K-ras oncogene mutation as a prognostic marker in non-small cell lung cancer: a combined analysis of 881 cases

Michael Huncharek1,2,5, Joshua Muscat3 and Jean-Francois Geschwind4

1 Department of Radiology, University of South Carolina School of Medicine, Columbia, SC,
2 Meta-Analysis Research Group, Columbia, SC,
3 Division of Epidemiology, American Health Foundation, New York, NY and
4 Department of Interventional Radiology, Johns Hopkins Hospital, Johns Hopkins School of Medicine, Baltimore, MD, USA


    Abstract
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The treatment of non-small cell lung cancer (NSCLC) remains unsatisfactory, with most patients succumbing to metastatic disease within 5 years of diagnosis. Improved selection of patients for aggressive adjuvant therapy may contribute to improved survival. Mutation of the k-ras oncogene is considered a potentially clinically useful prognostic biomarker for this purpose. This report presents the results of a meta-analysis performed to determine whether the existing data support such a role for k-ras mutations in NSCLC. Two year survival data derived from 881 NSCLC patients in eight published studies were analyzed using a general variance-based meta-analytical method employing confidence intervals. The outcome of interest was a summary relative risk (RRs) reflecting the risk of death at 2 years associated with k-ras mutation-positive versus k-ras mutation-negative disease. Prior to calculation of RRs, analysis for heterogeneity showed Q to equal 15.52 (7 df, P = 0.03). This indicated heterogeneity across the analyzed studies in terms of their estimate of effect. Possible sources of heterogeneity were identified and included selection bias and various other sources of uncontrolled confounding. Although a RRs of 2.35 (95% CI = 1.61–3.22) was found when all eight studies were combined (favoring a negative prognostic role for k-ras mutation), it is unclear whether the magnitude of the RRs would persist after adjusting for other well-established prognostic indicators (e.g. stage). The existing data suggest that k-ras mutation may be associated with shortened survival in NSCLC, although this finding awaits confirmation in well-designed multivariate analyses which adjust for the effect of known prognostic indicators.

Abbreviations: NSCLC, non-small cell lung cancer


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Lung cancer is the leading cause of cancer-related death in the USA, accounting for ~28% of all cancer deaths (1). Despite advances in clinical management, mortality and incidence have shown similar increasing trends over the last two decades, although there has been a recent decline (2). Unfortunately, only 13% of patients diagnosed with lung cancer survive 5 years, highlighting the need for improved therapeutic interventions (2).

The treatment of non-small cell lung cancer (NSCLC) remains a clinical challenge. At present, surgical resection of early stage disease represents the only treatment associated with a high likelihood of 5 year survival. Nonetheless, even among stage I and II patients undergoing potentially curative resection, 5 year survival ranges from 25 to 45% (2). Death is usually due to metastatic disease. Patients with more advanced disease, i.e. stages IIIa and IIIb, have a 5 year survival of 15 and 5%, respectively. Long-term survival with metastatic NSCLC is rare.

The fact that a large percentage of patients with early stage disease undergoing `curative' complete resection die of metastatic disease presents difficult management issues for the clinician. Ideally, identification of markers predictive of disease recurrence/treatment failure would potentially allow modifications of current treatment protocols to target certain patients for adjuvant treatment. In this way, aggressive therapy could be directed toward patient groups most likely to benefit from such treatment.

In recent years, developments in molecular biology suggest that multiple genetic changes characterize the carcinogenic process in many human tumors (3). NSCL tumors are known to exhibit multiple cytogenetic and molecular abnormalities consistent with the theory that malignant transformation requires a stepwise accumulation of genetic damage (4).

