Chiara Pastrello

Association between hsa-mir-146a genotype and tumor age-of-onset in BRCA1/BRCA2-negative familial breast and ovarian cancer patients.

An increasing body of evidence points to a possible role of microRNAs (miRNAs) in hereditary cancer syndromes. To evaluate the role of miRNA allelic variants in the susceptibility to familial breast and ovarian cancers in BRCA1/BRCA2-negative patients, we focused our attention on three miRNAs, miR-146a, miR-17 and miR-369, based on their affinity to either BRCA1 or BRCA2 messenger RNA and their localization on chromosome regions commonly deleted in those tumors. The analysis was performed on 101 Italian probands with ascertained familiarity for breast/ovarian cancer and tested negative for both BRCA1 and BRCA2 gene mutations. No allelic variant was detected for hsa-mir-17 and hsa-mir-369, and allelic and genotype frequencies for miR-146a rs2910164 single-nucleotide polymorphism (SNP) were comparable with that of 155 controls from the same population, ruling out a role for genetic variations in these three miRNAs as major determinants in cancer predisposition of BRCA1/BRCA2-negative patients. Instead, our study suggests that mir-146a rs2910164 SNP may impact on the age of cancer onset. In fact, subjects with mir-146a a GC or CC genotypes developed tumors at younger age compared with individuals with the GG genotype Thus, in contrast to a recent report, our data support the hypothesis by Shen and coworkers of an association between the C allele of hsa-mir-146a and early cancer onset and prompt further investigations on the relevance of this polymorphism in early familial breast/ovarian tumor development.

Molecular and clinicopathologic characterization of gastrointestinal stromal tumors (GISTs) of small size.

Although Gastrointestinal stromal tumors (GISTs) affect about 0.0014% of the population, GISTs smaller than 1 cm (microGISTs) are detectable in about 20% to 30% of elderly individuals. This suggests that microGISTs likely represent premalignant precursors that evolve only in a minute fraction of cases toward overt GISTs. We sought histopathologic and molecular explanations for the infrequent clinical progression in small GISTs. To investigate the mechanisms of GIST progression and identify subsets with differential malignant potential, we carried out a thorough characterization of 170 GISTs <2 cm and compared their KIT/PDGFRA status with overt GISTs. The proliferation was lower in microGISTs compared with GISTs from 1 to 2 cm (milliGISTs). In addition, microGISTs were more frequently incidental, gastric, spindle, showed an infiltrative growth pattern, a lower degree of cellularity, and abundant sclerosis. The progression was limited to 1 ileal and 1 rectal milliGISTs. KIT/PDGFRA mutations were detected in 74% of the cases. The overall frequency of KIT/PDGFRA mutation and, particularly, the frequency of KIT exon 11 mutations was significantly lower in small GISTs compared with overt GISTs. Five novel mutations, 3 in KIT (p.Phe506Leu, p.Ser692Leu, p.Glu695Lys) 2 in PDGFRA (p.Ser847X, p.Ser667Pro), plus 4 double mutations were identified. Small GISTs share with overt GIST KIT/PDGFRA mutation. Nevertheless, microGISTs display an overall lower frequency of mutations, particularly canonical KIT mutations, and also carry rare and novel mutations. These molecular features, together with the peculiar pathologic characteristics, suggest that the proliferation of these lesions is likely sustained by weakly pathogenic molecular events, supporting the epidemiologic evidence that microGISTs are self-limiting lesions.

Integrated interactions database: tissue-specific view of the human and model organism interactomes

IID (Integrated Interactions Database) is the first database providing tissue-specific protein–protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), C. elegans (worm), D. melonogaster (fly), R. norvegicus (rat), M. musculus (mouse) and H. sapiens (human)) and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables searches across species and tissues automatically by using their orthologs; for example, retrieving interactions in a given tissue, conserved in human and mouse. Interaction data in IID comprises three types of PPI networks: experimentally detected PPIs from major databases, orthologous PPIs and high-confidence computationally predicted PPIs. Interactions are assigned to tissues where their proteins pairs or encoding genes are expressed. IID is a major replacement of the I2D interaction database, with larger PPI networks (a total of 1,566,043 PPIs among 68,831 proteins), tissue annotations for interactions, and new query, analysis and data visualization capabilities. IID is available at http://ophid.utoronto.ca/iid.

In silico prediction of physical protein interactions and characterization of interactome orphans

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining–based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).

