Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression

Expanding Beyond Genetic Subtypes in B-Cell Acute Lymphoblastic Leukemia: A Pathway-Based Stratification of Patients for Precision Oncology

Precision oncology promises individually tailored drugs and clinical care for patients with cancer: That is, "the right drug, for the right patient, at the right dose, and at the right time." Although stratification of the risk for treatment resistance and toxicity is key to precision oncology, there are multiple ways in which such stratification can be achieved, for example, genetic, functional pathway based, among others. Moving toward precision oncology is sorely needed in the case of acute lymphoblastic leukemia (ALL) wherein adult patients display survival rates ranging from 30% to 70%. The present study reports on the pathway activity signature of adult B-ALL, with an eye to precision oncology. Transcriptome profiles from three different expression datasets, comprising 346 patients who were adolescents or adults with B-ALL, were harnessed to determine the activity of signaling pathways commonly disrupted in B-ALL. Pathway activity analyses revealed that Ph-like ALL closely resembles Ph-positive ALL. Although this was the case at the average pathway activity level, the pathway activity patterns in B-ALL differ from genetic subtypes. Importantly, clustering analysis revealed that five distinct clusters exist in B-ALL patients based on pathway activity, with each cluster displaying a unique pattern of pathway activation. Identifying pathway-based subtypes thus appears to be crucial, considering the inherent heterogeneity among patients with the same genetic subtype. In conclusion, a pathway-based stratification of the B-ALL could potentially allow for simultaneously targeting highly active pathways within each ALL subtype, and thus might open up new avenues of innovation for personalized/precision medicine in this cancer that continues to have poor prognosis in adult patients compared with the children.

Characterization of Cancer/Testis Antigens as Prognostic Markers of Ovarian Cancer

The main goal of this study was to characterize cancer/testis antigens (CTAs) as potential molecular markers of ovarian cancer. First, we gathered and analyzed a significantly large dataset of 21 selected CTAs that are encoded by 32 genes; the dataset consisted of the mutation data, expression data, and survival data of patients with ovarian cancer (n = 15,665). The 19 functionally significant missense mutations were identified in 9 CTA genes: ACRBP, CCT4, KDM5B, MAGEA1, MAGEA4, PIWIL1, PIWIL2, PRAME, and SPA17. The analysis of the mRNA expression levels of 21 CTAs in healthy and tumor ovarian tissue showed an up-regulation in the expression level of AKAP3, MAGEA4, PIWIL1, and PRAME in tumor samples and a down-regulation in the expression level of CTAG1A, CTAG1B, MAGEC1, and PIWIL2. The CCT4 up-regulation and PRAME mutations were correlated with a good prognosis for ovarian cancer, while higher levels of GAGE2A and CT45A1 mRNAs were correlated with a poor prognosis for ovarian cancer patients. Thus, GAGE2, CT45, CCT4, and PRAME cancer/testis antigens can be considered as potential prognostic markers for ovarian tumors, and GAGE2, CCT4, and PRAME were revealed to be correlated with the prognosis for ovarian cancer patients for the first time.

SEQENS: An ensemble method for relevant gene identification in microarray data

This paper describes an ensemble feature identification algorithm called SEQENS, and measures its capability to identify the relevant variables in a case-control study using a genetic expression microarray dataset. SEQENS uses Sequential Feature Search on multiple sample splitting to select variables showing stronger relation with the target, and a variable relevance ranking is finally produced. Although designed for feature identification, SEQENS could also serve as a basis for feature selection (classifier optimisation). Cliff, a ranking evaluation metric is also presented and used to assess the feature identification algorithms when a groundtruth of relevant variables is available. To test performance, three types of synthetic groundtruths emulating fictitious diseases are generated from ten randomly chosen variables following different target pattern distributions using the E-MTAB-3732 dataset. Several sample-to-dimensionality ratios ranging from 300 to 3,000 observations and 854 to 54,675 variables are explored. SEQENS is compared with other feature selection or identification state-of-the-art methods. On average, the proposed algorithm identifies better the relevant genes and exhibits a stronger stability. The algorithm is available to the community.

MPVNN: Mutated Pathway Visible Neural Network architecture for interpretable prediction of cancer-specific survival risk

MOTIVATION

Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with a lack of interpretability. More interpretable visible neural network architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types.

