Meta-Analysis Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Jul 24, 2024; 15(7): 920-935
Published online Jul 24, 2024. doi: 10.5306/wjco.v15.i7.920
Predictive value of tumor-infiltrating lymphocytes for neoadjuvant therapy response in triple-negative breast cancer: A systematic review and meta-analysis
Hai-Kuan Sun, Wen-Long Jiang, Shi-Lei Zhang, Peng-Cheng Xu, Li-Min Wei, Jiang-Bo Liu, Department of Thyroid and Breast Surgery, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, Luoyang 471000, Henan Province, China
ORCID number: Shi-Lei Zhang (0000-0001-6800-9464); Jiang-Bo Liu (0000-0002-1384-7353).
Co-first authors: Hai-Kuan Sun and Wen-Long Jiang.
Author contributions: Sun HK and Jiang WL acquisition of data, analysis, and interpretation of data, drafting the article, final approval; Zhang SL, Xu PC, and Wei LM interpretation of data, revising the article, final approval; Liu JB conception and design of the study, critical revision, final approval. Sun HK and Jiang WL contributed equally to this work as co-first authors. The reasons for designating Sun HK and Jiang WL as co-first authors are as follows: First, Sun HK and Jiang WL spent equal time and effort on acquisition of data, analysis, and interpretation of data during the literature eligibility and data analytical process. Second, during the preparation of our manuscript, Sun HK and Jiang WL achieve equal contribution in drafting and final approval of article. Finally, co-first authorship of Sun HK and Jiang WL indicate that the two co-first authors have equal responsibilities and burdens associated with the quality and reliability of the article. In summary, we believe that designating Sun HK and Jiang WL as co-first authors for our manuscript is appropriate as it accurately reflects our team's collaborative spirit and equal contributions.
Supported by Henan Province Medical Science and Technology Tackling Plan Joint Construction Project, No. LHGJ20220684.
Conflict-of-interest statement: The authors deny any conflict of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jiang-Bo Liu, Doctor, PhD, Associate Professor, Department of Thyroid and Breast Surgery, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology, No. 24 Jinghua Road, Luoyang 471000, Henan Province, China. jiangboliuxing@163.com
Received: March 9, 2024
Revised: May 21, 2024
Accepted: June 6, 2024
Published online: July 24, 2024
Processing time: 128 Days and 15 Hours

Abstract
BACKGROUND

The association between tumor-infiltrating lymphocyte (TIL) levels and the response to neoadjuvant therapy (NAT) in patients with triple-negative breast cancer (TNBC) remains unclear.

AIM

To investigate the predictive potential of TIL levels for the response to NAT in TNBC patients.

METHODS

A systematic search of the National Center for Biotechnology Information PubMed database was performed to collect relevant published literature prior to August 31, 2023. The correlation between TIL levels and the NAT pathologic complete response (pCR) in TNBC patients was assessed using a systematic review and meta-analysis. Subgroup analysis, sensitivity analysis, and publication bias analysis were also conducted.

RESULTS

A total of 32 studies were included in this meta-analysis. The overall meta-analysis results indicated that the pCR rate after NAT treatment in TNBC patients in the high TIL subgroup was significantly greater than that in patients in the low TIL subgroup (48.0% vs 27.7%) (risk ratio 2.01; 95% confidence interval 1.77-2.29; P < 0.001, I2 = 56%). Subgroup analysis revealed that the between-study heterogeneity originated from differences in study design, TIL level cutoffs, and study populations. Publication bias could have existed in the included studies. The meta-analysis based on different NAT protocols revealed that all TNBC patients with high levels of TILs had a greater rate of pCR after NAT treatment in all protocols (all P ≤ 0.01), and there was no significant between-protocol difference in the statistics among the different NAT protocols (P = 0.29). Additionally, sensitivity analysis demonstrated that the overall results of the meta-analysis remained consistent when the included studies were individually excluded.

CONCLUSION

TILs can serve as a predictor of the response to NAT treatment in TNBC patients. TNBC patients with high levels of TILs exhibit a greater NAT pCR rate than those with low levels of TILs, and this predictive capability is consistent across different NAT regimens.