Mutations of the k-ras oncogene have been found in a variety of human tumors, predominantly in adenocarcinomas of the pancreas, colon and ~30% of adenocarcinomas of the lung (5). This gene (as well as the other members of the ras family) encodes a protein that binds guanine nucleotides, has GTPase activity, is bound to the inner side of the cell membrane and is involved in signal transduction (6). Mutations of this gene are thought to play an important role in the development of a subset of NSCLCs and prior work suggests that such mutations represent a possible biomarker of poor survival, particularly in early stage disease. Unfortunately, this finding is not universal (7). The objective of this report was to present a meta-analysis/systematic review of the available data on the prognostic significance of k-ras mutations in NSCLC. This type of `clinical molecular epidemiological' study may provide a clearer understanding of the clinical relevance of this biomarker and suggest avenues for future research.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
The methods used in the design and execution of this study have been described previously (8,9). A study protocol was initially developed outlining a meta-analysis to examine the prognostic significance of k-ras mutations in NSCLC. Two year survival was the outcome of interest. Eligibility criteria for study inclusion were determined prospectively, as were the specific data elements to be extracted from each published report. The study protocol also included details of the planned statistical analysis.

A data extraction form was designed for recording relevant information from each selected paper. Data extraction was performed by two research physicians (one oncologist) with differences in extraction forms resolved by consensus.

Other data collected but not included in the eligibility criteria were number of patients included in each study, information on selection criteria for patients/tumor samples and specific k-ras codon(s) analyzed.

Literature search
Information retrieval was performed by previously described methods (8,9). Briefly, a MEDLARS search was conducted covering the years 1985–1997. CancerLit and the CD-ROM version of Current Contents were also reviewed. The search was limited to English language literature. If a series of papers was published, all data were retrieved from the most recent report. Hand searches of bibliographies of published papers, review articles and textbooks were also performed.

The initial citations (in the form of abstracts) from this literature search were screened by a physician investigator to exclude those that did not meet protocol-specified inclusion criteria. Reasons for rejection included studies examining NSCLC cell lines, studies including patients with non-NSCLC histologies, such as carcinoid or tumor metastatic to the lung, studies employing only immunohistochemistry to detect k-ras abnormalities, animal studies, abstracts and review articles. Copies of full articles for the remaining citations were obtained and screened using the following additional eligibility criteria: (i) published randomized controlled trials (RCTs), non-randomized controlled trials (nRCTs), case–control studies, cohort studies or case series examining the prognostic influence of k-ras mutations in NSCLC; (ii) studies employing paraffin-embedded or fresh tissue for k-ras mutation analysis; (iii) studies documenting k-ras mutations via DNA sequencing techniques; (iv) published studies with data on the outcome of interest, i.e. 2 year survival; (v) availability of information on disease stage.

Statistical methods
Data analysis was performed according to meta-analysis procedures described by Greenland (10). This meta-analysis method is a general variance-based method employing confidence intervals. Because the variance estimates are based on the adjusted measures of effect and on the 95% confidence interval (CI) for the adjusted measure, the CI methods do not ignore confounding factors and are the preferred methodology for non-randomized study data. Odds ratios reflecting the odds of death from lung cancer at 2 years are calculated for each included study. The natural logarithm of the estimated relative risk is determined for each study followed by an estimate of the variance. The estimate of the 95% CI from each study is used to estimate the variance of each study's effect measure. A weight for each included study is calculated as 1/variance followed by a summation of the weights. The product of the study weight and the natural logarithm of the estimated relative risk is then determined. A summation of these products is then performed. Finally, a summary relative risk (RRs) and 95% CI is calculated (10).

Prior to estimation of the RRs, a statistical test for heterogeneity was performed (Q). This procedure tests the hypothesis that the effect sizes are equal in all studies (8). If Q exceeds the upper tail critical value of the {chi}2 distribution at k 1 degrees of freedom (where k is the number of studies analyzed or the number of statistical comparisons), the observed variances in study effect sizes is significantly greater than would be expected by chance if all studies shared a common population effect size. If the hypothesis that the studies are homogenous is rejected, the studies are not measuring an effect of the same size and calculation of a pooled estimate of effect must be done cautiously. Possible explanations for the observed heterogeneity must be sought in order to provide the most rational interpretation of the RRs. Sensitivity and/or further stratified analyses were performed as needed based on the magnitude of Q and are discussed below.


