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The Cochrane Database of Systematic Reviews logoLink to The Cochrane Database of Systematic Reviews
. 2019 Jun 25;2019(6):CD013364. doi: 10.1002/14651858.CD013364

Checkpoint inhibitors for stage I to III non‐small cell lung cancer treated with surgery or radiotherapy with curative intent: a generic protocol

Zhen Zhu 1,2, Kexun Zhang 1,2, Ning Cai 1,2, Edward Charbek 3, Aaron C Miller 4, Sibo Zhu 2, Chen Suo 1,2,✉, Xingdong Chen 5, Huan Song 6,7,8
PMCID: PMC6592372

Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To evaluate the effectiveness and safety of immune checkpoint inhibitors in people with stage I to III NSCLC who receive surgery or radiotherapy with curative intent.

Background

Description of the condition

Worldwide, lung cancer remains the leading cause of cancer incidence and mortality with an estimated 2.1 million new cases (11.6% of all incident cancer cases), and 1.8 million deaths (18.4% of all cancer deaths) in 2018. Among males, it is the leading cause of death in most countries in Eastern Europe, Western Asia, Northern Africa, and specific countries in Eastern Asia (China) and South‐Eastern Asia. Among females, lung cancer is the leading cause of cancer death in 28 countries (GLOBOCAN 2018).

There are two main types of lung cancer: non‐small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). This protocol will focus on NSCLC, which is the most common type, accounting for approximately 80% to 85% of all lung cancer cases. The TNM staging system is a crucial component of lung cancer evaluation due to its role in guiding management decisions and providing valuable prognostic information (Detterbeck 2016). For example, a stage IA tumour is managed surgically and has the best prognosis, with a five‐year survival rate between 80% and 90% (Goldstraw 2016). However, more advanced stages, such as stage IIIA, have a much worse five‐year survival rate below 40% and frequently require medical management as opposed to surgical intervention (Goldstraw 2016). People with stage IV NSCLC have the lowest five‐year survival of only 6% (Goldstraw 2016).

Despite many improvements in the treatment of NSCLC, the overall survival rate remains low (Siegel 2017). For people with stage I and II NSCLC and an acceptable surgical risk, the currently preferred management approach is surgical resection with or without chemotherapy. Lobectomy with mediastinal node dissection remains the preferred surgical procedure for management of early‐stage NSCLC (Howington 2013). For unresectable, locally advanced NSCLC with stage IIIA or stage IIIB, therapeutic radiotherapy combined with chemotherapy is usually provided (Ramnath 2013). For stage IIIA and IIIB NSCLC, prospective randomized trials and meta‐analyses established concurrent chemoradiation as the standard treatment approach (Aupérin 2010). Despite these aggressive treatment options, patient outcomes remain poor. Only 15% of people with locally advanced NSCLC survive five years (Aupérin 2010). Cancer immunotherapy is a potential option to improve outcomes after chemoradiation, with an acceptable increase in adverse events. To date, the incidence of adverse effects from immunotherapy remains considerably low. Most of the adverse effects are mild or moderate in severity as reported in clinical trials involving programmed death‐1 (PD‐1) inhibitors, programmed death‐ligand (PD‐L1) inhibitors, and cytotoxic T lymphocyte‐associated protein‐4 (CTLA‐4) inhibitors (Genova 2017).

Description of the intervention

Normally, the immune system plays a pivotal role in identifying and eliminating malignant cells, thereby effectively suppressing tumour growth (Swann 2007). Furthermore, the immune system has many immunosuppressive mechanisms, which prevent the development of autoimmune disease. Immune checkpoints are the molecular pathways present on immune cells which activate immunosuppression and prevent excessive activity of effector T‐cells. When immune checkpoint signals are abnormal, cluster of differentiation (CD)25+ and CD4+ regulatory T‐cells proliferate excessively, resulting in an abnormal immune response to self‐antigens. Subsequently, the normal cellular structure is destroyed, and unwanted autoimmunity is triggered.