Key Words: Breast cancer; Tumor-infiltrating lymphocyte; Neoadjuvant therapy; Treatment response; Systematic review; Meta-analysis

Core Tip: The immune response status may have a significant impact on the effectiveness of chemotherapy. Tumor-infiltrating lymphocytes (TILs) can directly or indirectly participate in specific immune responses against tumor cells. However, the association between TIL levels and the response to neoadjuvant therapy (NAT) in patients with triple-negative breast cancer (TNBC) remains unclear. This systematic review and meta-analysis first investigated the relationship between TIL status and the response to NAT in TNBC patients. This systematic review and meta-analysis will provide clinical physicians with systematic evidence on the role of TILs to predict the response of TNBC patients to NAT.
INTRODUCTION

Global Cancer Statistics 2020 reported that in 2020, breast cancer (BC) was becoming the most common malignant tumor globally[1]. Triple-negative BC (TNBC) is characterized by extremely aggressive biological behavior and has a high recurrence rate and poor survival[2,3]. Extensive investigations on early diagnosis, precision treatment, and prognostic prediction have been conducted to improve TNBC patient survival[4-6]. Neoadjuvant therapy (NAT) can effectively decrease the clinical stage of TNBC, and patients who attain pathologic complete response (pCR) following NAT have significantly prolonged event-free survival (EFS) and overall survival compared with those having residual infiltrative carcinoma. Consequently, NAT has been widely recommended as the preferred preoperative standard treatment modality for TNBC patients with lymph node involvement and/or stage ≥ T1c disease[7,8].

The immune response status may have a significant impact on the effectiveness of chemotherapy[9,10]. Research findings indicate that in early-stage TNBC patients, the NAT protocol combining the immune checkpoint inhibitor pembrolizumab, which enhances the functionality of activated T cells, with conventional chemotherapy drugs has been correlated with increased rates of pCR and prolonged EFS[11,12]. Tumor-infiltrating lymphocytes (TILs) can directly or indirectly participate in specific immune responses against tumor cells, and their aggregation, interaction, and costimulation are essential for successful antitumor immune responses[13,14]. High levels of TILs within the tumor or the stroma are associated with a more favorable response to NAT in early-stage and locally advanced TNBC patients[15-19]. However, this result was not substantiated in a study that conducted a meta-analysis of individual patient data from a phase II study of TNBC NATs involving five different platinum-based regimens[20]. Therefore, further investigations are warranted to explore the correlation between TIL levels and therapeutic response in TNBC NATs.

Previously, a systematic review and meta-analysis on the correlation between TIL levels in different molecular subtypes of BC and NAT response showed that high levels of TILs are associated with pCR in a TNBC subgroup analysis including four studies[21]. Over the past decade, many clinical trials have further investigated the effectiveness of different NAT regimens for TNBC and employed TIL levels to predict treatment response and long-term prognosis. Consequently, this study was designed to analyze the ability of TILs in TNBC patients to predict the response to NAT through a more comprehensive systematic review and meta-analysis, with the objective of obtaining more current and robust research evidence. Additionally, this study examined the predictive importance of TIL levels for the therapeutic efficacy of different NAT regimens in TNBC patients.

MATERIALS AND METHODS

The present meta-analysis adhered to the reporting suggestions provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses[22].

Literature search and inclusion criteria

The literature search was conducted on the National Center for Biotechnology Information PubMed (MEDLINE) database to identify pertinent articles published prior to August 31, 2023. The search strategy involved utilizing a combination of the following MeSH terms, title/abstract keywords, or full-text search terms: “breast cancer, or breast carcinoma” , “triple-negative, or TNBC” , “neoadjuvant therapy, or neoadjuvant” , and “tumor-infiltrating lymphocytes, T lymphocytes, or TILs” . Additionally, a manual search of the literature and reference tracing were performed to identify any additional relevant studies.

The studies eligible for this meta-analysis met the following criteria: (1) Pathological and immunohistochemical-based molecular subtyping confirming the diagnosis of TNBC; (2) Reported TIL levels by hematoxylin and eosin staining evaluation according to the standardized method presented by the International TILs Working Group in 2014 or other explicit assays; (3) Reported the number or rate of pCR events in TNBC patients based on different TIL levels; and (4) Were published in either English or Chinese.

Three researchers (Sun HK, Jiang WL, and Zhang SL) independently evaluated the titles and abstracts of the candidate studies, excluding those not pertinent to the topic. Subsequently, both researchers thoroughly examined the full texts to determine their eligibility for inclusion. In cases where uncertainty arose or disagreements occurred regarding inclusion, the researchers resorted to the study designer (Liu JB) for a review and discussion to achieve consensus. Furthermore, if multiple publications involved the same study population, priority was given to the publication with a larger sample size or the most recent study for eligibility in the meta-analysis.

Data extraction and quality assessment

Three researchers (Sun HK, Jiang WL, and Zhang SL) independently collected the relevant information and data for each study that met the inclusion criteria using a predesigned table. These included details such as the first author, geographical location of the study population, publication year, study design, recruitment year, TNM staging, NAT regimen, number of high/low TILs, cutoff values and methodology used, treatment response endpoints and pCR criteria as well as the number and ratio of pCR events. Next, the quality of the cohort studies included was independently assessed by two researchers (Sun HK and Jiang WL) using the Newcastle-Ottawa Scale (NOS)[23].

