Pathway-based classification of cancer subtypes

Non-genetic heterogeneity and immune subtyping in breast cancer: Implications for immunotherapy and targeted therapeutics

Breast cancer (BC) is a complex and multifactorial disease, driven by genetic alterations that promote tumor growth and progression. However, recent research has highlighted the importance of non-genetic factors in shaping cancer evolution and influencing therapeutic outcomes. Non-genetic heterogeneity refers to diverse subpopulations of cancer cells within breast tumors, exhibiting distinct phenotypic and functional properties. These subpopulations can arise through various mechanisms, including clonal evolution, genetic changes, epigenetic changes, and reversible phenotypic transitions. Although genetic and epigenetic changes are important points of the pathology of breast cancer yet, the immune system also plays a crucial role in its progression. In clinical management, histologic and molecular classification of BC are used. Immunological subtyping of BC has gained attention in recent years as compared to traditional techniques. Intratumoral heterogeneity revealed by immunological microenvironment (IME) has opened novel opportunities for immunotherapy research. This systematic review is focused on non-genetic variability to identify and interlink immunological subgroups in breast cancer. This review provides a deep understanding of adaptive methods adopted by tumor cells to withstand changes in the tumor microenvironment and selective pressure imposed by medications. These adaptive methods include alterations in drug targets, immune system evasion, activation of survival pathways, and alterations in metabolism. Understanding non-genetic heterogeneity is essential for the development of targeted therapies.

A Paradoxical Role for Somatic Chromosomal Mosaicism and Chromosome Instability in Cancer: Theoretical and Technological Aspects

Somatic chromosomal mosaicism, chromosome instability, and cancer are intimately linked together. Addressing the role of somatic genome variations (encompassing chromosomal mosaicism and instability) in cancer yields paradoxical results. Firstly, somatic mosaicism for specific chromosomal rearrangement causes cancer per se. Secondly, chromosomal mosaicism and instability are associated with a variety of diseases (chromosomal disorders demonstrating less severe phenotypes, complex diseases), which exhibit cancer predisposition. Chromosome instability syndromes may be considered the best examples of these diseases. Thirdly, chromosomal mosaicism and instability are able to result not only in cancerous diseases but also in non-cancerous disorders (brain diseases, autoimmune diseases, etc.). Currently, the molecular basis for these three outcomes of somatic chromosomal mosaicism and chromosome instability remains incompletely understood. Here, we address possible mechanisms for the aforementioned scenarios using a system analysis model. A number of theoretical models based on studies dedicated to chromosomal mosaicism and chromosome instability seem to be valuable for disentangling and understanding molecular pathways to cancer-causing genome chaos. In addition, technological aspects of uncovering causes and consequences of somatic chromosomal mosaicism and chromosome instability are discussed. In total, molecular cytogenetics, cytogenomics, and system analysis are likely to form a powerful technological alliance for successful research against cancer.

Novel Therapies in Glioblastoma Treatment: Review of Glioblastoma; Current Treatment Options; and Novel Oncolytic Viral Therapies

One of the most prevalent primary malignant brain tumors is glioblastoma (GB). About 6 incidents per 100,000 people are reported annually. Most frequently, these tumors are linked to a poor prognosis and poor quality of life. There has been little advancement in the treatment of GB. In recent years, some innovative medicines have been tested for the treatment of newly diagnosed cases of GB and recurrent cases of GB. Surgery, radiotherapy, and alkylating chemotherapy are all common treatments for GB. A few of the potential alternatives include immunotherapy, tumor-treating fields (TTFs), and medications that target specific cellular receptors. To provide new multimodal therapies that focus on the molecular pathways implicated in tumor initiation and progression in GB, novel medications, delivery technologies, and immunotherapy approaches are being researched. Of these, oncolytic viruses (OVs) are among the most recent. Coupling OVs with certain modern treatment approaches may have significant benefits for GB patients. Here, we discuss several OVs and how they work in conjunction with other therapies, as well as virotherapy for GB. The study was based on the PRISMA guidelines. Systematic retrieval of information was performed on PubMed. A total of 307 articles were found in a search on oncolytic viral therapies for glioblastoma. Out of these 83 articles were meta-analyses, randomized controlled trials, reviews, and systematic reviews. A total of 42 articles were from the years 2018 to 2023. Appropriate studies were isolated, and important information from each of them was understood and entered into a database from which the information was used in this article. One of the most prevalent malignant brain tumors is still GB. Significant promise and opportunity exist for oncolytic viruses in the treatment of GB and in boosting immune response. Making the most of OVs in the treatment of GB requires careful consideration and evaluation of a number of its application factors.

Repurposing antifungal drugs for cancer therapy

BACKGROUND

Repurposing antifungal drugs in cancer therapy has attracted unprecedented attention in both preclinical and clinical research due to specific advantages, such as safety, high-cost effectiveness and time savings compared with cancer drug discovery. The surprising and encouraging efficacy of antifungal drugs in cancer therapy, mechanistically, is attributed to the overlapping targets or molecular pathways between fungal and cancer pathogenesis. Advancements in omics, informatics and analytical technology have led to the discovery of increasing "off-site" targets from antifungal drugs involved in cancerogenesis, such as smoothened (D477G) inhibition from itraconazole in basal cell carcinoma.

AIM OF REVIEW

This review illustrates several antifungal drugs repurposed for cancer therapy and reveals the underlying mechanism based on their original target and "off-site" target. Furthermore, the challenges and perspectives for the future development and clinical applications of antifungal drugs for cancer therapy are also discussed, providing a refresh understanding of drug repurposing.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review may provide a basic understanding of repurposed antifungal drugs for clinical cancer management, thereby helping antifungal drugs broaden new indications and promote clinical translation.

Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment

Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. Results : TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. Conclusions: Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.

MmCMS: mouse models' consensus molecular subtypes of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue.