    Results
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
A total of 1080 citations were obtained from the literature search. Initial screening of these citations eliminated ~95% from further consideration. From the remaining 5%, 18 full papers were obtained and reviewed (7,1127). Ten of these were excluded from the analysis for a variety of reasons. The paper by Isobe et al. (11) did not contain the required survival data, whereas the report by Mitsudomi et al. (14) employed only NSCLC cell lines without corresponding fresh or paraffin-embedded tissue. Rosell et al. (16) only presented a survival curve for patients with stage III disease without data on the remaining patients. Mitsudomi et al. (19) was excluded since only NSCLC cell lines were analyzed, whereas data from Rosell et al. (20,23) were included in Rosell et al. (22). Therefore, as specified previously, only data from Rosell et al. (22) were included in the meta-analysis. Likewise, information reported by Sugio et al. (17) were subsequently presented in Fukuyama et al. (24). Only the latter analysis was included in the present study.

Rodenhuis et al. (25) did not cite 2 year survival data and also included non-lung tumors, which was not consistent with the eligibility criteria. Rosell et al. (26) dealt primarily with replication error-type instability in NSCLC and did not provide independent data on the prognostic significance of ras mutations. Finally, the report by Kashii et al. (27) was excluded from our analysis since it included data on tumor histologies other than NSCLC which could not be stratified by tumor type. The remaining eight published papers provide the basis for the present analysis.

Table IGo provides an overview of the studies combined in the meta-analysis. A total of 881 tumor samples were analyzed, with 217 positive for k-ras mutations (25%). Four studies included patients with all stages of NSCLC (7,15,22,24) while one report presented only patients with advanced disease, i.e. stages III and IV (21). Although mutations of the k-ras oncogene have been detected primarily in adenocarcinomas, three reports (18,22,24) analyzed tumors of all NSCLC cell types. Likewise, although the majority of k-ras mutations occur at codon 12, four of the included studies sequenced codons 13 and 61 in addition to 12 (15,18,21,22).


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Table I. Study characteristics
 
Initially, all eight studies were included in an analysis for heterogeneity (Q), as previously described. Table IIGo displays the data for each study used for this purpose. Q was found to equal 15.52. With 7 degrees of freedom (df), the P value for a Q of this size is equal to 0.03. This indicates that the studies are heterogeneous, i.e. the studies are not measuring an effect of the same size. If the data from the eight studies are combined, an RRs of 2.35 is obtained with a 95% CI of 1.61–3.22 (favoring a prognostic role for k-ras mutations, i.e. mutations are associated with a greater chance of death at 2 years). Due to the observed heterogeneity, this summary estimate of effect must be presented with several caveats.


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Table II. Data for analysis of heterogeneity
 
Table IIGo shows that the odds ratios for the analyzed studies range from 1.08 to 10.14. Although these odds ratios generally favor a prognostic role for k-ras mutations, they range over an order of magnitude. Four studies have confidence intervals which include the null value (statistically non-significant), whereas three studies have very wide CIs with an upper limit exceeding 20 (ranging from 20.28 to 45.45). These characteristics further demonstrate the observed heterogeneity.

By examining the study characteristics as outlined in Tables I and IIIGoGo, other sources of variability were sought. These included differences in tumor histology, patient stage and codon analyzed. Combining studies including patients with all stages of lung cancer versus only early or late stage disease yielded a Q or 16.45 with 4 df, a strongly heterogeneous result. This suggests that the patient data included may be biased due to differences in stage distribution. If k-ras mutations differ in frequency with stage or if there are imbalances in the number of patients in each stage across studies (assuming a constant mutation frequency with stage), a biased RRs may result.


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Table III. Selected study characteristics which may contribute to heterogeneity
 
Since k-ras appears more frequently associated with adenocarcinoma than other non-small cell types, Q was recalculated combining only those papers including this tumor type. Q was found to equal 9.94 with 4 df. This is a heterogeneous result, although less so than that seen when all eight papers were pooled. Survival analyses may therefore be influenced by histology, i.e. the biological influence of k-ras may differ with tumor type.

Another potentially important source of heterogeneity is the type of mutational analysis performed. As stated earlier, the studies are inconsistent with the specific k-ras codon(s) analyzed. Some authors studied only codon 12 mutations (24), whereas others analyzed codons 12, 13 and 61 (12,15,18,21,22). One paper (12) analyzed codons 12, 13 and 61 in approximately half of the tumors and only codon 12 in the remainder. Pooling of studies which sequenced codons 12, 13 and 61 versus only 12 or only 12 and 13 showed a significant reduction in the observed heterogeneity (Q = 7.69 with 4 df, P = 0.11). It is possible that some k-ras mutations are more `biologically significant' than others in terms of their impact on tumor behavior. If the studies are not balanced with regard to such mutations, wide variability in the odds ratios could result, as observed in the present report. This could result in a spurious association between mutation and survival.