Statistical analysis

The meta-analysis was conducted in RevMan 5.4 software. The total cases of patients and the cases of patients who achieved pCR were recorded separately for the high TIL level group and low TIL level group in each study and input into RevMan software. The relative risk ratio (RR) and the associated 95% confidence interval (CI) were calculated per the following formula: The pCR rate in the high TIL level group divided by the pCR rate in the low TIL level group. RR > 1 and P < 0.05 indicated a greater pCR rate in the high TIL level subgroup than in the low TIL level subgroup.

In the meta-analysis, between-study heterogeneity was assessed using the I2 statistic (ranging from 0% to 100%). If an I2 value less than 50% or a P value greater than 0.05 indicated the absence or low between-study heterogeneity, a fixed-effects model was used for meta-analysis; otherwise, a random-effects model (REM) was used. Additionally, subgroup analysis was conducted to explore the source of between-study heterogeneity when significant heterogeneity was observed, and sensitivity analysis was performed to evaluate the influence of individual studies on the overall meta-analysis results. Publication bias was investigated using a funnel plot and Egger’s test. If funnel plot is asymmetric or a P value is less than 0.05 according to Egger’s test, publication bias was considered present[24]. Duval and Tweedie trim-and-fill method was used for testing and adjusting for publication bias in meta-analysis[25]. All the statistical tests were two-tailed, and P < 0.05 was considered indicative of statistical significance.

RESULTS
Study selection

A preliminary literature search identified 269 articles, and after reviewing the titles and abstracts, we selected 158 articles for full-text reading. Subsequently, 125 articles were excluded because of the eligibility criteria. Finally, 32 eligible studies comprising 5406 TNBC patients were included in this systematic review and meta-analysis. The NOS quality scores of the eligible studies ranged from 6 to 9, with a median score of 8 (Figure 1).

Figure 1
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Figure 1 PRISMA flow diagram for study selection of systematic review and meta-analysis. pCR: Pathological complete response; TIL: Tumor-infiltrating lymphocyte; TNBC: Triple-negative breast cancer.
Characteristics of the included studies

Table 1 displays the characteristics of all the studies included in the analysis. Among the 32 included studies, 16 studies provided descriptions of TNBC before NAT based on T staging, including 4051 cases in T1/T2, 48 cases in T2/T3, 341 cases in T2-T4, and 1007 cases in T3/T4; fifteen (15) studies described N staging of pre-NAT TNBC, including 2937 cases in N0 and 2,704 cases in N1-N3; additionally, 11 studies described the clinical TNM staging of pre-NAT TNBC, including 91 cases in stage I, 923 cases in stage II, and 762 cases in stage III; and five studies did not report T or N stage or clinical TNM staging. Among the 27 included studies, TIL levels were assessed per the standardized method proposed by the International TILs Working Group in 2014[26], while five studies[27-30] did not report the specific method used for TIL assessment. The cutoff value for TIL level most commonly reported was 10% (n = 10)[18,29,31-38], followed by 50% (n = 8)[17,27,39-44], 20% (n = 4)[15,16,45,46], 30% (n = 3)[17,28,47], 40% (n = 3)[48-50], and 60% (n = 2)[51,52].

