Prognostic value of tumour-associated regulatory T-cells as a biomarker in non-small cell lung cancer: a systematic review and meta-analysis

doi:10.21203/rs.3.rs-3949944/v1

Background: Despite continuous improvement, tumour, nodes, and metastases (TNM) staging has been deficient in prognosticating in patients suffering from non-small cell lung cancer (NSCLC). To supplement TNM staging, this systematic review and meta-analysis aimed to evaluate the prognostic value of the regulatory T cells (Treg).

Methods: A keyword search was conducted in the MEDLINE database through PubMed for full-text original human studies from any region published in English during the last 10 years. Eligible for inclusion were studies evaluating the prognostic value of the number of Treg cells and pre-specified biomarkers in NSCLC. Case studies, case series, systematic reviews, and meta-analyses were excluded. Two reviewers independently screened the studies and assessed risk-of-bias using the Quality in Prognosis Studies (QUIPS) tool. One reviewer used an automation tool for screening, which was also used to facilitate data extraction. Meta-analysis was done for studies reporting significant multivariate hazards ratio (HR).

Results: Out of 258 retrievals, 19 studies were included in the final review. The low number of Treg cells was found significantly associated with improved overall survival (pooled log OR: 1.626; 95% CI: 1.324, 1.928; p (2-tailed) <.001; SE: 0.1174), improved recurrence-free survival (HR: 1.99; 95% CI: 1.15, 3.46; p = .01), and worse disease-free survival (pooled log OR: 0.992; 95% CI: 0.820, 1.163; p (2-tailed) .009; SE: 0.0135), especially when identified by forkhead box P3 (FOXP3), in any stage or non-metastatic NSCLC.

Conclusion: A low number of Treg cells indicated better survival, suggesting its potential use as a prognostic biomarker in NSCLC.

Systematic review registration: The protocol of this review was prospectively registered on PROSPERO on August 28, 2021, and was assigned the registration number CRD42021270598. The protocol can be accessed from PROSPERO website.

Meta-analysis

non-small cell lung cancer

prognosis

regulatory T cells

systematic review

An estimated 1.8 million deaths and 2.1 million new cases occur annually due to lung cancer around the world (1). Almost half of the patients die within one year of diagnosis and less than 18% survive beyond five years (2). Further, among the patients with non-small cell lung cancer (NSCLC), around two-thirds of patients fail to survive beyond two years (3, 4).

The dismal prognosis is exacerbated by a lack of methods for early diagnosis and prognostication and limited access to opportune standard treatment. Historically, the clinical fraternity has relied upon the tumour, nodes, and metastases (TNM) staging as the gold standard prognostic tool for lung cancer (5), although, several researchers have raised concerns about various editions of the TNM classification (6–9). Recently, several studies have reported a profound prognostic impact of tumour-infiltrating lymphocytes (TILs) in malignant tumours (10–17) including lung cancer (18). Further, immune scoring based on TILs or their ratios for differentiating prognosis within each tumour, node, and metastasis have been used to enhance the prognostic value of TNM staging (19–22).

TILs present in the immune infiltrate called the tumour microenvironment (TME) include effector T cells, which can be T helper cells (1, 2, and 17), regulatory T (Treg) cells, T follicular helper cells, and cytotoxic T cells (18, 23, 24). These cells may be localized in the tumour parenchyma, invasive margin, or adjacent tumour stroma, where they interact with tumour cells in three phases of immuno-editing (25). During the escape phase, tumour cells induce the production of cytokines and growth factors and facilitate the recruitment of immunosuppressive cells, thereby causing immune suppression (26–28).

