Multi-omics integration for neuroblastoma clinical endpoint prediction

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework

Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities.

Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

Molecular Cluster Mining of Adrenocortical Carcinoma via Multi-Omics Data Analysis Aids Precise Clinical Therapy

Adrenocortical carcinoma (ACC) is a malignancy of the endocrine system. We collected clinical and pathological features, genomic mutations, DNA methylation profiles, and mRNA, lncRNA, microRNA, and somatic mutations in ACC patients from the TCGA, GSE19750, GSE33371, and GSE49278 cohorts. Based on the MOVICS algorithm, the patients were divided into ACC1-3 subtypes by comprehensive multi-omics data analysis. We found that immune-related pathways were more activated, and drug metabolism pathways were enriched in ACC1 subtype patients. Furthermore, ACC1 patients were sensitive to PD-1 immunotherapy and had the lowest sensitivity to chemotherapeutic drugs. Patients with the ACC2 subtype had the worst survival prognosis and the highest tumor-mutation rate. Meanwhile, cell-cycle-related pathways, amino-acid-synthesis pathways, and immunosuppressive cells were enriched in ACC2 patients. Steroid and cholesterol biosynthetic pathways were enriched in patients with the ACC3 subtype. DNA-repair-related pathways were enriched in subtypes ACC2 and ACC3. The sensitivity of the ACC2 subtype to cisplatin, doxorubicin, gemcitabine, and etoposide was better than that of the other two subtypes. For 5-fluorouracil, there was no significant difference in sensitivity to paclitaxel between the three groups. A comprehensive analysis of multi-omics data will provide new clues for the prognosis and treatment of patients with ACC.

ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification

With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.

Advances in proteomic profiling of pediatric kidney diseases

Chronic kidney disease (CKD) can progress to kidney failure and require dialysis or transplantation, while early diagnosis can alter the course of disease and lead to better outcomes in both pediatric and adult patients. Significant CKD comorbidities include the manifestation of cardiovascular disease, heart failure, coronary disease, and hypertension. The pathogenesis of chronic kidney diseases can present as subtle and especially difficult to distinguish between different glomerular pathologies. Early detection of adult and pediatric CKD and detailed mechanistic understanding of the kidney damage can be helpful in delaying or curtailing disease progression via precise intervention toward diagnosis and prognosis. Clinically, serum creatinine and albumin levels can be indicative of CKD, but often are a lagging indicator only significantly affected once kidney function has severely diminished. The evolution of proteomics and mass spectrometry technologies has begun to provide a powerful research tool in defining these mechanisms and identifying novel biomarkers of CKD. Many of the same challenges and advances in proteomics apply to adult and pediatric patient populations. Additionally, proteomic analysis of adult CKD patients can be transferred directly toward advancing our knowledge of pediatric CKD as well. In this review, we highlight applications of proteomics that have yielded such biomarkers as PLA2R, SEMA3B, and other markers of membranous nephropathy as well as KIM-1, MCP-1, and NGAL in lupus nephritis among other potential diagnostic and prognostic markers. The potential for improving the clinical toolkit toward better treatment of pediatric kidney diseases is significantly aided by current and future development of proteomic applications.

Ten simple rules for organizing a special session at a scientific conference

Special sessions are important parts of scientific meetings and conferences: They gather together researchers and students interested in a specific topic and can strongly contribute to the success of the conference itself. Moreover, they can be the first step for trainees and students to the organization of a scientific event. Organizing a special session, however, can be uneasy for beginners and students. Here, we provide ten simple rules to follow to organize a special session at a scientific conference.

