An integrated approach to uncover drivers of cancer

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

Exploring the role of sporadic BRAF and KRAS mutations during colorectal cancer pathogenesis: A spotlight on the contribution of the endosome-lysosome system

The highly heterogenous nature of colorectal cancer can significantly hinder its early and accurate diagnosis, eventually contributing to high mortality rates. The adenoma-carcinoma sequence and serrated polyp-carcinoma sequence are the two most common sequences in sporadic colorectal cancer. Genetic alterations in adenomatous polyposis coli (APC), v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) and tumour protein 53 (TP53) genes are critical in adenoma-carcinoma sequence, whereas v-Raf murine sarcoma viral oncogene homolog B (BRAF) and MutL Homolog1 (MLH1) are driving oncogenes in the serrated polyp-carcinoma sequence. Sporadic mutations in these genes contribute differently to colorectal cancer pathogenesis by introducing distinct alterations in several signalling pathways that rely on the endosome-lysosome system. Unsurprisingly, the endosome-lysosome system plays a pivotal role in the hallmarks of cancer and contributes to specialised colon function. Thus, the endosome-lysosome system might be distinctively influenced by different mutations and these alterations may contribute to the heterogenous nature of sporadic colorectal cancer. This review highlights potential connections between major sporadic colorectal cancer mutations and the diverse pathogenic mechanisms driven by the endosome-lysosome system in colorectal carcinogenesis.

A Quinquennial Review of Potent LSD1 Inhibitors Explored for the Treatment of Different Cancers, with Special Focus on SAR Studies

Cancer bears a significant share of global mortality. The enzyme Lysine Specific Demethylase 1 (LSD1, also known as KDM1A), since its discovery in 2004, has captured the attention of cancer researchers due to its overexpression in several cancers like acute myeloid leukaemia (AML), solid tumours, etc. The Lysine Specific Demethylase (LSD1) downregulation is reported to have an effect on cancer proliferation, migration, and invasion. Therefore, research to discover safer and more potent LSD1 inhibitors can pave the way for the development of better cancer therapeutics. These efforts have resulted in the synthesis of many types of derivatives containing diverse structural nuclei. The present manuscript describes the role of Lysine Specific Demethylase 1 (LSD1) in carcinogenesis, reviews the LSD1 inhibitors explored in the past five years and discusses their comprehensive structural activity characteristics apart from the thorough description of LSD1. Besides, the potential challenges, opportunities, and future perspectives in the development of LSD1 inhibitors are also discussed. The review suggests that tranylcypromine derivatives are the most promising potent LSD1 inhibitors, followed by triazole and pyrimidine derivatives with IC₅₀ values in the nanomolar and sub-micromolar range. A number of potent LSD1 inhibitors derived from natural sources like resveratrol, protoberberine alkaloids, curcumin, etc. are also discussed. The structural-activity relationships discussed in the manuscript can be exploited to design potent and relatively safer LSD1 inhibitors as anticancer agents.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI

Cancer is known as a heterogeneous disease. Cancer driver genes (CDGs) need to be inferred for understanding tumor heterogeneity in cancer. However, the existing computational methods have identified many common CDGs. A key challenge exploring cancer progression is to infer cancer subtype-specific driver genes (CSDGs), which provides guidane for the diagnosis, treatment and prognosis of cancer. The significant advancements in single-cell RNA-sequencing (scRNA-seq) technologies have opened up new possibilities for studying human cancers at the individual cell level. In this study, we develop a novel unsupervised method, CSDGI (Cancer Subtype-specific Driver Gene Inference), which applies Encoder-Decoder-Framework consisting of low-rank residual neural networks to inferring driver genes corresponding to potential cancer subtypes at the single-cell level. To infer CSDGs, we apply CSDGI to the tumor single-cell transcriptomics data. To filter the redundant genes before driver gene inference, we perform the differential expression genes (DEGs). The experimental results demonstrate CSDGI is effective to infer driver genes that are cancer subtype-specific. Functional and disease enrichment analysis shows these inferred CSDGs indicate the key biological processes and disease pathways. CSDGI is the first method to explore cancer driver genes at the cancer subtype level. We believe that it can be a useful method to understand the mechanisms of cell transformation driving tumours.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data

MOTIVATION

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges.

RESULTS

We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits.

