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.

Tumori primitivi multipli del polmone. Profilo clinico e biologico in pazienti affetti da neoplasie polmonari multiple. Analisi di elementi clinici e marcatori biologici come possibili fattori di differenziazione dei tumori polmonari multipli

Background: MPLC represents a diagnostic challenge. Topic of the discussion is how to distinguish these patients as a metastatic or a multifocal disease. While in case of the different histology there are less doubt on the opposite in case of same histology is mandatory to investigate on other clinical features to rule out this question. Matherials and Methods: A retrospective review identified all patients treated surgically for a presumed diagnosis of SPLC. Pre-operative staging was obtained with Total CT scan and fluoro-deoxy positron emission tomography and mediastinoscopy. Patients with nodes interest or extra-thoracic location were excluded from this study. Epidermal growth factor receptor (EGFR) expression with complete immunohistochemical analisis was evaluated. Survival was estimated using Kaplan-Meyer method, and clinical features were estimated using a long-rank test or Cox proportional hazards model for categorical and continuous variable, respectively. Results: According to American College Chest Physician, 18 patients underwent to surgical resection for a diagnosis of MPLC. Of these, 8 patients had 3 or more nodules while 10 patients had less than 3 nodules. Pathologic examination demonstrated that 13/18(70%) of patients with multiple histological types was Adenocarcinoma, 2/18(10%) Squamous carcinoma, 2/18(10%) large cell carcinoma and 1/18(5%) Adenosquamosu carcinoma. Expression of EGFR has been evaluated in all nodules: in 7 patients of 18 (38%) the percentage of expression of each nodule resulted different. Conclusions: MPLC represent a multifocal disease where interactions of clinical informations with biological studies reinforce the diagnosis. EGFR could contribute to differentiate the nodules. However, further researches are necessary to validate this hypothesis.

The search for Multiple Myeloma Stem cells: molecular characterization and self-renewal mechanisms involved in the disease persistence

The existence of Multiple Myeloma Stem cells (MMSCs)is supposed to be one of the major causes of MM drug-resistance. However, very little is known about the molecular characteristics of MMSCs, even if some studies suggested that these cells resembles the memory B cells. In order to molecularly characterize MMSCs, we isolated the 138+138- population. For each cell fraction we performed a VDJ rearrangement analysis. The complete set of aberrations were performed by SNP Array 6.0 and HG-U133 Plus 2.0 microarray analyses (Affymetrix). The VDJ rearrangement analyses confirmed the clonal relationship between the 138+ clone and the immature clone. Both BM and PBL 138+ clones showed exactly the same genomic macroalterations. In the BM and PBL 138-19+27+ cell fractions several micro-alterations (range: 1-350 Kb) unique of the memory B cells clone were highlighted. Any micro-alterations detected were located out of any genomic variants region and are presumably associated to the MM pathogenesis, as confirmed by the presence of KRAS, WWOX and XIAP genes among the amplified regions. To get insight into the biology of the clonotypic B cell population, we compared the gene expression profile of 8 MM B cells samples 5 donor B cells vs, thus showing a differential expression of 11480 probes (p-value: <0,05). Among the self-renewal mechanisms, we observed the down-regulation of Hedgehog pathway and the iperactivation of Notch and Wnt signaling. Moreover, these immature cells showed a particular phenotype correlated to resistance to proteasome inhibitors (IRE1α-XBP1: -18.0; -19.96. P<0,05). Data suggested that the MM 138+ clone might resume the end of the complex process of myelomagenesis, whereas the memory B cells have some intriguing micro-alterations and a specific transcriptional program, supporting the idea that these post germinal center cells might be involved in the transforming event that originate and sustain the neoplastic clone.