Classification of Non-Small Cell Lung Cancer Based on Copy Number Alterations

Li, Biqing; You, Jin; Huang, Tao; Cai, Yu-Dong

doi:10.1371/journal.pone.0088300

Cited by 33 publications

(30 citation statements)

References 65 publications

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“…There was no significant difference in CNVs for these 33 genes according to either gender or smoking history (data not shown). We also compared the performance of our 33‐gene classifier with that of another CNV classifier proposed by Li et al In their study, the classifier consisting of 266 probes was not tested by an independent validation dataset. According to their results, it had a comparable accuracy to our classifier; however, its sensitivity was much lower (0.65).…”

Section: Discussionmentioning

confidence: 99%

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Genome‐wide copy number variation pattern analysis and a classification signature for non‐small cell lung cancer

Qiu

Gazdar

et al. 2017

Genes Chromosomes & Cancer

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The accurate classification of non-small cell lung carcinoma (NSCLC) into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is essential for both clinical practice and lung cancer research. Although the standard WHO diagnosis of NSCLC on biopsy material is rapid and economic, more than 13% of NSCLC tumors in the USA are not further classified. The purpose of this study was to analyze the genome-wide pattern differences in copy number variations (CNVs) and to develop a CNV signature as an adjunct test for the routine histopathologic classification of NSCLCs. We investigated the genome-wide CNV differences between these two tumor types using three independent patient datasets. Approximately half of the genes examined exhibited significant differences between LUAD and LUSC tumors and the corresponding non-malignant tissues. A new classifier was developed to identify signature genes out of 20 000 genes. Thirty-three genes were identified as a CNV signature of NSCLC. Using only their CNV values, the classification model separated the LUADs from the LUSCs with an accuracy of 0.88 and 0.84, respectively, in the training and validation datasets. The same signature also classified NSCLC tumors from their corresponding non-malignant samples with an accuracy of 0.96 and 0.98, respectively. We also compared the CNV patterns of NSCLC tumors with those of histologically similar tumors arising at other sites, such as the breast, head, and neck, and four additional tumors. Of greater importance, the significant differences between these tumors may offer the possibility of identifying the origin of tumors whose origin is unknown.

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Section: Discussionmentioning

confidence: 99%

“…All of these facts point to the value of molecular classifications as nonsubjective adjuvant methods. These methods include digital nuclear imaging, mutation analysis, mRNA expression values and various other molecular methods, either alone or in combination …”

Section: Introductionmentioning

confidence: 99%

Genome‐wide copy number variation pattern analysis and a classification signature for non‐small cell lung cancer

Qiu

Gazdar

et al. 2017

Genes Chromosomes & Cancer

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“…Previous studies have shown that the CNV pattern between lung adenocarcinoma and squamous cell carcinoma are very different [12] and there are rich mutations and copy number changes in various cancers [13]. Here, we developed a computational method, which successfully uses the information of gene CNV levels to classify six types of cancers -breast adenocarcinoma (BRCA), colon and rectal carcinoma (COAD/READ), glioblastoma multiforme (GBM), kidney renal clear cell carcinoma (KIRC), ovarian serous carcinoma (OV), and uterine corpus endometrial carcinoma (UCEC).…”

Section: Introductionmentioning

confidence: 99%

Classification of cancers based on copy number variation landscapes

Zhang

Wang

Zhang

et al. 2016

Biochimica et Biophysica Acta (BBA) - General Subjects

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“…Gene copy number variation (CNV) caused by genomic instability has received attention as a potential parameter for the diagnosis of some tumor subtypes [5]. Among these, it has been found that the CNV-based classifier constructed by comparing the CNV pattern of lung adenocarcinoma and lung squamous cell carcinoma can be used to distinguish two subgroups of non-small cell lung cancer [6]. Xu et al found that CNV analysis could help to identify patients with chronic liver disease who are at high risk of hepatocellular carcinoma (HCC), which is of great value for early monitoring and intervention of HCC.…”

Section: Open Accessmentioning

confidence: 99%

Development of a risk scoring system for evaluating the prognosis of patients with Her2-positive breast cancer

Gao

Zhuang

et al. 2020

Cancer Cell Int

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Background: As one of the many breast cancer subtypes, human epidermal growth factor receptor 2 (Her2)-positive breast cancer has higher invasiveness and poor prognosis, although the advent of anti-Her2 drugs has brought good news to patients. However, the emergence of drug resistance still limits its clinical efficacy, so there is an urgent need to explore new targets and develop a risk scoring system to improve treatments and evaluate patient prognosis.Methods: Differentially expressed mRNAs associated with Her2-positive breast cancer were screened from a TCGA cohort. The prognostic risk scoring system was constructed according to univariate and Lasso Cox regression model analyses and combined with clinical factors (such as age and TNM) for univariate and multivariate analyses to verify the specificity and sensitivity of the risk scoring system. Finally, based on correlation and CNV mutation analyses, we explored the research value of the mRNAs involved in the system as key genes of the model. Results:In this study, six mRNAs were screened and identified to construct a prognostic risk scoring system, including four up-regulated mRNA (RDH16, SPC25, SPC24, and SCUBE3) and two down-regulated mRNA (DGAT2 and CCDC69). The risk scoring system can divide Her2-positive breast cancer samples into high-risk and low-risk groups to evaluate patient prognosis. In addition, whether through the time-dependent receiver operating characteristics curve or compared with clinical factors, the risk scoring system showed high predictive sensitivity and specificity. Moreover, some CNV mutations in mRNA increase patient risk by influencing expression levels.Conclusion: The risk scoring system constructed in this study is helpful to improve the screening of high-risk patients with Her2-positive breast cancer and is beneficial for implementing early diagnosis and personalized treatment. It is suggested that these mRNAs may play an important role in the progression of Her2-positive breast cancer.

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Classification of Non-Small Cell Lung Cancer Based on Copy Number Alterations

Cited by 33 publications

References 65 publications

Genome‐wide copy number variation pattern analysis and a classification signature for non‐small cell lung cancer

Genome‐wide copy number variation pattern analysis and a classification signature for non‐small cell lung cancer

Classification of cancers based on copy number variation landscapes

Development of a risk scoring system for evaluating the prognosis of patients with Her2-positive breast cancer

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