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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front Med    2013, Vol. 7 Issue (2) : 157-171     DOI: 10.1007/s11684-013-0272-4
Molecular classification of non-small-cell lung cancer: diagnosis, individualized treatment, and prognosis
Yue Yu, Jie He()
Department of Thoracic Surgery, Cancer Institute and Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100021, China
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Non-small-cell lung cancer (NSCLC) is the most common cause of premature death among the malignant diseases worldwide. The current staging criteria do not fully capture the complexity of this disease. Molecular biology techniques, particularly gene expression microarrays, proteomics, and next-generation sequencing, have recently been developed to facilitate effectively its molecular classification. The underlying etiology, pathogenesis, therapeutics, and prognosis of NSCLC based on an improved molecular classification scheme may promote individualized treatment and improve clinical outcomes. This review focuses on the molecular classification of NSCLC based on gene expression microarray technology reported during the past decade, as well as their applications for improving the diagnosis, staging and treatment of NSCLC, including the discovery of prognostic markers or potential therapeutic targets. We highlight some of the recent studies that may refine the identification of NSCLC subtypes using novel techniques such as epigenetics, proteomics, or deep sequencing.

Keywords non-small-cell lung cancer      molecular typing      individualized medicine      molecular-targeted therapy      gene expression profiling     
Corresponding Authors: He Jie,   
Issue Date: 05 June 2013
URL:     OR
Fig.1  Summary of potential molecular targets and therapeutic agents. EGFR, epidermal growth factor receptor; ERK, extracellular signal-regulated kinase; FGFR, fibroblast growth factor receptor; HER, human epidermal growth factor receptor; mTOR, mammalian target of rapamycin; PDGFR, platelet-derived growth factor receptor; PI3K (PIK3CA), phosphatidylinositol 3-kinase; VEGFR, vascular endothelial growth factor receptor. Partial data are obtained from Ausborn [].
Fig.2  Ten driver mutations in 60% (252/422) of lung adenocarcinomas. KRAS, v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog; EGFR, epidermal growth factor receptor; ALK fusion, anaplastic lymphoma kinase fusion gene; BRAF, v-raf murine sarcoma viral oncogene homolog B1; PIK3CA (PI3K), phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha; MET amp, met proto-oncogene amplification; HER2, human epidermal growth factor receptor-2; MEK1 (MAP2K1), mitogen-activated protein kinase kinase 1; NRAS, neuroblastoma RAS viral (v-ras) oncogene homolog; AKT1, v-akt murine thymoma viral oncogene homolog 1. Data were obtained from a report by the Lung Cancer Mutation Consortium [].
TypeSubtypeDescriptionPathwayPotentially relevant therapiesRelevant histologicalsubtypesStrength of evidencefor clinical use
11.1EGFR sensitizingmutationsEGFRTKIs and chemotherapyADCHigh
1.2EGFR resistancemutations includingT790MEGFRDual EGFR/HER2 TKI, c-MET inhibitors+/ - EGFR–TKIs, Hsp90 inhibitors, dual MET/VEGFR2 inhibitors, Chk1 inhibitorsADCHigh
1.3VeriStrat proteomicsignatureEGFRTKIs and bevacizumabADCHigh
2K-ras mutationsK-rasDual MAPK and AKT/PI3K inhibitors, Hsp90 inhibitorsADCHigh
3ALK fusionALK fusionALK inhibitors, Hsp90 inhibitorsADCHigh
44.1c-MET overexpressionc-METc-MET inhibitors, dual Met/VEGFR2 inhibitors, ALK/MET inhibitors, c-MET monoclonal antibodiesADC, SCLC, SCCMedium
4.2c-MET mutationsc-METc-MET inhibitors, dual Met/VEGFR2 inhibitors, ALK/MET inhibitors, c-MET monoclonal antibodiesADC, SCLC, SCC, LCCLow
55.1PI3KCA amplification, mutationsAKT/PI3KPI3K, AKT, mTOR inhibitorsADCLow
5.2PTEN deletions/methylationAKT/PI3KPI3K, AKT, mTOR inhibitorsADCLow
66.1VEGFR overexpressionVEGFRVEGFR inhibitorsSCLCLow
6.2Bcl-2 overexpressionP53/BCLBCL-2 InhibitorsSCLCLow
7ROS1 translocationROS-1ROS1 inhibitorsADC (1.5%)Medium
8Epigenetic alterations-HDAC inhibitors, epigenetic inhibitorswith cytotoxic agents-Low
9IGF alterationsIGFIGF1R monoclonal antibodies, IGF1R TKIsADC,SCC,SCLC-
Tab.1  Molecular classification of lung cancer based on genetic aberrations []
Shibata et al. [7]Hierarchical clustering analysisaCGH55 ADCsClassified three subgroups in ADC
