Molecular classification of non-small-cell lung cancer: diagnosis, individualized treatment, and prognosis

doi:10.1007/s11684-013-0272-4

Front Med

0, Vol.

Issue () : 157-171 https://doi.org/10.1007/s11684-013-0272-4

REVIEW

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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Abstract

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 Author(s): He Jie,Email:prof.hejie@263.net

Issue Date: 05 June 2013

Cite this article:

Yue Yu,Jie He. Molecular classification of non-small-cell lung cancer: diagnosis, individualized treatment, and prognosis[J]. Front Med, 0, (): 157-171.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-013-0272-4
https://academic.hep.com.cn/fmd/EN/Y0/V/I/157

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 [].

Tab.1 Molecular classification of lung cancer based on genetic aberrations []

Researchers	Signature	Technology	Samples	Results
Shibata et al. [7]	Hierarchical clustering analysis	aCGH	55 ADCs	Classified three subgroups in ADC
Gordon et al. [8]	8-gene signature	Microarray	47 MPMs and 166 ADCs	Distinguished MPM from lung cancer
Arai et al. [10]	Hot-spot mutation analysis	aCGH	131 synchronous double lung cancers and 230 T3 tumors	Distinguished second primary lung cancer from metastatic lesion
Giordano et al. [12]	20-gene signature	Microarray	57 lung, 51 colon, and 46 ovary ADCs	Identified an organ-specific molecular classification
Garber et al. [14]	Hierarchical clustering analysis	Microarray	56 lung cancers	Provided a molecular basis for the classification of lung cancer into four morphological subtypes
Bhattacharjee et al. [15]	Hierarchical clustering analysis	Oligonucleotide microarray	203 lung tumors and 17 normal lung specimens	Defined four distinct subclasses of primary lung ADC
Hosseinzadeh et al. [16]	Hierarchical clustering analysis and tree induction models	Gene Set Enrichment Analysis database	Public databases	Classified three classes of lung cancer
Xi et al. [18]	318-gene signature	Microarray	Two previously published lung ADC microarray data sets	Predicted lymph node metastasis in lung ADCs
Takada et al. [20]	23-, 55-, 43-, and 35-gene signatures	Microarray	92 NSCLCs (37 SCCs and 55 ADCs)	Identified different signatures for predicting lymph node metastasis in NSCLC
West et al. [27]	Different driver mutations	Rview	None	Identified a novel classification of lung cancer into molecular subtypes based on driver mutations
Yamauchi et al. [28]	139-gene signature	Microarray	439 ADCs and a publicly available data set	Predicted the aggressiveness of lung ADCs irrespective of EGFR mutations
Wan et al. [30]	12-gene signature	Statistical methods and machine learning algorithms	Three previously published lung ADC microarray data sets	Predicted recurrence risk and chemosensitivity
Chen et al. [31]	94-gene signature	Microarray	442 ADCs and two independent NSCLC microarray data sets	Predicted survival and chemosensitivity for early stage NSCLC
Hou et al. [33]	Hierarchical clustering analysis and a 25-gene signature	Microarray	90 NSCLCs	Defined six subclasses of NSCLC and predicted pemetrexed response
Eschrich et al. [35]	10-gene signature	Microarray	48 cell lines (including 4 NSCLC cell lines)	Predicted radiosensitivity for 12 cell lines (including 3 NSCLC cell lines)
Larsen et al. [37]	54-gene signature	Microarray	48 ADCs and two independent ADC microarray data sets	Predicted the risk of recurrent disease for early stage lung ADC
Tomida et al. [39]	82-gene signature	Microarray	117 ADCs and an independent ADC microarray data sets	Identified ADC patients at very high risk for relapse
Lu et al. [40]	51-gene signature	Microarray	142 Stage I ADCs and four independent data sets	Predicted tumor recurrence in Stage I NSCLC
Guo et al. [41]	37-gene signature and 12-gene signature	Previously reported gene expression profiles	170 ADCs	Predicted survival with 96% accuracy; 12-gene signature predicted stage of 94.2% of patients
Lu et al. [42]	64-gene signature	Microarray, RT-PCR	324 Stage I NSCLCs	Predicted survival with 87% accuracy
Lau et al. [49]	3-gene signature	RT-PCR	147 Stage I to II NSCLCs	Predicted overall survival and classified Stages I and II
Endoh et al. [43]	8-gene signature	RT-PCR	106 ADCs	Stratified patients by prognosis
Beer et al. [19]	50-gene signature	Oligonucleotide arrays	170 ADCs	Predicted survival
Sun et al. [44]	50-gene signature	Oligonucleotide arrays	390 NSCLCs	Predicted survival for NSCLC
Raponi et al. [45]	50-gene signature	RT-PCR, microarray	165 SCCs	Predicted survival for SCC
Roepman et al. [46]	72-gene signature	Microarray	241 NSCLCs	Associated the absence of disease and survival
Seike et al. [47]	11-cytokine gene signature	RT-PCR	120 ADCs	Predicted survival
Lu et al. [48]	34-microRNA signature and 27-microRNA signature	MicroRNA microarray	697 Stage I NSCLCs	Predicted relapse-free survival
Wan et al. [30]	12-gene signature	Microarray, RT-PCR	442ADCs	Predicted survival and chemoresponse in cell lines (NCI-60)
Chen et al. [31]	94-gene signature	Microarray, RT-PCR	692 NSCLCs	Predicted survival and response to adjuvant chemotherapy
Chen et al. [50]	5-gene signature	Microarray, RT-PCR	125 NSCLCs	Predicted survival
Xie et al. [52]	59-gene signature	Microarray	542 NSCLCs	Predicted survival for Stage I
Okayama et al. [51]	9-gene signature	Microarray, RT-PCR	343 ADCs with ALK-positive and EGFR/KRAS/ALK-negative	Predicted survival for ADCs with ALK-positive and EGFR/KRAS/ALK-negative

Tab.2 Studies evaluating molecular classification of NSCLC

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