METHODS

Using transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n = 577) and mouse (n = 57 across n = 8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers.

RESULTS

We developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours.

CONCLUSIONS

When applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package enables researchers to select suitable mouse models of human CRC subtype for their experimental testing.

Risk stratification and pathway analysis based on graph neural network and interpretable algorithm

Background

Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result.

Results

To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival.

Conclusion

PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04950-1.

Targeting the key cholesterol biosynthesis enzyme squalene monooxygenasefor cancer therapy

Cholesterol metabolism is often dysregulated in cancer. Squalene monooxygenase (SQLE) is the second rate-limiting enzyme involved in cholesterol synthesis. Since the discovery of SQLE dysregulation in cancer, compelling evidence has indicated that SQLE plays a vital role in cancer initiation and progression and is a promising therapeutic target for cancer treatment. In this review, we provide an overview of the role and regulation of SQLE in cancer and summarize the updates of antitumor therapy targeting SQLE.

Reassessment of Reliability and Reproducibility for Triple-Negative Breast Cancer Subtyping

Simple Summary

Triple-negative breast cancer (TNBC) is a heterogeneous disease. A proper classification system is needed to develop targetable biomarkers and guide personalized treatment in clinical practice. However, there has been no consensus on the molecular subtypes of TNBC, probably due to discrepancies in technical and computational methods chosen by different research groups. In this paper, we reassessed each major step for TNBC subtyping and provided suggestions, which promote rational workflow design and ensure reliable and reproducible results for future studies. We presented a recommended pipeline to the existing data, validated established TNBC subtypes with a larger sample size, and revealed two intermediate subtypes with prognostic significance. This work provides perspectives on issues and limitations regarding TNBC subtyping, indicating promising directions for developing targeted therapy based on the molecular characteristics of each TNBC subtype.

Abstract

Triple-negative breast cancer (TNBC) is a heterogeneous disease with diverse, often poor prognoses and treatment responses. In order to identify targetable biomarkers and guide personalized care, scientists have developed multiple molecular classification systems for TNBC based on transcriptomic profiling. However, there is no consensus on the molecular subtypes of TNBC, likely due to discrepancies in technical and computational methods used by different research groups. Here, we reassessed the major steps for TNBC subtyping, validated the reproducibility of established TNBC subtypes, and identified two more subtypes with a larger sample size. By comparing results from different workflows, we demonstrated the limitations of formalin-fixed, paraffin-embedded samples, as well as batch effect removal across microarray platforms. We also refined the usage of computational tools for TNBC subtyping. Furthermore, we integrated high-quality multi-institutional TNBC datasets (discovery set: n = 457; validation set: n = 165). Performing unsupervised clustering on the discovery and validation sets independently, we validated four previously discovered subtypes: luminal androgen receptor, mesenchymal, immunomodulatory, and basal-like immunosuppressed. Additionally, we identified two potential intermediate states of TNBC tumors based on their resemblance with more than one well-characterized subtype. In summary, we addressed the issues and limitations of previous TNBC subtyping through comprehensive analyses. Our results promote the rational design of future subtyping studies and provide new insights into TNBC patient stratification.

The emerging roles of E3 ubiquitin ligases in ovarian cancer chemoresistance

Epithelial cancer of the ovary exhibits the highest mortality rate of all gynecological malignancies in women today, since the disease is often diagnosed in advanced stages. While the treatment of cancer with specific chemical agents or drugs is the favored treatment regimen, chemotherapy resistance greatly impedes successful ovarian cancer chemotherapy. Thus, chemoresistance becomes one of the most critical clinical issues confronted when treating patients with ovarian cancer. Convincing evidence hints that dysregulation of E3 ubiquitin ligases is a key factor in the development and maintenance of ovarian cancer chemoresistance. This review outlines recent advancement in our understanding of the emerging roles of E3 ubiquitin ligases in ovarian cancer chemoresistance. We also highlight currently available inhibitors targeting E3 ligase activities and discuss their potential for clinical applications in treating chemoresistant ovarian cancer patients.

An Entropy-based Directed Random Walk for Pathway Activity Inference Using Topological Importance and Gene Interactions.. [DOI: 10.1101/2021.11.05.467449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The integration of microarray technologies and machine learning methods has become popular in predicting pathological condition of diseases and discovering risk genes. The traditional microarray analysis considers pathways as simple gene sets, treating all genes in the pathway identically while ignoring the pathway network’s structure information. This study, however, proposed an entropy-based directed random walk (e-DRW) method to infer pathway activity. This study aims (1) To enhance the gene-weighting method in Directed Random Walk (DRW) by incorporating t-test statistic scores and correlation coefficient values, (2) To implement entropy as a parameter variable for random walking in a biological network, and (3) To apply Entropy Weight Method (EWM) in DRW pathway activity inference. To test the objectives, the gene expression dataset was used as input datasets while the pathway dataset was used as reference datasets to build a directed graph. An equation was proposed to assess the connectivity of nodes in the directed graph via probability values calculated from the Shannon entropy formula. A direct proof of calculation based on the proposed mathematical formula was presented using e-DRW with gene expression data. Based on the results, there was an improvement in terms of sensitivity of prediction and accuracy of cancer classification between e-DRW and conventional DRW. The within-dataset experiments indicated that our novel method demonstrated robust and superior performance in terms of accuracy and number of predicted risk-active pathways compared to the other DRW methods. In conclusion, the results revealed that e-DRW not only improved prediction performance, but also effectively extracted topologically important pathways and genes that are specifically related to the corresponding cancer types.