Interestingly, tumour cells use various strategies to escape recognition by the immune system allowing unrestricted cancer cell growth. One key immune evasion strategy utilized by tumour cells is the suppression of anticancer responses by negative immune checkpoint signals to T‐cells (Pardoll 2012). This negative checkpoint signalling leads to the suppression of antitumour immune responses (Drake 2006), and to the development of cancer antigen tolerance in T‐cells (Pardoll 2012). In recent years, our knowledge of the immune system's role in tumour progression has led to the development of immune checkpoint inhibitors (ICIs) for the treatment of advanced malignant tumours. ICIs, especially PD‐1/PD‐L1 and CTLA‐4 monoclonal antibody therapies, have made remarkable progress in the clinical treatment of advanced NSCLC (Rizvi 2015). Compared with the direct killing of tumour cells by traditional radiotherapy and chemotherapy, ICIs aim to restore the effective T‐cells' specific recognition function and their capacity to kill tumour cells. Thus, the ICIs restore the immune system's ability to mitigate tumour growth. (Couzin‐Frankel 2013).

At present, monoclonal antibody therapy to block immune system checkpoints is one of the most widely used immunotherapies for NSCLC treatment. Many monoclonal antibody agents have gained Food and Drug Administration (FDA) approval for clinical trials in stage I to III NSCLC. For example, one phase 3 study compared the anti‐PD‐L1 antibody durvalumab as consolidation therapy with placebo in people with stage III NSCLC (Antonia 2017). This study suggested that the durvalumab could increase the overall survival time of people with stage III disease. Additionally, there were no significant adverse effect signals in the group using durvalumab. The safety profile of durvalumab was in accordance with that of other immunotherapies. Recently, the authors have updated and published the results (Antonia 2018). After a median follow‐up of 25.2 months, the overall survival of people treated with durvalumab was significantly longer than that of people treated with placebo (stratified hazard ratio for death 0.68, 99.73% confidence interval (CI) 0.470 to 0.997). There were no additional safety issues.

How the intervention might work

Among various ICIs, PD‐1/PD‐L1 and CTLA‐4 targeting regulatory pathways have been the most actively studied in clinical trials (Syn 2017). PD‐1 is one of the immune checkpoint receptors expressed on T‐lymphocytes, B‐lymphocytes, etc. When PD‐1 binds to the ligand PD‐L1, the activated PD‐1/PD‐L1 pathway plays a critical role in suppressing the excessive proliferation of T‐lymphocytes, such that it protects normal tissue from injury (Francisco 2010). However, the PD‐L1 ligand is not only expressed on normal tissue cells but also on cancer cells. Overexpressed PD‐L1 ligand on cancer cells would specifically bind to PD‐1 on T‐cells, such that the activated PD‐1/PD‐L1 pathway dampens the ability of the immune system to recognize and wipe out the cancer cells, leading to immune evasion of the cancer cells. Therefore, as novel therapeutic strategies, ICIs that target the PD‐1/PD‐L1 pathway may slow down the growth of certain tumours (Tsao 2016). Another important immunosuppressive molecule is CTLA‐4, which works similarly to PD‐1. The difference between CTLA‐4 and PD‐1 is that the former inhibits the proliferation of T‐cells in an early stage of antitumour response in lymph nodes, while the latter operates at a later stage within peripheral tissues (Buchbinder 2016).

The CTLA‐4 checkpoint proteins are mainly expressed on the surface of activated T‐cells, and have a high degree of homology with the costimulatory protein CD28. CTLA‐4 and CD28 share common ligand proteins, namely CD80 and CD86 (Figure 1). However, compared to CD28, CTLA‐4 is functionally opposite. When CTLA‐4 binds to a ligand protein, an inhibitory signal is generated that downregulates the activity of T‐cells (Quandt 2007). Because the affinity between CTLA‐4 and CD80/CD86 is higher than that of CD28, the stimulatory signals from CD28 are blocked and T‐cells are unable to become fully activated. The result of this signalling cascade impairs the immune system response, leading to cancer cells' evasion of immune detection. The checkpoint inhibitors suppress the expression of CTLA‐4 in order to stimulate the proliferation of immune cells and to induce or enhance antitumour immune responses (Topalian 2012). Figure 1 shows a simplified schema of immune checkpoints' role in the activation or suppression phases of antigen‐specific T‐cell responses.

Figure 1.

Figure 1

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A simplified schema of immune checkpoints in the activation or suppression phases of antigen‐specific T‐cell responses. APC: adenomatous polyposis coli; CD: cluster of differentiation; CTLA: cytotoxic T lymphocyte‐associated protein; MHC: major histocompatibility complex; PD: programmed death; PD‐L: programmed death‐ligand; TCR: T‐cell receptor.

