A nomogram model for predicting the risk of checkpoint inhibitor-related pneumonitis for patients with advanced non-small-cell lung cancer.

OBJECTIVE: Immunotherapy extensively treats advanced non-small-cell lung cancer (NSCLC). Although immunotherapy is generally better tolerated than chemotherapy, it can cause multiple immune-related adverse events (irAEs) involving multiple organs. Checkpoint inhibitor-related pneumonitis (CIP) is a relatively uncommon irAE that, in severe cases, can be fatal. Potential risk factors for the occurrence of CIP are currently poorly understood. This study sought to develop a novel scoring system for predicting the risk of CIP based on a nomogram model. METHODS: We retrospectively collected advanced NSCLC patients who received immunotherapy at our institution between January 1, 2018, and December 30, 2021. All patients who met the criteria were randomly divided into the training set and testing set (in a ratio of 7:3), and cases fulfilling the CIP diagnostic criteria were screened. The patients' baseline clinical characteristics, laboratory tests, imaging, and treatment information were extracted from the electronic medical records. The risk factors associated with the occurrence of CIP were identified based on the results of logistic regression analysis on the training set, and a nomogram prediction model was developed. The discrimination and prediction accuracy of the model was evaluated using the receiver operating characteristic (ROC) curve, the concordance index (C-index), and the calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical applicability of the model. RESULTS: The training set comprised 526 (CIP: 42 cases), and the testing set comprised 226 (CIP: 18 cases) patients, respectively. In the training set, the final multivariate regression analysis revealed that age (p = 0.014; odds ratio [OR] = 1.056; 95% Confidence Interval [CI] =1.011-1.102), Eastern Cooperative Oncology Group performance status (p = 0.002; OR = 6.170; 95% CI = 1.943-19.590), history of prior radiotherapy (p < 0.001; OR = 4.005; 95% CI = 1.920-8.355), baseline white blood cell count (WBC) (p < 0.001; OR = 1.604; 95% CI = 1.250-2.059), and baseline absolute lymphocyte count (ALC) (p = 0.034; OR = 0.288; 95% CI = 0.091-0.909) were identified as independent risk factors for the occurrence of CIP. A prediction nomogram model was developed based on these five parameters. The area under the ROC curve and C-index of the prediction model in the training set and testing set were 0.787 (95% CI: 0.716-0.857) and 0.874 (95% CI: 0.792-0.957), respectively. The calibration curves are in good agreement. The DCA curves indicate that the model has good clinical utility. CONCLUSION: We developed a nomogram model that proved to be a good assistant tool for predicting the risk of CIP in advanced NSCLC. This model has the potential power to help clinicians in making treatment decisions.

The predictive value of plasma exosomal lncRNAs/mRNAs in NSCLC patients receiving immunotherapy.

PURPOSE: There is an urgent need to explore the use of plasma-derived exosomal long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) as potential biomarkers to select the most suitable patient population to receive immunotherapy for advanced NSCLC with no actionable molecular markers. PATIENTS AND METHODS: In the present study, 7 patients with advanced NSCLC who received nivolumab were enrolled for molecular studies. Plasma-derived exosomal lncRNAs/mRNAs expression profiles differed between patients exhibiting differential immunotherapy efficacy. RESULTS: In the non-responders, 299 differentially expressed exosomal mRNAs and 154 lncRNAs were significantly upregulated. In GEPIA2, 10 mRNAs were upregulated in the NSCLC patients compared to that of the normal population. The up-regulation of CCNB1 related to the cis-regulation of lnc-CENPH-1 and lnc-CENPH-2. KPNA2, MRPL3, NET1 and CCNB1 were trans-regulated by lnc-ZFP3-3. In addition, IL6R exhibited a trend of increased expression in the non-responders at baseline, and this expression was further downregulated after treatment in responders. The association between CCNB1 with lnc-CENPH-1 and lnc-CENPH-2, as well as the lnc-ZFP3-3-TAF1 pair, may represent potential biomarkers of poor immunotherapy efficacy. Patients may obtain increased effector T cell function when IL6R is suppressed by immunotherapy. CONCLUSIONS: Our study suggests that plasma-derived exosomal lncRNA and mRNA expression profiles differ between responders and non-responders to nivolumab immunotherapy. Lnc-ZFP3-3-TAF1-CCNB1 pair and IL6R might be key factors predicting efficiency of immunotherapy. Large scale clinical studies seem warranted to further validate the potential of plasma-derived exosomal lncRNAs and mRNAs as a biomarker to aid the selection of NSCLC patients for nivolumab immunotherapy.