Toxicokinetics of Arenobufagin and its Cardiotoxicity Mechanism Exploration Based on Lipidomics and Proteomics Approaches in Rats

Arenobufagin (ArBu), one of the main active bufadienolides of toad venom with cardiotonic effect, analgesic effect, and outstanding anti-tumor potentiality, is also a potential cardiotoxic component. In the present study, the cardiac effect of ArBu and its underlying mechanism were explored by integrating data such as heart rates, toxicokinetics, myocardial enzyme and brain natriuretic peptide (BNP) activity, pathological sections, lipidomics and proteomics. Under different doses, the cardiac effects turned out to be different. The oral dose of 60 mg/kg of ArBu sped up the heart rate. However, 120 mg/kg ArBu mainly reduced the heart rate. Over time, they all returned to normal, consisting of the trend of ArBu concentration-time curve. High concentrations of myocardial enzymes and BNP indicated that ArBu inhibited or impaired the cardiac function of rats. Pathological sections of hearts also showed that ArBu caused myocardial fiber disorder and rupture, in which the high-dose group was more serious. At the same time, serum and heart tissue lipidomics were used to explore the changes in body lipid metabolism under different doses. The data indicated a larger difference in the high-dose ArBu group. There were likewise many significant differences in the proteomics of the heart. Furthermore, a multi-layered network was used to integrate the above information to explore the potential mechanism. Finally, 4 proteins that were shown to be significantly and differentially expressed were validated by targeted proteomics using parallel reaction monitoring (PRM) analysis. Our findings indicated that ArBu behaved as a bidirectional regulation of the heart. The potential mechanism of cardiac action was revealed with the increased dose, which provided a useful reference for the safety of clinical application of ArBu.

Representation Learning for the Clustering of Multi-Omics Data

The integration of several sources of data for the identification of subtypes of diseases has gained attention over the past few years. The heterogeneity and the high dimensions of the data sets calls for an adequate representation of the data. We summarize the field of representation learning for the multi-omics clustering problem and we investigate several techniques to learn relevant combined representations, using methods from group factor analysis (PCA, MFA and extensions) and from machine learning with autoencoders. We highlight the importance of appropriately designing and training the latter, notably with a novel combination of a disjointed deep autoencoder (DDAE) architecture and a layer-wise reconstruction loss. These different representations can then be clustered to identify biologically meaningful clusters of patients. We provide a unifying framework for model comparison between statistical and deep learning approaches with the introduction of a new weighted internal clustering index that evaluates how well the clustering information is retained from each source, favoring contributions from all data sets. We apply our methodology to two case studies for which previous works of integrative clustering exist, TCGA Breast Cancer and TARGET Neuroblastoma, and show how our method can yield good and well-balanced clusters across the different data sources.

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.

Molecular Mechanisms and Function of the p53 Protein Family Member - p73

Over 20 years after identification of p53 and its crucial function in cancer progression, two members of the same protein family were identified, namely p63 and p73. Since then, a body of information has been accumulated on each of these genes and their interrelations. Biological role of p73 has been elucidated thanks to four distinct knockout mice models: (i) with deletion of the entire TP73 gene, (ii) with deletion of exons encoding the full length TAp73 isoforms, (iii) with deletions of exons encoding the shorter DNp73 isoform, and (iv) with deletion of exons encoding C-terminal of the alpha isoform. This work, as well as expression studies in cancer and overwhelming body of molecular studies, allowed establishing major role of TP73 both in cancer and in neuro-development, as well as ciliogenesis, and metabolism. Here, we recapitulate the major milestones of this endeavor.

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

BACKGROUND

In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al.

METHODS

Data of 32 T1D subjects under free-living conditions for 4874 days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHOB), lunch (CHOL), dinner (CHOD), and snack (CHOS); breakfast timing (TB); and time between breakfast-lunch (TBL) and between lunch-dinner (TLD). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed.

RESULTS

Resulting PDF models were: gamma (CHOB, CHOS), lognormal (CHOL, TB), loglogistic (CHOD), and generalized-extreme-values (TBL, TLD). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (α = 0.05).

CONCLUSIONS

The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.