Why it is important to do this review

Immunotherapy targets both active and passive immune responses. Checkpoint inhibitors are one type of passive immunotherapy. In our previous review "Immunotherapy (excluding checkpoint inhibitors) for stage I to III non‐small cell lung cancer treated with surgery or radiotherapy with curative intent" (Zhu 2017), we excluded checkpoint inhibitors. Evidence from clinical trials on the ICIs among people with early‐stage NSCLC is emerging. Therefore, a systematic review of checkpoint inhibitors is needed to summarize and stringently evaluate the effectiveness of checkpoint inhibitors in the management of stage I to III NSCLC treated with curative intent.

Objectives

To evaluate the effectiveness and safety of immune checkpoint inhibitors in people with stage I to III NSCLC who receive surgery or radiotherapy with curative intent.

Methods

Criteria for considering studies for this review

Types of studies

We will only include randomized controlled trials (RCTs). Dose‐variable trials are ineligible and we will exclude them.

Types of participants

We will include histologically confirmed stage I to III NSCLC adults, aged 18 years or over, with or without chemotherapy after surgical resection, and unresectable locally advanced stage III NSCLC who have received radiotherapy only or radiotherapy combined with chemotherapy with curative intent.

Types of interventions

This is a generic protocol and we will generate three different systematic reviews that will address the following interventions.

  1. ICIs versus placebo/best supportive care/no intervention in people with locally advanced stage III NSCLC who have been treated by chemoradiotherapy.

  2. Neoadjuvant ICIs versus placebo/best supportive care/no intervention in people with stage I to III NSCLC with or without chemotherapy.

  3. Adjuvant ICIs versus placebo/best supportive care/no intervention in people with stage I to III NSCLC with or without chemotherapy.

Types of outcome measures

Primary outcomes

  1. Overall survival: defined as the time interval between the date of randomization and the date of death from any cause.

  2. Progression‐free survival: defined as the time from randomization to either death or disease progression, whichever occurs first. According to RECIST (Response Evaluation Criteria In Solid Tumors; Eisenhauer 2009), disease progression is defined as the sum of the longest diameters of the target lesions increased by at least 20%, taking the smallest sum of the longest diameter recorded since the treatment begins as reference, or that one or more new lesions appear.

  3. Adverse events/side effects: severity graded according to the National Cancer Institute‐Common Terminology Criteria for Adverse Events (NCI‐CTCAE) classification, including the percentage of treatment‐related deaths.

Secondary outcomes

  1. Overall response: response assessed according to RECIST guidelines (Eisenhauer 2009), and immune‐related response criteria (Wolchok 2009).

  2. Health‐related quality of life (HRQoL): measured by a validated scale.

Search methods for identification of studies

Electronic searches

We will conduct a literature search to identify all published RCTs with no language restrictions. We will search the following electronic databases to determine potential studies.

  1. Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane library, latest issue).

  2. MEDLINE (1966 to date of search; access via PubMed).

  3. Embase (1988 to date of search).

We will adapt the draft search strategies for CENTRAL (Appendix 1), MEDLINE (Appendix 2), and Embase (Appendix 3) to search remaining databases. We will create the validated filters for the retrieval of trials as suggested in the Cochrane Handbook for Systematic Reviews of Interventions (Section 6.3.2.2; Higgins 2011).

The search strategies are designed by the Cochrane Lung Cancer Review Group Information Specialists.

Searching other resources

We will search for the following clinical trial registers: www.ClinicalTrials.gov, www.controlled‐trials.com, FDA and the European Medicines Agency (EMA) databases, and the International Clinical Trial Registry's platform (ITCRP), for information of ongoing research.

We will also check reference lists of all major studies and review additional referenced articles. We will search and read review articles to identify relevant studies and ongoing clinical trials. We will ask experts in the field and related drug manufacturers to provide detailed information on outstanding clinical trials and any related unpublished materials. We will also contact authors of identified trials to identify other published and unpublished studies.

We will manually examine potential trials in abstracts and the following relevant conference proceedings (from 2016 to date of search):

  1. American Society for Clinical Oncology (ASCO);

  2. European Society of Oncology (ESMO);

  3. European Cancer Organisation (ECCO);

  4. International Lung Cancer Research Association (IASLC);

  5. World Lung Cancer Conference.

We will search for errata or retractions from eligible trials on www.ncbi.nlm.nih.gov/pubmed and report the date this was done within the review.

We will contact the authors of our included studies and experts in our field of investigation to identify relevant studies.

