Metformin +/- cyclic fasting mimicking diet in combination with platinum-pemetrexed chemotherapy for advanced LKB1 inactive lung adenocarcinoma: The FAME trial

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Epidermal growth factor receptor tyrosine kinase inhibitors for the treatment of central nervous system metastases from non-small cell lung cancer: the present and the future

Lung cancer is one of the major causes of cancer related mortality worldwide. Brain metastases (BM) complicate clinical evolution of non-small cell lung cancer (NSCLC) in approximately 25-40% of cases, adversely influencing quality of life (QoL) and overall survival (OS). Systemic therapy remains the standard strategy for metastatic disease. Nevertheless, the blood-brain barrier (BBB) makes central nervous system (CNS) a sanctuary site. To date, the combination of chemotherapy with whole brain radiation therapy (WBRT), surgery and/or stereotactic radiosurgery (SRS) represents the most used treatment for patients (pts) with intracranial involvement. However, due to their clinical conditions, many pts are not able to undergo local treatments. Targeted therapies directed against epidermal growth factor receptor (EGFR), such as gefitinib, erlotinib and afatinib, achieved important improvements in EGFR mutated NSCLC with favorable toxicity profile. Although their role is not well defined, the reported objective response rate (ORR) and the good tolerance make EGFR-tyrosine kinase inhibitors (TKIs) an interesting valid alternative for NSCLC pts with BM, especially for those harboring EGFR mutations. Furthermore, new-generation TKIs, such as osimertinib and rociletinib, have already shown important activity on intracranial disease and several trials are still ongoing to evaluate their efficacy. In this review we want to highlight literature data about the use and the effectiveness of EGFR-TKIs in pts with BM from NSCLC. © Translational lung cancer research. All rights reserved

Cognitive impairment and chemotherapy: a brief overview

Patients with cancer are experiencing long-term survivalfollowing chemotherapy, butthe treatment may also be associated with short and long-term toxicity, including the possibility of cognitive dysfunction. A literature overview indicated a significant association between chemotherapy and cognitive impairment but prospective longitudinal research is warranted to examine the degree and persisting nature of this decline. Although chemotherapeutic agents are unlikely to cross the blood-brain barrier, it has been alleged that the occurrence of neurotoxicity is linked to the pro-inflammatory cytokine pathways. Moreover in most cases many other factors could play an ancillary and concomitant role. The contribution of hormone therapy as well as emotional, social, behavioural and genetic factors should always be considered. Especially physical activity and cognitive training appear promising in the management of cognitive impairment but additional studies are required to establish their efficacy

Treatment of lung large cell neuroendocrine carcinoma

Lung large cell neuroendocrine carcinoma (L-LCNEC) is a rare, aggressive, and difficult-to-treat tumor. It is classified as a neuroendocrine subtype of large cell lung carcinoma (LCLC) belonging to the non-small cell lung cancer (NSCLC) group, but it is also included in the neuroendocrine tumor (NET) group. Most of the available data related to its treatment derive from retrospective analyses or small case series. For patients with L-LCNEC, prognosis is generally very poor. In early stages (I–II–III), surgery is recommended but does not seem to be sufficient. Platinum-based adjuvant chemotherapy may be useful while the role of neoadjuvant chemotherapy is still not well defined. In patients with advanced L-LCNEC, the chemotherapy regimens used in SCLC still remain the standard of treatment, but results are not satisfactory. Due to their peculiar clinical and biological features and the lack of literature data, there is an emerging need for a consensus on the best treatment strategy for L-LCNEC and for the identification of new therapeutic options. In this review, we will discuss the key aspects of L-LCNEC management with the aim to clarify the most controversial issues. © 2016, International Society of Oncology and BioMarkers (ISOBM)

Small-cell lung cancer: clinical management and unmet needs new perspectives for an old problem

Small cell lung cancer is a highly aggressive, difficult to treat neoplasm. Among all lung tumors, small cell lung cancers account for about 20%. Patients typically include heavy smokers in 70s age group, presenting with symptoms such as intrathoracic tumors growth, distant spread or paraneoplastic syndromes at the time of diagnosis. A useful and functional classification divides small cell lung cancers into limited disease and extensive disease. Concurrent chemo-radiotherapy is the standard treatment for limited disease, with improved survival when combined with prophylactic cranial irradiation. Platinum compounds (cisplatin/carboplatin) plus etoposide remain the cornerstone for extensive disease. Nevertheless, despite high chemo- and radio-sensitivity of this cancer, nearly all patients relapse within the first two years and the prognosis is extremely poor. A deeper understanding about small cell lung cancer carcinogenesis led to develop and test a considerable number of new and targeted agents but the results are currently weak or insufficient. To date, small cell lung cancer is still a challenge for researchers. In this review, key aspects of small cell lung cancer management and controversial points of standard and new treatments will be discussed

Systemic approach to malignant pleural mesothelioma: what news of chemotherapy, targeted agents and immunotherapy?

Malignant pleural mesothelioma is a rare cancer with a cause-effect relationship to asbestos exposure. The prognosis is poor and chemotherapy seems the best treatment option. In the last two decades a deeper understanding of mesothelioma carcinogenesis and invasiveness mechanisms has prompted research efforts to test new agents in patients with malignant pleural mesothelioma, but the results have been modest. Attractive preclinical data disappointed in subsequent experimental phases. Other promising agents failed to improve patient outcomes due to high toxicity. Interesting suggestions have come from preliminary data on immunotherapy. Several trials are ongoing and the results are eagerly awaited. The aim of this review is to discuss the most recent news on systemic therapy for advanced malignant pleural mesothelioma

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy

Simple Summary In this paper, the authors show that artificial intelligence (AI) and machine learning (ML) are useful approaches to integrate multifactorial data and helpful for personalized prediction. In detail, compared to PD-L1 for advanced non-small cell lung cancer (NSCLC), ML tools predicted better responder (R) and non-responder (NR) patients to immunotherapy (IO). It was also able to indirectly foresee OS and PFS of R and NR patients. Given the high incidence of NSCLC, and the absence of reliable biomarkers to predict the response to IO other than PD-L1, the authors believe this research may be of great interest to anyone involved in thoracic oncology. Furthermore, given the growing interest from the scientific community in AI and ML, the authors believe that this manuscript could represent a fascinating topic to anyone who needs to exploit the enormous potential of these tools in the treatment of cancer. (1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO