Towards accurate estimation of the proportion of true null hypotheses in multiple testing

Identification and validation of novel biomarkers and therapeutics for pulpitis using connectivity mapping

AIM

To create an irreversible pulpitis gene signature from microarray data of healthy and inflamed dental pulps, followed by a bioinformatics approach using connectivity mapping to identify therapeutic compounds that could potentially treat pulpitis.

METHODOLOGY

The Gene Expression Omnibus (GEO) database, an international public repository of genomics data sets, was searched for human microarray datasets assessing pulpitis. An irreversible pulpitis gene expression signature was generated by differential expression analysis. The statistically significant connectivity map (ssCMap) method was used to identify compounds with a highly correlating gene expression pattern. qPCR was used to validate novel pulpitis genes. An ex vivo pulpitis model was used to test the effects of the compounds identified, and the level of inflammatory cytokines was measured with qPCR, ELISA and multiplex array. Means were compared using the t-test or ANOVA with the level of significance set at p ≤ .05.

RESULTS

Pulpitis gene signatures were created using differential gene expression analysis at cutoff points p = .0001 and .000018. Top upregulated genes were selected as potential pulpitis biomarkers. Among these, IL8, IL6 and MMP9 were previously identified as pulpitis biomarkers. Novel upregulated genes, chemokine (C-C motif) ligand 21 (CCL21), metallothionein 1H (MT1H) and aquaporin 9 (AQP9) were validated in the pulp tissue of teeth clinically diagnosed with irreversible pulpitis using qPCR. ssCMap analysis identified fluvastatin (Statin) and dequalinium chloride (Quaternary ammonium) as compounds with the strongest correlation to the gene signatures (p = .0001). Fluvastatin reduced IL8, IL6, CCL21, AQP9 (p < .001) and MMP9 (p < .05) in the ex vivo pulpitis model, while dequalinium chloride reduced AQP9 (p < .001) but had no significant effect on the other biomarkers.

CONCLUSIONS

AQP9, MT1H and CCL21 were identified and validated as novel biomarkers for pulpitis. Fluvastatin and dequalinium chloride identified by the ssCMap as potential therapeutics for pulpitis reduced selected pulpitis biomarkers in an ex vivo pulpitis model. In vivo testing of these licenced drugs is warranted.

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

In vitro and clinical data analysis of Osteopontin as a prognostic indicator in colorectal cancer

Osteopontin (OPN) has been shown to promote colorectal cancer (CRC) progression; however, the mechanism of OPN-induced CRC progression is largely unknown. In this study, we found that OPN overexpression led to enhanced anchorage-independent growth, cell migration and invasion in KRAS gene mutant cells but to a lesser extent in KRAS wild-type cells. OPN overexpression also induced PI3K signalling, expression of Snail and Matrix metallopeptidase 9 (MMP9), and suppressed the expression of E-cadherin in KRAS mutant cells. In human CRC specimens, a high-level expression of OPN significantly predicted poorer survival in CRC patients and OPN expression was positively correlated with MMP9 expression, and negatively correlated with E-cadherin expression. Furthermore, we have found that 15 genes were co-upregulated in OPN highly expression CRC and a list of candidate drugs that may have potential to reverse the secreted phosphoprotein 1 (SPP1) gene signature by connectivity mapping. In summary, OPN is a potential prognostic indicator and therapeutic target for colon cancer.

An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping

BACKGROUND

Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer).

RESULTS

We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs.

CONCLUSIONS

A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.

A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping

Background

Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user’s perspective.

Results

We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a “gene signature progression” method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan female non-smoker lung cancer patients, while there is evidence from independent studies that gefitinib is highly effective in treating women, non-smoker or former light smoker, advanced non-small cell lung cancer patients of Asian origin.

Conclusions

In summary, we introduced a gene signature progression method into connectivity mapping, which enables a standardized procedure for constructing high quality gene signatures. This progression method is particularly useful when the number of differentially expressed genes identified is large, and when there is a need to prioritize them to be included in the query signature. The results from two case studies demonstrate that the approach we have developed is capable of obtaining pertinent candidate drugs with high precision.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1066-x) contains supplementary material, which is available to authorized users.

Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies

BACKGROUND

While the discovery of new drugs is a complex, lengthy and costly process, identifying new uses for existing drugs is a cost-effective approach to therapeutic discovery. Connectivity mapping integrates gene expression profiling with advanced algorithms to connect genes, diseases and small molecule compounds and has been applied in a large number of studies to identify potential drugs, particularly to facilitate drug repurposing. Colorectal cancer (CRC) is a commonly diagnosed cancer with high mortality rates, presenting a worldwide health problem. With the advancement of high throughput omics technologies, a number of large scale gene expression profiling studies have been conducted on CRCs, providing multiple datasets in gene expression data repositories. In this work, we systematically apply gene expression connectivity mapping to multiple CRC datasets to identify candidate therapeutics to this disease.

RESULTS

We developed a robust method to compile a combined gene signature for colorectal cancer across multiple datasets. Connectivity mapping analysis with this signature of 148 genes identified 10 candidate compounds, including irinotecan and etoposide, which are chemotherapy drugs currently used to treat CRCs. These results indicate that we have discovered high quality connections between the CRC disease state and the candidate compounds, and that the gene signature we created may be used as a potential therapeutic target in treating the disease. The method we proposed is highly effective in generating quality gene signature through multiple datasets; the publication of the combined CRC gene signature and the list of candidate compounds from this work will benefit both cancer and systems biology research communities for further development and investigations.

Estimating the proportion of true null hypotheses when the statistics are discrete

MOTIVATION

In high-dimensional testing problems π0, the proportion of null hypotheses that are true is an important parameter. For discrete test statistics, the P values come from a discrete distribution with finite support and the null distribution may depend on an ancillary statistic such as a table margin that varies among the test statistics. Methods for estimating π0 developed for continuous test statistics, which depend on a uniform or identical null distribution of P values, may not perform well when applied to discrete testing problems.

RESULTS

This article introduces a number of π0 estimators, the regression and 'T' methods that perform well with discrete test statistics and also assesses how well methods developed for or adapted from continuous tests perform with discrete tests. We demonstrate the usefulness of these estimators in the analysis of high-throughput biological RNA-seq and single-nucleotide polymorphism data.

AVAILABILITY AND IMPLEMENTATION

implemented in R.

Sequential Monte Carlo multiple testing

Motivation: In molecular biology, as in many other scientific fields, the scale of analyses is ever increasing. Often, complex Monte Carlo simulation is required, sometimes within a large-scale multiple testing setting. The resulting computational costs may be prohibitively high.

Results: We here present MCFDR, a simple, novel algorithm for false discovery rate (FDR) modulated sequential Monte Carlo (MC) multiple hypothesis testing. The algorithm iterates between adding MC samples across tests and calculating intermediate FDR values for the collection of tests. MC sampling is stopped either by sequential MC or based on a threshold on FDR. An essential property of the algorithm is that it limits the total number of MC samples whatever the number of true null hypotheses. We show on both real and simulated data that the proposed algorithm provides large gains in computational efficiency.

Availability: MCFDR is implemented in the Genomic HyperBrowser (http://hyperbrowser.uio.no/mcfdr), a web-based system for genome analysis. All input data and results are available and can be reproduced through a Galaxy Pages document at: http://hyperbrowser.uio.no/mcfdr/u/sandve/p/mcfdr.

Contact:geirksa@ifi.uio.no