Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

PURPOSE: The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community. EXPERIMENTAL DESIGN: We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models. RESULTS: We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia. CONCLUSIONS: Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

A framework towards digital twins for type 2 diabetes.

Introduction: A digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes. Methods: Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic-disease relationships. Results and discussion: Our findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Progress toward a universal biomedical data translator.

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

Synthetic lethal kinases in Ras/p53 mutant squamous cell carcinoma.

The oncogene Ras and the tumor suppressor gene p53 are frequently co-mutated in human cancer and mutations in Ras and p53 can cooperate to generate a more malignant cell state. To discover novel druggable targets for cancers carrying co-mutations in Ras and p53, we performed arrayed, kinome focused siRNA and oncology drug phenotypic screening utilizing a set of syngeneic Ras mutant squamous cell carcinoma (SCC) cell lines that also carried co-mutations in selected p53 pathway genes. These cell lines were derived from SCCs from carcinogen-treated inbred mice which harbored germline deletions or mutations in Trp53, p19Arf, Atm, or Prkdc. Both siRNA and drug phenotypic screening converge to implicate the phosphoinositol kinases, receptor tyrosine kinases, MAP kinases, as well as cell cycle and DNA damage response genes as targetable dependencies in SCC. Differences in functional kinome profiles between Ras mutant cell lines reflect incomplete penetrance of Ras synthetic lethal kinases and indicate that co-mutations cause a rewiring of survival pathways in Ras mutant tumors. This study describes the functional kinomic landscape of Ras/p53 mutant chemically-induced squamous cell carcinoma in both the baseline unperturbed state and following DNA damage and nominates candidate therapeutic targets, including the Nek4 kinase, for further development.

ToPP: Tumor online prognostic analysis platform for prognostic feature selection and clinical patient subgroup selection.

Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php.

A functional module states framework reveals transcriptional states for drug and target prediction.

Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery.

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.

Coordinated silencing of the Sp1-mediated long noncoding RNA MEG3 by EZH2 and HDAC3 as a prognostic factor in pancreatic ductal adenocarcinoma.

Objective: Pancreatic ductal adenocarcinoma (PDAC) is a disease with high mortality. Many so-called "junk" noncoding RNAs need to be discovered in PDAC. The purpose of this study was therefore to investigate the function and regulatory mechanism of the long noncoding RNA MEG3 in PDAC. Methods: The Gene Expression Omnibus database (GEO database) was used to determine the differential expression of long noncoding RNAs in PDAC, and MEG3 was selected for subsequent verification. Tissue and cell samples were used to verify MEG3 expression, followed by functional detection in vitro and in vivo. Microarrays were used to characterize long noncoding RNA and mRNA expression profiles. Competing endogenous RNA analyses were used to detect differential MEG3 and relational miRNA expression in PDAC. Finally, promoter analyses were conducted to explain the downregulation of MEG3 PDAC. Results: We generated a catalogue of PDAC-associated long noncoding RNAs in the GEO database. The ectopic expression of MEG3 inhibited PDAC growth and metastasis in vitro and in vivo, which was statistically significant (P < 0.05). Microarray analysis showed that multiple microRNAs interacted with MEG3. We also showed that MEG3, as a competing endogenous RNA, directly sponged miR-374a-5p to regulate PTEN expression. The transcription factor, Sp1, recruited EZH2 and HDAC3 to the promoter and transcriptionally repressed MEG3 expression. Finally, clinical data showed that MEG3 and miR-374a-5p expressions were correlated with clinicopathological features. Statistically, Sp1, EZH2, HDAC3, and miR-374a-5p were negatively correlated with MEG3 (P < 0.05). Conclusions: Reduced MEG3 levels played a crucial role in the PDAC malignant phenotype, which provided insight into novel and effective molecular targets of MEG3 for pancreatic cancer treatment.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories.

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 + and CD4 + T cells, and cytotoxic CD4 + T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

Intrinsic Genetic and Transcriptomic Patterns Reflect Tumor Immune Subtypes Facilitating Exploring Possible Combinatory Therapy.