Stability of BAT26 in tumours of hereditary nonpolyposis colorectal cancer patients with MSH2 intragenic deletion

Colon cancers arising in most patients with hereditary nonpolyposis colorectal cancer (HNPCC) show microsatellite instability (MSI). BAT26, a quasimonomorphic polyA stretch located just 3′ of MSH2 exon 5, is considered the most sensitive and specific marker of MSI. A total of 10 HNPCC families with large intragenic MSH2 deletions, encompassing exon 5 and intron 5, identified by multiplex ligation-dependent probe amplification (MLPA) were included in this study. The deletions under study were del1-16, del1-8, del1-7, del1-6, and del3-6, detected in 3, 1, 2, 3, and 1 families, respectively. Although all patients examined from these 10 families developed unstable tumours, 13/19 MSI-H tumours (68 %) surprisingly showed stability of BAT26. By MLPA and MSH2 sequence analyses of the BAT26-stable tumours, we demonstrated that the wild-type MSH2 allele was somatically inactivated by an identical large deletion, with complete loss of intron 5/BAT26 sequences at the tumour DNA level. We could infer that the apparent stability of BAT26 was due to the complete absence of target BAT26 sequences in the tumour sample, which results in exclusive amplification of contaminant normal DNA, containing a single copy of a wild-type stable BAT26 sequence. Identification of a subset of MSH2-related unstable tumours that are not recognized by analysis of BAT26 instability indicates that this marker should never be used alone for rapid MSI screening of HNPCC tumours. Moreover, our findings indicate that BAT26 stability in the context of MSI is strongly suggestive of the presence of a large intragenic MSH2 deletion.

Integrated analysis of unclassified variants in mismatch repair genes

Purpose: Lynch syndrome is a genetic disease that predisposes to colorectal tumors, caused by mutation in mismatch repair genes. The use of genetic tests to identify mutation carriers does not always give perfectly clear results, as happens when an unclassified variant is found. This study aimed to define the pathogenic role of 35 variants present in MSH2, MLH1, MSH6, and PMS2 genes identified in our 15-year case study.Methods: We collected clinical and molecular data of all carriers, and then we analyzed the variants pathogenic role with web tools and molecular analyses. Using a Bayesian approach, we derived a posterior probability of pathogenicity and classified each variant according to a standardized five-class system.Results: The MSH2 p.Pro349Arg, p.Met688Arg, the MLH1 p.Gly67Arg, p.Thr82Ala, p.Lys618Ala, the MSH6 p.Ala1236Pro, and the PMS2 p.Arg20Gln were classified as pathogenic, and the MSH2 p.Cys697Arg and the PMS2 p.Ser46Ile were classified as likely pathogenic. Seven variants were likely nonpathogenic, 3 were nonpathogenic, and 16 remained uncertain.Conclusion: Quantitative assessment of several parameters and their integration in a multifactorial likelihood model is the method of choice for classifying the variants. As such classifications can be associated with surveillance and testing recommendations, the results and the method developed in our study can be useful for helping laboratory geneticists in evaluation of genetic tests and clinicians in the management of carriers.

The role of MYH gene in genetic predisposition to colorectal cancer: Another piece of the puzzle

Biallelic germline mutations in the MYH gene cause MYH-Associated Polyposis but patients with a single mutation possibly have an increased colorectal cancer (CRC) risk. Using DNA from consecutive CRC patients we carried out a case-control study, with the aim to contribute data on the Italian population. Genotyping of four MYH mutations found two biallelic and two monoallelic carriers among 439 cases, and only one heterozygous individual among 247 age-matched controls. The frequencies of the mutant alleles were 0.68% (6/878) and 0.20% (1/494), respectively. These differences were not statistically significant. Results on the monoallelic carriers were combined with those from 11 studies on other populations, and the risk of developing a CRC was estimated with an OR=1.11 (95% CI=0.90; 1.36), yet not reaching a significant evidence of increased CRC risk.

Visual Data Mining: Effective Exploration of the Biological Universe

Visual Data Mining (VDM) is supported by interactive and scalable network visualization and analysis, which in turn enables effective exploration and communication of ideas within multiple biological and biomedical fields. Large networks, such as the protein interactome or transcriptional regulatory networks, contain hundreds of thousands of objects and millions of relationships. These networks are continuously evolving as new knowledge becomes available, and their content is richly annotated and can be presented in many different ways. Attempting to discover knowledge and new theories within this complex data sets can involve many workflows, such as accurately representing many formats of source data, merging heterogeneous and distributed data sources, complex database searching, integrating results from multiple computational and mathematical analyses, and effectively visualizing properties and results. Our experience with biology researchers has required us to address their needs and requirements in the design and development of a scalable and interactive network visualization and analysis platform, NAViGaTOR, now in its third major release.

A genetic model for determining MSH2 and MLH1 carrier probabilities based on family history and tumor microsatellite instability

Mutation‐predicting models can be useful when deciding on the genetic testing of individuals at risk and in determining the cost effectiveness of screening strategies at the population level. The aim of this study was to evaluate the performance of a newly developed genetic model that incorporates tumor microsatellite instability (MSI) information, called the AIFEG model, and in predicting the presence of mutations in MSH2 and MLH1 in probands with suspected hereditary non‐polyposis colorectal cancer. The AIFEG model is based on published estimates of mutation frequencies and cancer penetrances in carriers and non‐carriers and employs the program MLINK of the FASTLINK package to calculate the probands carrier probability. Model performance is evaluated in a series of 219 families screened for mutations in both MSH2 and MLH1, in which 68 disease‐causing mutations were identified. Predictions are first obtained using family history only and then converted into posterior probabilities using information on MSI. This improves predictions substantially. Using a probability threshold of 10% for mutation analysis, the AIFEG model applied to our series has 100% sensitivity and 71% specificity.

Visual data mining of biological networks: one size does not fit all.

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.