AVAILABILITY AND IMPLEMENTATION

The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

Extracellular vesicles in fatty liver promote a metastatic tumor microenvironment

Liver metastasis is a major cause of death in patients with colorectal cancer (CRC). Fatty liver promotes liver metastasis, but the underlying mechanism remains unclear. We demonstrated that hepatocyte-derived extracellular vesicles (EVs) in fatty liver enhanced the progression of CRC liver metastasis by promoting oncogenic Yes-associated protein (YAP) signaling and an immunosuppressive microenvironment. Fatty liver upregulated Rab27a expression, which facilitated EV production from hepatocytes. In the liver, these EVs transferred YAP signaling-regulating microRNAs to cancer cells to augment YAP activity by suppressing LATS2. Increased YAP activity in CRC liver metastasis with fatty liver promoted cancer cell growth and an immunosuppressive microenvironment by M2 macrophage infiltration through CYR61 production. Patients with CRC liver metastasis and fatty liver had elevated nuclear YAP expression, CYR61 expression, and M2 macrophage infiltration. Our data indicate that fatty liver-induced EV-microRNAs, YAP signaling, and an immunosuppressive microenvironment promote the growth of CRC liver metastasis.

Identification of a novel gene signature for neuroblastoma differentiation using a Boolean implication network

Although induction of differentiation represents an effective strategy for neuroblastoma treatment, the mechanisms underlying neuroblastoma differentiation are poorly understood. We generated a computational model of neuroblastoma differentiation consisting of interconnected gene clusters identified based on symmetric and asymmetric gene expression relationships. We identified a differentiation signature consisting of series of gene clusters comprised of 1251 independent genes that predicted neuroblastoma differentiation in independent datasets and in neuroblastoma cell lines treated with agents known to induce differentiation. This differentiation signature was associated with patient outcomes in multiple independent patient cohorts and validated the role of MYCN expression as a marker of neuroblastoma differentiation. Our results further identified novel genes associated with MYCN via asymmetric Boolean implication relationships that would not have been identified using symmetric computational approaches and that were associated with both neuroblastoma differentiation and patient outcomes. Our differentiation signature included a cluster of genes involved in intracellular signaling and growth factor receptor trafficking pathways that is strongly associated with neuroblastoma differentiation, and we validated the associations of UBE4B, a gene within this cluster, with neuroblastoma cell and tumor differentiation. Our findings demonstrate that Boolean network analyses of symmetric and asymmetric gene expression relationships can identify novel genes and pathways relevant for neuroblastoma tumor differentiation that could represent potential therapeutic targets.

Network Analysis of Biomarkers Associated with Occupational Exposure to Benzene and Malathion

Complex diseases are associated with the effects of multiple genes, proteins, and biological pathways. In this context, the tools of Network Medicine are compatible as a platform to systematically explore not only the molecular complexity of a specific disease but may also lead to the identification of disease modules and pathways. Such an approach enables us to gain a better understanding of how environmental chemical exposures affect the function of human cells, providing better perceptions about the mechanisms involved and helping to monitor/prevent exposure and disease to chemicals such as benzene and malathion. We selected differentially expressed genes for exposure to benzene and malathion. The construction of interaction networks was carried out using GeneMANIA and STRING. Topological properties were calculated using MCODE, BiNGO, and CentiScaPe, and a Benzene network composed of 114 genes and 2415 interactions was obtained. After topological analysis, five networks were identified. In these subnets, the most interconnected nodes were identified as: IL-8, KLF6, KLF4, JUN, SERTAD1, and MT1H. In the Malathion network, composed of 67 proteins and 134 interactions, HRAS and STAT3 were the most interconnected nodes. Path analysis, combined with various types of high-throughput data, reflects biological processes more clearly and comprehensively than analyses involving the evaluation of individual genes. We emphasize the central roles played by several important hub genes obtained by exposure to benzene and malathion.

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions.

SIGNIFICANCE

The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Ras superfamily GTPase activating proteins in cancer: Potential therapeutic targets?

To search more therapeutic strategies for Ras-mutant tumors, regulators of the Ras superfamily involved in the GTP/GDP (guanosine triphosphate/guanosine diphosphate) cycle have been well concerned for their anti-tumor potentials. GTPase activating proteins (GAPs) provide the catalytic group necessary for the hydrolysis of GTPs, which accelerate the switch by cycling between GTP-bound active and GDP-bound inactive forms. Inactivated GAPs lose their function in activating GTPase, leading to the continuous activation of downstream signaling pathways, uncontrolled cell proliferation, and eventually carcinogenesis. A growing number of evidence has shown the close link between GAPs and human tumors, and as a result, GAPs are believed as potential anti-tumor targets. The present review mainly summarizes the critically important role of GAPs in human tumors by introducing the classification, function and regulatory mechanism. Moreover, we comprehensively describe the relationship between dysregulated GAPs and the certain type of tumor. Finally, the current status, research progress, and clinical value of GAPs as therapeutic targets are also discussed, as well as the challenges and future direction in the cancer therapy.

