Immune checkpoint inhibitors (ICIs) have brought impressive clinical benefits in a variety of malignancies over the past years, which dramatically revolutionized the cancer treatment paradigm. Monotherapy or in combination with chemotherapy of ICIs targeting programmed death 1/programmed death ligand 1 (PD-L1) has emerged as an alternative treatment for patients with advanced non-small-cell lung cancer (NSCLC). However, constrained by primary or acquired resistance, most patients obtain limited benefits from ICIs and occasionally suffer from severe immune-related adverse events. Moreover, owing to the complexity of the tumor microenvironment and the technical limitations, clinical application of PD-L1 and tumor mutation burden as biomarkers shows many deficiencies. Thus, additional predictive biomarkers are required to further advance the precision of proper patient selection, avoiding the exposure of potential nonresponders to unnecessary immunotoxicity. Nowadays, an increasing number of investigations are focusing on peripheral blood as a noninvasive alternative to tissue biopsy in predicting and monitoring treatment outcomes. Herein, we summarize the emerging blood-based biomarkers that could predict the clinical response to checkpoint immunotherapy, specifically in patients with NSCLC.

Background Lymph node status of clinical T1 (diameter ≤ 3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information. Methods We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models. Results Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p = 0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models. Conclusion In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.

Integrase interactor 1 (INI1)-deficient lung cancer is extremely rare, often with poor prognosis, and lacks effective treatment. Previous studies have reported the efficacy of immunotherapy and enhancer of the zeste homolog 2 (EZH2) inhibitor tazemetostat in various types of INI1-deficient tumors, such as sarcomas. However, the effectiveness of these treatments in INI1-deficient lung cancer has not yet been verified. We hereby report a case of a patient who was diagnosed with advanced squamous lung cancer with INI1 deficiency and received chemotherapy, immunotherapy, and tazemetostat treatments successively. The patient showed optimal response in the initial chemotherapy combined with anti-programmed cell death protein 1 (PD-1) immunotherapy, made rapid progress in the subsequent stage of maintenance immunotherapy, and showed nonresponse to tazemetostat. To the best of our knowledge, this is the first case of a lung cancer patient with INI1 deficiency who received tazemetostat treatment.