POPBic: Pathway-Based Order Preserving Biclustering Algorithm Towards the Analysis of Gene Expression Data

To understand the underlying biological mechanisms of gene expression data, it is important to discover the groups of genes that have similar expression patterns under certain subsets of conditions. Biclustering algorithms have been effective in analyzing large-scale gene expression data. Recently, traditional biclustering has been improved by introducing biological knowledge along with the expression data during the biclustering process. In this paper, we propose the Pathway-based Order Preserving Biclustering (POPBic) algorithm by incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG) based on the hypothesis that two genes sharing similar pathways are likely to be similar. The basic principle of the POPBic approach is to apply the concept of Longest Common Subsequence between a pair of genes which have a high number of common pathways. The algorithm identifies the expression patterns from data using two major steps: (i) selection of significant seed genes and (ii) extraction of biclusters. We performe exhaustive experimentation with the POPBic algorithm using synthetic dataset to evaluate the bicluster model, finding its robustness in the presence of noise and identifying overlapping biclusters. We demonstrate that POPBic is able to discover biologically significant biclusters for four cancer microarray gene expression datasets. POPBic has been found to perform consistently well in comparison to its closest competitors.

Systems Cytogenomics: Are We Ready Yet?

With the introduction of systems theory to genetics, numerous opportunities for genomic research have been identified. Consequences of DNA sequence variations are systematically evaluated using the network- or pathway-based analysis, a technological basis of systems biology or, more precisely, systems genomics. Despite comprehensive descriptions of advantages offered by systems genomic approaches, pathway-based analysis is uncommon in cytogenetic (cytogenomic) studies, i.e. genome analysis at the chromosomal level. Here, we would like to express our opinion that current cytogenomics benefits from the application of systems biology methodology. Accordingly, systems cytogenomics appears to be a biomedical area requiring more attention than it actually receives.

Squalene epoxidase expression is associated with breast tumor progression and with a poor prognosis in breast cancer

Differentially expressed genes (DEGs) have been previously identified using massive parallel RNA sequencing in matched normal, breast cancer (BC) and nodal metastatic tissues. Squalene epoxidase (SQLE), one of these DEGs, is a key enzyme in cholesterol synthesis. The aim of the present study was to investigate the potential involvement of SQLE in the tumorigenic process of BC and to determine its association with the clinical outcome of BC. SQLE mRNA expression was measured using reverse transcription-quantitative PCR in 10 pairs of ductal carcinoma in situ (DCIS) and BC tissues and their adjacent normal tissues. Immunohistochemical staining of SQLE on tissue microarray was performed in 26 normal breast, 79 DCIS and 198 BC samples. The role of SQLE as a prognostic biomarker in patients with BC has been verified using BreastMark. SQLE mRNA expression was significantly increased in DCIS and BC tissues compared with that in their adjacent normal tissues. High SQLE expression was detected in 0, 48.1 and 40.4% of normal breast, DCIS and BC tissues, respectively. SQLE expression in DCIS and BC tissues was significantly higher than that in normal breast tissues. High SQLE expression was observed in DCIS with higher nuclear grade, comedo-type necrosis and HER2 positivity. High SQLE expression in BC was associated with larger tumor size, nodal metastases, higher stage, HER2 subtype and distant metastatic relapse. High SQLE expression was associated with poor disease-free and overall survival, and independently predicted poor disease-free survival in patients with BC. Following BreastMark analysis, high SQLE mRNA expression in BC was significantly associated with a poor prognosis in the ‘all’, lymph node negative, lymph node positive, luminal A subtype and luminal B subtype groups. Therefore, SQLE expression may be upregulated during the tumorigenic process of BC, and high SQLE expression may be a useful biomarker for predicting a poor prognosis in patients with BC.

Unravelling actionable biology using transcriptomic data to integrate mitotic index and Ki-67 in the management of lung neuroendocrine tumors

Pulmonary neuroendocrine tumors (NETs) are a heterogeneous family of malignancies whose classification relies on morphology and mitotic rate, unlike extrapulmonary neuroendocrine tumors that require both mitotic rate and Ki-67. As mitotic count is proportional to Ki-67, it is crucial to understand if Ki-67 can complement the existing diagnostic guidelines, as well as discover the benefit of these two markers to unravel the biological heterogeneity. In this study, we investigated the association of mitotic rate and Ki-67 at gene- and pathway-level using transcriptomic data in lung NET malignancies. Lung resection tumor specimens obtained from 28 patients diagnosed with NETs were selected. Mitotic rate, Ki-67 and transcriptomic data were obtained for all samples. The concordance between mitotic rate and Ki-67 was evaluated at gene-level and pathway-level using gene expression data. Our analysis revealed a strong association between mitotic rate and Ki-67 across all samples and cell cycle genes were found to be differentially ranked between them. Pathway analysis indicated that a greater number of pathways overlapped between these markers. Analyses based on lung NET subtypes revealed that mitotic rate in carcinoids and Ki-67 in large cell neuroendocrine carcinomas provided comprehensive characterization of pathways among these malignancies. Among the two subtypes, we found distinct leading-edge gene sets that drive the enrichment signal of commonly enriched pathways between mitotic index and Ki-67. Overall, our findings delineated the degree of benefit of the two proliferation markers, and offers new layer to predict the biological behavior and identify high-risk patients using a more comprehensive diagnostic workup.

Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities

The transcriptomic classification of glioblastoma (GBM) has failed to predict survival and therapeutic vulnerabilities. A computational approach for unbiased identification of core biological traits of single cells and bulk tumors uncovered four tumor cell states and GBM subtypes distributed along neurodevelopmental and metabolic axes, classified as proliferative/progenitor, neuronal, mitochondrial and glycolytic/plurimetabolic. Each subtype was enriched with biologically coherent multiomic features. Mitochondrial GBM was associated with the most favorable clinical outcome. It relied exclusively on oxidative phosphorylation for energy production, whereas the glycolytic/plurimetabolic subtype was sustained by aerobic glycolysis and amino acid and lipid metabolism. Deletion of the glucose-proton symporter SLC45A1 was the truncal alteration most significantly associated with mitochondrial GBM, and the reintroduction of SLC45A1 in mitochondrial glioma cells induced acidification and loss of fitness. Mitochondrial, but not glycolytic/plurimetabolic, GBM exhibited marked vulnerability to inhibitors of oxidative phosphorylation. The pathway-based classification of GBM informs survival and enables precision targeting of cancer metabolism.

