Outcome of HIV-infected patients with hepatocellular carcinoma: a comparison with HIV negative controls

Background and rationale for the study. This study investigated whether human immunodeficiency virus (HIV) infection adversely affects the prognosis of patients diagnosed with hepatocellular carcinoma (HCC).Thirty-four HIV-positive patients with chronic liver disease, consecutively diagnosed with HCC from 1998 to 2007 were one-to-one matched with 34 HIV negative controls for: sex, liver function (Child-Turcotte-Pugh class [CTP]), cancer stage (BCLC model) and, whenever possible, age, etiology of liver disease and modality of cancer diagnosis. Survival in the two groups and independent prognostic predictors were assessed. Results. Among HIV patients 88% were receiving HAART. HIV-RNA was undetectable in 65% of cases; median lymphocyte CD4+ count was 368.5/mmc. Etiology of liver disease was mostly related to HCV infection. CTP class was: A in 38%, B in 41%, C in 21% of cases. BCLC cancer stage was: early in 50%, intermediate in 23.5%, advanced in 5.9%, end-stage in 20.6% of cases. HCC treatments and death causes did not differ between the two groups. Median survival did not differ, being 16 months (95% CI: 6-26) in HIV positive and 23 months (95% CI: 5-41) in HIV negative patients (P=0.391). BCLC cancer stage and HCC treatment proved to be independent predictors of survival both in the whole population and in HIV patients. Conclusions. Survival of HIV infected patients receiving antiretroviral therapy and diagnosed with HCC is similar to that of HIV negative patients bearing this tumor. Prognosis is determined by the cancer bulk and its treatment.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.