Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.

Cryo-EM structures of adenosine receptor A3AR bound to selective agonists.

The adenosine A3 receptor (A3AR), a key member of the G protein-coupled receptor family, is a promising therapeutic target for inflammatory and cancerous conditions. The selective A3AR agonists, CF101 and CF102, are clinically significant, yet their recognition mechanisms remained elusive. Here we report the cryogenic electron microscopy structures of the full-length human A3AR bound to CF101 and CF102 with heterotrimeric Gi protein in complex at 3.3-3.2 Å resolution. These agonists reside in the orthosteric pocket, forming conserved interactions via their adenine moieties, while their 3-iodobenzyl groups exhibit distinct orientations. Functional assays reveal the critical role of extracellular loop 3 in A3AR's ligand selectivity and receptor activation. Key mutations, including His3.37, Ser5.42, and Ser6.52, in a unique sub-pocket of A3AR, significantly impact receptor activation. Comparative analysis with the inactive A2AAR structure highlights a conserved receptor activation mechanism. Our findings provide comprehensive insights into the molecular recognition and signaling of A3AR, paving the way for designing subtype-selective adenosine receptor ligands.

Improving Cancer Survival Prediction via Graph Convolutional Neural Network Learning on Protein-Protein Interaction Networks.

Cancer is one of the most challenging health problems worldwide. Accurate cancer survival prediction is vital for clinical decision making. Many deep learning methods have been proposed to understand the association between patients' genomic features and survival time. In most cases, the gene expression matrix is fed directly to the deep learning model. However, this approach completely ignores the interactions between biomolecules, and the resulting models can only learn the expression levels of genes to predict patient survival. In essence, the interaction between biomolecules is the key to determining the direction and function of biological processes. Proteins are the building blocks and principal undertakings of life activities, and as such, their complex interaction network is potentially informative for deep learning methods. Therefore, a more reliable approach is to have the neural network learn both gene expression data and protein interaction networks. We propose a new computational approach, termed CRESCENT, which is a protein-protein interaction (PPI) prior knowledge graph-based convolutional neural network (GCN) to improve cancer survival prediction. CRESCENT relies on the gene expression networks rather than gene expression levels to predict patient survival. The performance of CRESCENT is evaluated on a large-scale pan-cancer dataset consisting of 5991 patients from 16 different types of cancers. Extensive benchmarking experiments demonstrate that our proposed method is competitive in terms of the evaluation metric of the time-dependent concordance index( Ctd) when compared with several existing state-of-the-art approaches. Experiments also show that incorporating the network structure between genomic features effectively improves cancer survival prediction.

Specifying the Starting Point for S1 Iliosacral Screw Placement in the Dysmorphic Sacrum.

BACKGROUND: Sacral dysmorphism is not uncommon and complicates S1 iliosacral screw placement partially because of the difficulty of determining the starting point accurately on the sacral lateral view. We propose a method of specifying the starting point. METHODS: The starting point for the S1 iliosacral screw into the dysmorphic sacrum was specifically set at a point where the ossification of the S1/S2 intervertebral disc (OSID) intersected the posterior vertebral cortical line (PVCL) on the sacral lateral view, followed by guidewire manipulation and screw placement on the pelvic outlet and inlet views. Computer-simulated virtual surgical procedures based on pelvic computed tomography (CT) data on 95 dysmorphic sacra were performed to determine whether the starting point was below the iliac cortical density (ICD) and in the S1 oblique osseous corridor and to evaluate the accuracy of screw placement (with 1 screw being used, in the left hemipelvis). Surgical procedures on 17 patients were performed to verify the visibility of the OSID and PVCL, to check the location of the starting point relative to the ICD, and to validate the screw placement safety as demonstrated with postoperative CT scans. RESULTS: In the virtual surgical procedures, the starting point was consistently below the ICD and in the oblique osseous corridor in all patients and all screws were Grade 1. In the clinical surgical procedures, the OSID and PVCL were consistently visible and the starting point was always below the ICD in all patients; overall, 21 S1 iliosacral screws were placed in these 17 patients without malpositioning or iatrogenic injury. CONCLUSIONS: On the lateral view of the dysmorphic sacrum, the OSID and PVCL are visible and intersect at a point that is consistently below the ICD and in the oblique osseous corridor, and thus they can be used to identify the starting point. LEVEL OF EVIDENCE: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

An Online Mammography Database with Biopsy Confirmed Types.

Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespan. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. Although some public mammography datasets are useful, there is still a lack of open access datasets that expand beyond the white population as well as missing biopsy confirmation or with unknown molecular subtypes. To fill this gap, we build a database containing two online breast mammographies. The dataset named by Chinese Mammography Database (CMMD) contains 3712 mammographies involved 1775 patients, which is divided into two branches. The first dataset CMMD1 contains 1026 cases (2214 mammographies) with biopsy confirmed type of benign or malignant tumors. The second dataset CMMD2 includes 1498 mammographies for 749 patients with known molecular subtypes. Our database is constructed to enrich the diversity of mammography data and promote the development of relevant fields.

Evolutionary route of nasopharyngeal carcinoma metastasis and its clinical significance.

It is critical to understand factors associated with nasopharyngeal carcinoma (NPC) metastasis. To track the evolutionary route of metastasis, here we perform an integrative genomic analysis of 163 matched blood and primary, regional lymph node metastasis and distant metastasis tumour samples, combined with single-cell RNA-seq on 11 samples from two patients. The mutation burden, gene mutation frequency, mutation signature, and copy number frequency are similar between metastatic tumours and primary and regional lymph node tumours. There are two distinct evolutionary routes of metastasis, including metastases evolved from regional lymph nodes (lymphatic route, 61.5%, 8/13) and from primary tumours (hematogenous route, 38.5%, 5/13). The hematogenous route is characterised by higher IFN-γ response gene expression and a higher fraction of exhausted CD8+ T cells. Based on a radiomics model, we find that the hematogenous group has significantly better progression-free survival and PD-1 immunotherapy response, while the lymphatic group has a better response to locoregional radiotherapy.

Similarity Fusion via Exploiting High Order Proximity for Cancer Subtyping.

Identifying cancer subtypes holds essential promise for improving prognosis and personalized treatment. Cancer subtyping based on multi-omics data has become a hotspot in bioinformatics research. One of the critical approaches of handling data heterogeneity in multi-omics data is first modeling each omics data as a separate similarity graph. Then, the information of multiple graphs is integrated into a unified graph. However, a significant challenge is how to measure the similarity of nodes in each graph and preserve cluster information of each graph. To that end, we exploit a new high order proximity in each graph and propose a similarity fusion method to fuse the high order proximity of multiple graphs while preserving cluster information of multiple graphs. Compared with the current techniques employing the first order proximity, exploiting high order proximity contributes to attaining accurate similarity. The proposed similarity fusion method makes full use of the complementary information from multi-omics data. Experiments in six benchmark multi-omics datasets and two individual cancer case studies confirm that our proposed method achieves statistically significant and biologically meaningful cancer subtypes.

Identifying miRNA-Gene Common and Specific Regulatory Modules for Cancer Subtyping by a High-Order Graph Matching Model.

Identifying regulatory modules between miRNAs and genes is crucial in cancer research. It promotes a comprehensive understanding of the molecular mechanisms of cancer. The genomic data collected from subjects usually relate to different cancer statuses, such as different TNM Classifications of Malignant Tumors (TNM) or histological subtypes. Simple integrated analyses generally identify the core of the tumorigenesis (common modules) but miss the subtype-specific regulatory mechanisms (specific modules). In contrast, separate analyses can only report the differences and ignore important common modules. Therefore, there is an urgent need to develop a novel method to jointly analyze miRNA and gene data of different cancer statuses to identify common and specific modules. To that end, we developed a High-Order Graph Matching model to identify Common and Specific modules (HOGMCS) between miRNA and gene data of different cancer statuses. We first demonstrate the superiority of HOGMCS through a comparison with four state-of-the-art techniques using a set of simulated data. Then, we apply HOGMCS on stomach adenocarcinoma data with four TNM stages and two histological types, and breast invasive carcinoma data with four PAM50 subtypes. The experimental results demonstrate that HOGMCS can accurately extract common and subtype-specific miRNA-gene regulatory modules, where many identified miRNA-gene interactions have been confirmed in several public databases.