Tregs are a highly immune-suppressive subset of clusters of differentiation (CD) 4⁺ T cells (26–28), which play an important role in the preservation of self-tolerance and modulation of the overall immune responses against tumour cells through numerous cellular and humoral mechanisms (29, 30). Studies have shown that the composition of Tregs is altered in the TME, where the effector Treg numbers are increased in NSCLC patients as compared to healthy individuals (31, 32). Transforming growth factor (TGF) -𝛽1 and interleukin (IL) -2 pro-inflammatory cytokines which are present at a high level in tumour tissues of NSCLC patients promote the differentiation of naïve T-cells into Tregs (31). Moreover, due to self-antigens released in the TME by dying cancer cells, nTregs are converted into effector Tregs by expressing a higher level of activation biomarkers (31). In a study, Erfani et al. reported that the NSCLC patients had almost twice Treg cells than the healthy controls (33). Further, they reported that the metastatic stages had almost two times more Treg cells than the non-metastatic stages (33). This makes Tregs an ideal therapeutic target and candidate for prognostication of lung cancer, especially NSCLC, which comprises about 85% of all lung cancer cases (34). While the therapeutic targeting of Treg by pathways like the blockade of immune checkpoint molecules has been studied by many authors (29), the prognostic value of measurement and localization of Treg cells has not been fully evaluated in all study populations, with various prognostic factor variables, causing individual studies to report piecemeal and mixed results.

The objective of the current systematic review and meta-analysis was to evaluate whether the “number of Treg cells” has any prognostic value in predicting the survival of NSCLC patients in various study populations, considering varied prognostic factor variables and survival outcomes.

The study conducted adheres to and is in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki 1975, as revised in 2000) for experiments involving humans. The systematic review did not deal with any patient-level data or disclose any identifiable patient data; hence Institutional Review Board Approval or Informed Consent of the patients was not sought.

2.1 Design and Setting

The design of the current study was a systematic review and meta-analysis. An online electronic database search was conducted from July 19 to 25, 2022. Retrieved studies underwent screening, quality assessment, and extraction of the required information. The systematic review process was facilitated by an automation tool “MaiA”, an artificial intelligence-based proprietary platform developed in-house by Genpro Research Pvt Ltd.

2.2 Search Strategy

PubMed was used to conduct the search in the MEDLINE database, parallelly by a manual reviewer and another reviewer using the automation tool. Retrievals were sought for the relevant published prospective as well as retrospective studies describing the prognostic value of presence, degree, measurement, or localization of Tregs in tumour tissue or body fluids of patients with NSCLC, through histological or cytological procedures like immunohistochemistry, flow cytometry, or quantitative real-time-polymerase chain reaction, assessing either Treg alone or in combination with at least one biomarker out of CD4⁺, CD25⁺, CD127⁻, Helios⁺, and/or forkhead box P3 (FOXP3)⁺ (29).

Free full-text studies presenting original research in humans, published in English in any region during the last 10 years were included in the review. Protocols or design papers, opinion papers, education literature, meeting reports, position papers, case studies, case series, systematic reviews, or meta-analyses were excluded. Letters to the editor and brief communications were eligible for inclusion only if they described original research with its results. Cross-references from review articles were reviewed to ensure that no eligible article was missed. The search was not rerun before the final analysis and no unpublished study was sought. The last search was done on July 25, 2022.

Using the same predefined eligibility criteria based on the “Population, Prognostic Factor, Outcome” (PFO) approach, two reviewers screened the unblinded titles, abstracts, and full text of the studies independently in a standardized manner. One reviewer performed screening manually, while the other used “MaiA”. The disagreements were resolved by consultation with other co-authors. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (35) Flow Diagram was created based on the retrieved literature, screened records, excluded studies, and included studies (Fig. 1). For the quality assessment of the studies after screening, Cochrane’s Quality in Prognosis Studies (QUIPS) tool was used. Studies with low risk of bias for “study participation”, “prognostic factor measurement”, and “outcome measurement” domains, while low to moderate risk of bias for “study attrition”, “study confounding”, and “statistical analysis and reporting” domains were included for extraction of data for the final narrative synthesis and quantitative analysis. Data extraction was done by the first reviewer and verified by a second reviewer. The automation tool “MaiA” was used to facilitate the extraction of data. The detailed search strategy including keywords used, retrieval, eligibility criteria, risk of bias assessment and extraction are provided in the Additional Note 1.

