Stochastic methods in cancer research : applications to genomics and angiogenesis

In recent years, interactions between mathematicians and biomedical researchers have increased due to both the complexity of the biological/medical issues and the development of new technologies, producing \u201clarge\u201d data rich of information. Biomathematics is applied in many areas, such as epidemiology, clinical trial design, neuroscience, disease modeling, genomics, proteomics, etc. Cancer is a multistep process where the accumulation of genomic lesions alters cell biology. The latter is under control of several pathways and, thus, cancer can origin via different mechanisms affecting different pathways. However, usually, more than one of these mechanisms needs to be damaged before a cell becomes cancerous. Due to the general complexity of this disease and the different type of tumors, the efforts of cancer research cover several research areas such as, for example, immunology, genetics, cell biology, angiogenesis. As a consequence, many biostatistical topics can be applied. The thesis is divided into two parts. In the former, two Bayesian regression methods for the analysis of two types of cancer genomic data are proposed. In the latter, the properties of two estimators of the intensity of a stationary fibre process are studied, which can be applied for the characterization of angiogenic and vascular processes

Intensity estimation of stationary fibre processes from digital images with a learned detector

Stationary fibre processes are processes of curves in a higher dimensional space, whose distribution is translation invariant. In practical applications, they can be used to model several real objects, such as roots, vascular networks and fibres of materials. Often it is required to compare processes showing similar shape, thus a quantitative approach to describe their stochastic geometry is necessary. One of the basic geometric characteristics of these processes is the intensity (i.e., mean total length per unit area or volume). Here, a general computational-statistical approach is proposed for the estimation of this quantity from digital images of the process, thus only planar fibre processes or projections of processes onto a plane are considered. Differently from approaches based on segmentation, it does not depend on the particular application. The statistical estimator of the intensity is proportional to the number of intersections between the process under study and an independent motion invariant test fibre process. The intersections are detected on the real digital image by a learned detector, easily trained by the user. Under rather mild regularity conditions on the fibre process under study, the method also allows to estimate approximate confidence intervals for the intensity, which is useful especially for comparison purposes

Modeling, optimization, and comparable efficacy of T cell and hematopoietic stem cell gene editing for treating hyper-IgM syndrome

Precise correction of the CD40LG gene in T cells and hematopoietic stem/progenitor cells (HSPC) holds promise for treating X-linked hyper-IgM Syndrome (HIGM1), but its actual therapeutic potential remains elusive. Here, we developed a one-size-fits-all editing strategy for effective T-cell correction, selection, and depletion and investigated the therapeutic potential of T-cell and HSPC therapies in the HIGM1 mouse model. Edited patients' derived CD4 T cells restored physiologically regulated CD40L expression and contact-dependent B-cell helper function. Adoptive transfer of wild-type T cells into conditioned HIGM1 mice rescued antigen-specific IgG responses and protected mice from a disease-relevant pathogen. We then obtained similar to 25% CD40LG editing in long-term repopulating human HSPC. Transplanting such proportion of wild-type HSPC in HIGM1 mice rescued immune functions similarly to T-cell therapy. Overall, our findings suggest that autologous edited T cells can provide immediate and substantial benefits to HIGM1 patients and position T-cell ahead of HSPC gene therapy because of easier translation, lower safety concerns and potentially comparable clinical benefits.Transplantation and immunomodulatio

Stochastic methods in cancer research. Applications to genomics and angiogenesis

In recent years, interactions between mathematicians and biomedical researchers have increased due to both the complexity of the biological/medical issues and the development of new technologies, producing \u201clarge\u201d data rich of information. Biomathematics is applied in many areas, such as epidemiology, clinical trial design, neuroscience, disease modeling, genomics, proteomics, etc. Cancer is a multistep process where the accumulation of genomic lesions alters cell biology. The latter is under control of several pathways and, thus, cancer can origin via different mechanisms affecting different pathways. However, usually, more than one of these mechanisms needs to be damaged before a cell becomes cancerous. Due to the general complexity of this disease and the different type of tumors, the efforts of cancer research cover several research areas such as, for example, immunology, genetics, cell biology, angiogenesis. As a consequence, many biostatistical topics can be applied. The thesis is divided into two parts. In the former, two Bayesian regression methods for the analysis of two types of cancer genomic data are proposed. In the latter, the properties of two estimators of the intensity of a stationary fibre process are studied, which can be applied for the characterization of angiogenic and vascular processes. (Pubblicata - vedi http://hdl.handle.net/2434/159517

Estimators of the intensity of fibre processes and applications

Many objects in the real world can be modeled as fibres (i.e. lines in 2D or 3D space). If the process is invariant under translations, one of its characteristics is the mean length per unit area (called intensity). Under suitable conditions, two estimators of the intensity have been shown to be asymptotically normal when the sample is \u201cenriched\u201d by enlarging the window of observation. We discuss the applicability of these estimators in practice, by using both simulated and real images of fibre processes

