- freely available
Microarrays 2013, 2(4), 318-339; doi:10.3390/microarrays2040318
Published: 13 December 2013
Abstract: Microarrays have been used for more than two decades in preclinical research. The tumor transcriptional profiles were analyzed to select cancer-associated genes for in-deep functional characterization, to stratify tumor subgroups according to the histopathology or diverse clinical courses, and to assess biological and cellular functions behind these gene sets. In lung cancer—the main type of cancer causing mortality worldwide—biomarker research focuses on different objectives: the early diagnosis of curable tumor diseases, the stratification of patients with prognostic unfavorable operable tumors to assess the need for further therapy regimens, or the selection of patients for the most efficient therapies at early and late stages. In non-small cell lung cancer, gene and miRNA signatures are valuable to differentiate between the two main subtypes’ squamous and non-squamous tumors, a discrimination which has further implications for therapeutic schemes. Further subclassification within adenocarcinoma and squamous cell carcinoma has been done to correlate histopathological phenotype with disease outcome. Those tumor subgroups were assigned by diverse transcriptional patterns including potential biomarkers and therapy targets for future diagnostic and clinical applications. In lung cancer, none of these signatures have entered clinical routine for testing so far. In this review, the status quo of lung cancer gene signatures in preclinical and clinical research will be presented in the context of future clinical perspectives.
The ultimate goal reducing the high mortality rate in lung cancer disease is strongly linked to an increased efficacy of cancer prevention strategies and screening approaches for risk assessment and early detection of lung cancer in a curable stage. Major risk factors for lung cancer onset are smoking and increasing air pollution in metropolitan areas . Despite enhanced prevention campaigns in the last two decades, lung cancer still represents the second most frequent malignancy and the highest cancer-related death rate in western countries. In 2013, about 228,190 new cases and 159,480 related deaths were estimated for cancer in the lung and bronchus in the United States . About 55%–60% of patients are diagnosed at late incurable stages with distant metastases. As a consequence, the five-year survival rate is only 13%–16% for all stages. Non-small cell lung cancer (NSCLC) is the most common bronchial tumor, which is classified into the two major histological subtypes adenocarcinoma and squamous cell carcinoma. Both subtypes strongly differ in DNA copy number, DNA methylation, gene mutations, transcriptome, proteome and putative biomarkers as outlined in the following chapters. The stratification of diverse lung cancer entities based on clinico-histopathology and molecular alterations also determines disease outcome and therapy options. Despite significant progress in the development of novel targeted therapies, the high mortality rate in lung cancer strongly emphasizes the need for efficient lung cancer prevention and screening approaches, and the better stratification of patients who will benefit from a particular therapy regimen. A survey of clinical studies between 2009 and 2012 indicated enlarged activities of biomarker analyses to almost half of all interventional studies . Biomarker-based patient selection for therapy decision clearly increased up from 7.9%–25.8%. The major goal is to identify and validate specific biomarkers or signatures in lung cancer tissues and patient surrogates, that will shift lung cancer diagnosis towards a curable stage, better stratify patients with resectable tumors for the need of adjuvant therapies, and guide clinicians to select the most beneficial therapy regimens for their patients after first diagnosis and disease progression.
2. Risk Assessment and Early Detection of Lung Cancer
In the National Lung Screening Trial, low-dose computed tomography-based lung cancer screening reduced cancer mortality in high-risk individuals . However, CT screening is also accompanied with a high rate of suspicious cases without confirming a malignancy, cancer over-diagnosis and economic challenges . A future diagnostic testing scenario may also include screening approaches for cancer-related nucleic acid, peptide or metabolic molecules. So far, no molecular test for lung cancer diagnosis has been established in routine health care. Serum proteins like CEA, CYFRA 21-1 or MALDI/MS signatures might have been valid to detect lung cancer subtypes, but did not overcome clinical studies .
An immunobiomarker test (EarlyCDT®-Lung) measuring autoantibodies to a panel of seven antigens (p53, NY-ESO-1, CAGE, GBU4-5, SOX2, HuD, and MAGE A4) in serum has been developed [7,8], and is actually evaluated in a phase 2 trial. This commercially available assay objects the early detection of lung cancer in high-risk individuals (long standing smokers) and risk stratification in patients with pulmonary nodules detected by CT scans. Depending on cut-off criteria, sensitivity (49%) and specificity (93%) leads to the diagnosis of one lung cancer patient from seven positive tested individuals . Another in vitro diagnostic test assay (Epi proLung BL Reflex Assay, Epigenomics, AG) measures SHOX2 DNA methylation in bronchial aspirates of patients which are suspected for lung cancer (78% sensitivity, 96% specificity), and is suggested as diagnostic adjunct when cytology results are negative or suspicious . Additionally, a clinical trial is ongoing to test the accuracy of mediastinal staging by SHOX2 methylation level in transbronchial needle aspiration . Moreover, a 4-gene methylation signature (p16, TERT, WT1, and RASSF1) was reported based on 655 bronchial washings to diagnose lung cancer with 82% sensitivity and 91% specificity . However, it remains a challenge to sequentially combine different methods like CT and genomic signatures in a screening approach and to avoid a high number of false positives.
Circulating miRNAs, robustly detected in serum and plasma, are suggested as promising biomarkers in cancer patients. Different abundance of specific miRNAs was detected in serum and plasma of lung cancer patients, which might improve risk group assessment for further CT and invasive diagnostics [13,14,15,16]. One study outlined a weighted linear combination of the expression levels of 34-miRNAs measured in 253 patients separated in a training, validation and an additional clinical validation cohort finally displaying 71% sensitivity and 90% specificity . A 10-miRNA signature developed in a screening and validation study including serum from 620 NSCLC patients and controls proposed a better accuracy (90% sensitivity and 93% specificity) for early lung cancer detection . Of note, expression of a serum miRNA pair (miR-15b and miR-27b) promised a 100% sensitivity and 84% specificity . The increasing number of studies reporting circulating miRNAs as putative biomarkers in cancer patients indicates the great potential in biomarker discovery and translational research. Blood cells were also analyzed for non-invasive biomarkers. For example, a large gene classifier was identified and validated in blood of 233 patients using Illumina microarrays . In contrast to serum and plasma, overall RNA expression will be strongly affected by various compositions of blood cell types.
Molecular alterations have been associated with the individual risk for lung tissue damage and tumorigenesis. The comparison between lung cancer and benign tissues revealed diverse transcriptional profiles and putative diagnostic biomarkers [18,19,20]. For example, comprehensive meta-analysis of 20 studies comprising over 1100 lung tumor and benign tissues resulted in a robust tumor-associated 15-miRNA signature in NSCLC . miRNA detection methods include both microarray and qPCR technology. Technical studies revealed a higher variation of miRNA quantification between different microarray platforms compared with qPCR and sequencing, and proposed a higher sensitivity and specificity for qPCR-based miRNA expression analysis [21,22]. Recently, an 8-miRNA signature (miR-96, miR-450a, miR-183, miR-9, miR-577, Let-7i, miR-27b miR-34a) was proposed to diagnose NSCLC in minimal biopsy material . In non-tumor lung tissues of 853 lung cancer patients large and consistent gene expression variances caused by smoking were identified by using microarray technology . Furthermore, gene expression changes along the airways were analyzed in order to investigate if easily accessible epithelial cells like in the nose, mouth or main bronchus reflect early oncogenic alteration in lung tissues caused by toxic agents like cigarette smoke [25,26]. Here, a prospective study (DECAMP-1) was started in 2013 aiming at the validation of gene, protein and cytokine signatures identified in bronchial airway and serum of cancer patients .
Alternative lung specimens like endobronchial epithelial lining fluid, bronchial lavage and sputum are collected for biomarker research. For example, DNA methylation changes, specific gene and miRNA expression signatures were identified in the presence of cancer cells [28,29,30]. However, standardization of sampling procedures for these approaches is much more challenging, because the general health condition and comorbidities strongly affect sample recovery and cellular components.
As non-invasive approach, GC/MS or electronic noses were used to identify diverse signatures of volatile organic compounds (VOCs) in exhaled breath of cancer patients compared to healthy individuals [31,32,33]. Distinct VOC profiles could be assigned to patients suffering from lung cancer. In addition to analytical techniques, the excellent olfactory sense of dogs might contribute to early lung cancer detection . Here, a phase-2 trial was terminated in 2013 because of inconsistent training status of sniffer dogs. Such diagnostic methods have the potential to improve the stratification of high-risk individuals and suspicious cases before invasive diagnostic bronchoscopy. Ongoing validation of diagnostic biomarker profiles and an earlier focus on standardization parameters of the techniques are prerequisites for application into clinical practice.
3. Molecular Stratification of Non-Small Cell Lung Cancer Subtypes and Outcome
3.1. Molecular Profiling of NSCLC Tumor Subtypes
After lung cancer detection, clinicopathological parameters like tumor histology, staging and localization of metastases determine the disease outcome and current therapeutic interventions [35,36]. The clinical practice guidelines differentiate between small cell lung cancer (SCLC) and NSCLC, and between the major NSCLC subtypes squamous and non-squamous cell carcinoma assessed by standard histopathology. Concerning the status of present blood biomarkers, different serum levels and ratios of ProGRP, CEA, SCC, CA 125, CYFRA 21-1 and NSE have been proposed to distinguish between the two major NSCLC subtypes adenocarcinoma and squamous cell carcinoma, and SCLC, respectively . Here, the precise determination of protein isoform signatures in lung cancer patients may further improve testing accuracy . Recently, Roche Diagnostics launched Elecsys ProGRP test for a more precise diagnosis of SCLC from patients’ serum and plasma.
