Pancreatic ductal adenocarcinoma (PDA) is one of the most lethal forms of human cancer, characterized by unrestrained progression, invasiveness and treatment resistance. To date, there are limited curative options, with surgical resection as the only effective strategy, hence the urgent need to discover novel therapies. A platform of onco-immunology targets is represented by molecules that play a role in the reprogrammed cellular metabolism as one hallmark of cancer. Due to the hypoxic tumor microenvironment (TME), PDA cells display an altered glucose metabolism—resulting in its increased uptake—and a higher glycolytic rate, which leads to lactate accumulation and them acting as fuel for cancer cells. The consequent acidification of the TME results in immunosuppression, which impairs the antitumor immunity. This review analyzes the genetic background and the emerging glycolytic enzymes that are involved in tumor progression, development and metastasis, and how this represents feasible therapeutic targets to counteract PDA. In particular, as the overexpressed or mutated glycolytic enzymes stimulate both humoral and cellular immune responses, we will discuss their possible exploitation as immunological targets in anti-PDA therapeutic strategies.

Pancreatic Ductal Adenocarcinoma (PDA) is an aggressive malignancy with a very poor outcome. Although chemotherapy (CT) treatment has poor efficacy, it can enhance tumor immunogenicity. Tumor-Associated Antigens (TAA) are self-proteins that are overexpressed in tumors that may induce antibody production and can be PDA theranostic targets. However, the prognostic value of TAA-antibody association as Circulating Immune Complexes (CIC) has not yet been elucidated, mainly due to the lack of techniques that lead to their identification. In this study, we show a novel method to separate IgG, IgM, and IgA CIC from sera to use them as prognostic biomarkers of CT response. The PDA Immune-Complexome (IC) was identified using a LTQ-Orbitrap mass spectrometer followed by computational analysis. The analysis of the IC of 37 PDA patients before and after CT revealed differential associated antigens (DAA) for each immunoglobulin class. Our method identified different PDA-specific CIC in patients that were associated with poor prognosis patients. Finally, CIC levels were significantly modified by CT suggesting that they can be used as effective prognostic biomarkers to follow CT response in PDA patients.

A main aspect in computational modelling of biological systems is the determination of model structure and model parameters. Due to economical and technical reasons, only part of these details are well characterized, while the rest are unknown. To deal with this difficulty, many reverse engineering and parameter estimation methods have been proposed in the literature, however these methods often need an amount of experimental data not always available. In this paper we propose an alternative approach, which overcomes model indetermination solving an Optimization Problem (OP) with an objective function that, similarly to Flux Balance Analysis, is derived from an empirical biological knowledge and does not require large amounts of data. The system behaviour is described by a set of Ordinary Differential Equations (ODE). Model indetermination is resolved selecting time-varying coefficients that maximize/ minimize the objective function at each ODE integration step. Moreover, to facilitate the modelling phase we provide a graphical formalism, based on Petri Nets, which can be used to derive the corresponding ODEs and OP. Finally, the approach is illustrated on a case study focused on cancer metabolism.

A main aspect in computational modelling of biological sys- tems is the determination of model structure and model pa- rameters. Due to economical and technical reasons, only part of these details are well characterized, while the rest are unknown. To deal with this difficulty, many reverse en- gineering and parameter estimation methods have been pro- posed in the literature, however these methods often need an amount of experimental data not always available. In this paper we propose an alternative approach, which overcomes model indetermination solving an Optimization Problem (OP) with an objective function that, similarly to Flux Balance Analysis, is derived from an empirical bio- logical knowledge and does not require large amounts of data. The system behaviour is described by a set of Or- dinary Differential Equations (ODE). Model indetermina- tion is resolved selecting time-varying coefficients that max- imize/minimize the objective function at each ODE inte- gration step. Moreover, to facilitate the modelling phase we provide a graphical formalism, based on Petri Nets, which can be used to derive the corresponding ODEs and OP. Fi- nally, the approach is illustrated on a case study focused on cancer metabolism.

DNA methylation is a DNA modification playing an important role in several diseases, including cancer. The gold-standard technique for measuring DNA methylation is Bisulfite Sequencing (BS). The treatment with bisulfite alters the sequence of DNA making the analysis of BS data computationally difficult. There are many tools for analysing BS data but the choice of which to use is difficult due to the extensive biological and technical variability of the data. Synthetic and real datasets can be exploited to evaluate the tool performance and to obtain an accurate data analysis. Today, Sherman is the only available tool to generate BS synthetic datasets. However, this tool does not report any information about the methylated cytosines. For this purpose, in this paper we present MethyIFASTQ, an easy-to-use bioinformatics tool that generates synthetic bisulfite datasets in FASTQ format. MethylFASTQ works in parallel manner using producer-consumer approach. It returns: i) a complete dataset in FASTQ format simulating the results of a BS experiment ii) a report file storing the information about the methylation level of the dataset (i.e. methylated cytosines). First, we test MethylFASTQ performances with an increasing number of concurrent processes and we report the comparison of MethylFASTQ with respect to Sherman tool. Then, we also describe an application of synthetic datasets generated with our tool and we use them as input for two bisulfite mapping and methylation calling tools. Finally, we propose MethylFASTQ as a tool to generate synthetic bisulfite sequencing data.