Bayesian Joint Estimation of CN and LOH Aberrations

SNP-microarrays are able to measure simultaneously both copy number and genotype at several single nucleotide polymorphism positions. Combining the two data, it is possible to better identify genomic aberrations. For this purpose, we propose a Bayesian piecewise constant regression which infers the type of aberration occurred, taking into account all the possible influence in the microarray detection of the genotype, resulting from an altered copy number level. Namely, we model the distributions of the detected genotype given a specific genomic alteration and we estimate the hyper-parameters used on public reference datasets

Tumor dormancy and frailty models: A novel approach

Frailty models are here proposed in the tumor dormancy framework, in order to account for possible unobservable dependence mechanisms in cancer studies where a non-negligible proportion of cancer patients relapses years or decades after surgical removal of the primary tumor. Relapses do not seem to follow a memory-less process, since their timing distribution leads to multimodal hazards. From a biomedical perspective, this behavior may be explained by tumor dormancy, i.e., for some patients microscopic tumor foci may remain asymptomatic for a prolonged time interval and, when they escape from dormancy, micrometastatic growth results in a clinical disease appearance. The activation of the growth phase at different metastatic states would explain the occurrence of metastatic recurrences and mortality at different times (multimodal hazard). We propose a new frailty model which includes in the risk function a random source of heterogeneity (frailty variable) affecting the components of the hazard function. Thus, the individual hazard rate results as the product of a random frailty variable and the sum of basic hazard rates. In tumor dormancy, the basic hazard rates correspond to micrometastatic developments starting from different initial states. The frailty variable represents the heterogeneity among patients with respect to relapse, which might be related to unknown mechanisms that regulate tumor dormancy. We use our model to estimate the overall survival in a large breast cancer dataset, showing how this improves the understanding of the underlying biological process

Along‐tract statistics of neurite orientation dispersion and density imaging diffusion metrics to enhance MR tractography quantitative analysis in healthy controls and in patients with brain tumors

Along-tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities, new parameters reflecting the relative contribution of different diffusion compartments in the tissue can be estimated through advanced diffusion MRI methods as neurite orientation dispersion and density imaging (NODDI), leading to a more specific microstructural characterization. In this study, we extracted both DTI- and NODDI-derived quantitative microstructural diffusion metrics along the most eloquent fiber tracts in 15 healthy subjects and in 22 patients with brain tumors. We obtained a robust intraprotocol reference database of normative along-tract microstructural metrics, and their corresponding plots, from healthy fiber tracts. Each diffusion metric of individual patient's fiber tract was then plotted and statistically compared to the normative profile of the corresponding metric from the healthy fiber tracts. NODDI-derived metrics appeared to account for the pathological microstructural changes of the peritumoral tissue more accurately than DTI-derived ones. This approach may be useful for future studies that may compare healthy subjects to patients diagnosed with other pathological conditions

Chromosome 11q23.1 is an unstable region in B-cell tumor cell lines

Chromosome 11q23 region is a frequent target of chromosome aberrations in B-cell lymphoid tumors. Here, we present the cytogenetic and molecular characterization of an amplification affecting 11q23.1 in four cell lines derived from B-cell lymphoid tumors. A minimal common region of amplification of 330kb was identified in three cell lines using Affymetrix Human Mapping 250K arrays. When analyzed with three BAC clones, the amplifications appeared different at cytogenetic level in each cell line. Possibly affected transcripts were evaluated using tiling arrays, and validated by real time PCR. Since no effect of the amplification at the local transcription level was observed, it is possible that 11q23 amplification might mainly represent the effect of unstable chromosomal region

Preoperative predictive factors of laparoscopic distal pancreatectomy difficulty

Background: Laparoscopic distal pancreatectomy (LDP) is a challenging operation due to technical complexity and tumor-related factors. Aim of this study was to identify preoperative risk factors affecting LDP difficulty. Methods: Consecutive patients who underwent LDP between 2015 and 2018 at San Raffaele Hospital and Policlinico S.Orsola-Malpighi Hospital were enrolled retrospectively. Three variables were used to define surgical difficulty: conversion to open, duration of surgery >3rd quartile and intraoperative blood loss >3rd quartile. The presence of 651 of these 3 variables was considered as another measure of difficulty. Results: Overall, 191 patients were included. Conversion to open was required in 25 patients (13%). At multiple regression analysis, tumor proximity to major vessels was the only independent predictor of conversion from laparoscopic to open (p < 0.001). No variables independently predicted an excessive duration of surgery. Male gender (p = 0.033) and increasing parenchymal thickness at resection line (p = 0.018) were independent predictors of excessive blood loss. Increasing parenchymal thickness at resection line (p = 0.014) and tumor proximity to major vessels (p = 0.002) were significant risk factors for the presence of 651 outcome of surgical difficulty. Conclusion: Male gender, increasing parenchymal thickness at resection line and tumor proximity to major vessels represent preoperative risk factors of LDP difficulty