LIGHT promotes osteolytic bone metastases in NSCLC patients

2016 ◽  
Author(s):  
Giacomina Brunetti ◽  
Dimas Carolina Belisario ◽  
Valentina Alliod ◽  
Lucio Buffoni ◽  
Silvia Colucci ◽  
...  
Keyword(s):  
Bone Metastases ◽  
Nsclc Patients

scholarly journals Clinicopathologic correlates of pembrolizumab efficacy in patients with advanced NSCLC and a PD-L1 expression of ≥ 50%

2020 ◽  
Author(s):  
Alessio Cortellini ◽  
Marcello Tiseo ◽  
Giuseppe L Banna ◽  
Federico Cappuzzo ◽  
Joachim GJV Aerts ◽  
...  
Keyword(s):  
Liver Metastases ◽  
Bone Metastases ◽  
Recursive Partitioning ◽  
Single Agent ◽  
Multivariable Analysis ◽  
Tobacco Exposure ◽  
Former Smokers ◽  
Never Smokers ◽  
Ecog Ps ◽  
Nsclc Patients

AbstractBackgroundSingle agent pembrolizumab represents the standard first line option for metastatic non-small-cell-lung-cancer (NSCLC) patients with a PD-L1 (programmed death-ligand 1) expression of ≥ 50%.MethodsWe conducted a multicenter study aimed at evaluating the clinicopathologic correlates of pembrolizumab efficacy in patients with treatment-naïve NSCLC and a PD-L1 TPS ≥ 50%.Results1026 consecutive patients were included. ECOG-PS ≥ 2 (p < 0.0001) and bone metastases (p = 0.0003) were confirmed to be independent predictors of a worse ORR. Former smokers (p = 0.0002), but not current smokers (p = 0.0532) were confirmed to have a significantly prolonged PFS compared to never smokers at multivariate analysis. ECOG-PS (p < 0.0001), bone metastases (p < 0.0001) and liver metastases (p < 0.0001) were also confirmed to be independent predictors of a worse PFS. Previous palliative RT was significantly related to a shortened OS (p = 0.0104), while previous non-palliative RT was significantly related to a prolonged OS (p = 0.0033). Former smokers (p = 0.0131), but not current smokers (p = 0.3433) were confirmed to have a significantly prolonged OS compared to never smokers. ECOG-PS (p < 0.0001), bone metastases (p < 0.0001) and liver metastases (p < 0.0001) were also confirmed to be independent predictors of a shortened OS. A PD-L1 expression of ≥ 90%, as assessed by recursive partitioning, was associated with significantly higher ORR (p = 0.0204), and longer and OS (p = 0.0346) at multivariable analysis.Conclusionspembrolizumab was effective in a large cohort of NSCLC patients treated outside of clinical trials. We confirmed that the absence of tobacco exposure, and the presence of bone and liver metastasis are associated with worse clinical outcomes to pembrolizumab. Increasing levels of PD-L1 expression may help identifying a subset of patients who derive a greater benefit from pembrolizumab monotherapy.

91P GEFITINIB PLUS ZOLEDRONIC ACID ROLE IN BONE METASTASES FROM EGFR MUT+VE NSCLC PATIENTS

Lung Cancer ◽  
2013 ◽  
Vol 80 ◽  
pp. S39
Author(s):  
E. Del Signore ◽  
B. Gori ◽  
C. D'Antonio ◽  
S. Ricciardi ◽  
A. Fulvi ◽  
...  
Keyword(s):  
Zoledronic Acid ◽  
Bone Metastases ◽  
Nsclc Patients

The effect of pain on survival in non-small cell lung cancer (NSCLC) patients with bone metastases receiving chemotherapy

2008 ◽  
Vol 26 (15_suppl) ◽  
pp. 20666-20666
Author(s):  
K. Yoh ◽  
K. Kubota ◽  
H. Ohmatsu ◽  
K. Goto ◽  
S. Niho ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Bone Metastases ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