Table 1 Characteristics of the impact of tumor-infiltrating lymphocytes on the response to neoadjuvant therapy in triple-negative breast cancer patients included in the meta-analysis.
Ref.
Data collection
Recruitment period
Sample size
Age in yr, median/mean (range)
TNM stage
Neoadjuvant regimen
Number of high/middle TILs as %, cut-off, and method
End point and pCR standard
Number of overall pCR as %
pCR rates as high TILs vs low TILs
OR or RR
Cerbelli et al[36], GermanyRetrospective consecutive cohort2011.6-2017.66150 (28-74)T1: 8; T2: 46; T3: 3; T4: 4; N0: 32; N1-N3: 29AC×4 (Q3W) →T×12 (QW)49 (17/32) (80.3), (50%) 10%, HEpCR, ypT023 (37.7)18 (36.7) vs 5 (41.7)OR: [U] 0.41 (0.17-0.95), 0.037; [M] 2.39 (0.96-5.96), 0.062
Galvez et al[17], PeruRetrospective cohort2003.1-2014.1243549 (24-84)II: 72, III: 363; AC×4 (Q3W) →T×12 (QW)181 (41.6), 50%, HE pCR, ypT046 (11.0)26 (14.4) vs 20 (7.9)NR
Abdelrahman et al[39], EgyptProspective cohort2017.1-2019.55045 (22-72)T1: 20; T2: 30; N0: 18; N1-N3: 32AC→T14 (28.0), 50%, HEpCR, ypT020 (40.0)10 (71.4) vs 10 (27.8)NR
Jung et al[53], KoreaRetrospective cohort2009.1-2014.12143NRT1-T2: 91; T3: 52; N0: 64; N1-N3: 79AC→T74 (51.7), 30%, HEpCR, ypT066 (46.2)43 (58.1) vs 23 (33.3)OR: [U] 2.774 (1.404-5.481), 0.003; [M] 3.484 (1.407-8.627), 0.007
Russo et al[47], VenezuelaRetrospective cohort2008-201341NRII: 80, III: 107;AC→T14 (34.1), 30%, HEpCR, ypT015 (36.6)11 (78.6) vs 4 (14.8)OR: [U] 8.85 (3.62-21.66), 0.001
Vicent et al[48], SpainRetrospective cohort1998-201516449 (29-81)II: 63, III: 37AC×4 (Q3W) →T×12 (QW)58 (35.4), 40%, HEpCR, ypT0/is, ypN061 (37.2)51 (88.0) vs 10 (9.0)NR
Ochi et al[32], JapanRetrospective consecutive cohort2001-20098052 (27-75)NRAC→T55 (19/36) (68.8), (50%) 10%, HEpCR, ypT025 (31.3)24 (43.6) vs 1 (4.0)NR
Bockstal et al[49], BelgiumRetrospective consecutive cohort2015.1-2020.33555.8 ± 13.3NRAC→T10 (28.6), 40%, HEpCR, ypT013 (37.1)8 (80.0) vs 5 (20.0)NR
Rangan et al[43], IndiaNRNR75NRT1-T3: 49; T4: 26; N0: 36; N1-N3: 39NR57 (76.0), 50%, HEpCR, ypT027 (36.0)25 (43.9) vs 2 (11.1)OR: [U] 6.25 (1.312-29.763), 0.025
Pang et al[18], ChiNRRetrospective cohort2010.1-2018.12310NRT1-2: 298; T3-4: 97AC→T177 (85/92) (57.1), (20%) 10%, HEpCR, ypT088 (28.4)53 (31.1) vs 33 (34.5)NR
Zhang et al[52], AmericaRetrospective cohort2005-20165846 (24-64)T1: 7; T2-T4: 51; N0: 30; N1-N3: 28AC×4 (Q3W) →T×12 (QW)17 (29.3), 60%, HEpCR, ypT026 (44.8)12 (70.6) vs 14 (34.1)NR
Zhao et al[50], ChiNRRetrospective cohort2017-201812650.1 ± 11.2T1: 78; T2-T3: 48; N0: 74; N1-N3: 52AC→T42 (33.3), 40%, HEpCR, ypT076 (60.3)38 (90.5) vs 38 (45.2)NR
Cerbelli et al[40], ItalyRetrospective consecutive cohort2011.1-2016.125450 (28-75)T1: 7; T2-T4: 47; N0: 24; N1-N3: 30AC×4 (Q3W) →T×12 (QW)22 (40.7), 50%, HEpCR, ypT0/is, N019 (35.2)11 (50.0) vs 8 (25.0)OR: [U] 1.61 (0.40-6.52), 0.025
Rao et al[30], ChiNRRetrospective consecutive cohort2009.7-2014.65246.9 (23-67)II: 34, III: 16;TAC21 (40.4), CD8: ≥ 0.15, HEpCR, ypT0 DFS OS14 (26.9)CD8: 10 (47.6) vs 4 (12.9) CD8 OR: [U] 6.14 (1.6-23.8), 0.010
Lusho et al[28], JapanRetrospective consecutive cohort2008-201912056 (28-86)NRTAC18 (15.0), 30%, HEpCR, ypT0/Tis ypN034 (28.3)10 (55.6) vs 24 (23.5)NR
Hida et al[37], JapanRetrospective cohort2007-20144856 (22-79)T1: 93; T2: 59; T3: 2; N0: 98; N1-N3: 56AC×4 (Q3W) →T×12 (QW)31 (11/20) (64.6), (50%) 10%, HEpCR, ypT0/is, ypN021 (43.8)18 (58.0) vs 3 (17.6)NR
