[Frontiers in Bioscience E3, 46-50, January 1, 2011]

Bioinformatics approach for the validation of non-small cell lung cancer biomarkers

Carolina Beatriz Muller, Rafael Longhi Sampaio de Barros, Jose Claudio Fonseca Moreira, Fabio Klamt

Centro de Estudos em Estresse Oxidativo, Departamento de Bioquimica, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos 2600-anexo, Porto Alegre 90035-003, Brazil


1. Abstract
2. Introduction
3. Searching for NSCLC potential biomarker
3.1. Literature search
3.2. Tumor samples and microarray data
3.3. Survival data analysis
4. Discussion
5. Acknowledgements
6. References


Non-small cell lung cancer (NSCLC) accounts for nearly 1 million deaths annually, worldwide. Conventional treatments offer limited benefits and patients have a survival rate of approximately 1 year. A biomarker for NSCLC could provide the potential benefits of early diagnosis, prognosis and could lead to important applications such as drug targeting. In a search for a biomarker with prognostic value, we reviewed the literature and tested potential biomarkers by performing a meta-data analysis using public databank of NSCLC biopsies containing gene expression data and clinical and pathologic information from 111 patients. We generated standard Kaplan-Meier mortality curves by clustering patients according to either biomarker expression levels or NSCLC stage grouping. Our statistical analyses show that all 60 potential biomarkers analyzed here have no prognostic value for NSCLC patient outcome.


Lung cancer is the leading cause of cancer deaths worldwide, accounting for 1.3 million deaths annually (data from World Health Organization, 2008). The high mortality associated with this disease is primarily due to the fact that the majority of lung cancers are not detected until they have progressed to an advanced stage (1). Non-small cell lung cancer (NSCLC) represents nearly 85% of lung cancer cases. Compared with other major types of cancer such as colon, prostate, and breast cancers, the clinical outcome of conventional therapies for NSCLC remains poor, with a median survival of 9-11 months (2).

Therefore, there is an urgent need for more effective therapies, drugs, or treatments that could help decrease the incidence of NSCLC. Alternatively, cancer biomarker gives good guidance on many areas of cancer biology. Unlike uniformity of long-established TNM system, the international standard protocol that allows the staging of carcinoma according to the extent of disease in the patient, cancer biomarkers are considered to be more suitable to the heterogeneous nature of cancer (3). In 2008, Sawyers discussed the three types of cancer biomarkers. These can be used for prognosis, to predict the natural course of a tumor, indicating whether the outcome for the patient is likely to be good or poor. They can also be used in prediction, helping to decide which patients are likely to respond to a given drug and at what dose it might be most effective (4). A biomarker for NSCLC could provide the potential benefits of early diagnosis, considering that the disease is usually detected in late stages when surgical removal of the tumor is no longer an option, and additionally, could lead to other important applications such as prognosis and drug targeting (5). An impressive number of molecular markers have been implicated in the prognosis of NSCLC; however, the results reported in literature are conflicting and none are in use in clinics. Thus, further investigation, newer molecular assays and the development of appropriate panel of molecular markers are still required (6).

Systematic analysis of gene expression using high-throughput screening of cDNA microarray libraries has been considered as an effective approach for identifying and validating potentials biomarkers for NSCLC (7). However, final validation should be done by testing a collection of well-defined clinical samples. Herein, we describe a bioinformatics-based approach to test and validate the prognostic value of potential NSCLC biomarkers. Our research group have been studying many aspects of tumor biology by different bioinformatics approaches (8-9). Here our approach is to correlate data collected from the literature with data on gene expression of a large and well-defined collection of NSCLC biopsies containing information on patients' clinical status and pathology to clinically evaluate the efficacy of potential biomarkers to predict patients outcome. Validation by clinical trials in large cohorts of patients is necessary before cancer-related phenotypes can be translated into the clinic as reliable biomarkers.


3.1. Literature search

The list of genes presented in (Table 1) was compiled by searching the PubMed database for articles published in English between January 1985 and December 2009. Search criteria included subject heading terms for "biomarker", "prognosis", "gene expression" and "lung cancer". Genes reported in two or more articles during the period of our search, or in one article at least during the last 3 years were selected. Those articles describing pooled biomarkers into the same analysis were excluded from the list (i.e. combined performance for multiple genes). A total of 60 NSCLC was tested.

3.2. Tumor samples and microarray data

For the clinical validation of potential NSCLC prognostic biomarkers, we used a large, homogeneous, well-defined collection of samples from lung cancer biopsies, along with respective gene expression data and relevant clinical and pathologic information -such as age, sex, cancer histological type, and NSCLC staging in a cohort follow-up period of 72 months- on 111 patients (10). Data were obtained from the GEO database (http://www.ncbi.nlm.nih.gov/projects/geo/; Series GSE3141) and from the Duke Institute for Genome Sciences & Policy website (http://data.cgt.duke.edu/oncogene.php). All gene array data was on Affymetrix U133A GeneChip, from core biopsies of patients' tumor. A sine qua non condition for select a given gene was the presence of two or more microarray probes in NSCLC cohort used.

3.3. Survival data analysis

We used the SPSS software (SPSS for Windows, release 14.0.0, SPSS Inc., Chicago, IL) to generate Standard Kaplan-Meier mortality curves with their significance levels, for patient clusters. Survival curves were compared using the log-rank test; patients were clustered according to biomarker expression level or NCSLC stage grouping (i.e. initial and advanced staging).


Through a systematic MEDLINE literature inspection, we selected 60 genes as potential biomarkers to be validated using a clinical databank. These biomarkers constitute a diverse group of genes involved in different cellular functions. They code proteins such as transcription factors (TFAP2B, MYC), protein kinases (PIK3CA, STAT1, KRAS, STYK1, LCK), protein phosphatases (DUSP6), receptors (AGER, EGFR, AMFR), and several DNA repair systems (ALKBH5, ALKBH3, FGFR1OP, ERCC1) (See table 1 for complete list of potential prognostic biomarkers). These potential biomarkers are related in the most distinct ways with lung cancer; for example, mutations in the genetic region encoding the kinase domain of the epidermal growth-factor receptor (EGFR) predict the sensitivity of lung tumors to erlotinib or gefitinib (11), as well different mutation in KRAS predict that patients with lung cancer will fail to respond to these inhibitors (12).

Some genes of DNA repair system are also considered to be potential biomarkers to NSCLC. They have been constantly described as being related with sensitivity to chemotherapeutic drugs (13-14), specially alkylating agents, which are the most largely used anti-cancer drug for NSCLC treatment (15). These drugs are mutagenic, genotoxic and have the ability to damage DNA (16). Cisplatin is an alkylating agent widely used in NSCLC treatment; however, this type of cancer can presents inherently resistance against it. Like many DNA alkylators, cisplatin acts inhibiting DNA replication, which is the critical target in cancer treatment. In this case, the resistance against cisplatin is mainly determinate by the expression levels of nucleotide excision repair genes (NER). ERCC, a component of the NER complex, is a potential marker involved in prediction of resistance to cisplatin, which has been described to be related with its mRNA expression (17-19).

Despite the large literature about markers in NSCLC, our study shows that none of the genes we tested have their mRNA levels directly correlated with patient outcome. It is important to state that our analysis was based on gene expression status, not taking into account other relevant parameters, like gene mutation pattern, methylation, or cohort subgroups. As an example, ERBB2 gene has prognostic value in patients with NSCLC when considering specific cohort gender (20). Likewise, ERCC1 is effective in stage IIB-IIIA-IIIB of NSCLC (21) and CD9 has prognostic value given the mutation status of KRAS gene (22).

Different experimental approaches have been used to establish each of the genes listed in table 1 as potentials biomarkers for predicting patient outcome. This approach shows that biomarker candidates should be carefully tested in clinical samples and exemplifies a rational use of public high-throughput clinical data. In theory, it could be applied to validate any possible biomarker, optimizing the use of the information available in public databanks and serving as standard tool to guide future clinical trials. Thus, we would be maximizing the use of information already generated and increasing its applicability.

The panel generated by this tool must be further analyzed. Microarrays are well described as capable of determining the expression levels of thousands of genes simultaneously (23) and the ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signature has been demonstrated in multiple studies (24). Bild & Col. described the activation status of several oncogenic pathway based on the statistical combination of gene expression signatures (8). Nevertheless, we believe that the gene signatures should be obtained based on biological (not statistical) combination of high-throughput screening of cDNA microarray probes. In this scenario, fluctuation of gene expression within biological networks can be evaluated by landscape analysis, which can represent different functional states of the same gene network (25)

In summary, our research describes a reliable tool able to discriminate biomarkers performance, revealing that none of the 60 genes individually tested had shown sufficient statistical power to be safely included in clinical use, when compared to TNM system, which is considered gold standard by physicians. Therefore, these approach may strengthens the development of new biomarkers, since up-to-date there is still no prognostic biomarker (based on gene expression) available for NSCLC. As stated by Dr. Goldstraw in the last World Conference on Lung Cancer (26), it is still uncertain how to integrate the predictive information from biomarkers with the anatomical extent of disease described by the TNM system, which rises the possibility that T, N, and M could be joined by a B (biological) factor. Biomarkers will probably be the next major development in NSCLC staging.


This project is supported by Brazilian MCT/CNPq Universal funds (479860/2006-8 & 476114/2008-0) by MCT/CNPq INCT-TM (573671/2008-7) funds. C.B.M. was supported by a training grant from FAPERGS (08511895) and F.K. received a fellowship from MCT/CNPq (303613/2008-4). The author would like to thanks Dr Márcia Triunfol for critical review of this manuscript.


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Key Words: Bioinformatic approach, Non-Small Cell Lung Cancer, prognostic biomarkers

Send correspondence to: Fabio Klamt, Centro de Estudos em Estresse Oxidativo, Departamento de Bioquimica, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos 2600-anexo, Porto Alegre 90035-003, Brazil, Tel: 555133085577, Fax: 55513308 535, E-mail:00025267@ufrgs.br