Identify Multiple Gene-Drug Common Modules via Constrained Graph Matching.

Identifying gene-drug interactions is vital to understanding biological mechanisms and achieving precise drug repurposing. High-throughput technologies produce a large amount of pharmacological and genomic data, providing an opportunity to explore the associations between oncogenic genes and therapeutic drugs. However, most studies only focus on "one-to-one" or "one-to-many" interactions, ignoring the multivariate patterns between genes and drugs. In this article, a high-order graph matching model with hypergraph constraints is proposed to discover the gene-drug common regulatory modules. Moreover, the prior knowledge is formulated into hypergraph constraints to reveal their multiple correspondences, penalizing the tensor matching process. The experimental results on the synthetic data demonstrate the proposed model is robust to noise contamination and outlier corruption, achieving a better performance than four state-of-the-art methods. We then evaluate the statistical power of our proposed method on the pharmacogenomics data. Our identified gene-drug common modules not only show significantly enriched pathways associated with cancer but also manifest the highly close gene-drug interactions.

Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold.

BACKGROUND: Patient subgroups are important for easily understanding a disease and for providing precise yet personalized treatment through multiple omics dataset integration. Multiomics datasets are produced daily. Thus, the fusion of heterogeneous big data into intrinsic structures is an urgent problem. Novel mathematical methods are needed to process these data in a straightforward way. RESULTS: We developed a novel method for subgrouping patients with distinct survival rates via the integration of multiple omics datasets and by using principal component analysis to reduce the high data dimensionality. Then, we constructed similarity graphs for patients, merged the graphs in a subspace, and analyzed them on a Grassmann manifold. The proposed method could identify patient subgroups that had not been reported previously by selecting the most critical information during the merging at each level of the omics dataset. Our method was tested on empirical multiomics datasets from The Cancer Genome Atlas. CONCLUSION: Through the integration of microRNA, gene expression, and DNA methylation data, our method accurately identified patient subgroups and achieved superior performance compared with popular methods.

SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance.

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets.

NPCNet: Jointly Segment Primary Nasopharyngeal Carcinoma Tumors and Metastatic Lymph Nodes in MR Images.

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose survivability is greatly improved if early diagnosis and timely treatment are provided. Accurate segmentation of both the primary NPC tumors and metastatic lymph nodes (MLNs) is crucial for patient staging and radiotherapy scheduling. However, existing studies mainly focus on the segmentation of primary tumors, eliding the recognition of MLNs, and thus fail to comprehensively provide a landscape for tumor identification. There are three main challenges in segmenting primary NPC tumors and MLNs: variable location, variable size, and irregular boundary. To address these challenges, we propose an automatic segmentation network, named by NPCNet, to achieve segmentation of primary NPC tumors and MLNs simultaneously. Specifically, we design three modules, including position enhancement module (PEM), scale enhancement module (SEM), and boundary enhancement module (BEM), to address the above challenges. First, the PEM enhances the feature representations of the most suspicious regions. Subsequently, the SEM captures multiscale context information and target context information. Finally, the BEM rectifies the unreliable predictions in the segmentation mask. To that end, extensive experiments are conducted on our dataset of 9124 samples collected from 754 patients. Empirical results demonstrate that each module realizes its designed functionalities and is complementary to the others. By incorporating the three proposed modules together, our model achieves state-of-the-art performance compared with nine popular models.

The Small Open Reading Frame-Encoded Peptides: Advances in Methodologies and Functional Studies.

