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First-line treatment of advanced epidermal growth factor receptor (EGFR) mutation positive non-squamous non-small cell lung cancer

  1. John A Green1,*,
  2. Victoria Bates2,
  3. Janette Greenhalgh3,
  4. Angela Boland3,
  5. Pooja Jain4,
  6. Rumona C Dickson3,
  7. Kerry Dwan5,
  8. Fabio Vecchio3

Editorial Group: Cochrane Lung Cancer Group

Published Online: 28 FEB 2013

Assessed as up-to-date: 5 JUL 2012

DOI: 10.1002/14651858.CD010383

How to Cite

Green JA, Bates V, Greenhalgh J, Boland A, Jain P, Dickson RC, Dwan K, Vecchio F. First-line treatment of advanced epidermal growth factor receptor (EGFR) mutation positive non-squamous non-small cell lung cancer (Protocol). Cochrane Database of Systematic Reviews 2013, Issue 2. Art. No.: CD010383. DOI: 10.1002/14651858.CD010383.

Author Information

  1. 1

    University of Liverpool, Institute of Translational Medicine, Bebington, Merseyside, UK

  2. 2

    The Clatterbridge Cancer Centre NHS FT, Wirral, UK

  3. 3

    University of Liverpool, Liverpool Reviews and Implementation Group, Liverpool, UK

  4. 4

    Clatterbridge Cancer Centre, Bebington, Merseyside, UK

  5. 5

    University of Liverpool, Institute of Child Health, Liverpool, England, UK

*John A Green, Institute of Translational Medicine, University of Liverpool, Clatterbridge Cancer Centre, Clatterbridge Road, Bebington, Merseyside, CH63 4JY, UK. J.A.Green@liverpool.ac.uk.

Publication History

  1. Publication Status: Edited (no change to conclusions)
  2. Published Online: 28 FEB 2013

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Background

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest
 

Description of the condition

Lung cancer is the most common cancer in the world and the second most common cancer diagnosed in Europe, after breast cancer (Cancer Research UK). In 2009 there were 41,428 new cases of lung cancer diagnosed in the UK (Cancer Research UK) and 1.61 million diagnosed globally in 2008, representing 12.7% of all cancers (GLOBOCAN 2008). Lung cancer is rarely diagnosed in people younger than 40 years of age and 75% of cases occur in people over the age of 65 years (Cancer Research UK). In both men and women smoking is the primary cause of lung cancer and the prognosis is poor. Early stage lung cancer is often asymptomatic, and over three-quarters of patients are diagnosed at a late stage (National Cancer Institute).

Non-small cell lung cancer (NSCLC) accounts for approximately 84% of lung cancer cases and comprises two main histological subgroups, squamous cell carcinoma and non-squamous cell carcinoma (Schiller 2002). Squamous cell carcinoma accounts for 33% of all NSCLC cases while non-squamous carcinoma (including adenocarcinoma and large cell carcinoma) accounts for 29% of NSCLC instances. Approximately 36% of patients have NSCLC that is ‘not-otherwise specified’ with the diagnosis based on cytology alone and 1% have carcinoma in situ (Schiller 2002). The prognosis for patients with metastatic NSCLC is poor, with a median survival in the order of 11 months (Schiller 2002).

In recent years the clinical subtypes of NSCLC have become relevant to the selection of treatment regimens. It is estimated that 10% to 15% of patients with non-squamous NSCLC will have tumours with activating mutations of the epidermal growth factor receptor (EGFR M+) (Peters 2012; Rosell 2012). EGFR, a protein located on the cell surface, binds to the activated epidermal growth factor. This binding induces receptor dimerization and tyrosine kinase autophosphorylation, leading to cell proliferation (NCBI). This mechanism provides the rationale for this subtype being dependent on activated EGFR. An EGFR mutation frequency of 21% was reported by Shigematsu 2005 and is more frequently observed in never smokers than ever smokers (51% versus 10%), in adenocarcinomas versus cancer of other histologies (40% versus 3%), in patients of East Asian ethnicity versus other ethnicities (30% versus 8%), and in females versus males (42% versus 14%). These findings are supported by other studies demonstrating EGFR mutations (exons 18 to 21) in 17% to 20% of samples of NSCLC (Rosell 2009; Ulivi 2012) and almost exclusively in never smoking females (P = 0.0067) (Scoccianti 2012). The identification of patients with EGFR M+ tumours has led to the development of targeted therapies directed at the EGFR to treat this group, and five randomised trials have been published in full to date demonstrating longer progression-free survival in first–line treatment (Han 2012; Maemondo 2010; Mitsudomi 2010; Mok 2009; Rosell 2012; Zhou 2011).

 

Description of the intervention

Treatment for patients with NSCLC is determined not only by the histological subtype and genetic subtype of the patient, but also by disease stage, co-morbidity, and performance status. Chemotherapy for advanced disease can extend overall survival by several months compared to best supportive care and may improve quality of life, but it may not be appropriate for many patients with poor performance status (Spiro 2004).

Patients of interest to this review are chemotherapy-naive patients with locally advanced or metastatic (Stage IIIB or IV) EGFR M+ NSCLC who are not suitable for treatment with curative intent, such as surgery or radical radiotherapy.

In the UK, specific first-line chemotherapy treatment options for patients with EGFR M+ NSCLC include erlotinib and gefitinib; both drugs are tyrosine kinase inhibitors of EGFR. These drugs target proteins on the cancer cells. A number of other drugs (for example cetuximab) are currently under clinical investigation but are not yet licensed for the first-line treatment of patients with EGFR M+ disease. In Europe, the recommended treatment for NSCLC with EGFR M+ is erlotinib or gefinitib (European Medicines Agency; Peters 2012) while in the USA erlotinib is recommended (US Food and Drug Administration). Gefinitib is not licensed in the USA, and globally there is considerable variation in the use of each of these drugs in NSCLC.

In the UK, the National Institute for Health and Clinical Excellence (NICE) has recommended two first-line treatment options for patients with previously untreated locally advanced or metastatic EGFR M+ NSCLC. Gefitinib was recommended in TA192 (NICE 2010) and in guidance that was published in June 2012. Erlotinib was also recommended as a first-line treatment.

 

Why it is important to do this review

Treatments for patients with NSCLC are evolving rapidly. Up until early 2000, patients with NSCLC were offered standard cytotoxic chemotherapy treatments (for example docetaxel, vinorelbine, paclitaxel and gemcitabine). However, in recent years patients have been treated with drugs according to their disease histology (for example pemetrexed for non-squamous disease). Even more recently, as understanding of NSCLC has evolved, targeted treatments have been developed (for example tyrosine kinase inhibitors and monoclonal antibodies) to treat specific groups of patients. It is estimated that around 10% (n ≈ 4000 annually) of all lung cancer patients in the UK have locally advanced or metastatic EGFR M+ NSCLC (NICE 2010). It is, therefore, important to synthesise the clinical effectiveness evidence of these innovative treatments to ensure that patients are being treated with the most clinically effective drugs for their specific disease type.

 

Objectives

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest

The systematic review will examine the clinical effectiveness of single or combination EGFR therapies used in the first-line treatment of patients with locally advanced or metastatic EGFR M+NSCLC compared with other cytotoxic agents used alone or in combination, or best supportive care. Maintenance or second-line strategies will not be evaluated.

 

Methods

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest
 

Criteria for considering studies for this review

 

Types of studies

Only parallel randomised controlled trials (RCTs) will be considered for this study.

 

Types of participants

Chemotherapy-naive patients with locally advanced or metastatic (Stage IIIB or IV) EGFR M+ NSCLC who are not suitable for treatment with curative intent, such as surgery or radical radiotherapy.

 

Types of interventions

EGFR M+ targeted agents, alone or in combination with cytotoxic agents, compared with cytotoxic agents used alone or in combination, or best supportive care.

Studies comparing single agents or combinations of cytotoxic chemotherapy without a targeted therapy in either arm will be excluded.

 

Types of outcome measures

 

Primary outcomes

The primary outcome will be overall survival.

 

Secondary outcomes

Additional outcomes will be considered for inclusion.

Progression-free survival.
Tumour response.
Toxicity and adverse effects of treatment.
Quality of life (QoL) (e.g. Functional Assessment of Cancer Therapy - Lung (FACT_L) and Trial Outcome Index (TOI)).
Symptom palliation.

 

Search methods for identification of studies

The search will incorporate a number of methods to identify completed or ongoing studies.

 

Electronic searches

The following electronic databases will be searched for relevant published literature. Searches will not be restricted by language.
CENTRAL (Cochrane Central Register of Controlled Trials) (The Cochrane Library).
CDSR (Cochrane Database of Systematic Reviews).
DARE (Database of Abstracts of Reviews of Effectiveness).
EMBASE (OvidSP).
Health Technology Assessment (HTA) database.
ISI Web of Science - Proceedings (Index to Scientific & Technical Proceedings).
MEDLINE (accessed via PubMed and OvidSP).
ISI Web of Science - Science Citation Index Expanded.

The draft search strategy that will be used to explore MEDLINE (via Ovid) is outlined in Appendix 1 and will be adapted, as appropriate, for the remaining databases.

 

Searching other resources

Other resources include: searching of bibliographies of identified sources, use of Evidence Review Group (ERG) reports to NICE. Where electronic search facilities exist, proceedings of relevant conferences such as the American Society for Clinical Oncology (ASCO) will also be searched. If data are available, they will be considered for inclusion in the review.

A database of relevant references will be developed using EndNote X5 software.

 

Data collection and analysis

 

Selection of studies

Two review authors will independently take part in all stages of study selection. Firstly, review authors will independently scan the titles and abstracts of references identified by searching. Full details of possibly relevant studies will be obtained and assessed independently for inclusion in the review. If a disagreement occurs, the review authors will attempt to reach a consensus by discussion, or involve a third review author. Studies that do not meet all of the inclusion criteria will be excluded and their bibliographic details listed with reasons for exclusion. Ongoing studies that do not report relevant outcomes but meet the inclusion criteria will be listed for future use. For studies published in abstract form only, if it is clear that a study is eligible then it will be included. If it is not clear, authors will be contacted for further information and the study will be placed in ‘awaiting assessment’ until a reply is received (in an appropriate timeframe).

 

Data extraction and management

Data extraction will be carried out by one review author using pre-tested data extraction forms and the data independently checked for accuracy by a second review author. Data relating to the outcome measures as well as information on study design and participants (for example baseline characteristics) will be extracted. Data from studies presented in multiple publications will be extracted and reported as a single study with all other relevant publications listed.

 

Assessment of risk of bias in included studies

Included trials will be independently assessed for risk of bias using criteria outlined in Chapter 8 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). Risk of bias will be assessed using the seven evidence-based domains listed.

1. Random sequence generation (selection bias).

2. Allocation concealment (selection bias).

3. Blinding of participants (performance bias).

4. Blinding of outcome assessment (detection bias).

5. Incomplete outcome data (attrition bias).

6. Selective outcome reporting (reporting bias).

7. Any other identified bias, including inappropriate influence of funders.

Bias will be reported as either high, low or unclear (further details of reporting bias are outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). The domains blinding and incomplete outcome data will be assessed at the outcome level.

Summary of findings tables will be created for outcomes and each outcome will be graded accordingly using the GRADE approach (GRADE Working Group 2004).

 

Measures of treatment effect

For binary outcomes, where sufficient data are available, relative treatment effects will be presented in the form of relative risks (RR) with 95% confidence intervals (CI). For continuous outcomes, mean differences (MD) and 95% CIs will be calculated provided there is no evidence that the data are subject to skew. If statistical tests used in the original paper are for skewed data, or if median and interquartile ranges are reported, we will assume the data are skewed. Standardised mean differences (SMDs) will be calculated for QoL variables where appropriate. For time to event outcomes, log hazard ratios (log HR) will be extracted, when available, with 95% CI. If the log HR is not reported, individual patient data will be requested from authors.

Trials that: (1) provide only unplanned, interim findings, and (2) are continuing to recruit patients will be considered for inclusion in the review but will not be included in the meta-analysis.

 

Unit of analysis issues

Cross-over trials will not be included in the review.

 

Dealing with missing data

Authors (and sponsors) of the studies will be contacted for missing data.

 

Assessment of heterogeneity

Statistical heterogeneity between studies will be assessed visually by inspection of the forest plots and using the Chi2 test (P < 0.1 will be considered significant due to the low power of the test). The I2 statistic will also be calculated, this describes the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error (chance). Values of I2 range from 0 to 100, with 0 representing no heterogeneity and 100 representing considerable heterogeneity.

For this review:

  • 0% to 40%, heterogeneity might not be important;
  • 30% to 60% may represent moderate heterogeneity;
  • 50% to 90% may represent substantial heterogeneity and
  • 75% to 100%, considerable heterogeneity.

 

Assessment of reporting biases

If a sufficient number of studies are identified (approximately 10), a funnel plot will be constructed. If asymmetry is present other possible causes will be explored, such as heterogeneity or outcome reporting bias.

 

Data synthesis

Individual study data will be summarised in structured tables and as a narrative description. For time to event outcomes, data will be combined using the generic inverse variance method. For continuous outcomes, the inverse variance method will be used and for dichotomous outcomes the Mantel-Haenszel method will be used.

Meta-analyses will be conducted using the fixed-effect model unless there is substantial heterogeneity (I2 > 50%) and then a random-effects model will be used. If there is considerable heterogeneity (I2 > 75%) data will still be combined but conclusions will highlight the amount of heterogeneity.

Indirect comparisons and network-meta-analysis

If studies are identified that compare different interventions, indirect comparisons will be made for competing interventions that have not been compared directly. Multiple treatment meta-analysis (also referred to as network meta-analysis) will combine direct and indirect comparisons using multivariate meta-analysis as this will also take into account any multi arm studies. A random-effects model will be used.STATA will be used to conduct analyses using code from www.mtm.uoi.gr.

Transitivity will be evaluated clinically. We will compare the distributions of possible effect modifiers (smoking status; age, gender, ethnicity and performance status)across comparisons using subgroup analysis. As the review is only considering first line treatment, indications are similar.

Consistency will be evaluated using a loop specific approach (Salanti 2009) and design interaction consistency model (Higgins 2012) will also be used. If inconsistency is identified, the network meta-analysis will not be presented.

Estimates of treatment effects and between trial variance from network meta-analysis and regular pairwise meta-analysis will be presented.

Prior to analysis a diagram of the network for all relevant interventions wil be drawn, indicating the number of trials per comparison. Ranking probabilities for each treatment will be derived and displayed using the Surface Under the Cumulative RAnking curve (SUCRA) plot and rankograms (Salanti 2011).

The possible effects of risk of bias on the clinical effectiveness data and review findings will be discussed.

 

Subgroup analysis and investigation of heterogeneity

Where data are available, subgroup analysis will be performed for the following subgroups:

  • smoker versus non-smoker;
  • age < 65 years versus age ≥ 65 years;
  • male versus female;
  • ethnicity;
  • performance status.

 

Sensitivity analysis

Sensitivity analyses will be conducted based on the overall risk of bias of the included studies. Overall risk of bias will be based on sequence generation, allocation concealment and blinding (for the specific outcome), and the summary assessment will be based on recommendations in Table 8.7a of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).

 

Acknowledgements

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest

The authors are pleased to acknowledge the support of Desiree West (Consumer at the Cochrane Lung Cancer Group) who provided feedback on the draft of the protocol.

 

Appendices

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest
 

Appendix 1. MEDLINE (via Ovid; 1946 onwards)

1. exp Carcinoma, Non-Small-Cell Lung/

2. (lung and (cancer$ or carcin$ or neoplasm$ or tumour$ or tumor$) and ((non-small or nonsmall) and cell)).ti,ab.

3. (erlotinib or tarceva).af.

4. (gefitinib or iressa).af.

5. (tyrosine kinase inhibit$ or monoclonal antibod$ or EGFR).tw.

6. 1 or 2

7. or/3-5

8. 6 and 7

9. randomized controlled trial.pt

10. controlled clinical trial.pt.

11. randomize.pt.

12. placebo.ab.

13. drug therapy.fs.

14. randomly.ab.

15. trial.ab.

16.groups.ab.

17. 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16

18. 8 and 17

19. exp animals/not humans. sh.

20. 18 not 19

 

What's new

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest

Last assessed as up-to-date: 5 July 2012.


DateEventDescription

3 April 2013AmendedNetwork meta-analysis section updated



 

Contributions of authors

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest

All review authors listed below will contribute to the text or data section, or both, and analysis. All review authors will take part in the editing and production of the review.

J Green: input into all aspects of the review

VB: data extraction, entry and analysis

J Greenhalgh: project co-ordination

AB: project management

PJ:

FV: searching, data extraction, entry and analysis

KD: statistical advisor

RD: methodological supervisor

 

Declarations of interest

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Appendices
  7. What's new
  8. Contributions of authors
  9. Declarations of interest

John Green has received support from Roche Pharmaceuticals to attend an international conference.

Pooja Jain has received sponsorship for attendance at national and international conferences from Eli Lilly and Company (UK), Roche Pharmaceuticals Ltd, Pierre Fabre Ltd and Boehringer Ingelheim. She has also served on advisory boards for Eli Lilly and Company Ltd, Roche Pharmaceuticals Ltd and Boehringer Ingelheim.

References

Additional references

  1. Top of page
  2. Abstract
  3. Background
  4. Objectives
  5. Methods
  6. Acknowledgements
  7. Appendices
  8. What's new
  9. Contributions of authors
  10. Declarations of interest
  11. Additional references
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