Synthetic lethality-mediated precision oncology via the tumor transcriptome.

Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.

Altered tumor signature and T-cell profile after chemotherapy reveal new therapeutic opportunities in high-grade serous ovarian carcinoma.

Chemotherapy combined with debulking surgery is the standard treatment protocol for high-grade serous ovarian carcinoma (HGSOC). Nonetheless, a significant number of patients encounter relapse due to the development of chemotherapy resistance. To better understand and address this resistance, we conducted a comprehensive study investigating the transcriptional alterations at the single-cell resolution in tissue samples from patients with HGSOC, using single-cell RNA sequencing and T-cell receptor sequencing techniques. Our analyses unveiled notable changes in the tumor signatures after chemotherapy, including those associated with epithelial-mesenchymal transition and cell cycle arrest. Within the immune compartment, we observed alterations in the T-cell profiles, characterized by naïve or pre-exhausted populations following chemotherapy. This phenotypic change was further supported by the examination of adjoining T-cell receptor clonotypes in paired longitudinal samples. These findings underscore the profound impact of chemotherapy on reshaping the tumor landscape and the immune microenvironment. This knowledge may provide clues for the development of future therapeutic strategies to combat treatment resistance in HGSOC.

Genomic characteristics of invasive mucinous adenocarcinoma of the lung with multiple pulmonary sites of involvement.

Invasive mucinous adenocarcinoma (IMA) of the lung frequently presents with diffuse pneumonic-type features or multifocal lesions, which are regarded as a pattern of intrapulmonary metastases. However, the genomics of multifocal IMAs have not been well studied. We performed whole exome sequencing on samples taken from 2 to 5 regions in seven patients with synchronous multifocal IMAs of the lung (24 regions total). Early initiating driver events, such as KRAS, NKX2-1, TP53, or ARID1A mutations, are clonal mutations and were present in all multifocal IMAs in each patient. The tumor mutational burden of multifocal IMAs was low (mean: 1.13/mega base), but further analyses suggested intra-tumor heterogeneity. The mutational signature analysis found that IMAs were predominantly associated with endogenous mutational process (signature 1), APOBEC activity (signatures 2 and 13), and defective DNA mismatch repair (signature 6), but not related to smoking signature. IMAs synchronously located in the bilateral lower lobes of two patients with background usual interstitial pneumonia had different mutation types, suggesting that they were double primaries. In conclusion, genomic evidence found in this study indicated the clonal intrapulmonary spread of diffuse pneumonic-type or multifocal IMAs, although they can occur in multicentric origins in the background of usual interstitial pneumonia. IMAs exhibited a heterogeneous genomic landscape despite the low somatic mutation burden. Further studies are warranted to determine the clinical significance of the genomic characteristics of IMAs in expanded cohorts.

miR-22-3p and miR-30e-5p Are Associated with Prognosis in Cervical Squamous Cell Carcinoma.

Alteration in expression of miRNAs can cause various malignant changes and the metastatic process. Our aim was to identify the miRNAs involved in cervical squamous cell carcinoma (SqCC) and metastasis, and to test their utility as indicators of metastasis and survival. Using microarray technology, we performed miRNA expression profiling on primary cervical SqCC tissue (n = 6) compared with normal control (NC) tissue and compared SqCC that had (SqC-M; n = 3) and had not (SqC-NM; n = 3) metastasized. Four miRNAs were selected for validation by qRT-PCR on 29 SqC-NM and 27 SqC-M samples, and nine metastatic lesions (ML-SqC), from a total of 56 patients. Correlation of miRNA expression and clinicopathological parameters was analyzed to evaluate the clinical impact of candidate miRNAs. We found 40 miRNAs differentially altered in cervical SqCC tissue: 21 miRNAs were upregulated and 19 were downregulated (≥2-fold, p < 0.05). Eight were differentially altered in SqC-M compared with SqC-NM samples: four were upregulated (miR-494, miR-92a-3p, miR-205-5p, and miR-221-3p), and four were downregulated (miR-574-3p, miR-4769-3p, miR-1281, and miR-1825) (≥1.5-fold, p < 0.05). MiR-22-3p might be a metastamiR, which was gradually further downregulated in SqC-NM > SqC-M > ML-SqC. Downregulation of miR-30e-5p significantly correlated with high stage, lymph node metastasis, and low survival rate, suggesting an independent poor prognostic factor.

HumanNet v2: human gene networks for disease research.