The ZNF750-RAC1 axis as potential prognostic factor for breast cancer

The human zinc finger (C2H2-type) protein ZNF750 is a transcription factor regulated by p63 that plays a critical role in epithelial tissues homoeostasis, as well as being involved in the pathogenesis of cancer. Indeed, missense mutations, truncation and genomic deletion have been found in oesophageal squamous cell carcinoma. In keeping, we showed that ZNF750 negatively regulates cell migration and invasion in breast cancer cells; in particular, ZNF750 binds and recruits KDM1A and HDAC1 on the LAMB3 and CTNNAL1 promoters. This interaction, in turn, represses the transcription of LAMB3 and CTNNAL1 genes, which are involved in cell migration and invasion. Given that ZNF750 is emerging as a crucial transcription factor that acts as tumour suppressor gene, here, we show that ZNF750 represses the expression of the small GTPase, Ras-related C3 botulinum toxin substrate 1 (RAC1) in breast cancer cell lines, by directly binding its promoter region. In keeping with ZNF750 controlling RAC1 expression, we found an inverse correlation between ZNF750 and RAC1 in human breast cancer datasets. More importantly, we found a significant upregulation of RAC1 in human breast cancer datasets and we identified a direct correlation between RAC1 expression and the survival rate of breast cancer patient. Overall, our findings provide a novel molecular mechanism by which ZNF750 acts as tumour suppressor gene. Hence, we report a potential clinical relevance of ZNF750/RAC1 axis in breast cancer.

Liquid biopsies and cancer omics

The development of the sequencing technologies allowed the generation of huge amounts of molecular data from a single cancer specimen, allowing the clinical oncology to enter the era of the precision medicine. This massive amount of data is highlighting new details on cancer pathogenesis but still relies on tissue biopsies, which are unable to capture the dynamic nature of cancer through its evolution. This assumption led to the exploration of non-tissue sources of tumoral material opening the field of liquid biopsies. Blood, together with body fluids such as urines, or stool, from cancer patients, are analyzed applying the techniques used for the generation of omics data. With blood, this approach would allow to take into account tumor heterogeneity (since the circulating components such as CTCs, ctDNA, or ECVs derive from each cancer clone) in a time dependent manner, resulting in a somehow "real-time" understanding of cancer evolution. Liquid biopsies are beginning nowdays to be applied in many cancer contexts and are at the basis of many clinical trials in oncology.

Commensal microbes and p53 in cancer progression

Aetiogenesis of cancer has not been fully determined. Recent advances have clearly defined a role for microenvironmental factors in cancer progression and initiation; in this context, microbiome has recently emerged with a number of reported correlative and causative links implicating alterations of commensal microbes in tumorigenesis. Bacteria appear to have the potential to directly alter physiological pathways of host cells and in specific circumstances, such as the mutation of the tumour suppressive factor p53, they can also directly switch the function of a gene from oncosuppressive to oncogenic. In this minireview, we report a number of examples on how commensal microbes alter the host cell biology, affecting the oncogenic process. We then discuss more in detail how interaction with the gut microbiome can affect the function of p53 mutant in the intestinal tumorigenesis.

Pathophysiology of Crohn's disease inflammation and recurrence

Chron’s Disease is a chronic inflammatory intestinal disease, first described at the beginning of the last century. The disease is characterized by the alternation of periods of flares and remissions influenced by a complex pathogenesis in which inflammation plays a key role. Crohn’s disease evolution is mediated by a complex alteration of the inflammatory response which is characterized by alterations of the innate immunity of the intestinal mucosa barrier together with a remodeling of the extracellular matrix through the expression of metalloproteins and increased adhesion molecules expression, such as MAcCAM-1. This reshaped microenvironment enhances leucocytes migration in the sites of inflammation, promoting a T_H1 response, through the production of cytokines such as IL-12 and TNF-α. IL-12 itself and IL-23 have been targeted for the medical treatment of CD. Giving the limited success of medical therapies, the treatment of the disease is invariably surgical. This review will highlight the role of inflammation in CD and describe the surgical approaches for the prevention of the almost inevitable recurrence.

Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data

Predicting the prognosis of pancreatic cancer is important because of the very low survival rates of patients with this particular cancer. Although several studies have used microRNA and gene expression profiles and clinical data, as well as images of tissues and cells, to predict cancer survival and recurrence, the accuracies of these approaches in the prediction of high-risk pancreatic adenocarcinoma (PAAD) still need to be improved. Accordingly, in this study, we proposed two biological features based on multi-omics datasets to predict survival and recurrence among patients with PAAD. First, the clonal expansion of cancer cells with somatic mutations was used to predict prognosis. Using whole-exome sequencing data from 134 patients with PAAD from The Cancer Genome Atlas (TCGA), we found five candidate genes that were mutated in the early stages of tumorigenesis with high cellular prevalence (CP). CDKN2A, TP53, TTN, KCNJ18, and KRAS had the highest CP values among the patients with PAAD, and survival and recurrence rates were significantly different between the patients harboring mutations in these candidate genes and those harboring mutations in other genes (p = 2.39E-03, p = 8.47E-04, respectively). Second, we generated an autoencoder to integrate the RNA sequencing, microRNA sequencing, and DNA methylation data from 134 patients with PAAD from TCGA. The autoencoder robustly reduced the dimensions of these multi-omics data, and the K-means clustering method was then used to cluster the patients into two subgroups. The subgroups of patients had significant differences in survival and recurrence (p = 1.41E-03, p = 4.43E-04, respectively). Finally, we developed a prediction model for prognosis using these two biological features and clinical data. When support vector machines, random forest, logistic regression, and L2 regularized logistic regression were used as prediction models, logistic regression analysis generally revealed the best performance for both disease-free survival (DFS) and overall survival (OS) (accuracy [ACC] = 0.762 and area under the curve [AUC] = 0.795 for DFS; ACC = 0.776 and AUC = 0.769 for OS). Thus, we could classify patients with a high probability of recurrence and at a high risk of poor outcomes. Our study provides insights into new personalized therapies on the basis of mutation status and multi-omics data.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools

In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Predictability of drug-induced liver injury by machine learning

Background

Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucial advantage. In 2018, the Critical Assessment Massive Data Analysis group proposed the CMap Drug Safety challenge focusing on DILI prediction.

Methods and results

The challenge data included Affymetrix GeneChip expression profiles for the two cancer cell lines MCF7 and PC3 treated with 276 drug compounds and empty vehicles. Binary DILI labeling and a recommended train/test split for the development of predictive classification approaches were also provided. We devised three deep learning architectures for DILI prediction on the challenge data and compared them to random forest and multi-layer perceptron classifiers. On a subset of the data and for some of the models we additionally tested several strategies for balancing the two DILI classes and to identify alternative informative train/test splits. All the models were trained with the MAQC data analysis protocol (DAP), i.e., 10x5 cross-validation over the training set. In all the experiments, the classification performance in both cross-validation and external validation gave Matthews correlation coefficient (MCC) values below 0.2. We observed minimal differences between the two cell lines. Notably, deep learning approaches did not give an advantage on the classification performance.

Discussion

We extensively tested multiple machine learning approaches for the DILI classification task obtaining poor to mediocre performance. The results suggest that the CMap expression data on the two cell lines MCF7 and PC3 are not sufficient for accurate DILI label prediction.

Reviewers

This article was reviewed by Maciej Kandula and Paweł P. Labaj.

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

BACKGROUND

The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the "small n large p" problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process.

METHODS

We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients' omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers.

RESULTS

We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality.

CONCLUSIONS

Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.

A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models

Background

Recently high-throughput technologies have been massively used alongside clinical tests to study various types of cancer. Data generated in such large-scale studies are heterogeneous, of different types and formats. With lack of effective integration strategies novel models are necessary for efficient and operative data integration, where both clinical and molecular information can be effectively joined for storage, access and ease of use. Such models, combined with machine learning methods for accurate prediction of survival time in cancer studies, can yield novel insights into disease development and lead to precise personalized therapies.

Results

We developed an approach for intelligent data integration of two cancer datasets (breast cancer and neuroblastoma) − provided in the CAMDA 2018 ‘Cancer Data Integration Challenge’, and compared models for prediction of survival time. We developed a novel semantic network-based data integration framework that utilizes NoSQL databases, where we combined clinical and expression profile data, using both raw data records and external knowledge sources. Utilizing the integrated data we introduced Tumor Integrated Clinical Feature (TICF) − a new feature for accurate prediction of patient survival time. Finally, we applied and validated several machine learning models for survival time prediction.

