Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients

2016 ◽  
Vol 63 (5) ◽  
pp. 1034-1043 ◽  
Author(s):  
Nastaran Emaminejad ◽  
Wei Qian ◽  
Yubao Guan ◽  
Maxine Tan ◽  
Yuchen Qiu ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
pp. 69-78
Author(s):  
Hojin Moon ◽  
Alex Nguyen ◽  
Evan Lee

Aims: Our goal is to find predictive genomic biomarkers in order to identify subgroups of early-stage lung cancer patients that are most likely to benefit from adjuvant chemotherapy with surgery (ACT). Background: Receiving ACT appears to have a better prognosis for more severe early-stage non-small cell lung cancer patients than surgical resection only. However, not all patients benefit from chemotherapy. Objective: Preliminary studies suggest that the application of ACT is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. Given the immense personal and financial costs associated with ACT, finding the patients who are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to find treatment-associated genomic biomarkers. Methods: To investigate the treatment effect, a modified-covariate regularized Cox regression model with lasso penalty is implemented using National Cancer Institute gene expression data to find genomic biomarkers. Results: This research utilized an independent validation dataset involving 318 lung cancer patients to validate the models. In the validation set with 318 patients, the modified covariate Cox model with lasso penalty were able to show patients who followed their predicted recommendation (either ACT for low-risk group or OBS for the high-risk group, n = 171) have higher survival benefits than 147 patients who did not follow the recommendations (p < .0001). Conclusion: Based on validation data, patients who follow our predicted recommendation by genomic biomarkers selected from the proposed model will likely benefit from ACT.


PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252304
Author(s):  
Dirk Stefani ◽  
Balazs Hegedues ◽  
Stephane Collaud ◽  
Mohamed Zaatar ◽  
Till Ploenes ◽  
...  

Background Torque teno virus (TTV) is a ubiquitous non-pathogenic virus, which is suppressed in immunological healthy individuals but replicates in immune compromised patients. Thus, TTV load is a suitable biomarker for monitoring the immunosuppression also in lung transplant recipients. Since little is known about the changes of TTV load in lung cancer patients, we analyzed TTV plasma DNA levels in lung cancer patients and its perioperative changes after lung cancer surgery. Material and methods Patients with lung cancer and non-malignant nodules as control group were included prospectively. TTV DNA levels were measured by quantiative PCR using DNA isolated from patients plasma and correlated with routine circulating biomarkers and clinicopathological variables. Results 47 patients (early stage lung cancer n = 30, stage IV lung cancer n = 10, non-malignant nodules n = 7) were included. TTV DNA levels were not detected in seven patients (15%). There was no significant difference between the stage IV cases and the preoperative TTV plasma DNA levels in patients with early stage lung cancer or non-malignant nodules (p = 0.627). While gender, tumor stage and tumor histology showed no correlation with TTV load patients below 65 years of age had a significantly lower TTV load then older patients (p = 0.022). Regarding routine blood based biomarkers, LDH activity was significantly higher in patients with stage IV lung cancer (p = 0.043), however, TTV load showed no correlation with LDH activity, albumin, hemoglobin, CRP or WBC. Comparing the preoperative, postoperative and discharge day TTV load, no unequivocal pattern in the kinetics were. Conclusion Our study suggest that lung cancer has no stage dependent impact on TTV plasma DNA levels and confirms that elderly patients have a significantly higher TTV load. Furthermore, we found no uniform perioperative changes during early stage lung cancer resection on plasma TTV DNA levels.


2020 ◽  
Author(s):  
Lingling Wan ◽  
Yutong He ◽  
Qingyi Liu ◽  
Di Liang ◽  
Yongdong Guo ◽  
...  

Abstract Background: Lung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis. Methods: In this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients, and the early diagnostic value of these assays in lung cancer was analyzed. Results: 62.5% (30/48) of lung cancer patients had ≥ 1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC > 0.9). Conclusions: Our results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer. This possibility requires more in-depth large-sample research for verification.


CHEST Journal ◽  
2007 ◽  
Vol 132 (4) ◽  
pp. 654A
Author(s):  
Themistokles P. Chamogeorgakis ◽  
Constantine E. Anagnostopoulos ◽  
Faiz Y. Bhora ◽  
Ioannis K. Toumpoulis ◽  
Andy Nabong ◽  
...  

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