The access of resected tumor material enables a comprehensive cancer-cell related diagnostics and molecular profiling. In the past, numerous microarray-based profiles were reported after millennium. Diagnostic and prognostic gene signatures using gene expression microarrays were outlined in Tables A1 and A2. Based on transcriptional profiles lung adenocarcinomas were stratified in molecular subgroups proposing diverse cellular characteristics and prognosis [18,39,40,41,42,43]. Gene expression patterns were associated with diverse adenocarcinoma subtypes named bronchioid, squamoid, and magnoid, dependent on transcriptomic similarities with histologically defined bronchioalveolar carcinoma, squamous cell carcinoma, and large-cell carcinoma . These expression profiles were further investigated across six independent studies and about 1,000 patients . Here, distinct molecular alterations, mutations, copy number variation and methylation could be assigned to these intrinsic subtypes with implications for further therapy modalities. Another study differentiated between two different molecular subtypes with respect to a prognostic 193-gene signature . Recently, architectural classification of invasive pulmonary adenocarcinomas described five predominant patterns and has been shown to be a stage-independent predictor of survival [45,46]. Few molecular markers like TTF-1 have been investigated across IASLC/ATS/ERS adenocarcinoma architectures and were associated with disease recurrence [47,48]. The occurrence of different histological pattern in one tumor impedes the evaluation of the predominant architecture. Moreover, the histopathological AC patterns do not directly correspond with the molecular subtypes mentioned above. However, gene expression analysis of distinct architectural patterns upon tissue microdissection is under way to select for specific signatures and novel targets.
Similar subtype analyses were done for squamous cell carcinomas of the lung predicting different survival outcomes [19,49,50]. The stratification of four different SCC gene expression subtypes named primitive, classical, secretory and basal generated by a nearest-centroid predictor from microarray data was reproducible across independent microarray and RNA sequencing datasets including about 600 SCCs [50,51]. The primitive subtype was associated with the worst survival outcome. Furthermore, gene expression patterns in distinct molecular subtypes were attributed to the activation status of biological and cellular processes, and oncogenic pathways. A better understanding of the relationship between histopathological diversification of lung tumors, molecular characteristics and disease outcome will contribute to molecular pathology and biomarker development in the future.
Nowadays, comprehensive genomic and transcriptomic sequencing allows an integrative analysis of gene expression, mutations, copy number variations and DNA methylation to assign more complex signatures in tumor subgroups [44,51]. In lung cancer clinics, invasive tissue sampling challenges the evaluation of tumor histology. Especially in small biopsy samples from advanced tumors, specific biomarkers and signatures would be highly useful to guide tumor stratification and outcome prediction. For example, a histology expression predictor for adenocarcinoma, carcinoid, small cell carcinoma, and squamous cell carcinoma was developed using RT-qPCR in FFPE samples . Direct comparison between molecular predictor and pathologist indicated similar accuracy and precision of the biomarker approach. Several qPCR- and microarray-based studies identified miRNA patterns specific for lung cancer subtypes. For example, stratification of adenocarcinoma and SCC histology was done by a 34-miRNA panel measured in FFPE specimen from 205 male smokers . Moreover, two small miRNA panels measured in FFPE samples and bronchial brushing lung specimens have been reported to discriminate SCLC from NSCLC, and SCC from adenocarcinoma (AUC = 0.94–0.99), respectively . Similar accuracy has been achieved by an overlapping 8-miRNA panel measured in preoperative cytologic samples . Ongoing comprehensive screening for specific gene expression signatures, driver mutations and other genomic alterations are promising to identify biomarkers and targets for therapeutic intervention in distinct tumor architectures.
3.2. Gene Signatures Associated with Disease Prognosis and Outcome
In the cited microarray studies above, differences between histological and molecular tumor subgroups were often characterized by large gene signatures, which are helpful for the understanding of tumor progression and differentiation but very challenging for the translation into molecular diagnostics. In addition, the tumor-intrinsic subclasses are not implicitly associated with disease outcome. At early tumor stage, stringent signatures are needed to predict individual relapse risk and survival, and to support further therapy decisions. Several microarray study reports stated diverse prognostic gene signatures in adenocarcinoma [39,56,57,58,59], SCC [49,60], or NSCLC in general [58,61,62,63,64,65,66,67]. Prognostic classifier was usually generated from a single microarray dataset by applying a Cox model (endpoints survival time or relapse time) to a training set, and validated in an independent test set or by leave-one-out-cross-validation. Thus, most of the signatures are linked to the local study protocols including patient cohort selection, starting biomaterial, microarray platform and statistics, variables, which explain the large divergence of contributing genes and the challenge of reproducibility. The accuracy and robustness of few gene signatures were successfully tested across independent microarray datasets and study centers. Based on multi-site microarray profiles from 442 adenocarcinomas, it has been shown that the predictive value of gene signatures can profit from the addition of clinical covariates . The large collection of microarray datasets from lung cancer tissues provides a valuable source for the selection and validation of clinically relevant gene signatures. This is also reflected by numerous meta-analysis studies integrating public microarray data for searching and validating prognostic gene sets [42,68,69,70,71,72,73,74]. For example, a 64-gene signature predicting survival of stage I NSCLC patients was derived from a consensus expression set of 4,905 genes across seven different microarray datasets .
Small prognostic and predictive gene sets in early-stage NSCLC were described to be applicable to standardized analytical techniques like qPCR technology [63,75,76,77,78,79,80,81,82]. Chen and colleagues validated a five-gene signature (DUSP6, MMD, STAT1, ERBB3, and LCK) for relapse-free and overall survival across 271 stage I-III NSCLC tumors . It was proposed to test in prospective, large-scale, multicenter studies if patients with a high-risk gene signature might benefit from adjuvant therapy. Furthermore, a 14-gene quantitative PCR assay on formalin-fixed paraffin-embedded tissue predicting survival in resected stage I adenocarcinoma was developed in a cohort of 361 patients and validated in two independent cohorts comprising nearly 1,439 patients . Of note, this assay was successful in ethnically distinct cohorts including US and Chinese lung cancer patients.
Moreover, several prognostic miRNA signatures in lung cancer tissues have been described [53,84,85,86]. For example, a five miRNA signature (let-7a, miR-221, miR-137, miR-372, and miR-182*) was generated from fresh frozen tumor tissues of a training set (n = 56) and tested in two independent patient cohorts (n = 118) to be associated with survival and cancer relapse in NSCLC patients . Similarly, Landis and colleagues reported another five miRNA signature (miR-25, miR-34c-5p, miR-191, let-7e, and miR-34a) measured in FFPE tissues predicting survival (p = 0.017) in squamous cell carcinoma patients . Two further prognostic miRNA signatures were proposed after microarray profiling of tissues from 527 stage I NSCLC patients dependent on the inclusion of AC and SCC subtypes . As outline for gene signatures above, diverse miRNA signatures are likely reasoned by differences in the selected patient cohorts, biomaterials, platform technologies and statistics. Of note, five frequently reported miRNAs (miR-21, miR-29b, miR-34a/b/c, miR-155 and let-7a) could not be confirmed as prognostic or predictive biomarker in a large cohort (IALT trial) including 639 patients with resectable NSCLC receiving adjuvant chemotherapy . A future diagnostic test based on tissue sections has to consider feasible tissue repository in the clinics, and valid standards to evaluate biomarker molecule quality and tumor cell content.
Distinct circulating miRNA signatures were detected in serum or plasma from early-stage NSCLC patients and associated with recurrence risk and survival [88,89,90]. For example, a signature of four ‘high-risk’ serum miRNAs (miR-486, miR-1, miR-499, miR-30d) was reported to predict overall survival in NSCLC patients (n = 303) after surgery and adjuvant chemotherapy . For advanced NSCLC, a combined 17-miRNA signature was able to calculate a 2.5-fold increased risk of death between low- and high-risk score patients . The usage of specific blood biomarkers for risk assessment would facilitate diagnostics, especially for inoperable NSCLC patients where the access of representative tumor tissue is difficult.
4. Predictive Biomarkers for Lung Cancer Therapies
4.1. Prognostic and Predictive Biomarkers for Systemic Therapies
Adjuvant therapies are recommended for patients with operable lung cancer dependent on tumor staging. However, the stratification of those patients is still not accurate with regard to the relapse risk and individual response. In other words, no clinical parameter or biomarker is available to predict complete cure after removal of early-stage lung tumor, or to calculate effectiveness of adjuvant chemotherapy in preventing disease relapse. Numerous prognostic signatures in tumor tissues or patient surrogates of early disease patients were generated to potentially improve therapy management if adapted to clinical routine. It has been shown that single biomarkers often failed to efficiently predict therapy response. The well-studied DNA repair genes ERCC1 and RRM1 as predictive biomarkers for chemotherapy did not enter clinical routine . Weakness in the performance of detection methods and the intratumoral heterogeneity of biomarkers limited the value of single biomarkers like ERCC1 [93,94]. Cross-validation analysis of Mucin by immunohistochemistry staining in 780 patients indicated that the biomarker was not predictive for overall survival after chemotherapy . Similarly, the presence of KRAS mutations was not recommended to select patient for adjuvant chemotherapy . In advanced NSCLC patients, the immunohistochemical status of beta-3 tubulin was not predictive for the benefit of ixabepilone- or paclitaxel-containing regimens reported in a phase II study . In SCLC patients, a combination of serum biomarkers like nucleosomes, NSE, ProGRP and CYFRA 21-1 achieved up to 47% sensitivity at 95% specificity to predict insufficient response to first-line chemotherapy .
Of note, several prognostic signatures reviewed in the paragraph before were addressed to the need of adjuvant chemotherapy in early-stage lung cancer patients. For example, a 15-gene signature in tumor tissues from stage IB and II NSCLC patients was reported to predict the benefit from adjuvant chemotherapy . An ongoing NCI observation study is recorded to further validate this signature in FFPE specimen by using quantitative nuclease protection and NanoString assays. Moreover, a 12-gene signature predicting the benefit from adjuvant chemotherapy with cisplatin/ vinorelbine was identified by integrative analysis of genetic aberration, genome-wide RNAi data, and mRNA expression data, and successfully validated in two independent datasets . Both retrospective studies integrated published microarray data in order to validate their predictive gene signatures. The variable validation success rates might be reasoned by different microarray platforms, gene probe qualities, and heterogeneous patient cohorts.
