Deciphering causal and statistical relations of molecular aberrations and gene expressions in NCI-60 cell lines

Putative effectors for prognosis in lung adenocarcinoma are ethnic and gender specific

Lung adenocarcinoma possesses distinct patterns of EGFR/KRAS mutations between East Asian and Western, male and female patients. However, beyond the well-known EGFR/KRAS distinction, gender and ethnic specific molecular aberrations and their effects on prognosis remain largely unexplored. Association modules capture the dependency of an effector molecular aberration and target gene expressions. We established association modules from the copy number variation (CNV), DNA methylation and mRNA expression data of a Taiwanese female cohort. The inferred modules were validated in four external datasets of East Asian and Caucasian patients by examining the coherence of the target gene expressions and their associations with prognostic outcomes. Modules 1 (cis-acting effects with chromosome 7 CNV) and 3 (DNA methylations of UBIAD1 and VAV1) possessed significantly negative associations with survival times among two East Asian patient cohorts. Module 2 (cis-acting effects with chromosome 18 CNV) possessed significantly negative associations with survival times among the East Asian female subpopulation alone. By examining the genomic locations and functions of the target genes, we identified several putative effectors of the two cis-acting CNV modules: RAC1, EGFR, CDK5 and RALBP1. Furthermore, module 3 targets were enriched with genes involved in cell proliferation and division and hence were consistent with the negative associations with survival times. We demonstrated that association modules in lung adenocarcinoma with significant links of prognostic outcomes were ethnic and/or gender specific. This discovery has profound implications in diagnosis and treatment of lung adenocarcinoma and echoes the fundamental principles of the personalized medicine paradigm.

Design and activity of AP endonuclease-1 inhibitors

Apurinic/apyrimidinic endonuclease-1/redox effector factor-1 (APE-1) is a critical component of base excision repair that excises abasic lesions created enzymatically by the action of DNA glycosylases on modified bases and non-enzymatically by hydrolytic depurination/depyrimidination of nucleobases. Many anticancer drugs generate DNA adducts that are processed by base excision repair, and tumor resistance is frequently associated with enhanced APE-1 expression. Accordingly, APE-1 is a potential therapeutic target to treat cancer. Using computational approaches and the high resolution structure of APE-1, we developed a 5-point pharmacophore model for APE-1 small molecule inhibitors. One of the nM APE-1 inhibitors (AJAY-4) that was identified based on this model exhibited an overall median growth inhibition (GI50) of 4.19 μM in the NCI-60 cell line panel. The mechanism of action is shown to be related to the buildup of abasic sites that cause PARP activation and PARP cleavage, and the activation of caspase-3 and caspase-7, which is consistent with cell death by apoptosis. In a drug combination growth inhibition screen conducted in 10 randomly selected NCI-60 cell lines and with 20 clinically used non-genotoxic anticancer drugs, a synergy was flagged in the SK-MEL-5 melanoma cell line exposed to combinations of vemurafenib, which targets melanoma cells with V600E mutated BRAF, and AJAY-4, our most potent APE-1 inhibitor. The synergy between AJAY-4 and vemurafenib was not observed in cell lines expressing wild-type B-Raf protein. This synergistic combination may provide a solution to the resistance that develops in tumors treated with B-Raf-targeting drugs.

A meta-analysis of multiple matched copy number and transcriptomics data sets for inferring gene regulatory relationships

Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments.

An integrative characterization of recurrent molecular aberrations in glioblastoma genomes

Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults. Decades of investigations and the recent effort of the Cancer Genome Atlas (TCGA) project have mapped many molecular alterations in GBM cells. Alterations on DNAs may dysregulate gene expressions and drive malignancy of tumors. It is thus important to uncover causal and statistical dependency between ‘effector’ molecular aberrations and ‘target’ gene expressions in GBMs. A rich collection of prior studies attempted to combine copy number variation (CNV) and mRNA expression data. However, systematic methods to integrate multiple types of cancer genomic data—gene mutations, single nucleotide polymorphisms, CNVs, DNA methylations, mRNA and microRNA expressions and clinical information—are relatively scarce. We proposed an algorithm to build ‘association modules’ linking effector molecular aberrations and target gene expressions and applied the module-finding algorithm to the integrated TCGA GBM data sets. The inferred association modules were validated by six tests using external information and datasets of central nervous system tumors: (i) indication of prognostic effects among patients; (ii) coherence of target gene expressions; (iii) retention of effector–target associations in external data sets; (iv) recurrence of effector molecular aberrations in GBM; (v) functional enrichment of target genes; and (vi) co-citations between effectors and targets. Modules associated with well-known molecular aberrations of GBM—such as chromosome 7 amplifications, chromosome 10 deletions, EGFR and NF1 mutations—passed the majority of the validation tests. Furthermore, several modules associated with less well-reported molecular aberrations—such as chromosome 11 CNVs, CD40, PLXNB1 and GSTM1 methylations, and mir-21 expressions—were also validated by external information. In particular, modules constituting trans-acting effects with chromosome 11 CNVs and cis-acting effects with chromosome 10 CNVs manifested strong negative and positive associations with survival times in brain tumors. By aligning the information of association modules with the established GBM subclasses based on transcription or methylation levels, we found each subclass possessed multiple concurrent molecular aberrations. Furthermore, the joint molecular characteristics derived from 16 association modules had prognostic power not explained away by the strong biomarker of CpG island methylator phenotypes. Functional and survival analyses indicated that immune/inflammatory responses and epithelial-mesenchymal transitions were among the most important determining processes of prognosis. Finally, we demonstrated that certain molecular aberrations uniquely recurred in GBM but were relatively rare in non-GBM glioma cells. These results justify the utility of an integrative analysis on cancer genomes and provide testable characterizations of driver aberration events in GBM.

