Comparative Analysis of Atezolizumab Plus Bevacizumab and Hepatic Artery Infusion Chemotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter, Propensity Score Study.

This study aimed to compare the prognosis and characteristics of patients with advanced hepatocellular carcinoma treated with first-line atezolizumab plus bevacizumab (AB) combination therapy and hepatic artery infusion chemotherapy (HAIC). We retrospectively assessed 193 and 114 patients treated with HAIC and AB combination therapy, respectively, between January 2018 and May 2023. The progression-free survival (PFS) of patients treated with AB combination therapy was significantly superior to that of patients treated with HAIC (p < 0.05), but there was no significant difference in overall survival (OS). After propensity score matching, our data revealed no significant differences in OS and PFS between patients who received AB combination therapy and those who received HAIC therapy (p = 0.5617 and 0.3522, respectively). In conclusion, our propensity score study reveals no significant differences in OS and PFS between patients treated with AB combination therapy and those treated with HAIC.

Higher Number of Tumor-Infiltrating PD-L1+ Cells Is Related to Better Response to Multikinase Inhibitors in Hepatocellular Carcinoma.

Multikinase inhibitors (MKIs) such as sorafenib and lenvatinib are first-line treatments for unresectable hepatocellular carcinoma (HCC) and are known to have immunomodulatory effects. However, predictive biomarkers of MKI treatment in HCC patients need to be elucidated. In the present study, thirty consecutive HCC patients receiving lenvatinib (n = 22) and sorafenib (n = 8) who underwent core-needle biopsy before treatment were enrolled. The associations of CD3, CD68, and programmed cell death-ligand-1 (PD-L1) immunohistochemistry with patient outcomes, including overall survival (OS), progression-free survival (PFS), and objective response rate (ORR), were evaluated. High and low subgroups were determined according to median CD3, CD68, and PD-L1 values. Median CD3 and CD68 counts were 51.0 and 46.0 per 20,000 µm2, respectively. The median combined positivity score (CPS) of PD-L1 was 2.0. Median OS and PFS were 17.6 and 4.4 months, respectively. ORRs of the total, lenvatinib, and sorafenib groups were 33.3% (10/30), 12.5% (1/8), and 40.9% (9/22), respectively. The high CD68+ group had significantly better PFS than the low CD68+ group. The high PD-L1 group had better PFS than the low subgroup. When we analyzed the lenvatinib subgroup, PFS was also significantly better in the high CD68+ and PD-L1 groups. These findings suggest that high numbers of PD-L1-expressing cells within tumor tissue prior to MKI treatment can serve as a biomarker to predict favorable PFS in HCC patients.

Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes.

Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify patient-specific cancer driver genes, we first generated patient-specific gene networks before using modified PageRank to generate feature vectors that represented the impacts genes had on the patient-specific gene network. Subsequently, the feature vectors of the good and poor prognosis groups were used to train the deep feedforward network. For the 11 cancer types in the TCGA data, the proposed method showed a significantly better prediction performance than the existing state-of-the-art methods for three cancer types (BRCA, CESC and PAAD), better performance for five cancer types (COAD, ESCA, HNSC, KIRC and STAD), and a similar or slightly worse performance for the remaining three cancer types (BLCA, LIHC and LUAD). Furthermore, the case study for the identified breast cancer and cervical squamous cell carcinoma prognostic genes and their subnetworks included several pathways associated with the progression of breast cancer and cervical squamous cell carcinoma. These results suggested that heterogeneous cancer driver information may be associated with cancer prognosis.

Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder.

In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed model was the Stacked Conditional Variation AutoEncoder (Stack-CVAE), which acts as an agent in reinforcement learning so that the resulting chemical formulas have the desired chemical properties and show high binding affinity with specific target proteins. We generated 1000 chemical formulas using the chemical properties of sorafenib and the three target kinases of sorafenib. Then, we confirmed that Stack-CVAE generates more of the valid and unique chemical compounds that have the desired chemical properties and predicted binding affinity better than other generative models. More detailed analysis for 100 of the top scoring molecules show that they are novel ones not found in existing chemical databases. Moreover, they reveal significantly higher predicted binding affinity score for Raf kinases than for other kinases. Furthermore, they are highly druggable and synthesizable.

