Classificazione su base molecolare dei tumori mammari della cagna mediante metodica immunoistochimica

Background. A new classification system of human breast tumours based on the immunohistochemical characterization has been applied to mammary tumours of the female dog with the aim to verify its association with invasion and grade, and prognostic aid in veterinary medicine. Methods. Forty-five canine mammary carcinomas with a two-year post-mastectomy follow-up were selected from our database, and the following antibodies were applied: anti-cytokeratines 14, 5/6, oestrogen receptor (ER), progesterone receptor (PR), and ERB-B2. . The tumours were grouped for phenotype as: luminal-like (ER+ and/or PR+, CK14-, CK5/6-) type A (ERB-B2-), and B (ERB-B2+); basal-like (ER-, PR-, CK14+ and/or CK5/6+, ERB-B2-); ERB-B2 (ER-, PR-, CK14-, CK5/6-, ERB-B2+). Association with invasion, grade and histotypes were evaluated and Kaplan-Meier survival curves estimated, then compared by survival analysis. Results. Thirty-five cases with luminal pattern (ER+ and PR+) were subgrouped into 13 A type and 22 B type, if ERB-B2 positive or negative . Most luminal-like A and basal-like cases were grade 1 carcinomas, while the percentage of luminal B cases was higher in grade 2 and 3 (Pearson Chi-square P=0.009). No difference in the percentage of molecular subtypes was evidenced between simple and complex/mixed carcinomas (Pearson Chi-square P=0.47). No significant results were obtained by survival analysis, even if basal-like had a more favourable prognosis than luminal-like. Conclusion. The panel of antibodies identified only 3 groups (luminal-like A and B, and basal-like) in the dog. Even though canine mammary tumours may be a model of human breast cancer, the existence of the same types of carcinoma as in the woman need to be confirmed. Canine mammary carcinomas show high molecular heterogeneity, which would benefit from a classification based on molecular differences. However, by multivariate analysis, the molecular classification appears a variable with a dependent value if compared to invasion and grade that are independent, suggesting that, at present, caution should be used in the application of such a classification to the dog, in which invasion and grade supply the most important prognostic information.

New routes to enantioenriched substances through small organic molecules

In this thesis we will disclose the results obtained from the diastereoisomeric salt formation (n salt, p salt and p1,n1 salt) between non-racemic trans-chrysanthemic acid (trans-ChA) and pure enantiomers of threo-2-dimethylamino-1-phenyl-1,3-propanediol (DMPP). The occurrence of p1,n1 salt formation can have profound effects on enantiomer separation of scalemic (non-racemic) mixtures. This phenomenon when accompanied by substrate self-association impedes the complete recovery of the major enantiomer through formation of an inescapable racemate cage. A synthetic sequence for the asymmetric synthesis of bicyclo[3.2.0]heptanones and bicyclo[3.2.0]hept-3-en-6-ones through a cycloaddition strategy is reported. The fundamental step is a [2+2]-cycloaddition of an enantiopure amide derived from the reaction between a set of acids and an oxazolidinone as the chiral auxiliary. The inter- and intramolecular cycloaddition of in situ-generated keteniminium salts gives bicycles with a good enantioselection. A key intermediate of Iloprost, a chemically stable and biologically active mimic of prostacyclin PGI2 is synthesized following a ‘green approach’. An example of simple optical resolution of this racemic intermediate involving the diastereoisomeric salt formation is described.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.