Testing the utility of an integrated analysis of copy number and transcriptomics datasets for inferring gene regulatory relationships

Correlation patterns between matched copy number variation and gene expression data in cancer samples enable the inference of causal gene regulatory relationships by exploiting the natural randomization of such systems. The aim of this study was to test and verify experimentally the accuracy of a causal inference approach based on genomic randomization using esophageal cancer samples. Two candidates with strong regulatory effects emerging from our analysis are components of growth factor receptors, and implicated in cancer development, namely ERBB2 and FGFR2. We tested experimentally two ERBB2 and three FGFR2 regulated interactions predicted by the statistical analysis, all of which were confirmed. We also applied the method in a meta-analysis of 10 cancer datasets and tested 15 of the predicted regulatory interactions experimentally. Three additional predicted ERBB2 regulated interactions were confirmed, as well as interactions regulated by ARPC1A and FANCG. Overall, two thirds of experimentally tested predictions were confirmed.

miRConnect 2.0: identification of oncogenic, antagonistic miRNA families in three human cancers

BACKGROUND

Based on their function in cancer micro(mi)RNAs are often grouped as either tumor suppressors or oncogenes. However, miRNAs regulate multiple tumor relevant signaling pathways raising the question whether two oncogenic miRNAs could be functional antagonists by promoting different steps in tumor progression. We recently developed a method to connect miRNAs to biological function by comparing miRNA and gene array expression data from the NCI60 cell lines without using miRNA target predictions (miRConnect).

RESULTS

We have now extended this analysis to three primary human cancers (ovarian cancer, glioblastoma multiforme, and kidney renal clear cell carcinoma) available at the Cancer Genome Atlas (TCGA), and have correlated the expression of the clustered miRNAs with 158 oncogenic signatures (miRConnect 2.0). We have identified functionally antagonistic groups of miRNAs. One group (the agonists), which contains many of the members of the miR-17 family, correlated with c-Myc induced genes and E2F gene signatures. A group that was directly antagonistic to the agonists in all three primary cancers contains miR-221 and miR-222. Since both miR-17 ~ 92 and miR-221/222 are considered to be oncogenic this points to a functional antagonism of different oncogenic miRNAs. Analysis of patient data revealed that in certain patients agonistic miRNAs predominated, whereas in other patients antagonists predominated. In glioblastoma a high ratio of miR-17 to miR-221/222 was predictive of better overall survival suggesting that high miR-221/222 expression is more adverse for patients than high miR-17 expression.

CONCLUSION

miRConnect 2.0 is useful for identifying activities of miRNAs that are relevant to primary cancers. The new correlation data on miRNAs and mRNAs deregulated in three primary cancers are available at miRConnect.org.

Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties

Background

High-throughput (omic) data have become more widespread in both quantity and frequency of use, thanks to technological advances, lower costs and higher precision. Consequently, computational scientists are confronted by two parallel challenges: on one side, the design of efficient methods to interpret each of these data in their own right (gene expression signatures, protein markers, etc.) and, on the other side, realization of a novel, pressing request from the biological field to design methodologies that allow for these data to be interpreted as a whole, i.e. not only as the union of relevant molecules in each of these layers, but as a complex molecular signature containing proteins, mRNAs and miRNAs, all of which must be directly associated in the results of analyses that are able to capture inter-layers connections and complexity.

Results

We address the latter of these two challenges by testing an integrated approach on a known cancer benchmark: the NCI-60 cell panel. Here, high-throughput screens for mRNA, miRNA and proteins are jointly analyzed using factor analysis, combined with linear discriminant analysis, to identify the molecular characteristics of cancer. Comparisons with separate (non-joint) analyses show that the proposed integrated approach can uncover deeper and more precise biological information. In particular, the integrated approach gives a more complete picture of the set of miRNAs identified and the Wnt pathway, which represents an important surrogate marker of melanoma progression. We further test the approach on a more challenging patient-dataset, for which we are able to identify clinically relevant markers.

Conclusions

The integration of multiple layers of omics can bring more information than analysis of single layers alone. Using and expanding the proposed integrated framework to integrate omic data from other molecular levels will allow researchers to uncover further systemic information. The application of this approach to a clinically challenging dataset shows its promising potential.