Choosing the optimal immunotherapeutic strategies for non-small cell lung cancer based on clinical factors

Epithelial-mesenchymal transition to mitigate age-related progression in lung cancer

Epithelial-Mesenchymal Transition (EMT) is a fundamental biological process involved in embryonic development, wound healing, and cancer progression. In lung cancer, EMT is a key regulator of invasion and metastasis, significantly contributing to the fatal progression of the disease. Age-related factors such as cellular senescence, chronic inflammation, and epigenetic alterations exacerbate EMT, accelerating lung cancer development in the elderly. This review describes the complex mechanism among EMT and age-related pathways, highlighting key regulators such as TGF-β, WNT/β-catenin, NOTCH, and Hedgehog signalling. We also discuss the mechanisms by which oxidative stress, mediated through pathways involving NRF2 and ROS, telomere attrition, regulated by telomerase activity and shelterin complex, and immune system dysregulation, driven by alterations in cytokine profiles and immune cell senescence, upregulate or downregulate EMT induction. Additionally, we highlighted pathways of transcription such as SNAIL, TWIST, ZEB, SIRT1, TP53, NF-κB, and miRNAs regulating these processes. Understanding these mechanisms, we highlight potential therapeutic interventions targeting these critical molecules and pathways.

First-line treatment of driver gene-negative metastatic lung adenocarcinoma with malignant pleural effusion: Should chemotherapy be combined with an immune checkpoint inhibitor or bevacizumab?

Patients with metastatic lung adenocarcinoma (MLA) and malignant pleural effusion (MPE) without driver gene mutations have a poor prognosis. None of the standard treatment strategies is recommended for such patients. We retrospectively analyzed the efficacy of the first-line treatment for this specific population: standard platinum-based doublet chemotherapy (CT), CT plus an immune checkpoint inhibitor (CT plus ICI), and CT plus bevacizumab (CT plus Bev). A total of 323 eligible patients were enrolled: CT alone (n = 166), CT plus Bev (n = 72), and CT plus ICI (n = 85). Treatment efficacy assessments were performed every two cycles according to the RECIST guidelines. The endpoints were overall survival (OS) and progression-free survival (PFS). Kaplan-Meier (K‒M) curves and the log-rank test were used to compare OS and PFS. p < 0.05 was the threshold of significance (statistical software: SPSS). The median follow-up was 11.4 months (range, 2.1-49.6 months). PFS and OS in the CT plus ICI/CT plus Bev cohort were significantly longer than those in the CT group (PFS: 7.8/6.4/3.9 months, p < 0.0001; OS: 16.4/15.6/9.6 months, p < 0.0001, respectively). CT plus Bev had better PFS and OS than CT plus ICI/CT in PD-L1 < 1% patients (PFS: 8.4/5.0/3.8 months, p < 0.0001; OS: 15.6/12.9/9.3 months, p < 0.0001). Among patients with PD-L1 1-49%, CT plus ICI led to a longer PFS and OS (PFS: 8.9/5.8/4.2 months, p = 0.009; OS: 24.2/18.8/11.5 months, p = 0.03). In the cohort with PD-L1 ≥ 50%, CT plus ICI was still the best first-line treatment (PFS: 19.7/13.8/9.6 months, p = 0.033; OS: 27.2/19.6/14.9 months, p = 0.047). In driver gene-negative MLA with MPE, CT plus Bev or ICI better controlled MPE and significantly prolonged survival compared to CT alone. PD-L1 expression (negative/positive) may be a key factor influencing the choice of CT plus Bev or ICI.

Screening biomarkers for predicting the efficacy of immunotherapy in patients with PD-L1 overexpression

PURPOSE

Immunotherapy plays an important role in non-small cell lung cancer (NSCLC); in particular, immune checkpoint inhibitors (ICIs) therapy has good therapeutic effects in PD-L1-positive patients. This study aims to screen NSCLC patients with PD-L1-positive expression and select effective biomarkers for ICI immunotherapy.

METHODS

Collected tumor samples from the Affiliated Cancer Hospital of Xinjiang Medical University and 117 patients with stage III-IV NSCLC were included in the study. All patients were on first- or second-line therapy and not on targeted therapy. Based on the molecular profiles and clinical features, we screened biomarkers for predicting the efficacy of immunotherapy in patients with PD-L1 overexpression.

RESULTS

117 NSCLC patients receiving ICIs immunotherapy were enrolled. First, we found that immunotherapy was more effective in patients with positive PD-L1 expression. Second, we found that ROS1 gene mutations, KRAS gene mutations, tumor stage, and the endocrine system diseases history are independent prognostic factors for PD-L1 positive patients. Then we combined independent risk factors and constructed a new Nomogram to predict the therapeutic efficacy of ICIs immunotherapy in PD-L1 positive patients. The Nomogram integrates these factors into a prediction model, and the predicted C-statistic of 3 months, 6 months and 12 months are 0.85, 0.84 and 0.85, which represents the high predictive accuracy of the model.

CONCLUSIONS

We have established a model that can predict the efficacy of ICIs immunotherapy in PD-L1 positive patients. The model consists of ROS1 gene mutations, KRAS gene mutations, tumor staging, and endocrine system disease history, and has good predictive ability.

Nanoparticles overcome adaptive immune resistance and enhance immunotherapy via targeting tumor microenvironment in lung cancer

Lung cancer is one of the common malignant cancers worldwide. Immune checkpoint inhibitor (ICI) therapy has improved survival of lung cancer patients. However, ICI therapy leads to adaptive immune resistance and displays resistance to PD-1/PD-L1 blockade in lung cancer, leading to less immune response of lung cancer patients. Tumor microenvironment (TME) is an integral tumor microenvironment, which is involved in immunotherapy resistance. Nanomedicine has been used to enhance the immunotherapy in lung cancer. In this review article, we described the association between TME and immunotherapy in lung cancer. We also highlighted the importance of TME in immunotherapy in lung cancer. Moreover, we discussed how nanoparticles are involved in regulation of TME to improve the efficacy of immunotherapy, including Nanomedicine SGT-53, AZD1080, Nanomodulator NRF2, Cisplatin nanoparticles, Au@PG, DPAICP@ME, SPIO NP@M-P, NBTXR3 nanoparticles, ARAC nanoparticles, Nano-DOX, MS NPs, Nab-paclitaxel, GNPs-hPD-L1 siRNA. Furthermore, we concluded that targeting TME by nanoparticles could be helpful to overcome resistance to PD-1/PD-L1 blockade in lung cancer.