Identification of functional gene modules by integrating multi-omics data and known molecular interactions

Multi-omics data integration has emerged as a promising approach to identify patient subgroups. However, in terms of grouping genes (or gene products) into co-expression modules, data integration methods suffer from two main drawbacks. First, most existing methods only consider genes or samples measured in all different datasets. Second, known molecular interactions (e.g., transcriptional regulatory interactions, protein-protein interactions and biological pathways) cannot be utilized to assist in module detection. Herein, we present a novel data integration framework, Correlation-based Local Approximation of Membership (CLAM), which provides two methodological innovations to address these limitations: 1) constructing a trans-omics neighborhood matrix by integrating multi-omics datasets and known molecular interactions, and 2) using a local approximation procedure to define gene modules from the matrix. Applying Correlation-based Local Approximation of Membership to human colorectal cancer (CRC) and mouse B-cell differentiation multi-omics data obtained from The Cancer Genome Atlas (TCGA), Clinical Proteomics Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO) and ProteomeXchange database, we demonstrated its superior ability to recover biologically relevant modules and gene ontology (GO) terms. Further investigation of the colorectal cancer modules revealed numerous transcription factors and KEGG pathways that played crucial roles in colorectal cancer progression. Module-based survival analysis constructed four survival-related networks in which pairwise gene correlations were significantly correlated with colorectal cancer patient survival. Overall, the series of evaluations demonstrated the great potential of Correlation-based Local Approximation of Membership for identifying modular biomarkers for complex diseases. We implemented Correlation-based Local Approximation of Membership as a user-friendly application available at https://github.com/free1234hm/CLAM.

Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM

Despite the abundance of multimodal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here, we present SparseGMM, a statistical approach for gene regulatory network discovery. SparseGMM uses latent variable modeling with sparsity constraints to learn Gaussian mixtures from multiomic data. By combining coexpression patterns with a Bayesian framework, SparseGMM quantitatively measures confidence in regulators and uncertainty in target gene assignment by computing gene entropy. We apply SparseGMM to liver cancer and normal liver tissue data and evaluate discovered gene modules in an independent single-cell RNA sequencing (scRNA-seq) dataset. SparseGMM identifies PROCR as a regulator of angiogenesis and PDCD1LG2 and HNF4A as regulators of immune response and blood coagulation in cancer. Furthermore, we show that more genes have significantly higher entropy in cancer compared with normal liver. Among high-entropy genes are key multifunctional components shared by critical pathways, including p53 and estrogen signaling.

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) coexpression network analyses focusing on correlations between genes and (ii) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: A slight change of parameterization or dataset could lead to marked alterations of outcomes. Here, we propose Stabilized COre gene and Pathway Election (SCOPE), a tool integrating bootstrapped least absolute shrinkage and selection operator and coexpression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD, and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations toward complex interactions using transcriptome data.

Protective effects of inhalation of essential oils from Mentha piperita leaf on tight junctions and inflammation in allergic rhinitis

Allergic rhinitis is one of the most common diseases, which is caused by IgE-mediated reactions to inhaled allergens. Essential oils from the Mentha piperita leaf (EOM) are known to be effective for various diseases, such as respiratory diseases. However, the effect of inhalation of EOM on tight junctions and inflammation related to allergic rhinitis is not yet known. The purpose of this research was to explain the effects of the inhalation of EOM on tight junctions and inflammation of allergic rhinitis through network pharmacology and an experimental study. For that purpose, a pharmacology network analysis was conducted comprising major components of EOM. Based on the network pharmacology prediction results, we evaluated the effect of EOM on histological changes in mice with ovalbumin and PM10-induced allergic rhinitis. Allergic symptoms, infiltration of inflammatory cells, and regulation of ZO-1 were investigated in mice with allergic rhinitis. Other allergic parameters were also analyzed by reverse transcription polymerase chain reaction and western blot in nasal epithelial cells. In the network analysis, the effects of EOM were closely related to tight junctions and inflammation in allergic rhinitis. Consistent with the results from the network analysis, EOM significantly decreased epithelial thickness, mast cell degranulation, goblet cell secretion, and the infiltration of inflammatory cells in nasal tissue. EOM also regulated the MAPK-NF-κB signaling pathway, which was related to tight junctions in nasal epithelial cells. This research confirmed that inhalation of EOM effectively restores tight junctions and suppresses inflammation in the allergic rhinitis model. These results reveal that EOM has a therapeutic mechanism to treat allergic rhinitis.

Comparative transcriptome and miRNA analysis of skin pigmentation during embryonic development of Chinese soft-shelled turtle (Pelodiscus sinensis)

BACKGROUND

Aquatic animals show diverse body coloration, and the formation of animal body colour is a complicated process. Increasing evidence has shown that microRNAs (miRNAs) play important regulatory roles in many life processes. The role of miRNAs in pigmentation has been investigated in some species. However, the regulatory patterns of miRNAs in reptile pigmentation remain to be elucidated. In this study, we performed an integrated analysis of miRNA and mRNA expression profiles to explore corresponding regulatory patterns in embryonic body colour formation in the soft-shelled turtle Pelodiscus sinensis.