Biomarker studies accompanying radiotherapy or immunotherapy are rare. Several studies suggested putative biomarkers to predict the response or toxicity upon radiotherapy [100,101,102,103]. For example, a gene expression classifier was calculated to predict radiosensitivity by comparing microarray expression profiles of the NCI 60 cell line panel and clonogenic survival assay outcome after 2 Gy of radiation . In NSCLC patients, a blood biomarker panel (CRP, LDH, Osteopontin, CA-9 IL-6, IL-8, CEA, CYFRA 21-1, and α-2M) has been successfully tested to predict survival after (chemo-) radiotherapy , and a prospective clinical trial has been currently started to correlate blood biomarkers with overall survival. Similarly, a decrease of serum ProGRP has been associated with response to chemo- and radio-chemotherapy in SCLC . So far, the activation status of oncogenic drivers like EGFR is most conclusive to contribute to radiotherapy efficacy . It has been shown that targeting EGFR pathway increased radiosensitivity of tumor cell [107,108]. In contrast, radiation itself may activate diverse oncogenic pathways and benefit disease relapse . Therefore, it is important to better understand the interaction between radiotherapy and oncogenic signaling in tumor cells. Based on phase II trials including melanoma and NSCLC patients treated with immunotherapeutic recombinant MAGE-A3 protein, an 84-gene gene signature was associated with clinical response . The further validation of this gene signature was announced for two phase III trials. Diverse immune signatures were compared and further dissected for their contribution of the tumor genome, host genetic background and environmental factors [111,112]. The increasing number of clinical trials focusing on immunotherapies may strongly benefit from valid predictive biomarkers.
4.2. Prognostic and Predictive Biomarkers for Targeted Therapies
Substantial progress has been achieved in the field of targeted therapies for lung cancer. At advanced inoperable tumor stage, molecular pathology plays an increasing role for tumor characterization, target identification and individualized therapy options. The era of high-throughput tumor genome sequencing and personalized medicine enables a further classification into molecular subtypes based on activated, therapeutically targetable oncogenes. So far, more than 50% of adenocarcinoma and squamous cell carcinoma can be characterized by mutations, fusion genes or amplifications leading to driver activation with potentially effective targeted drugs . Tumor histology guides driver mutation testing and the ability of targeted therapy approaches. For lung adenocarcinoma, EGFR mutation and ALK rearrangement testing is recommended, KRAS mutation testing is suggested by the NCCN guidelines . The biomarker testing is further specified by frequent mutations and reliable analytical techniques . Tumors with EGFR mutations preferentially respond to EGFR tyrosine kinase inhibitors (TKIs), tumors with ALK rearrangements are associated with response to crizotinib [115,116]. The value of the most frequently mutated gene KRAS as predictive biomarker for EGFR-TKI insensitivity is controversially discussed [117,118]. Further targeted drugs are investigated in clinical trials for molecular subtypes harboring BRAF, PIK3CA or HER2 mutations, ROS1 or RET rearrangements, or c-MET amplification . In about 35% of squamous cell carcinoma, aberrant FGFR1, PDGFRA, AKT1 or DDR2 are putative drug targets for individualized therapy schemes.
In a simplified diagnostic scenario, genomic alterations in lung cancer are tested for relevant drivers in order to apply suitable drugs. Back to reality, the assessment of an individual drug scheme is impeded by limited predictive value of present biomarkers, less robust testing techniques, pressure of therapy timing, and subsequently initial and acquired resistance. For example, a significant number of EGFR wild-type tested lung cancer patients respond to EGFR-TKIs . In contrast, 30%–40% of patients with EGFR-mutated tumors do not respond to this therapy, and most of the responders develop resistance after few months . Based on specific peaks in mass spectrometry, a commercial serum/plasma-based assay (VeriStrat®) was developed to predict response to EGFR TKI therapy, and retrospectively tested in 441 patients of the BR.21 phase III trial comparing outcome of erlotinib versus placebo treatment . In this study the test was prognostic for progression-free and overall survival, but was not able to predict for differential survival benefit from erlotinib. The stratification of patients for EGFR-TKI drug sensitivity depends on the mutation type of the target itself, the activation status of EGFR downstream actors and potential bypass signaling . A better understanding of the acquired resistance mechanism can disclose novel therapy options using combinatorial treatments to prevent bypass signaling, or to assess a suitable therapy after relapse against the novel acquired molecular subtype . Thus, additional predictive biomarkers are urgently needed to improve patient stratification and to suggest targets for mono- or combinatorial therapies for the primary tumor and after disease relapse.
Microarrays have been used to identify gene signatures associated with driver mutations and response to targeted therapies [123,124,125,126]. A 76-gene signature associated with epithelial-mesenchymal transition was generated from gene expression profiles of cell lines and tissues of NSCLC patients, and proposed to predict resistance to EGFR and PI3K inhibitors . Recently, a 47-gene signature associated with sorafenib sensitivity was retrospectively analyzed based on the BATTLE trial . The signature was reported to serve as additional biomarker for the definition of a subgroup of patients with tumors wild-type for EGFR that may benefit from sorafenib treatment. Furthermore, a large gene set derived from microarrays well stratified lung adenocarcinomas in one ALK-mutated and two EGFR/KRAS/ALK-mutation negative subgroups . Based on distinct profiles, novel target candidates have been identified in patients of a triple-negative subgroup with worse prognosis. Large comprehensive genomic studies in lung cancer are ongoing to precisely define clinically relevant tumor subtypes by combining histopathology, mutation status, DNA copy number variation, gene and protein expression, and disease outcome. Future lung cancer diagnosis and therapy will benefit from a continuous histological and molecular characterization of the tumor, and its diversity and mutability during an individual disease course, to be one step ahead of the beast.
5. Conclusions and Outlook
In the last years, the number of clinical trials accompanied by biomarker studies has been continuously increasing . The implementation of novel therapies more and more depend on a parallel development of biomarkers for patient stratification. So far, the translation of promising findings from biomarker research studies into valid test assays is an exceptional case for lung cancer. None of the aforementioned diagnostic or prognostic biomarkers and signatures is implemented in actual lung cancer clinical practice guidelines . For advanced lung cancer, immunohistochemistry staining of protein markers can help to assess tumor histology in small biopsies and limited biomaterial . In the case of non-squamous NSCLC EGFR and ALK testing are recommended by the NCCN guidelines to stratify patients for targeted therapy approaches, and hopefully represent the starting point for a wide range of targeted therapy options in future.
The progress in chip-based molecular stratification of breast cancer patients for therapeutic intervention indicates the potential of molecular diagnostics for cancer patient care [128,129,130]. An evaluation of six different genomic tests also emphasizes the need of large prospective randomized trial and the potential benefit of integrated clinicopathological factors . The controversial debates and the reservation against genomic tests may also reflect social-economic challenges and competition with well-established clinicopathological standards. In the context of intratumoral heterogeneity and clonal selection throughout disease course, it is very likely that a combination of molecular biomarkers and clinicopathological factors can increase the power of diagnostic tests and therapy decision.
The knowledge about histopathological and molecular subtypes cleared the way for genomic testing of specific drivers beneficial for a small subset of affected lung cancer patients. In contrast, the statistical requirements for diagnostic and prognostic molecular biomarkers in most of the studies included both a high sensitivity and specificity in an epidemiologically or clinically defined risk population. Of course, a high specificity is very important to avoid a large number of suspicious cases, which would not be manageable in further clinical programs. However, do we really need a high sensitivity? If a biomarker approach for early diagnosis would be able to shift one third of advanced lung cancer diagnoses towards a potentially curable stage, this would have dramatic consequences on therapy options and cancer mortality. The numerous preclinical and clinical studies reporting diagnostic, prognostic, and predictive biomarkers and signatures well reflect the huge activity in the fields of tumor detection, prognostic stratification and molecular subtyping. So far, the clinical utility of many reported microarray-based prognostic gene signatures in lung cancer is questionable . The future translation of genomic tests into clinical practice will strongly depend on the answer to the unambiguous clinical question, the inclusion criteria of the target population, the availability of required biomaterial, robust analytical techniques and standards, and the validation of the biomarker assay in large, prospective, randomized trials.
The author thanks Holger Sültmann, Helen Hülsmann and Sajo Kaduthanam from the Unit Cancer Genome Research at the DKFZ, Michael Meister and Thomas Muley from the Section Translational Research at the Thoraxklinik Heidelberg, and Rainer Hipfel for discussion about biomarker research and challenges in lung cancer.
Conflicts of Interest
The author declares no conflict of interest.