RESULTS

We propose a novel Mutated Pathway Visible Neural Network (MPVNN) architecture, designed using prior signaling pathway knowledge and random replacement of known pathway edges using gene mutation data simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction of MPVNN over other similar-sized NN and standard survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that is important in risk prediction for particular cancer types, is reliable.

AVAILABILITY AND IMPLEMENTATION

The data and code are available at https://github.com/gourabghoshroy/MPVNN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessment of deep learning and transfer learning for cancer prediction based on gene expression data

BACKGROUND

Machine learning is now a standard tool for cancer prediction based on gene expression data. However, deep learning is still new for this task, and there is no clear consensus about its performance and utility. Few experimental works have evaluated deep neural networks and compared them with state-of-the-art machine learning. Moreover, their conclusions are not consistent.

RESULTS

We extensively evaluate the deep learning approach on 22 cancer prediction tasks based on gene expression data. We measure the impact of the main hyper-parameters and compare the performances of neural networks with the state-of-the-art. We also investigate the effectiveness of several transfer learning schemes in different experimental setups.

CONCLUSION

Based on our experimentations, we provide several recommendations to optimize the construction and training of a neural network model. We show that neural networks outperform the state-of-the-art methods only for very large training set size. For a small training set, we show that transfer learning is possible and may strongly improve the model performance in some cases.

GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression

MOTIVATION

Medical care is becoming more and more specific to patients' needs due to the increased availability of omics data. The application to these data of sophisticated machine learning models, in particular deep learning, can improve the field of precision medicine. However, their use in clinics is limited as their predictions are not accompanied by an explanation. The production of accurate and intelligible predictions can benefit from the inclusion of domain knowledge. Therefore, knowledge-based deep learning models appear to be a promising solution.

RESULTS

In this paper, we propose GraphGONet, where the Gene Ontology is encapsulated in the hidden layers of a new self-explaining neural network. Each neuron in the layers represents a biological concept, combining the gene expression profile of a patient, and the information from its neighboring neurons. The experiments described in the paper confirm that our model not only performs as accurately as the state-of-the-art (non-explainable ones) but also automatically produces stable and intelligible explanations composed of the biological concepts with the highest contribution. This feature allows experts to use our tool in a medical setting.

AVAILABILITY

GraphGONet is freely available at https://forge.ibisc.univ-evry.fr/vbourgeais/GraphGONet.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Characterization of SLC34A2 as a Potential Prognostic Marker of Oncological Diseases

The main goal of this study is to consider SLC34A2 as a potential prognostic marker of oncological diseases using the mutational, expression, and survival data of cancer studies which are publicly available online. We collected data from four databases (cBioPortal, The Cancer Genome Atlas; cBioPortal, Genie; International Cancer Genome Consortium; ArrayExpress). In total, 111,283 samples were categorized according to 27 tumor locations. Ninety-nine functionally significant missense mutations and twelve functionally significant indel mutations in SLC34A2 were found. The most frequent mutations were SLC34A2-ROS1, p.T154A, p.P506S/R/L, p.G257A/E/R, p.S318W, p.A396T, p.P410L/S/H, p.S461C, p.A473T/V, and p.Y503H/C/F. The upregulation of SLC34A2 was found in samples of myeloid, bowel, ovarian, and uterine tumors; downregulation was found in tumor samples of breast, liver, lung, and skin cancer tumors. It was found that the life expectancy of breast and thymus cancer patients with an SLC34A2 mutation is lower, and it was revealed that SLC34A2 overexpression reduced the life span of patients with brain, ovarian, and pancreatic tumors. Thereby, for these types of oncological diseases, the mutational profile of SLC34A2 can be a potential prognostic marker for breast and thymus cancers, and the upregulation of SLC34A2 can be a potential prognostic marker for brain, ovarian, and pancreatic cancers.