Bone metastases as predictors of treatment response and rapidly progressive disease in NSCLC patients on PD-1/PD-L1 immune checkpoint inhibitors (ICI).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20667-e20667
Author(s):  
Sally CM Lau ◽  
Lisa W Le ◽  
Elliot Charles Smith ◽  
Sze Wah Samuel Chan ◽  
Malcolm Ryan ◽  
...  
Keyword(s):  
Liver Metastases ◽  
Bone Metastases ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Smoking Status ◽  
Clinicopathologic Characteristics ◽  
Metastatic Site ◽  
Overall Response ◽  
Nsclc Patients ◽  
Sites Of Metastases

e20667 Background: The tissue microenvironment associated with specific organ metastases potentially influences the efficacy of checkpoint inhibitors. The presence of liver metastases is a predictor of poor response and survival in melanoma and is correlated with reduced CD8+ T cell infiltration. Our study examined clinicopathologic characteristics, focusing on sites of metastatic disease, that are associated with poor outcomes. Methods: Advanced NSCLC patients treated with ≥1 cycle of ICI were reviewed. Baseline age, sex, histology, stage, smoking status, ethnicity, PD-L1 expression and sites of metastases were recorded. Best overall response (BOR) was determined by clinical imaging response and categorized ordinally as shrinkage, stable, or progression, adapted from RECIST for CR/PR, SD, PD. A rapidly progressive phenotype (RPP) was defined as BOR of progression and ICI use of ≤2 months. The association between sites of metastases and clinical outcomes were investigated using logistic and cox regression models. Results: Among 219 eligible patients, bone was the most common metastatic site (34.7%), followed by brain (21.5%), adrenals (14.2%), and liver (13.7%). Bone metastases (OR 0.45, p = 0.004) were associated with a worse BOR, while only a trend was observed for liver metastases (OR 0.47, p = 0.06). Adrenal metastases were associated with a better BOR (OR 2.08, p = 0.04). But thorax limited disease did not associate with BOR (OR 1.08, p = 0.76). In a multivariate model, bone was the only metastatic site associated with a worse BOR (OR 0.50, p = 0.01). Further, bone metastases were associated with RPP (adjusted OR 1.91, p = 0.04). Both bone (adjusted hazard ratio/aHR 1.61, p = 0.01) and liver metastases (aHR 1.80, p = 0.02) were associated with a shorter time-to-treatment-failure. The presence of liver (aHR 2.63, p < 0.001) but not bone (aHR 1.04, p = 0.86) metastases was a significant predictor of poor OS. Conclusions: We report a novel finding that the presence of bone metastases was associated with a worse clinical overall response on ICI and a rapidly progressive phenotype. Further investigations into the mechanisms of RPP in the presence of bone metastases are needed.

RB1 mutation had influenced on bone metastases in advanced treatment-naïve lung cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21668-e21668
Author(s):  
Chengzhi Zhou ◽  
Ying Xu ◽  
Xinqing Lin ◽  
Fei Wang ◽  
Huojin Deng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Bone Metastases ◽  
Cell Lung Cancer ◽  
Suppressor Gene ◽  
Small Cell ◽  
Small Cell Lung ◽  
Treatment Naïve ◽  
Cancer Types ◽  
Nsclc Patients

e21668 Background: RB1 is one of the most vital cancer suppressor genes in various cancer types. RB1 mutation always occurs in treatment-naïve small cell lung cancer (SCLC) patients or in resistance to EGFR TKI therapy of non-small cell lung cancer (NSCLC) patients. However, we observed a group of clinical NSCLC patients, and found that RB1 mutations also occurred in treatment-naïve patients. Methods: RB1 is one of the most vital cancer suppressor genes in various cancer types. RB1 mutation always occurs in treatment-naïve small cell lung cancer (SCLC) patients or in resistance to EGFR TKI therapy of non-small cell lung cancer (NSCLC) patients. However, we observed a group of clinical NSCLC patients, and found that RB1 mutations also occurred in treatment-naïve patients. Results: Nonsense and frame-shift mutations were the major pathogenic mutations found in RB1 gene, which predicted RB1 protein-deficient in these mutational patients. Furthermore, we found these RB1 mutation patients had TP53 mutations at the same time. Patients with RB1 mutation had a higher smoking prevalence than patients who had wildtype RB1 genes (P < 0.0001). Gene testing showed RB1 mutations patients also developed EGFR driver mutations, while the mutation frequency was significantly lower than RB1 wildtype patients ( P = 0.0368). Eight RB1 mutated patients diagnosed with actionable EGFR mutations, including exon 19 del, L858R, L861Q, G719S and S768I, while no patients diagnosed with EGFR exon 20 insertion. 10 (62.5%, 10/16) patients in RB1 mutated group, and 25(32.1%, 25/78) patients with RB1 wildtype had bone metastases. The proportion of bone metastases in patients with RB1 mutation was significantly increased than others ( P = 0.0216). Conclusions: This study demonstrates that the function of tumor suppressor gene RB1 in advanced stage of NSCLC. From the results, we considered RB1 mutation had influence on bone metastases. Although loss-of-function mutations in tumor suppressor gene (TSG) RB1 always had no targets therapeutic drugs, it also suggested that some other clinically treatment can be performed as soon as possible.