- Alberg, A.J.; Brock, M.V.; Ford, J.G.; Samet, J.M.; Spivack, S.D. Epidemiology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest 2013, 143, e1S–e29S. [Google Scholar] [CrossRef]
- Siegel, R.; Naishadham, D.; Jemal, A. Cancer statistics, 2013. Cancer J. Clin. 2013, 63, 11–30. [Google Scholar] [CrossRef]
- Subramanian, J.; Regenbogen, T.; Nagaraj, G.; Lane, A.; Devarakonda, S.; Zhou, G.; Govindan, R. Review of ongoing clinical trials in non-small-cell lung cancer: A status report for 2012 from the ClinicalTrials.gov Web site. J. Thoracic Oncol. 2013, 8, 860–865. [Google Scholar] [CrossRef]
- Kovalchik, S.A.; Tammemagi, M.; Berg, C.D.; Caporaso, N.E.; Riley, T.L.; Korch, M.; Silvestri, G.A.; Chaturvedi, A.K.; Katki, H.A. Targeting of low-dose CT screening according to the risk of lung-cancer death. N. Engl. J. Med. 2013, 369, 245–254. [Google Scholar] [CrossRef]
- Aberle, D.R.; Abtin, F.; Brown, K. Computed tomography screening for lung cancer: Has it finally arrived? Implications of the national lung screening trial. J. Clin. Oncol. 2013, 31, 1002–1008. [Google Scholar] [CrossRef]
- Hassanein, M.; Callison, J.C.; Callaway-Lane, C.; Aldrich, M.C.; Grogan, E.L.; Massion, P.P. The state of molecular biomarkers for the early detection of lung cancer. Cancer Prev. Res. 2012, 5, 992–1006. [Google Scholar] [CrossRef]
- Chapman, C.J.; Healey, G.F.; Murray, A.; Boyle, P.; Robertson, C.; Peek, L.J.; Allen, J.; Thorpe, A.J.; Hamilton-Fairley, G.; Parsy-Kowalska, C.B.; et al. EarlyCDT®-Lung test: Improved clinical utility through additional autoantibody assays. Tumour Biol. 2012, 33, 1319–1326. [Google Scholar] [CrossRef]
- Lam, S.; Boyle, P.; Healey, G.F.; Maddison, P.; Peek, L.; Murray, A.; Chapman, C.J.; Allen, J.; Wood, W.C.; Sewell, H.F.; et al. EarlyCDT-Lung: An immunobiomarker test as an aid to early detection of lung cancer. Cancer Prev. Res. 2011, 4, 1126–1134. [Google Scholar] [CrossRef]
- Macdonald, I.K.; Murray, A.; Healey, G.F.; Parsy-Kowalska, C.B.; Allen, J.; McElveen, J.; Robertson, C.; Sewell, H.F.; Chapman, C.J.; Robertson, J.F. Application of a high throughput method of biomarker discovery to improvement of the EarlyCDT®-Lung test. PLoS One 2012, 7. [Google Scholar] [CrossRef]
- Dietrich, D.; Kneip, C.; Raji, O.; Liloglou, T.; Seegebarth, A.; Schlegel, T.; Flemming, N.; Rausch, S.; Distler, J.; Fleischhacker, M.; et al. Performance evaluation of the DNA methylation biomarker SHOX2 for the aid in diagnosis of lung cancer based on the analysis of bronchial aspirates. Int. J. Oncol. 2012, 40, 825–832. [Google Scholar]
- Darwiche, K.; Zarogoulidis, P.; Baehner, K.; Welter, S.; Tetzner, R.; Wohlschlaeger, J.; Theegarten, D.; Nakajima, T.; Freitag, L. Assessment of SHOX2 methylation in EBUS-TBNA specimen improves accuracy in lung cancer staging. Ann. Oncol. 2013, 24, 2866–2870. [Google Scholar] [CrossRef]
- Nikolaidis, G.; Raji, O.Y.; Markopoulou, S.; Gosney, J.R.; Bryan, J.; Warburton, C.; Walshaw, M.; Sheard, J.; Field, J.K.; Liloglou, T. DNA methylation biomarkers offer improved diagnostic efficiency in lung cancer. Cancer Res. 2012, 72, 5692–5701. [Google Scholar] [CrossRef]
- Bianchi, F.; Nicassio, F.; Marzi, M.; Belloni, E.; Dall’olio, V.; Bernard, L.; Pelosi, G.; Maisonneuve, P.; Veronesi, G.; di Fiore, P.P. A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. EMBO Mol. Med. 2011, 3, 495–503. [Google Scholar] [CrossRef]
- Boeri, M.; Verri, C.; Conte, D.; Roz, L.; Modena, P.; Facchinetti, F.; Calabro, E.; Croce, C.M.; Pastorino, U.; Sozzi, G. MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer. Proc. Natl. Acad. Sci. USA 2011, 108, 3713–3718. [Google Scholar] [CrossRef]
- Chen, X.; Hu, Z.; Wang, W.; Ba, Y.; Ma, L.; Zhang, C.; Wang, C.; Ren, Z.; Zhao, Y.; Wu, S.; et al. Identification of ten serum microRNAs from a genome-wide serum microRNA expression profile as novel noninvasive biomarkers for nonsmall cell lung cancer diagnosis. Int. J. Cancer 2012, 130, 1620–1628. [Google Scholar] [CrossRef]
- Hennessey, P.T.; Sanford, T.; Choudhary, A.; Mydlarz, W.W.; Brown, D.; Adai, A.T.; Ochs, M.F.; Ahrendt, S.A.; Mambo, E.; Califano, J.A. Serum microRNA biomarkers for detection of non-small cell lung cancer. PLoS One 2012, 7. [Google Scholar] [CrossRef]
- Zander, T.; Hofmann, A.; Staratschek-Jox, A.; Classen, S.; Debey-Pascher, S.; Maisel, D.; Ansen, S.; Hahn, M.; Beyer, M.; Thomas, R.K.; et al. Blood-based gene expression signatures in non-small cell lung cancer. Clin. Cancer Res. 2011, 17, 3360–3367. [Google Scholar] [CrossRef]
- Bhattacharjee, A.; Richards, W.G.; Staunton, J.; Li, C.; Monti, S.; Vasa, P.; Ladd, C.; Beheshti, J.; Bueno, R.; Gillette, M.; et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl. Acad. Sci. USA 2001, 98, 13790–13795. [Google Scholar] [CrossRef]
- Inamura, K.; Fujiwara, T.; Hoshida, Y.; Isagawa, T.; Jones, M.H.; Virtanen, C.; Shimane, M.; Satoh, Y.; Okumura, S.; Nakagawa, K.; et al. Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization. Oncogene 2005, 24, 7105–7113. [Google Scholar] [CrossRef]
- Vosa, U.; Vooder, T.; Kolde, R.; Vilo, J.; Metspalu, A.; Annilo, T. Meta-analysis of microRNA expression in lung cancer. Int. J. Cancer 2013, 132, 2884–2893. [Google Scholar] [CrossRef]
- Jang, J.S.; Simon, V.A.; Feddersen, R.M.; Rakhshan, F.; Schultz, D.A.; Zschunke, M.A.; Lingle, W.L.; Kolbert, C.P.; Jen, J. Quantitative miRNA expression analysis using fluidigm microfluidics dynamic arrays. BMC Genomics 2011, 12. [Google Scholar] [CrossRef]
- Pradervand, S.; Weber, J.; Lemoine, F.; Consales, F.; Paillusson, A.; Dupasquier, M.; Thomas, J.; Richter, H.; Kaessmann, H.; Beaudoing, E.; et al. Concordance among digital gene expression, microarrays, and qPCR when measuring differential expression of microRNAs. BioTechniques 2010, 48, 219–222. [Google Scholar] [CrossRef]
- Bediaga, N.G.; Davies, M.P.; Acha-Sagredo, A.; Hyde, R.; Raji, O.Y.; Page, R.; Walshaw, M.; Gosney, J.; Alfirevic, A.; Field, J.K.; et al. A microRNA-based prediction algorithm for diagnosis of non-small lung cell carcinoma in minimal biopsy material. Br. J. Cancer 2013, 109, 2404–2411. [Google Scholar] [CrossRef]
- Bosse, Y.; Postma, D.S.; Sin, D.D.; Lamontagne, M.; Couture, C.; Gaudreault, N.; Joubert, P.; Wong, V.; Elliott, M.; van den Berge, M.; et al. Molecular signature of smoking in human lung tissues. Cancer Res. 2012, 72, 3753–3763. [Google Scholar] [CrossRef]
- Beane, J.; Vick, J.; Schembri, F.; Anderlind, C.; Gower, A.; Campbell, J.; Luo, L.; Zhang, X.H.; Xiao, J.; Alekseyev, Y.O.; et al. Characterizing the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq. Cancer Prev. Res. 2011, 4, 803–817. [Google Scholar] [CrossRef]
- Spira, A.; Beane, J.E.; Shah, V.; Steiling, K.; Liu, G.; Schembri, F.; Gilman, S.; Dumas, Y.M.; Calner, P.; Sebastiani, P.; et al. Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nat. Med. 2007, 13, 361–366. [Google Scholar] [CrossRef]
- Brothers, J.F.; Hijazi, K.; Mascaux, C.; El-Zein, R.A.; Spitz, M.R.; Spira, A. Bridging the clinical gaps: Genetic, epigenetic and transcriptomic biomarkers for the early detection of lung cancer in the post-National Lung Screening Trial era. BMC Med. 2013, 11. [Google Scholar] [CrossRef]
- Kahn, N.; Meister, M.; Eberhardt, R.; Muley, T.; Schnabel, P.A.; Bender, C.; Johannes, M.; Keitel, D.; Sultmann, H.; Herth, F.J.; et al. Early detection of lung cancer by molecular markers in endobronchial epithelial-lining fluid. J. Thoracic Oncol. 2012, 7, 1001–1008. [Google Scholar] [CrossRef]
- Leng, S.; Do, K.; Yingling, C.M.; Picchi, M.A.; Wolf, H.J.; Kennedy, T.C.; Feser, W.J.; Baron, A.E.; Franklin, W.A.; Brock, M.V.; et al. Defining a gene promoter methylation signature in sputum for lung cancer risk assessment. Clin. Cancer Res. 2012, 18, 3387–3395. [Google Scholar] [CrossRef]