Band-based similarity indices for gene expression classification and clustering

The concept of depth induces an ordering from centre outwards in multivariate data. Most depth definitions are unfeasible for dimensions larger than three or four, but the Modified Band Depth (MBD) is a notable exception that has proven to be a valuable tool in the analysis of high-dimensional gene expression data. This depth definition relates the centrality of each individual to its (partial) inclusion in all possible bands formed by elements of the data set. We assess (dis)similarity between pairs of observations by accounting for such bands and constructing binary matrices associated to each pair. From these, contingency tables are calculated and used to derive standard similarity indices. Our approach is computationally efficient and can be applied to bands formed by any number of observations from the data set. We have evaluated the performance of several band-based similarity indices with respect to that of other classical distances in standard classification and clustering tasks in a variety of simulated and real data sets. However, the use of the method is not restricted to these, the extension to other similarity coefficients being straightforward. Our experiments show the benefits of our technique, with some of the selected indices outperforming, among others, the Euclidean distance.

Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data

BACKGROUND

With the rapid advancement of genomic sequencing techniques, massive production of gene expression data is becoming possible, which prompts the development of precision medicine. Deep learning is a promising approach for phenotype prediction (clinical diagnosis, prognosis, and drug response) based on gene expression profile. Existing deep learning models are usually considered as black-boxes that provide accurate predictions but are not interpretable. However, accuracy and interpretation are both essential for precision medicine. In addition, most models do not integrate the knowledge of the domain. Hence, making deep learning models interpretable for medical applications using prior biological knowledge is the main focus of this paper.

RESULTS

In this paper, we propose a new self-explainable deep learning model, called Deep GONet, integrating the Gene Ontology into the hierarchical architecture of the neural network. This model is based on a fully-connected architecture constrained by the Gene Ontology annotations, such that each neuron represents a biological function. The experiments on cancer diagnosis datasets demonstrate that Deep GONet is both easily interpretable and highly performant to discriminate cancer and non-cancer samples.

CONCLUSIONS

Our model provides an explanation to its predictions by identifying the most important neurons and associating them with biological functions, making the model understandable for biologists and physicians.

Extreme Downregulation of Chromosome Y and Cancer Risk in Men

BACKGROUND

Understanding the biological differences between sexes in cancer is essential for personalized treatment and prevention. We hypothesized that the extreme downregulation of chromosome Y gene expression (EDY) is a signature of cancer risk in men and the functional mediator of the reported association between the mosaic loss of chromosome Y (LOY) and cancer.

METHODS

We advanced a method to measure EDY from transcriptomic data. We studied EDY across 47 nondiseased tissues from the Genotype Tissue-Expression Project (n = 371) and its association with cancer status across 12 cancer studies from The Cancer Genome Atlas (n = 1774) and seven other studies (n = 7562). Associations of EDY with cancer status and presence of loss-off function mutations in chromosome X were tested with logistic regression models, and a Fisher's test was used to assess genome-wide association of EDY with the proportion of copy number gains. All statistical tests were two-sided.

RESULTS

EDY was likely to occur in multiple nondiseased tissues (P < .001) and was statistically significantly associated with the EGFR tyrosine kinase inhibitor resistance pathway (false discovery rate = 0.028). EDY strongly associated with cancer risk in men (odds ratio [OR] = 3.66, 95% confidence interval [CI] = 1.58 to 8.46, P = .002), adjusted by LOY and age, and its variability was largely explained by several genes of the nonrecombinant region whose chromosome X homologs showed loss-of-function mutations that co-occurred with EDY during cancer (OR = 2.82, 95% CI = 1.32 to 6.01, P = .007). EDY associated with a high proportion of EGFR amplifications (OR = 5.64, 95% CI = 3.70 to 8.59, false discovery rate < 0.001) and EGFR overexpression along with SRY hypomethylation and nonrecombinant region hypermethylation, indicating alternative causes of EDY in cancer other than LOY. EDY associations were independently validated for different cancers and exposure to smoking, and its status was accurately predicted from individual methylation patterns.

CONCLUSIONS

EDY is a male-specific signature of cancer susceptibility that supports the escape from X-inactivation tumor suppressor hypothesis for genes that protect women compared with men from cancer risk.