RESULTS

We identified 8 866 novel genes and 9 061 mature miRNAs in the skin of Chinese soft-shelled turtles in three embryonic stages (initial period: IP, middle period: MP, final period: FP). A total of 16 563 target genes of the miRNAs were identified. Furthermore, we identified 2 867, 1 840 and 4 290 different expression genes (DEGs) and 227, 158 and 678 different expression miRNAs (DEMs) in IP vs. MP, MP vs. FP, and IP vs. FP, respectively. Among which 72 genes and 25 miRNAs may be related to turtle pigmentation in embryonic development. Further analysis of the novel miRNA families revealed that some novel miRNAs related to pigmentation belong to the miR-7386, miR-138, miR-19 and miR-129 families. Novel_miR_2622 and novel_miR_2173 belong to the miR-19 family and target Kit and Gpnmb, respectively. The quantification of novel_miR_2622 and Kit revealed negative regulation, indicating that novel_miR_2622 may participate in embryonic pigmentation in P. sinensis by negatively regulating the expression of Kit.

CONCLUSIONS

miRNA act as master regulators of biological processes by controlling the expression of mRNAs. Considering their importance, the identified miRNAs and their target genes in Chinese soft-shelled turtle might be useful for investigating the molecular processes involved in pigmentation. All the results of this study may aid in the improvement of P. sinensis breeding traits for aquaculture.

An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types

Cancer cells harbor molecular alterations at all levels of information processing. Genomic/epigenomic and transcriptomic alterations are inter-related between genes, within and across cancer types and may affect clinical phenotypes. Despite the abundant prior studies of integrating cancer multi-omics data, none of them organizes these associations in a hierarchical structure and validates the discoveries in extensive external data. We infer this Integrated Hierarchical Association Structure (IHAS) from the complete data of The Cancer Genome Atlas (TCGA) and compile a compendium of cancer multi-omics associations. Intriguingly, diverse alterations on genomes/epigenomes from multiple cancer types impact transcriptions of 18 Gene Groups. Half of them are further reduced to three Meta Gene Groups enriched with (1) immune and inflammatory responses, (2) embryonic development and neurogenesis, (3) cell cycle process and DNA repair. Over 80% of the clinical/molecular phenotypes reported in TCGA are aligned with the combinatorial expressions of Meta Gene Groups, Gene Groups, and other IHAS subunits. Furthermore, IHAS derived from TCGA is validated in more than 300 external datasets including multi-omics measurements and cellular responses upon drug treatments and gene perturbations in tumors, cancer cell lines, and normal tissues. To sum up, IHAS stratifies patients in terms of molecular signatures of its subunits, selects targeted genes or drugs for precision cancer therapy, and demonstrates that associations between survival times and transcriptional biomarkers may vary with cancer types. These rich information is critical for diagnosis and treatments of cancers.

Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients

BACKGROUND

Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment.

OBJECTIVE

This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models.

METHODS

We performed machine-learning (ML) analysis to screen pathogenic survival-related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L, MYH9, and DRD4 as candidate genes. These three genes and tumor stages were used to generate survival prediction models. Moreover, the genes were validated by experimental and clinical analyses, and the theranostic application of the survival prediction models was assessed.

RESULTS

RABGAP1L, MYH9, and DRD4 were identified as survival-related candidate genes by ML and in silico system analysis. The survival prediction model using the expression of the three genes showed higher predictive performance when applied to predict the prognosis of CC patients. A series of functional analyses revealed that each knockdown of three genes reduced the protumor activity of CC cells. In particular, validation with an independent cohort of CC patients confirmed that the coexpression of MYH9 and DRD4 gene expression reflected poorer clinical outcomes in terms of overall survival and disease-free survival.

CONCLUSIONS

Our survival prediction approach will contribute to providing information on patients and developing a therapeutic strategy for CC patients.

SYSMut: decoding the functional significance of rare somatic mutations in cancer

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological significance. We present SYSMut, an extendable systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multiomics profiles in primary tumors. We establish SYSMut's improved performance vis-à-vis state-of-the-art driver gene identification methodologies by recapitulating the functional impact of known driver genes, while additionally identifying novel functionally impactful mutated genes across 29 cancers. Subsequent application of SYSMut on low-frequency gene mutations in head and neck squamous cell (HNSC) cancers, followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC cancers. SYSMut is thus a robust scalable framework that enables the discovery of new targetable avenues in cancer.

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Identification of a Five-Gene Panel to Assess Prognosis for Gastric Cancer

Methods

Two datasets were used as training and validation cohorts to establish the predictive model. We used three types of screening criteria: background analysis, pathway analysis, and functional analysis provided by the cBioportal website. Fisher's exact test and multivariable logistic regression were performed to screen out related genes. Furthermore, we performed receiver operating characteristic (ROC) and Kaplan–Meier curve analyses to evaluate the correlation between the selected genes and overall survival.