- Yu, L.; Todd, N.W.; Xing, L.; Xie, Y.; Zhang, H.; Liu, Z.; Fang, H.; Zhang, J.; Katz, R.L.; Jiang, F. Early detection of lung adenocarcinoma in sputum by a panel of microRNA markers. Int. J. Cancer 2010, 127, 2870–2878. [Google Scholar] [CrossRef]
- Bajtarevic, A.; Ager, C.; Pienz, M.; Klieber, M.; Schwarz, K.; Ligor, M.; Ligor, T.; Filipiak, W.; Denz, H.; Fiegl, M.; et al. Noninvasive detection of lung cancer by analysis of exhaled breath. BMC Cancer 2009, 9. [Google Scholar] [CrossRef]
- Phillips, M.; Altorki, N.; Austin, J.H.; Cameron, R.B.; Cataneo, R.N.; Greenberg, J.; Kloss, R.; Maxfield, R.A.; Munawar, M.I.; Pass, H.I.; et al. Prediction of lung cancer using volatile biomarkers in breath. Canc. Biomarkers 2007, 3, 95–109. [Google Scholar]
- Ulanowska, A.; Kowalkowski, T.; Trawinska, E.; Buszewski, B. The application of statistical methods using VOCs to identify patients with lung cancer. J. Breath Res. 2011, 5. [Google Scholar] [CrossRef]
- McCulloch, M.; Turner, K.; Broffman, M. Lung cancer detection by canine scent: Will there be a lab in the lab? Eur. Respir. J. 2012, 39, 511–512. [Google Scholar] [CrossRef]
- Ettinger, D.S.; Akerley, W.; Borghaei, H.; Chang, A.C.; Cheney, R.T.; Chirieac, L.R.; D’Amico, T.A.; Demmy, T.L.; Ganti, A.K.; Govindan, R.; et al. Nccn, non-small cell lung cancer. J. Natl. Compr. Cancer Netw. 2012, 10, 1236–1271. [Google Scholar]
- Kalemkerian, G.P.; Akerley, W.; Bogner, P.; Borghaei, H.; Chow, L.Q.; Downey, R.J.; Gandhi, L.; Ganti, A.K.; Govindan, R.; Grecula, J.C.; et al. Small cell lung cancer. J. Natl. Compr. Cancer Netw. 2013, 11, 78–98. [Google Scholar]
- Molina, R.; Auge, J.M.; Bosch, X.; Escudero, J.M.; Vinolas, N.; Marrades, R.; Ramirez, J.; Carcereny, E.; Filella, X. Usefulness of serum tumor markers, including progastrin-releasing peptide, in patients with lung cancer: correlation with histology. Tumour Biol. 2009, 30, 121–129. [Google Scholar] [CrossRef]
- Torsetnes, S.B.; Nordlund, M.S.; Paus, E.; Halvorsen, T.G.; Reubsaet, L. Digging deeper into the field of the small cell lung cancer tumor marker ProGRP: A method for differentiation of its isoforms. J. Proteome Res. 2013, 12, 412–420. [Google Scholar] [CrossRef]
- Beer, D.G.; Kardia, S.L.; Huang, C.C.; Giordano, T.J.; Levin, A.M.; Misek, D.E.; Lin, L.; Chen, G.; Gharib, T.G.; Thomas, D.G.; et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 2002, 8, 816–824. [Google Scholar]
- Garber, M.E.; Troyanskaya, O.G.; Schluens, K.; Petersen, S.; Thaesler, Z.; Pacyna-Gengelbach, M.; van de Rijn, M.; Rosen, G.D.; Perou, C.M.; Whyte, R.I.; et al. Diversity of gene expression in adenocarcinoma of the lung. Proc. Natl. Acad. Sci. USA 2001, 98, 13784–13789. [Google Scholar] [CrossRef]
- Hayes, D.N.; Monti, S.; Parmigiani, G.; Gilks, C.B.; Naoki, K.; Bhattacharjee, A.; Socinski, M.A.; Perou, C.; Meyerson, M. Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. J. Clin. Oncol. 2006, 24, 5079–5090. [Google Scholar] [CrossRef]
- Park, Y.Y.; Park, E.S.; Kim, S.B.; Kim, S.C.; Sohn, B.H.; Chu, I.S.; Jeong, W.; Mills, G.B.; Byers, L.A.; Lee, J.S. Development and validation of a prognostic gene-expression signature for lung adenocarcinoma. PLoS One 2012, 7. [Google Scholar] [CrossRef]
- Takeuchi, T.; Tomida, S.; Yatabe, Y.; Kosaka, T.; Osada, H.; Yanagisawa, K.; Mitsudomi, T.; Takahashi, T. Expression profile-defined classification of lung adenocarcinoma shows close relationship with underlying major genetic changes and clinicopathologic behaviors. J. Clin. Oncol. 2006, 24, 1679–1688. [Google Scholar] [CrossRef]
- Wilkerson, M.D.; Yin, X.; Walter, V.; Zhao, N.; Cabanski, C.R.; Hayward, M.C.; Miller, C.R.; Socinski, M.A.; Parsons, A.M.; Thorne, L.B.; et al. Differential pathogenesis of lung adenocarcinoma subtypes involving sequence mutations, copy number, chromosomal instability, and methylation. PLoS One 2012, 7. [Google Scholar] [CrossRef]
- Warth, A.; Muley, T.; Meister, M.; Stenzinger, A.; Thomas, M.; Schirmacher, P.; Schnabel, P.A.; Budczies, J.; Hoffmann, H.; Weichert, W. The novel histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification system of lung adenocarcinoma is a stage-independent predictor of survival. J. Clin. Oncol. 2012, 30, 1438–1446. [Google Scholar] [CrossRef]
- Travis, W.D.; Brambilla, E.; Riely, G.J. New pathologic classification of lung cancer: Relevance for clinical practice and clinical trials. J. Clin. Oncol. 2013, 31, 992–1001. [Google Scholar] [CrossRef]
- Kadota, K.; Nitadori, J.; Sarkaria, I.S.; Sima, C.S.; Jia, X.; Yoshizawa, A.; Rusch, V.W.; Travis, W.D.; Adusumilli, P.S. Thyroid transcription factor-1 expression is an independent predictor of recurrence and correlates with the IASLC/ATS/ERS histologic classification in patients with stage I lung adenocarcinoma. Cancer 2013, 119, 931–938. [Google Scholar] [CrossRef]
- Solis, L.M.; Behrens, C.; Raso, M.G.; Lin, H.Y.; Kadara, H.; Yuan, P.; Galindo, H.; Tang, X.; Lee, J.J.; Kalhor, N.; et al. Histologic patterns and molecular characteristics of lung adenocarcinoma associated with clinical outcome. Cancer 2012, 118, 2889–2899. [Google Scholar] [CrossRef]
- Raponi, M.; Zhang, Y.; Yu, J.; Chen, G.; Lee, G.; Taylor, J.M.; Macdonald, J.; Thomas, D.; Moskaluk, C.; Wang, Y.; et al. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung. Cancer Res. 2006, 66, 7466–7472. [Google Scholar] [CrossRef]
- Wilkerson, M.D.; Yin, X.; Hoadley, K.A.; Liu, Y.; Hayward, M.C.; Cabanski, C.R.; Muldrew, K.; Miller, C.R.; Randell, S.H.; Socinski, M.A.; et al. Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types. Clin. Cancer Res. 2010, 16, 4864–4875. [Google Scholar] [CrossRef]
- The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 2012, 489, 519–525. [Google Scholar] [CrossRef]
- Wilkerson, M.D.; Schallheim, J.M.; Hayes, D.N.; Roberts, P.J.; Bastien, R.R.; Mullins, M.; Yin, X.; Miller, C.R.; Thorne, L.B.; Geiersbach, K.B.; et al. Prediction of lung cancer histological types by RT-qPCR gene expression in FFPE specimens. J. Mol. Diagn. 2013, 15, 485–497. [Google Scholar] [CrossRef]
- Landi, M.T.; Zhao, Y.; Rotunno, M.; Koshiol, J.; Liu, H.; Bergen, A.W.; Rubagotti, M.; Goldstein, A.M.; Linnoila, I.; Marincola, F.M.; et al. MicroRNA expression differentiates histology and predicts survival of lung cancer. Clin. Cancer Res. 2010, 16, 430–441. [Google Scholar] [CrossRef]
- Huang, W.; Hu, J.; Yang, D.W.; Fan, X.T.; Jin, Y.; Hou, Y.Y.; Wang, J.P.; Yuan, Y.F.; Tan, Y.S.; Zhu, X.Z.; et al. Two microRNA panels to discriminate three subtypes of lung carcinoma in bronchial brushing specimens. Am. J. Respir. Crit. Care Med. 2012, 186, 1160–1167. [Google Scholar] [CrossRef]
- Gilad, S.; Lithwick-Yanai, G.; Barshack, I.; Benjamin, S.; Krivitsky, I.; Edmonston, T.B.; Bibbo, M.; Thurm, C.; Horowitz, L.; Huang, Y.; et al. Classification of the four main types of lung cancer using a microRNA-based diagnostic assay. J. Mol. Diagn. 2012, 14, 510–517. [Google Scholar] [CrossRef]
- Director’s Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma; Shedden, K.; Taylor, J.M.; Enkemann, S.A.; Tsao, M.S.; Yeatman, T.J.; Gerald, W.L.; Eschrich, S.; Jurisica, I.; Giordano, T.J.; et al. Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study. Nat. Med. 2008, 14, 822–827. [Google Scholar] [CrossRef]
- Larsen, J.E.; Pavey, S.J.; Passmore, L.H.; Bowman, R.V.; Hayward, N.K.; Fong, K.M. Gene expression signature predicts recurrence in lung adenocarcinoma. Clin. Cancer Res. 2007, 13, 2946–2954. [Google Scholar] [CrossRef]
- Li, Y.; Tang, H.; Sun, Z.; Bungum, A.O.; Edell, E.S.; Lingle, W.L.; Stoddard, S.M.; Zhang, M.; Jen, J.; Yang, P.; et al. Network-based approach identified cell cycle genes as predictor of overall survival in lung adenocarcinoma patients. Lung Cancer 2013, 80, 91–98. [Google Scholar] [CrossRef]
- Tomida, S.; Takeuchi, T.; Shimada, Y.; Arima, C.; Matsuo, K.; Mitsudomi, T.; Yatabe, Y.; Takahashi, T. Relapse-related molecular signature in lung adenocarcinomas identifies patients with dismal prognosis. J. Clin. Oncol. 2009, 27, 2793–2799. [Google Scholar] [CrossRef]
- Larsen, J.E.; Pavey, S.J.; Passmore, L.H.; Bowman, R.; Clarke, B.E.; Hayward, N.K.; Fong, K.M. Expression profiling defines a recurrence signature in lung squamous cell carcinoma. Carcinogenesis 2007, 28, 760–766. [Google Scholar]
- Botling, J.; Edlund, K.; Lohr, M.; Hellwig, B.; Holmberg, L.; Lambe, M.; Berglund, A.; Ekman, S.; Bergqvist, M.; Ponten, F.; et al. Biomarker discovery in non-small cell lung cancer: Integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin. Cancer Res. 2013, 19, 194–204. [Google Scholar] [CrossRef]
- Hou, J.; Aerts, J.; den Hamer, B.; van Ijcken, W.; den Bakker, M.; Riegman, P.; van der Leest, C.; van der Spek, P.; Foekens, J.A.; Hoogsteden, H.C.; et al. Gene expression-based classification of non-small cell lung carcinomas and survival prediction. PLoS One 2010, 5. [Google Scholar] [CrossRef]
- Mitra, R.; Lee, J.; Jo, J.; Milani, M.; McClintick, J.N.; Edenberg, H.J.; Kesler, K.A.; Rieger, K.M.; Badve, S.; Cummings, O.W.; et al. Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model. Clin. Cancer Res. 2011, 17, 2934–2946. [Google Scholar] [CrossRef]
- Roepman, P.; Jassem, J.; Smit, E.F.; Muley, T.; Niklinski, J.; van de Velde, T.; Witteveen, A.T.; Rzyman, W.; Floore, A.; Burgers, S.; et al. An immune response enriched 72-gene prognostic profile for early-stage non-small-cell lung cancer. Clin. Cancer Res. 2009, 15, 284–290. [Google Scholar] [CrossRef]
- Xie, Y.; Xiao, G.; Coombes, K.R.; Behrens, C.; Solis, L.M.; Raso, G.; Girard, L.; Erickson, H.S.; Roth, J.; Heymach, J.V.; et al. Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients. Clin. Cancer Res. 2011, 17, 5705–5714. [Google Scholar] [CrossRef]
- Zhu, C.Q.; Ding, K.; Strumpf, D.; Weir, B.A.; Meyerson, M.; Pennell, N.; Thomas, R.K.; Naoki, K.; Ladd-Acosta, C.; Liu, N.; et al. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J. Clin. Oncol. 2010, 28, 4417–4424. [Google Scholar] [CrossRef]
- Lee, E.S.; Son, D.S.; Kim, S.H.; Lee, J.; Jo, J.; Han, J.; Kim, H.; Lee, H.J.; Choi, H.Y.; Jung, Y.; et al. Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. Clin. Cancer Res. 2008, 14, 7397–7404. [Google Scholar] [CrossRef]
- Boutros, P.C.; Lau, S.K.; Pintilie, M.; Liu, N.; Shepherd, F.A.; Der, S.D.; Tsao, M.S.; Penn, L.Z.; Jurisica, I. Prognostic gene signatures for non-small-cell lung cancer. Proc. Natl. Acad. Sci. USA 2009, 106, 2824–2828. [Google Scholar] [CrossRef]
- Chen, D.T.; Hsu, Y.L.; Fulp, W.J.; Coppola, D.; Haura, E.B.; Yeatman, T.J.; Cress, W.D. Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer. J. Natl. Cancer Instit. 2011, 103, 1859–1870. [Google Scholar] [CrossRef]
- Guo, N.L.; Wan, Y.W.; Tosun, K.; Lin, H.; Msiska, Z.; Flynn, D.C.; Remick, S.C.; Vallyathan, V.; Dowlati, A.; Shi, X.; et al. Confirmation of gene expression-based prediction of survival in non-small cell lung cancer. Clin. Cancer Res. 2008, 14, 8213–8220. [Google Scholar] [CrossRef]
- Lu, Y.; Lemon, W.; Liu, P.Y.; Yi, Y.; Morrison, C.; Yang, P.; Sun, Z.; Szoke, J.; Gerald, W.L.; Watson, M.; et al. A gene expression signature predicts survival of patients with stage I non-small cell lung cancer. PLoS Med. 2006, 3. [Google Scholar] [CrossRef]
- Lu, Y.; Wang, L.; Liu, P.; Yang, P.; You, M. Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients. PLoS One 2012, 7. [Google Scholar] [CrossRef]
- Sun, Z.; Wigle, D.A.; Yang, P. Non-overlapping and non-cell-type-specific gene expression signatures predict lung cancer survival. J. Clin. Oncol. 2008, 26, 877–883. [Google Scholar] [CrossRef]
- Van Laar, R.K. Genomic signatures for predicting survival and adjuvant chemotherapy benefit in patients with non-small-cell lung cancer. BMC Med. Genomics 2012, 5. [Google Scholar] [CrossRef]
- Akagi, I.; Okayama, H.; Schetter, A.J.; Robles, A.I.; Kohno, T.; Bowman, E.D.; Kazandjian, D.; Welsh, J.A.; Oue, N.; Saito, M.; et al. Combination of protein coding and noncoding gene expression as a robust prognostic classifier in stage I lung adenocarcinoma. Cancer Res. 2013, 73, 3821–3832. [Google Scholar] [CrossRef]
- Bianchi, F.; Nuciforo, P.; Vecchi, M.; Bernard, L.; Tizzoni, L.; Marchetti, A.; Buttitta, F.; Felicioni, L.; Nicassio, F.; di Fiore, P.P. Survival prediction of stage I lung adenocarcinomas by expression of 10 genes. J. Clin. Investig. 2007, 117, 3436–3444. [Google Scholar] [CrossRef]
- Chen, H.Y.; Yu, S.L.; Chen, C.H.; Chang, G.C.; Chen, C.Y.; Yuan, A.; Cheng, C.L.; Wang, C.H.; Terng, H.J.; Kao, S.F.; et al. A five-gene signature and clinical outcome in non-small-cell lung cancer. N. Engl. J. Med. 2007, 356, 11–20. [Google Scholar] [CrossRef]
- Lau, S.K.; Boutros, P.C.; Pintilie, M.; Blackhall, F.H.; Zhu, C.Q.; Strumpf, D.; Johnston, M.R.; Darling, G.; Keshavjee, S.; Waddell, T.K.; et al. Three-gene prognostic classifier for early-stage non small-cell lung cancer. J. Clin. Oncol. 2007, 25, 5562–5569. [Google Scholar] [CrossRef]
- Raz, D.J.; Ray, M.R.; Kim, J.Y.; He, B.; Taron, M.; Skrzypski, M.; Segal, M.; Gandara, D.R.; Rosell, R.; Jablons, D.M. A multigene assay is prognostic of survival in patients with early-stage lung adenocarcinoma. Clin. Cancer Res. 2008, 14, 5565–5570. [Google Scholar] [CrossRef]
- Seike, M.; Yanaihara, N.; Bowman, E.D.; Zanetti, K.A.; Budhu, A.; Kumamoto, K.; Mechanic, L.E.; Matsumoto, S.; Yokota, J.; Shibata, T.; et al. Use of a cytokine gene expression signature in lung adenocarcinoma and the surrounding tissue as a prognostic classifier. J. Natl. Cancer Instit. 2007, 99, 1257–1269. [Google Scholar] [CrossRef]
- Wistuba, I.I.; Behrens, C.; Lombardi, F.; Wagner, S.; Fujimoto, J.; Raso, M.G.; Spaggiari, L.; Galetta, D.; Riley, R.; Hughes, E.; et al. Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma. Clin. Cancer Res. 2013, 19, 6261–6271. [Google Scholar] [CrossRef]
- Skrzypski, M.; Jassem, E.; Taron, M.; Sanchez, J.J.; Mendez, P.; Rzyman, W.; Gulida, G.; Raz, D.; Jablons, D.; Provencio, M.; et al. Three-gene expression signature predicts survival in early-stage squamous cell carcinoma of the lung. Clin. Cancer Res. 2008, 14, 4794–4799. [Google Scholar] [CrossRef]
- Kratz, J.R.; He, J.; van den Eeden, S.K.; Zhu, Z.H.; Gao, W.; Pham, P.T.; Mulvihill, M.S.; Ziaei, F.; Zhang, H.; Su, B.; et al. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: Development and international validation studies. Lancet 2012, 379, 823–832. [Google Scholar] [CrossRef]
- Lu, Y.; Govindan, R.; Wang, L.; Liu, P.Y.; Goodgame, B.; Wen, W.; Sezhiyan, A.; Pfeifer, J.; Li, Y.F.; Hua, X.; et al. MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer. Carcinogenesis 2012, 33, 1046–1054. [Google Scholar] [CrossRef]
- Yanaihara, N.; Caplen, N.; Bowman, E.; Seike, M.; Kumamoto, K.; Yi, M.; Stephens, R.M.; Okamoto, A.; Yokota, J.; Tanaka, T.; et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006, 9, 189–198. [Google Scholar] [CrossRef]
- Yu, S.L.; Chen, H.Y.; Chang, G.C.; Chen, C.Y.; Chen, H.W.; Singh, S.; Cheng, C.L.; Yu, C.J.; Lee, Y.C.; Chen, H.S.; et al. MicroRNA signature predicts survival and relapse in lung cancer. Cancer Cell 2008, 13, 48–57. [Google Scholar] [CrossRef]
- Voortman, J.; Goto, A.; Mendiboure, J.; Sohn, J.J.; Schetter, A.J.; Saito, M.; Dunant, A.; Pham, T.C.; Petrini, I.; Lee, A.; et al. MicroRNA expression and clinical outcomes in patients treated with adjuvant chemotherapy after complete resection of non-small cell lung carcinoma. Cancer Res. 2010, 70, 8288–8298. [Google Scholar] [CrossRef]
- Hu, Z.; Chen, X.; Zhao, Y.; Tian, T.; Jin, G.; Shu, Y.; Chen, Y.; Xu, L.; Zen, K.; Zhang, C.; et al. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J. Clin. Oncol. 2010, 28, 1721–1726. [Google Scholar] [CrossRef]
- Kaduthanam, S.; Gade, S.; Meister, M.; Brase, J.C.; Johannes, M.; Dienemann, H.; Warth, A.; Schnabel, P.A.; Herth, F.J.; Sultmann, H.; et al. Serum miR-142-3p is associated with early relapse in operable lung adenocarcinoma patients. Lung Cancer 2013, 80, 223–227. [Google Scholar] [CrossRef]