Data collection and analysis

Selection of studies

Two review authors (CS, NC) will independently screen titles and abstracts that are identified by the search methods. We will mark studies as 'retrieve' (eligible or potentially eligible/unclear) or 'do not retrieve'. For those coded as 'retrieve', we will refer to the full‐text research reports/publications. Two review authors (CS, NC) will independently screen all the documents. The procedures for inclusion and exclusion of studies will be documented using a standard screening form. We will resolve any disagreement through discussion or, if necessary, we will consult a third review author (HS). We will record studies rejected at this or subsequent stages in the 'Characteristics of excluded studies' table, with our reasons for exclusion.

We will identify and exclude duplicates and examine and group multiple reports for the same study so that each study, but not each report, is the unit in the review. We will document the selection process in sufficient detail to complete the PRISMA flow diagram (Liberati 2009) and a 'Characteristics of exclusion studies' table.

Data extraction and management

We will use the data collection form to obtain study characteristics and outcome data. we will pilot the form on at least one RCT in the review. One review author (NC) will extract study characteristics from the included studies. We will extract the following study characteristics.

  1. Methods: study design (e.g. parallel or cross‐over design), number and location of study centres (country), total duration (e.g. study date, follow‐up time, early cessation of trials), method of randomization (including unbalanced randomization ratio), methods of allocation concealment, methods of blinding.

  2. Participants: total number, age, gender, Eastern Co‐operative Oncology Group (ECOG) performance status, medical history, severity of condition (stage), diagnostic criteria, inclusion criteria, exclusion criteria.

  3. Interventions: interventions, comparisons, concomitant medications, excluded medications.

  4. Outcomes: primary and secondary results, reported time points.

  5. Type of questionnaires used to assess HRQoL.

  6. Notes: funding for trial, or any notable authors' conflict of interest.

Two review authors (CS, NC) will independently extract outcome data from included studies. We will record in the 'Characteristics of the included studies' table if outcomes are reported in an unusable way. We will resolve disagreements through consensus or refer to a third review author (HS). One review author (NC) will copy the data from the data collection form into Review Manager 5 (Review Manager 2014). We will double‐check that the data are entered correctly by comparing the study reports with the data presented in the systematic review. A second review author (CS) will spot‐check study characteristics for accuracy against the trial report.

Assessment of risk of bias in included studies

Two review authors (CS, ZZ) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve disagreements through consensus or refer to a third review author (HS). We will assess the risk of bias according as 'low', 'high' or 'unclear' risk of bias at study level and for each outcome if possible and support the rating of each domain by a brief description. We will summarize the risk of bias for each outcome within a study considering all the domains relevant to the outcome (i.e. both study‐level entries, such as allocation sequence concealment, and outcome specific entries, such as blinding). We will provide figures to summarize the risk of bias, such as presented in the Figure 8.6.C of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will assess the following domains.

  1. Random sequence generation.

  2. Allocation concealment.

  3. Blinding of participants and personnel.

  4. Blinding of outcome assessment.

  5. Incomplete outcome data.

  6. Selective outcome reporting.

  7. Other potential bias.

We will classify each potential source of bias as high, low, or unclear and provide a quote from the study report together with a justification for our judgement in the 'Risk of bias' table. We will summarize the risk of bias judgements across different studies for each of the domains listed above. For overall risk of bias, we will consider studies having adequate random sequence generation, adequate allocation concealment, adequate blinding, adequate handling of incomplete outcome report, no selective outcome reporting, and without other risks of bias, as being at low risk of bias overall. We will consider studies that are at high or unclear risk of bias in the majority of the domains as being at high risk of bias overall; and the remaining studies to be at moderate risk of bias. If necessary, we will consider blinding separately for different key outcomes (e.g. in unblinded outcome assessment, risk of bias for all‐cause mortality may be very different to a participant‐reported pain scale). Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the 'Risk of bias' table.

