Association between pretreatment emotional distress and immune checkpoint inhibitor response in non-small-cell lung cancer.

Emotional distress (ED), commonly characterized by symptoms of depression and/or anxiety, is prevalent in patients with cancer. Preclinical studies suggest that ED can impair antitumor immune responses, but few clinical studies have explored its relationship with response to immune checkpoint inhibitors (ICIs). Here we report results from cohort 1 of the prospective observational STRESS-LUNG study, which investigated the association between ED and clinical efficacy of first-line treatment of ICIs in patients with advanced non-small-cell lung cancer. ED was assessed by Patient Health Questionnaire-9 and Generalized Anxiety Disorder 7-item scale. The study included 227 patients with 111 (48.9%) exhibiting ED who presented depression (Patient Health Questionnaire-9 score ≥5) and/or anxiety (Generalized Anxiety Disorder 7-item score ≥5) symptoms at baseline. On the primary endpoint analysis, patients with baseline ED exhibited a significantly shorter median progression-free survival compared with those without ED (7.9 months versus 15.5 months, hazard ratio 1.73, 95% confidence interval 1.23 to 2.43, P = 0.002). On the secondary endpoint analysis, ED was associated with lower objective response rate (46.8% versus 62.1%, odds ratio 0.54, P = 0.022), reduced 2-year overall survival rate of 46.5% versus 64.9% (hazard ratio for death 1.82, 95% confidence interval 1.12 to 2.97, P = 0.016) and detriments in quality of life. The exploratory analysis indicated that the ED group showed elevated blood cortisol levels, which was associated with adverse survival outcomes. This study suggests that there is an association between ED and worse clinical outcomes in patients with advanced non-small-cell lung cancer treated with ICIs, highlighting the potential significance of addressing ED in cancer management. ClinicalTrials.gov registration: NCT05477979 .

Immunometabolism: a new dimension in immunotherapy resistance.

Immune checkpoint inhibitors (ICIs) have demonstrated unparalleled clinical responses and revolutionized the paradigm of tumor treatment, while substantial patients remain unresponsive or develop resistance to ICIs as a single agent, which is traceable to cellular metabolic dysfunction. Although dysregulated metabolism has long been adjudged as a hallmark of tumor, it is now increasingly accepted that metabolic reprogramming is not exclusive to tumor cells but is also characteristic of immunocytes. Correspondingly, people used to pay more attention to the effect of tumor cell metabolism on immunocytes, but in practice immunocytes interact intimately with their own metabolic function in a way that has never been realized before during their activation and differentiation, which opens up a whole new frontier called immunometabolism. The metabolic intervention for tumor-infiltrating immunocytes could offer fresh opportunities to break the resistance and ameliorate existing ICI immunotherapy, whose crux might be to ascertain synergistic combinations of metabolic intervention with ICIs to reap synergic benefits and facilitate an adjusted anti-tumor immune response. Herein, we elaborate potential mechanisms underlying immunotherapy resistance from a novel dimension of metabolic reprogramming in diverse tumor-infiltrating immunocytes, and related metabolic intervention in the hope of offering a reference for targeting metabolic vulnerabilities to circumvent immunotherapeutic resistance.

Opportunities and challenges of neoadjuvant targeted therapy in nonsmall cell lung cancer.

PURPOSE OF REVIEW: The aim of this study is to summarize the completed and ongoing clinical trials of neoadjuvant targeted therapy, discuss tolerability and efficacy, and explain the role of neoadjuvant targeted therapy in patients with resectable nonsmall cell lung cancer (NSCLC). At the same time, the existing challenges are presented, including assessment methods, biomarkers, surrogate endpoints and so on. We also put forward our views on possible ways to make improvements and establish neoadjuvant therapy a standard treatment in resectable NSCLC. RECENT FINDINGS: The mortality of lung cancer has decreased in the last 10 years, which can partly be attributed to advancement of targeted therapy. Targeted therapy has become the first-line treatment for patients with advanced mutation gene positive NSCLC, achieving the effect of prolonging overall survival (OS). Compared with chemotherapy, targeted therapy is associated with good tolerability and high response rate. Neoadjuvant targeted therapy has emerged in recent years and attracted attention of researchers. Early findings proved that neoadjuvant targeted therapy alone can improve patients' disease-free survival (DFS) and the efficacy of combining with other forms of neoadjuvant therapy is also being explored by researchers. SUMMARY: Neoadjuvant targeted therapy is playing an important role in NSCLC and worth more in-depth research.

Tertiary Lymphoid Structures: A Potential Biomarker for Anti-Cancer Therapy.

A tertiary lymphoid structure (TLS) is a special component in the immune microenvironment that is mainly composed of tumor-infiltrating lymphocytes (TILs), including T cells, B cells, DC cells, and high endothelial venules (HEVs). For cancer patients, evaluation of the immune microenvironment has a predictive effect on tumor biological behavior, treatment methods, and prognosis. As a result, TLSs have begun to attract the attention of researchers as a new potential biomarker. However, the composition and mechanisms of TLSs are still unclear, and clinical detection methods are still being explored. Although some meaningful results have been obtained in clinical trials, there is still a long way to go before such methods can be applied in clinical practice. However, we believe that with the continuous progress of basic research and clinical trials, TLS detection and related treatment can benefit more and more patients. In this review, we generalize the definition and composition of TLSs, summarize clinical trials involving TLSs according to treatment methods, and describe possible methods of inducing TLS formation.

Adjuvant EGFR tyrosine kinase inhibitors for patients with resected EGFR-mutated non-small-cell lung cancer: a network meta-analysis.

Aim: To investigate the efficacy and safety of adjuvant EGFR tyrosine kinase inhibitors for resected EGFR-mutated non-small-cell lung cancer. Materials & methods: Eligible phase II/III randomized controlled trials were included for the network meta-analyses (PROSPERO CRD42021275150). Results: Nine records and 831 patients were involved. Adjuvant chemotherapy followed with osimertinib significantly prolonged disease-free survival compared with chemotherapy (hazard ratio [HR]: 0.2; 95% CI: 0.14-0.29), chemotherapy followed with erlotinib (HR: 0.33; 95% CI: 0.18-0.6), chemotherapy followed with gefitinib (HR: 0.36; 95% CI: 0.16-0.82), gefitinib (HR: 0.26; 95% CI: 0.17-0.41) and icotinib (HR: 0.56; 95% CI: 0.3-0.98). Icotinib was the least likely to cause grade ≥3 adverse events. Conclusion: Chemotherapy followed with osimertinib brings about the best disease-free survival. Icotinib monotherapy shows the best safety.

Patients with early-stage non-small-cell lung cancer have about a one in five chance of cancer recurrence, even after complete resection. Using agents targeting EGFR, known as adjuvant EGFR tyrosine kinase inhibitors, after surgery is an effective way for patients with EGFR-mutated non-small-cell lung cancer to prevent recurrence. However, the optimal agent with favorable efficacy and safety is yet to be determined. This study showed that adjuvant chemotherapy followed with osimertinib had the best efficacy, while adjuvant icotinib monotherapy had the best safety.

A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning.

Background: Renal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment. Methods: The retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model. Results: Bone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM. Conclusions: This XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.