Blood-based biomarkers for immune-based therapy in advanced HCC: Promising but a long way to go.

The introduction of immune checkpoint inhibitors (ICIs) represents a key shift in the management strategy for patients with hepatocellular carcinoma (HCC). However, there is a paucity of predictive biomarkers that facilitate the identification of patients that would respond to ICI therapy. Although several researchers have attempted to resolve the issue, the data is insufficient to alter daily clinical practice. The use of minimally invasive procedures to obtain patient-derived specimen, such as using blood-based samples, is increasingly preferred. Circulating tumor DNA (ctDNA) can be isolated from the blood of cancer patients, and liquid biopsies can provide sufficient material to enable ongoing monitoring of HCC. This is particularly significant for patients for whom surgery is not indicated, including those with advanced HCC. In this review, we summarize the current state of understanding of blood-based biomarkers for ICI-based therapy in advanced HCC, which is promising despite there is still a long way to go.

Intrahepatic inflammatory IgA+PD-L1high monocytes in hepatocellular carcinoma development and immunotherapy.

BACKGROUND: IgA neutralizes pathogens to prevent infection at mucosal sites. However, emerging evidence shows that IgA contributes to aggravating inflammation or dismantling antitumor immunity in human diseased liver. The aim of this study was to elucidate the roles of inflammation-induced intrahepatic inflammatory IgA+ monocytes in the development of hepatocellular carcinoma (HCC). METHODS: Patient cohorts including steatohepatitis cohort (n=61) and HCC cohort (n=271) were established. Patients' surgical and biopsy specimens were analyzed using immunohistochemistry. Multicolor flow cytometry was performed with a subset of patient samples. Single-cell RNA-Seq analysis was performed using Gene Expression Omnibus (GEO) datasets. Additionally, we performed in vitro differentiation of macrophages, stimulation with coated IgA, and RNA sequencing. Hepa1-6 cells and C57BL/6N mice were used to obtain HCC syngeneic mouse models. RESULTS: Serum IgA levels were associated (p<0.001) with fibrosis progression and HCC development in patients with chronic liver diseases. Additionally, immunohistochemical staining of inflamed livers or HCC revealed IgA positivity in monocytes, with a correlation between IgA+ cell frequency and IgA serum levels. Compared with IgA- monocytes, intrahepatic IgA+ monocytes expressed higher levels of programmed death-ligand 1 (PD-L1) in inflamed livers and in HCC tumor microenvironment. Single-cell RNA sequencing using NCBI GEO database indicated an upregulation in inflammation-associated genes in the monocytes of patients whose plasma cell IGHA1 expression was greater than or equal to the median value. Bulk RNA sequencing demonstrated that in vitro stimulation of M2-polarized macrophages using coated IgA complex induced PD-L1 upregulation via YAP-mediated signaling. In vivo blockade of IgA signaling decreased the number of tumor-infiltrating IgA+PD-L1high macrophages and increased the number of CD69+CD8+ T cells to enhance antitumor effects in HCC mice models. CONCLUSIONS: Overall, the findings of this study showed that serum IgA levels was correlated with intrahepatic and intratumoral infiltration of inflammatory IgA+PD-L1high monocytes in chronic liver diseases and HCC, providing potential therapeutic targets.

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein-ligand complex is ongoing. RESULTS: In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein-ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein-ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. CONCLUSIONS: We confirmed that an attention mechanism can capture the binding sites in a protein-ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA .

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN.

Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especially deep learning-based methods have been receiving increasing attention, mostly owing to their good prediction accuracy in many applications. Machine learning methods can be applied to high throughput DNA mutation or RNA expression data to predict cancer stage. However, because the number of genes or markers generally exceeds 10,000, a considerable number of data samples is required to guarantee high prediction accuracy. To solve this problem of a small number of clinical samples, we used a Generative Adversarial Networks (GANs) to augment the samples. Because GANs are not effective with whole genes, we first selected significant genes using DNA mutation data and random forest feature ranking. Next, RNA expression data for selected genes were expanded using GANs. We compared the classification accuracies using original dataset and expanded datasets generated by proposed and existing methods, using random forest, Deep Neural Networks (DNNs), and 1-Dimensional Convolutional Neural Networks (1DCNN). When using the 1DCNN, the F1 score of GAN5 (a 5-fold increase in data) was improved by 39% in relation to the original data. Moreover, the results using only 30% of the data were better than those using all of the data. Our attempt is the first to use GAN for augmentation using numeric data for both DNA and RNA. The augmented datasets obtained using the proposed method demonstrated significantly increased classification accuracy for most cases. By using GAN and 1DCNN in the prediction of cancer stage, we confirmed that good results can be obtained even with small amounts of samples, and it is expected that a great deal of the cost and time required to obtain clinical samples will be reduced. The proposed sample augmentation method could also be applied for other purposes, such as prognostic prediction or cancer classification.

GVES: machine learning model for identification of prognostic genes with a small dataset.

Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited prediction accuracy for cancer outcomes, primarily owing to small sample numbers and relatively large number of features. In this paper, we provide a description of GVES (Gene Vector for Each Sample), a proposed machine learning model that can be efficiently leveraged even with a small sample size, to increase the accuracy of identification of genes with prognostic value. GVES, an adaptation of the continuous bag of words (CBOW) model, generates vector representations of all genes for all samples by leveraging gene expression and biological network data. GVES clusters samples using their gene vectors, and identifies genes that divide samples into good and poor outcome groups for the prediction of cancer outcomes. Because GVES generates gene vectors for each sample, the sample size effect is reduced. We applied GVES to six cancer types and demonstrated that GVES outperformed existing machine learning methods, particularly for cancer datasets with a small number of samples. Moreover, the genes identified as prognosticators were shown to reside within a number of significant prognostic genetic pathways associated with pancreatic cancer.

RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.

Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine learning models have been developed for prediction of drug responses using various genomic data and diverse drug molecular information, but those methods are ineffective to predict drug response to untrained drugs and gene expression patterns, which is known as the cold-start problem. In this study, we present a novel deep neural network model, termed RefDNN, for improved prediction of drug resistance and identification of biomarkers related to drug response. RefDNN exploits a collection of drugs, called reference drugs, to learn representations for a high-dimensional gene expression vector and a molecular structure vector of a drug and predicts drug response labels using the reference drug-based representations. These calculations come from the observation that similar chemicals have similar effects. The proposed model not only outperformed existing computational prediction models in most comparative experiments, but also showed more robust prediction for untrained drugs and cancer types than traditional machine learning models. RefDNN exploits the ElasticNet regularization to deal with high-dimensional gene expression data, which allows identification of gene markers associated with drug resistance. Lastly, we described an application of RefDNN in exploring a new candidate drug for liver cancer. As the proposed model can guarantee good prediction of drug responses to untrained drugs for given gene expression patterns, it may be of potential benefit in drug repositioning and personalized medicine.

Novel deep learning model for more accurate prediction of drug-drug interaction effects.

BACKGROUND: Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or in vitro, but room remains for improvement in prediction performance. RESULTS: In this study, we propose a novel deep learning model to predict the effect of DDIs more accurately.. The proposed model uses autoencoders and a deep feed-forward network that are trained using the structural similarity profiles (SSP), Gene Ontology (GO) term similarity profiles (GSP), and target gene similarity profiles (TSP) of known drug pairs to predict the pharmacological effects of DDIs. The results show that GSP and TSP increase the prediction accuracy when using SSP alone, and the autoencoder is more effective than PCA for reducing the dimensions of each profile. Our model showed better performance than the existing methods, and identified a number of novel DDIs that are supported by medical databases or existing research. CONCLUSIONS: We present a novel deep learning model for more accurate prediction of DDIs and their effects, which may assist in future research to discover novel DDIs and their pharmacological effects.

RN+: A Novel Biclustering Algorithm for Analysis of Gene Expression Data Using Protein-Protein Interaction Network.