Result

We screened five genes (KNL1, NRXN1, C6, CCDC169-SOHLH2, and TTN) that were highly related to recurrence of GC. The area under the receiver operating characteristic (ROC) curve was 0.813, which was much higher than that of the baseline model (AUC = 0.699). This result suggested that the mutation of five selected genes had a significant effect on the prediction of recurrence compared with other factors (age, stages, history, etc.). Furthermore, the Kaplan-Meier estimator also revealed that the mutation of five genes positively correlated with patient survival.

Conclusions

The patients who have mutations in these five genes may experience longer survival than those who do not have mutations. This five-gene panel will likely be a practical tool for prognostic evaluation and will provide another possible way for clinicians to determine therapy.

BHMPS Inhibits Breast Cancer Migration and Invasion by Disrupting Rab27a-Mediated EGFR and Fibronectin Secretion

Simple Summary

Numerous studies targeting Rab GTPases and its multiple effectors have been attempted since exocytosis has been shown to alter tumor malignancy by modulating cancer cell behavior and tumor microenvironment. Here, we demonstrated that BHMPS inhibits migration and invasion of breast cancer cells by blocking the interaction between Rab27a and Slp4. BHMPS interfered with vesicle trafficking and secretion by decreasing FAK and JNK activation. In addition, BHMPS suppressed tumor growth in Rab27a-overexpressing MDA-MB-231 xenograft mice. This study highlighted the importance of understanding the mechanisms of Rab27a-mediated metastasis in improving the therapeutic options for metastatic cancers.

Abstract

Our previous work demonstrated that (E)-N-benzyl-6-(2-(3, 4-dihydroxybenzylidene) hydrazinyl)-N-methylpyridine-3-sulfonamide (BHMPS), a novel synthetic inhibitor of Rab27aSlp(s) interaction, suppresses tumor cell invasion and metastasis. Here, we aimed to further investigate the mechanisms of action and biological significance of BHMPS. BHMPS decreased the expression of epithelial-mesenchymal transition transcription factors through inhibition of focal adhesion kinase and c-Jun N-terminal kinase activation, thereby reducing the migration and invasion of breast cancer. Additionally, knockdown of Rab27a inhibited tumor migration, with changes in related signaling molecules, whereas overexpression of Rab27a reversed this phenomenon. BHMPS effectively prevented the interaction of Rab27a and its effector Slp4, which was verified by co-localization, immunoprecipitation, and in situ proximity ligation assays. BHMPS decreased the secretion of epidermal growth factor receptor and fibronectin by interfering with vesicle trafficking, as indicated by increased perinuclear accumulation of CD63-positive vesicles. Moreover, administration of BHMPS suppressed tumor growth in Rab27a-overexpressing MDA-MB-231 xenograft mice. These findings suggest that BHMPS may be a promising candidate for attenuating tumor migration and invasion by blocking Rab27a-mediated exocytosis.

Detection of gene mutations and gene-gene fusions in circulating cell-free DNA of glioblastoma patients: an avenue for clinically relevant diagnostic analysis

Glioblastoma (GBM) is the most common type of glioma and is uniformly fatal. Currently, tumour heterogeneity and mutation acquisition are major impedances for tailoring personalized therapy. We collected blood and tumour tissue samples from 25 GBM patients and 25 blood samples from healthy controls. Cell‐free DNA (cfDNA) was extracted from the plasma of GBM patients and from healthy controls. Tumour DNA was extracted from fresh tumour samples. Extracted DNA was sequenced using a whole‐genome sequencing procedure. We also collected 180 tumour DNA datasets from GBM patients publicly available at the TCGA/PANCANCER project. These data were analysed for mutations and gene–gene fusions that could be potential druggable targets. We found that plasma cfDNA concentrations in GBM patients were significantly elevated (22.6 ± 5 ng·mL⁻¹), as compared to healthy controls (1.4 ± 0.4 ng·mL⁻¹) of the same average age. We identified unique mutations in the cfDNA and tumour DNA of each GBM patient, including some of the most frequently mutated genes in GBM according to the COSMIC database (TP53, 18.75%; EGFR, 37.5%; NF1, 12.5%; LRP1B, 25%; IRS4, 25%). Using our gene–gene fusion database, ChiTaRS 5.0, we identified gene–gene fusions in cfDNA and tumour DNA, such as KDR–PDGFRA and NCDN–PDGFRA, which correspond to previously reported alterations of PDGFRA in GBM (44% of all samples). Interestingly, the PDGFRA protein fusions can be targeted by tyrosine kinase inhibitors such as imatinib, sunitinib, and sorafenib. Moreover, we identified BCR–ABL1 (in 8% of patients), COL1A1–PDGFB (8%), NIN–PDGFRB (8%), and FGFR1–BCR (4%) in cfDNA of patients, which can be targeted by analogues of imatinib. ROS1 fusions (CEP85L–ROS1 and GOPC–ROS1), identified in 8% of patient cfDNA, might be targeted by crizotinib, entrectinib, or larotrectinib. Thus, our study suggests that integrated analysis of cfDNA plasma concentration, gene mutations, and gene–gene fusions can serve as a diagnostic modality for distinguishing GBM patients who may benefit from targeted therapy. These results open new avenues for precision medicine in GBM, using noninvasive liquid biopsy diagnostics to assess personalized patient profiles. Moreover, repeated detection of druggable targets over the course of the disease may provide real‐time information on the evolving molecular landscape of the tumour.

