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Marino F.Z.,Pathology Unit | Liguori G.,Pathology Unit | Aquino G.,Pathology Unit | Mantia E.L.,Pathology Unit | And 14 more authors.

Background Non Small Cell Lung Cancer is a highly heterogeneous tumor. Histologic intratumor heterogeneity could be 'major', characterized by a single tumor showing two different histologic types, and 'minor', due to at least 2 different growth patterns in the same tumor. Therefore, a morphological heterogeneity could reflect an intratumor molecular heterogeneity. To date, few data are reported in literature about molecular features of the mixed adenocarcinoma. The aim of our study was to assess EGFR-mutations and ALK-rearrangements in different intratumor subtypes and/or growth patterns in a series of mixed adenocarcinomas and adenosquamous carcinomas. Methods 590 Non Small Cell Lung Carcinomas tumor samples were revised in order to select mixed adenocarcinomas with available tumor components. Finally, only 105 mixed adenocarcinomas and 17 adenosquamous carcinomas were included in the study for further analyses. Two TMAs were built selecting the different intratumor histotypes. ALK-rearrangements were detected through FISH and IHC, and EGFR-mutations were detected through IHC and confirmed by RT-PCR. Results 10/122 cases were ALK-rearranged and 7 from those 10 showing an intratumor heterogeneity of the rearrangements. 12/122 cases were EGFR-mutated, uniformly expressing the EGFR-mutated protein in all histologic components. Conclusion Our data suggests that EGFR-mutations is generally homogeneously expressed. On the contrary, ALK-rearrangement showed an intratumor heterogeneity in both mixed adenocarcinomas and adenosquamous carcinomas. The intratumor heterogeneity of ALK-rearrangements could lead to a possible impact on the therapeutic responses and the disease outcomes. © 2015 Marino et al. Source

De Marco C.,University of Catanzaro | De Marco C.,Institute for Genetic Research Gaetano Salvatore | Rinaldo N.,Institute for Genetic Research Gaetano Salvatore | Bruni P.,Casa di Cura Malzoni Villa dei Platani | And 12 more authors.

The phosphatidylinositol 3-kinase (PI3K)/AKT pathway is activated in multiple cancers including ovarian carcinoma (OC). However, the relative contribution of the single components within the PI3K pathway to AKT activation in OC is still unclear. We examined 98 tumor samples from Italian OC patients for alterations in the members of the PI3K pathway. We report that AKT is significantly hyperactive in OC compared to normal tissue (n = 93; p<0.0001) and that AKT activation is preferentially observed in the elderly (>58 years old; n = 93; p<0.05). The most frequent alteration is the overexpression of the p110α catalytic subunit of PI3K (63/93, ~68%); less frequent alterations comprise the loss of PTEN (24/89, 27%) and the overexpression of AKT1 (18/96, 19%) or AKT2 (11/88,12.5%). Mutations in the PIK3CA or KRAS genes were detected at lower frequency (12% and 10%, respectively) whereas mutations in AKT1 or AKT2 genes were absent. Although many tumors presented a single lesion (28/93, of which 23 overexpressed PIK3CA, 1 overexpressed AKT and 4 had lost PTEN), many OC (35/93) presented multiple alterations within the PI3K pathway. Apparently, aberrant PI3K signalling was mediated by activation of the canonical downstream AKT-dependent mTOR/S6K1/4EBP1 pathway and by regulation of expression of oncogenic transcription factors that include HMGA1, JUN-B, FOS and MYC but not by AKT-independent activation of SGK3. FISH analysis indicated that gene amplification of PIK3CA, AKT1 and AKT2 (but not of PI3KR1) and the loss of PTEN are common and may account for changes in the expression of the corresponding proteins. In conclusion, our results indicate that p110α overexpression represents the most frequent alteration within the PI3K/AKT pathway in OC. However, p110α overexpression may not be sufficient to activate AKT signalling and drive ovarian tumorigenesis since many tumors overexpressing PI3K presented at least one additional alteration. © 2013 De Marco et al. Source

Scrima M.,Institute for Genetic Research Gaetano Salvatore | De Marco C.,Institute for Genetic Research Gaetano Salvatore | De Marco C.,University of Catanzaro | De Vita F.,Institute for Genetic Research Gaetano Salvatore | And 8 more authors.
American Journal of Pathology

The aim of the present work was to identify protein tyrosine phosphatases (PTPs) as novel, candidate tumor suppressor genes in lung cancer. Among the 38 PTPs in the human genome that show specificity for phosphotyrosine, we identified six PTPs by quantitative RT-PCR whose mRNA expression levels were significantly down-regulated in lung cancerderived cell lines (ie, PTPRE, PTPRF, PTPRU, PTPRK, PTPRD, and PTPN13). After validation in primary samples of nonsmall cell lung cancer (NSCLC), we selected PTPN13 for further studies. The results presented here demonstrate that PTPN13 is a candidate tumor suppressor gene that is frequently inactivated in NSCLC through the loss of either mRNA and protein expression (64/87, 73%) or somatic mutation (approximately 8%). Loss of PTPN13 expression was apparently due to the loss of one or both copies of the PTPN13 locus at 4q (approximately 26% double deletion and approximately 37% single deletion) but not to promoter methylation. Finally, the manipulation of PTPN13 expression in lung cancer cells (ie, NCI-H292, A549) demonstrated that PTPN13 negatively regulates anchorage-dependent and anchorage-independent growth in vitro and restrains tumorigenicity in vivo, possibly through the control of the tyrosine phosphorylation of both EGFR and HER2. In conclusion, the expression screening of PTPs in lung cancer reported here has identified PTPN13 as a novel candidate tumor suppressor in NSCLC whose loss increases signaling from epidermal growth factor receptor and HER2 tyrosine kinase receptors. © 2012 American Society for Investigative Pathology. Source

Scrima M.,Institute for Genetic Research Gaetano Salvatore | de Marco C.,Institute for Genetic Research Gaetano Salvatore | de Marco C.,University of Catanzaro | Fabiani F.,University of Catanzaro | And 14 more authors.

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. © 2012 Scrima et al. Source

Zoppoli P.,University of Sannio | Zoppoli P.,Institute for Genetic Research Gaetano Salvatore | Morganella S.,University of Sannio | Morganella S.,Institute for Genetic Research Gaetano Salvatore | And 2 more authors.
BMC Bioinformatics

Background: One 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.Results: In 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.Conclusions: Here 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. © 2010 Zoppoli et al; licensee BioMed Central Ltd. Source

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