Michele Ceccarelli

Transforming Fusions of FGFR and TACC Genes in Human Glioblastoma

Oncogenic TACC-tics Human cancers exhibit many types of genomic rearrangements—including some that juxtapose sequences from two unrelated genes—thereby creating fusion proteins with oncogenic activity. Functional analysis of these fusion genes can provide mechanistic insights into tumorigenesis and potentially lead to effective drugs, as famously illustrated by the BCR-ABL gene in chronic myelogenous leukemia. Singh et al. (p. 1231, published online 26 July) identify and characterize a fusion gene present in 3% of human glioblastomas, a deadly brain cancer. In the resultant fusion protein, the tyrosine kinase region of the fibroblast growth factor receptor (FGFR) is joined to a domain from a transforming acidic coiled-coil (TACC) protein. The TACC-FGFR protein is oncogenic, shows unregulated kinase activity, localizes to the mitotic spindle, and disrupts chromosome segregation. In mice, FGFR inhibitors slowed the growth of tumors driven by the TACC-FGFR gene, suggesting that a subset of glioblastoma patients may benefit from these types of drugs. A fusion gene detected in a small subset of human brain tumors encodes a potentially druggable target. The brain tumor glioblastoma multiforme (GBM) is among the most lethal forms of human cancer. Here, we report that a small subset of GBMs (3.1%; 3 of 97 tumors examined) harbors oncogenic chromosomal translocations that fuse in-frame the tyrosine kinase coding domains of fibroblast growth factor receptor (FGFR) genes (FGFR1 or FGFR3) to the transforming acidic coiled-coil (TACC) coding domains of TACC1 or TACC3, respectively. The FGFR-TACC fusion protein displays oncogenic activity when introduced into astrocytes or stereotactically transduced in the mouse brain. The fusion protein, which localizes to mitotic spindle poles, has constitutive kinase activity and induces mitotic and chromosomal segregation defects and triggers aneuploidy. Inhibition of FGFR kinase corrects the aneuploidy, and oral administration of an FGFR inhibitor prolongs survival of mice harboring intracranial FGFR3-TACC3–initiated glioma. FGFR-TACC fusions could potentially identify a subset of GBM patients who would benefit from targeted FGFR kinase inhibition.

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach

BackgroundOne of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory.ResultsIn this paper we show how the ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) algorithm can be used for gene regulatory network inference in the case of time-course expression profiles. The resulting method is called TimeDelay-ARACNE. It just tries to extract dependencies between two genes at different time delays, providing a measure of these dependencies in terms of mutual information. The basic idea of the proposed algorithm is to detect time-delayed dependencies between the expression profiles by assuming as underlying probabilistic model a stationary Markov Random Field. Less informative dependencies are filtered out using an auto calculated threshold, retaining most reliable connections. TimeDelay-ARACNE can infer small local networks of time regulated gene-gene interactions detecting their versus and also discovering cyclic interactions also when only a medium-small number of measurements are available. We test the algorithm both on synthetic networks and on microarray expression profiles. Microarray measurements concern S. cerevisiae cell cycle, E. coli SOS pathways and a recently developed network for in vivo assessment of reverse engineering algorithms. Our results are compared with ARACNE itself and with the ones of two previously published algorithms: Dynamic Bayesian Networks and systems of ODEs, showing that TimeDelay-ARACNE has good accuracy, recall and F-score for the network reconstruction task.ConclusionsHere we report the adaptation of the ARACNE algorithm to infer gene regulatory networks from time-course data, so that, the resulting network is represented as a directed graph. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.

Upregulation of miR-21 by Ras in vivo and its role in tumor growth

miR-21 is a microRNA (miRNA) frequently overexpressed in human cancers. Here we show that miR-21 is upregulated both in vitro and in vivo by oncogenic Ras, thus linking this miRNA to one of the most frequently activated oncogenes in human cancers. Ras regulation of miR-21 occurs with a delayed kinetic and requires at least two Ras downstream pathways. A screen of human thyroid cancers and non-small-cell lung cancers for the expression of miR-21 reveals that it is overexpressed mainly in anaplastic thyroid carcinomas, the most aggressive form of thyroid cancer, whereas in lung its overexpression appears to be inversely correlated with tumor progression. We also show that a LNA directed against miR-21 slows down tumor growth in mice. Consistently, a search for mRNAs downregulated by miR-21 shows an enrichment for mRNAs encoding cell cycle checkpoints regulators, suggesting an important role for miR-21 in oncogenic Ras-induced cell proliferation.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGAs research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

Learning gene regulatory networks from only positive and unlabeled data

BackgroundRecently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.ResultsA recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement.ConclusionsCompared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