From DNA Copy Number Gains and Tumor Dependencies to Novel Therapeutic Targets for High-Risk Neuroblastoma

Neuroblastoma is a pediatric tumor arising from the sympatho-adrenal lineage and a worldwide leading cause of childhood cancer-related deaths. About half of high-risk patients die from the disease while survivors suffer from multiple therapy-related side-effects. While neuroblastomas present with a low mutational burden, focal and large segmental DNA copy number aberrations are highly recurrent and associated with poor survival. It can be assumed that the affected chromosomal regions contain critical genes implicated in neuroblastoma biology and behavior. More specifically, evidence has emerged that several of these genes are implicated in tumor dependencies thus potentially providing novel therapeutic entry points. In this review, we briefly review the current status of recurrent DNA copy number aberrations in neuroblastoma and provide an overview of the genes affected by these genomic variants for which a direct role in neuroblastoma has been established. Several of these genes are implicated in networks that positively regulate MYCN expression or stability as well as cell cycle control and apoptosis. Finally, we summarize alternative approaches to identify and prioritize candidate copy-number driven dependency genes for neuroblastoma offering novel therapeutic opportunities.

In vivo Roles of Rab27 and Its Effectors in Exocytosis

The monomeric GTPase Rab27 regulates exocytosis of a broad range of vesicles in multicellular organisms. Several effectors bind GTP-bound Rab27a and/or Rab27b on secretory vesicles to execute a series of exocytic steps, such as vesicle maturation, movement along microtubules, anchoring within the peripheral F-actin network, and tethering to the plasma membrane, via interactions with specific proteins and membrane lipids in a local milieu. Although Rab27 effectors generally promote exocytosis, they can also temporarily restrict it when they are involved in the rate-limiting step. Genetic alterations in Rab27-related molecules cause discrete diseases manifesting pigment dilution and immunodeficiency, and can also affect common diseases such as diabetes and cancer in complex ways. Although the function and mechanism of action of these effectors have been explored, it is unclear how multiple effectors act in coordination within a cell to regulate the secretory process as a whole. It seems that Rab27 and various effectors constitutively reside on individual vesicles to perform consecutive exocytic steps. The present review describes the unique properties and in vivo roles of the Rab27 system, and the functional relationship among different effectors coexpressed in single cells, with pancreatic beta cells used as an example.Key words: membrane trafficking, regulated exocytosis, insulin granules, pancreatic beta cells.

Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.

Whole genome copy number analyses reveal a highly aberrant genome in TP53 mutant lung adenocarcinoma tumors

BACKGROUND

Genetic alterations are common in non-small cell lung cancer (NSCLC), and DNA mutations and translocations are targets for therapy. Copy number aberrations occur frequently in NSCLC tumors and may influence gene expression and further alter signaling pathways. In this study we aimed to characterize the genomic architecture of NSCLC tumors and to identify genomic differences between tumors stratified by histology and mutation status. Furthermore, we sought to integrate DNA copy number data with mRNA expression to find genes with expression putatively regulated by copy number aberrations and the oncogenic pathways associated with these affected genes.

METHODS

Copy number data were obtained from 190 resected early-stage NSCLC tumors and gene expression data were available from 113 of the adenocarcinomas. Clinical and histopathological data were known, and EGFR-, KRAS- and TP53 mutation status was determined. Allele-specific copy number profiles were calculated using ASCAT, and regional copy number aberration were subsequently obtained and analyzed jointly with the gene expression data.

RESULTS

The NSCLC tumors tissue displayed overall complex DNA copy number profiles with numerous recurrent aberrations. Despite histological differences, tissue samples from squamous cell carcinomas and adenocarcinomas had remarkably similar copy number patterns. The TP53-mutated lung adenocarcinomas displayed a highly aberrant genome, with significantly altered copy number profiles including gains, losses and focal complex events. The EGFR-mutant lung adenocarcinomas had specific arm-wise aberrations particularly at chromosome7p and 9q. A large number of genes displayed correlation between copy number and expression level, and the PI(3)K-mTOR pathway was highly enriched for such genes.

CONCLUSIONS

The genomic architecture in NSCLC tumors is complex, and particularly TP53-mutated lung adenocarcinomas displayed highly aberrant copy number profiles. We suggest to always include TP53-mutation status when studying copy number aberrations in NSCLC tumors. Copy number may further impact gene expression and alter cellular signaling pathways.