Cancer subtype classification and modeling by pathway attention and propagation

MOTIVATION

Biological pathway is an important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only one-third of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification.

RESULTS

We present an explainable deep-learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. Then, a multi-attention-based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway-gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer datasets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions.

AVAILABILITY AND IMPLEMENTATION

The source code is available at http://biohealth.snu.ac.kr/software/GCN_MAE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pharmacoproteomics Identifies Kinase Pathways that Drive the Epithelial-Mesenchymal Transition and Drug Resistance in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a complex and deadly disease lacking druggable genetic mutations. The limited efficacy of systemic treatments for advanced HCC implies that predictive biomarkers and drug targets are urgently needed. Most HCC drugs target protein kinases, indicating that kinase-dependent signaling networks drive HCC progression. To identify HCC signaling networks that determine responses to kinase inhibitors (KIs), we apply a pharmacoproteomics approach integrating kinome activity in 17 HCC cell lines with their responses to 299 KIs, resulting in a comprehensive dataset of pathway-based drug response signatures. By profiling patient HCC samples, we identify signatures of clinical HCC drug responses in individual tumors. Our analyses reveal kinase networks promoting the epithelial-mesenchymal transition (EMT) and drug resistance, including a FZD2-AXL-NUAK1/2 signaling module, whose inhibition reverses the EMT and sensitizes HCC cells to drugs. Our approach identifies cancer drug targets and molecular signatures of drug response for personalized oncology.

An integration analysis of mRNAs and miRNAs microarray data to identify key regulators for ovarian endometriosis based on competing endogenous RNAs

This study aimed to uncover effects of non-coding RNA transcripts on ovarian endometriosis (OEM) development. Two transcription datasets (GSE105764 and GSE105765) about OME were downloaded from Gene Expression Omnibus (GEO) database and the differentially expressed mRNAs, lncRNAs and miRNAs (DEmRNAs, DElncRNAs and DEmiRNAs) between OEM cases and controls were identified followed by protein-protein interaction analysis. Then, co-expression analysis was conducted and DEmiRNAs-DEmRNAs as well as DElncRNAs-DEmiRNAs pairs were predicted to construct the ceRNA network followed by sub-ceRNA network associated with OEM extraction. Functional analyses of DEmRNAs in ceRNA and sub-module network and the survival analysis were also performed to evaluate the correlation of key regulators and OV outcomes. Totally, 1910 DEmRNAs, 158 DElncRNAs and 118 DEmiRNAs were screened between OEM cases and controls and the functional analyses of DEmRNAs showed that they were significantly enriched in cell adhesion. Furthermore, there were 505 nodes in PPI network and ceRNA network included 762 interaction pairs among 357 DEmRNAs, 28 DElncRNAs and 24 DEmiRNAs. The KEGG analysis suggested several genes including FOXO1, STAT5A and RUNX1 were predominately associated with pathways in cancer while IL15 was primarily enriched in cytokine-cytokine receptor interaction pathway. Importantly, these two pathways were also found to be implicated with OEM development. Finally, survival analysis implied that overexpression of ZFPM2-AS1 had a good clinical outcome while the under-expression levels of FOXO1, JUP, STAT5A, RUNX1 and PRKG1-AS1 exhibited a better prognosis for OV. FOXO1, STAT5A, RUNX1 and IL15, PRKG1-AS1 and ZFPM2-AS1 were promising diagnostic makers for OME.

Predicting and affecting response to cancer therapy based on pathway-level biomarkers

Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.

Differential stromal reprogramming in benign and malignant naturally occurring canine mammary tumours identifies disease-modulating stromal components

While cancer-associated stroma (CAS) in malignant tumours is well described, stromal changes in benign forms of naturally occurring tumours remain poorly characterized. Spontaneous canine mammary carcinomas (mCA) are viewed as excellent models of human mCA. We have recently reported highly conserved stromal reprogramming between canine and human mCA based on transcriptome analysis of laser-capture-microdissected FFPE specimen. To identify stromal changes between benign and malignant mammary tumours, we have analysed matched normal and adenoma-associated stroma (AAS) from 13 canine mammary adenomas and compared them to previous data from 15 canine mCA. Our analyses reveal distinct stromal reprogramming even in small benign tumours. While similarities between AAS and CAS exist, the stromal signature clearly distinguished adenomas from mCA. The distinction between AAS and CAS is further substantiated by differential enrichment in several hallmark signalling pathways as well as differential abundance in cellular composition. Finally, we identify COL11A1, VIT, CD74, HLA-DRA, STRA6, IGFBP4, PIGR, and TNIP1 as strongly discriminatory stromal genes between adenoma and mCA, and demonstrate their prognostic value for human breast cancer. Given the relevance of canine CAS as a model for the human disease, our approach identifies disease-modulating stromal components with implications for both human and canine breast cancer.

Comprehensive analysis of mRNA-level and miRNA-level subpathway activities for identifying robust ovarian cancer prognostic signatures

Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA‐ or miRNA‐based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function‐level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high‐throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub‐training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA‐miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA‐miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.

Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma

OBJECTIVE

To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications.

METHODS

Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes.

RESULTS

The five-gene diagnostic signature including keratin 5 (KRT5), mucin 1 (MUC1), triggering receptor expressed on myeloid cells 1 (TREM1), complement C3 (C3) and transmembrane serine protease 2 (TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide (ADH1C), alpha-2-glycoprotein 1, zinc-binding (AZGP1), clusterin (CLU), cyclin dependent kinase 1 (CDK1) and paternally expressed 10 (PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set.

CONCLUSIONS

Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.