Biclustering is a process of finding groups of genes that behave similarly under a subset of conditions. In this article, we propose an efficient biclustering algorithm, namely RN+, to identify biologically meaningful biclusters in gene expression data. The RN+ algorithm finds biologically meaningful biclusters through a novel gene filtering using protein-protein interaction network, gene searching, gene grouping, and queuing process. It also efficiently removes duplicate biclusters. We tested the proposed RN+ on five real microarray datasets, and compared its performance with seven competitive biclustering algorithms. The experimental results show that RN+ efficiently finds functionally enriched and biologically meaningful biclusters for large gene expression datasets, and outperforms the other tested biclustering algorithms on real datasets.

DockerBIO: web application for efficient use of bioinformatics Docker images.

BACKGROUND AND OBJECTIVE: Docker is a light containerization program that shows almost the same performance as a local environment. Recently, many bioinformatics tools have been distributed as Docker images that include complex settings such as libraries, configurations, and data if needed, as well as the actual tools. Users can simply download and run them without making the effort to compile and configure them, and can obtain reproducible results. In spite of these advantages, several problems remain. First, there is a lack of clear standards for distribution of Docker images, and the Docker Hub often provides multiple images with the same objective but different uses. For these reasons, it can be difficult for users to learn how to select and use them. Second, Docker images are often not suitable as a component of a pipeline, because many of them include big data. Moreover, a group of users can have difficulties when sharing a pipeline composed of Docker images. Users of a group may modify scripts or use different versions of the data, which causes inconsistent results. METHODS AND RESULTS: To handle the problems described above, we developed a Java web application, DockerBIO, which provides reliable, verified, light-weight Docker images for various bioinformatics tools and for various kinds of reference data. With DockerBIO, users can easily build a pipeline with tools and data registered at DockerBIO, and if necessary, users can easily register new tools or data. Built pipelines are registered in DockerBIO, which provides an efficient running environment for the pipelines registered at DockerBIO. This enables user groups to run their pipelines without expending much effort to copy and modify them.

An Improved Method for Prediction of Cancer Prognosis by Network Learning.

Accurate identification of prognostic biomarkers is an important yet challenging goal in bioinformatics. Many bioinformatics approaches have been proposed for this purpose, but there is still room for improvement. In this paper, we propose a novel machine learning-based method for more accurate identification of prognostic biomarker genes and use them for prediction of cancer prognosis. The proposed method specifies the candidate prognostic gene module by graph learning using the generative adversarial networks (GANs) model, and scores genes using a PageRank algorithm. We applied the proposed method to multiple-omics data that included copy number, gene expression, DNA methylation, and somatic mutation data for five cancer types. The proposed method showed better prediction accuracy than did existing methods. We identified many prognostic genes and their roles in their biological pathways. We also showed that the genes identified from different omics data were complementary, which led to improved accuracy in prediction using multi-omics data.

PISTON: Predicting drug indications and side effects using topic modeling and natural language processing.

The process of discovering novel drugs to treat diseases requires a long time and high cost. It is important to understand side effects of drugs as well as their therapeutic effects, because these can seriously damage the patients due to unexpected actions of the derived candidate drugs. In order to overcome these limitations, computational methods for predicting the therapeutic effects and side effects have been proposed. In particular, text mining is a widely used technique in the field of systems biology, because it can discover hidden relationships between drugs, genes and diseases from a large amount of literature data. Compared with in vivo/in vitro experiments, text mining derives meaningful results with less time and cost. In this study, we propose an algorithm for predicting novel drug-phenotype associations and drug-side effect associations using topic modeling and natural language processing (NLP). We extract sentences in which drugs and genes co-occur from the abstracts of the literature and identify words that describe the relationship between them using NLP. Considering the characteristics of the identified words, we determine if the drug has an up-regulation effect or a down-regulation effect on the gene. Based on genes that affect drugs and their regulatory relationships, we group the frequently occurring genes and regulatory relationships into topics, and build a drug-topic probability matrix by calculating the score that the drug will have a topic using topic modeling. Using the matrix, a classifier is constructed for predicting the novel indications and side effects of drugs considering the characteristics of known drug-phenotype associations or drug-side effect associations. The proposed method predicts both indications and side effects with a single algorithm, and it can exclude drugs with serious side effects or side effects that patients do not want to experience from among the candidate drugs provided for the treatment of the phenotype. Furthermore, lists of novel candidate drugs for phenotypes and side effects can be continuously updated with our algorithm every time a document is added. More than a thousand documents are produced per day, and it is possible for our algorithm to efficiently derive candidate drugs because it requires less cost than the existing drug repositioning methods. The resource of PISTON is available at databio.gachon.ac.kr/tools/PISTON.