Measures of treatment effect

We will analyze the primary outcomes based on intention‐to‐treat (ITT) analysis, where available. The included studies that do not present ITT data will be considered as ITT and we will perform a subgroup analysis where only studies that present ITT data are included. We will measure effect assessment by hazard ratios (HRs) for time‐to‐event variables, and risk ratios (RRs) for binary variables and their corresponding 95% confidence intervals (CIs). For continuous variables using the same scale, we will calculate mean differences (MDs) and their corresponding 95% CIs. If studies use different scales, we will use standardized mean differences (SMDs) and 95% CIs. We will contact the corresponding authors for missing information on the standard deviations or standard errors. If summary data for each intervention group (for example, numbers of events and participants, or means and standard deviations) can not be extracted, if necessary, we will attempt to calculate effect estimates from P values, t statistics, or other statistics as appropriate (Section 7.7.7.1, Higgins 2011), by including the study in a meta‐analysis using the generic inverse variance method. A limitation of this approach is that estimates and standard errors of the same effect measure must be calculated for all the other studies in the same meta‐analysis, even if they provide the summary data by intervention group. For example, if numbers in each outcome category by intervention group are known for some studies, but only odds ratios (ORs) are available for other studies, then ORs would need to be calculated for the first set of studies under the generic inverse variance outcome type to enable meta‐analysis with the second set of studies.

Unit of analysis issues

We will evaluate studies with non‐standard designs, such as cluster randomized trials for potential 'unit‐of‐analysis errors', in aspects of recruitment bias, baseline imbalance, loss of clusters and comparability with individually randomized trials. In addition, we will apply proper statistical methods, such as multi‐level model and generalized estimating equations, for analysis according to the Cochrane Handbook for Systematic Review of Interventions (Higgins 2011). Unit of analysis will be the group of participants at randomization.

If there are data from a cross‐over trial with eligible intervention performances, we will use data within the first phase only, from the time point of randomization to the point of cross‐over.

Where multiple trial arms are reported in a single trial, we will include the relevant arms only. We will list additional arms in the 'Characteristics of included studies' table. If two comparisons (e.g. drug A versus placebo and drug B versus placebo) must be entered into the same meta‐analysis, we will halve the control group to avoid double counting.

If there are studies where only a proportion of the participants are histologically confirmed, we would include the study if the proportion of histologically confirmed participants is at least 80%, or if the results are presented separately. The certainty of evidence would be rated down by indirectness for studies including a mix of participants.

Dealing with missing data

We will contact investigators or study sponsors to verify key study characteristics and obtain missing numerical outcome data where possible (e.g. when only an abstract is available for a study). In addition, for the studies with full‐text reports, if there are any unreported details relevant to our analysis, we will try to find out more information from the corresponding authors.

Assessment of heterogeneity

We will carry out tests for heterogeneity using the Chi² test to assess whether observed differences in results are compatible with chance alone. Further, we will use the I² statistic to quantify inconsistency across studies. The presence of heterogeneity will be defined by a P value of less than 0.05 from the Chi² test and an I² value greater than 50% (Higgins 2011). If moderate or higher heterogeneity (50% to 100%) is detected, we will carry out a thorough exploration of possible sources of heterogeneity by means of subgroup and sensitivity analyses (as stated below). Given the limitations of the methods, we will use the P value from the Chi² test and the value of I² only as a guide, and we will interpret the results with caution.

Assessment of reporting biases

We will attempt to communicate with study authors to provide missing outcome data. When this is not feasible, and the missing data may introduce serious bias, we will explore the impact of including such studies in the overall assessment of results by a sensitivity analysis.

If we are able to pool more than 10 trials, we will create and examine a funnel plot where intervention effect estimate is plotted against standard error of intervention effect estimate to explore possible publication biases. If we find funnel plot asymmetry, we will further investigate clinical diversity of studies as a possible explanation. If there are enough studies (more than 10), we also will use the 'contour‐enhanced' funnel plot to differentiate asymmetry due to publication bias from that due to other factors (Peters 2008). In the case that the supposed missing studies are in areas of higher statistical significance, the cause of the asymmetry is highly suggested to be due to factors other than publication bias.

Data synthesis

We will use Review Manager 5 to pool data and perform statistical analyses (Review Manager 2014). We will use a random‐effects model in the first instance. If the studies are homogeneous, we will switch to a fixed‐effect model and generate forest plots.

'Summary of findings' table

We will create a 'Summary of findings' table using GRADEpro software (GRADEpro 2014). We will use the five GRADE considerations for study limitations, consistency of effect, imprecision, indirectness and publication bias to assess the certainty of a body of evidence as it relates to the studies which contribute data to the meta‐analyses for the prespecified outcomes. We will justify decisions to downgrade or upgrade the quality of studies using footnotes and we will make comments to aid the reader's understanding of the review, where necessary.

We will include: overall survival, progression‐free survival, overall response and HRQoL in the 'Summary of findings' table.