Emerging roles of F-box proteins in cancer drug resistance

Chemotherapy continues to be a major treatment strategy for various human malignancies. However, the frequent emergence of chemoresistance compromises chemotherapy efficacy leading to poor prognosis. Thus, overcoming drug resistance is pivotal to achieve enhanced therapy efficacy in various cancers. Although increased evidence has revealed that reduced drug uptake, increased drug efflux, drug target protein alterations, drug sequestration in organelles, enhanced drug metabolism, impaired DNA repair systems, and anti-apoptotic mechanisms, are critically involved in drug resistance, the detailed resistance mechanisms have not been fully elucidated in distinct cancers. Recently, F-box protein (FBPs), key subunits in Skp1-Cullin1-F-box protein (SCF) E3 ligase complexes, have been found to play critical roles in carcinogenesis, tumor progression, and drug resistance through degradation of their downstream substrates. Therefore, in this review, we describe the functions of FBPs that are involved in drug resistance and discuss how FBPs contribute to the development of cancer drug resistance. Furthermore, we propose that targeting FBPs might be a promising strategy to overcome drug resistance and achieve better treatment outcome in cancer patients. Lastly, we state the limitations and challenges of using FBPs to overcome chemotherapeutic drug resistance in various cancers.

Gene set based systematic analysis of prostate cancer and its subtypes

Aim: A gene set based systematic analysis strategy is used to investigate prostate tumors and its subclusters with focuses on similarities and differences of biological functions. Results: Dysregulation of methylation status, as well as RAS/RAF/ERK and PI3K-ATK signaling pathways, were found to be the most dramatic changes during prostate cancer tumorigenesis. Besides, neural and inflammation microenvironment is also significantly divergent between tumor and adjacent tissues. Insights of subclasses within prostate tumor cohorts revealed four different clusters with distinct gene expression patterns. We found that samples are mainly clustered by immune environments and proliferation traits. Conclusion: The findings of this article may help to advance the progress of identifying better diagnosis biomarkers and therapeutic targets.

Predictive modelling using pathway scores: robustness and significance of pathway collections

BACKGROUND

Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a 'pathway space'. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity.

RESULTS

Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases.

CONCLUSIONS

Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.

A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data

Background

Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification.

Results

A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods.

Conclusion

The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes.

Squalene Epoxidase Correlates E-Cadherin Expression and Overall Survival in Colorectal Cancer Patients: The Impact on Prognosis and Correlation to Clinicopathologic Features

Squalene epoxidase (SE), coded by SQLE, is an important rate-limiting enzyme in the cholesterol biosynthetic pathway. Recently, the aberrant expression of SQLE, which is responsible for epithelial to mesenchymal transition (EMT), has been reported in various types of cancer. This study was undertaken to clarify the clinicopathologic implications of SE in patients with stage I to IV colorectal cancer (CRC). We also analyzed the expression patterns of SE in association with E-cadherin in a series of CRCs. We detected the cytoplasmic expression of SE in 59.4% of carcinoma samples by immunohistochemistry (IHC). There was a significant correlation between a high level of SE expression and lymphovascular (LV) invasion (p < 0.001), tumor budding (p < 0.001), invasion depth (p = 0.002), regional lymph node metastasis (p < 0.001), and pathologic TNM stage (p < 0.001). SE is more abundantly expressed at the invasive front, and reversely correlated with E-cadherin expression. Patients with SE-positive CRC had shorter recurrence-free survival (RFS) and poor overall survival (OS) than those with SE-negative CRC in multivariate analysis (p < 0.001 and p < 0.001, respectively). These data suggest that SE can serve as a valuable biomarker for unfavorable prognosis, and as a possible therapeutic target in CRCs.

Differentially mutated subnetworks discovery

PROBLEM

We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard.

ALGORITHM

We propose a novel and efficient algorithm, called DAMOKLE, to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available.

EXPERIMENTAL RESULTS

We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights into the molecular mechanisms of the disease not revealed by standard methods.

Differential Expression of Genes for Ubiquitin Ligases in Medulloblastoma Subtypes

Using publically available datasets on gene expression in medulloblastoma (MB) subtypes, we selected genes for ubiquitin ligases and identified statistically those that best predicted each of the four major MB subgroups as separate disease entities. We identify a gene coding for an ubiquitin ligase, ZNRF3, whose overexpression alone can predict the WNT subgroup for 100% in the Pfister dataset. For the SHH subgroup, we identify a gene for a regulatory subunit of the protein phosphatase 2A (PP2A), PPP2R2C, as the major predictor among the E3 ligases genes. The ubiquitin and ubiquitin-like conjugation database (UUCD) lists PPP2R2C as coding for a Cullin Ring ubiquitin ligase adaptor. For group 3 MBs, the best ubiquitin ligase predictor was PPP2R2B, a gene which codes for another regulatory subunit of the PP2A holoenzyme. For group 4, the best E3 gene predictors were MID2, ZBTB18, and PPP2R2A, which codes for a third PP2A regulatory subunit. Heatmap analysis of the E3 gene data shows that expression of ten genes for ubiquitin ligases can be used to classify MBs into the four major consensus subgroups. This was illustrated by analysis of gene expression of ubiquitin ligases of the Pfister dataset and confirmed in the dataset of Cavalli. We conclude that genes for ubiquitin ligases can be used as genetic markers for MB subtypes and that the proteins coded for by these genes should be investigated as subtype specific therapeutic targets for MB.