G2Vec: Distributed gene representations for identification of cancer prognostic genes.

Identification of cancer prognostic genes is important in that it can lead to accurate outcome prediction and better therapeutic trials for cancer patients. Many computational approaches have been proposed to achieve this goal; however, there is room for improvement. Recent developments in deep learning techniques can aid in the identification of better prognostic genes and more accurate outcome prediction, but one of the main problems in the adoption of deep learning for this purpose is that data from cancer patients have too many dimensions, while the number of samples is relatively small. In this study, we propose a novel network-based deep learning method to identify prognostic gene signatures via distributed gene representations generated by G2Vec, which is a modified Word2Vec model originally used for natural language processing. We applied the proposed method to five cancer types including liver cancer and showed that G2Vec outperformed extant feature selection methods, especially for small number of samples. Moreover, biomarkers identified by G2Vec was useful to find significant prognostic gene modules associated with hepatocellular carcinoma.

Coregulation of alternative splicing by hnRNPM and ESRP1 during EMT.

The epithelial-mesenchymal transition (EMT) is a fundamental developmental process that is abnormally activated in cancer metastasis. Dynamic changes in alternative splicing occur during EMT. ESRP1 and hnRNPM are splicing regulators that promote an epithelial splicing program and a mesenchymal splicing program, respectively. The functional relationships between these splicing factors in the genome scale remain elusive. Comparing alternative splicing targets of hnRNPM and ESRP1 revealed that they coregulate a set of cassette exon events, with the majority showing discordant splicing regulation. Discordant splicing events regulated by hnRNPM show a positive correlation with splicing during EMT; however, concordant events do not, indicating the role of hnRNPM in regulating alternative splicing during EMT is more complex than previously understood. Motif enrichment analysis near hnRNPM-ESRP1 coregulated exons identifies guanine-uridine rich motifs downstream from hnRNPM-repressed and ESRP1-enhanced exons, supporting a general model of competitive binding to these cis-elements to antagonize alternative splicing. The set of coregulated exons are enriched in genes associated with cell migration and cytoskeletal reorganization, which are pathways associated with EMT. Splicing levels of coregulated exons are associated with breast cancer patient survival and correlate with gene sets involved in EMT and breast cancer subtyping. This study identifies complex modes of interaction between hnRNPM and ESRP1 in regulation of splicing in disease-relevant contexts.

Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.

Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

MOTIVATION: Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. RESULTS: To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/mathcom/CPR. CONTACT: jgahn@inu.ac.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model.

Adverse drug reactions (ADRs) are one of the major concerns threatening public health and have resulted in failures in drug development. Thus, predicting ADRs and discovering the mechanisms underlying ADRs have become important tasks in pharmacovigilance. Identification of potential ADRs by computational approaches in the early stages would be advantageous in drug development. Here we propose a computational method that elucidates the action mechanisms of ADRs and predicts potential ADRs by utilizing ADR genes, drug features, and protein-protein interaction (PPI) networks. If some ADRs share similar features, there is a high possibility that they may appear together in a drug and share analogous mechanisms. Proceeding from this assumption, we clustered ADRs according to interactions of ADR genes in the PPI networks and the frequency of co-occurrence of ADRs in drugs. ADR clusters were verified based on a side effect database and literature data regarding whether ADRs have relevance to other ADRs in the same cluster. Gene networks shared by ADRs in each cluster were constructed by cumulating the shortest paths between drug target genes and ADR genes in the PPI network. We developed a classification model to predict potential ADRs using these gene networks shared by ADRs and calculated cross-validation AUC (area under the curve) values for each ADR cluster. In addition, in order to demonstrate correlations between gene networks shared by ADRs and ADRs in a cluster, we applied the Wilcoxon rank sum statistical test to the literature data and results of a Google query search. We attained statistically meaningful p-values (<0.05) for every ADR cluster. The results suggest that our approach provides insights into discovering the action mechanisms of ADRs and is a novel attempt to predict ADRs in a biological aspect.