- Sanfiorenzo, C.; Ilie, M.I.; Belaid, A.; Barlesi, F.; Mouroux, J.; Marquette, C.H.; Brest, P.; Hofman, P. Two panels of plasma microRNAs as non-invasive biomarkers for prediction of recurrence in resectable NSCLC. PLoS One 2013, 8. [Google Scholar] [CrossRef]
- Wang, Y.; Gu, J.; Roth, J.A.; Hildebrandt, M.A.; Lippman, S.M.; Ye, Y.; Minna, J.D.; Wu, X. Pathway-based serum microRNA profiling and survival in patients with advanced stage non-small cell lung cancer. Cancer Res. 2013, 73, 4801–4809. [Google Scholar] [CrossRef]
- Besse, B.; Olaussen, K.A.; Soria, J.C. ERCC1 and RRM1: Ready for prime time? J. Clin. Oncol. 2013, 31, 1050–1060. [Google Scholar] [CrossRef]
- Friboulet, L.; Olaussen, K.A.; Pignon, J.P.; Shepherd, F.A.; Tsao, M.S.; Graziano, S.; Kratzke, R.; Douillard, J.Y.; Seymour, L.; Pirker, R.; et al. ERCC1 isoform expression and DNA repair in non-small-cell lung cancer. N. Engl. J. Med. 2013, 368, 1101–1110. [Google Scholar] [CrossRef]
- Jakobsen, J.N.; Santoni-Rugiu, E.; Ravn, J.; Sorensen, J.B. Intratumour variation of biomarker expression by immunohistochemistry in resectable non-small cell lung cancer. Eur. J. Cancer 2013. [Google Scholar] [CrossRef]
- Graziano, S.L.; Lacas, B.; Vollmer, R.; Kratzke, R.; Popper, H.; Filipits, M.; Seymour, L.; Shepherd, F.A.; Rosell, R.; Veillard, A.S.; et al. Cross-validation analysis of the prognostic significance of mucin expression in patients with resected non-small cell lung cancer treated with adjuvant chemotherapy: Results from IALT, JBR.10 and ANITA. Lung Cancer 2013, 82, 149–155. [Google Scholar] [CrossRef]
- Shepherd, F.A.; Domerg, C.; Hainaut, P.; Janne, P.A.; Pignon, J.P.; Graziano, S.; Douillard, J.Y.; Brambilla, E.; le Chevalier, T.; Seymour, L.; et al. Pooled analysis of the prognostic and predictive effects of KRAS mutation status and KRAS mutation subtype in early-stage resected non-small-cell lung cancer in four trials of adjuvant chemotherapy. J. Clin. Oncol. 2013, 31, 2173–2181. [Google Scholar] [CrossRef]
- Edelman, M.J.; Schneider, C.P.; Tsai, C.M.; Kim, H.T.; Quoix, E.; Luft, A.V.; Kaleta, R.; Mukhopadhyay, P.; Trifan, O.C.; Whitaker, L.; et al. Randomized phase II study of ixabepilone or paclitaxel plus carboplatin in patients with non-small-cell lung cancer prospectively stratified by beta-3 tubulin status. J. Clin. Oncol. 2013, 31, 1990–1996. [Google Scholar] [CrossRef]
- Holdenrieder, S.; von Pawel, J.; Dankelmann, E.; Duell, T.; Faderl, B.; Markus, A.; Siakavara, M.; Wagner, H.; Feldmann, K.; Hoffmann, H.; et al. Nucleosomes, ProGRP, NSE, CYFRA 21-1, and CEA in monitoring first-line chemotherapy of small cell lung cancer. Clin. Cancer Res. 2008, 14, 7813–7821. [Google Scholar] [CrossRef]
- Tang, H.; Xiao, G.; Behrens, C.; Schiller, J.; Allen, J.; Chow, C.W.; Suraokar, M.; Corvalan, A.; Mao, J.; White, M.A.; et al. A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients. Clin. Cancer Res. 2013, 19, 1577–1586. [Google Scholar] [CrossRef]
- Chai, Y.; Lam, R.K.; Calaf, G.M.; Zhou, H.; Amundson, S.; Hei, T.K. Radiation-induced non-targeted response in vivo: Role of the TGFbeta-TGFBR1-COX-2 signalling pathway. Br. J. Cancer 2013, 108, 1106–1112. [Google Scholar] [CrossRef]
- Yuan, S.T.; Ellingrod, V.L.; Schipper, M.; Stringer, K.A.; Cai, X.; Hayman, J.A.; Yu, J.; Lawrence, T.S.; Kong, F.M. Genetic variations in TGFbeta1, tPA, and ACE and radiation-induced thoracic toxicities in patients with non-small-cell lung cancer. J. Thoracic Oncol. 2013, 8, 208–213. [Google Scholar] [CrossRef]
- Niu, N.; Qin, Y.; Fridley, B.L.; Hou, J.; Kalari, K.R.; Zhu, M.; Wu, T.Y.; Jenkins, G.D.; Batzler, A.; Wang, L. Radiation pharmacogenomics: A genome-wide association approach to identify radiation response biomarkers using human lymphoblastoid cell lines. Genome Res. 2010, 20, 1482–1492. [Google Scholar] [CrossRef]
- Torres-Roca, J.F.; Eschrich, S.; Zhao, H.; Bloom, G.; Sung, J.; McCarthy, S.; Cantor, A.B.; Scuto, A.; Li, C.; Zhang, S.; et al. Prediction of radiation sensitivity using a gene expression classifier. Cancer Res. 2005, 65, 7169–7176. [Google Scholar] [CrossRef]
- Dehing-Oberije, C.; Aerts, H.; Yu, S.; de Ruysscher, D.; Menheere, P.; Hilvo, M.; van der Weide, H.; Rao, B.; Lambin, P. Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325). Int. J. Radiat. Oncol. Biol. Phys. 2011, 81, 360–368. [Google Scholar] [CrossRef]
- Ono, A.; Naito, T.; Ito, I.; Watanabe, R.; Shukuya, T.; Kenmotsu, H.; Tsuya, A.; Nakamura, Y.; Murakami, H.; Kaira, K.; et al. Correlations between serial pro-gastrin-releasing peptide and neuron-specific enolase levels, and the radiological response to treatment and survival of patients with small-cell lung cancer. Lung Cancer 2012, 76, 439–444. [Google Scholar] [CrossRef]
- Koh, P.K.; Faivre-Finn, C.; Blackhall, F.H.; de Ruysscher, D. Targeted agents in non-small cell lung cancer (NSCLC): Clinical developments and rationale for the combination with thoracic radiotherapy. Cancer Treatm. Rev. 2012, 38, 626–640. [Google Scholar] [CrossRef]
- Das, A.K.; Chen, B.P.; Story, M.D.; Sato, M.; Minna, J.D.; Chen, D.J.; Nirodi, C.S. Somatic mutations in the tyrosine kinase domain of epidermal growth factor receptor (EGFR) abrogate EGFR-mediated radioprotection in non-small cell lung carcinoma. Cancer Res. 2007, 67, 5267–5274. [Google Scholar] [CrossRef]
- Wang, M.; Morsbach, F.; Sander, D.; Gheorghiu, L.; Nanda, A.; Benes, C.; Kriegs, M.; Krause, M.; Dikomey, E.; Baumann, M.; et al. EGF receptor inhibition radiosensitizes NSCLC cells by inducing senescence in cells sustaining DNA double-strand breaks. Cancer Res. 2011, 71, 6261–6269. [Google Scholar] [CrossRef]
- Contessa, J.N.; Hampton, J.; Lammering, G.; Mikkelsen, R.B.; Dent, P.; Valerie, K.; Schmidt-Ullrich, R.K. Ionizing radiation activates Erb-B receptor dependent Akt and p70 S6 kinase signaling in carcinoma cells. Oncogene 2002, 21, 4032–4041. [Google Scholar] [CrossRef]
- Ulloa-Montoya, F.; Louahed, J.; Dizier, B.; Gruselle, O.; Spiessens, B.; Lehmann, F.F.; Suciu, S.; Kruit, W.H.; Eggermont, A.M.; Vansteenkiste, J.; et al. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J. Clin. Oncol. 2013, 31, 2388–2395. [Google Scholar] [CrossRef]
- Galon, J.; Angell, H.K.; Bedognetti, D.; Marincola, F.M. The continuum of cancer immunosurveillance: Prognostic, predictive, and mechanistic signatures. Immunity 2013, 39, 11–26. [Google Scholar] [CrossRef]
- Morse, M.A.; Osada, T.; Hobeika, A.; Patel, S.; Lyerly, H.K. Biomarkers and correlative endpoints for immunotherapy trials. Am. Soc. Clin. Oncol. Educ. Book 2013, 2013, 287–293. [Google Scholar]
- Pikor, L.A.; Ramnarine, V.R.; Lam, S.; Lam, W.L. Genetic alterations defining NSCLC subtypes and their therapeutic implications. Lung Cancer 2013, 82, 179–189. [Google Scholar] [CrossRef]
- Cagle, P.T.; Sholl, L.M.; Lindeman, N.I.; Alsabeh, R.; Divaris, D.X.; Foulis, P.; Lee, G.; Neal, J.W.; Nowak, J.A.; Yu, P.P. Template for reporting results of biomarker testing of specimens from patients with non-small cell carcinoma of the lung. Arch. Pathol. Lab. Med. 2013. [Google Scholar] [CrossRef]
- Neal, J.W.; Sequist, L.V. Targeted therapies: Optimal first-line therapy for NSCLC with EGFR mutations. Nat. Rev. Clin. Oncol. 2010, 7, 71–72. [Google Scholar] [CrossRef]
- Shaw, A.T.; Yeap, B.Y.; Solomon, B.J.; Riely, G.J.; Gainor, J.; Engelman, J.A.; Shapiro, G.I.; Costa, D.B.; Ou, S.H.; Butaney, M.; et al. Effect of crizotinib on overall survival in patients with advanced non-small-cell lung cancer harbouring ALK gene rearrangement: A retrospective analysis. Lancet Oncol. 2011, 12, 1004–1012. [Google Scholar] [CrossRef]
- Mao, C.; Qiu, L.X.; Liao, R.Y.; Du, F.B.; Ding, H.; Yang, W.C.; Li, J.; Chen, Q. KRAS mutations and resistance to EGFR-TKIs treatment in patients with non-small cell lung cancer: A meta-analysis of 22 studies. Lung Cancer 2010, 69, 272–278. [Google Scholar] [CrossRef]