Small open reading frames (sORFs) are an important class of genes with less than 100 codons. They were historically annotated as noncoding or even junk sequences. In recent years, accumulating evidence suggests that sORFs could encode a considerable number of polypeptides, many of which play important roles in both physiology and disease pathology. However, it has been technically challenging to directly detect sORF-encoded peptides (SEPs). Here, we discuss the latest advances in methodologies for identifying SEPs with mass spectrometry, as well as the progress on functional studies of SEPs.

Machine learning-based prediction of anatomical outcome after idiopathic macular hole surgery.

BACKGROUND: To develop a machine learning (ML) model for the prediction of the idiopathic macular hole (IMH) status at 1 month after vitrectomy and internal limiting membrane peeling (VILMP) surgery. METHODS: A total of 288 IMH eyes from four ophthalmic centers were enrolled. All eyes underwent optical coherence tomography (OCT) examinations upon admission and one month after VILMP. First, 1,792 preoperative macular OCT parameters and 768 clinical variables of 256 eyes from two ophthalmic centers were used to train and internally validate ML models. Second, 224 preoperative macular OCT parameters and 96 clinical variables of 32 eyes from the other two centers were utilized for external validation. To fulfill the purpose of predicting postoperative IMH status (i.e., closed or open), five ML algorithms were trained and internally validated by the ten-fold cross-validation method, while the best-performing algorithm was further tested by an external validation set. RESULTS: In the internal validation, the mean area under the receiver operating characteristic curves (AUCs) of the five ML algorithms were 0.882-0.951. The AUC, accuracy, sensitivity, and specificity of the best-performing algorithm (i.e., random forest, RF) were 0.951, 0.892, 0.973, and 0.904, respectively. In the external validation, the AUC of RF was 0.940, with an accuracy of 0.875, a specificity of 0.875, and a sensitivity of 0.958. CONCLUSIONS: Based on the preoperative OCT parameters and clinical variables, our ML model achieved remarkable accuracy in predicting IMH status after VILMP. Therefore, ML models may help optimize surgical planning for IMH patients in the future.

IOAT: an interactive tool for statistical analysis of omics data and clinical data.

BACKGROUND: With the development of high-throughput sequencing technology, a huge amount of multi-omics data has been accumulated. Although there are many software tools for statistical analysis and visual development of omics data, these tools are not suitable for private data and non-technical users. Besides, most of these tools have specialized in only one or perhaps a few data typesare, without combining clinical information. What's more, users could not choose data processing and model selection flexibly when using these tools. RESULTS: To help non-technical users to understand and analyze private multi-omics data and ensure data security, we developed an interactive desk tool for statistical analysis and visualization of omics and clinical data (shortly IOAT). Our mainly targets csv format data, and combines clinical data with high-dimensional multi-omics data. It also contains various operations, such as data preprocessing, feature selection, risk assessment, clustering, and survival analysis. By using this tool, users can safely and conveniently try a combination of various methods on their private multi-omics data to find a model suitable for their data, conduct risk assessment and determine their cancer subtypes. At the same time, the tool can also provide them with references to genes that are closely related to tumor staging, facilitating the development of precision oncology. We review IOAT's main features and demonstrate its analysis capabilities on a lung from TCGA. CONCLUSIONS: IOAT is a local desktop tool, which provides a set of multi-omics data integration solutions. It can quickly perform a complete analysis of cancer genome data for subtype discovery and biomarker identification without security issues and writing any code. Thus, our tool can enable cancer biologists and biomedicine researchers to analyze their data more easily and safely. IOAT can be downloaded for free from https://github.com/WlSunshine/IOAT-software .