Hida et al[27], JapanRetrospective consecutive cohort2007-201480NRN0: 56; N1-N3: 24TAC23 (28.8), 50%, HEpCR, ypT0/is, N028 (35.0)12 (52.2) vs 16 (28.1)NR
Kolberg et al[51], GermanyRetrospective cohortNR311NRNRAC→T59 (19.0), 60%, HEpCR, ypT0110 (35.4)35 (59.3) vs 75 (29.8)OR: [U] 3.44 (1.92-6.18), 0.001
Foldi et al[38], AmericaII RCT2015.12-2018.1154NRI: 12, II: 33, III: 14; T→ddAC- Durvalumab (3 and 10 mg/kg)26 (16/10) (48.1), (30%) 10%, HEpCR, ypT0/Tis ypN023 (42.6)15 (57.7) vs 8 (28.6)NR
Abuhadra et al[16], AmericaProspective cohort2015.10-2019.1131852.5 (24-77)I: 38, II: 210, III: 70; ddAC→T+ (Atezolizumab/ Panitumumab/ Bevacizumab)106 (33.3), 20%, HEpCR, ypT0130 (40.9)68 (64.2) vs 62 (29.2)NR
Denkert et al[33], GermanyRCT IPD pooled analysis2010.1-2016.12906NRNRT+ Bevacizumab646 (273/373) (71.3), (60%) 10%, HEpCR, ypT0333 (36.8)253 (39.2) vs 80 (30.8)NR
Yuan et al[34], AmericaII RCT2012.1-2018.86352 (28-79)II: 55, III: 12;TCb28 (6/22) (45.9), (60%) 10%, HEpCR, ypT030 (47.6)17 (60.7) vs 13 (39.3)Medium vs low1: OR: [U] 2.23 (0.74- 6.69), 0.16; high vs low1: OR: [U] 3.06 (0.49-9.30), 0.23
Sharma et al[46], AmericaII RCT2015.7-2018.510051 (29–70)T1: 19; T2: 70; T3-T4: 11; N0: 70; N1-N3: 30Arm-A: CbP + AC; Arm-B: CbD39 (43.3), 20%, HEpCR ypT0/is, ypN051 (56.7)26 (66.7) vs 25 (49.0)OR: [U] 2.08 (0.88-4.93), 0.096
Pons et al[45], SpainNR2016-202267NRT1-T2: 59; T3: 10; N0: 43; N1-N3: 26TCb + ddAC24 (35.8), 20%, HEpCR, ypT0/is, ypN036 (53.7)14 (58.3) vs 22 (51.2)NR
Abuhadra et al[15], AmericaNR2015.10-2020.1040851 (23–77)I: 41, II: 284, III: 83AC→TCb143 (35.0), 20%, HEpCR, ypT0/is, N0166 (40.7)85 (59.4) vs 81 (30.6)NR
Asano et al[31], JapanRetrospective cohort2007-201361NRT1: 24; T2-T4: 153; N0: 41; N1-N3: 136FEC→T48 (78.7), 10%, HEpCR, ypT028 (45.9)26 (54.2) vs 2 (15.4)NR
Ono et al[54], JapanNR1999-20079252 (23-76)II: 23, III: 36;AC→T
CEF		67 (72.8)1, high: (3-5), HEpCR, ypT029 (31.5)25 (37.3) vs 4 (16.0)NR
Wang et al[35], AmericaNR2007-201472NRT1: 5; T2: 48; T3: 15; T4: 5; N0: 38; N1-N3: 34NR53 (1/52) (73.6), (50%) 10%, HEpCR, ypT038 (52.8)35 (66.0) vs 3 (15.8)NR
Dong et al[29], ChiNRRetrospective cohort2010.1-2014.12170NRT1-2: 110; T3-4: 60TAC 122 (74/48) (71.8), (20%) 10%, HEpCR, ypT0 DFS OS48 (28.2)38 (31.1) vs 10 (24.8)NR
Würfel et al [44], GermanyNR2015.5-2017.4146NRT1: 59; T2-T4: 90NR24 (16.4), 50%, HEpCR ypT0 ypN056 (38.4)16 (66.7) vs 40 (32.8)NR
Hamy et al[42], FranceNR2015.1-2017.3717NRT1-T2: 529; T3: 189; N0: 282; N1-N3: 435NR81 (11.3), 50%, HEpCR, ypT0202 (28.2)48 (59.2) vs 154 (24.2)OR: [U] 5.02 (4.27-5.77), 0.001
Cerbelli et al[41], ItalyRetrospective consecutive cohortNR5949 (28-74)II: 36, III: 24NR17 (28.8), 50%, HEpCR, ypT022 (37.3)13 (76.5) vs 9 (21.4)NR
1High: Tumor-infiltrating lymphocyte proportion > 10% (2 points) combined with mild (1 point) or marked (2 points) intensity.
DFS: Disease-free survival; HE: Hematoxylin eosin staining; IPD: Individual patient data; M: Multivariate analysis; NR: Not reported; OR: Odds ratio; OS: Overall survival; pCR: Pathological complete response; RCT: Randomized controlled trial; RR: Risk ratio; TIL: Tumor-infiltrating lymphocyte; U: Univariate analysis.
Association between preoperative TIL levels and therapeutic efficacy of NAT in TNBC patients

