Prognostic value and therapeutic implications of expanded molecular testing for resected early stage lung adenocarcinoma

Lung Cancer ◽  
2020 ◽  
Vol 143 ◽  
pp. 60-66 ◽  
Author(s):  
Peter J. Kneuertz ◽  
David P. Carbone ◽  
Desmond M. D’Souza ◽  
Konstantin Shilo ◽  
Mahmoud Abdel-Rasoul ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Early Stage ◽  
Molecular Testing ◽  
Therapeutic Implications

scholarly journals Prognostic value of OCT4A and SPP1C transcript variant co-expression in early-stage lung adenocarcinoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Seijiro Koshimune ◽  
Mitsuko Kosaka ◽  
Nobuhiko Mizuno ◽  
Hiromasa Yamamoto ◽  
Tomoyuki Miyamoto ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Early Stage ◽  
Transcript Variant

scholarly journals P3.16-06 Does Comprehensive Mutation Analysis Add Prognostic Value in Resected Early Stage Lung Adenocarcinoma?

2018 ◽  
Vol 13 (10) ◽  
pp. S1005
Author(s):  
P. Kneuertz ◽  
D. Carbone ◽  
L. Luo ◽  
D. D'Souza ◽  
S. Moffatt-Bruce ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Mutation Analysis ◽  
Prognostic Value ◽  
Early Stage

Use of a proliferation-based mRNA signature to predict outcome in early-stage non-small cell lung adenocarcinoma.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 7023-7023
Author(s):  
Carmen Behrens ◽  
Francesca Lombardi ◽  
Susanne Wagner ◽  
Junya Fujimoto ◽  
Maria G Raso ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Multivariate Analysis ◽  
Lung Adenocarcinoma ◽  
Quantitative Pcr ◽  
Prognostic Value ◽  
Early Stage ◽  
Treatment Decisions ◽  
Cancer Center ◽  
Data Set ◽  
Ffpe Samples

7023 Background: Adjuvant treatment of patients with early-stage lung adenocarcinoma is based on post-surgical pathological staging and patient performance status. Disparate outcomes within each staging group suggest that additional prognostic markers could improve our understanding of risk-benefit and potentially lead to better treatment decisions. A proliferation-based, mRNA expression profile was applied to public microarray data of surgically treated lung adenocarcinomas and a cohort of FFPE samples to test its potential prognostic utility. Methods: Public expression data (Director’s Consortium, DC) were derived from Affymetrix HG-U133A arrays. Clinical FFPE samples were assayed by quantitative PCR. A cell cycle progression (CCP) score was calculated from the expression average of 31 cell cycle genes normalized by 15 housekeeper genes. The prognostic value of the CCP score to predict stage I and II patient outcomes was evaluated by Cox proportional hazards analysis with disease-related death as the primary outcome measure. Results: In 256 DC cases, the CCP score was a significant predictor of death in univariate (p=0.0001) and multivariate analysis (p=0.001, HR 1.57, 95%CI 1.20-2.05) using age, stage, gender, smoking status and treatment as covariates. Similarly, in a second data set (GSE31210, n=204) the CCP score was highly associated with death (univariate, p=0.001; multivariate analysis, p=0.003, HR 1.81, 95% CI 1.24-2.66). Using quantitative PCR, the signature was applied to 381 FFPE samples with a median follow-up of 5 years collected at the MD Anderson Cancer Center and the European Institute for Oncology. In the presence of clinical covariates (as above and tumor size and pleural invasion), the CCP score remained the most significant predictor of death in univariate (p=0.0003) and multivariate analysis (p=0.007, HR 1.50, 95% CI 1.11-2.02). Conclusions: A 46 gene mRNA signature is a significant predictor of disease-related death in early-stage lung adenocarcinoma, providing independent prognostic value in the presence of clinical variables. This molecular predictor of cancer survival will be studied in additional cohorts for its ability to impact clinical treatment decisions.

scholarly journals The prognostic value of a Methylome-based Malignancy Density Scoring System to predict recurrence risk in early-stage Lung Adenocarcinoma

Theranostics ◽  
2020 ◽  
Vol 10 (17) ◽  
pp. 7635-7644
Author(s):  
Lu Yang ◽  
Jing Zhang ◽  
Guangjian Yang ◽  
Haiyan Xu ◽  
Jing Lin ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Scoring System ◽  
Prognostic Value ◽  
Early Stage ◽  
Recurrence Risk

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

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