Gordon et al. [8]8-gene signatureMicroarray47 MPMs and 166 ADCsDistinguished MPM from lung cancer
Arai et al. [10]Hot-spot mutation analysisaCGH131 synchronous double lung cancers and 230 T3 tumorsDistinguished second primary lung cancer from metastatic lesion
Giordano et al. [12]20-gene signatureMicroarray57 lung, 51 colon, and 46 ovary ADCsIdentified an organ-specific molecular classification
Garber et al. [14]Hierarchical clustering analysisMicroarray56 lung cancersProvided a molecular basis for the classification of lung cancer into four morphological subtypes
Bhattacharjee et al. [15]Hierarchical clustering analysisOligonucleotide microarray203 lung tumors and 17 normal lung specimensDefined four distinct subclasses of primary lung ADC
Hosseinzadeh et al. [16]Hierarchical clustering analysis and tree induction modelsGene Set Enrichment Analysis databasePublic databasesClassified three classes of lung cancer
Xi et al. [18]318-gene signatureMicroarrayTwo previously published lung ADC microarray data setsPredicted lymph node metastasis in lung ADCs
Takada et al. [20]23-, 55-, 43-, and 35-gene signaturesMicroarray92 NSCLCs (37 SCCs and 55 ADCs)Identified different signatures for predicting lymph node metastasis in NSCLC
West et al. [27]Different driver mutationsRviewNoneIdentified a novel classification of lung cancer into molecular subtypes based on driver mutations
Yamauchi et al. [28]139-gene signatureMicroarray439 ADCs and a publicly available data setPredicted the aggressiveness of lung ADCs irrespective of EGFR mutations
Wan et al. [30]12-gene signatureStatistical methods and machine learning algorithmsThree previously published lung ADC microarray data setsPredicted recurrence risk and chemosensitivity
Chen et al. [31]94-gene signatureMicroarray442 ADCs and two independent NSCLC microarray data setsPredicted survival and chemosensitivity for early stage NSCLC
Hou et al. [33]Hierarchical clustering analysis and a 25-gene signatureMicroarray90 NSCLCsDefined six subclasses of NSCLC and predicted pemetrexed response
Eschrich et al. [35]10-gene signatureMicroarray48 cell lines (including 4 NSCLC cell lines)Predicted radiosensitivity for 12 cell lines (including 3 NSCLC cell lines)
Larsen et al. [37]54-gene signatureMicroarray48 ADCs and two independent ADC microarray data setsPredicted the risk of recurrent disease for early stage lung ADC
Tomida et al. [39]82-gene signatureMicroarray117 ADCs and an independent ADC microarray data setsIdentified ADC patients at very high risk for relapse
Lu et al. [40]51-gene signatureMicroarray142 Stage I ADCs and four independent data setsPredicted tumor recurrence in Stage I NSCLC
Guo et al. [41]37-gene signature and 12-gene signaturePreviously reported gene expression profiles170 ADCsPredicted survival with 96% accuracy; 12-gene signature predicted stage of 94.2% of patients
Lu et al. [42]64-gene signatureMicroarray, RT-PCR324 Stage I NSCLCsPredicted survival with 87% accuracy
Lau et al. [49]3-gene signatureRT-PCR147 Stage I to II NSCLCsPredicted overall survival and classified Stages I and II
Endoh et al. [43]8-gene signatureRT-PCR106 ADCsStratified patients by prognosis
Beer et al. [19]50-gene signatureOligonucleotide arrays170 ADCsPredicted survival
Sun et al. [44]50-gene signatureOligonucleotide arrays390 NSCLCsPredicted survival for NSCLC
Raponi et al. [45]50-gene signatureRT-PCR, microarray165 SCCsPredicted survival for SCC
Roepman et al. [46]72-gene signatureMicroarray241 NSCLCsAssociated the absence of disease and survival
Seike et al. [47]11-cytokine gene signatureRT-PCR120 ADCsPredicted survival
Lu et al. [48]34-microRNA signature and 27-microRNA signatureMicroRNA microarray697 Stage I NSCLCsPredicted relapse-free survival
Wan et al. [30]12-gene signatureMicroarray, RT-PCR442ADCsPredicted survival and chemoresponse in cell lines (NCI-60)
Chen et al. [31]94-gene signatureMicroarray, RT-PCR692 NSCLCsPredicted survival and response to adjuvant chemotherapy
Chen et al. [50]5-gene signatureMicroarray, RT-PCR125 NSCLCsPredicted survival
Xie et al. [52]59-gene signatureMicroarray542 NSCLCsPredicted survival for Stage I
Okayama et al. [51]9-gene signatureMicroarray, RT-PCR343 ADCs with ALK-positive and EGFR/KRAS/ALK-negativePredicted survival for ADCs with ALK-positive and EGFR/KRAS/ALK-negative
Tab.2  Studies evaluating molecular classification of NSCLC
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