSE12417), 5-gene prognostic signature was established to predict survival of CN-AML patients. The prognostic performance of this prognostic signature was further validated in testing dataset (TCGA CN-AML cohort) and validation dataset (GSE6891 CN-AML cohort). RESULTS: In training, testing and validation datasets, the increased 5-gene risk score was significantly related with inferior overall survival (OS) of patients, and the area under the receiver operating characteristic curve (AUC) demonstrated that our prognostic signature had overall prediction accuracy. The excellent prognostic value of the 5-gene prognostic signature was also supported by the comparison with three previously proposed prognostic models. For the intermediate-risk CN-AML patients and the CN-AML patients with FLT3 or NPM1 mutation, our model could also well dichotomize them into two subgroups with distinct prognosis. Multivariate analysis demonstrated that 5-gene risk score was the only independent risk factor in TCGA CN-AML cohort. Nomogram including the 5-gene risk score performed well in predicting 1-year, 2-year and 3-year OS. CONCLUSION: In summary, our novel 5-gene prognostic signature facilitated the improvement in risk stratification of CN-AML patients.

Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors.

BACKGROUND: To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. METHODS: We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. RESULTS:  Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. CONCLUSIONS:  We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.

A modified primary culture method of rat pulmonary vein smooth muscle cells.

BACKGROUND: Although the pressure of pulmonary vein increases before pulmonary artery in pulmonary hypertension due to left heart disease (PH-LHD), only a few studies have assessed pulmonary vein smooth muscle cells (PVSMCs) because of the lack of a simple and feasible isolation method. METHODS: In this study, we introduced a simple method to obtain PVSMCs. Primary pulmonary veins were removed by puncture needle cannula guidance. Then, PVSMCs were cultured by the tissue explant method and purified by the differential adhesion method. The cells were characterized by hematoxylin-eosin (HE) staining, immunohistochemistry, western blotting, and immunofluorescence to observe the morphology and verify the expression of alpha-smooth muscle actin (α-SMA). RESULTS: The HE staining results showed that the pulmonary vein media was thinner than the pulmonary artery, the intima and adventitia of the pulmonary vein were removed by this method, and the obtained cells with good activity exhibited morphological characteristics of smooth muscle cells. In addition, higher α-SMA expression was observed in the cells obtained by our isolation method than in the traditional method. CONCLUSION: This study established a simple and feasible method to isolate and culture PVSMCs that might facilitate the cytological experiments for PH-LHD.

Temporal contexts for motion tracking in ultrasound sequences with information bottleneck.

BACKGROUND: Recently, deep convolutional neural networks (CNNs) have been widely adopted for ultrasound sequence tracking and shown to perform satisfactorily. However, existing trackers ignore the rich temporal contexts that exists between consecutive frames, making it difficult for these trackers to perceive information about the motion of the target. PURPOSE: In this paper, we propose a sophisticated method to fully utilize temporal contexts for ultrasound sequences tracking with information bottleneck. This method determines the temporal contexts between consecutive frames to perform both feature extraction and similarity graph refinement, and information bottleneck is integrated into the feature refinement process. METHODS: The proposed tracker combined three models. First, online temporal adaptive convolutional neural network (TAdaCNN) is proposed to focus on feature extraction and enhance spatial features using temporal information. Second, information bottleneck (IB) is incorporated to achieve more accurate target tracking by maximally limiting the amount of information in the network and discarding irrelevant information. Finally, we propose temporal adaptive transformer (TA-Trans) that efficiently encodes temporal knowledge by decoding it for similarity graph refinement. The tracker was trained on 2015 MICCAI Challenge on Liver Ultrasound Tracking (CLUST) dataset to evaluate the performance of the proposed method by calculating the tracking error (TE) between the predicted landmarks and the ground truth landmarks for each frame. The experimental results are compared with 13 state-of-the-art methods, and ablation studies are conducted. RESULTS: On CLUST 2015 dataset, our proposed model achieves a mean TE of 0.81 ± 0.74 mm and a maximum TE of 1.93 mm for 85 point-landmarks across 39 ultrasound sequences in the 2D sequences. Tracking speed ranged from 41 to 63 frames per second (fps). CONCLUSIONS: This study demonstrates a new integrated workflow for ultrasound sequences motion tracking. The results show that the model has excellent accuracy and robustness. Reliable and accurate motion estimation is provided for applications requiring real-time motion estimation in the context of ultrasound-guided radiation therapy.