2.3 Statistical Analysis

The primary outcome measured by this review was how the number of Treg cells affected the survival of the patients with NSCLC in terms of overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), or recurrence-free survival (RFS), with point estimates as risk and ratios such as relative risk (RR), odds ratio (OR), or hazards ratio (HR). OS was defined as the time between the first diagnosis of NSCLC and the date of death regardless of the cause. PFS, DFS, and RFS were defined as the time between the date of diagnosis and the date of progression, recurrent symptoms, and first relapse respectively. The secondary outcome of the study was to quantify recurrence and metastasis.

The data were pooled for quantitative synthesis from the individual studies through meta-analysis using Statistical Package for the Social Sciences (SPSS). As per the Cochrane Consumers and Communication Group review for meta-analysis, a minimum of two studies with the same type of survival outcome were required to subject them to quantitative synthesis. In case enough studies were not available for certain survival outcomes, findings from the individual studies were summarized in the narrative.

The survival analysis was reflected by the individual studies as HR, 95% confidence interval (CI), and p-value. Studies with a significant p-value (< .05) for multivariate HR were considered for meta-analysis to avoid bias due to confounding by co-factors. Studies having a low number of Tregs as the reference category for HR were eventually included in the meta-analysis.

For pooling HRs, a meta-analysis for binary outcomes with pre-calculated effect sizes was run in SPSS. Confidence intervals were converted to variance. Since the variables related to the study population, prognostic factor, outcome, and study design were not completely similar among the included studies, the random effect model was applied. Inverse-variance method was used to determine the weight of the studies. DerSimonian-Laird was used as the estimation method. Standard error adjustment was done using the Knapp-Hartung adjustment method. The low number of Treg became the control category for the pooled OR. A sensitivity analysis for the standard method was also run but eventually, the significant results obtained from the Knapp-Hartung adjustment method were reported in the results. The summary effect estimate (log OR) and its confidence interval were determined by generating forest plots using the HRs and associated variance for survival outcomes. The I² statistic was calculated for determining heterogeneity where an I² value lower than 30% was considered indicative of homogeneity. The symmetry of funnel plots generated using effect size and variance was used to assess publication bias visually. No subgroup analysis was performed. The data generated during screening, risk-of-bias assessment, and extraction process was uploaded in the online Harvard Dataverse Repository.

3.1 Included Studies: Flow Through the Review and Characteristics

Out of a total of 258 retrievals through automation, manual search, and cross-references, 152 studies were filtered, 76 studies were screened, two studies were not available in full text, and nine studies could not clear quality assessment. Eventually, 19 studies (16 from manual search and three from cross-references) were included in the review (Fig. 1) (36, 37, 46–54, 38–45).

Most studies had the study population as non-metastatic NSCLC (n = 5) or any NSCLC (n = 4). Most of the studies included all sub-types of NSCLC (n = 16). Six studies included patients who underwent surgery without any neo-adjuvant therapy, while two studies included patients who did not receive any adjuvant therapy. Most studies performed tests on Formalin-fixed paraffin-embedded (FFPE) sections or preserved tissue-microarray blocks, either alone or with other types of samples (n = 14), commonly using immuno-histochemistry method (n = 13). FOXP3 (n = 7) was the commonest biomarker used. Most studies reported OS (n = 13) either as the only survival outcome or along with other outcomes. Sixteen studies (84.2%) were designed as an analytical cross-sectional study with or without a comparison group. Out of the seven studies with a comparison group (Cross-sectional: 5, Cohort: 1, Controlled before-after: 1), six used healthy volunteers (three used age-matched healthy volunteers, one used both age- and sex-matched healthy volunteers, and two studies used unmatched healthy volunteers). Thirteen studies were retrospective in nature, while five were prospective, and one study was both prospective and retrospective. Report characteristics and study characteristics of the included studies are detailed in Table 1. The study-wise details of report characteristics, population under study, prognostic factor, outcome, and study design are provided in Additional Table 1.

