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Background: High expression of programmed death-ligand 1 (PD-L1) has been recognized as a marker of improved efficacy of immunotherapy in gastroesophageal adenocarcinoma (GEA); however, the optimal PD-L1 cut-off is still debated. The aim of the present review was to analyze available phase III trials and to identify the appropriate PD-L1 expression cut-off for GEA., Methods: Phase III trials investigating the efficacy of anti-programmed cell death protein 1 (PD-1) therapies in addition to standard chemotherapy versus standard chemotherapy in the first-line setting were selected. Progression-free survival (PFS), overall survival (OS) and objective response rate (ORR) were the analyzed outcome measures. Pooled treatment effects were assessed in the unselected population and in subpopulations with different levels of PD-L1 expression., Results: PD-1 blockade efficacy was found to consistently increase in a linear manner with higher combined positive score (CPS) of PD-L1 expression: pooled hazard ratio (HR) for OS and PFS and pooled odds ratio (OR) for ORR of 0.80, 0.75 and 1.51, respectively, in the unselected population versus 0.67, 0.63 and 1.90, respectively, in the CPS ≥10 population (all P values < 0.0001). In the PD-L1-negative population (CPS <1) a significant benefit of anti-PD-1 agents could not be demonstrated in terms of OS and PFS (P = 0.28 and 0.12, respectively), but it was seen in terms of ORR (P = 0.03). PD-1 blockade was effective in the CPS <10 population (P value for pooled OS HR, PFS HR and response OR are all 0.01), while in the CPS <5 population the effect was of borderline significance for OS (P = 0.07) and significant for PFS and ORR (P = 0.02 and 0.03, respectively)., Conclusion: The present meta-analysis confirmed that the benefit of PD-1 blockade in GEA patients is related to PD-L1 CPS, with increased benefit observed for higher CPS cut-offs and no OS benefit in the CPS <1 subset. Overall, data indicate that PD-L1 CPS ≥5 could represent an acceptable cut-off to optimize the risk/benefit ratio of such agents. Our data suggest a potential clinical benefit of immunotherapy in selected patients within the CPS 1-4 population which needs further investigation., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Machine learning models learn about general behavior from data by finding the relationships between features. Our purpose was to develop a predictive model to identify and predict which subset of colorectal cancer patients are more likely to experience chemotherapy-induced toxicity and to determine the specific attributes that influence the presence of treatment-related side effects., Methods: The predictor was general toxicity, and for the construction of our data training, we selected 95 characteristics that represent the health state of 74 patients prior to their first round of chemotherapy. After the data were processed, Random Forest models were trained to offer an optimal balance between accuracy and interpretability., Results: We constructed a machine learning predictor with an emphasis on assessing the importance of numerical and categorical variables in relation to toxicity., Conclusions: The incorporation of artificial intelligence in personalizing colorectal cancer management by anticipating and overseeing toxicities more effectively illustrates a pivotal shift towards more personalized and precise medical care.

The interplay between the immune system and chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC) is complex and multifaceted. In COPD, chronic inflammation and oxidative stress can lead to immune dysfunction that can exacerbate lung damage, further worsening the respiratory symptoms. In NSCLC, immune cells can recognise and attack the cancer cells, which, however, can evade or suppress the immune response by various mechanisms, such as expressing immune checkpoint proteins or secreting immunosuppressive cytokines, thus creating an immunosuppressive tumour microenvironment that promotes cancer progression and metastasis. The interaction between COPD and NSCLC further complicates the immune response. In patients with both diseases, COPD can impair the immune response against cancer cells by reducing or suppressing the activity of immune cells, or altering their cytokine profile. Moreover, anti-cancer treatments can also affect the immune system and worsen COPD symptoms by causing lung inflammation and fibrosis. Immunotherapy itself can also cause immune-related adverse events that could worsen the respiratory symptoms in patients with COPD-compromised lungs. In the present review, we tried to understand the interplay between the two pathologies and how the efficacy of immunotherapy in NSCLC patients with COPD is affected in these patients.