Genomic profiling of early-stage lung cancer for patterns of recurrence.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23126-e23126
Author(s):  
Costanzo A DiPerna ◽  
Leah Fine
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Early Stage ◽  
Molecular Profiling ◽  
Stage I ◽  
Genomic Profiling ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Adjuvant Therapies ◽  
Stage Lung Cancer

e23126 Background: Currently there is no evidence to support molecular profiling for early stage resected lung cancer patients. However, up to 50% of patients experience recurrence following resection. This is a first-of-a-kind study utilizing comprehensive genomic profiling technology to characterize genomic patterns for risk of recurrence in early stage lung cancer patients within the community hospital setting who have undergone lung surgery. Methods: A total of 60 Stage I-II lung cancer patients, all having undergone pulmonary resection, were evaluated with molecular profiling of their primary lung cancer tissue using Foundation Medicine’s FoundationOneÒ test. Patient age in years ranged from 39-86, and all patients were confirmed Stage I or II based on final pathologic analysis. Samples were taken from three community hospitals that are part of the Addario Lung Cancer Foundation Centers of Excellence program. Patients whose tumors were resected between the years of 2009-2017 were included in this combined retrospective and prospective study. Results: More than 300 genes were evaluated using FoundationOneÒ and patients were segmented to establish similarities and differences. Analysis of segments include gender, recurrence, smoking status among others. Gene patterns across segments are beginning to reveal possible predictive profiles. Final analysis will be completed shortly. Conclusions: Genomic profiling could help predict lung cancer recurrence for early stage lung cancer patients. Similarities amongst patients with recurrences imply that early genomic profiling of lung cancer patients could help predict those patients who would benefit from adjuvant therapies including conventional chemotherapy. Genomic profiling for early stage lung cancer should be studied further and in greater detail to help predict those patients who would benefit from potentially early adjuvant therapies.

Circulating tumor DNA (ctDNA) mutation and epigenomic patterns in early-stage lung cancer patients and its utility in identifying patients at high risk for early recurrence.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14557-e14557
Author(s):  
Jong Ho Cho ◽  
Il-Jin Kim ◽  
Junghee Lee ◽  
Hong Kwan Kim ◽  
Jinseon Lee ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Somatic Mutations ◽  
Early Stage ◽  
Circulating Tumor Dna ◽  
Stage I ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Tumor Dna ◽  
Stage Lung Cancer

e14557 Background: Circulating tumor DNA (ctDNA) analysis has been successfully applied to therapy selection and treatment monitoring in advanced cancer patients. However, it is not yet established whether ctDNA can be used clinically for early cancer detection or predicting tumor recurrence in early stage lung cancer patients. Methods: We analyzed pre-operative plasma samples from 55 early stage NSCLC patients (stages I-IIIA) using next-generation sequencing to detect somatic mutations and differential epigenomics patterns, including methylation signatures. Results: Using somatic mutation analysis alone, ctDNA was detected in 42% (23/55) of patients, whereas combined mutational and epigenomic analysis detected ctDNA in 71%. ctDNA detection rate also varied markedly between lung squamous cell carcinoma (SCC) and adenocarcinoma (ADC);using combined analysis of somatic mutations and epigenomic patterns, ctDNA was detected in all SCC patients, while only 55% of ADC (12/22) were ctDNA-positive (p= 0.006). Within the ADC subgroup, ctDNA detection rates using the combined approach were dependent on disease stage: 47% (8/17) in stage I, 100% (2/2) in stage II, and 100% (2/2) in stage IIIA. Importantly, pre-operative ctDNA status was correlated with tumor recurrence post-resection; three of eight (38%) ctDNA-positive stage I ADC patients recurred within 2 years of resection, while only one of nine (11%) ctDNA-negative stage I ADC patients recurred (p= 0.29). Conclusions: Taken together, we show that the combination of somatic mutation detection and epigenomic analysis outperforms each individual biomarker in the detection of ctDNA in early stage lung cancer. Importantly, we also demonstrate that pre-operative ctDNA detection may identify a high-risk population of early stage lung cancer patients that may benefit from (neo)adjuvant therapy.

Automated imaging-based stratification of early-stage lung cancer patients prior to receiving surgical resection using deep learning applied to CTs.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1552-1552
Author(s):  
Felipe Soares Torres ◽  
Shazia Akbar ◽  
Srinivas Raman ◽  
Kazuhiro Yasufuku ◽  
Felix Baldauf-Lenschen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Surgical Resection ◽  
Mortality Risk ◽  
Early Stage ◽  
Stage I ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Stage Lung Cancer

1552 Background: Computed tomography (CT) imaging is an important tool to guide further investigation and treatment in patients with lung cancer. For patients with early stage lung cancer, surgery remains an optimal treatment option. Artificial intelligence applied to pretreatment CTs may have the ability to quantify mortality risk and stratify patients for more individualized diagnostic, treatment and monitoring decisions. Methods: A fully automated, end-to-end model was designed to localize the 36cm x 36cm x 36cm space centered on the lungs and learn deep prognostic features using a 3-dimensional convolutional neural network (3DCNN) to predict 5-year mortality risk. The 3DCNN was trained and validated in a 5-fold cross-validation using 2,924 CTs of 1,689 lung cancer patients from 6 public datasets made available in The Cancer Imaging Archive. We evaluated 3DCNN’s ability to stratify stage I & II patients who received surgery into mortality risk quintiles using the Cox proportional hazards model. Results: 260 of the 1,689 lung cancer patients in the withheld validation dataset were diagnosed as stage I or II, received a surgical resection within 6 months of their pretreatment CT and had known 5-year disease and survival outcomes. Based on the 3DCNN’s predicted mortality risk, patients in the highest risk quintile had a 14.2-fold (95% CI 4.3-46.8, p < 0.001) increase in 5-year mortality hazard compared to patients in the lowest risk quintile. Conclusions: Deep learning applied to pretreatment CTs provides personalised prognostic insights for early stage lung cancer patients who received surgery and has the potential to inform treatment and monitoring decisions.[Table: see text]

scholarly journals Torque Teno Virus load in lung cancer patients correlates with age but not with tumor stage

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252304
Author(s):  
Dirk Stefani ◽  
Balazs Hegedues ◽  
Stephane Collaud ◽  
Mohamed Zaatar ◽  
Till Ploenes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Early Stage ◽  
Stage Iv ◽  
Tumor Stage ◽  
Plasma Dna ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Stage Iv Lung Cancer ◽  
Stage Lung Cancer

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.

Circulating Tumor Cell and Metabolites as Novel Biomarkers for Early-stage Lung Cancer Diagnosis

2020 ◽  
Author(s):  
Lingling Wan ◽  
Yutong He ◽  
Qingyi Liu ◽  
Di Liang ◽  
Yongdong Guo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Diagnosis ◽  
Cancer Patients ◽  
Early Stage ◽  
Untargeted Metabolomics ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Patients ◽  
Diagnosis Of Lung Cancer ◽  
Metabolomics Analysis ◽  
Stage Lung Cancer

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.

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