Pathway-based classification of genetic diseases

Background

In medical genetics, diseases are classified according to the nature (hypothetical nature) of the underlying genetic defect. The classification is “gene-centric” and “factor-centric”; a disease may be, thereby, designated as monogenic, oligogenic or polygenic/multifactorial. Chromosomal diseases/syndromes and abnormalities are generally considered apart from these designations due to distinctly different formation mechanisms and simultaneous encompassing from several to several hundreds of co-localized genes. These definitions are ubiquitously used and are perfectly suitable for human genetics issues in historical and academic perspective. However, recent achievements in systems biology have offered a possibility to explore the consequences of a genetic defect from genomic variations to molecular/cellular pathway alterations unique to a disease. Since pathogenetic mechanisms (pathways) are more influential on our understating of disease presentation and progression than genetic defects per se, a need for a disease classification reflecting both genetic causes and molecular/cellular mechanisms appears to exist. Here, we propose an extension to the common disease classification based on the underlying genetic defects, which focuses on disease-specific molecular pathways.

Conclusion

The basic idea of our classification is to propose pathways as parameters for designating a genetic disease. To proceed, we have followed the tradition of using ancient Greek words and prefixes to create the terms for the pathway-based classification of genetic diseases. We have chosen the word “griphos” (γρῖφος), which simultaneously means “net” and “puzzle”, accurately symbolizing the term “pathway” currently used in molecular biology and medicine. Thus, diseases may be classified as monogryphic (single pathway is altered to result in a phenotype), digryphic (two pathways are altered to result in a phenotype), etc.; additionally, diseases may be designated as oligogryphic (several pathways are altered to result in a phenotype), polygryphic (numerous pathways or cascades of pathways are altered to result in a phenotype) and homeogryphic in cases of comorbid diseases resulted from shared pathway alterations. We suppose that classifying illness this way using both “gene-centric” and “pathway-centric” concepts is able to revolutionize current views on genetic diseases.

Gene expression patterns in synchronized islet populations

In vivo levels of insulin are oscillatory with a period of ~5-10 minutes, indicating that the islets of Langerhans within the pancreas are synchronized. While the synchronizing factors are still under investigation, one result of this behavior is expected to be coordinated and oscillatory intracellular factors, such as intracellular Ca²⁺ levels, throughout the islet population. In other cell types, oscillatory intracellular signals, like intracellular Ca²⁺, have been shown to affect specific gene expression. To test how the gene expression landscape may differ between a synchronized islet population with its reproducible intracellular oscillations and an unsynchronized islet population with heterogeneous oscillations, gene set enrichment analysis (GSEA) was used to compare an islet population that had been synchronized using a glucose wave with a 5-min period, and an unsynchronized islet population. In the population exposed to the glucose wave, 58/62 islets showed synchronization as evidenced by coordinated intracellular Ca²⁺ oscillations with an average oscillation period of 5.1 min, while in the unsynchronized population 29/62 islets showed slow oscillations with an average period of 5.2 min. The synchronized islets also had a significantly smaller drift of their oscillation period during the experiment as compared to the unsynchronized population. GSEA indicated that the synchronized population had reduced expression of gene sets related to protein translation, protein turnover, energy expenditure, and insulin synthesis, while those that were related to maintenance of cell morphology were increased.

PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data

Background

Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.

Results

In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.

Conclusions

PASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.

FBXL7 Upregulation Predicts a Poor Prognosis and Associates with a Possible Mechanism for Paclitaxel Resistance in Ovarian Cancer

Paclitaxel (PTX) is a common regimen used to treat patients with ovarian cancer. Although approximately 60% of ovarian cancer patients exhibit a pathologic complete response (pCR), approximately 40% of patients appear to be insensitive to PTX adjuvant therapy. Thus, identifying a useful biomarker to predict pCR would be of great help to ovarian cancer patients who decide to receive PTX treatment. We found that FBXL7 was downregulated in OVSAHO (PTX-sensitive) but upregulated in KURAMOCHI (PTX-resistant) cells after PTX treatment at cytotoxic concentrations. Moreover, our data showed that the fold change of FBXL7 expression post-treatment with PTX was causally correlated with the 50% inhibitory concentrations (IC₅₀) of PTX in a panel of ovarian cancer cell lines. In assessments of progression-free survival probability, high levels of FBXL7 transcript strongly predicted a poor prognosis and unfavorable response to PTX-based chemotherapy in patients with ovarian cancer. The knockdown of FBXL7 predominantly enhanced the cytotoxic effectiveness of PTX on the PTX-resistant KURAMOCHI cells. FBXL7 may be a useful biomarker for predicting complete pathologic response in ovarian cancer patients who decide to receive post-operative PTX therapy.

Identifying SNPs and candidate genes for three litter traits using single-step GWAS across six parities in Landrace and Large White pigs

Total number born (TNB), number born alive (NBA), and litter weight born alive (LWB) are critically important traits in pig production. The sow's parity is one of the major factors influencing litter traits. Because of monogenic or polygenic contributions and the presence of temporal gene effects in different sows' parities, it is difficult to clarify the biological and genetic background. To systematically explore the genetic mechanism of litter traits, we conducted 18 GWASs using single-step GWAS (ssGWAS) based on two breeds (908 Landrace and 1,130 Large White sow litter records) for each litter trait in different parities. A total of 300 Landrace and 300 Large White sows were genotyped by sequencing (GBS). ssGWAS was performed separately for each breed and each parity due to population stratification and temporal gene effect. In summary, we identified 80 (15 for Landrace and 65 for Large White), 227 (52 for Landrace, 175 for Large White), and 187 (34 for Landrace, 153 for Large White) single nucleotide polymorphisms (SNPs) affecting TNB, NBA, and LWB, respectively. Of them, we suggest that a total of 22 loci (SSC1: 125098202, SSC1: 117560058, SSC14: 147794697, SSC8: 84823302, SSC9: 143554876, and SSC9: 138766097 for Landrace; SSC1: 4023577, SSC1: 3859573, SSC1: 4891063, SSC16: 5197665, SSC10: 32050819, SSC13: 13552924, SSC13: 92819, SSC17: 3579607, SSC13: 196698221, SSC7: 30918403, SSC16: 46221484, SSC16: 46169204, SSC2: 41988642, SSC2: 44475457, SSC2: 42521875, and SSC7: 58411951 for Large White) are shared by TNB, NBA, and LWB. These results indicate the existence of gene temporal effect in each parity. Furthermore, our findings suggest four interesting candidate genes (FBXL7, ALDH1A2, LEPR, and DDX1) associated with litter traits in different parities that have a major effect on embryonic development progression. In conclusion, 22 crucial SNPs and four interesting candidate genes were identified for three litter traits across six parities. These findings advance our understanding of the genetic architecture of litter traits and confirm the presence of temporal gene effects in different parities. Importantly, functional validation studies for findings of particular interest are recommended in litter traits.