Specific organ metastases and survival of advanced non-small cell lung cancer (NSCLC) patients with EGFR mutation.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21527-e21527
Author(s):  
Tiantian Shi ◽  
Long Wang ◽  
Zhisong Fan ◽  
Li Feng ◽  
Jing Han ◽  
...  
Keyword(s):  
Lymph Node ◽  
Liver Metastases ◽  
Bone Metastases ◽  
Lymph Node Metastases ◽  
Egfr Mutation ◽  
Poor Response ◽  
The Poor ◽  
Node Metastases ◽  
Tki Treatment ◽  
Nsclc Patients

e21527 Background: The retrospective study was performed to evaluate the clinicopathological characteristics and survival associated with distant metastasis from advanced NSCLC with EGFR mutation. Methods: The records of metastasis NSCLC patients with EGFR mutation at the time of diagnosis between 2012 and 2018 were reviewed. The Kaplan-Meier method was used to assess survival curves and the log-rank test was used in the univariate analysis. The multivariate survival analysis were performed using the Cox proportional hazards model to investigate the effects of clinicopathological factors on survival. All the statistical analyses were performed using SPSS 23.0 statistical software and a statistically significant difference was determined as P < 0.05. Results: A total of 258 NSCLC patients with EGFR mutation were enrolled in this study, including 146 cases of female (56.6%), 241 cases of adenocarcinoma (93.4%) and 212 cases of non-smokers (82.2%). Among these patients, 65(25.2%), 111(43.0%), 22(8.5%), 107 (41.5%), 11(4.3%), 87 (33.7%), 65 (25.2%) had brain, bone, liver, lung, adrenal gland, pleural metastasis and extrathoracic lymph node metastases, respectively. The median OS of total patients was 32.9 months (95% CI: 29.8-36.0). In the univariate analysis, patients with metastases to the bone (p = 0.001), liver (p = 0.012), extrathoracic lymph node(p = 0.006), and pleural(p = 0.008) exhibited a poorer survival compared to those without metastases to these regions. Abdominal metastases (p = 0.005) and extremity metastases (p = 0.002) were statistically independent prognostic factors. Association between metastatic region and the response to TKI treatment, liver metastases (p = 0.033), extrathoracic lymph node metastases (p = 0.000) and bone metastases (p = 0.009) were correlated with the poor response of TKI treatment, and the abdominal metastasis ( p= 0.029) and extremity metastases ( p= 0.016) were correlated with the poor response of TKI treatment. Conclusions: Bone metastases, liver metastases, extrathoracic lymph node metastases and pleural metastases were independent prognostic factors of NSCLC patients with EGFR mutation. Liver metastases, extrathoracic lymph node metastases, and bone metastases were correlated with the poor response of TKI treatment.

scholarly journals An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhangheng Huang ◽  
Chuan Hu ◽  
Changxing Chi ◽  
Zhe Jiang ◽  
Yuexin Tong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Survival Rate ◽  
Small Cell Lung Cancer ◽  
Bone Metastases ◽  
Cell Lung Cancer ◽  
Predictive Models ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training (n=4183) and validation (n=1790) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1-year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies.

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