A Novel Therapeutic Target for Small-Cell Lung Cancer: Tumor-Associated Repair-like Schwann Cells.

Small-cell lung cancer (SCLC), representing 15-20% of all lung cancers, is an aggressive malignancy with a distinct natural history, poor prognosis, and limited treatment options. We have previously identified Schwann cells (SCs), the main glial cells of the peripheral nervous system, in tumor tissues and demonstrated that they may support tumor spreading and metastasis formation in the in vitro and in vivo models. However, the role of SCs in the progression of SCLC has not been investigated. To clarify this issue, the cell proliferation assay, the annexin V apoptosis assay, and the transwell migration and invasion assay were conducted to elucidate the roles in SCLC of tumor-associated SCs (TA-SCs) in the proliferation, apoptosis, migration, and invasion of SCLC cells in vitro, compared to control group. In addition, the animal models to assess SC action's effects on SCLC in vivo were also developed. The result confirmed that TA-SCs have a well-established and significant role in facilitating SCLC cell cancer migration and invasion of SCLC in vitro, and we also observed that SC promotes tumor growth of SCLC in vivo and that TA-SCs exhibited an advantage and show a repair-like phenotype, which allowed defining them as tumor-associated repair SCs (TAR-SCs). Potential molecular mechanisms of pro-tumorigenic activity of TAR-SCs were investigated by the screening of differentially expressed genes and constructing networks of messenger-, micro-, and long- non-coding RNA (mRNA-miRNA-lncRNA) using DMS114 cells, a human SCLC, stimulated with media from DMS114-activated SCs, non-stimulated SCs, and appropriate controls. This study improves our understanding of how SCs, especially tumor-activated SCs, may promote SCLC progression. Our results highlight a new functional phenotype of SCs in cancer and bring new insights into the characterization of the nervous system-tumor crosstalk.

A nomogram for predicting hyperprogressive disease after immune checkpoint inhibitor treatment in lung cancer.

Background: Immune checkpoint inhibitor (ICI) therapy is an emerging type of treatment for lung cancer (LC). However, hyperprogressive disease (HPD) has been observed in patients treated with ICIs that lacks a prognostic prediction model. There is an urgent need for a simple and easily implementable predictive model to predict the occurrence of HPD. This study aimed to establish a novel scoring system based on a nomogram for the occurrence of HPD. Methods: We retrospectively identified 1473 patients with stage III-IV LC or inoperable stage I-II LC (1147 in training set, and 326 in testing set), who had undergone ICI therapy at the Shanghai Chest Hospital between January 2017 and March 2022. Available computed tomography (CT) data from the previous treatment, before ICI administration, and at least 2 months after the first the course of ICI administration is collected to confirm HPD. Data from these patients' common blood laboratory test results before ICI administration were analyzed by the univariable and multivariable logistic regression analysis, then used to develop nomogram predictive model, and made validation in testing set. Results: A total of 1,055 patients were included in this study (844 in the training set, and 211 in the testing set). In the training set, 93 were HPD and 751were non-HPD. Multivariate logistic regression analyses demonstrated that lactate dehydrogenase [LDH, P<0.001; odds ratio (OR) =0.987; 95% confidence interval (CI): 0.980-0.995], mean corpuscular hemoglobin concentration (MCHC, P=0.038; OR =1.021; 95% CI: 1.003-1.033), and erythrocyte sedimentation rate (ESR, P=0.012; OR =0.989; 95% CI: 0.977-0.997) were significantly different. The prediction model was established and validated based on these 3 variables. The concordance index were 0.899 (95% CI: 0.859-0.918) and 0.924 (95% CI: 0.866-0.983) in training set and testing set, and the calibration curve was acceptable. Conclusions: This model, which was developed from a laboratory examination of LC patients undergoing ICI treatment, is the first nomogram model to be developed to predict HPD occurrence and exhibited good sensitivity and specificity.