PASI: A novel pathway method to identify delicate group effects

Pathway analysis is a common approach in diverse biomedical studies, yet the currently-available pathway tools do not typically support the increasingly popular personalized analyses. Another weakness of the currently-available pathway methods is their inability to handle challenging data with only modest group-based effects compared to natural individual variation. In an effort to address these issues, this study presents a novel pathway method PASI (Pathway Analysis for Sample-level Information) and demonstrates its performance on complex diseases with different levels of group-based differences in gene expression. PASI is freely available as an R package.

Analytical Strategy to Prioritize Alzheimer's Disease Candidate Genes in Gene Regulatory Networks Using Public Expression Data

Alzheimer’s disease (AD) progressively destroys cognitive abilities in the aging population with tremendous effects on memory. Despite recent progress in understanding the underlying mechanisms, high drug attrition rates have put a question mark behind our knowledge about its etiology. Re-evaluation of past studies could help us to elucidate molecular-level details of this disease. Several methods to infer such networks exist, but most of them do not elaborate on context specificity and completeness of the generated networks, missing out on lesser-known candidates. In this study, we present a novel strategy that corroborates common mechanistic patterns across large scale AD gene expression studies and further prioritizes potential biomarker candidates. To infer gene regulatory networks (GRNs), we applied an optimized version of the BC3Net algorithm, named BC3Net10, capable of deriving robust and coherent patterns. In principle, this approach initially leverages the power of literature knowledge to extract AD specific genes for generating viable networks. Our findings suggest that AD GRNs show significant enrichment for key signaling mechanisms involved in neurotransmission. Among the prioritized genes, well-known AD genes were prominent in synaptic transmission, implicated in cognitive deficits. Moreover, less intensive studied AD candidates (STX2, HLA-F, HLA-C, RAB11FIP4, ARAP3, AP2A2, ATP2B4, ITPR2, and ATP2A3) are also involved in neurotransmission, providing new insights into the underlying mechanism. To our knowledge, this is the first study to generate knowledge-instructed GRNs that demonstrates an effective way of combining literature-based knowledge and data-driven analysis to identify lesser known candidates embedded in stable and robust functional patterns across disparate datasets.

Squalene epoxidase as a promising metabolic target in cancer treatment

Oncogenic alteration of the cholesterol synthesis pathway is a recognized mechanism of metabolic adaptation. In the present review, we focus on squalene epoxidase (SE), one of the two rate-limiting enzymes in cholesterol synthesis, retracing its history since its discovery as an antimycotic target to its description as an emerging metabolic oncogene by amplification with clinical relevance in cancer. We review the published literature assessing the association between SE over-expression and poor prognosis in this disease. We assess the works demonstrating how SE promotes tumor cell proliferation and migration, and displaying evidence of cancer cell demise in presence of human SE inhibitors in in vitro and in vivo models. Taken together, robust scientific evidence has by now accumulated pointing out SE as a promising novel therapeutic target in cancer treatment.

Semi-supervised identification of cancer subgroups using survival outcomes and overlapping grouping information

Identification of cancer patient subgroups using high throughput genomic data is of critical importance to clinicians and scientists because it can offer opportunities for more personalized treatment and overlapping treatments of cancers. In spite of tremendous efforts, this problem still remains challenging because of low reproducibility and instability of identified cancer subgroups and molecular features. In order to address this challenge, we developed Integrative Genomics Robust iDentification of cancer subgroups (InGRiD), a statistical approach that integrates information from biological pathway databases with high-throughput genomic data to improve the robustness for identification and interpretation of molecularly-defined subgroups of cancer patients. We applied InGRiD to the gene expression data of high-grade serous ovarian cancer from The Cancer Genome Atlas and the Australian Ovarian Cancer Study. The results indicate clear benefits of the pathway-level approaches over the gene-level approaches. In addition, using the proposed InGRiD framework, we also investigate and address the issue of gene sharing among pathways, which often occurs in practice, to further facilitate biological interpretation of key molecular features associated with cancer progression. The R package "InGRiD" implementing the proposed approach is currently available in our research group GitHub webpage ( https://dongjunchung.github.io/INGRID/ ).

Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors

Background

Phenotypic classification is problematic because small samples are ubiquitous; and, for these, use of prior knowledge is critical. If knowledge concerning the feature-label distribution – for instance, genetic pathways – is available, then it can be used in learning. Optimal Bayesian classification provides optimal classification under model uncertainty. It differs from classical Bayesian methods in which a classification model is assumed and prior distributions are placed on model parameters. With optimal Bayesian classification, uncertainty is treated directly on the feature-label distribution, which assures full utilization of prior knowledge and is guaranteed to outperform classical methods.

Results

The salient problem confronting optimal Bayesian classification is prior construction. In this paper, we propose a new prior construction methodology based on a general framework of constraints in the form of conditional probability statements. We call this prior the maximal knowledge-driven information prior (MKDIP). The new constraint framework is more flexible than our previous methods as it naturally handles the potential inconsistency in archived regulatory relationships and conditioning can be augmented by other knowledge, such as population statistics. We also extend the application of prior construction to a multinomial mixture model when labels are unknown, which often occurs in practice. The performance of the proposed methods is examined on two important pathway families, the mammalian cell-cycle and a set of p53-related pathways, and also on a publicly available gene expression dataset of non-small cell lung cancer when combined with the existing prior knowledge on relevant signaling pathways.

