The MET gene, known as MET proto-oncogene receptor tyrosine kinase, was first identified to induce tumor cell migration, invasion, and proliferation/survival through canonical RAS-CDC42-PAK-Rho kinase, RAS-MAPK, PI3K-AKT-mTOR, and β-catenin signaling pathways, and its driver mutations, such as MET gene amplification (METamp) and the exon 14 skipping alterations (METex14), activate cell transformation, cancer progression, and worse patient prognosis, principally in lung cancer through the overactivation of their own oncogenic and MET parallel signaling pathways. Because of this, MET driver alterations have become of interest in lung adenocarcinomas since the FDA approval of target therapies for METamp and METex14 in 2020. However, after using MET target therapies, tumor cells develop adaptative changes, favoring tumor resistance to drugs, the main current challenge to precision medicine. Here, we review a link between the resistance mechanism and MET signaling pathways, which is not only limited to MET. The resistance impacts MET parallel tyrosine kinase receptors and signals shared hubs. Therefore, this information could be relevant in the patient’s mutational profile evaluation before the first target therapy prescription and follow-up to reduce the risk of drug resistance. However, to develop a resistance mechanism to a MET inhibitor, patients must have access to the drugs. For instance, none of the FDA approved MET inhibitors are registered as such in Chile and other developing countries. Constant cross-feeding between basic and clinical research will thus be required to meet future challenges imposed by the acquired resistance to targeted therapies.

Background: Programmed cell death-ligand 1 (PD-L1) protein expression is one of the most extensively studied biomarkers in patients with non-small cell lung cancer (NSCLC). However, there is scarce information regarding its association with distinct adenocarcinoma subtypes. This study evaluated the frequency of PD-L1 expression according to the IASLC/ATS/ERS classification and other relevant histological and clinical features.Patients and Methods: PD-L1 expression was assessed by immunohistochemistry (IHC). According to its positivity in tumor cells membrane, we stratified patients in three different tumor proportions score (TPS) cut-off points: a) <1% (negative), b) between 1 and 49%, and c) ≥50%; afterward, we analyzed the association among PD-L1 expression and lung adenocarcinoma (LADC) predominant subtypes, as well as other clinical features. As an exploratory outcome we evaluated if a PD-L1 TPS score ≥15% was useful as a biomarker for determining survival.Results: A total of 240 patients were included to our final analysis. Median age at diagnosis was 65 years (range 23–94 years). A PD-L1 TPS ≥1% was observed in 52.5% of the entire cohort; regarding specific predominant histological patterns, a PD-L1 TPS ≥1 was documented in 31.2% of patients with predominant-lepidic pattern, 46.2% of patients with predominant-acinar pattern, 42.8% of patients with a predominant-papillary pattern, and 68.7% of patients with predominant-solid pattern (p = 0.002). On the other hand, proportion of tumors with PD-L1 TPS ≥50% was not significantly different among adenocarcinoma subtypes. At the univariate survival analysis, a PD-L1 TPS cut-off value of ≥15% was associated with a worse PFS and OS.Conclusion: According to IASLC/ATS/ERS lung adenocarcinoma classification, the predominant-solid pattern is associated with a higher proportion of PD-L1 positive samples, no subtype was identified to be associated with a high (≥50%) TPS PD-L1.

Prostate cancer (PCa) is one of the most frequent causes of cancer death worldwide. Historically, diagnosis was based on physical examination, transrectal (TRUS) images, and TRUS biopsy resulting in overdiagnosis and overtreatment. Recently magnetic resonance imaging (MRI) has been identified as an evolving tool in terms of diagnosis, staging, treatment decision, and follow-up. In this review we provide the key studies and concepts of MRI as a promising tool in the diagnosis and management of prostate cancer in the general population and in challenging scenarios, such as anteriorly located lesions, enlarged prostates determining extracapsular extension and seminal vesicle invasion, and prior negative biopsy and the future role of MRI in association with artificial intelligence (AI).