Clinical value of an electromagnetic navigation system for CT-guided percutaneous lung biopsy of peripheral lung lesions.

BACKGROUND: The purpose of this study was to retrospectively evaluate the clinical value of an electromagnetic navigation system for CT-guided percutaneous lung biopsy of peripheral lung lesions. METHODS: This was a retrospective study. Patients with peripheral lung lesions in our institution between January 2019 and December 2020, who underwent lung biopsy assisted by the electromagnetic navigation system were included in Group A, and those who underwent lung biopsy using conventional CT-guided percutaneous lung biopsy were included in Group B. The general features and clinical and technical information of each patient were collected and evaluated in both groups. RESULTS: A total of 141 patients were included in Group A (78 males and 63 females; median age, 65 years; range, 32-79 years), and 96 patients were included in group B (57 males and 39 females; median age, 65 years; range, 34-80 years). The technical success rate was 100% in both groups. The technical efficacy rate was 92.9% and 90.6% in Groups A and B (P=0.525), respectively. There was no significant difference in surgical time and the number of CT scans between the two groups, and only grade 1-2 complications occurred in the patients. CONCLUSIONS: The electromagnetic navigation system is an effective and safe auxiliary tool for CT-guided percutaneous lung biopsy of peripheral lung lesions.

The genomic characteristics of different progression patterns in advanced non-small cell lung cancer patients treated with immune checkpoint inhibitors.

BACKGROUND: Fast progression (FP), hyperprogressive disease (HPD), and early death (ED) are the newly reported cancer progression patterns in response to immune checkpoint inhibitor (ICI) treatment. This study aimed to investigate the clinical and genomic characteristics of FP, HPD, and ED following the ICI treatment of advanced non-small cell lung cancer (NSCLC). METHODS: We retrospectively reviewed 117 patients with advanced NSCLC who were treated with ICIs from March 2017 to October 2019. FP was defined as (I) time to treatment failure (TTF) <1.5 months; and (II) ≥50% increase in the sum of the longest diameter (SLD) of target lesions. HPD was defined as (I) TTF <2 months; and (II) ≥50% change in tumor growth rate compared with before ICI initiation. ED was defined as overall survival (OS) <3 months. Tissue samples from 18 FP/HPD/ED patients and 5 partial response (PR) patients were subjected to genomic profiling. Genomic data from 693 tumor mutational burden- and histology-matched lung cancer samples were retrieved from an internal database as a control. RESULTS: FP, HPD, and ED occurred in 7.21%, 9.38%, and 11.97% patients, respectively. The progression-free survival was comparable among the 3 groups. The median overall survival for FP, HPD, and ED were 3.19, 11.2, and 1.84 months, respectively. The genomic landscape revealed 1 EGFR amplification, 1 ALK fusion, 6 KRAS mutations, 1 ERBB2 amplification, 1 MET amplification, and 1 RET fusion among the 18 patients with FP/HPD/ED. Compared with the Control group, ED patients showed higher mutation frequencies for KRAS (P<0.01), CDKN1B (P<0.01), and NTRK1 (P=0.04). Mutations in RAD54L (P=0.018) and MYC (P=0.04) were more common in FP patients; HPD patients showed more frequent RAD54L mutations (P<0.001). CONCLUSIONS: We demonstrated different genomic characteristics across different progression patterns following ICI treatment, which might assist clinicians in the prediction of a patient's response, identifying candidates for more effective ICI therapy.

