Background
Non-small cell lung cancer (NSCLC) comprises about 85% of all lung cancers. About 32.3% of NSCLC have mutation(s) of epidermal growth factor receptor (EGFR), ranging from 17.4% in Caucasian to 38.8% in Asian [
1]. In addition to Asian ethnicity,
EGFR mutations are well known for being more common among females and never smokers diagnosed with NSCLC [
1,
2].
EGFR gene mutations associated with NSCLC occur in the tyrosine kinase domain (exons 18 to 21) and lead to constitutive activation of the EGFR tyrosine kinase [
3]. Some constitutively activated mutant
EGFR proteins are sensitive to EGFR tyrosine kinase inhibitor (TKI) drugs, such as those encoded by
EGFR genes with exon 19 deletion mutations or exon 21 L858R point mutation, whereas others are not, such as those encoded by
EGFR genes with exon 20 insertion mutations [
3]. When first introduced into clinical use, EGFR-TKIs were approved for use for any patient with NSCLC without molecular selection [
4]. Since then, several randomised trials have shown that NSCLC patients with activating
EGFR gene mutations are responsive to EGFR tyrosine kinase inhibitors (EGFR-TKI) such as gefitinib and erlotinib [
5‐
12]. A meta-analysis including seven trials showed that EGFR-TKIs resulted in prolonged PFS overall and in all subgroups compared to chemotherapy, with greater benefits in patients with exon 19 deletions, no smoking history and in female patients [
13].
Testing for
EGFR mutations has become a critical first step in personalised treatment of lung cancer. For several years now, clinical practice guidelines have recommended
EGFR mutation testing for most patients with NSCLC, for individualising treatment and selecting patients for EGFR-TKI therapy [
14‐
16]. These guidelines recommend against using demographic or clinicopathological factors for selecting patients for testing [
14‐
16]. Not testing all eligible patients risks missing some patients with
EGFR mutations, who will miss out on treatment with EGFR-TKIs and their well-known clinical benefits. Not testing also risks treating some patients without
EGFR mutations with EGFR-TKIs, who have little or no chance of benefit.
EGFR mutation testing methodologies have improved in recent times, for example, in their analytical sensitivity for detecting low levels of mutations in tissue specimens and body fluids, such as blood plasma and pleural effusions [
17].
Despite clinical guidelines and improved methodologies for testing, the potential of personalised treatment of lung cancer for improving patient outcomes has not yet been fully realised in the setting of routine care. Testing rates remain low in many parts of the world, fuelled by sample limitations, funding constraints and selective testing referral practices. For example, our recent systematic review of studies from throughout the globe that had evaluated the utilisation of
EGFR mutation testing in the setting of routine care, found that less than one third of a total of over 50,000 patients from 18 eligible studies were tested for
EGFR mutations [
18]. So, the implementation of
EGFR mutation testing into routine clinical practice appears to have been less successful than might have been expected. Further effort will be required beyond aspirational guidelines and new testing methods to increase testing rates and appropriate use of EGFR-TKIs. To do so, estimation of pretest probability of
EGFR mutations from universally available demographic factors has been suggested as a potential adjunct to mutation testing [
19].
EGFR-TKIs became available in New Zealand from October 2010 [
20].
EGFR gene mutation testing has been recommended in New Zealand for all NSCLC patients, except those with confidently diagnosed squamous cell carcinoma, since May 2013 [
20]. Soon after testing had commenced in New Zealand, we began a population-based cohort study of non-squamous NSCLC patients presenting in northern New Zealand, which is on-going. Previously we reported on the uptake and impact of
EGFR mutation testing in 1857 cohort patients diagnosed up until April 2014 [
20];
EGFR mutation retesting of a subgroup of 532 cohort patients [
21]; the impact of incomplete uptake of testing on estimates of mutation prevalence in 2701 cohort patients diagnosed up until December 2015 [
22], and screening for ALK gene rearrangements in 3130 cohort patients diagnosed up until July 2016 [
23]. In this large population-based study, in northern New Zealand, only 3.7% of non-squamous NSCLC patients were tested in 2010; this increased to 64.6% in 2014 and remained stable afterwards [
20,
22]. These suboptimal testing rates were explained by selective referral practices and the lack of suitable tumour specimens being available for testing [
20,
22].
EGFR mutation testing of plasma (liquid biopsy) offers one solution [
24,
25] but it is prone to false negative test results, and it is expensive and not readily available in New Zealand. Thus, a good estimate of
EGFR mutation probabilities would assist clinical decision making for treatment with EGFR-TKIs for patients with no test result available.
In a literature review up to Aug 2019, we identified nine
EGFR mutation prediction models [
26‐
34] that had been validated in an independent dataset. However, those studies were based on limited numbers of patients, confined to non-Asian patient populations, or included predictors that are routinely unavailable such as certain radiological features. The validity of these models in the New Zealand context is unknown, and may be more limited as New Zealand has diverse ethnic groups including Māori and Pacific people. Thus, we aimed to develop and validate a model based on the New Zealand patient data to estimate the probability of
EGFR mutations in patients with non-squamous NSCLC. To do so, we further expanded our population-based retrospective cohort study to include a total of 3556 patients from northern New Zealand diagnosed with non-squamous NSCLC up until July 2017. Our analysis confirmed associations of
EGFR mutations with gender, ethnicity and smoking status in a New Zealand context, and allowed us to develop and validate a statistical model for estimating the
EGFR mutation probability, based on readily available demographic factors, in our local patient population.
Discussion
We developed a model to estimate the probability of
EGFR mutation based on a population-based series of 1176 non-squamous NSCLC patients in northern New Zealand. Our model included three predictors that were significantly associated with the
EGFR mutation status in the multivariable analysis: sex, ethnicity and smoking status. The female sex, Asian ethnicity and being a non-smoker were highly associated with higher prevalence of
EGFR mutation, as observed in previous studies [
1,
2].
We presented the fitted model using a nomogram, which is an increasingly used format for clinical prediction models for its ability to provide exact predictions [
44]. We validated the model using established performance measures [
44]. The model showed good calibration with the mean predicted probabilities being within the 95% limits of the observed values in all the groups for both development and validation. The goodness-of-fit was slightly better in the validation group than the development group. The AUCs of 0.78 in the development group and 0.75 in the validation group inferred that our model performed reasonably well. Further, in a retrospective group of NSCLC patients treated with EGFR-TKIs without
EGFR mutation testing, patients with higher
EGFR mutation probabilities estimated from the model had significantly longer overall survival and longer duration of EGFR-TKI treatment than those with lower
EGFR mutation probabilities.
We considered possible limitations of our model. The patients included in our model were of necessity those who had been tested for the
EGFR gene mutation. Our earlier work showed that
EGFR mutation testing increased from 3.7% of all patients in 2010 to 64.6% in 2014 in this population-based retrospective cohort [
20]. In parallel, recorded
EGFR mutation rates decreased from 43.8% in 2010 to 16.8% in 2014, reflecting decreases in selective testing [
22]. Taking into account this variation, we assessed the external validity of the model in the independent earlier period dataset, and the results were similar to those in the development group. The
EGFR mutation prevalence in this study is within the range of the largest systematic review, being 47% in Asia-Pacific region and 12% in Australia [
2]. The predictive model does not provide information about what particular
EGFR mutation may be present, which could be important for clinical decision-making.
Models with combined clinical factors and imaging features may improve performance in predicting
EGFR mutation status [
26,
28,
33,
45‐
48]. However, extracting radiological features from clinical or radiological reports is complex unless a particular recording system is added to routine records for this purpose. For instance, in Zhang et al. [
28] study, as many as 485 CT features were used for their Rad_signature scoring system, which is unlikely to be feasible in our setting. Thus, we developed the current model with the important available clinical factors only.
Our model includes New Zealand specific ethnicities including Māori and Pacific people. Māori and Pacific people have a higher incidence of lung cancer and poorer survival, compared to the New Zealand European population [
49]. But, the testing rate was particularly low in Māori patients compared to other ethnic groups [
22]. Our model may be helpful in addressing ethnic disparity in lung cancer patients in New Zealand. Moreover, a combined nomogram for both Asian and non-Asian populations showed unsatisfactory accuracy in the study of Gevaert et al. [
26]. It claimed that Asian patients had substantially different distributions of the predictors. Thus, developing ethnic specific models may be relevant in future research.
We categorised the patients into three groups based on the probability of
EGFR mutation positivity: low (< 0.2), medium (0.2–0.6) and high (> 0.6) probability groups. We then compared the duration of benefit and the overall survival from the start of EGFR-TKI treatment between the three probability subgroups in a group who had been treated with EGFR-TKIs second-line, without a tissue test result for mutations. The outcomes were significantly more favourable in the higher probability group than the lower probability group with outcomes of the medium probability group being intermediate of the other two. These findings demonstrate that our model has the potential to predict mutation status and can differentiate between untested patients who have good outcomes from EGFR-TKI treatment and those who will have poor treatment outcomes. Thus, when testing is not possible, those in the high probability group could be considered for EGFR-TKI treatment. Conversely, those in the low probability group should not receive an EGFR-TKI. These findings are consistent with published randomised controlled clinical trials showing the relative benefits of EGFR-TKIs versus chemotherapy for untested NSCLC patients to critically depend upon the proportion of patients demonstrated to have
EGFR mutations by post hoc mutation testing [
6,
7,
50‐
52].
EGFR mutation status can also be estimated by liquid biopsy to detect circulating DNA in plasma. The sensitivity of this, compared with tissue biopsy, varies considerably in different series and with the methods used, but may be about 85% in advanced disease, but lower in less advanced cases [
24,
25]. However, these methods are expensive and not readily available in New Zealand. False negative results are of concern. While our
EGFR mutation predictive model cannot replace molecular testing, in patients for whom tissue biopsy is difficult, it could be used in conjunction with liquid biopsy, giving further attention to patients with a high estimated probability, but a negative liquid biopsy result, suggesting a false negative.
Our study is moderate in size, and applies to a multi-ethnic population in New Zealand, so application to other populations requires further studies. Our model used only three factors, and other factors such as radiological appearances, blood markers such as CEA [
53], or more precise classification of smoking history, may yield improved models.
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