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of causalities and medical resources insufficiency in many countries. Reverse-transcriptase polymerase chain reaction (RT-PCR) testing is adopted as biopsy tool for confirmation of virus infection. However, its accuracy is as low as 60-70%, which is inefficient to uncover the infected. In comparison, the chest CT has been considered as the prior choice in diagnosis and monitoring progress of COVID-19 infection. Although the COVID-19 diagnostic systems based on artificial intelligence have been developed for assisting doctors in diagnosis, the small sample size and the excessive time consumption limit their applications. To this end, this paper proposed a diagnosis prototype system for COVID-19 infection testing. The proposed deep learning model is trained and is tested on 2267 CT sequences from 1357 patients clinically confirmed with COVID-19 and 1235 CT sequences from non-infected people. The main highlights of the prototype system are: (1) no data augmentation is needed to accurately discriminate the COVID-19 from normal controls with the specificity of 0.92 and sensitivity of 0.93; (2) the raw DICOM image is not necessary in testing. Highly compressed image like Jpeg can be used to allow a quick diagnosis; and (3) it discriminates the virus infection within 6 seconds and thus allows an online test with light cost. We also applied our model on 48 asymptomatic patients diagnosed with COVID-19. We found that: (1) the positive rate of RT-PCR assay is 63.5% (687/1082). (2) 45.8% (22/48) of the RT-PCR assay is negative for asymptomatic patients, yet the accuracy of CT scans is 95.8%. The online detection system is available: http://212.64.70.65/covid .

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images. METHODS: In the training set, 12,365 OCT images were extracted from a public data set and an ophthalmic center. A total of 656 OCT images were extracted from another ophthalmic center for external validation. The presence or absence of three OCT patterns of DME, including diffused retinal thickening, cystoid macular edema, and serous retinal detachment, was labeled with 1 or 0, respectively. A DL model was trained to detect three OCT patterns of DME. The occlusion test was applied for the visualization of the DL model. RESULTS: Applying 5-fold cross-validation method in internal validation, the area under the receiver operating characteristic curve for the detection of three OCT patterns (i.e., diffused retinal thickening, cystoid macular edema, and serous retinal detachment) was 0.971, 0.974, and 0.994, respectively, with an accuracy of 93.0%, 95.1%, and 98.8%, respectively, a sensitivity of 93.5%, 94.5%, and 96.7%, respectively, and a specificity of 92.3%, 95.6%, and 99.3%, respectively. In external validation, the area under the receiver operating characteristic curve was 0.970, 0.997, and 0.997, respectively, with an accuracy of 90.2%, 95.4%, and 95.9%, respectively, a sensitivity of 80.1%, 93.4%, and 94.9%, respectively, and a specificity of 97.6%, 97.2%, and 96.5%, respectively. The occlusion test showed that the DL model could successfully identify the pathologic regions most critical for detection. CONCLUSION: Our DL model demonstrated high accuracy and transparency in the detection of OCT patterns of DME. These results emphasized the potential of artificial intelligence in assisting clinical decision-making processes in patients with DME.

Evaluation of gene-drug common module identification methods using pharmacogenomics data.

Accurately identifying the interactions between genomic factors and the response of cancer drugs plays important roles in drug discovery, drug repositioning and cancer treatment. A number of studies revealed that interactions between genes and drugs were 'many-genes-to-many drugs' interactions, i.e. common modules, opposed to 'one-gene-to-one-drug' interactions. Such modules fully explain the interactions between complex biological regulatory mechanisms and cancer drugs. However, strategies for effectively and robustly identifying the underlying common modules among pharmacogenomics data remain to be improved. In this paper, we aim to provide a detailed evaluation of three categories of state-of-the-art common module identification techniques from a machine learning perspective, including non-negative matrix factorization (NMF), partial least squares (PLS) and network analyses. We first evaluate the performance of six methods, namely SNMNMF, NetNMF, SNPLS, O2PLS, NSBM and HOGMMNC, using two series of simulated data sets with different noise levels and outlier ratios. Then, we conduct experiments using a real world data set of 2091 genes and 101 drugs in 392 cancer cell lines and compare the real experimental results from the aspect of biological process term enrichment, gene-drug and drug-drug interactions. Finally, we present interesting findings from our evaluation study and discuss the advantages and drawbacks of each method. Supplementary information: Supplementary file is available at Briefings in Bioinformatics online.