Overall meta-analysis: A meta-analysis of 32 studies revealed that the patients with high TIL levels had a high proportion of pCR events (46.7%, 1004/2092) than patients with low TIL levels (26.4%, 900/3254) with a significant difference (P < 0.001, REM, I2 = 56%) (Figure 2). Sensitivity analysis using leave-one-out approach indicated that the meta-analytical statistics were not changed by any single study: Excluding the study with the largest effect size[32], the calculated RR was 1.99 (95%CI: 1.75-2.26, REM, I2 = 55%).

Figure 2
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Figure 2 Forest plot demonstrating the correlation between tumor-infiltrating lymphocyte levels and the pathological complete response rate in triple-negative breast cancer patients receiving neoadjuvant therapy. TIL: Tumor-infiltrating lymphocyte.

Publication bias analysis: An asymmetric funnel plot and Egger’s test P value (P = 0.001) less than 0.05 suggested potential publication bias in the included studies of overall meta-analysis. Additionally, the trim-and-fill method was further employed for assessing and adjusting for publication bias, the analytical result showed that nine missing studies were interpolated during the analysis to account for potential bias. It was observed that there was no significant asymmetry in the trimmed funnel plot and still significant overall meta-analytical effect size after adjusting for publication bias, suggesting that there was limited or insignificant publication bias (Figure 3).

Figure 3
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Figure 3 Funnel plot illustrating the correlation between tumor-infiltrating lymphocyte levels and the pathological complete response rate in studies investigating neoadjuvant therapy in triple-negative breast cancer patients. A: An asymmetric funnel plot and Egger’s test P value (P = 0.001) less than 0.05 suggested potential publication bias in the included studies of overall meta-analysis; B: Trim-and-fill method showed that there was no significant asymmetry in the trimmed funnel plot and still significant overall meta-analytical effect size after adjusting for publication bias, suggesting that there was limited or insignificant publication bias.

Subgroup analysis: Due to significant heterogeneity among the included studies in the overall meta-analysis, subgroup analysis was conducted based on important variables, including study design, TIL cutoff value, sample size, and geographical region, to explore the sources of between-study heterogeneity. The analytical results indicated that the statistical effect sizes of all subgroup analyses were consistent with the overall meta-analysis results, and there were no significant differences in the statistics among the subgroups. However, there were noticeable differences in the heterogeneity among the subgroups. Subgroup analysis revealed that the sources of between-study heterogeneity could stem from the subgroup of retrospective cohort studies (I2 = 58%) (Figure 4), the subgroups with cutoff values of 40% (I2 = 78%) and 20% (I2 = 67%), the subgroup with sample sizes > 80 (I2 = 69%), and the subgroup with European populations (I2 = 77%) (Table 2).

Figure 4
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Figure 4 Forest plot illustrating subgroup analysis based on study design of included meta-analysis. TIL: Tumor-infiltrating lymphocyte; RCT: Randomized controlled trial.
Table 2 Subgroup analysis examining heterogeneity among the included studies.
Analysis
No. of studies
Risk ratio (95%CI)
I2 statistic (%)
P value for
heterogeneity
Analytical
model
P value for subgroup differences
Study design
    RCT51.42 (1.23-1.64)410.15FEM
    Prospective cohort22.24 (1.77-2.83)00.64FEM
    Retrospective cohort 182.27 (1.84-2.80)580.01REM
    Not reported72.05 (1.77-2.36)450.09FEM0.02
Cut-off
    60%22.01 (1.57-2.58)00.90FEM
    50%82.31 (1.95-2.74)00.71FEM
    40%33.06 (1.60-5.84)780.01REM
    30%32.33 (1.61-3.37)460.16FEM
    20%41.68 (1.29-2.20)670.03REM
    10%101.63 (1.24-2.15)490.04REM
Locations
    Asia121.90 (1.62-2.24)460.04FEM
    Europe112.07 (1.58-2.71)770.01REM
    Americas92.01 (1.76-2.30)340.14FEM0.35
Sample size
    n ≤ 80162.62 (2.14-3.20)350.08FEM
    n > 80161.82 (1.56-2.12)690.01REM0.04
NAT regimens
    AC-T142.13 (1.72-2.63)560.01REM
    TAC41.99 (1.43-2.75)00.44FEM
    AC-T + targeted therapy31.73 (1.12-2.67)820.01REM
    AC-TCb41.57 (1.31-1.90)430.15FEM
    AC-T + Fu22.75 (1.28-5.92)00.61FEM0.02
AC: Anthracycline combined with cyclophosphamide; AC-T: Anthracycline combined with cyclophosphamide followed by paclitaxel or docetaxel; AC-T + Fu: Anthracycline combined with cyclophosphamide followed by paclitaxel or docetaxel, and fluorouracil; AC-TCb: Anthracycline combined with cyclophosphamide followed by paclitaxel or docetaxel, and platinum; FEM: Fixed-effects model; NAT: Neoadjuvant therapy; TAC: Paclitaxel or docetaxel combined with anthracycline, and cyclophosphamide; REM: Random-effects model.
Meta-analysis of different NAT regimens

Among the 32 studies, except for five studies[35,41-44] without a description of the NAT regimen, the reported NAT regimens in 27 included 14 studies with anthracycline combined with cyclophosphamide (AC) followed by sequential paclitaxel (T) (AC-T) [15,17,18,32,36,37,39,40,47-50,52,53], three studies with AC followed by sequential T in combination with anti-HER2 targeted therapy (AC-T + targeted therapy)[16,33,38], four studies with AC followed by sequential T in combination with platinum (Cb) agents (AC-TCb)[34,45,46,51], two studies with AC followed by sequential T in combination with fluorouracil (Fu) (AC-T + Fu)[31,54], and four studies with AC combined with T (TAC)[27-30].