Human gene networks have proven useful in many aspects of disease research, with numerous network-based strategies developed for generating hypotheses about gene-disease-drug associations. The ability to predict and organize genes most relevant to a specific disease has proven especially important. We previously developed a human functional gene network, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes. Here, we present HumanNet v2 (http://www.inetbio.org/humannet), a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms. HumanNet now comprises a hierarchy of human gene networks, allowing for more flexible incorporation of network information into studies. HumanNet performs well in ranking disease-linked gene sets with minimal literature-dependent biases. We observe that incorporating model organisms' protein-protein interactions does not markedly improve disease gene predictions, suggesting that many of the disease gene associations are now captured directly in human-derived datasets. With an improved interactive user interface for disease network analysis, we expect HumanNet will be a useful resource for network medicine.

SoyNet: a database of co-functional networks for soybean Glycine max.

Soybean (Glycine max) is a legume crop with substantial economic value, providing a source of oil and protein for humans and livestock. More than 50% of edible oils consumed globally are derived from this crop. Soybean plants are also important for soil fertility, as they fix atmospheric nitrogen by symbiosis with microorganisms. The latest soybean genome annotation (version 2.0) lists 56 044 coding genes, yet their functional contributions to crop traits remain mostly unknown. Co-functional networks have proven useful for identifying genes that are involved in a particular pathway or phenotype with various network algorithms. Here, we present SoyNet (available at www.inetbio.org/soynet), a database of co-functional networks for G. max and a companion web server for network-based functional predictions. SoyNet maps 1 940 284 co-functional links between 40 812 soybean genes (72.8% of the coding genome), which were inferred from 21 distinct types of genomics data including 734 microarrays and 290 RNA-seq samples from soybean. SoyNet provides a new route to functional investigation of the soybean genome, elucidating genes and pathways of agricultural importance.

COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH).

The use of high-throughput array and sequencing technologies has produced unprecedented amounts of gene expression data in central public depositories, including the Gene Expression Omnibus (GEO). The immense amount of expression data in GEO provides both vast research opportunities and data analysis challenges. Co-expression analysis of high-dimensional expression data has proven effective for the study of gene functions, and several co-expression databases have been developed. Here, we present a new co-expression database, COEXPEDIA (www.coexpedia.org), which is distinctive from other co-expression databases in three aspects: (i) it contains only co-functional co-expressions that passed a rigorous statistical assessment for functional association, (ii) the co-expressions were inferred from individual studies, each of which was designed to investigate gene functions with respect to a particular biomedical context such as a disease and (iii) the co-expressions are associated with medical subject headings (MeSH) that provide biomedical information for anatomical, disease, and chemical relevance. COEXPEDIA currently contains approximately eight million co-expressions inferred from 384 and 248 GEO series for humans and mice, respectively. We describe how these MeSH-associated co-expressions enable the identification of diseases and drugs previously unknown to be related to a gene or a gene group of interest.

GWAB: a web server for the network-based boosting of human genome-wide association data.

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10-8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

Function-driven discovery of disease genes in zebrafish using an integrated genomics big data resource.

Whole exome sequencing (WES) accelerates disease gene discovery using rare genetic variants, but further statistical and functional evidence is required to avoid false-discovery. To complement variant-driven disease gene discovery, here we present function-driven disease gene discovery in zebrafish (Danio rerio), a promising human disease model owing to its high anatomical and genomic similarity to humans. To facilitate zebrafish-based function-driven disease gene discovery, we developed a genome-scale co-functional network of zebrafish genes, DanioNet (www.inetbio.org/danionet), which was constructed by Bayesian integration of genomics big data. Rigorous statistical assessment confirmed the high prediction capacity of DanioNet for a wide variety of human diseases. To demonstrate the feasibility of the function-driven disease gene discovery using DanioNet, we predicted genes for ciliopathies and performed experimental validation for eight candidate genes. We also validated the existence of heterozygous rare variants in the candidate genes of individuals with ciliopathies yet not in controls derived from the UK10K consortium, suggesting that these variants are potentially involved in enhancing the risk of ciliopathies. These results showed that an integrated genomics big data for a model animal of diseases can expand our opportunity for harnessing WES data in disease gene discovery.

MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates.

Laboratory mouse, Mus musculus, is one of the most important animal tools in biomedical research. Functional characterization of the mouse genes, hence, has been a long-standing goal in mammalian and human genetics. Although large-scale knockout phenotyping is under progress by international collaborative efforts, a large portion of mouse genome is still poorly characterized for cellular functions and associations with disease phenotypes. A genome-scale functional network of mouse genes, MouseNet, was previously developed in context of MouseFunc competition, which allowed only limited input data for network inferences. Here, we present an improved mouse co-functional network, MouseNet v2 (available at http://www.inetbio.org/mousenet), which covers 17 714 genes (>88% of coding genome) with 788 080 links, along with a companion web server for network-assisted functional hypothesis generation. The network database has been substantially improved by large expansion of genomics data. For example, MouseNet v2 database contains 183 co-expression networks inferred from 8154 public microarray samples. We demonstrated that MouseNet v2 is predictive for mammalian phenotypes as well as human diseases, which suggests its usefulness in discovery of novel disease genes and dissection of disease pathways. Furthermore, MouseNet v2 database provides functional networks for eight other vertebrate models used in various research fields.