Table 1

Characteristics of the Included Studies
Report Characteristics	n	Study Characteristics	n	Study Characteristics	n
Year of publication (N = 19)		Population under study (N = 19)		Flow cytometry and antibody staining	1
2014	1	Any NSCLC	4	Immuno-histochemistry with FOXP3 staining	1
2015	1	Any stage NSCLC	1	Biomarkers used for Treg (N = 19)
2016	5	Chemotherapy-naïve any stage NSCLC	1	FOXP3+	7
2017	3	Newly diagnosed any stage NSCLC	1	CD4 + CD25+/++ CD127-/dim	4
2018	1	Stage 1 NSCLC	1	CD4 + FOXP3+	2
2019	2	Non-metastatic NSCLC	5	CD4+	1
2020	3	Chemotherapy-naïve non-metastatic NSCLC	1	Helios + FOXP3+	1
2021	3	Metastatic NSCLC	1	CD3 + FOXP3+	1
Language (N = 19)		First diagnosis and relapsed NSCLC	1	CD8 + FOXP3+	1
English	19	NSCLC with KPS > 80%	1	CD3 + CD45RO + FOXP3+	1
Region (N = 19)		Resected NSCLC without neo-adjuvant chemo-radiotherapy	1	No biomarker	1
China	7	Solitary lesion in localized NSCLC	1	Outcomes (N = 19)
Japan	3	Prognostic factor (N = 19)		Overall survival	6
Brazil	2	Samples for Treg assessment		Disease-free survival	3
Germany	2	FFPE sections or preserved tissue-microarray blocks	11	Progression-free survival	3
France	1	Peripheral blood	4	Recurrence-free survival	1
Greece	1	Both FFPE and peripheral blood	2	Both overall survival and disease-free survival	2
Australia	1	Fresh surgical specimens	1	Both overall survival and progression-free survival	1
Spain	1	FFPE and fresh tumour biopsies	1	Both overall survival and recurrence-free survival	3
United States of America	1	Tests conducted on samples		Study design (N = 19)
Species (N = 19)		Immuno-histochemistry	8	Analytical cross-sectional design	11
Humans	19	Flow cytometry	3	Analytical cross-sectional design with comparison group	5
Article type (N = 19)		Flow cytometry and immuno-histochemistry	3	Longitudinal	1
Original research article	18	Multiplex immuno-fluorescence staining	2	Controlled before after study	1
Letter to the editor	1	Immuno-histochemistry and immuno-fluorescence staining	1	Cohort study	1
CD, clusters of differentiation; FFPE, formalin-fixed paraffin-embedded; FOXP3, forkhead box P3; KPS, Karnofsky Performance Status; NSCLC, non-small cell lung cancer; Treg, regulatory T cell.

3.2 Quantitative Synthesis of the Study Results

Out of the 19 included studies, only ten studies, reported significant (p ≤ .05) multivariate analysis. Among these studies, eight studies reported ten OS values, of which, eventually, six OS values from six studies were included in the meta-analysis (Fig. 2a). The population for these studies were non-metastatic NSCLC (n = 2), any stage NSCLC (n = 2), naïve NSCLC (n = 1), and NSCLC with Karnofsky Performance Status (KPS) > 80% (n = 1). Four studies measured Treg in FFPE sections by immunohistochemistry, while two studies measured the same in peripheral blood using flow cytometry. Four studies measured FOXP3 as the biomarker for Treg and two studies measured multiple biomarkers. All six studies ran Cox proportional hazards model for survival analysis. All of them were analytical cross-sectional in design. Two of these studies were prospective and four studies were retrospective.

Among the ten studies reporting significant multivariate analysis, eventually, two studies reporting two values for DFS were included in the meta-analysis (Fig. 2b) and two studies reporting two values of PFS were included in the meta-analysis (Fig. 2c). Only one study reported RFS among the ten studies reporting significant multivariate analysis. Since a minimum of two studies were required for performing a meta-analysis, quantitative synthesis could not be done for RFS.

Out of the final 19 studies included in the review, except one study which evaluated the FOXP3⁺ category and the FOXP3- category in non-metastatic NSCLC, all the remaining 18 studies assessed a high and low number of Tregs, however, the low Treg was defined differently in different studies as described in Table 2.

Table 2

Definitions of Low Treg Used in Different Studies
Study Population	Definition of Low Treg Used in Different Studies (n = 18)
Only stage 1 NSCLC patients	• low cell counts of less than 45 per high power field (n = 1)
Non-metastatic NSCLC patients	• less than 10% of the total lymphocyte count (n = 1) • low area under the curve than a cut-off (n = 1) • lower than the median (n = 1) • below the optimal cutoff point according to the built-in risk scoring formula in X-tile (n = 1) • not specified (although the text mentions the comparative frequency of Tregs in patients and healthy controls) (n = 1)
Only metastatic NSCLC patients	• lower than the median (n = 1)
NSCLC patients irrespective of the stage	• lower than the median (n = 2) • lower than mean (n = 2) • lower than a cut-off concerning percent of CD4 count (n = 2) • less than 95% of controls (n = 1) • low concentration in tumour tissue than a pre-specified level (n = 1) • high score (score 2–3) and low score (score 0–1), calculated as per the proportion of positively stained cells out of the total nucleated cells (n = 1) • below 25 in number (n = 1) • unspecified (n = 1)

3.3 Summary Estimates from the Meta-analysis

The six studies analysed for OS had a Q value less than the degree of freedom (K-1), hence I² was zero and the included studies had low heterogeneity. The summary log OR was 1.626; 95% CI: 1.324, 1.928; p (2-tailed) < .001; standard error (SE): 0.1174 (Fig. 3a). The two studies pooled for DFS had low heterogeneity (I² = 0), and the summary log OR was 0.992; 95% CI: 0.820, 1.163; p (2-tailed) .009; SE: 0.0135 (Fig. 3b). The two studies pooled for PFS had low heterogeneity (I² = 0), and the summary log OR as 2.128; 95% CI: -7.983, 12.239; p (2-tailed) = .228; SE: 0.7958 (Fig. 3c). The one study which evaluated RFS reported multivariate HR as 1.99; 95% CI: 1.15, 3.46; p = .01. The funnel plots for OS, DFS, and PFS did not show any publication bias as depicted in Additional Fig. 1.

The studies included in the review also reported other results such as quantification, localization, recurrence, metastasis, and correlation of programmed cell death protein 1 (PD-L1) with the level of Tregs. These results are provided in a Additional Note 2.

The results of the review indicate that the number of Treg cells was significantly associated with OS (p < .001) and DFS (p = .009) in NSCLC. The pooled log OR of more than one indicates that the reference category as a low number of Treg was associated with improved OS. On the contrary, DFS was better with the high number of Tregs, although the analysis pooled only two studies and both used different biomarkers and study populations. The low number of Tregs also showed higher PFS, although the same was not statistically significant (p = .228). Based on one study that reported significant HR for RFS, a low number of Treg had significantly better RFS (p = .01). The log OR generated by the forest plot for OS was significant for the study population “any stage NSCLC” and FOXP3 as the biomarker, while the one generated for DFS was significant for the study population “any stage NSCLC” and “non-metastatic NSCLC” and biomarker as FOXP3 and CD4⁺FOXP3. The study reporting significant HR for RFS also evaluated FOXP3 in any stage NSCLC. Overall, the meta-analysis reflected that a low number of Treg cells indicated better survival, especially in any stage NSCLC or non-metastatic NSCLC, while using FOXP3 as the biomarker.

Most of the past studies reported findings similar to our study, although few studies contradicted our conclusion. Peng et al. concluded that a low number of Tregs had significantly poor DFS (37). Kotsakis et al. found that a high number of terminal effector Tregs was associated with significantly better OS and PFS (46). On univariate Cox regression, Muto et al. reported significantly better OS (49), while Ameratunga et al. reported significantly better DFS in patients with a high number of Treg cells (40). None of the previous studies could highlight the role of Tregs in different study populations, considering different prognostic factor variables, outcomes, and study designs. None of the published reviews on this topic was designed as a meta-analysis and/or a systematic review(30, 55). On the contrary, our review was designed as a systematic review and meta-analysis and was comprehensive with respect to varied study populations, prognostic factor variables, outcomes, and several other aspects.

The current study had certain limitations. Searching only one portal might have introduced bias, although the unavailability of filters appropriate for clinical studies in search portals like Scopus and Google Scholar discouraged the authors from including them in the search strategy. Since MEDLINE is the biggest database for clinical studies, the authors chose to conduct the review on the PubMed portal. Moreover, variation in study populations, prognostic factor variables (such as types of Tregs, biomarkers, samples, and procedures of measurement), outcomes, study designs, analytical tests, reference categories, quantification, and localization of Tregs, among the included studies may have introduced imprecision in results and may be a source of bias.