Elevated Expression of Cathepsin K in Periodontal Ligament Fibroblast by Inflammatory Cytokines Accelerates Osteoclastogenesis via Paracrine Mechanism in Periodontal Disease

Cathepsin K (CTSK) is a cysteine protease that is mainly produced from mature osteoclasts and contributes to the destruction of connective tissues and mineralized matrix as a consequence of periodontal disease (PD). However, few studies have reported its regulatory role in osteoclastogenesis-supporting cells in inflammatory conditions. Here, we investigated the role of CTSK in osteoclastogenesis-supporting cells, focusing on the modulation of paracrine function. Microarray data showed that CTSK was upregulated in PD patients compared with healthy individuals, which was further supported by immunohistochemistry and qPCR analyses performed with human gingival tissues. The expression of CTSK in the osteoclastogenesis-supporting cells, including dental pulp stem cells, gingival fibroblasts, and periodontal ligament fibroblasts (PDLFs) was significantly elevated by treatment with inflammatory cytokines such as TNFα and IL-1β. Moreover, TNFα stimulation potentiated the PDLF-mediated osteoclastogenesis of bone marrow-derived macrophages. Interestingly, small interfering RNA-mediated silencing of CTSK in PDLF noticeably attenuated the TNFα-triggered upregulation of receptor activator of nuclear factor kappa-B ligand (RANKL), macrophage colony-stimulating factor, and RANKL/osteoprotegerin ratio, thereby abrogating the enhanced osteoclastogenesis-supporting activity of PDLF. Collectively, these results suggest a novel role of CTSK in the paracrine function of osteoclastogenesis-supporting cells in periodontal disease.

Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

Biological interpretation of deep neural network for phenotype prediction based on gene expression

BACKGROUND

The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field.

RESULTS

We focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians.

CONCLUSION

We propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.

Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials

BACKGROUND

The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases.

METHODS AND FINDINGS

Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5-29, 49-69 versus 70-82 months, χ2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI -1 to 31, 92-120 versus 113-129 months, χ2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8+ T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6-36, 49-73 versus 74-90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies.

CONCLUSIONS

In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies.

Secreted modular calcium-binding proteins in pathophysiological processes and embryonic development

Objective:

Secreted modular calcium-binding proteins (SMOCs) are extracellular glycoproteins of the secreted protein, acidic, and rich in cysteine-related modular calcium-binding protein family and include two isoforms, SMOC1 and SMOC2, in humans. Functionally, SMOCs bind to calcium for various cell functions. In this review, we provided a summary of the most recent advancements in and findings of SMOC1 and SMOC2 in development, homeostasis, and disease states.

Data sources:

All publications in the PubMed database were searched and retrieved (up to July 24, 2019) using various combinations of keywords searching, including SMOC1, SMOC2, and diseases.

Study selection:

All original studies and review articles of SMOCs in human diseases and embryo development written in English were retrieved and included.

Results:

SMOC1 and SMOC2 regulate embryonic development, cell homeostasis, and disease pathophysiology. They play an important role in the regulation of cell cycle progression, cell attachment to the extracellular matrix, tissue fibrosis, calcification, angiogenesis, birth defects, and cancer development.

Conclusions:

SMOC1 and SMOC2 are critical regulators of many cell biological processes and potential therapeutic targets for the control of human cancers and birth defects.

Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder

Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.

ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells

Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer

Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions

Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein-protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics

Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.

RefBool: a reference-based algorithm for discretizing gene expression data

Motivation

The identification of genes or molecular regulatory mechanisms implicated in biological processes often requires the discretization, and in particular booleanization, of gene expression measurements. However, currently used methods mostly classify each measurement into an active or inactive state regardless of its statistical support possibly leading to downstream analysis conclusions based on spurious booleanization results.

Results

In order to overcome the lack of certainty inherent in current methodologies and to improve the process of discretization, we introduce RefBool, a reference-based algorithm for discretizing gene expression data. Instead of requiring each measurement to be classified as active or inactive, RefBool allows for the classification of a third state that can be interpreted as an intermediate expression of genes. Furthermore, each measurement is associated to a p- and q-value indicating the significance of each classification. Validation of RefBool on a neuroepithelial differentiation study and subsequent qualitative and quantitative comparison against 10 currently used methods supports its advantages and shows clear improvements of resulting clusterings.

Availability and Implementation

The software is available as MATLAB files in the Supplementary Information and as an online repository ( https://github.com/saschajung/RefBool ).

