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Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy associated with occupational or environmental exposure to asbestos. Effective management of MPM remains challenging due to its prolonged latency period and the typically late onset of clinical symptoms. Accordingly, there is an increasing demand for the implementation of reliable, non-invasive, and data-driven diagnostic strategies within large-scale screening programs. In this context, the chemical profiling of volatile organic compounds (VOCs) in exhaled breath has recently gained recognition as a promising and non-invasive approach for the early detection of cancer, including MPM. Therefore, in this cross-sectional observational study, an overall number of 125 individuals, including 64 MPM patients and 61 healthy controls (HC), were enrolled. End-tidal breath fraction (EXP) was collected directly onto two-bed adsorbent cartridges by an automated sampling system and analyzed by thermal desorption–gas chromatography–mass spectrometry (TD-GC/MS). A machine learning approach based on a random forest (RF) algorithm and trained using a 10-fold cross-validation framework was applied to experimental data, yielding remarkable results (AUC = 86%). Fifteen VOCs reflecting key metabolic alterations characteristic of MPM pathophysiology were found to be able to discriminate between MPM and HC. Moreover, twenty breath samples from asymptomatic former asbestos-exposed (AEx) and eight MPM patients during follow-up (FUMPM) were exploratively analyzed, processed, and tested as blinded samples by the validated statistical method. Good agreement was found between model output and clinical information obtained by CT. These findings underscore the potential of breath VOC analysis as a non-invasive diagnostic approach for MPM and support its feasibility for longitudinal patient and at-risk subjects monitoring.

Despite promising results obtained in the early diagnosis of several pathologies, breath analysis still remains an unused technique in clinical practice due to the lack of breath sampling standardized procedures able to guarantee a good repeatability and comparability of results. The most diffuse on an international scale breath sampling method uses polymeric bags, but, recently, devices named Mistral and ReCIVA, able to directly concentrate volatile organic compounds (VOCs) onto sorbent tubes, have been developed and launched on the market. In order to explore performances of these new automatic devices with respect to sampling in the polymeric bag and to study the differences in VOCs profile when whole or alveolar breath is collected and when pulmonary wash out with clean air is done, a tailored experimental design was developed. Three different breath sampling approaches were compared: (a) whole breath sampling by means of Tedlar bags, (b) the end-tidal breath collection using the Mistral sampler, and (c) the simultaneous collection of the whole and alveolar breath by using the ReCIVA. The obtained results showed that alveolar fraction of breath was relatively less affected by ambient air (AA) contaminants (p-values equal to 0.04 for Mistral and 0.002 for ReCIVA Low) with respect to whole breath (p-values equal to 0.97 for ReCIVA Whole). Compared to Tedlar bags, coherent results were obtained by using Mistral while lower VOCs levels were detected for samples (both breath and AA) collected by ReCIVA, likely due to uncorrected and fluctuating flow rates applied by this device. Finally, the analysis of all data also including data obtained by explorative analysis of the unique lung cancer (LC) breath sample showed that a clean air supply might determine a further confounding factor in breath analysis considering that lung wash-out is species-dependent.

Recently, the fifth edition of the WHO classification recognized the thoracic SMARCA4-deficient undifferentiated tumor (SMARCA4-UT) as a separate entity from conventional non-small cell lung cancer with SMARCA4 deficiency because of the different clinicopathological characteristics of these two diseases. SMARCA4-UT mainly occurs in young to middle-aged adults and involves a large mass compressing the tissues surrounding the mediastinum and lung parenchyma. Unfortunately, SMARCA4-UT shows a high probability of recurrence after upfront surgery as well as radiotherapy resistance; moreover, chemotherapy has low efficacy. Moreover, given the recent classification of SMARCA4-UT, no data concerning specific clinical trials are currently available. However, several case reports show immunotherapy efficacy in patients with this disease not only in a metastatic setting but also in a neoadjuvant manner, supporting the development of clinical trials. In addition, preclinical data and initial clinical experiences suggest that inhibiting pathways such as CDK4/6, AURKA, ATR, and EZH2 may be a promising therapeutic approach to SMARCA4-UT.

Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30–55% of recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment of cancer patients. The radiomic analysis of CT images has already shown great potential in solving this task; specifically, Convolutional Neural Networks (CNNs) have already been proposed providing good performances. Recently, Vision Transformers (ViTs) have been introduced, reaching comparable and even better performances than traditional CNNs in image classification. The aim of the proposed paper was to compare the performances of different state-of-the-art deep learning algorithms to predict cancer recurrence in NSCLC patients. In this work, using a public database of 144 patients, we implemented a transfer learning approach, involving different Transformers architectures like pre-trained ViTs, pre-trained Pyramid Vision Transformers, and pre-trained Swin Transformers to predict the recurrence of NSCLC patients from CT images, comparing their performances with state-of-the-art CNNs. Although, the best performances in this study are reached via CNNs with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.91, 0.89, 0.85, 0.90, and 0.78, respectively, Transformer architectures reach comparable ones with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.90, 0.86, 0.81, 0.89, and 0.75, respectively. Based on our preliminary experimental results, it appears that Transformers architectures do not add improvements in terms of predictive performance to the addressed problem.

