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Simple Summary: The search for precision medicine applications in mesotheliomas (MM) is taking its first steps. After platinum and pemetrexed chemotherapy, the treatment for relapsed MM remains an unmet clinical need, and the prognosis of MM remains poor even if the recent introduction of immunotherapy. It is known that MM is mainly characterized by inactivanting tumor suppressor alterations and that these, along with some cellular targets or metabolic enzymes, could be potentially amenable to specific therapies The purpose of this review is to take a comprehensive excursus of the main targets and the related evidence regarding possible treatment activities intended for them. Mesothelioma (MM) is an aggressive and lethal disease with few therapeutic opportunities. Platinum-pemetrexed chemotherapy is the backbone of first-line treatment for MM. The introduction of immunotherapy (IO) has been the only novelty of the last decades, allowing an increase in survival compared to standard chemotherapy (CT). However, IO is not approved for epithelioid histology in many countries. Therefore, therapy for relapsed MM remains an unmet clinical need, and the prognosis of MM remains poor, with an average survival of only 18 months. Increasing evidence reveals MM complexity and heterogeneity, of which histological classification fails to explain. Thus, scientific focus on possibly new molecular markers or cellular targets is increasing, together with the search for target therapies directed towards them. The molecular landscape of MM is characterized by inactivating tumor suppressor alterations, the most common of which is found in CDKN2A, BAP1, MTAP, and NF2. In addition, cellular targets such as mesothelin or metabolic enzymes such as ASS1 could be potentially amenable to specific therapies. This review examines the major targets and relative attempts of therapeutic approaches to provide an overview of the potential prospects for treating this rare neoplasm. [ABSTRACT FROM AUTHOR]

ROS proto-oncogene 1 (ROS1) rearrangements occur in 0.9–2.6% of patients with non small cell lung cancer (NSCLC), conferring sensitivity to treatment with specific tyrosine-kinase inhibitors (TKI). Crizotinib, a first-generation TKI, was the first target-therapy approved for the first-line treatment of ROS1-positive NSCLC. Recently, entrectinib, a multitarget inhibitor with an anti-ROS1 activity 40 times more potent than crizotinib and better activity on the central nervous system (CNS), received approval for treatment-naive patients. After a median time-to-progression of 5.5–20 months, resistance mechanisms can occur, leading to tumor progression. Therefore, newer generation TKI with greater potency and brain penetration have been developed and are currently under investigation. This review summarizes the current knowledge on clinicopathological characteristics of ROS1-positive NSCLC and its therapeutic options. [ABSTRACT FROM AUTHOR]

Tissue biopsy is essential for NSCLC diagnosis and treatment management. Over the past decades, liquid biopsy has proven to be a powerful tool in clinical oncology, isolating tumor-derived entities from the blood. Liquid biopsy permits several advantages over tissue biopsy: it is non-invasive, and it should provide a better view of tumor heterogeneity, gene alterations, and clonal evolution. Consequentially, liquid biopsy has gained attention as a cancer biomarker tool, with growing clinical applications in NSCLC. In the era of precision medicine based on molecular typing, non-invasive genotyping methods became increasingly important due to the great number of oncogene drivers and the small tissue specimen often available. In our work, we comprehensively reviewed established and emerging applications of liquid biopsy in NSCLC. We made an excursus on laboratory analysis methods and the applications of liquid biopsy either in early or metastatic NSCLC disease settings. We deeply reviewed current data and future perspectives regarding screening, minimal residual disease, micrometastasis detection, and their implication in adjuvant and neoadjuvant therapy management. Moreover, we reviewed liquid biopsy diagnostic utility in the absence of tissue biopsy and its role in monitoring treatment response and emerging resistance in metastatic NSCLC treated with target therapy and immuno-therapy. [ABSTRACT FROM AUTHOR]

The severe prognosis linked with a lung cancer diagnosis has changed with the discovery of oncogenic molecularly driven subgroups and the use of tailored treatment. ALK-translocated advanced lung cancer is the most interesting model, having achieved the longest overall survival. Here, we report the most important paradigmatic shifts in the prognosis and treatment for this subgroup population occurred among lung cancer. [ABSTRACT FROM AUTHOR]

Osimertinib is currently the preferred first-line therapy in patients with non-small cell lung cancer (NSCLC) with common epidermal growth factor receptor (EGFR) mutation and the standard second-line therapy in T790M-positive patients in progression to previous EGFR tyrosine kinase inhibitor. Osimertinib is a highly effective treatment that shows a high response rate and long-lasting disease control. However, a resistance to the treatment inevitably develops among patients. Understanding the secondary mechanisms of resistance and the possible therapeutic options available is crucial to define the best management of patients in progression to osimertinib. We provide a comprehensive review of the emerging molecular resistance mechanism in EGFR-mutated NSCLC pre-treated with osimertinib and its future treatment applications. [ABSTRACT FROM AUTHOR]

Patients with non-small cell lung cancer (NSCLC) develop bone metastasis (BoM) in more than 50% of cases during the course of the disease. This metastatic site can lead to the development of skeletal related events (SREs), such as severe pain, pathological fractures, spinal compression, and hypercalcemia, which reduce the patient's quality of life. Recently, the treatment of advanced NSCLC has radically changed due to the advent of immunotherapy. Immune checkpoint inhibitors (ICI) alone or in combination with chemotherapy have become the main therapeutic strategy for advanced or metastatic NSCLC without driver gene mutations. Since survival has increased, it has become even more important to treat bone metastasis to prevent SRE. We know that the presence of bone metastasis is a negative prognostic factor. The lower efficacy of immunotherapy treatments in BoM+ patients could be induced by the presence of a particular immunosuppressive tumor and bone microenvironment. This article reviews the most important pre-clinical and clinical scientific evidence on the reasons for this lower sensitivity to immunotherapy and the need to combine bone target therapies (BTT) with immunotherapy to improve patient outcome. [ABSTRACT FROM AUTHOR]

The therapeutic landscape in patients with advanced non-small-cell lung cancer harboring oncogenic biomarkers has radically changed with the development of targeted therapies. Although lung cancers are known to frequently metastasize to the brain, oncogene-driven non-small-cell lung cancer patients show a higher incidence of both brain metastases at baseline and a further risk of central nervous system progression/relapse. Recently, a new generation of targeted agents, highly active in the central nervous system, has improved the control of intracranial disease. The intracranial activity of these drugs poses a crucial issue in determining the optimal management sequence in oncogene-addicted non-small-cell lung cancer patients with brain metastases, with a potential change of paradigm from primary brain irradiation to central nervous system penetrating targeted inhibitors. [ABSTRACT FROM AUTHOR]

Simple Summary: The interest in using Machine-Learning (ML) techniques in clinical research is growing. We applied ML to build up a novel prognostic model from patients affected with Mantle Cell Lymphoma (MCL) enrolled in a phase III open-labeled, randomized clinical trial from the Fondazione Italiana Linfomi (FIL)—MCL0208. This is the first application of ML in a prospective clinical trial on MCL lymphoma. We applied a novel ML pipeline to a large cohort of patients for which several clinical variables have been collected at baseline, and assessed their prognostic value based on overall survival. We validated it on two independent data series provided by European MCL Network. Due to its flexibility, we believe that ML would be of tremendous help in the development of a novel MCL prognostic score aimed at re-defining risk stratification. Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential. [ABSTRACT FROM AUTHOR]

Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313)., Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0-9.6; High→Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential.