Literature DB >> 29141854

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma.

Yanding Zhao1, Frederick S Varn1, Guoshuai Cai1, Feifei Xiao2, Christopher I Amos1,3,4, Chao Cheng5,3,4.   

Abstract

Background: Lung cancer is associated with the highest mortality rate of all cancer types, and the most common histologic subtype of lung cancer is adenocarcinoma. To apply more effective therapeutic treatment, molecular markers that are able to predict the recurrence risk of patients with adenocarcinoma are critically needed. Mutations in TP53 tumor suppressor gene have been found in approximately 50% of lung adenocarcinoma cases, but the presence of a TP53 mutation does not always associate with increased mortality.
Methods: The Cancer Genome Atlas RNA sequencing data of lung adenocarcinoma were used to define a novel gene signature for P53 deficiency. This signature was then used to calculate a sample-specific P53 deficiency score based on a patient's transcriptomic profile and tested in four independent lung adenocarcinoma microarray datasets.
Results: In all datasets, P53 deficiency score was a significant predictor for recurrence-free survival where high P53 deficiency score was associated with poor survival. The score was prognostic even after adjusting for several key clinical variables including age, tumor stage, smoking status, and P53 mutation status. Furthermore, the score was able to predict recurrence-free survival in patients with stage I adenocarcinoma and was also associated with smoking status.Conclusions: The P53 deficiency score was a better predictor of recurrence-free survival compared with P53 mutation status and provided additional prognostic values to established clinical factors.Impact: The P53 deficiency score can be used to stratify early-stage patients into subgroups based on their risk of recurrence for aiding physicians to decide personalized therapeutic treatment. Cancer Epidemiol Biomarkers Prev; 27(1); 86-95. ©2017 AACR. ©2017 American Association for Cancer Research.

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Year:  2017        PMID: 29141854      PMCID: PMC5839302          DOI: 10.1158/1055-9965.EPI-17-0478

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


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2.  Genomic Characterization of Six Virus-Associated Cancers Identifies Changes in the Tumor Immune Microenvironment and Altered Genetic Programs.

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3.  MYC Activity Inference Captures Diverse Mechanisms of Aberrant MYC Pathway Activation in Human Cancers.

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5.  Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model.

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6.  Single-cell RNA sequencing reveals the impact of chromosomal instability on glioblastoma cancer stem cells.

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7.  Risk stratification for lung adenocarcinoma on EGFR and TP53 mutation status, chemotherapy, and PD-L1 immunotherapy.

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9.  A network-based signature to predict the survival of non-smoking lung adenocarcinoma.

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10.  Mutated p53 Promotes the Symmetric Self-Renewal of Cisplatin-Resistant Lung Cancer Stem-Like Cells and Inhibits the Recruitment of Macrophages.

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