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Precision medicine has driven a major change in the treatment of many forms of cancer. The discovery that each patient is different and each tumor mass has its own characteristics has shifted the focus of basic and clinical research to the singular individual. Liquid biopsy (LB), in this sense, presents new scenarios in personalized medicine through the study of molecules, factors, and tumor biomarkers in blood such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), exosomes and circulating tumor microRNAs (ct-miRNAs). Moreover, its easy application and complete absence of contraindications for the patient make this method applicable in a great many fields. Melanoma, given its highly heterogeneous characteristics, is a cancer form that could significantly benefit from the information linked to liquid biopsy, especially in the treatment management. In this review, we will focus our attention on the latest applications of liquid biopsy in metastatic melanoma and possible developments in the clinical setting. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments. [ABSTRACT FROM AUTHOR]

New evidence on the impact of dysregulation of the CDK4/6 pathway on breast cancer (BC) cell proliferation has led to the development of selective CDK4/6 inhibitors, which have radically changed the management of advanced BC. Despite the improved outcomes obtained by CDK4/6 inhibitors, approximately 10% of tumors show primary resistance, whereas acquired resistance appears to be an almost ubiquitous occurrence, leading to treatment failure. The identification of differentially expressed genes or genomic mutational signatures able to predict sensitivity or resistance to CDK4/6 inhibitors is critical for medical decision making and for avoiding or counteracting primary or acquired resistance against CDK4/6 inhibitors. In this review, we summarize the main mechanisms of resistance to CDK4/6 inhibitors, focusing on those associated with potentially relevant biomarkers that could predict patients' response/resistance to treatment. Recent advances in biomarker identification are discussed, including the potential use of liquid biopsy for BC management and the role of multiple microRNAs as molecular predictors of cancer cell sensitivity and resistance to CDK4/6 inhibitors. [ABSTRACT FROM AUTHOR]

We describe an original electroporation protocol for in vivo plasmid DNA transfection. The right hind limbs of C57 mice are exposed to a specifically designed train of permeabilizing electric pulses by transcutaneous application of tailored needle electrodes, immediately after the injection of pEGFP-C1 plasmid encoding GFP (Green Fluorescente Protein). The electroporated rodents show a greater GFP expression than the controls at three different time points (4, 10, and 15 days). The electroporated muscles display only mild interstitial myositis, with a significant increase in inflammatory cell infiltrates. Finally, mild gait abnormalities are registered in electroporated mice only in the first 48 h after the treatment. This protocol has proven to be highly efficient in terms of expression levels of the construct, is easy to apply since it does not require surgical exposure of the muscle and is well tolerated by the animals because it does not cause evident morphological and functional damage to the electroporated muscle. [ABSTRACT FROM AUTHOR]

Background: According to the cancer stem cells (CSCs) hypothesis, a population of cancer cells with stem cell properties is responsible for tumor propagation, drug resistance, and disease recurrence. Study of the mechanisms responsible for lung CSCs propagation is expected to provide better understanding of cancer biology and new opportunities for therapy. Methods: The Lung Adenocarcinoma (LUAD) NCI-H460 cell line was grown either as 2D or as 3D cultures. Transcriptomic and genome-wide chromatin accessibility studies of 2D vs. 3D cultures were carried out using RNA-sequencing and Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq), respectively. Reverse transcription polymerase chain reaction (RT-PCR) was also carried out on RNA extracted from primary cultures derived from malignant pleural effusions to validate RNA-seq results. Results: RNA-seq and ATAC-seq data disentangled transcriptional and genome accessibility variability of 3D vs. 2D cultures in NCI-H460 cells. The examination of genomic landscape of genes upregulated in 3D vs. 2D cultures led to the identification of 2D cultures led to the identification of Beta-1,4-galactosyltranferase 1 (B4GALT1) as the top candidate. B4GALT1 as the top candidate. B4GALT1 was validated as a stemness factor, since its silencing caused strong inhibition of 3D spheroid formation. Conclusion: Combined transcriptomic and chromatin accessibility study of 3D vs. 2D LUAD cultures led to the identification of B4GALT1 as a new factor involved in the propagation and maintenance of LUAD CSCs. [ABSTRACT FROM AUTHOR]

Identifying putative biomarkers of clinical outcomes in cancer is crucial for successful enrichment, and for the selection of patients who are the most likely to benefit from a specific therapeutic approach. Indeed, current research in personalized cancer therapy focuses on the possibility of identifying biomarkers that predict prognosis, sensitivity or resistance to therapies. Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a tumor suppressor gene that regulates several crucial cell functions such as proliferation, survival, genomic stability and cell motility through both enzymatic and non-enzymatic activities and phosphatidylinositol 3-kinase (PI3K)-dependent and -independent mechanisms. Despite its undisputed role as a tumor suppressor, assessment of PTEN status in sporadic human tumors has yet to provide clinically robust prognostic, predictive or therapeutic information. This is possibly due to the exceptionally complex regulation of PTEN function, which involves genetic, transcriptional, post-transcriptional and post-translational events. This review shows a brief summary of the regulation and function of PTEN and discusses its controversial aspects as a prognostic/predictive biomarker. [ABSTRACT FROM AUTHOR]

The characterization of a gene product function is a process that involves multiple laboratory techniques in order to silence the gene itself and to understand the resulting cellular phenotype via several omics profiling. When it comes to tumor cells, usually the translation process from in vitro characterization results to human validation is a difficult journey. Here, we present a simple algorithm to extract mRNA signatures from cancer datasets, where a particular gene has been deleted at the genomic level, ICAro. The process is implemented as a two-step workflow. The first one employs several filters in order to select the two patient subsets: the inactivated one, where the target gene is deleted, and the control one, where large genomic rearrangements should be absent. The second step performs a signature extraction via a Differential Expression analysis and a complementary Random Forest approach to provide an additional gene ranking in terms of information loss. We benchmarked the system robustness on a panel of genes frequently deleted in cancers, where we validated the downregulation of target genes and found a correlation with signatures extracted with the L1000 tool, outperforming random sampling for two out of six L1000 classes. Furthermore, we present a use case correlation with a published transcriptomic experiment. In conclusion, deciphering the complex interactions of the tumor environment is a challenge that requires the integration of several experimental techniques in order to create reproducible results. We implemented a tool which could be of use when trying to find mRNA signatures related to a gene loss event to better understand its function or for a gene-loss associated biomarker research. [ABSTRACT FROM AUTHOR]