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.

Mast cells (MCs) are multifunctional immune cells implicated in both physiological and pathological processes. Among the latter, MCs play a crucial role in cancer. Many studies have shown a correlation between MCs and tumor progression in several solid and hematological malignancies. In particular, MCs can directly promote tumor growth via c‐kit/stem cell factor–dependent signaling and via the release of histamine, which modulate tumor growth through H1 and H2 receptors. At the same time, MCs can increase tumor progression by stimulating angiogenesis via both proangiogenic cytokines stored in their cytoplasm, and by acting on the tumor microenvironment and extracellular matrix. With regard to NSCLC, the role of MCs has not yet been established, with studies showing a correlation with a poor prognosis on the one hand and suggesting a protective effect of MCs on the other hand. These controversial evidences are at least, in part, due to the heterogeneity of the studies exploring the role of MCs in NSCLC, with some studies describing only the MC count without specification of the activation and degranulation state, and without reporting the intratumoral localization and the proximity to other immune and cancer cells. A better knowledge of the role of MCs in NSCLC is mandatory, not only to define their prognostic and predictive proprieties but also because targeting them could be a possible therapeutic strategy. MCs have multiple roles in NSCLC with both protumor and antitumor capabilities according to the tumor context. MCs express a large quantity of molecules promoting tumor progression such as histamine, SCF, FGF‐2, IL‐8, VEGF, PDGF, and NGF. At the same time MCs secrete TGF‐β and TNF‐α, molecules with ambivalent features, able to exert both antitumoral and tumorigenic effects based on the context. Moreover, MCs produce serine proteases activating metalloproteinases and remodeling the extracellular matrix (ECM). In particular, tryptase can degrade the ECM increasing the space available for neovascularization and releasing angiogenic factors included in the matrix. At the same time, tryptase activates PAR‐2 inducing endothelial cell proliferation. Another serine protease, namely chymase, induces angiogenesis by converting angiotensin I to angiotensin II. On the other hand, MCs can contribute to tumor rejection by activating dendritic cells and inhibiting Treg cells and myeloid‐derived suppressor cells (MDSCs). Moreover, chondroitin sulfate, which is secreted by MCs, inhibits the development of metastasis.

Non-small-cell lung cancer, histologically classified into adenocarcinoma (AD) and squamous cell carcinoma, is one of the most deadly malignancies worldwide. Lung AD (LUAD) could benefit of a plethora of target therapies and, in the last few years, also of immunotherapies. Here we focused on a real-life cohort of LUAD and The Cancer Genome Atlas (TCGA)-LUAD dataset aiming to gain insights into the immune contexture of such a malignancy. We explored the mutational status of 41 genes and the expression of 94 genes, related to immune-checkpoint, inflammation, and stromal microenvironment. Surprisingly, we found that our cohort has a very low mutational burden if we consider our panel as its surrogate. Regarding gene expression data, we identified 31 genes significantly deregulated in tumor tissues compared with a pool of normal samples. Unsupervised hierarchical clustering of the deregulated genes is able to identify two clusters of tumor samples, differently enriched in alterations in actionable genes. In particular, we identified a cluster enriched in patients carrying KRAS alterations. In silico deconvolution, that is the inferring of tumor microenvironment composition by gene expression data, through TIMER algorithm has been performed to explore immune microenvironment. Estimation performed on our gene expression matrix showed that B cell infiltration is lower in the KRAS-mutated enriched cluster, as in the TCGA-LUAD dataset. Such a finding has been validated in situ through immunohistochemistry in an independent cohort. Moreover, cases in LUAD-TCGA with low B cell infiltration have a significantly worse overall survival than those with higher levels. In the real-life cohort we observed that cases belonging to cluster enriched in KRAS-mutated patients have a poor outcome. LUAD driven by KRAS mutation represents an unmet clinical need, being refractory to pharmacological inhibition. Our results link KRAS mutations to B cell infiltration. Thus, the present findings could be helpful in a better definition of immunotherapeutic approaches for KRAS mutated patients.