A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity

[Figure: see text].

From Oncological Paradigms to Non-Communicable Disease Pandemic. The Need of Recovery Human Biology Evolution

The paradigm of the Somatic Mutation Theory (SMT) is failing, and a new paradigm is underway but not yet established. What is being challenged is a conceptual approach that involves the entire human biology and the development of chronic diseases. The behavior of breast and other solid cancers is compatible with the concept that the primary tumor is able to control its microscopic metastases, in the same way that an organ (e.g., the liver) is able to control its physiological size. This finding suggested that cancer and its metastases may behave as an organoid. The new paradigm under construction considers the origin of tumors as a disturbance in the communication network between tissue cell populations and between cells and extracellular matrix, and supports a systemic approach to the study of both healthy and pathologic tissues. The commentary provides a rationale for the role of physical exercise in the control of tumor dormancy according to a human evolutionary perspective.

Deep Subspace Mutual Learning For Cancer Subtypes Prediction

MOTIVATION

Precise prediction of cancer subtypes is of significant importance in cancer diagnosis and treatment. Disease etiology is complicated existing at different omics levels, hence integrative analysis provides a very effective way to improve our understanding of cancer.

RESULTS

We propose a novel computational framework, named Deep Subspace Mutual Learning (DSML). DSML has the capability to simultaneously learn the subspace structures in each available omics data and in overall multi-omics data by adopting deep neural networks, which thereby facilitates the subtypes prediction via clustering on multi-level, single level, and partial level omics data. Extensive experiments are performed in five different cancers on three levels of omics data from The Cancer Genome Atlas. The experimental analysis demonstrates that DSML delivers comparable or even better results than many state-of-the-art integrative methods.

AVAILABILITY

An implementation and documentation of the DSML is publicly available at https://github.com/polytechnicXTT/Deep-Subspace-Mutual-Learning.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

OmicsAnalyst: a comprehensive web-based platform for visual analytics of multi-omics data

Data analysis and interpretation remain a critical bottleneck in current multi-omics studies. Here, we introduce OmicsAnalyst, a user-friendly, web-based platform that allows users to perform a wide range of well-established data-driven approaches for multi-omics integration, and visually explore their results in a clear and meaningful manner. To help navigate complex landscapes of multi-omics analysis, these approaches are organized into three visual analytics tracks: (i) the correlation network analysis track, where users choose among univariate and multivariate methods to identify important features and explore their relationships in 2D or 3D networks; (ii) the cluster heatmap analysis track, where users apply several cutting-edge multi-view clustering algorithms and explore their results via interactive heatmaps; and (iii) the dimension reduction analysis track, where users choose among several recent multivariate techniques to reveal global data structures, and explore corresponding scores, loadings and biplots in interactive 3D scatter plots. The three visual analytics tracks are equipped with comprehensive options for parameter customization, view customization and targeted analysis. OmicsAnalyst lowers the access barriers to many well-established methods for multi-omics integration via novel visual analytics. It is freely available at https://www.omicsanalyst.ca.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Computational methods for cancer driver discovery: A survey

Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a “one-stop” reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survival.

Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction

One type of cancer usually consists of several subtypes with distinct clinical implications, thus the cancer subtype prediction is an important task in disease diagnosis and therapy. Utilizing one type of data from molecular layers in biological system to predict is difficult to bridge the cancer genome to cancer phenotypes, since the genome is neither simple nor independent but rather complicated and dysregulated from multiple molecular mechanisms. Similarity Network Fusion (SNF) has been recently proposed to integrate diverse omics data for improving the understanding of tumorigenesis. SNF adopts Euclidean distance to measure the similarity between patients, which shows some limitations. In this article, we introduce a novel prediction technique as an extension of SNF, namely Deep Subspace Fusion Clustering (DSFC). DSFC utilizes auto-encoder and data self-expressiveness approaches to guide a deep subspace model, which can achieve effective expression of discriminative similarity between patients. As a result, the dissimilarity between inter-cluster is delivered and enhanced compactness of intra-cluster is achieved at the same time. The validity of DSFC is examined by extensive simulations over six different cancer through three levels omics data. The survival analysis demonstrates that DSFC delivers comparable or even better results than many state-of-the-art integrative methods.

Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

Production of a SCID mouse model of medulloblastoma to explore the therapeutic value of targeting tumor driver genes

Tumor driver genes are genes where structural or sequence mutations confer a selective advantage for cancer cells. The individualized targeting of tumor driver genes has become a topic of interest for tumor treatment. The prognosis for medulloblastoma (MB), a common type of malignant intracranial tumor found in children, is poor. The tumor driver genes and the corresponding targeted drugs remain to be studied. The present study analyzed tumor driver genes from tumor tissue and peripheral blood from one patient with nodular desmoplastic MB with Sonic Hedgehog activation and screened targeted drugs for the tumor driver genes. Additionally, MB tissue was successfully implanted into the SCID mouse which were then used for subsequent drug screening. The present study explored novel treatments for MB from the perspective of tumor driver genes, and may provide new ideas for choosing individualized targeted therapies for patients with MB.