Contact

antonio.delsol@uni.lu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Deviations of the immune cell landscape between healthy liver and hepatocellular carcinoma

Tumor-infiltrating immune cells are highly relevant for prognosis and identification of immunotherapy targets in hepatocellular carcinoma (HCC). The recently developed CIBERSORT method allows immune cell profiling by deconvolution of gene expression microarray data. By applying CIBERSORT, we assessed the relative proportions of immune cells in 41 healthy human livers, 305 HCC samples and 82 HCC adjacent tissues. The obtained immune cell profiles provided enumeration and activation status of 22 immune cell subtypes. Mast cells were evaluated by immunohistochemistry in ten HCC patients. Activated mast cells, monocytes and plasma cells were decreased in HCC, while resting mast cells, total and naïve B cells, CD4+ memory resting and CD8+ T cells were increased when compared to healthy livers. Previously described S1, S2 and S3 molecular HCC subclasses demonstrated increased M1-polarized macrophages in the S3 subclass with good prognosis. Strong total immune cell infiltration into HCC correlated with total B cells, memory B cells, T follicular helper cells and M1 macrophages, whereas weak infiltration was linked to resting NK cells, neutrophils and resting mast cells. Immunohistochemical analysis of patient samples confirmed the reduced frequency of mast cells in human HCC tumor tissue as compared to tumor adjacent tissue. Our data demonstrate that deconvolution of gene expression data by CIBERSORT provides valuable information about immune cell composition of HCC patients.

Cancer transcriptome profiling at the juncture of clinical translation

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

Overexpression of SMOC2 Attenuates the Tumorigenicity of Hepatocellular Carcinoma Cells and Is Associated With a Positive Postoperative Prognosis in Human Hepatocellular Carcinoma

Secreted modular calcium binding protein-2 (SMOC2), a recently identified matricellular protein that belongs to the SPARC protein family, has been reported to be downregulated in various cancers. The purpose of this study was to investigate the clinical significance and biological function of SMOC2 in human hepatocellular carcinoma. Real-time quantitative PCR and western blotting analyses revealed that SMOC2 mRNA and protein levels were significantly downregulated in human HCC tissues compared to the matched adjacent normal tissues. Clinicopathological analysis indicated that SMOC2 expression was significantly associated with tumor size, number of tumors, tumor-node-metastasis (TNM) stage and distant metastasis. Kaplan-Meier survival analysis showed that high tumor SMOC2 expression was associated with improved overall survival and disease-free survival in patients with HCC. Functional analyses (cell proliferation and colony formation assays, cell migration and invasion assays, cell cycle and apoptosis assays) demonstrated that stable overexpression of SMOC2 using a lentiviral vector significantly inhibited cell proliferation, colony formation, migration and invasion, and induced G0/G1 phase arrest in HCC cells in vitro. In addition, experiments with a mouse model revealed the suppressed effect of SMOC2 on HCC tumorigenicity and metastases in vivo. These results suggest that SMOC2 functions as a tumor suppressor during the development of HCC and may represent an effective prognostic factor and novel therapeutic target for HCC.

HEROD: a human ethnic and regional specific omics database

Motivation

Genetic and gene expression variations within and between populations and across geographical regions have substantial effects on the biological phenotypes, diseases, and therapeutic response. The development of precision medicines can be facilitated by the OMICS studies of the patients of specific ethnicity and geographic region. However, there is an inadequate facility for broadly and conveniently accessing the ethnic and regional specific OMICS data.

Results

Here, we introduced a new free database, HEROD, a human ethnic and regional specific OMICS database. Its first version contains the gene expression data of 53 070 patients of 169 diseases in seven ethnic populations from 193 cities/regions in 49 nations curated from the Gene Expression Omnibus (GEO), the ArrayExpress Archive of Functional Genomics Data (ArrayExpress), the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Geographic region information of curated patients was mainly manually extracted from referenced publications of each original study. These data can be accessed and downloaded via keyword search, World map search, and menu-bar search of disease name, the international classification of disease code, geographical region, location of sample collection, ethnic population, gender, age, sample source organ, patient type (patient or healthy), sample type (disease or normal tissue) and assay type on the web interface.

Availability and implementation

The HEROD database is freely accessible at http://bidd2.nus.edu.sg/herod/index.php. The database and web interface are implemented in MySQL, PHP and HTML with all major browsers supported.

Contact

phacyz@nus.edu.sg.