Subgroup analysis and investigation of heterogeneity

If there is a sufficient number of studies (at least three for each subgroup), we will perform the following exploratory subgroup analyses:

  1. people receiving ICIs with different stage at presentation: people with I/II/III stage NSCLC.

  2. people with a specific biomarker (e.g. people with a gene signature profile (PDL‐1 or PD‐1 positive).

We will only use primary outcomes in subgroup analyses. We will use the same model (random/fixed) for the main analyses and subgroups analyses to ensure comparability between results.

We will examine differences between subgroups by visual inspection of their CIs at first; non‐overlapping CIs indicate a statistically significant difference in treatment effect between subgroups. In addition, we will use the approach of Borenstein 2009 to formally investigate differences between two or more subgroups. We will conduct analyses using Review Manager 5 (Review Manager 2014). We will conduct the subgroup analysis regardless of statistical heterogeneity of the overall analysis. We will only perform subgroup tests for those outcomes with three or more trials contributing to the data.

Sensitivity analysis

We will utilize sensitivity analyses defined a priori to assess the robustness of our conclusions. We will conduct the sensitivity analysis regardless of statistical heterogeneity of the overall analysis. This will be realized by repeating the analyses in order to explore the impact of the following factors on effect size:

  1. exclusion of unpublished studies;

  2. exclusion of low‐quality studies (those at high or unclear risk of bias related to randomization, blinding or attrition);

  3. use of a random‐effects model when a fixed‐effect model was finally applied in the main analysis;

  4. exclusion of trials with a mix of histologically confirmed participants and non‐histologically confirmed participants.

Acknowledgements

We would like to express our thanks to Corynne Marchal, Managing Editor, Cochrane Lung Cancer Review Group for providing administrative and logistical support for conduct of the current protocol; Virginie Westeel, Renee Manser, Anne‐Claire Toffard and Sophie Paget‐Bailly for their comments on our protocol; and Francois Calais and Giorgio Maria Agazzi for the design of our search strategies.

We would also like to thank authors of the review "Immunotherapy (excluding checkpoint inhibitors) for stage I to III non‐small cell lung cancer treated with surgery or radiotherapy with curative intent" for the use of their partial background and method section (Zhu 2017).

We would like to acknowledge that Dr. Xingdong Chen and Dr. Chen Suo contributed equally to the correspondence work, and Ning Cai and Zhen Zhu contributed equally to the study.

Appendices

Appendix 1. CENTRAL search strategy

#1. MeSH descriptor: [Carcinoma, Non‐Small‐Cell Lung] explode all trees #2. nsclc #3. lung cancer* #4. lung carcinom* #5. lung neoplasm* #6. lung tumor* #7. lung tumour* #8. non small cell* #9. nonsmall cell* #. 10(#3 or #4 or #5 or #6 or #7) and (#8 or #9) #11. first line #12. naive #13. #11 or #12 #14. MeSH descriptor: [Programmed Cell Death 1 Receptor] explode all trees #15. Programmed Cell Death 1 #16. PD‐1 Receptor #17. CD279 Antigen* #18. PD1 Receptor #19. MeSH descriptor: [Programmed Cell Death 1 Ligand 2 Protein] explode all trees #20. CD 273 #21. PD L2 Ligand #22. B7 DC Ligand #23. B7 DC Antigen* #24. programmed death #25. pd l1 #26. pd l2 #27. pd 2 #28. MeSH descriptor: [Immunotherapy] explode all trees #29. Immunotherap* #30. durvalumab #31. MEDI4736 #32. MEDI‐4736 #33. Imfinzi #34. avelumab #35. MSB0010718C #36. atezolizumab #37. MPDL3280A #38. Tecentriq #39. RG7446 #40. RG 7446 #41. pembrolizumab #42. lambrolizumab #43. Keytruda #44. MK3475 #45. MK 3475 #46. nivolumab #47. MDX‐1106 #48. ONO‐4538 #49. BMS‐936558 #50. Opdivo #51. immune checkpoint inhibitor* #52. MeSH descriptor: [Ipilimumab] explode all trees #53. Ipilimumab #54. Yervoy #55. MDX010 #56. MDX 010 #57. tremelimumab #58. ticilimumab #59. CP 675* #60. CP675* #61. MeSH descriptor: [Drug Therapy] explode all trees #62. Drug Therap* #63. Chemotherap* #64. Pharmacotherap* #65. MeSH descriptor: [Antineoplastic Agents] explode all trees #66. Antineoplas* #67. Antitumo* #68. Anticancer #69. #14 or #15 or #16 or #17 or #18 or #19 or #20 or #21 or #22 or #23 or #24 or #25 or #26 or #27 or #28 or #29 or #30 or #31 or #32 or #33 or #34 or #35 or #36 or #37 or #38 or #39 or #40 or #41 or #42 or #43 or #44 or #45 or #46 or #47 or #48 or #49 or #50 or #51 or #52 or #53 or #54 or #55 or #56 or #57 or #58 or #59 or #60 or #61 or #62 or #63 or #64 or #65 or #66 or #67 or #68 #70. #10 and #13 and #69