Integrative analysis reveals novel driver genes and molecular subclasses of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous disease with various genetic and epigenetic abnormalities. Previous studies of HCC driver genes were primarily based on frequency of mutations and copy number alterations. Here, we performed an integrative analysis of genomic and epigenomic data from 377 HCC patients to identify driver genes that regulate gene expression in HCC. This integrative approach has significant advantages over single-platform analyses for identifying cancer drivers. Using this approach, HCC tissues were divided into four subgroups, based on expression of the transcription factor E2F and the mutation status of TP53. HCC tissues with E2F overexpression and TP53 mutation had the highest cell cycle activity, indicating a synergistic effect of E2F and TP53. We found that overexpression of the identified driver genes, stratifin (SFN) and SPP1, correlates with tumor grade and poor survival in HCC and promotes HCC cell proliferation. These findings indicate SFN and SPP1 function as oncogenes in HCC and highlight the important role of enhancers in the regulation of gene expression in HCC.

Versatile Role of Rab27a in Glioma: Effects on Release of Extracellular Vesicles, Cell Viability, and Tumor Progression

Introduction: Glioma cells exert influence over the tumor-microenvironment in part through the release of extracellular vesicles (EVs), membrane-enclosed structures containing proteins, lipids, and RNAs. In this study, we evaluated the function of Ras-associated protein 27a (Rab27a) in glioma and evaluated the feasibility of assessing its role in EV release in glioma cells in vitro and in vivo. Methods: Rab27a was knocked down via a short hairpin RNA (shRNA) stably expressed in mouse glioma cell line GL261, with a scrambled shRNA as control. EVs were isolated by ultracentrifugation and quantified with Nanoparticle Tracking Analysis (NTA) and Tunable Resistive Pulse Sensing (TRPS). CellTiter-Glo viability assays and cytokine arrays were used to evaluate the impact of Rab27a knockdown. GL261.shRab27a cells and GL261.shControl were implanted into the left striatum of eight mice to assess tumor growth and changes in the tumor microenvironment. Results: Knockdown of Rab27a in GL261 glioma cells decreased the release of small EVs isolated at 100,000 × g in vitro (p = 0.005), but not the release of larger EVs, isolated at 10,000 × g. GL261.shRab27a cells were less viable compared to the scramble control in vitro (p < 0.005). A significant increase in CCL2 expression in shRab27a GL261 cells was also observed (p < 0.001). However, in vivo there was no difference in tumor growth or overall survival between the two groups, while shRab27a tumors showed lower proliferation at the tumor borders. Decreased infiltration of IBA1 positive macrophages and microglia, but not FoxP3 positive regulatory T cells was observed. Conclusion: Rab27a plays an important role in the release of small EVs from glioma cells, and also in their viability and expression of CCL2 in vitro. As interference in Rab27a expression influences glioma cell viability and expression profiles, future studies should be cautious in using the knockdown of Rab27a as a means of studying the role of small EVs in glioma growth.

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. They are characterized by a high clinical and genetic heterogeneity and in most patients, the responsible gene is unknown. Diagnosis is based on the identification of the causative gene that allows genetic counseling, prenatal diagnosis, understanding of pathological mechanisms, and personalized therapeutic approaches. Despite the emergence of Next Generation Sequencing (NGS), to date, more than one out of two patients has no diagnosis in the absence of identification of the responsible gene. Technologies currently used for detecting causal variants (genetic alterations) is far from complete, leading many variants of unknown significance (VUS) and mainly based on the use of whole exome sequencing thus neglecting the identification of non-coding variants. The complexity of human genome and its regulation at multiple levels has led biologists to develop several assays to interrogate the different aspects of biological processes. While one-dimension single omics investigation offers a peek of this complex system, the combination of different omics data allows the discovery of coherent signatures. The community of computational biologists and bioinformaticians, in order to integrate data from different omics, has developed several approaches and tools. However, it is difficult to understand which suits the best to predict diverse phenotypic outcome. First attempts to use multi-omics approaches showed an improvement of the diagnostic power. However, we are far from a complete understanding of MD and their diagnosis. After reviewing multi-omics algorithms developed in the latest years, we are proposing here a novel data-driven classification and we will discuss how multi-omics will change and improve the diagnosis of MD. Due to the growing use of multi-omics approaches in MD, we foresee that this work will contribute to set up good practices to perform multi-omics data integration to improve the prediction of phenotypic outcomes and the diagnostic power of MD.

Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data

Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways.