The prognostic impact of lymph node metastasis in patients with non-small cell lung cancer and distant organ metastasis

Distant metastatic patterns in young and old non-small cell lung cancer patients: A dose‒response analysis based on SEER population

Background

Few research has explored the risk of distant metastasis dynamically changes with age in non-small cell lung cancer patients. The objective of this study was to explore the risk factors of developing distant metastasis with changing age.

Methods

This retrospective cohort study was based on a large population of patients with non-small cell lung cancer from the SEER database. Logistic regression was applied to identify risk factors for distant metastasis. The clinicopathological features were compared between the young group (≤50 years old) and the old group (>50 years old). Dose-response analyses were conducted to explore risk of distant metastasis changes with age.

Results

A total of 18,711 patients were studied in this study. According to the univariate and multivariate logistic regression analyses, ten factors were found to be risk factors for distant metastasis. Young patients have a greater incidence of each pattern of metastasis. However, the survival time of younger patients was longer. Dose-response analyses indicated that the risks of pleural or pericardial metastasis and overall distant metastasis gradually decreased with age at younger ages, but they intend to increase at older ages.

Conclusions

Age, sex, ethnicity, histology, T category, N category, differentiation grade, primary site of the tumor, ipsilateral metastases are factors associated with distant metastasis in NSCLC patients. Young patients have a greater risk of distant metastasis. The distant metastasis may decrease with the increasing age in patients younger than 70 years, but increase with the climb of age for patients older than 70 years.

Tankyrase 2 promotes lung cancer cell malignancy

BACKGROUND

Tankyrase 2 (TNKS2) is a potential candidate molecular target for the prognosis and treatment of non-small cell lung cancer (NSCLC), but its biological functions are unclear.

AIM

To investigate the biological functions of TNKS2 in NSCLC.

METHODS

Using a lentiviral vector, we generated H647 model cells with TNKS2 knockdown by RNA interference and A549 model cells with TNKS2 overexpression by transfection with a TNKS2 overexpressing plasmid. Increased and decreased expression levels of TNKS2 in the two cell lines were verified using real-time reverse transcriptase-polymerase chain reaction and Western blot analyses. Cell apoptosis, proliferation, and migration were determined using flow cytometry, carboxyfluorescein succinimidyl ester staining, and scratch assay, respectively. Immunofluorescence staining was conducted to examine TNKS2 and β-catenin expression levels in the two transfected cell lines and the non-transfected cells.

RESULTS

TNKS2 mRNA and protein expression was significantly higher in the highly malignant NCI-H647 cells, while it remained at a low level in the less malignant A549 cells. Lentivirus-mediated overexpression of TNKS2 in A549 cells resulted in a 3-fold increase in gene expression and a 1.7-fold increase in protein expression (P < 0.01). Conversely, shRNA interference targeting TNKS2 Led to an 8-fold decrease in gene expression and a 3-fold decrease in protein expression (P < 0.01) in NCI-H647 cells. Furthermore, the cell apoptosis rate was significantly reduced (50%) and cell migration rate was increased (35%) in the TNKS2 overexpression group than in the control group (P < 0.05). In contrast, shTNKS2 promoted apoptosis by more than one fold and reduced migration by 60% (P < 0.05). Immunofluorescence analysis revealed enhanced nuclear localization of β-catenin fluorescence signal associated with high TNKS2 expression levels. Western blot analysis investigating TNKS2/β-catenin-related proteins indicated consistent changes between TNKS2 and β-catenin expression in lung cancer cells, whereas Axin displayed an opposite trend (P < 0.05).

CONCLUSION

The obtained results revealed that TNKS2 may serve as an adverse prognostic factor and a potential therapeutic target in NSCLC.

A novel algorithm to differentiate between primary lung tumors and distant liver metastasis in lung cancers using an exosome based multi gene biomarker panel

The lack of non-invasive methods for detection of early metastasis is a crucial reason for the poor prognosis of lung cancer (LC) liver metastasis (LM) patients. In this study, the goal was to identify circulating biomarkers based on a biomarker model for the early diagnosis and monitoring of patients with LCLM. An 8-gene panel identified in our previous study was validated in CTC, cfRNA and exosomes isolated from primary lung cancer with & without metastasis. Further multivariate analysis including PCA & ROC was performed to determine the sensitivity and specificity of the biomarker panel. Model validation cohort (n = 79) was used to verify the stability of the constructed predictive model. Further, clinic-pathological factors, survival analysis and immune infiltration correlations were also performed. In comparison to our previous tissue data, exosomes demonstrated a good discriminative value with an AUC of 0.7247, specificity (72.48%) and sensitivity (96.87%) for the 8-gene panel. Further individual gene patterns led us to a 5- gene panel that showed an AUC of 0.9488 (p =  < 0.001) and 0.9924 (p =  < 0.001) respectively for tissue and exosomes. Additionally, on validating the model in a larger cohort a risk score was obtained (RS > 0.2) for prediction of liver metastasis with an accuracy of 95%. Survival analysis and immune filtration markers suggested that four exosomal markers were independently associated with poor overall survival. We report a novel blood-based exosomal biomarker panel for early diagnosis, monitoring of therapeutic response, and prognostic evaluation of patients with LCLM.

Evolving landscape of treatments targeting the microenvironment of liver metastases in non-small cell lung cancer

ABSTRACT

Liver metastases (LMs) are common in lung cancer. Despite substantial advances in diagnosis and treatment, the survival rate of patients with LM remains low as the immune-suppressive microenvironment of the liver allows tumor cells to evade the immune system. The impact of LMs on the outcomes of immune checkpoint inhibitors in patients with solid tumors has been the main focus of recent translational and clinical research. Growing evidence indicates that the hepatic microenvironment delivers paracrine and autocrine signals from non-parenchymal and parenchymal cells. Overall, these microenvironments create pre- and post-metastatic conditions for the progression of LMs. Herein, we reviewed the epidemiology, physiology, pathology and immunology, of LMs associated with non-small cell lung cancer and the role and potential targets of the liver microenvironment in LM in each phase of metastasis. Additionally, we reviewed the current treatment strategies and challenges that should be overcome in preclinical and clinical investigations. These approaches target liver elements as the basis for future clinical trials, including combinatorial interventions reported to resolve hepatic immune suppression, such as immunotherapy plus chemotherapy, immunotherapy plus radiotherapy, immunotherapy plus anti-angiogenesis therapy, and surgical resection.

A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study

Non-small-cell lung cancer (NSCLC) remains the most common malignant cancer. We identified 43140 advanced NSCLC patients from the SEER database to develop and validate a new prognostic model. The prognostic performance was evaluated by P value, concordance index, net reclassification index, integrated discrimination improvement, and decision curve analysis. The following variables were contained in the final prognostic model: age, sex, race, TNM stage, and grade and treatment options. Compared to the AJCC staging system, this prognostic model is conducive to the implementation of individualized clinical treatment schemes and can be an important part of the precise medical care of NSCLC tumors.

Nomogram for predicting the likelihood of liver metastases at initial diagnosis in patients with Siewert type II gastroesophageal junction adenocarcinoma

The liver is one of the most ordinary metastatic sites of gastroesophageal junction adenocarcinoma and significantly affects its prognosis. Therefore, this study tried to construct a nomogram that can be applied to predict the likelihood of liver metastases from gastroesophageal junction adenocarcinoma. 3001 eligible patients diagnosed with gastroesophageal junction adenocarcinoma between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were involved in the analysis. Patients were randomly divided into a training cohort and an internal validation cohort using R software, with an allocation ratio of 7:3. According to the consequences of univariate and multivariate logistic regression, we constructed a nomogram for predicting the risk of liver metastases. The discrimination and calibration ability of the nomogram was appraised by the C-index, ROC curve, calibration plots, and decision curve analysis (DCA). We also used Kaplan-Meier survival curves to compare differences in overall survival in patients with gastroesophageal junction adenocarcinoma with and without liver metastases. Liver metastases developed in 281 of 3001 eligible patients. The overall survival of patients with gastroesophageal junction adenocarcinoma with liver metastases before and after propensity score matching (PSM) was obviously lower than that of patients without liver metastases. Six risk factors were finally recognized by multivariate logistic regression, and a nomogram was constructed. The C-index was 0.816 in the training cohort and 0.771 in the validation cohort, demonstrating the good predictive capacity of the nomogram. The ROC curve, calibration curve, and decision curve analysis further demonstrated the good performance of the predictive model. The nomogram can accurately predict the likelihood of liver metastases in gastroesophageal junction adenocarcinoma patients.

Fatores Associados ao Karnofsky Performance Status e sua Trajetória no Último Mês de Vida em Pacientes com Câncer Terminal

Introdução: O Karnofsky Performance Status (KPS) pode caracterizar o impacto da doença em pacientes com câncer. Objetivo: Avaliar os fatores associados ao KPS e a sua trajetória no último mês de vida em pacientes com câncer terminal. Método: Estudo de coorte retrospectivo, com pacientes com câncer terminal internados em uma unidade de cuidados paliativos, falecidos entre julho e agosto de 2019. A variável dependente foi o KPS avaliado diariamente no último mês de vida. Uma análise transversal dos fatores associados ao KPS inicial foi realizada por meio de regressões logísticas ordinais. Para verificar a trajetória do KPS no último mês de vida, foram realizadas análises gráficas longitudinais. Resultados: Foram avaliados 108 pacientes, cuja maioria possuía >60 anos (68,5%) e era do sexo feminino (62,0%). Os sítios tumorais mais prevalentes foram o trato gastrointestinal (TGI) (24,3%), mama (18,7%) e cabeça e pescoço (CP) (16,8%). No modelo múltiplo, os sítios tumorais primários permaneceram associados ao KPS. Durante o último mês de vida, a redução do KPS foi mais pronunciada naqueles com tumor no TGI, CP e tecido ósseo conjuntivo, que apresentaram valores mais elevados de KPS no trigésimo dia antes do óbito quando comparados aos demais. Por outro lado, aqueles com câncer no sistema nervoso central e pulmão iniciaram o período de seguimento com valores de KPS mais baixos e tiveram redução menos exacerbada que os demais. Conclusão: Os valores de KPS diminuem no último mês de vida, porém com intensidade diferente de acordo com o local do tumor em pacientes com câncer terminal.

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Objective

The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.

Methods

Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.

Results

A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.

Conclusion

This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

Colon metastasis from lung adenocarcinoma with BRAF V600E mutation: A case report

Symptomatic colon metastasis from primary lung cancer is rare in clinical practice. We report the case of a 58-year-old patient with advanced lung adenocarcinoma who developed abdominal symptoms, including abdominal distention and difficulty defecating, after immunotherapy and chemotherapy. The patient was diagnosed with lung adenocarcinoma, and systemic positron emission tomography-computed tomography confirmed multiple lymph node, pleural, and adrenal metastases. Molecular detection indicated BRAF V600E mutation and high programmed death-ligand 1 (PD-L1) expression. After first-line anti-programmed cell death protein 1 immunotherapy combined with chemotherapy, the nodes in the chest remarkably diminished. However, it was followed by colon obstruction, incomplete ileus, and bone metastasis. Endoscopic histological examination confirmed adenocarcinoma but could not identify primary or secondary tumors due to insufficient tissue. We performed colon resection to remove the obstruction, and postoperative tissue pathological microscopy confirmed metastasis from the lung adenocarcinoma. We corroborated the BRAF V600E mutation and high PD-L1 expression and supported the molecular features of lung adenocarcinoma. During hospitalization, the patient presented with unbearable pain in the bone metastases, and palliative radiotherapy was administered. Then, the patient received dabrafenib plus trametinib as the second-line therapy. This report discusses the clinical characteristics, pathology, imaging, molecular profile assessments, and treatment of primary lung adenocarcinoma with colon metastasis.

Oncogenic Alterations in Histologically Negative Lymph Nodes Are Associated with Prognosis of Patients with Stage I Lung Adenocarcinoma

Simple Summary

Lymph nodes (LNs) metastasis is one of the most important factors affecting the outcome of non-small cell lung. The aim of this study is to explore whether presence of oncogenic alterations in histologically-negative lymph nodes (LNs) can be of prognostic significance in stage I lung adenocarcinoma (LUAD). We confirmed that presence of oncogenic alterations in regional LN may be associated with higher risks of postsurgical recurrence of Stage I LUAD, particularly for certain molecular subgroups. These results warranted future studies on larger cohort of NSCLC patients using more comprehensive cancer gene panels to establish the clinical impact of molecular LN occult metastasis for localized NSCLC and identify Stage I patients at high risks for recurrence for appropriate adjuvant therapy.

Abstract

Background: Survival of patients with stage I non-small cell lung cancer (NSCLC) varies greatly. We sought to explore whether presence of oncogenic alterations in histologically-negative lymph nodes (LNs) can be of prognostic significance in stage I lung adenocarcinoma (LUAD). Methods: Genomic analysis of oncogenic alterations was applied to 123 stage I LUAD tumors. The same genomic variants identified in primary tumors were examined in corresponding histologically-negative LNs. Results: A total of 102 (82.9%) patients had at least one canonical oncogenic alteration detected in primary tumors, and 57 LNs from 12 patients (11.8%) were found to carry the identical oncogenic alterations detected in the corresponding primary tumor tissues, including EGFR mutations (six cases), KRAS mutations (three cases), ALK fusion (one case), BRAF mutation (one case) and HER2 & NRAS co-mutations (one case). None of these LNs was found to have occult tumor cells by routine pathological assessment or immunohistochemistry staining using antibodies against pan-cytokeratins (AE1/AE3) and the epithelial marker Ber-EP4. The detection rate of oncogenenic alterations in LN was significantly higher in RAS-mutant tumors than EGFR mutant tumors (36.36% verse 7.41%, p = 0.017). Patients with oncogenic alterations in LN showed inferior disease-free survival (DFS, p = 0.025) and overall survival (OS, p = 0.027). Furthermore, patients with RAS-mutations detected in LN had the worst DFS and OS (p = 0.001). Among the 11 patients with RAS mutation in primary tumors, DFS and OS in the four patients with mutations detected in LN were significantly shorter than the remaining seven patients without mutations LN (DFS, p = 0.001, OS, p = 0.002). Conclusions: Genomic analysis has the potential to detect oncogenic alterations in regional LNs for localized LUAD and presence of oncogenic alterations in regional LN may be associated with inferior clinical outcome of stage I LUAD, particularly for certain molecular subgroups. ClinicalTrials.gov ID NCT04266691

Brain Metastasis in Patients with Small Cell Lung Cancer

Purpose

To characterize the risk factors for brain metastasis (BM) at presentation and analyze the prognostic factors for patients with small cell lung cancer (SCLC).

Patients and Methods

Patients were recruited from the SEER database between 2010 and 2016. They were divided into two groups according to BM status. The incidence trends of SCLC and its BM were analyzed by joinpoint software. The risk factors for BM in SCLC were identified by binary logistic regression models. The prognostic factors for SCLC patients with BM were identified by Cox proportional hazard models.

Results

The incidence of SCLC and its BM significantly decreased after 2010. Totally 11,093 patients were collected, including 1717 (15.5%) patients with BM and 9376 (84.5%) patients without BM. In multivariate logistic regression analysis, age, male and higher T stage were independent risk factors for BM in SCLC patients at presentation. SCLC patients with BM showed inferior survival to those without BM. In multivariate Cox regression analysis, increasing age, large tumor size, and higher N stage were risk factors for poor prognosis, while other race, surgery, adjuvant radiotherapy, and chemotherapy were protective factors for SCLC patients with BM. A nomogram was developed for prognosis evaluation of such patients.

Conclusion

Age, male and higher T stage were risk factors for BM in SCLC patients at presentation. Increasing age, large tumor size, and advanced N stage may predict poor survival for SCLC patients with BM. Multidisciplinary therapies may provide clinical benefits. This study will help identify patients with higher BM risk and hopefully improve their clinical outcome.

Predictive and Prognostic Biomarkers for Lung Cancer Bone Metastasis and Their Therapeutic Value

Lung cancer is the leading cause of cancer-related death worldwide. Bone metastasis, which usually accompanies severe skeletal-related events, is the most common site for tumor distant dissemination and detected in more than one-third of patients with advanced lung cancer. Biopsy and imaging play critical roles in the diagnosis of bone metastasis; however, these approaches are characterized by evident limitations. Recently, studies regarding potential biomarkers in the serum, urine, and tumor tissue, were performed to predict the bone metastases and prognosis in patients with lung cancer. In this review, we summarize the findings of recent clinical research studies on biomarkers detected in samples obtained from patients with lung cancer bone metastasis. These markers include the following: (1) bone resorption-associated markers, such as N-terminal telopeptide (NTx)/C-terminal telopeptide (CTx), C-terminal telopeptide of type I collagen (CTx-I), tartrate-resistant acid phosphatase isoform 5b (TRACP-5b), pyridinoline (PYD), and parathyroid hormone related peptide (PTHrP); (2) bone formation-associated markers, including total serum alkaline phosphatase (ALP)/bone specific alkaline phosphatase(BAP), osteopontin (OP), osteocalcin (OS), amino-terminal extension propeptide of type I procollagen/carboxy-terminal extension propeptide of type I procollagen (PICP/PINP); (3) signaling markers, including epidermal growth factor receptor/Kirsten rat sarcoma/anaplastic lymphoma kinase (EGFR/KRAS/ALK), receptor activator of nuclear factor κB ligand/receptor activator of nuclear factor κB/osteoprotegerin (RANKL/RANK/OPG), C-X-C motif chemokine ligand 12/C-X-C motif chemokine receptor 4 (CXCL12/CXCR4), complement component 5a receptor (C5AR); and (4) other potential markers, such as calcium sensing receptor (CASR), bone sialoprotein (BSP), bone morphogenetic protein 2 (BMP2), cytokeratin 19 fragment/carcinoembryonic antigen (CYFRA/CEA), tissue factor, cell-free DNA, long non-coding RNA, and microRNA. The prognostic value of these markers is also investigated. Furthermore, we listed some clinical trials targeting hotspot biomarkers in advanced lung cancer referring for their therapeutic effects.