Histologic subtyping affecting outcome of triple negative breast cancer: a large Sardinian population-based analysis.

BACKGROUND: Triple Negative breast cancer (TNBC) includes a heterogeneous group of tumors with different clinico-pathological features, molecular alterations and treatment responsivity. Our aim was to evaluate the clinico-pathological heterogeneity and prognostic significance of TNBC histologic variants, comparing "special types" to high-grade invasive breast carcinomas of no special type (IBC-NST). METHODS: This study was performed on data obtained from TNBC Database, including pathological features and clinical records of 1009 TNBCs patients diagnosed between 1994 and 2015 in the four most important Oncology Units located in different hospitals in Sardinia, Italy. Kaplan-Meier analysis, log-rank test and multivariate Cox proportional-hazards regression were applied for overall survival (OS) and disease free survival (DFS) according to TNBC histologic types. RESULTS: TNBC "special types" showed significant differences for several clinico-pathological features when compared to IBC-NST. We observed that in apocrine carcinomas as tumor size increased, the number of metastatic lymph nodes manifestly increased. Adenoid cystic carcinoma showed the smallest tumor size relative to IBC-NST. At five-year follow-up, OS was 92.1, 100.0, and 94.5% for patients with apocrine, adenoid cystic and medullary carcinoma, respectively; patients with lobular and metaplastic carcinoma showed the worst OS, with 79.7 and 84.3%, respectively. At ten-years, patients with adenoid cystic (100.0%) and medullary (94.5%) carcinoma showed a favourable prognosis, whereas patients with lobular carcinoma showed the worst prognosis (73.8%). TNBC medullary type was an independent prognostic factor for DFS compared to IBC-NST. CONCLUSIONS: Our study confirms that an accurate and reliable histopathologic definition of TNBC subtypes has a significant clinical utility and is effective in the therapeutic decision-making process, with the aim to develop innovative and personalized treatments.

Clinical and pathological factors influencing survival in a large cohort of triple-negative breast cancer patients.

BACKGROUND: To provide further information on the clinical and pathological prognostic factors in triple-negative breast cancer (TNBC), for which limited and inconsistent data are available. METHODS: Pathological characteristics and clinical records of 841 TNBCs diagnosed between 1994 and 2015 in four major oncologic centers from Sardinia, Italy, were reviewed. Multivariate hazard ratios (HRs) for mortality and recurrence according to various clinicopathological factors were estimated using Cox proportional hazards models. RESULTS: After a mean follow-up of 4.3 years, 275 (33.3%) TNBC patients had a progression of the disease and 170 (20.2%) died. After allowance for study center, age at diagnosis, and various clinicopathological factors, all components of the TNM staging system were identified as significant independent prognostic factors for TNBC mortality. The HRs were 3.13, 9.65, and 29.0, for stage II, III and IV, respectively, vs stage I. Necrosis and Ki-67 > 16% were also associated with increased mortality (HR: 1.61 and 1.99, respectively). Patients with tumor histotypes other than ductal invasive/lobular carcinomas had a more favorable prognosis (HR: 0.40 vs ductal invasive carcinoma). No significant associations with mortality were found for histologic grade, tumor infiltrating lymphocytes, and lymphovascular invasion. Among lymph node positive TNBCs, lymph node ratio appeared to be a stronger predictor of mortality than pathological lymph nodes stage (HR: 0.80 for pN3 vs pN1, and 3.05 for >0.65 vs <0.21 lymph node ratio), respectively. Consistent results were observed for cancer recurrence, except for Ki-67 and necrosis that were not found to be significant predictors for recurrence. CONCLUSIONS: This uniquely large study of TNBC patients provides further evidence that, besides tumor stage at diagnosis, lymph node ratio among lymph node positive tumors is an additional relevant predictor of survival and tumor recurrence, while Ki-67 seems to be predictive of mortality, but not of recurrence.

Fragment Prioritization on a Large Mutagenicity Dataset.