Conclusion

The new proposed general prior construction framework extends the prior construction methodology to a more flexible framework that results in better inference when proper prior knowledge exists. Moreover, the extension of optimal Bayesian classification to multinomial mixtures where data sets are both small and unlabeled, enables superior classifier design using small, unstructured data sets. We have demonstrated the effectiveness of our approach using pathway information and available knowledge of gene regulating functions; however, the underlying theory can be applied to a wide variety of knowledge types, and other applications when there are small samples.

Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information

Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible.

De novo pathway-based biomarker identification

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.

Finding disagreement pathway signatures and constructing an ensemble model for cancer classification

Cancer classification based on molecular level is a relatively routine research procedure with advances in high-throughput molecular profiling techniques. However, the number of genes typically far exceeds the number of the sample size in gene expression studies. The existing gene selection methods are almost based on statistics and machine learning, overlooking relevant biological principles or knowledge while working with biological data. Here, we propose a robust ensemble learning paradigm, which incorporates multiple pathways information, to predict cancer classification. We compare the proposed method with other methods, such as Elastic SCAD and PPDMF, and estimate the classification performance. The results show that the proposed method has the higher performances on most metrics and robust performance. We further investigate the biological mechanism of the ensemble feature genes. The results demonstrate that the ensemble feature genes are associated with drug targets/clinically-relevant cancer. In addition, some core biological pathways and biological process underlying clinically-relevant phenotypes are identified by function annotation. Overall, our research can provide a new perspective for the further study of molecular activities and manifestations of cancer.

Identifying dynamic pathway interactions based on clinical information

In this paper, we introduce approaches for inferring dynamic pathway interactions by converting static datasets into dynamic datasets using patients' clinical information. One approach uses survival time-based dynamic datasets, and the other uses grade- and stage-based dynamic datasets. Based on cancer grades and stages, we generated six dynamic levels and obtained two pairs of significant pathways out of twelve enriched pathways. One pair of the pathways included CELL ADHESION MOLECULES CAMS and SYSTEMIC LUPUS ERYTHEMATOSUS (correlation coefficient=1.00), in which CD28, CD86, HLA-DOA, and HLA-DOB were identified as common genes in the pathways. The other pair of the pathways included SPLICEOSOME and PRIMARY IMMUNODEFICIENCY (correlation coefficient=0.94) with no common genes identified.

Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data

BACKGROUND

The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data.

RESULTS

The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively.

CONCLUSIONS

Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into distinctive associations between pathway activities in case and control samples.

SQLE induces epithelial-to-mesenchymal transition by regulating of miR-133b in esophageal squamous cell carcinoma

Increasing evidence suggests that microRNAs, which control gene expression at the post-transcriptional level, are aberrantly expressed in cancers and play significant roles in carcinogenesis and cancer progression. In this study, we show differential miR-133b down-expression in human esophageal squamous cell carcinoma (ESCC) cells and tissues. In addition, squalene epoxidase (SQLE), a key enzyme of cholesterol synthesis, is identified as the direct downstream target gene of miR-133b by luciferase gene reporter assay. Furthermore, ectogenic miR-133b expression and SQLE knockdown can inhibit proliferation, invasion, and metastasis, and diminish epithelial-to-mesenchymal transition (EMT) traits of ESCC in vitro, implying that miR-133b-dependent SQLE can induce tumorigenicity and that SQLE is an EMT inducer. Xenograft experiment results also proved the biological function of SQLE in vivo. Therefore, we conclude that miR-133b-dependent SQLE plays a critical role in the potential metastasis mechanisms in ESCC.

Identification of Genes Discriminating Multiple Sclerosis Patients from Controls by Adapting a Pathway Analysis Method

The focus of analyzing data from microarray experiments has shifted from the identification of associated individual genes to that of associated biological pathways or gene sets. In bioinformatics, a feature selection algorithm is usually used to cope with the high dimensionality of microarray data. In addition to those algorithms that use the biological information contained within a gene set as a priori to facilitate the process of feature selection, various gene set analysis methods can be applied directly or modified readily for the purpose of feature selection. Significance analysis of microarray to gene-set reduction analysis (SAM-GSR) algorithm, a novel direction of gene set analysis, is one of such methods. Here, we explore the feature selection property of SAM-GSR and provide a modification to better achieve the goal of feature selection. In a multiple sclerosis (MS) microarray data application, both SAM-GSR and our modification of SAM-GSR perform well. Our results show that SAM-GSR can carry out feature selection indeed, and modified SAM-GSR outperforms SAM-GSR. Given pathway information is far from completeness, a statistical method capable of constructing biologically meaningful gene networks is of interest. Consequently, both SAM-GSR algorithms will be continuously revaluated in our future work, and thus better characterized.

Network-Assisted Disease Classification and Biomarker Discovery

Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput "omics" data is an active research topic in the field of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identified from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identification. In this chapter, after giving a general introduction to the problem of disease classification and biomarker discovery, we will review two types of network-assisted approaches: (1) approaches inferring activity scores for specific pathways which are subsequently used for classification and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.

Disease-specific classification using deconvoluted whole blood gene expression

Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.

Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data

We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful for clustering cancer cohorts into subtypes. Variants of the method allow it to be used on datasets that do and do not contain noncancerous samples. Activity levels of all types of pathways, broadly grouped into metabolic, cellular processes and signaling, and immune system, are useful for separating the pan-cancer cohort. In the clustering of specific cancer types, certain pathway types become more valuable depending on the site being studied. For lung cancer, signaling pathways dominate; for pancreatic cancer, signaling and metabolic pathways dominate; and for melanoma, immune system pathways are the most useful. This work suggests the utility of pathway-level genomic analysis and points in the direction of using pathway classification for predicting the efficacy and side effects of drugs and radiation.