- Pao, W.; Wang, T.Y.; Riely, G.J.; Miller, V.A.; Pan, Q.; Ladanyi, M.; Zakowski, M.F.; Heelan, R.T.; Kris, M.G.; Varmus, H.E. KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. PLoS Med. 2005, 2. [Google Scholar] [CrossRef]
- Laurie, S.A.; Goss, G.D. Role of epidermal growth factor receptor inhibitors in epidermal growth factor receptor wild-type non-small-cell lung cancer. J. Clin. Oncol. 2013, 31, 1061–1069. [Google Scholar] [CrossRef]
- Carbone, D.P.; Ding, K.; Roder, H.; Grigorieva, J.; Roder, J.; Tsao, M.S.; Seymour, L.; Shepherd, F.A. Prognostic and predictive role of the VeriStrat plasma test in patients with advanced non-small-cell lung cancer treated with erlotinib or placebo in the NCIC Clinical Trials Group BR.21 trial. J. Thoracic Oncol. 2012, 7, 1653–1660. [Google Scholar] [CrossRef]
- Martini, M.; Vecchione, L.; Siena, S.; Tejpar, S.; Bardelli, A. Targeted therapies: How personal should we go? Nat. Rev. Clin. Oncol. 2012, 9, 87–97. [Google Scholar]
- Sequist, L.V.; Waltman, B.A.; Dias-Santagata, D.; Digumarthy, S.; Turke, A.B.; Fidias, P.; Bergethon, K.; Shaw, A.T.; Gettinger, S.; Cosper, A.K.; et al. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci. Transl. Med. 2013, 7, 75ra26. [Google Scholar] [CrossRef]
- Byers, L.A.; Diao, L.; Wang, J.; Saintigny, P.; Girard, L.; Peyton, M.; Shen, L.; Fan, Y.; Giri, U.; Tumula, P.K.; et al. An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin. Cancer Res. 2013, 19, 279–290. [Google Scholar] [CrossRef]
- Blumenschein, G.R.; Saintigny, P.; Liu, S.; Kim, E.S.; Tsao, A.S.; Herbst, R.; Alden, C.M.; Lee, J.J.; Tang, X.; Stewart, D.J.; et al. Comprehensive biomarker analysis and final efficacy results of sorafenib in the BATTLE (Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination) trial. Clin. Cancer Res. 2013. [Google Scholar] [CrossRef]
- Okayama, H.; Kohno, T.; Ishii, Y.; Shimada, Y.; Shiraishi, K.; Iwakawa, R.; Furuta, K.; Tsuta, K.; Shibata, T.; Yamamoto, S.; et al. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 2012, 72, 100–111. [Google Scholar] [CrossRef]
- Chitale, D.; Gong, Y.; Taylor, B.S.; Broderick, S.; Brennan, C.; Somwar, R.; Golas, B.; Wang, L.; Motoi, N.; Szoke, J.; et al. An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors. Oncogene 2009, 28, 2773–2783. [Google Scholar] [CrossRef]
- Ettinger, D.S.; Akerley, W.; Borghaei, H.; Chang, A.C.; Cheney, R.T.; Chirieac, L.R.; D’Amico, T.A.; Demmy, T.L.; Govindan, R.; Grannis, F.W.; et al. Non-small cell lung cancer, version 2.2013. J. Natl. Compr. Cancer Netw. 2013, 11, 645–653. [Google Scholar]
- Muller, B.M.; Keil, E.; Lehmann, A.; Winzer, K.J.; Richter-Ehrenstein, C.; Prinzler, J.; Bangemann, N.; Reles, A.; Stadie, S.; Schoenegg, W.; et al. The endopredict gene-expression assay in clinical practice–performance and impact on clinical decisions. PLoS One 2013, 8. [Google Scholar] [CrossRef]
- Rouzier, R.; Pronzato, P.; Chereau, E.; Carlson, J.; Hunt, B.; Valentine, W.J. Multigene assays and molecular markers in breast cancer: Systematic review of health economic analyses. Breast Cancer Res. Treatm. 2013, 139, 621–637. [Google Scholar] [CrossRef]
- Azim, H.A., Jr.; Michiels, S.; Zagouri, F.; Delaloge, S.; Filipits, M.; Namer, M.; Neven, P.; Symmans, W.F.; Thompson, A.; Andre, F.; et al. Utility of prognostic genomic tests in breast cancer practice: The IMPAKT 2012 Working Group Consensus Statement. Ann. Oncol. 2013, 24, 647–654. [Google Scholar] [CrossRef]
- Subramanian, J.; Simon, R. Gene expression-based prognostic signatures in lung cancer: Ready for clinical use? J. Natl. Cancer Instit. 2010, 102, 464–474. [Google Scholar]
- Bild, A.H.; Yao, G.; Chang, J.T.; Wang, Q.; Potti, A.; Chasse, D.; Joshi, M.B.; Harpole, D.; Lancaster, J.M.; Berchuck, A.; et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006, 439, 353–357. [Google Scholar]
- Ding, L.; Getz, G.; Wheeler, D.A.; Mardis, E.R.; McLellan, M.D.; Cibulskis, K.; Sougnez, C.; Greulich, H.; Muzny, D.M.; Morgan, M.B.; et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 2008, 455, 1069–1075. [Google Scholar] [CrossRef]
|Table A1. Gene expression microarray studies describing gene clusters or gene signatures in lung cancer or NSCLC.|
|Clinical focus||Tumor Type||Biomaterial||Gene signature||Screening||Validation||Technology||References|
|Diagnosis||NSCLC||Blood cells||484-feature classifier||n = 77||n = 156||Illumina microarrays|||
|Diagnosis||Lung cancer||normal large-airway epithelial cells||80-gene signature||n = 77||n = 52||Affymetrix microarrays|||
|Risk, Smoking||Lung cancer||non-tumor lung tissue||599-feature set||n = 344||n = 509||Affymetrix microarrays|||
|Prognosis||NSCLC||Tissues||6-gene signature, clinical covariates||n = 56||n = 59||Affymetrix microarrays|||
|Prognosis||NSCLC||Tissues||72-gene signature||n = 103||n = 69||Agilent oligo microarray|||
|Prognosis||NSCLC||Tissues||17-gene signature||n = 91||public dataset; Potti, 2006||Affymetrix microarrays|||
|Prognosis; Chemotherapy prediction||NSCLC||Tissues||15-gene signature||n = 133||public datasets; Potti, 2006; Raponi, 2006; Shedden, 2008; Roepman, 2009; qPCR (n = 30)||Affymetrix microarrays|||
|Prognosis||NSCLC||Tissues||4-gene signature, clinical covariates||n = 27||n = 138||Affymetrix microarrays|||
|Prognosis||NSCLC||Tissues||59-gene signature||n = 55||public datasets; Bhattacharjee, 2001; Bild, 2006||Affymetrix microarrays|||
|Prognosis||NSCLC||Tissues||450-gene signature||n = 196||public datasets; Bild, 2006; Raponi, 2006; Shedden, 2008; Zhu, 2010; Hou, 2010||Affymetrix microarrays|||
|Prognosis||NSCLC||Tissues||5-gene signature||n = 125||n = 60; public datasets; Beer, 2002||cDNA microarray, qPCR arrays|||
|In vitro model; Pathway||Lung cancer||Cell lines, Tissues||Oncogenic pathway signatures||cell line, lung cancer (n = 111)||none||Affymetrix microarrays|||
|Table A2. Gene expression microarray studies describing gene clusters or gene signatures in adenocarcinoma or squamous cell carcinoma.|
|Clinical focus||Tumor Type||Biomaterial||Gene signature||Screening||Validation||Technology||References|
|AC-Subtypes; Prognosis||Lung cancer–AC||Tissues||Gene clusters||n = 139||none||Affymetrix microarrays|||
|AC-Subtypes; Prognosis||Lung cancer–AC||Tissues||Gene clusters||n = 67||none||cDNA microarray|||
|AC-Subtypes; Prognosis||NSCLC–AC||Tissues||Gene clusters||n = 149||none||Agilent oligo microarray|||
|AC-Subtypes; Prognosis||AC||Tissues||50-gene signature||n = 43||n = 43||Affymetrix microarrays|||
|Prognosis||AC||Tissues||54-gene signature||n = 48||n = 95||Agilent oligo microarray|||
|Prognosis||AC||Tissues||Gene classifiers; clinical covariates||n = 256||n = 186||Affymetrix microarrays|||
|Prognosis||AC||Tissues||82-feature signature||n = 60||n = 57||Agilent oligo microarray|||
|Prognosis||AC||Tissues||3-gene signature||n = 82||public datasets; Bhattacharjee, 2001; Shedden, 2008||Illumina microarrays|||
|Integrative analysis||AC||Tissues||None||n = 75||none||Affymetrix microarrays|||
|Integrative analysis||AC||Tissues||EGFR and KRAS associated gene signatures||n = 193||none||Affymetrix microarrays|||
|Genomic subtypes||AC||Tissues||Gene signatures||n = 226||none||Affymetrix microarrays|||
|SCC-Subtypes; Prognosis||NSCLC–SCC||Tissues||Gene clusters||n = 48||none||cDNA microarray|||
|SCC-Subtypes; Prognosis||NSCLC–SCC||Tissues||Gene clusters, 50-gene signature||n = 129||n = 36||Affymetrix microarrays|||
|SCC-Subtypes; Prognosis||SCC||Tissues||Subtype predictor||public datasets; Bild, 2006; Lee, 2008; Raponi, 2006; Roepman, 2009||n =56||Agilent oligo microarray|||
|SCC-Subtypes||SCC||Tissues||Subtype predictor||Wilkerson, 2010||n = 178||Agilent oligo microarray|||
|Prognosis||SCC||Tissues||111-gene signature||n = 51||n = 58||Operon oligo microarray|||
© 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).