The identification of structural alerts is one of the simplest tools used for the identification of potentially toxic chemical compounds. Structural alerts have served as an aid to quickly identify chemicals that should be either prioritized for testing or for elimination from further consideration and use. In the recent years, the availability of larger datasets, often growing in the context of collaborative efforts and competitions, created the raw material needed to identify new and more accurate structural alerts. This work applied a method to efficiently mine large toxicological dataset for structural alert showing a strong statistical association with mutagenicity. In details, we processed a large Ames mutagenicity dataset comprising 14,015 unique molecules obtained by joining different data sources. After correction for multiple testing, we were able to assign a probability value to each fragment. A total of 51 rules were identified, with p-value < 0.05. Using the same method, we also confirmed the statistical significance of several mutagenicity rules already present and largely recognized in the literature. In addition, we have extended the application of our method by predicting the mutagenicity of an external data set.

A generalizable definition of chemical similarity for read-across.

BACKGROUND: Methods that provide a measure of chemical similarity are strongly relevant in several fields of chemoinformatics as they allow to predict the molecular behavior and fate of structurally close compounds. One common application of chemical similarity measurements, based on the principle that similar molecules have similar properties, is the read-across approach, where an estimation of a specific endpoint for a chemical is provided using experimental data available from highly similar compounds. RESULTS: This paper reports the comparison of multiple combinations of binary fingerprints and similarity metrics for computing the chemical similarity in the context of two different applications of the read-across technique. CONCLUSIONS: Our analysis demonstrates that the classical similarity measurements can be improved with a generalizable model of similarity. The proposed approach has already been used to build similarity indices in two open-source software tools (CAESAR and VEGA) that make several QSAR models available. In these tools, the similarity index plays a key role for the assessment of the applicability domain.

Implementing the "Best Template Searching" tool into Adenosiland platform.

BACKGROUND: Adenosine receptors (ARs) belong to the G protein-coupled receptors (GCPRs) family. The recent release of X-ray structures of the human A2A AR (h A2A AR ) in complex with agonists and antagonists has increased the application of structure-based drug design approaches to this class of receptors. Among them, homology modeling represents the method of choice to gather structural information on the other receptor subtypes, namely A1, A2B, and A3 ARs. With the aim of helping users in the selection of either a template to build its own models or ARs homology models publicly available on our platform, we implemented our web-resource dedicated to ARs, Adenosiland, with the "Best Template Searching" facility. This tool is freely accessible at the following web address: http://mms.dsfarm.unipd.it/Adenosiland/ligand.php. FINDINGS: The template suggestions and homology models provided by the "Best Template Searching" tool are guided by the similarity of a query structure (putative or known ARs ligand) with all ligands co-crystallized with hA2A AR subtype. The tool computes several similarity indexes and sort the outcoming results according to the index selected by the user. CONCLUSIONS: We have implemented our web-resource dedicated to ARs Adenosiland with the "Best Template Searching" facility, a tool to guide template and models selection for hARs modelling. The underlying idea of our new facility, that is the selection of a template (or models built upon a template) whose co-crystallized ligand shares the highest similarity with the query structure, can be easily extended to other GPCRs.

Adenosiland: walking through adenosine receptors landscape.

Adenosine receptors (ARs) belong to the family of G protein-coupled receptors. Four distinct subtypes are known, termed adenosine A(1), A(2A), A(2B) and A(3). receptors and they are regulated by adenosine which is one of the most ancient and widespread chemical messengers in the animal and plant kingdoms. Moreover, ARs are widely distributed in human body and they are expressed with different density in diverse tissues. It is not surprising that they are involved in the regulation of several physiopathological processes. Adenosiland represents the first tentative of an integrated bioinformatics and chemoinformatics web-resource dedicated to adenosine receptors. This informatics platform provides a wide-ranging of structure based and ligand based query functions to facilitate the exploration of adenosine receptor structures from primary sequences to three-dimensional architectures. Here, we present an overview of Adenosiland platform describing the most valuable searching tools and their functionalities. Adenosiland can be freely accessed at http://mms.dsfarm.unipd.it/Adenosiland/.