Identification of nine mutant genes and establishment of three prediction models of organ tropism metastases of non-small cell lung cancer.

BACKGROUND: Most Non-small cell lung cancer (NSCLC) patients tend to have metastases at the initial diagnosis. However, limited knowledge has been established regarding which factors, are associated with its metastases. This study aims to identify more biomarkers associated with its organ tropism metastasis and to establish models for prediction of its metastatic organs. METHODS: We performed targeted next-generation sequencing (NGS) to detect genes related to lung cancer in 272 patients with primary advanced NSCLC from Northeast China. We adopted Fisher test, multivariate logistic regression analysis to identify metastasis-related gene mutations and to establish prediction models. RESULTS: Mutations of EGFR (p = 0.0003, OR = 2.554) (especially EGFR L858R [p = 0.02, OR = 2.009]), ATM (p = 0.008, OR = 11.032), and JAK2 (p = 0.009, OR = Inf) were positively and of TP53 exon4mut (p = 0.001, OR = 0.173) was negatively correlated with lung metastasis, and those of CSF1R (p = 0.01, OR = Inf), KIT (p = 0.03, OR = 4.746), MYC (p = 0.05, OR = 7.938), and ERBB2 (p = 0.02, OR = 2.666) were positively correlated with pleural dissemination; those of TP53 (p = 0.01, OR = 0.417) was negatively, while of SMAD4 (p = 0.03, OR = 4.957) was positively correlated with brain metastasis of NSCLC. Additionally, smoking history (p = 0.004, OR = 0.004) was negatively correlated with pleural dissemination of NSCLC. Furthermore, models for prediction of lung metastasis (AUC = 0.706), pleural dissemination (AUC = 0.651), and brane metastasis (AUC = 0.629) were established. CONCLUSION: Taken together, this study revealed nine mutant genes and smoking history associated with organ tropism metastases of NSCLC and provided three models for the prediction of metastatic organs. This study enables us to predict the organs to which non-small cell lung cancer metastasizes before it does develop.

Body mass index and serum markers associated with progression-free survival in lung cancer patients treated with immune checkpoint inhibitors.

BACKGROUND: ICIs have remarkably affected the treatment strategies for numerous malignancies, including lung cancer. However, only a fraction of patients experience durable responses to ICIs; thus, there is an urgent need to identify the parameters related to ICI therapeutic effects. In this study, we investigated nutritional status surrogates and several serum markers to estimate the efficacy of ICIs. MATERIALS AND METHODS: The records of 66 patients with stage III/IV lung cancer who received ICIs were retrospectively analyzed. Features of patients' clinical pathology, including age, sex, histology, line of treatment, BMI, serum albumin, serum creatinine, and serum inflammatory markers such as LMR and PLR, were examined. Progression-free survival was the primary endpoint. Relationships among categorical variables were assessed by the chi-squared test. Survival analysis was performed using the Kaplan-Meier method followed by the log-rank test. Cox multivariate analysis was performed to analyze the association between each variable and the survival time of patients. RESULTS: The patients with BMI ≥ 25 (kg/m2), serum ALB≥37 (g/dL), serum creatinine ≥61.8 (µmol/L), LMR ≥ 2.12 had a significantly prolonged PFS in comparison with BMI<25 (kg/m2), ALB<37 (g/dL), creatinine<61.8 (µmol/L), LMR<2.12 (p < 0.05). No statistically significant difference was detected between patients with PLR < 135 and PLR ≥ 135 (p = 0.612). Multivariate analysis revealed that ALB≥37 (g/dL) and creatinine ≥ 61.8 (µmol/L) were associated with prolonged PFS, while statistical significance was not achieved in the BMI groups. CONCLUSIONS: The current results indicated that high BMI is related to longer PFS in lung cancer patients treated with ICIs, which may be correlated with high levels of serum albumin and creatinine.

Molecular and clinical characteristics of IDH mutations in Chinese NSCLC patients and potential treatment strategies.