Signaling Networks Associated with AKT Activation in Non-Small Cell Lung Cancer (NSCLC): New Insights on the Role of Phosphatydil-Inositol-3 kinase

Aberrant activation of PI3K/AKT signalling represents one of the most common molecular alterations in lung cancer, though the relative contribution of the single components of the cascade to the NSCLC development is still poorly defined. In this manuscript we have investigated the relationship between expression and genetic alterations of the components of the PI3K/AKT pathway [KRAS, the catalytic subunit of PI3K (p110α), PTEN, AKT1 and AKT2] and the activation of AKT in 107 surgically resected NSCLCs and have analyzed the existing relationships with clinico-pathologic features. Expression analysis was performed by immunohistochemistry on Tissue Micro Arrays (TMA); mutation analysis was performed by DNA sequencing; copy number variation was determined by FISH. We report that activation of PI3K/AKT pathway in Italian NSCLC patients is associated with high grade (G3–G4 compared with G1–G2; n = 83; p<0.05) and more advanced disease (TNM stage III vs. stages I and II; n = 26; p<0.05). In addition, we found that PTEN loss (41/104, 39%) and the overexpression of p110α (27/92, 29%) represent the most frequent aberration observed in NSCLCs. Less frequent molecular lesions comprised the overexpression of AKT2 (18/83, 22%) or AKT1 (17/96, 18%), and KRAS mutation (7/63, 11%). Our results indicate that, among all genes, only p110α overexpression was significantly associated to AKT activation in NSCLCs (p = 0.02). Manipulation of p110α expression in lung cancer cells carrying an active PI3K allele (NCI-H460) efficiently reduced proliferation of NSCLC cells in vitro and tumour growth in vivo. Finally, RNA profiling of lung epithelial cells (BEAS-2B) expressing a mutant allele of PIK3 (E545K) identified a network of transcription factors such as MYC, FOS and HMGA1, not previously recognised to be associated with aberrant PI3K signalling in lung cancer.

UHRF1 coordinates peroxisome proliferator activated receptor gamma (PPARG) epigenetic silencing and mediates colorectal cancer progression

Peroxisome proliferator-activated receptor gamma (PPARG) inactivation has been identified as an important step in colorectal cancer (CRC) progression, although the events involved have been partially clarified. UHRF1 is emerging as a cofactor that coordinates the epigenetic silencing of tumor suppressor genes, but its role in CRC remains elusive. Here, we report that UHRF1 negatively regulates PPARG and is associated with a higher proliferative, clonogenic and migration potential. Consistently, UHRF1 ectopic expression induces PPARG repression through its recruitment on the PPARG promoter fostering DNA methylation and histone repressive modifications. In agreement, UHRF1 knockdown elicits PPARG re-activation, accompanied by positive histone marks and DNA demethylation, corroborating its role in PPARG silencing. UHRF1 overexpression, as well as PPARG-silencing, imparts higher growth rate and phenotypic features resembling those occurring in the epithelial-mesenchymal transition. In our series of 110 sporadic CRCs, high UHRF1-expressing tumors are characterized by an undifferentiated phenotype, higher proliferation rate and poor clinical outcome only in advanced stages III–IV. In addition, the inverse relationship with PPARG found in vitro is detected in vivo and UHRF1 prognostic significance appears closely related to PPARG low expression, as remarkably validated in an independent dataset. The results demonstrate that UHRF1 regulates PPARG silencing and both genes appear to be part of a complex regulatory network. These findings suggest that the relationship between UHRF1 and PPARG may have a relevant role in CRC progression.

Using multivariate time series and association rules to detect logical change coupling: An empirical study

In recent years, techniques based on association rules discovery have been extensively used to determine change-coupling relations between artifacts that often changed together. Although association rules worked well in many cases, they fail to capture logical coupling relations between artifacts modified in subsequent change sets.

An eclectic approach for change impact analysis

Change impact analysis aims at identifying software artifacts being affected by a change. In the past, this problem has been addressed by approaches relying on static, dynamic, and textual analysis. Recently, techniques based on historical analysis and association rules have been explored. This paper proposes a novel change impact analysis method based on the idea that the mutual relationships between software objects can be inferred with a statistical learning approach. We use the bivariate Granger causality test, a multivariate time series forecasting approach used to verify whether past values of a time series are useful for predicting future values of another time series. Results of a preliminary study performed on the Samba daemon show that change impact relationships inferred with the Granger causality test are complementary to those inferred with association rules. This opens the road towards the development of an eclectic impact analysis approach conceived by combining different techniques.