Prognostic Implication of the Expression Level of PECAM-1 in Non-small Cell Lung Cancer.

Background: Lung cancer is a malignant disease that threatens human health. Hence, it is crucial to identify effective prognostic factors and treatment targets. Single-cell RNA sequencing can quantify the expression profiles of transcripts in individual cells. Methods: GSE117570 profiles were downloaded from the Gene Expression Omnibus database. Key ligand-receptor genes in the tumor and the normal groups were screened to identify integrated differentially expressed genes (DEGs) from the GSE118370 and The Cancer Genome Atlas Lung Adenocarcinoma databases. DEGs associated with more ligand-receptor pairs were selected as candidate DEGs for Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analysis. In addition, we conducted validation immunohistochemical experiments on postoperative specimens of 30 patients with lung cancer. Results: A total of 18 candidate DEGs were identified from the tumor and the normal groups. The analysis of the GO biological process revealed that these DEGs were mainly enriched in wound healing, in response to wounding, cell migration, cell motility, and regulation of cell motility, while the KEGG pathway analysis found that these DEGs were mainly enriched in proteoglycans in cancer, bladder cancer, malaria, tyrosine kinase inhibitor resistance in Epidermal Growth Factor Receptor (EGFR), and the ERBB signaling pathway. Survival analysis showed that a high, rather than a low, expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) was associated with improved survival. Similarly, in postoperative patients with lung cancer, we found that the overall survival of the PECAM-1 high-expression group shows a better trend than the PECAM-1 low-expression group (p = 0.172). Conclusions: The candidate DEGs identified in this study may play some important roles in the occurrence and development of lung cancer, especially PECAM-1, which may present potential prognostic biomarkers for the outcome.

Tumor genomics and response to chemotherapy in advanced non-small cell lung cancer with exon 20 insertion epidermal growth factor receptor mutations.

BACKGROUND: To characterize the effects of mutation subtypes and concomitant pathogenic mutations on progression-free survival (PFS) and overall survival (OS) in advanced non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) exon 20 insertion (ex20ins) mutations treated with chemotherapy. METHODS: We retrospectively found that patients who underwent genomic analysis from January 2017 to December 2019, and 101 patients with advanced EGFR ex20ins NSCLC were found. Binary logistic regression and Cox regression were used to determine how EGFR ex20ins mutation subtypes and concomitant mutations are associated with PFS and OS. RESULTS: A total of 8,348 patients were screened and 101 advanced EGFR ex20ins NSCLC patients were detected. Fifty-five patients who received chemotherapy (n=49) or TKIs (n=6) as first-line treatment were recorded for PFS and OS. PFS and OS were significantly longer in the platinum-based chemotherapy group (median PFS: 7.6 versus 5.6 months; P=0.001; median OS: 19.9 versus 7.4 months; P=0.027) than in the TKI group. Common mutations include Ala767_Val769dupAlaSerVal (A767_V769dupASV), Ser768_Asp770dupSerValAsp (S768_D770dupSVD) and Ala763_Tyr764insPheGlnGluAla (A763_Y764insFQEA). On binary logistic regression, common mutations (OR =17.04, 95% CI: 1.39-209.56; P=0.027) and number of concomitant mutations ≤1 (OR =34.67, 95% CI: 2.02-595.48; P=0.015) is significantly associated with durable clinical benefit (DCB). On multivariable analysis, common mutations (HR =0.26, 95% CI: 0.0.10-0.63; P=0.003) and the number of concomitant mutations ≤1 (HR =0.33, 95% CI: 0.15-0.73; P=0.006) were significantly associated with longer PFS. CONCLUSIONS: Common mutations and the number of concomitant mutations ≤1 correlate with a biomarker that predicts benefit from chemotherapy and confers excellent prognosis for advanced patients with advanced EGFR ex20ins NSCLC. Patients with common mutations and with only one concomitant mutation had the greatest PFS and patients with uncommon mutations, and with over one concomitant mutation had the worst prognosis.