An integrated systems biology approach identifies positive cofactor 4 as a factor that increases reprogramming efficiency.

Spermatogonial stem cells (SSCs) can spontaneously dedifferentiate into embryonic stem cell (ESC)-like cells, which are designated as multipotent SSCs (mSSCs), without ectopic expression of reprogramming factors. Interestingly, SSCs express key pluripotency genes such as Oct4, Sox2, Klf4 and Myc. Therefore, molecular dissection of mSSC reprogramming may provide clues about novel endogenous reprogramming or pluripotency regulatory factors. Our comparative transcriptome analysis of mSSCs and induced pluripotent stem cells (iPSCs) suggests that they have similar pluripotency states but are reprogrammed via different transcriptional pathways. We identified 53 genes as putative pluripotency regulatory factors using an integrated systems biology approach. We demonstrated a selected candidate, Positive cofactor 4 (Pc4), can enhance the efficiency of somatic cell reprogramming by promoting and maintaining transcriptional activity of the key reprograming factors. These results suggest that Pc4 has an important role in inducing spontaneous somatic cell reprogramming via up-regulation of key pluripotency genes.

AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species.

Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes. We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes. More recently, we have observed significant growth in the availability of omics data for A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance the integrated database of co-functional networks. Here, we present an updated co-functional gene network for A. thaliana, AraNet v2 (available at http://www.inetbio.org/aranet), which covers approximately 84% of the coding genome. We demonstrate significant improvements in both genome coverage and accuracy. To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.

FlyNet: a versatile network prioritization server for the Drosophila community.

Drosophila melanogaster (fruit fly) has been a popular model organism in animal genetics due to the high accessibility of reverse-genetics tools. In addition, the close relationship between the Drosophila and human genomes rationalizes the use of Drosophila as an invertebrate model for human neurobiology and disease research. A platform technology for predicting candidate genes or functions would further enhance the usefulness of this long-established model organism for gene-to-phenotype mapping. Recently, the power of network prioritization for gene-to-phenotype mapping has been demonstrated in many organisms. Here we present a network prioritization server dedicated to Drosophila that covers ∼95% of the coding genome. This server, dubbed FlyNet, has several distinctive features, including (i) prioritization for both genes and functions; (ii) two complementary network algorithms: direct neighborhood and network diffusion; (iii) spatiotemporal-specific networks as an additional prioritization strategy for traits associated with a specific developmental stage or tissue and (iv) prioritization for human disease genes. FlyNet is expected to serve as a versatile hypothesis-generation platform for genes and functions in the study of basic animal genetics, developmental biology and human disease. FlyNet is available for free at http://www.inetbio.org/flynet.

RiceNet v2: an improved network prioritization server for rice genes.

Rice is the most important staple food crop and a model grass for studies of bioenergy crops. We previously published a genome-scale functional network server called RiceNet, constructed by integrating diverse genomics data and demonstrated the use of the network in genetic dissection of rice biotic stress responses and its usefulness for other grass species. Since the initial construction of the network, there has been a significant increase in the amount of publicly available rice genomics data. Here, we present an updated network prioritization server for Oryza sativa ssp. japonica, RiceNet v2 (http://www.inetbio.org/ricenet), which provides a network of 25 765 genes (70.1% of the coding genome) and 1 775 000 co-functional links. Ricenet v2 also provides two complementary methods for network prioritization based on: (i) network direct neighborhood and (ii) context-associated hubs. RiceNet v2 can use genes of the related subspecies O. sativa ssp. indica and the reference plant Arabidopsis for versatility in generating hypotheses. We demonstrate that RiceNet v2 effectively identifies candidate genes involved in rice root/shoot development and defense responses, demonstrating its usefulness for the grass research community.

MORPHIN: a web tool for human disease research by projecting model organism biology onto a human integrated gene network.

Despite recent advances in human genetics, model organisms are indispensable for human disease research. Most human disease pathways are evolutionally conserved among other species, where they may phenocopy the human condition or be associated with seemingly unrelated phenotypes. Much of the known gene-to-phenotype association information is distributed across diverse databases, growing rapidly due to new experimental techniques. Accessible bioinformatics tools will therefore facilitate translation of discoveries from model organisms into human disease biology. Here, we present a web-based discovery tool for human disease studies, MORPHIN (model organisms projected on a human integrated gene network), which prioritizes the most relevant human diseases for a given set of model organism genes, potentially highlighting new model systems for human diseases and providing context to model organism studies. Conceptually, MORPHIN investigates human diseases by an orthology-based projection of a set of model organism genes onto a genome-scale human gene network. MORPHIN then prioritizes human diseases by relevance to the projected model organism genes using two distinct methods: a conventional overlap-based gene set enrichment analysis and a network-based measure of closeness between the query and disease gene sets capable of detecting associations undetectable by the conventional overlap-based methods. MORPHIN is freely accessible at http://www.inetbio.org/morphin.

YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae.

Saccharomyces cerevisiae, i.e. baker's yeast, is a widely studied model organism in eukaryote genetics because of its simple protocols for genetic manipulation and phenotype profiling. The high abundance of publicly available data that has been generated through diverse 'omics' approaches has led to the use of yeast for many systems biology studies, including large-scale gene network modeling to better understand the molecular basis of the cellular phenotype. We have previously developed a genome-scale gene network for yeast, YeastNet v2, which has been used for various genetics and systems biology studies. Here, we present an updated version, YeastNet v3 (available at http://www.inetbio.org/yeastnet/), that significantly improves the prediction of gene-phenotype associations. The extended genome in YeastNet v3 covers up to 5818 genes (∼99% of the coding genome) wired by 362 512 functional links. YeastNet v3 provides a new web interface to run the tools for network-guided hypothesis generations. YeastNet v3 also provides edge information for all data-specific networks (∼2 million functional links) as well as the integrated networks. Therefore, users can construct alternative versions of the integrated network by applying their own data integration algorithm to the same data-specific links.

WormNet v3: a network-assisted hypothesis-generating server for Caenorhabditis elegans.

High-throughput experimental technologies gradually shift the paradigm of biological research from hypothesis-validation toward hypothesis-generation science. Translating diverse types of large-scale experimental data into testable hypotheses, however, remains a daunting task. We previously demonstrated that heterogeneous genomics data can be integrated into a single genome-scale gene network with high prediction power for ribonucleic acid interference (RNAi) phenotypes in Caenorhabditis elegans, a popular metazoan model in the study of developmental biology, neurobiology and genetics. Here, we present WormNet version 3 (v3), which is a new network-assisted hypothesis-generating server for C. elegans. WormNet v3 includes major updates to the base gene network, which substantially improved predictions of RNAi phenotypes. The server generates various gene network-based hypotheses using three complementary network methods: (i) a phenotype-centric approach to 'find new members for a pathway'; (ii) a gene-centric approach to 'infer functions from network neighbors' and (iii) a context-centric approach to 'find context-associated hub genes', which is a new method to identify key genes that mediate physiology within a specific context. For example, we demonstrated that the context-centric approach can be used to identify potential molecular targets of toxic chemicals. WormNet v3 is freely accessible at http://www.inetbio.org/wormnet.

Genetic dissection of the biotic stress response using a genome-scale gene network for rice.

Rice is a staple food for one-half the world's population and a model for other monocotyledonous species. Thus, efficient approaches for identifying key genes controlling simple or complex traits in rice have important biological, agricultural, and economic consequences. Here, we report on the construction of RiceNet, an experimentally tested genome-scale gene network for a monocotyledonous species. Many different datasets, derived from five different organisms including plants, animals, yeast, and humans, were evaluated, and 24 of the most useful were integrated into a statistical framework that allowed for the prediction of functional linkages between pairs of genes. Genes could be linked to traits by using guilt-by-association, predicting gene attributes on the basis of network neighbors. We applied RiceNet to an important agronomic trait, the biotic stress response. Using network guilt-by-association followed by focused protein-protein interaction assays, we identified and validated, in planta, two positive regulators, LOC_Os01g70580 (now Regulator of XA21; ROX1) and LOC_Os02g21510 (ROX2), and one negative regulator, LOC_Os06g12530 (ROX3). These proteins control resistance mediated by rice XA21, a pattern recognition receptor. We also showed that RiceNet can accurately predict gene function in another major monocotyledonous crop species, maize. RiceNet thus enables the identification of genes regulating important crop traits, facilitating engineering of pathways critical to crop productivity.

Towards establishment of a rice stress response interactome.

Rice (Oryza sativa) is a staple food for more than half the world and a model for studies of monocotyledonous species, which include cereal crops and candidate bioenergy grasses. A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30%-60% yield losses globally each year. To elucidate stress response signaling networks, we constructed an interactome of 100 proteins by yeast two-hybrid (Y2H) assays around key regulators of the rice biotic and abiotic stress responses. We validated the interactome using protein-protein interaction (PPI) assays, co-expression of transcripts, and phenotypic analyses. Using this interactome-guided prediction and phenotype validation, we identified ten novel regulators of stress tolerance, including two from protein classes not previously known to function in stress responses. Several lines of evidence support cross-talk between biotic and abiotic stress responses. The combination of focused interactome and systems analyses described here represents significant progress toward elucidating the molecular basis of traits of agronomic importance.