The included studies were analyzed according to the NAT regimens, and the results revealed that patients with high TIL levels in different NAT regimens, such as AC-T, AC-TCb, AC-T + targeted therapy, AC-T + FU, and TAC, had 1.57 to 2.75 times greater rates of pCR events than those with low TIL levels. Moreover, there was no significant difference in the statistics among the various NAT regimens (P = 0.29). The detailed meta-analysis data of TILs associated with treatment response to different NAT regimens in TNBC patients are presented in Figure 5 and Table 2.

Figure 5
Open in New Tab   Full Size Figure   Download Figure
Figure 5 Forest plot illustrating the correlation between tumor-infiltrating lymphocyte levels and pathological complete response rates across various neoadjuvant therapy regimens. TIL: Tumor-infiltrating lymphocyte; AC: Anthracycline combined with cyclophosphamide; AC-T: Anthracycline combined with cyclophosphamide followed by paclitaxel or docetaxel; TAC: Paclitaxel or docetaxel combined with anthracycline, and cyclophosphamide; AC-TCb: Anthracycline combined with cyclophosphamide followed by paclitaxel or docetaxel, and platinum; AC-T + Fu: Anthracycline combined with cyclophosphamide followed by paclitaxel or docetaxel, and fluorouracil.
DISCUSSION

Tumor immunity plays a crucial role in the body’s defense against tumors and in mediating the response to anti-cancer treatments. The presence of TILs in breast tumors has been associated with improved clinical outcomes[55]. The role of TILs in the NAT response in TNBC patients has been extensively studied. Based on the existing studies evaluating the correlation between TIL assessment and NAT treatment outcomes in TNBC patients, we conducted a systematic review and meta-analysis of the relationship between TIL status and the response to NAT in TNBC patients. The results showed that TNBC patients with high levels of TILs had greater NAT pCR rates than did those with low TIL levels. Furthermore, analysis based on different NAT regimens revealed that TIL levels were significantly associated with treatment response in all NAT regimens incorporating anthracycline combined with taxane drugs. This suggests that TILs have predictive value for treatment response in these NAT regimens. To our knowledge, this is the first comprehensive and specific evaluation of the ability of TILs to predict the response of TNBC patients to NAT, which offers important insights into predicting treatment response based on pretreatment tumor immune status in TNBC patients.

TILs play a vital role in the surveillance and defense against tumors within the tumor immune microenvironment. The positioning, clustering, interaction, and costimulation of TIL subgroups are crucial for effective antitumor immune responses[13]. TILs can directly eliminate cancer cells through various mechanisms, including the specific recognition of endogenous antigen peptide-MHC class I molecule complexes by CD8+ T cells, the secretion of substances such as perforin and granzymes to induce tumor cell death through proteolytic activity, and the expression of FasL or the secretion of tumor necrosis factor (TNF)-alpha to induce apoptosis in cancer cells by binding to the death receptor Fas and TNF receptor on the surface of target cells[56]. Studies have shown that chemotherapy drugs can not only directly kill cancer cells through cytotoxic effects but also regulate TILs to eliminate cancer cells. For example, T cells pretreated with doxorubicin, cyclophosphamide, and paclitaxel in a coculture system with tumor organoids showed a greater proportion of cancer cell apoptosis than did T cells that were only pretreated with doxorubicin and cyclophosphamide and cocultured with tumor organoids. In another study, no significant difference was observed in T-cell pretreatment between doxorubicin, cyclophosphamide, and carboplatin combination therapy and doxorubicin and cyclophosphamide alone. This suggests that paclitaxel can modulate the cytotoxicity of T cells and exert an antitumor effect[57]. Furthermore, research has shown that BC patients with higher levels of TILs have better clinical responses to chemotherapy containing paclitaxel than to adjuvant chemotherapy regimens without taxanes, confirming this concept at the clinical level[58].

