Patrizia Chiarappa

Genetic heterogeneity by comparative genomic hybridization in BRCAx breast cancers

The chromosomal changes in eight familial BRCAx breast cancers (i.e., negative for BRCA1 or BRCA2) were analyzed by comparative genomic hybridization (CGH) to investigate intratumor heterogeneity. This was the first step in a study of most frequent chromosomal aberrations in BRCAx familial breast cancers. Laser microdissection analysis of paraffin tissue samples was followed by whole-genome amplification. CGH was performed on DNA isolated from two to three different cell groups per case to detect any cytogenetic aberrations in important clones that might have been missed when analyzing DNA extracted from large numbers of cells. The results were compared, to evaluate the influence of tumor heterogeneity on CGH, and the heterogeneity was confirmed comparing CGH with fluorescence in situ hybridization results. Different chromosomal aberrations were detected between adjacent clones within the same section, which highlights the utility of microdissection in addressing the problem of heterogeneity in whole-genome studies. Some chromosomal regions were more frequently altered in the eight BRCAx tumors; loss of 2q, 3p, 3q, 8p, 9p, and 15q and gains of 1p, 4p, 4q, 5p, 6q, 12q, and 19p were the most common. Further studies focusing on specific genes and sequences with more sensitive approaches, such as array-CGH, are warranted to confirm these findings.

Touch imprint cytology in tumor tissue banks for the confirmation of neoplastic cellularity and for DNA extraction.

CONTEXT Learning the characteristics of frozen tissue samples stored in tumor banks for biological studies remains a problem. OBJECTIVE To assess the use of touch imprint cytology on fresh tissue samples as a rapid and reliable method of determining the presence and quantity of neoplastic cells before freezing. DESIGN Touch imprint cytology was performed on 259 specimens of operable breast cancer. Touch imprints were prepared from fresh tissue specimens before freezing samples for storage. Each tumor sample was imprinted on a glass slide and stained with hematoxylin-eosin. Tumor cellularity was quantified as negative, poor, moderate, or rich. RESULTS A significant correlation was found between samples with a tumor size greater than 2 cm and high tumor cellularity (P = .03; chi(2) test). Furthermore, 35% of ductal tumors showed higher tumor cellularity compared with lobular tumors (P < .001; chi(2) test). No association was found between lymph node status and tumor grade. When samples for which more than 2 imprints were available were examined, tumor cellularity among imprints of the same sample showed an overall agreement of 0.67 (P < .001; kappa statistic). It was also determined that the higher the cellularity, the higher the agreement. Our data also showed concordance of 0.87 (P < .001; kappa statistic) between touch imprint cytology imprints and histologic sections from contiguous tumor. Moreover, 11 randomly selected samples underwent DNA extraction, polymerase chain reaction, and sequencing to verify the feasibility of DNA analyses. We found that DNA from touch imprint cytology was amplifiable and suitable for direct sequencing. CONCLUSIONS Touch imprint cytology may represent an important step in the quality control of tumor cellularity of breast cancer specimens designed to be stored in tumor biobanks and a valid method for assessing the suitability of such tissue for further biomorphologic and biomolecular applications.

Induction of fuzzy rules with artificial immune systems in acgh based er status breast cancer characterization

Genomic DNA copy number aberrations are frequent in solid tumours although their underlying causes remain obscure. In this paper we show how Artificial Immune System (AIS) paradigm can be successfully employed in the elucidation of biological dynamics of cancerous processes using a novel fuzzy rule induction system for data mining (IFRAIS). Competitive results have been obtained using IFRAIS. A biological interpretation of the results, carried out using Gene Ontology, followed the statistical assessment and put in evidence interesting patterns that are currently under investigation.

Identification of tumor evolution patterns by means of inductive logic programming.

In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an inductive logic programming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elucidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained on a real-world dataset, we try to give some hints about further approach to the knowledge-driven validations of such hypotheses.

Fuzzy rule induction and artificial immune systems in female breast cancer familiarity profiling

Genomic DNA copy number aberrations are frequent in solid tumours although their underlying causes of chromosomal instability in tumours remain obscure. In this paper we show how Artificial Immune System (AIS) paradigm can be successfully employed in the elucidation of biological dynamics of cancerous processes using a novel fuzzy rule induction system for data mining (IFRAIS) [1] of aCGH data. Competitive results have been obtained using IFRAIS. A biological interpretation of the results carried out using Gene Ontology is currently under investigation.