Background The addition of immune checkpoint inhibitors (ICIs) to chemotherapy is the new standard of care in the first‐line treatment of small cell lung cancer (SCLC). However, although the concomitant use of immunotherapy and chemotherapy can increase the antitumor efficacy, it can also increase toxicity. The present study evaluated the tolerability of immune‐based combinations in the first‐line treatment of SCLC. Methods Relevant trials were identified by searching electronic databases and conference meetings. Seven phase II and III randomized controlled trials and 3766 SCLC patients were included in the meta‐analysis (immune‐based combinations = 2133; chemotherapy = 1633). Outcomes of interest included treatment‐related adverse events (TRAEs) and the rate of discontinuation due to TRAEs. Results Immune‐based combination treatment was associated with a higher risk of grade 3–5 TRAEs (odds ratio [OR], 1.16; 95% confidence interval [CI]: 1.01–1.35). Immune‐based combinations were associated with a higher risk of TRAEs leading to discontinuation (OR, 2.30; 95% CI: 1.17–4.54). No differences were observed in grade 5 TRAEs (OR, 1.56; 95% CI: 0.93–2.63). Conclusion This meta‐analysis indicates that the addition of immunotherapy to chemotherapy in SCLC patients is associated with a higher risk of toxicity and probably of treatment discontinuation. Tools for identifying SCLC patients that would not benefit from immune‐based therapy are urgently needed. The addition of immune checkpoint inhibitors (ICIs) to chemotherapy is the new standard of care in the first‐line treatment of small cell lung cancer (SCLC). However, although the concomitant use of immunotherapy and chemotherapy can increase the antitumor efficacy, it can also increase toxicity. We performed a meta‐analysis to compare any grade treatment‐related adverse events (TRAEs), grade 3–5 TRAEs, grade 5 TRAEs, and treatment discontinuation due to TRAEs between ES‐SCLC patients treated with front‐line immune‐based combinations and those receiving chemotherapy. Seven phase II and III trials with 3766 patients were included in the analysis. Immune‐based combination treatment was associated with higher risk than chemotherapy of any grade TRAEs (odds ratio [OR], 1.63, 95% confidence interval [CI]: 1.31–2.03; p = 0.86). Unfortunately, predictive factors for toxicity have not been established for SCLC patients treated with immune‐based combinations. Tools for identifying SCLC patients that would not benefit from immune‐based therapy are urgently needed.

Background Potential relationships with the prognosis of patients with extensive‐stage non‐small cell lung cancer (ES‐SCLC) have been investigated without valid results. Methods A retrospective analysis of real‐world data of consecutive patients with ES‐SCLC admitted to our Medical Thoracic Oncology Unit was carried out from 2010 to 2020, focusing on identification of prognostic factors. Kaplan–Meier analysis was used to represent progression‐free survival (PFS) and overall survival (OS). Univariable and multivariable Cox models were used to investigate prognostic factors. Results The analysis included 244 patients. The median OS was 8 months (95% confidence interval [CI]: 8–10) and the median PFS was 5 months (95% CI: 5–6). The univariable analysis showed that factors associated with shorter OS were older age (p = 0.047), TNM stage 4 versus 3 (p < 0.001), Eastern Cooperative Oncology Group (ECOG) performance status (PS) 1 and 2 versus 0 (p < 0.001), and >2 metastatic sites (p = 0.004). Mediastinal radiotherapy (RT) (p < 0.001), >1 irradiated site (p = 0.026), 3 and 4 chemotherapy (CT) lines versus 1 (p = 0.044 and 0.001, respectively), prophylactic cranial irradiation (PCI) (p < 0.001), and surgery (p = 0.001) correlated with longer OS. The multivariable analysis revealed statistically significant associations for TNM, ECOG PS 2 versus 0, number of CT lines, PCI, and surgery. A total of 23 patients (9.4%) survived ≥24 months, 39% of whom had received four CT lines and 48% had mediastinal RT. Conclusions Our data suggest that tumor burden, PS, and mediastinal RT strongly correlate with outcome. With the addition of immunotherapy to CT, the identification of new biomarkers as predictive factors is urgently required. A retrospective analysis of real‐world data of consecutive patients with ES‐SCLC admitted to our Medical Thoracic Oncology Unit was carried out from 2010 to 2020, focusing on identification of prognostic factors. Kaplan–Meier analysis was used to represent progression‐free survival (PFS) and overall survival (OS). Univariable and multivariable Cox models were used to investigate prognostic factors. The analysis included 244 patients. The median OS was 8 months (95% confidence interval [CI]: 8–10) and the median PFS was 5 months (95% CI: 5–6). The univariable analysis showed that factors associated with shorter OS were TNM stage 4 versus 3 (p < 0.001), Eastern Cooperative Oncology Group (ECOG) performance status (PS) 1 and 2 versus 0 (p < 0.001), and >2 metastatic sites (p = 0.004). Mediastinal radiotherapy (RT) (p < 0.001), >1 irradiated site (p = 0.026), 3 and 4 chemotherapy (CT) lines versus 1 (p = 0.044 and 0.001, respectively), prophylactic cranial irradiation (PCI) (p < 0.001) correlated with longer OS. The multivariable analysis revealed statistically significant associations for TNM, ECOG PS 2 versus 0, number of CT lines, and PCI.

Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancers, and most NSCLC is diagnosed in the advanced stage. The advent of immune check point inhibitors (ICIs) changed the therapeutic scenario both in metastatic disease (in first and subsequent lines) and earlier settings. Comorbidities, reduced organ function, cognitive deterioration, and social impairment give reasons for a greater probability of adverse events, making the treatment of elderly patients challenging. The reduced toxicity of ICIs compared to standard chemotherapy makes this approach attractive in this population. The effectiveness of ICIs varies according to age, and patients older than 75 years may benefit less than younger patients. This may be related to the so-called immunosenescence, a phenomenon that refers to the reduced activity of immunity with older age. Elders are often under-represented in clinical trials, even if they are a large part of the patients in a clinical practice. In this review, we aim to explore the biological aspects of immunosenescence and to report and analyze the most relevant and recent literature findings on the role of immunotherapy in elderly patients with NSCLC.