The systematic assessment and meta-analysis conducted herein provide substantial evidence that TNBC patients exhibiting high TIL levels exhibit superior treatment responses regardless of the specific NAT scheme employed, particularly in terms of higher pCR rates. Moreover, an increase in the TIL level following NAT treatment is associated with improved therapeutic outcomes in BC patients. The study findings indicate that the administration of anthracycline-based chemotherapy drugs along with cyclophosphamide augments TIL levels in BC patients receiving NAT, and this increase in TIL levels is positively correlated with an improved pCR rate[33,59]. A study that stratified TNBC cohorts into lymphocyte-predominant BC (LPBC) and non-LPBC based on stromal TIL levels revealed that higher levels of stromal TILs in TNBC patients not only correlated with a greater pCR rate but also supported a greater pCR rate in LPBC patients than in non-LPBC patients. Additionally, even within the LPBC subgroup, the inclusion of platinum-based drugs in anthracycline-based chemotherapy followed by sequential paclitaxel yielded more significant benefits than in non-LPBC patients[60]. These clinical findings have been validated in various established experimental models of carcinogen-induced BC. In these animal models, the administration of doxorubicin amplifies the tumor antigen-specific proliferation of CD8+ T cells in tumor-draining lymph nodes in a homologous antigen-specific manner. Furthermore, it augments the ratio of CD8+ T cells infiltrating the tumor tissue and elicits tumor antigen-specific interferon-gamma production by these CD8+ TILs. Ultimately, the therapeutic effects of doxorubicin are mediated through these two mechanisms[61].

Due to the substantial heterogeneity observed in the meta-analysis of the 32 eligible studies, we performed subgroup analysis to investigate the sources of heterogeneity. The subgroup analysis showed that TNBC patients with high preoperative TIL counts exhibited increased pCR rates, irrespective of the study design. However, there were significant variations in heterogeneity among the different subgroups. In particular, the subgroup of randomized controlled trials and prospective cohort studies showed no interstudy heterogeneity, whereas the subgroup of retrospective cohort studies demonstrated considerable interstudy heterogeneity. Therefore, the primary contributor to the interstudy heterogeneity among the overall meta-analysis was attributed to the included retrospective cohort studies. These findings highlight the essential requirement for rigorous and well-designed research, including prospective designs and/or randomized controlled designs in future research protocols, to ensure the consistency and accuracy of clinical trial outcomes. Consequently, when assessing the predictive value of TILs for TNBC NAT treatment response, the meta-analysis results from the subgroup of randomized controlled trials and prospective cohort studies, which exhibit good consistency, can be considered robust evidence for clinical decision-making. Additionally, subgroup analysis was performed to explore the influence of high TIL cutoff values, the source of the study population, and the median sample size on the heterogeneity observed in the current meta-analysis. The analytical results presented that the differences in the cutoff values and the source of the study population were also potential sources of interstudy heterogeneity. Sensitivity analysis, carried out by sequentially excluding individual studies from the overall meta-analysis results, showed that the overall findings were not affected by any single study, but the heterogeneity varied. Notably, exclusion of the study conducted by Denkert et al[33] resulted in the lowest level of heterogeneity (I2 = 45%).

Despite our comprehensive evaluation of the association between TIL levels in preoperative BC tissue treated with NATs and pCR in TNBC patients, our systematic review and meta-analysis has several limitations. First, the assessment of TILs is subjective, and there may be substantial variations in determining TIL levels among different studies due to the subjective judgments of various pathology experts. This subjectivity may impact the true relationship between TIL levels and treatment response and introduce heterogeneity across studies. Additionally, the analysis was limited by the paucity of studies that examined the correlation between TIL levels and NAT treatment response according to different molecular marker types of TILs. Consequently, it was not possible to more comprehensively conduct a subgroup analysis based on TIL molecular subtypes to explore the relationship between TIL levels and NAT treatment response. Finally, the restriction to studies published in English or Chinese may introduce language bias in this analysis. Therefore, given these considerations, it is advisable to interpret the results of this meta-analysis with caution.

CONCLUSION

In summary, this systematic review and meta-analysis indicated that TNBC patients with elevated TILs exhibited significantly greater pCR after NAT than those with low TILs, even among different NAT regimens and in TNBC patients from diverse populations. Therefore, it can be concluded that high TIL levels in preoperative TNBC tissue have the potential to predict treatment response to various NAT regimens in all TNBC patients. Additionally, the subgroup analysis results of homogeneous randomized controlled trials support the use of high TIL levels as Class Ia clinical evidence to predict NAT treatment response in TNBC patients, and the results of homogeneous prospective cohort studies are classified as class 2a evidence. Therefore, in clinical practice, adopting appropriate threshold to define high levels of TILs can effectively predict the response to NAT and aid in making NAT decisions for TNBC patients.

ACKNOWLEDGEMENTS

The authors wish to acknowledge Dr. Ping ZG, Professor of College of Public Health, Zhengzhou University, for his help in reviewing and guiding the statistical methods of this study.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Oncology

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade C

Novelty: Grade B

Creativity or Innovation: Grade B

Scientific Significance: Grade B

P-Reviewer: Dauyey K, Kazakhstan S-Editor: Qu XL L-Editor: Filipodia P-Editor: Zhao YQ

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