Extracellular Vesicles and Epidermal Growth Factor Receptor Activation: Interplay of Drivers in Cancer Progression

Extracellular vesicles (EVs) are of great interest to study the cellular mechanisms of cancer development and to diagnose and monitor cancer progression. EVs are a highly heterogeneous population of cell derived particles, which include microvesicles (MVs) and exosomes (EXOs). EVs deliver intercellular messages transferring proteins, lipids, nucleic acids, and metabolites with implications for tumour progression, invasiveness, and metastasis. Epidermal Growth Factor Receptor (EGFR) is a major driver of cancer. Tumour cells with activated EGFR could produce EVs disseminating EGFR itself or its ligands. This review provides an overview of EVs (mainly EXOs and MVs) and their cargo, with a subsequent focus on their production and effects related to EGFR activation. In particular, in vitro studies performed in EGFR-dependent solid tumours and/or cell cultures will be explored, thus shedding light on the interplay between EGFR and EVs production in promoting cancer progression, metastases, and resistance to therapies. Finally, an overview of liquid biopsy approaches involving EGFR and EVs in the blood/plasma of EGFR-dependent tumour patients will also be discussed to evaluate their possible application as candidate biomarkers

Aberrant MET activation impairs perinuclear actin cap organization with YAP1 cytosolic relocation

: Little is known about the signaling network responsible for the organization of the perinuclear actin cap, a recently identified structure holding unique roles in the regulation of nuclear shape and cell directionality. In cancer cells expressing a constitutively active MET, we show a rearrangement of the actin cap filaments, which crash into perinuclear patches associated with spherical nuclei, meandering cell motility and inactivation of the mechano-transducer YAP1. MET ablation is sufficient to reactivate YAP1 and restore the cap, leading to enhanced directionality and flattened nuclei. Consistently, the introduction of a hyperactive MET in normal epithelial cells, enhances nuclear height and alters the cap organization, as also confirmed by TEM analysis. Finally, the constitutively active YAP1 mutant YAP5SA is able to overcome the effects of oncogenic MET. Overall, our work describes a signaling axis empowering MET-mediated YAP1 dampening and actin cap misalignment, with implications for nuclear shape and cell motility

The <i>Cancermuts </i>software package for the prioritization of missense cancer variants:a case study of AMBRA1 in melanoma

Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software