Appendix 2. MEDLINE search strategy (access through PubMed, 1966 to date of search)

#1. Carcinoma, Non‐Small‐Cell Lung[MeSH Terms]

#2. nsclc

#3. lung cancer*

#4. lung carcinom*

#5. lung neoplasm*

#6. lung tumor*

#7. lung tumour*

#8. non small cell*

#9. nonsmall cell*

#10. (#3 OR #4 OR #5 OR #6 OR #7) AND (#8 OR #9)

#11. #1 OR #2 OR #10

#12. (first line) OR naive

#13. (((((((((((((((((((((((programmed cell death 1 receptor[MeSH Terms]) OR PD 1 Receptor) OR CD279 Antigen) OR PD1 Receptor) OR CD279 Antigen*) OR programmed cell death 1 ligand 2 protein[MeSH Terms]) OR CD273 Antigen*) OR PD L2 Ligand) OR B7 DC Ligand) OR B7 DC Antigen*) OR Programmed Cell Death 1 Ligand) OR programmed death) OR target* pd‐l1) OR target* pd‐l2) OR target* pd‐1) OR pd‐l1 block*) OR pd‐l2 block*) OR pd‐1 block*) OR pd‐l1 inhibitor*) OR pd‐l2 inhibitor*) OR pd‐1 inhibitor*) OR anti pd‐l1) OR anti pd‐l2) OR anti pd‐1

#14. (((((((((((((((((((((((((((((((((((((((((((immunotherapy[MeSH Terms]) OR immunotherap*) OR durvalumab) OR MEDI4736) OR MEDI‐4736) OR Imfinzi) OR durvalumab[Supplementary Concept]) OR avelumab[Supplementary Concept]) OR avelumab) OR MSB0010718C) OR atezolizumab[Supplementary Concept]) OR atezolizumab) OR MPDL3280A) OR Tecentriq) OR RG7446) OR RG‐7446) OR pembrolizumab[Supplementary Concept]) OR pembrolizumab) OR lambrolizumab) OR Keytruda) OR MK‐3475) OR nivolumab[Supplementary Concept]) OR nivolumab) OR MDX‐1106) OR ONO‐4538) OR BMS‐936558) OR Opdivo) OR immune checkpoint inhibitor*) OR ipilimumab[MeSH Terms]) OR Yervoy) OR MDX 010) OR MDX010) OR MDX CTLA 4) OR tremelimumab[Supplementary Concept]) OR ticilimumab) OR CP 675) OR CP675) OR CP 675206) OR CP675206) OR CTLA‐4 Antigen[MeSH Terms]) OR CTLA‐4 Antigen) OR CD152 Antigen*) OR Cytotoxic T‐Lymphocyte Antigen 4) OR Cytotoxic T‐Lymphocyte‐Associated Antigen 4

#15. ((((((drug therapy[MeSH Terms]) OR chemotherap*) OR Pharmacotherap*) OR Antineoplastic Agents[MeSH Terms]) OR Antineoplastic) OR Antitumo*) OR Anticancer*

#16. #13 OR #14 OR #15

#17. #11 AND #12 AND #16

#18. randomized controlled trial[Publication Type] OR controlled clinical trial[Publication Type] OR randomized[Title/Abstract] OR placebo[Title/Abstract] OR drug therapy[MeSH Subheading] OR randomly[Title/Abstract] OR trial[Title/Abstract] OR groups[Title/Abstract]

#19. animals[MeSH Terms] NOT humans[MeSH Terms]