Several factors influence the function of Tregs in the TME by acting on the development, maturation, and differentiation of Tregs, or by providing favourable conditions for the functioning of Tregs (55–64). Moreover, multiple mechanisms of action of Tregs pose difficulty in explaining the exact mechanism contributing to the results of the individual studies (29, 30). Since the current review did not assess any of these factors influencing the function of Tregs or the underlying mechanisms, they could have potentially confounded the results. Further, due to the low number of studies included in the final meta-analysis, sub-group analysis could not be conducted for many variables such as study population and biomarkers. Nevertheless, the present review applied meta-analysis to synthesize the best available evidence for use of the number of Treg cells as a prognostic indicator in NSCLC.

Based on the results of this meta-analysis, authors recommend using the “number of Treg cells” as a prognostic biomarker, especially in any stage NSCLC or non-metastatic NSCLC, while using FOXP3 expression as the specific marker, although further experimental studies designed for various populations, biomarkers, and outcomes are needed to confirm these findings. By highlighting the significance of Treg cells in the prognosis of NSCLC, this study can pave way for developing better prognostic tests, supplementing the TNM staging-based clinical-decision making, and formulating novel therapeutic approaches in cancer treatment.

Ethics approval and consent to participate

The study conducted adheres to and is in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki 1975, as revised in 2000) for experiments involving humans. The systematic review did not deal with any patient-level data or disclose any identifiable patient data; hence Institutional Review Board Approval or Informed Consent of the patients to participate was not sought.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and/or analyzed during the current study during screening, risk-of-bias assessment, and extraction process are available in the online Harvard Dataverse Repository, https://doi.org/10.7910/DVN/JQOI9V (65).

Competing interests

The authors declare that they have no competing interests.

Funding

No funding was received for this work.

Authors’ contributions (CRediT Taxonomy)

KK contributed to concept and design, investigation, project administration, and critical revision of the manuscript. MG contributed to acquisition of data, investigation, and methodology. MG analyzed the data and wrote the original draft of the manuscript. SM contributed to concept and design, critical revision of the manuscript, and supervised the review. RK contributed to interpretation of data, validation of findings of the review, and critical revision of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We acknowledge Mr. Ujjwal Das, an independent consultant from Indian Institute of Management, Udaipur for inputs in planning for statistical analysis.

Authors' information

KK is Ph.D., MG is M.B.B.S, Ph.D., RK is D.M. Oncology, and SM is M.D.

Systematic review registration

The protocol of this review was prospectively registered on PROSPERO on August 28, 2021, and was assigned the registration number CRD42021270598. The protocol can be accessed from PROSPERO website.

Reporting guideline

This manuscript is written in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

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AdditionalNote1.docx
Additional Note 1. Detailed Search Strategy, Risk of Bias Assessment, and Data Extraction (.docx)
AdditionalNote2.docx
Additional Note 2. Other Results Reported by the Studies Included in the Review (.docx)
AdditionalFigure1.docx
Additional Figure 1. Funnel Plots (.docx) Additional Figure 1a. Funnel plot for overall survival Additional Figure 1b. Funnel plot for disease-free survival Additional Figure 1c. Funnel plot for progression-free survival
AdditionalTable1.StudywisePFOTable.xlsx
Additional Table 1. Study-wise PFO Table (.xlsx)
PRISMA2020checklist.docx

Prognostic value of tumour-associated regulatory T-cells as a biomarker in non-small cell lung cancer: a systematic review and meta-analysis

Status:

Journal Publication

Version 1

Abstract

Figures

1 Background

2 Methods

2.1 Design and Setting

2.2 Search Strategy

2.3 Statistical Analysis

3 Results

3.1 Included Studies: Flow Through the Review and Characteristics

3.2 Quantitative Synthesis of the Study Results

3.3 Summary Estimates from the Meta-analysis

4 Discussion

5 Conclusions

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1