Table IIIGo summarizes additional study characteristics which may contribute to differences in outcome across the analyzed papers. One of the most striking is the lack of a systematic method for tumor sample selection. Most study designs did not incorporate a systematic method of specimen selection. Arbitrary sample collection based on availability of paraffin-embedded blocks or random collection of fresh tumor samples from resected patients could result in significant selection bias. Another possible source of selection bias is the inclusion only of samples obtained from resected patients. Only two studies (15,21) included data on some non-resected patients, e.g. those undergoing only biopsy. Especially in stage III disease, only a small proportion of patients are suitable for surgery (stage IIIa), whereas most stage IIIb patients are not considered surgical candidates.


    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
ras gene mutations appear to play an important role in the development of a variety of human tumors. Activation of ras is thought to represent only one step in the `genetic cascade' of events leading to malignant transformation. In vitro work demonstrates that transfection of an activated ras gene can transform normal mouse fibroblasts (6). This finding raises the question whether mutations of this gene provide a selective growth advantage to such cells. If a selective growth advantage exists, it may manifest itself clinically in the form of aggressive tumor growth and/or resistance to various medical interventions, e.g. chemotherapy. These are important considerations in relation to NSCLC since available therapeutic interventions produce few long-term survivors even among patients with early stage disease.

Numerous investigators have suggested that activating mutations of the k-ras oncogene are an important prognostic biomarker in NSCLC (5). Our combined analysis of almost 900 patients shows that k-ras mutations may be associated with an almost doubled risk of death at 2 years from NSCLC. As discussed above, this finding awaits confirmation due to numerous limitations of the available data.

The presented data demonstrate considerable statistical heterogeneity across the included studies. Such factors as uncontrolled confounding factors and selection bias in tumor sample procurement could potentially introduce great variability in study outcomes. The odds ratios of the eight studies included are all >1.0 (favoring an association between k-ras mutation and decreased survival). Nonetheless, they vary over an order of magnitude, suggesting that other characteristics are influencing patient survival in addition to k-ras mutation. Mutation type may be important in that certain k-ras amino acid substitutions could contribute to more or less biologically aggressive disease.

Siegfried et al. (7) present data on 181 adenocarcinomas yielding 57 k-ras mutations. Codons 12 and 13 were analyzed using PCR and DNA sequencing. No difference was observed in the survival of patients based on the presence or absence of k-ras mutations as a group. Approximately 95% of the mutations were found in codon 12. Interestingly, substitution of cysteine, arginine or aspartate for the wild-type glycine was associated with a poor prognosis (P < 0.001). In a multivariate analysis adjusting for stage and age, the prognostic significance of these amino acid changes was not maintained (P = 0.346). Stage was found to be the best predictor of survival (P < 0.001).

Since treatment for NSCLC differs by stage, differences in stage distribution across studies could bias the results if analyses are not controlled for this factor. If ras mutation has any impact on chemotherapy or radiation therapy response, stage distribution will be of even greater concern. Without a clearer understanding of the biological significance of such findings, integration of these molecular markers with `standard' clinical prognostic parameters is limited.

A prognostic marker of clinical significance would represent an important advance in the management of NSCLC. Future studies should incorporate methods to reduce selection bias in tumor sample procurement, include a sample size sufficient to demonstrate an effect of a predetermined magnitude, consider mutation type as a possibly important prognostic variable and provide a multivariate analysis which includes known and suspected prognostic variables. Multivariate analyses would provide insight into whether k-ras mutation type/status is of greater prognostic predictive value than other known clinical or biological parameters.


    Notes
 
5 To whom correspondence should be addressed at: Meta-Analysis Research Group, 1520 Senate Street, Suite 74, Columbia, SC 29201, USA Email: metaresearch{at}hotmail.com Back


    References
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Received August 21, 1998; revised March 3, 1999; accepted March 17, 1999.