#20. #18 NOT #19

#21. #17 AND #20

Appendix 3. Embase search strategy

#1. 'lung tumor'/exp OR 'non small cell lung cancer'/exp OR ((lung NEXT/1 carcinom*):ab,ti) OR ((lung NEXT/1 neoplasm*):ab,ti) OR 'lung cancer':ab,ti OR nsclc:ab,ti OR 'non small cell lung':ab,ti OR ((lung NEXT/1 tumour*):ab,ti) OR 'nonsmall cell lung':ab,ti OR 'non‐small cell lung':ab,ti OR 'lung carcinoma'/exp OR 'lung cancer'/exp

#2. 'first line':ti,ab OR 'first‐line':ti,ab OR naive:ti,ab

#3. 'programmed death 1 receptor'/exp OR 'programmed death 1 ligand 2'/exp OR 'programmed death 1 ligand 1'/exp OR 'anti programmed death':ab,ti OR 'programmed death ligand':ab,ti OR 'programmed death 2':ab,ti OR 'programmed death 1':ab,ti OR 'target* pd‐l1':ab,ti OR 'target* pd‐l2':ab,ti OR 'target* pd‐1':ab,ti OR 'pd‐l1 block*':ab,ti OR 'pd‐l2 block*':ti,ab OR 'pd‐1 block*':ab,ti OR 'pd‐l1 inhibitor*':ab,ti OR 'pd‐l2 inhibitor*':ab,ti OR 'pd‐1 inhibitor*':ab,ti OR 'anti pd‐l1':ab,ti OR 'anti pd‐l2':ab,ti OR 'anti pd‐1':ab,ti OR 'immunotherap*':ab,ti OR 'immunotherapy'/exp OR 'durvalumab':ab,ti OR 'avelumab':ab,ti OR 'atezolizumab':ab,ti OR 'pembrolizumab':ab,ti OR 'nivolumab':ab,ti OR 'immune checkpoint inhibitor*':ab,ti OR 'ono‐4538':ti,ab OR 'bms‐936558':ti,ab OR 'ipilimumab':ti,ab OR 'tremelimumab':ti,ab OR 'ticilimumab':ti,ab OR 'pembrolizumab':ti,ab OR 'lambrolizumab':ti,ab OR 'cytotoxic t lymphocyte antigen 4'/exp OR ctla4:ti,ab OR keytruda:ti,ab OR mdx1106:ti,ab OR opdivo:ti,ab OR 'mk 3475':ti,ab OR mpdl3280a:ti,ab OR yervoy:ti,ab OR 'mdx 010':ti,ab OR 'mdx 101':ti,ab OR 'cp 675,206':ti,ab OR 'chemotherap*':ab,ti OR 'chemotherapy'/exp OR 'antineoplastic agent'/exp

#4. 'crossover procedure'/exp OR 'double‐blind procedure'/exp OR 'randomized controlled trial'/exp OR 'single‐blind procedure'/exp OR random* OR factorial* OR crossover* OR (cross NEXT/1 over*) OR placebo* OR (doubl* NEAR/1 blind*) OR (singl* NEAR/1 blind*) OR assign* OR allocat* OR volunteer*

#5. #1 AND #2 AND #3 AND #4

Contributions of authors

Conceiving the protocol: HS.

Designing the protocol: HS, CS, XC.

Co‐ordinating the protocol: CS, XC, HS.

Designing search strategies: Cochrane Lung Cancer Review Group.

Writing the protocol: ZZ, NC, CS, KZ, EC, SZ, ACM.

Providing general advice on the protocol: CS, HS, XC, SZ, ACM.

Securing funding for the protocol: XC, CS.

Performing previous work that was the foundation of the current study: HS, CS.

Sources of support

Internal sources

  • Department of Epidemiology, School of Public Health, Fudan University, China.

  • Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.

External sources

  • This work was funded by National Key Research and Development Program of China 2017YFC0907002 (XC and CS), National Key Research and Development Program of China 2017YFC0907500 (XC), 1.3.5 project for disciplines of excellence ZYJC18010, West China Hospital, Sichuan University (HS) and National Nature Science Foundation of China 31600673 (CS), China.

Declarations of interest

ZZ: none.

KZ: none.

NC: none.

EC: none.

ACM: none.

SZ: none.

CS: none.

XC: none.

HS: none.

Notes

Some passages in the methods section of this protocol are taken from the Cochrane Review: "Immunotherapy (excluding checkpoint inhibitors) for stage I to III non‐small cell lung cancer treated with surgery or radiotherapy with curative intent" with the agreement of all of the authors of the said review (Zhu 2017).

New

References

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