Conclusion

We developed a framework for semantic integration of clinical and omics data that can borrow information across multiple cancer studies. By linking data with external domain knowledge sources our approach facilitates enrichment of the studied data by discovery of internal relations. The proposed and validated machine learning models for survival time prediction yielded accurate results.

Reviewers

This article was reviewed by Eran Elhaik, Wenzhong Xiao and Carlos Loucera.

The application of artificial neural networks in metabolomics: a historical perspective

BACKGROUND

Metabolomics data, with its complex covariance structure, is typically modelled by projection-based machine learning (ML) methods such as partial least squares (PLS) regression, which project data into a latent structure. Biological data are often non-linear, so it is reasonable to hypothesize that metabolomics data may also have a non-linear latent structure, which in turn would be best modelled using non-linear equations. A non-linear ML method with a similar projection equation structure to PLS is artificial neural networks (ANNs). While ANNs were first applied to metabolic profiling data in the 1990s, the lack of community acceptance combined with limitations in computational capacity and the lack of volume of data for robust non-linear model optimisation inhibited their widespread use. Due to recent advances in computational power, modelling improvements, community acceptance, and the more demanding needs for data science, ANNs have made a recent resurgence in interest across research communities, including a small yet growing usage in metabolomics. As metabolomics experiments become more complex and start to be integrated with other omics data, there is potential for ANNs to become a viable alternative to linear projection methods.

AIM OF REVIEW

We aim to first describe ANNs and their structural equivalence to linear projection-based methods, including PLS regression. We then review the historical, current, and future uses of ANNs in the field of metabolomics.

KEY SCIENTIFIC CONCEPT OF REVIEW

Is metabolomics ready for the return of artificial neural networks?

Integrated Proteomics and Lipidomics Investigation of the Mechanism Underlying the Neuroprotective Effect of N-benzylhexadecanamide

Macamides are very important secondary metabolites produced by Lepidium meyenii Walp, which possess multiple bioactivities, especially in the neuronal system. In a previous study, we observed that macamides exhibited excellent effects in the recovery of injured nerves after 1-methyl-4-phenylpyridinium (MPP⁺)-induced dopaminergic neuronal damage in zebrafish. However, the mechanism underlying this effect remains unclear. In the present study, we observed that N-benzylhexadecanamide (XA), which is a typical constituent of macamides, improved the survival rate of neurons in vitro. We determined the concentration of neurotransmitters in MN9D cells and used it in conjunction with an integrated proteomics and lipidomics approach to investigate the mechanism underlying the neuroprotective effects of XA in an MPP⁺-induced neurodegeneration cell model using QqQ MS, Q-TOF MS, and Orbitrap MS. The statistical analysis of the results led to the identification of differentially-expressed biomarkers, including 11 proteins and 22 lipids, which may be responsible for the neuron-related activities of XA. All these potential biomarkers were closely related to the pathogenesis of neurodegenerative diseases, and their levels approached those in the normal group after treatment with XA. Furthermore, seven lipids, including five phosphatidylcholines, one lysophosphatidylcholine, and one phosphatidylethanolamine, were verified by a relative quantitative approach. Moreover, four proteins (Scarb2, Csnk2a2, Vti1b, and Bnip2) were validated by ELISA. The neurotransmitters taurine and norepinephrine, and the cholinergic constituents, correlated closely with the neuroprotective effects of XA. Finally, the protein–lipid interaction network was analyzed. Based on our results, the regulation of sphingolipid metabolism and mitochondrial function were determined to be the main mechanisms underlying the neuroprotective effect of XA. The present study should help us to better understand the multiple effects of macamides and their use in neurodegenerative diseases.

Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach

BACKGROUND

One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients.

RESULTS

We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models.

CONCLUSION

We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data.

REVIEWERS

This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.