MIRAGAA--a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer

Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine

MicroRNAs (miRNAs) have key roles in breast cancer progression, and their expression levels are heterogeneous across individual breast cancer patients. Traditional methods aim to identify differentially expressed miRNAs in populations rather than in individuals and are affected by the expression intensities of miRNAs in different experimental batches or platforms. Thus it is urgent to conduct miRNA differential expression analysis at an individual level for further personalized medicine research. We proposed a straightforward method to determine the differential expression of each miRNA in an individual patient by utilizing the reversal expression order of miRNA pairs between two conditions (cancer and normal tissue). We applied our method to breast cancer miRNA expression profiles from The Cancer Genome Atlas and two other independent data sets. In total, 292 miRNAs were differentially expressed in individual breast cancer patients. Using the differential expression profile of miRNAs in individual patients, we found that the deregulations of miRNA tend to occur in specific breast cancer subtypes. We investigated the coordination effect between the miRNA and its target, based on the hypothesis that one gene function can be changed by copy number alterations of the corresponding gene or deregulation of the miRNA. We revealed that patients exhibiting an upregulation of hsa-miR-92b and patients with deletions of PTEN did not tend to overlap, and hsa-miR-92b and PTEN coordinately regulated the pathway of 'cell cycle' and so on. Moreover, we discovered a new prognostic signature, hsa-miR-29c, whose downregulation was associated with poor survival of breast cancer patients.

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.

Analysis of pathway mutation profiles highlights collaboration between cancer-associated superpathways

The biological interpretation of the complexity of cancer somatic mutation profiles is a major challenge in current cancer research. It has been suggested that mutations in multiple genes that participate in different pathways are collaborative in conferring growth advantage to tumor cells. Here, we propose a powerful pathway-based approach to study the functional collaboration of gene mutations in carcinogenesis. We successfully identify many pairs of significantly comutated pathways for a large-scale somatic mutation profile of lung adenocarcinoma. We find that the coordinated pathway pairs detected by comutations are also likely to be coaltered by other molecular changes, such as alterations in multifunctional genes in cancer. Then, we cluster comutated pathways into comutated superpathways and show that the derived superpathways also tend to be significantly coaltered by DNA copy number alterations. Our results support the hypothesis that comprehensive cooperation among a few basic functions is required for inducing cancer. The results also suggest biologically plausible models for understanding the heterogeneous mechanisms of cancers. Finally, we suggest an approach to identify candidate cancer genes from the derived comutated pathways. Together, our results provide guidelines to distill the pathway collaboration in carcinogenesis from the complexity of cancer somatic mutation profiles.

Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival

Cooperative dysregulation of gene sequence and expression may contribute to cancer formation and progression. The Cancer Genome Atlas (TCGA) Network recently catalogued gene sequence and expression data for a collection of glioblastoma multiforme (GBM) tumors. We developed an automated, model-free method to rapidly and exhaustively examine the correlation among somatic mutation and gene expression and interrogated 149 GBM tumor samples from the TCGA. The method identified 41 genes whose mutation status is highly correlated with drastic changes in the expression (z-score ± 2.0), across tumor samples, of other genes. Some of the 41 genes have been previously implicated in GBM pathogenesis (e.g., NF1, TP53, RB1, and IDH1) and others, while implicated in cancer, had not previously been highlighted in studies using TCGA data (e.g., SYNE1, KLF6, FGFR4, and EPHB4). The method also predicted that known oncogenes and tumor suppressors participate in GBM via drastic over- and underexpression, respectively. In addition, the method identified a known synthetic lethal interaction between TP53 and PLK1, other potential synthetic lethal interactions with TP53, and correlations between IDH1 mutation status and the overexpression of known GBM survival genes.

Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis

BACKGROUND

It has been suggested that, in the human protein-protein interaction network, changes of co-expression between highly connected proteins ("hub") and their interaction neighbours might have important roles in cancer metastasis and be predictive disease signatures for patient outcome. However, for a cancer, such disease signatures identified from different studies have little overlap.

RESULTS

Here, we propose a systemic approach to evaluate the reproducibility of disease signatures at multiple levels, on the basis of some statistically testable biological models. Using two datasets for breast cancer metastasis, we showed that different signature hubs identified from different studies were highly consistent in terms of significantly sharing interaction neighbours and displaying consistent co-expression changes with their overlapping neighbours, whereas the shared interaction neighbours were significantly over-represented with known cancer genes and enriched in pathways deregulated in breast cancer pathogenesis. Then, we showed that the signature hubs identified from the two datasets were highly reproducible at the protein interaction and pathway levels in three other independent datasets.

CONCLUSIONS

Our results provide a possible biological model that different signature hubs altered in different patient cohorts could disturb the same pathways associated with cancer metastasis through their interaction neighbours.

Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme

Background

Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.

Methods

We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.

Results

We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.

Conclusions

Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.

Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/

The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/

The future role of personalized medicine in the treatment of glioblastoma multiforme

Glioblastoma multiforme (GBM) remains one of the most malignant primary central nervous system tumors. Personalized therapeutic approaches have not become standard of care for GBM, but science is fast approaching this goal. GBM's heterogeneous genomic landscape and resistance to radiotherapy and chemotherapy make this tumor one of the most challenging to treat. Recent advances in genome-wide studies and genetic profiling show that there is unlikely to be a single genetic or cellular event that can be effectively targeted in all patients. Instead, future therapies will likely require personalization for each patient's tumor genotype or proteomic profile. Over the past year, many investigations specifically focused simultaneously on strategies to target oncogenic pathways, angiogenesis, tumor immunology, epigenomic events, glioma stem cells (GSCs), and the highly migratory glioma cell population. Combination therapy targeting multiple pathways is becoming a fast growing area of research, and many studies put special attention on small molecule inhibitors. Because GBM is a highly vascular tumor, therapy that directs monoclonal antibodies or small molecule tyrosine kinase inhibitors toward angiogenic factors is also an area of focus for the development of new therapies. Passive, active, and adoptive immunotherapies have been explored by many studies recently, and epigenetic regulation of gene expression with microRNAs is also becoming an important area of study. GSCs can be useful targets to stop tumor recurrence and proliferation, and recent research has found key molecules that regulate GBM cell migration that can be targeted by therapy. Current standard of care for GBM remains nonspecific; however, pharmacogenomic studies are underway to pave the way for patient-specific therapies that are based on the unique aberrant pathways in individual patients. In conclusion, recent studies in GBM have found many diverse molecular targets possible for therapy. The next obstacle in treating this fatal tumor is ascertaining which molecules in each patient should be targeted and how best to target them, so that we can move our current nonspecific therapies toward the realm of personalized medicine.

Bioinformatics and systems biology of cancers

Molecular databases and bioinformatics methods and tools are essential for modern cancer research. Multilevel analyses of all the protein-coding genes, thousands of proteins, and hundreds of metabolites require integration in terms of signaling and metabolic pathways and networks. This chapter provides background and examples of genomic, gene expression, epigenomic, proteomic, and metabolomic investigations of cancer progression and emergence of invasive and metastatic properties of cancers.