While genes and RNA encode information about cellular status, proteins are considered the engine of the cellular machine, as they are the effective elements that drive all cellular functions including proliferation, migration, differentiation, and apoptosis. Consequently, investigations of the cellular protein network are considered a fundamental tool for understanding cellular functions.Alteration of the cellular homeostasis driven by elaborate intra- and extracellular interactions has become one of the most studied fields in the era of personalized medicine and targeted therapy. Increasing interest has been focused on developing and improving proteomic technologies that are suitable for analysis of clinical samples. In this context, reverse-phase protein microarrays (RPPA) is a sensitive, quantitative, high-throughput immunoassay for protein analyses of tissue samples, cells, and body fluids.RPPA is well suited for broad proteomic profiling and is capable of capturing protein activation as well as biochemical reactions such as phosphorylation, glycosylation, ubiquitination, protein cleavage, and conformational alterations across hundreds of samples using a limited amount of biological material. For these reasons, RPPA represents a valid tool for protein analyses and generates data that help elucidate the functional signaling architecture through protein-protein interaction and protein activation mapping for the identification of critical nodes for individualized or combinatorial targeted therapy.

CD133-positive CTC may represent a suitable prognostic marker to stratify the risk of patients who undergo liver resection for CRC metastasis, which opens the avenue to identifying and potentially monitoring the patients who are most likely to benefit from adjuvant treatments.

Cancer is the consequence of intra- and extracellular signaling network deregulation that derives from alteration of genetic and proteomic cellular homeostasis. Mapping the individual molecular circuitry of a patient's tumor cells is the starting point for rational personalized therapy.While genes and RNA encode information about cellular status, proteins are considered the engine of the cellular machine, as they are the effective elements that drive cellular functions, such as proliferation, migration, differentiation, and apoptosis. Consequently, investigations of the cellular protein network are considered a fundamental tool to understand cellular functions. In the last decades, increasing interest has been focused on the improvement of new technologies for proteomic analysis. In this context, reverse-phase protein microarrays (RPMAs) have been developed to study and analyze posttranslational modifications that are responsible for principal cell functions and activities. This innovative technology allows the investigation of protein activation as a consequence of protein-protein interaction or biochemical reactions, such as phosphorylation, glycosylation, ubiquitination, protein cleavage, and conformational alterations.Intracellular balance is carefully conserved by constant rearrangements of proteins through the activity of a series of kinases and phosphatases. Therefore, knowledge of the key cellular signaling cascades reveal information regarding the cellular processes driving a tumor's growth (such as cellular survival, proliferation, invasion, and cell death) and response to treatment.Alteration to cellular homeostasis, driven by elaborate intra- and extracellular interactions, has become one of the most studied fields in the era of personalized medicine and targeted therapy. RPMA technology is a valid tool that can be applied to protein analysis of several diseases for the potential to generate protein interaction and activation maps that lead to the identification of critical nodes for individualized or combinatorial target therapy.

The diagnosis and follow-up of bladder cancer are mainly based on cystoscopy, an invasive method which could be negative in case of flat malignancies such as carcinoma in situ. Other noninvasive diagnostic methods have not yet given satisfactory results. There is a need for a reliable yet noninvasive method for the detection of bladder cancer. Our aim was to investigate whether cell-free DNA quantified in urine (ucf-DNA) could be a useful marker for the diagnosis of bladder cancer. A standard urine test was performed in 150 naturally voided morning urine samples that were processed to obtain a quantitative evaluation of ucf-DNA. Leukocyturia and/or bacteriuria were found in 18 subjects, who were excluded from the study. Statistical analysis was performed on 45 bladder cancer patients and 87 healthy subjects. Ucf-DNA was extracted from urine samples by a spin column-based method and quantified using four different methods: GeneQuant Pro (Amersham Biosciences, Pittsburg, PA, USA), Quant-iT DNA high-sensitivity assay kit (Invitrogen, Carlsbad, CA, USA), Real-Time PCR (Applied Biosystems, Foster City, CA, USA), and NanoDrop 1000 (NanoDrop Technologies, Houston, TX, USA). Median free DNA quantification did not differ statistically between bladder cancer patients and healthy subjects. A receiver-operating characteristic (ROC) curve was developed to evaluate the diagnostic performance of ucf-DNA quantification for each method. The area under the ROC curve was 0.578 for GeneQuant Pro, 0.573 for the Quant-iT DNA high-sensitivity assay kit, 0.507 for Real-Time PCR, and 0.551 for NanoDrop 1000, which indicated that ucf-DNA quantification by these methods is not able to discriminate between the presence and absence of bladder cancer. No association was found between ucf-DNA quantification and tumor size or tumor focality. In conclusion, ucf-DNA isolated by a spin column-based method and quantified by GeneQuant Pro, Quant-iT DNA high-sensitivity assay kit, Real-Time PCR or NanoDrop 1000 does not seem to be a reliable marker for the diagnosis of bladder cancer.

The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to synchronise cells, these usually work only in the short-term. Here, we build a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a controller algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing an avenue for yeast cell cycle synchronisation, our work shows that control engineering can be used to automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles.