miR-136 Suppresses the Aggressive Proliferation of Non-Small Cell Lung Cancer Through Restraining Histone Deacetylase 1 (HDAC1) and Phosphorylation of the Janus Kinase 2/Signal Transducer and Activator of Transcription 3 (Jak2/STAT3) Pathway

microRNA-136 can inhibit the proliferating activity of malignant cells and also participate in chemotherapy resistance of colorectal cancer via modulating HDAC1. This study assessed miR-136’s effect on NSCLC cell proliferation and underlying mechanisms. Tumor tissues and paracancerous tissues from NSCLC patients were collected to measure miR-136 and HDAC1 level. Cells were transfected with miR-136-mimics, miR-136-inhibitors or miR-136 mimics+HDAC1-OE followed by analysis of cell viability and apoptosis by CCK-8 method and flow cytometry, phosphorylation of Jak2/STAT3 by western blot. miR-136 was significantly downregulated in tumor tissues and NSCLC cells, accompanied by upregulated HDAC1. miR-136 overexpression suppressed HDAC1 expression, retarded phosphorylation and activation of Jak2/STAT3 signaling, reduced NSCLC cell viability and enhanced apoptosis. In addition, co-transfection of miR-136-mimics and HDAC1-OE reversed the inhibitory effects of miR-136 on NSCLC cells. In conclusion, miR-136 is reduced and HDAC1 is increased in NSCLC and miR-136 overexpression inhibited NSCLC cell proliferation and increased apoptosis possibly through regulating HDAC1/Jak2/STAT3 signal pathway, indicating that miR-136 might be a novel target for the treatment of NSCLC.

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

COL1A1 is a potential prognostic marker and therapeutic target in non-small cell lung cancer.

Background: Nowadays, non-small cell lung cancer (NSCLC) is a common and highly fatal malignancy in worldwide. Therefore, to identify the potential prognostic markers and therapeutic targets is urgent for patients. Objective: This study aims to find hub targets associated with NSCLC using multiple databases. Methods: Differentially expressed genes (DEGs) from Genome Expression Omnibus (GEO) cohorts were employed for the enrichment analyses of Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. Candidate key genes, filtered from the topological parameter 'Degree' and validated using the The Cancer Genome Atlas (TCGA) cohort, were analyzed for their association with clinicopathological features and prognosis of NSCLC. Meanwhile, immunohistochemical cohort analyses and biological verification were further evaluated. Results: A total of 146 DEGs were identified following data preprocessing, and a protein-protein interaction (PPI) systematic network was constructed based on them. The top ten candidate core genes were further extracted from the above PPI network by using 'Degree' value, among which COL1A1 was shown to associate with overall survival (OS) of NSCLC as determined by using the Kaplan-Meier analysis (p=0.028), and could serve as an independent prognostic factor for OS in NSCLC patients (HR, 0.814; 95% CI, 0.665-0.996; p=0.046). We then analyzed the clinical stages, PPI, mutations, potential biological functions and immune regulations of COL1A1 in NSCLC patients using multiple bioinformatics tools, including GEPIA, GeneMANIA, cBioPortal, GESA and TISIDB. Finally, we further experimentally validated the overexpression of COL1A1 in NSCLC samples, and found that inhibition of COL1A1 expression moderately sensitized NSCLC cells to cisplatin. Conclusion: Thus, our results show that COL1A1 may serve as a potential prognostic marker and therapeutic target in NSCLC.

Genomic features and tumor immune microenvironment alteration in NSCLC treated with neoadjuvant PD-1 blockade

Several clinical trials have shown the safety and effectiveness of PD-1/PD-L1 inhibitors in neoadjuvant therapy in resectable non-small cell lung cancer (NSCLC). However, 18–83% patients can benefit from it. In this study, we aimed to assess the association of PD-L1 expression, tumor mutation burden, copy number alteration (CNA, including copy number gain and loss) burden with the pathologic response to neoadjuvant PD-1 blockade and investigate the changes in the tumor immune microenvironment (TIME) during neoadjuvant immunotherapy in NSCLC. Pre-immunotherapy treatment tumor samples from twenty-nine NSCLC patients who received neoadjuvant immunotherapy with sintilimab, an anti-PD-1 drug, were subjected to targeted DNA sequencing and PD-L1 immunochemistry staining. The pathological response was positively correlated with tumor proportion score (TPS) of PD-L1 and negatively correlated with copy number gain (CNgain) burden. Of note, the combination of CNgain burden and TPS can better stratify major pathological response (MPR) patients than did CNgain or TPS alone. Whereas, TMB showed a limited correlation with pathological regression. Additionally, PD-1 blockade led to an increase in CD8+PD-1−T cells which was clinically relevant to MPR as evaluated by multiplex immunofluorescence. A significant reduction in CD19+ cells was observed in the Non-MPR group but not in the MPR group, indicating the involvement of B cells in improving neoadjuvant immunotherapy response in NSCLC. Together, our study provides new data for the correlation of PD-L1 expression and genomic factors with drug response in neoadjuvant immunotherapy settings in NSCLC. The changes of TIME may provide novel insight into the immune responses to neoadjuvant anti-PD-1 therapy.

Peripheral lymphocyte count as a surrogate marker of immune checkpoint inhibitor therapy outcomes in patients with non-small-cell lung cancer

Degree of expression of programmed death-ligand 1 (PD-L1) is related with Immune check point inhibitors (ICIs) response but it needs sufficient tumor tissue. There is unmet need for easily accessible and prognostic peripheral blood (PB) biomarkers. We investigated the application of serum peripheral lymphocyte count (PLC) as a predictive PB biomarker for ICI response in patients with NSCLC. We conducted a retrospective study and reviewed the patients with NSCLC who were treated with ICIs from April 1, 2016, to March 31, 2019. The PLC before and after 1 month of immunotherapy was collected. We evaluated the association between PLC and progression-free survival (PFS), overall survival (OS) and adverse events. A total of 231 patients were treated with ICIs for NSCLC. The median follow-up period was 4.7 months and the disease progressed in 138 patients (59.7%). Compared with the lowest quartile (Q1: the lowest 25%), the highest quartile (Q4: the highest 25%) of post-treatment PLC showed a significantly higher PFS (HR 0.28, 95% CI 0.16–0.52) and OS (HR 0.35, 95% CI 0.19–0.65) in the adjusted model. An association between adverse events and PLC was not observed. We revealed that an increased pre- and post-treatment PLC was associated with favorable PFS and OS with NSCLC patients treated with ICIs. PLC could be a helpful for ICI responses in NSCLC.

Evaluation of Log Odds of Positive Lymph Nodes in Predicting the Survival of Neoadjuvant Therapy Patients with Non-Small Cell Lung Cancer After Surgery: A SEER Cohort-Based Study

Background log odds of positive lymph nodes (LODDS) is a novel lymph node (LN) descriptor, demonstrating promising prognostic value in many tumors. However, there was limited information on LODDS in non-small cell lung cancer (NSCLC) patients, especially those receiving neoadjuvant therapy followed by lung surgery. Methods A total of 2,059 NSCLC patients who received neoadjuvant therapy and surgery were identified in the Surveillance, Epidemiology, and End Results (SEER) database. We used the X-tile software to calculate the cut-off value of LODDS. Kaplan-Meier survival analysis and receiver operating characteristics (ROC) curve were used to compare the predictive value of the American Joint Committee on Cancer (AJCC) N staging descriptor and LODDS. Univariate and multivariate Cox regression and inverse probability of treatment weighting (IPTW) analyses were conducted to construct the model predicting the prognosis. Results LODDS showed better differentiating ability in survival analysis than N staging descriptor (Log-rank test, P<0.0001 vs. P=0.031). The ROC curve demonstrated that the AUC of LODDS was significantly higher than the N staging descriptor in 1-year, 3-year, and 5-year survival analyses (All P<0.05). Univariate and multivariate Cox regression analysis showed that the LODDS was an independent risk factor for NSCLC patients receiving neoadjuvant therapy followed by surgery, both before and after IPTW (all P<0.001). A clinicopathological model with LODDS, age, gender, T, and radiotherapy could better predict the prognosis. Conclusions Compared with the AJCC N staging descriptor, LODDS exhibits better predictive ability for NSCLC patients receiving neoadjuvant therapy followed by surgery. A multivariate clinicopathological model with LODDS included demonstrates sound performance in predicting the prognosis.

Detection of Mitochondrial DNA Copy Number in Plasma Exosomes of Patients with Non-small Cell Lung Cancer

For the prevalence of lung cancer and its poor diagnosis, the seeking of the efficient biomarkers for this disease is an urgent requirement, especially from non-invasive samples such as plasma. The mitochondria DNA (mtDNA) copy number change has been evaluated as a potential indicator of cancer risk, however, there have been few studies regarding mtDNA in plasma derived exosomes. In this study, the mtDNA copy number was measured on 29 plasma exosome samples of patients with non-small cell lung cancer (NSCLC) and 29 plasma exosome samples of cancer-free controls by real-time PCR assay, then being statistically analyzed to evaluate the relationship between these figures and several pathological features of NSCLC patients. As the results, the existence of mtDNA in exosomes isolated from plasma was detected through PCR assay using primers covering most of the mtDNA length. The relative mtDNA copy numbers determined in the exosomes of the disease and control groups were 1619.1 ± 2589.0 and 1207.0 ± 1550.0, respectively, whereas these values in two disease stages were 783.6 ± 759.3 (stage I-II) and 2647.0 ± 3584.0 (stage III-IV). Comparing among these groups, the difference was only statistically significant between the disease groups of stage I-II and stage III-IV (p<0.05), the group of stage III-IV and the control group (p<0.05). Indeed, the mtDNA copy number is associated with tumor stage and stage N (p<0.05). On the other aspect, the smoking habit of NSCLC patients could be an underlying reason behind the alteration in mtDNA copy number in the plasma exosomes. In short, our study demonstrates that the mtDNA copy number in exosomes resourced from plasma could be a potential biomarker for the detection and prognosis of NSCLC.

Somatic copy number alteration predicts clinical benefit of lung adenocarcinoma patients treated with cytokine-induced killer plus chemotherapy

Somatic copy number alterations (SCNA), which are widespread in cancer, can predict the efficacy of immune checkpoint inhibitors in non-small-cell lung cancer (NSCLC). However, the usefulness of SCNA for predicting the survival of patients treated with cytokine-induced killer (CIK) cells or chemotherapy (CT) is unknown. This study aimed to explore the correlation between SCNA and clinical outcome in NSCLC patients treated with CIK + CT or CT alone. We performed whole-exome sequencing on 45 NSCLC patients treated with CIK + CT, as well as 305 NSCLC patients treated with CT alone, from The Cancer Genome Atlas, which showed SCNA had a superiority in predicting the progression-free survival (PFS) over tumor mutation burden (TMB) and SCNA + TMB in NSCLC patients treated with CIK + CT, especially in lung adenocarcinoma, while SCNA could not predict the efficacy of CT alone. Additionally, we investigated the association between SCNA and immune cell infiltration by RNA sequencing and immunohistochemistry. The results revealed that SCNA was negatively associated with the expression of dendritic cells. Collectively, this study revealed a negative correlation between SCNA and response to CIK + CT and showed that SCNA is a predictive indicator in LUAD patients treated with CIK + CT.