The classification of immune subtypes was based on immune signatures highlighting the tumor immuno-microenvironment. It was found that immune subtypes associated with mutation and expression patterns in the tumor. How the intrinsic genetic and transcriptomic alterations contribute to the immune subtypes and how to select drug combinations from both targeted drugs and immune therapeutic drugs according to different immune subtypes are still not clear. Through statistical analysis of genetic alterations and transcriptional profiles of breast invasive carcinoma (BRCA) samples, we found significant differences in the number of somatic missense mutations and frameshift deletions among the different immune subtypes. The high mutation load for somatic missense mutations and frameshift deletions may be explained by the high frequency of mutations and high expression of DNA double-strand break repair pathway genes. Extensive analysis of signaling pathways in both the genetic and transcriptomic levels reveals significantly altered pathways such as tumor protein Tumor Protein P53 (TP53) and receptor tyrosine kinase (RTK)/RAS signaling pathways among different subtypes. Drug targets in the signaling pathways such as mitogen-activated protein kinase kinase kinase 1 (MAP3K1) and Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) show genetic alteration in specific subtypes, which may be potential targets for patients of a specific subtype. More drug targets which show transcriptional difference among immune subtypes were discovered, such as cyclin-dependent kinase (CDK)4, CDK6, Erb-B2 receptor tyrosine kinase 2 (ERBB2), etc. Moreover, differences in functional activity between tumor growth and immune-related pathways also elucidate the extrinsic factors of differences in prognosis and suggest potential drug combinations for different immune subtypes. These results help to explain how intrinsic alterations are associated with the immune subtypes and provide clues for possible combination therapy for different immune subtypes.

Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data.

BACKGROUND: Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. RESULTS: Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. CONCLUSIONS: Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors.

Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma.

Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia1. Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50-70%2. Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)-high expression of which is a signature specific to the S-III subtype-alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it.

Dual extra-retinal origins of microglia in the model of retinal microglia repopulation.

Elucidating the origin of microglia is crucial for understanding their functions and homeostasis. Previous study has indicated that Nestin-positive progenitor cells differentiate into microglia and replenish the brain after depleting most brain microglia. Microglia have also shown the capacity to repopulate the retina after eliminating all retinal microglia. However, the origin(s) of repopulated retinal microglia is/are unknown. In this study, we aim to investigate the origins of repopulated microglia in the retina. Interestingly, we find that repopulated retinal microglia are not derived from Nestin-positive progenitor cells. Instead, they have two origins: the center-emerging microglia are derived from residual microglia in the optic nerve and the periphery-emerging microglia are derived from macrophages in the ciliary body/iris. Therefore, we have for the first time identified the extra-retinal origins of microglia in the adult mammalian retina by using a model of microglial repopulation, which may shed light on the target exploration of therapeutic interventions for retinal degenerative disorders.

Repopulated microglia are solely derived from the proliferation of residual microglia after acute depletion.

Newborn microglia rapidly replenish the whole brain after selective elimination of most microglia (>99%) in adult mice. Previous studies reported that repopulated microglia were largely derived from microglial progenitor cells expressing nestin in the brain. However, the origin of these repopulated microglia has been hotly debated. In this study, we investigated the origin of repopulated microglia by a series of fate-mapping approaches. We first excluded the blood origin of repopulated microglia via parabiosis. With different transgenic mouse lines, we then demonstrated that all repopulated microglia were derived from the proliferation of the few surviving microglia (<1%). Despite a transient pattern of nestin expression in newly forming microglia, none of repopulated microglia were derived from nestin-positive non-microglial cells. In summary, we conclude that repopulated microglia are solely derived from residual microglia rather than de novo progenitors, suggesting the absence of microglial progenitor cells in the adult brain.

Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles.

OBJECTIVE: Synergistic drug combinations are promising therapies for cancer treatment. However, effective prediction of synergistic drug combinations is quite challenging as mechanisms of drug synergism are still unclear. Various features such as drug response, and target networks may contribute to prediction of synergistic drug combinations. In this study, we aimed to construct a computational model to predict synergistic drug combinations. METHODS: We designed drug physicochemical features and network features, including drug chemical structure similarity, target distance in protein-protein network and targeted pathway similarity. At the same time, we designed fifteen pharmacogenomics features using drug treated gene expression profiles based on the background of cancer-related biology network. Based on these eighteen features, we built a prediction model for Synergistic Drug combination using Random forest algorithm (SyDRa). RESULTS: Our model achieved a quite good performance with AUC value of 0.89 and Out-of-bag estimate error rate of 0.15 in training dataset. Using the random anti-cancer drug combinations which have transcriptional profile data in the Connectivity Map dataset as the testing dataset, we identified 28 potentially synergistic drug combinations, three out of which had been reported to be effective drug combinations by literatures. CONCLUSIONS: We studied eighteen features for drug combinations and built a computational model using random forest algorithm. The model was evaluated using an independent test dataset. Our model provides an efficient strategy to identify potentially synergistic drug combinations for cancer and may help reduce the search space for high-throughput synergistic drug combinations screening.

Biomolecular Network-Based Synergistic Drug Combination Discovery.

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.