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.

SMO inhibitor specifically targets the Hedgehog Pathway and reverts the drug-resistance of Leukemic Stem Cells

Abnormal Hedgehog signaling is associated with human malignancies. Smo, a key player of that signaling, is the most suitable target to inhibit this pathway. To this aim several molecules, antagonists of Smo, have been synthesized, and some of them have started the phase I in clinical trials. Our hospital participated to one of these studies which investigated the oral administration of a new selective inhibitor of Smo (SMOi). To evaluate ex vivo SMOi efficacy and to identify new potential clinical biomarkers of responsiveness, we separated bone marrow CD34+ cells from 5 acute myeloid leukemia (AML), 1 myelofibrosis (MF), 2 blastic phases chronic myeloid leukemia (CML) patients treated with SMOi by immunomagnetic separation, and we analysed their gene expression profile using Affimetrix HG-U133 Plus 2.0 platform. This analysis, showed differential expression after 28 days start of therapy (p-value ≤ 0.05) of 1,197 genes in CML patients and 589 genes in AML patients. This differential expression is related to Hedgehog pathway with a p-value = 0.003 in CML patients and with a p-value = 0.0002 in AML patients, suggesting that SMOi targets specifically this pathway. Among the genes differentially expressed we observed strong up-regulation of Gas1 and Kif27 genes, which may work as biomarkers of responsiveness of SMOi treatment in CML CD34+ cells whereas Hedgehog target genes (such as Smo, Gli1, Gli2, Gli3), Bcl2 and Abca2 were down-regulated, in both AML and CML CD34+ cells. It has been reported that Bcl-2 expression could be correlated with cancer therapy resistance and that Hedgehog signaling modulate ATP-binding (ABC) cassette transporters, whose expression has been correlated with chemoresistance. Moreover we confirmed that in vitro SMOi treatment targets Hedgehog pathway, down-regulate ABC transporters, Abcg2 and Abcb1 genes, and in combination with tyrosine kinase inhibitors (TKIs) could revert the chemoresistance mechanism in K562 TKIs-resistant cell line.

Targeting the p53–MDM2 interaction by the small-molecule MDM2 antagonist Nutlin-3a: a new challenged target therapy in adult Philadelphia positive acute lymphoblastic leukemia patients

The human p53 tumor suppressor, known as the “guardian of the genome”, is one of the most important molecules in human cancers. One mechanism for suppressing p53 uses its negative regulator, MDM2, which modulates p53 by binding directly to and decreasing p53 stability. In testing novel therapeutic approaches activating p53, we investigated the preclinical activity of the MDM2 antagonist, Nutlin-3a, in Philadelphia positive (Ph+) and negative (Ph-) leukemic cell line models, and primary B-Acute lymphoblastic leukemia (ALL) patient samples. In this study we demonstrated that treatment with Nutlin-3a induced grow arrest and apoptosis mediated by p53 pathway in ALL cells with wild-type p53, in time and dose-dependent manner. Consequently, MDM2 inhibitor caused an increase of pro-apoptotic proteins and key regulators of cell cycle arrest. The dose-dependent reduction in cell viability was confirmed in primary blast cells from Ph+ ALL patients with the T315I Bcr-Abl kinase domain mutation. In order to better elucidate the implications of p53 activation and to identify biomarkers of clinical activity, gene expression profiling analysis in sensitive cell lines was performed. A total of 621 genes were differentially expressed (p < 0.05). We found a strong down-regulation of GAS41 (growth-arrest specific 1 gene) and BMI1 (a polycomb ring-finger oncogene) (fold-change -1.35 and -1.11, respectively; p-value 0.02 and 0.03, respectively) after in vitro treatment as compared to control cells. Both genes are repressors of INK4/ARF and p21. Given the importance of BMI in the control of apoptosis, we investigated its pattern in treated and untreated cells, confirming a marked decrease after exposure to MDM2 inhibitor in ALL cells. Noteworthy, the BMI-1 levels remained constant in resistant cells. Therefore, BMI-1 may be used as a biomarker of response. Our findings provide a strong rational for further clinical investigation of Nutlin-3a in Ph+ and Ph-ALL.

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.

Il trattamento ortodontico nei bambini con particolari necessità sanitarie (SHCN): una valutazione della durata e del risultato clinico utilizzando l'indice PAR (Peer Assessment Rating), la componente DHC (Dental Health Component) e la componente AC (Aesthetic Component) dell'indice IOTN (Orthodontic Treatment Need Index)

Obbiettivo: Valutazione delle eventuali differenze nel trattamento ortodontico di un gruppo di bambini con particolari necessità sanitarie (SHCN) rispetto ad un gruppo di bambini non diagnosticati con SHCN. Materiali e Metodi: Il gruppo campione (SHCN) è costituito da 50 bambini con SHCN. Il gruppo di controllo (NO SHCN) è costituito da 50 bambini non diagnosticati con SHCN pienamente corrispondenti per età, genere e tipo di apparecchio ortodontico utilizzato con i pazienti del gruppo di studio. I dati riguardanti i gruppi SHCN e NO SHCN sono stati analizzati in modo retrospettivo, valutando: - il punteggio pre- e post-trattamento e la riduzione finale dei valori dell'indice PAR (Peer Assessment Rating), della componente DHC (Dental Health Component) e della componente AC (Aesthetic Component) dell'indice IOTN (Orthodontic Treatment Need Index), - il numero di appuntamenti, - il numero di sedute semplici e complesse, - la durata complessiva del trattamento, - l'età all’inizio ed alla fine della terapia. Risultati: Non sono state rilevate differenze statisticamente significative tra i due gruppi per quanto concerne il numero di appuntamenti, la durata complessiva del trattamento, l'età all’inizio ed alla fine della terapia ortodontica (valori del p-value:0.682, 0.458, 0.535, 0.675). Sono state rilevate differenze statisticamente significative tra i due gruppi per quanto riguarda i punteggi dell’indice PAR, delle componenti DHC e AC dello IOTN pre- e post-trattamento, il numero di sedute semplici e complesse (valori del p-value:0.030, 0.000, 0.020, 0.023, 0.000, 0.000, 0.043, 0.037). Per quanto concerne la riduzione finale del valore dell’indice PAR, della componente DHC e di quella AC dello IOTN non sono state riscontrate differenze statisticamente significative tra i due gruppi (valori del p-value:0.060, 0.765, 0.825). Conclusioni: Lo studio incoraggia gli ortodontisti a trattare i bambini con SHCN nell'obiettivo di migliorarne la qualità di vita, pur evidenziando la necessità di un maggior numero di sedute complesse.