Efficacy of second-line treatments for patients with advanced human epidermal growth factor receptor 2 positive breast cancer after trastuzumab-based treatment: a systematic review and bayesian network analysis

Purpose: Different second-line treatments of patients with trastuzumab-resistant human epidermal growth factor receptor 2 (HER2) positive breast cancer were examined in randomized controlled trials (RCTs). A network meta-analysis is helpful to evaluate the comparative survival benefits of different options. Methods: We performed a bayesian network meta-analysis using R-4.0.0 software and fixed consistency model to compare the progression free survival (PFS) and overall survival (OS) benefits of different second-line regimens. Results: 13 RCTs (19 publications, 4313 patients) remained for qualitative synthesis and 12 RCTs (17 publications, 4022 patients) were deemed eligible for network meta-analysis. For PFS, we divided network analysis into two parts owing to insufficient connections among treatments. The first part involved 8 treatments in 9 studies and we referred it as PFS (#1). Amid the following 8 interventions: pyrotinib + capecitabine, T-DM1 + atezolizumab, pertuzumab + trastuzumab + capecitabine, T-DM1, trastuzumab + capecitabine, lapatinib + capecitabine, neratinib, and capecitabine, we found consistent benefits between the first three interventions; moreover, pyrotinib + capecitabine was most likely to be associated with the best benefits; capecitabine monotherapy was associated with the worst PFS. The second part included 3 treatments in 2 studies and we referred it as PFS (#2): everolimus + trastuzumab + vinorelbine had better PFS benefits versus trastuzumab + vinorelbine and afatinib + vinorelbine. For OS, we analyzed 7 treatments in 7 studies, and observed T-DM1 + atezolizumab, pertuzumab + trastuzumab + capecitabine, and T-DM1 had similar effectiveness, and the first had the highest probability to yield the longest OS; capecitabine or neratinib alone yielded the worst OS benefits. Conclusions: Our work comprehensively summarized and analyzed current available RCT-based evidence of the second-line treatments for trastuzumab-treated, HER2-positive, advanced breast cancer. These results provide clinicians and oncologists meaningful references for clinical drug administration and the development of novel effective therapies.


Rationale
3 Describe the rationale for the review in the context of what is already known, including mention of why a network meta-analysis has been conducted.

3-4
Objectives 4 Provide an explicit statement of questions being addressed, with reference to participants, interventions, 4 comparisons, outcomes, and study design (PICOS).

Eligibility criteria 6
Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. Clearly describe eligible treatments included in the treatment network, and note whether any have been clustered or merged into the same node (with justification).

5
Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

4-5
Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
Supplementary materials page 5-6 Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

7-8
Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

Data items 11
List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

5
Geometry of the network S1 Describe methods used to explore the geometry of the treatment network under study and potential biases related to it. This should include how the evidence base has been graphically summarized for presentation, and what characteristics were compiled and used to describe the evidence base to readers.

5
Risk of bias within individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

5-6
Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means). Also describe the use of additional summary measures assessed, such as treatment rankings and surface under the cumulative ranking curve (SUCRA) values, as well as modified approaches used to present summary findings from meta-analyses.

6-7
Planned methods of analysis 14 Describe the methods of handling data and combining results of studies for each network meta-analysis. This should include, but not be limited to: • Handling of multi-arm trials; • Selection of variance structure; • Selection of prior distributions in Bayesian analyses; and • Assessment of model fit.

6-7
Assessment of Inconsistency S2 Describe the statistical methods used to evaluate the agreement of direct and indirect evidence in the treatment network(s) studied. Describe efforts taken to address its presence when found.

6-7
Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

8
Additional analyses

16
Describe methods of additional analyses if done, indicating which were pre-specified. This may include, but not be limited to, the following: • Sensitivity or subgroup analyses; • Meta-regression analyses; • Alternative formulations of the treatment network; and • Use of alternative prior distributions for Bayesian analyses (if applicable).

Study selection 17
Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

7
Presentation of network structure S3 Provide a network graph of the included studies to enable visualization of the geometry of the treatment network.

11
Summary of network geometry S4 Provide a brief overview of characteristics of the treatment network. This may include commentary on the abundance of trials and randomized patients for the different interventions and pairwise comparisons in the network, gaps of evidence in the treatment network, and potential biases reflected by the network structure.

11
Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

Summary of evidence 24
Summarize the main findings, including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy-makers).

16-21
Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review level (e.g., incomplete retrieval of identified research, reporting bias). Comment on the validity of the assumptions, such as transitivity and consistency. Comment on any concerns regarding network geometry (e.g., avoidance of certain comparisons).

Conclusions 26
Provide a general interpretation of the results in the context of other evidence, and implications for future research.

Funding 27
Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. This should also include information regarding whether funding has been received from manufacturers of treatments in the network and/or whether some of the authors are content experts with professional conflicts of interest that could affect use of treatments in the network. 23 PICOS = population, intervention, comparators, outcomes, study design.

Fig. S1
Node-splitting analysis of inconsistency for comparisons within closed loops. P ≤ 0.05 indicates a significant inconsistency between the direct and indirect estimates.