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.

Deptor is an important protein that belongs to the mTORC1 and mTORC2 complexes, able to interact with mTOR and to inhibit its kinase activity. As a natural mTOR inhibitor, Deptor is involved in several molecular pathways, such as cell growth, apoptosis, autophagy and ER stress response. For this reason, Deptor seems to play an important role in controlling cellular homeostasis. Despite several recent insights characterizing Deptor functions and regulation, its complete role within cells has not yet been completely clarified. Indeed, quite recently, Deptor has been associated with chromatin, and it has been demonstrated having a role in transcriptional regulation, controlling in such way endoplasmatic reticulum activity. From all these observations it is not surprising that Deptor can behave either as an oncogene or oncosuppressor, depending on the cell- or tissue-contexts. This review highlights recent progresses made in our understanding of the many activities of Deptor, describing its transcriptional and post-transcriptional regulation in different cancer cell types. Moreover, here we discuss the possibility of using compounds able to inhibit Deptor or to disrupt its interaction with mTOR as novel approaches for cancer therapy.

Personalized cancer vaccines based on neoantigens have reached the clinical trial stage in melanoma. Different vaccination protocols showed efficacy in preclinical models without a clear indication of the quality and the number of neoantigens required for an effective cancer vaccine. In an effort to develop potent and efficacious neoantigen-based vaccines, we have developed different neoantigen minigene (NAM) vaccine vectors to determine the rules for a successful neoantigen cancer vaccine (NCV) delivered by plasmid DNA and electroporation. Immune responses were analyzed at the level of single neoantigen by flow cytometry and correlated with tumor growth. Adoptive T cell transfer, from HLA-2.1.1 mice, was used to demonstrate the efficacy of the NCV pipeline against human-derived tumors. In agreement with previous bodies of evidence, immunogenicity was driven by predicted affinity. A strong poly-functional and poly-specific immune response was observed with high affinity neoantigens. However, only a high poly-specific vaccine vector was able to completely protect mice from subsequent tumor challenge. More importantly, this pipeline - from the selection of neoantigens to vaccine design - applied to a new model of patient derived tumor xenograft resulted in therapeutic treatment. These results suggest a feasible strategy for a neoantigen cancer vaccine that is simple and applicable for clinical developments.

The mechanism by which c-Myc exerts its oncogenic functions is not completely clear and different hypotheses are still under investigation. The knowledge of the capacity of c-Myc to bind exclusively E-box sequences determined the discrepancy between, on the one hand, genomic studies showing the binding of c-Myc to all active promoters and, on the other hand, the evidence that only 60% or less of the binding sites have E-box sequences. In this review, we provide support to the hypothesis that the cooperation of c-Myc with transcriptional cofactors mediates c-Myc-induced cellular functions. We produce evidence that recently identified cofactors are involved in c-Myc control of survival mechanisms of cancer cells. The identification of new c-Myc cofactors could favor the development of therapeutic strategies able to compensate the difficulty of targeting c-Myc.

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.

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.