BACKGROUND: Isocitrate dehydrogenase (IDH) is an appealing target for anticancer therapy, and IDH (IDH1/2) inhibitors have been approved for targeted therapy of acute myeloid leukemia (AML) and Cholangiocarcinoma. The therapeutic potential of IDH inhibitors for non-small-cell lung cancer (NSCLC) patients is under active clinical investigation. Thus, it would be necessary and meaningful to study the molecular and clinical characteristics of IDH mutation in NSCLC patients, especially in the Chinese population. METHODS: A total of 17,978 Chinese patients with NSCLC who underwent next -generation sequencing (NGS) testing were retrospectively reviewed. RESULTS: We identified 161 unique IDH mutations in 361 of 17,978 patients (2.01%). Common active-site mutations, including IDH1R100 , IDH1R132 , IDH2R140 , and IDH2R172 , were detected in 154 patients (0.86%) and were associated with male sex (p = 0.004) and older age (p = 0.02). The IDH mutation spectra observed in NSCLC were quite different from those in glioma or AML. Patients with IDH active-site mutations exhibited significantly higher coalterations in KRAS (p. G12/13/61, 22.1% vs. 8.2%, p < 0.001) or BRAF (p. V600E, 6.5% vs. 1.0%, p < 0.001), but significantly lower coalterations in activating EGFR (e18-e20, 22.7 vs. 37.9%, p < 0.001) than IDH wild-type patients. Furthermore, we found that active-site IDH mutations were correlated with a short PFS (2-5.6 months) and short OS (2-9.5 months), which may arise as a resistance mechanism against common targeted drugs. In vitro, we experimentally observed that the combination of an IDH inhibitor and EGFR TKI could better inhibit lung cancer cell proliferation than an EGFR TKI alone. CONCLUSIONS: Taken together, this study reveals the molecular and clinical characteristics of IDH mutations in Chinese NSCLC patients and provides a theoretical basis for IDH-directed treatment. The potential of IDH mutations as response markers for targeted therapy warrants further investigation.

Inhibition of lncRNA PART1 Chemosensitizes Wild Type but Not KRAS Mutant NSCLC Cells.

BACKGROUND: Lung cancer has the highest incidence among solid tumors in men and is the third most common cancer in women. Despite improved understanding of genomic and mutational landscape in non-small cell lung cancer (NSCLC), the five-year survival in these patients has remained stagnant at a dismal 15%. The first line of treatment commonly adapted for NSCLC patients with somatic mutation in EGFR is tyrosine kinase inhibitor gefitinib or erlotinib. EGFR mutant cells seem to be intrinsically sensitive to tyrosine kinase inhibitors; however, the remaining 20-30% patients are resistant to tyrosine kinase inhibitor. MATERIALS AND METHODS: Here we show, using in vitro normal and NSCLS cell lines, that the lncRNA Prostate androgen-regulated transcript 1 (PART1) is expressed at higher levels in NSCLC cells compared to normal lung epithelial cell line, corroborating two earlier studies. RESULTS: We additionally show that these cells are resistant to erlotinib which is reversed in some, but not all, cell lines following suppression of PART1 expression. The differential response to erlotinib following siRNA-mediated knockdown of PART1 was found to be related to the mutational status of KRAS. Only in cells with wild-type KRAS suppression of PART1 sensitized them to erlotinib. Knockdown of mutant KRAS did not sensitize those cell lines to erlotinib. But knockdown of mutant KRAS along with suppression of PART1 sensitized the cells to treatment with erlotinib. The results from the study reveal a yet undefined and important role of lncRNA PART1 in defining sensitivity to erlotinib. This action is mediated by mutation status of KRAS. CONCLUSION: Even though preliminary, our results indicate PART1 might be a potential candidate for targeted therapy or used as a predictor of chemosensitivity in patients with NSCLC.

Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale.

Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation.