The value of inflammation-related indicators in chemotherapy efficacy and DFS of triple-negative breast cancer

doi:10.21203/rs.3.rs-5311464/v1

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The value of inflammation-related indicators in chemotherapy efficacy and DFS of triple-negative breast cancer

https://doi.org/10.21203/rs.3.rs-5311464/v1

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Background: Neoadjuvant chemotherapy (NAC) is an important measure for improving the prognosis of early operable Triple-negative breast cancer (TNBC) patients. Inflammation-related indicators can predict the outcome of TNBC patients.

Methods: Kaplan-Meier curves were used to evaluate survival. The correlation between these indicators and NAC efficacy was analyzed using t-tests. Models for univariate and multivariate Cox regression analyses were performed to determine the independent risk factors. A nomogram was used for the prediction of one-, two-, and three-year disease-free survival (DFS).

Results: IL-6, PLR, SII, and Ki-67 levels were associated with neoadjuvant efficacy. IL-6, PLR, SII, Ki-67, and lymphocyte count were associated with DFS. The risk score for each TNBC patient was obtained using LASSO regression analysis to construct a prognostic model. In the prognostic model, patients in the high-risk score group showed worse DFS than those in the low-risk group. Risk score and tumor size were independently correlated with outcomes in multivariate Cox regression analysis. A nomogram was constructed using IL-6, PLR, SII, Ki67, and Miller-Payne (MP) scores. Calibration curves demonstrated good consistency between the actual and predictive values of the nomogram.

Conclusion: A prognostic model was established by combining four prognosis-related indicators in TNBC patients who underwent NAC.

TNBC

inflammation-related indicators

prognostic model

nomogram

Breast cancer (BC) is a heavy burden on women worldwide. The latest data reported that the incidence rate of BC ranks second among women, and the mortality rate ranks seventh, with a gradually increasing trend[1]. Triple-negative breast cancer (TNBC) is widely acknowledged for its high invasiveness, recurrence, and metastasis rates after surgery and drug resistance. The survival time of most patients is less than 18 months[2]. In most cases, TNBC used for immunohistochemistry lacks the expression of estrogen receptors (ER) and progesterone receptors (RP), and HER-2[3, 4] has low (1 + or 2 + FISH−) or negative expression.

Due to the lack of targeted and endocrine therapy, chemotherapy remains the standard adjuvant treatment for both operable patients and recurrent or metastatic patients with TNBC[5]. Neoadjuvant chemotherapy (NAC) has been widely used to improve the prognosis of patients with operable early-stage TNBC. The current pathological complete response (pCR) rate of NAC is 40–50%[6]. NAC combined with immune checkpoint inhibitors can improve the pCR rate and event-free survival (EFS) in TNBC patients with early-stage disease, but due to the high price, not all patients can afford it[7, 8].

Systemic inflammation, particularly chronic inflammation, is prominently linked to the occurrence, progression, metastasis, and recurrence of malignant tumors[9]. Chronic inflammation causes continuous tissue damage and repair, and cell metaplasia often occurs during this long-term process. If cell metaplasia cannot be reversed, it will further develop into atypical hyperplasia[10]. In this state, the tissues and organs may become cancerous. Inflammatory mediators, particularly interleukin (IL)-6, play vital roles in cancer progression. Previous studies have demonstrated that inflammatory factors and chemokines in tumor cells or tumor-associated leukocytes can lead to tumor progression[9]. Many inflammation-related indicators, including C-reactive protein (CPR) level, platelet-lymphocyte ratio (PLR), and neutrophil-lymphocyte ratio (NLR), have been recognized as prognostic indicators for the outcomes of BC patients[11, 12]. Compared with traditional single indicators, the systemic immune-inflammation index (SII), a composite immune-inflammatory indicator, comprehensively reflects inflammation and immune status[13]. Previous studies have shown that SII is advantageous in outcome prediction in patients with TNBC[14, 15].

This study aimed to investigate the prognostic predictive value of inflammation-related indicators in patients with TNBC and establish a nomogram and prognostic model.

Subjects

In total, 100 patients diagnosed with TNBC were enrolled in this study. The case-screening flowchart was displayed in Fig. 1. Inclusion criteria: 1. Patients aged 18–75 (18 and 75 years); 2, the biopsy pathology before treatment was ER- and PR-negative; HER-2 expression according to immunohistochemistry was 0, 1+, or 2 + and FISH-; 3, the patient underwent eight cycles of standard NAC; 4, standard radical mastectomy for BC after NAC; 5, Eastern Cooperative Oncology Group (ECOG) score of 0–1. Exclusion criteria: 1. Patients who did not undergo NAC or direct surgery; 2, patients who failed to complete NAC or were unable to tolerate NAC; 3, no surgery after completion of NAC; 4, before treatment, the patient's hematological parameters could not be assessed; 5, the efficacy cannot be evaluated after NAC.

Blood samples: Peripheral blood samples of all patients were collected within one week before the biopsy. Blood samples were collected between 6:00 am and 7:30 am to eliminate the influence of circadian rhythm on inflammatory indicators. A hematology analyzer (Sysmex XE-9100; Sysmex, Kobe, Japan) was used to analyze routine blood samples. A fully automatic biochemical analyzer (Hitachi 7600; Hitachi, Tokyo, Japan) was used to detect the CRP levels.

Construction of the prognostic model and validation model

Prognostic risk scores were calculated using univariate Cox regression analysis and Lasso-penalized Cox regression[16]. The formula is as follows:

Risk score = \(\:\sum\:_{i=1}^{k}\left(Ci\times\:Vi\right)\)

In the formula, k represents the number of prognostic indicators involved in the model, Ci represents the coefficient of the prognostic indicators in multivariate Cox regression analysis, and Vi represents the specific value of the prognostic indicator.

Follow-up disease-free survival (DFS) was estimated from the date of diagnosis to the date of postoperative recurrence, metastasis, last clinical assessment, or death.

Statistical analysis was performed using GraphPad Prism (version 8.0, San Diego, USA) and SPSS software (version 19.0, Illinois, USA). Survival data analysis was performed using Kaplan-Meier curves, while statistical analysis was performed using the log-rank test. The correlation between indicators and NAC efficacy was analyzed using the t-test. Models for univariate and multivariate Cox regression analyses determined independent risk factors. A nomogram was established using the rms package in R software (version 4.0.3; https://www.r-project.org/) for predicting one-, two-, and three-year DFS. The nomogram was quantified in terms of discrimination and calibration. Statistical significance was set at p < 0.05.

1. Clinical characteristics

This study included 100 patients diagnosed with TNBC who underwent NAC combined with surgery. The case-screening flowchart was shown in Fig. 1. The median age of participants was 52. There were 8, 37, and 55 patients with tumor stages T1, T2, and T3, respectively. A total of 82 patients had lymph node metastasis at diagnosis, whereas the other 18 did not. Among the tumor immunohistochemistry results, 45 patients were HER-2 negative, 35 were 1+, and 20 were 2 + FISH-. A total of 76 patients had a high Ki-67 level (> 30), and 24 had a low level (≤ 30). The clinical and pathological characteristics of the patients with TNBC were presented in Table 1.

Table 1

Clinical feature
Clinical feature
Age (years)	Median	52
	Rang	23–75
Tumor size	T1	8
	T2	37
	T3	55
HER-2	0	45
	1	35
	2 (FISH-)	20
N	have	82
	none	18
Ki-67	≤ 30	24
	> 30	76

2. The relationship between inflammation indicators and the efficacy of NAC

IL-6 (p = 0.0398), PLT (p = 0.0157), PLR (p = 0.0143), and SII (p = 0.0108) were significantly lower in the pCR group than in the non-pCR group, whereas Ki-67 (p = 0.0137) in the pCR group was distinctly higher than that in the non-pCR group, as displayed in Fig. 2.

3. Prognostic relevance of inflammation indicators and clinical features

Indicators were divided into two groups based on their median values. The high-level IL-6 (p = 0.0157), PLR (p = 0.0001), CRP (p = 0.0298), and SII (p = 0.0011) levels were significantly correlated with worse DFS compared to the low-level group. Additionally, the prognosis of the group with high lymphocyte (p = 0.0267) and Ki-67 (p = 0.0298) levels was better than that of the low-level group, as showed in Fig. 3.

4. Development of a prognostic model

The present study suggests that IL-6, SII, PLR, and Ki-67 can serve as predictive indicators of NAC efficacy and postoperative DFS. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to construct an optimization model utilizing these four indicators. The risk model formula based on prognostic parameters is as follows:

Risk score = (IL-6 value * 0.05196) + (SII value * 0.00029) + (Ki-67 value * -0.01468) + (PLR value * 0.00109).

The patients were divided into low- and high-risk groups based on the median risk score. Survival curves were analyzed using the Kaplan-Meier method. Patients in the low-risk score group exhibited a significantly better DFS than those in the high-risk score group (Fig. 4A; p = 0.0007; HR, 3.630; 95% CI: 1.787–7.375). All participants were randomly assigned to either the test or training group using R package to validate the accuracy of the prognostic model. Prognostic analysis was conducted for both groups of patients. Regardless of whether or not the test was performed (Fig. 4B; p = 0.0262; HR, 2.936; 95% CI: 1.137–7.749) or training (Fig. 4C; p = 0.0111; HR, 3.228; 95% CI: 1.169–8.912) group, patients with a low-risk score consistently demonstrated better DFS.

5. Cox regression analysis of the prognostic model

Univariate regression analysis revealed that a high-risk score (HR, 3.932; 95% CI: 1.676–9.224; p = 0.002) and small tumor size (HR, 2.408; 95% CI: 1.061–5.463; p = 0.036) were correlated with worse DFS, as listed in Table 2. Multifactor analysis identified a high-risk score (HR, 4.597; 95% CI: 1.782–11.858; p = 0.002) and small tumor size (HR, 2.485; 95% CI: 1.084–5.696; p = 0.032) as independent risk factors (Table 2).

Table 2

COX regression analysis of prognostic model
univariate analyses			multivariate analyses
	P value	Hazard ratio		P value	Hazard ratio
Risk scores	0.002	3.932 (1.676–9.224)	Risk scores	0.002	4.597 (1.782–11.858)
Tumor size	0.036	2.408 (1.061–5.463)	Tumor size	0.032	2.458 (1.084–5.696)
Age (years)	0.588	0.815 (0.389–1.710)	Age (years)	-	-
HER2	0.633	1.198 (0.571–2.511)	HER2	-	-

6. Evaluation of prognostic model accuracy

The accuracy of the prognostic model was further validated using the ROC curve, as displayed in Fig. 5.

7. Establishment and validation of the nomogram

Four indicators were utilized along with the Miller-Payne (MP) scores[17] to develop a nomogram for predicting the one-, two-, and three-year DFS. Calibration curves were generated to illustrate acceptable consistency between the actual and nomogram-predicted survival rates. (Fig. 6)

TNBC is one of the most lethal tumors in women. Neoadjuvant therapy presents an opportunity to improve the prognosis of patients with early operability. However, there remains a cohort that is susceptible to metastasis post-surgery or even during chemotherapy. This study investigated common inflammation-related indicators, including IL-6, PLR, SII, and pre-chemotherapy platelet levels, as potential predictors of NAC efficacy. Furthermore, IL6, PLR, and SII were identified as the prognostic factors for postoperative DFS. Notably, among all clinical features detailed in Table 1, Ki-67 emerged as a significant predictor of both NAC efficacy and DFS. Consequently, four prognostic factors capable of reflecting the efficacy of NAC and predicting DFS were identified. Using univariate Cox and LASSO regression, a risk model was constructed using these four prognostic indicators. Patients classified in the low-risk group consistently demonstrated better DFS than their counterparts in the high-risk group. Additionally, the study successfully developed and validated a nomogram utilizing these prognostic factors, further enhancing prognostic accuracy in clinical settings.

Inflammation plays a pivotal role in cancer progression. The TME, which is characterized by inflammatory cells and mediators, significantly contributes to tumor initiation and progression. Chronic inflammation is acknowledged as a catalyst in the development of solid tumors such as BC[18]. Inflammation can also affect the immune system. Tumor-associated inflammation can facilitate the evasion of tumor cells via immune surveillance. It can also drive the alteration of the TME to a milieu, favoring tumor growth and directly transmitting pro-tumorigenic signals to epithelial and cancer cells[19].

Interleukin-6 (IL-6) is a key regulator of inflammation and immune responses. Within the tumor immune microenvironment (TIME), IL-6 plays a pivotal role as a major immunosuppressive factor, dampening the activity of dendritic cells (DCs) and CD8 + T cells[20, 21]. Dysregulation of the IL-6/JAK/STAT3 signaling pathway is crucial in driving tumor initiation, progression, metastasis, and recurrence. Despite numerous clinical investigations targeting IL-6/JAK/STAT3 in the treatment of TNBC[22, 23], no approved drugs that specifically target this pathway exist.

Studies such as those by Tawara et al. have underscored a significant association between elevated IL-6 levels and poor outcomes in TNBC[24]. C-reactive protein (CRP), an inflammatory biomarker synthesized in the liver, serves as an indicator of inflammatory response intensity. Previous research has consistently demonstrated a positive correlation between elevated CRP levels and the invasiveness, recurrence, and poor prognosis of BC. Furthermore, increased CRP expression has been observed in highly aggressive TNBC cell lines[11]. Studies have shown that knockout of the CRP gene markedly suppresses the proliferation, migration, and invasion of TNBC cell lines[25].

In the current study, not all inflammation-related indicators effectively reflected short-term or long-term efficacy in patients. Given the intricate nature of inflammation and the interplay of multiple inflammatory markers, our understanding of their roles in the onset and progression of TNBC remains incomplete. This complexity likely explains why not all indicators serve as reliable outcome predictors.

The SII has garnered attention for its ability to provide a comprehensive reflection of inflammatory status. It is a composite indicator that incorporates neutrophils, lymphocytes, and platelets, thus surpassing the predictive capacity of single or dual inflammatory markers. Hence, the SII was included in our study, revealing a significant correlation between elevated SII levels and poorer NAC efficacy and DFS in patients with TNBC. Consistent with findings from a meta-analysis involving 13 BC studies, including two focused on TNBC, this study underscores that patients with high SII scores experience worse DFS than those with low score[11]. Additionally, these patients exhibited poor OS outcomes.

However, the present study has some limitations. First, the sample size of TNBC cases included in the retrospective analysis was relatively small, limiting its generalizability. This is because this study primarily reflects a single geographic region and lacks external validation data. Second, the study did not include patients who underwent treatment with immune checkpoint inhibitors in combination with chemotherapy. Future investigations should aim to address these limitations by collecting and analyzing data from patients receiving such combined therapies. These efforts will contribute to a more comprehensive understanding of the efficacy and outcomes of diverse treatment modalities for patients with TNBC.

In summary, the analysis conducted on multiple inflammation-related indicators among patients with TNBC undergoing NAC revealed significant associations between IL-6, PLR, and SII levels and NAC efficacy. Additionally, this study found significant associations between IL-6, PLR, and SII levels and postoperative DFS among patients with TNBC undergoing NAC. These inflammation-related indicators offer an economic, safe, and simple means of assessment. In addition, they demonstrated high clinical feasibility and potential for widespread clinical use. This study aimed to use these inflammatory indicators to predict the efficacy of neoadjuvant chemotherapy for TNBC. This study will provide a foundation for future TNBC treatment strategies.

Author contributions

(I) Conception and design: J Zhu, HH Shi, Y Meng; (II) Administrative support: HH Shi, Y Meng; (III) Provision of study materials or patients: J Zhu, JL Cheng, YY Ma; (IV) Collection and assembly of data: WJ Wang; (V) Data analysis and interpretation: Y Wang, ZH Zou; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Funding

This study was supported by the Science and Technology Project Foundation of Suzhou (grant No. SKJY2021126); Clinical Oncology Research Foundation of Beijing CSCO (grant No. Y-Young2021-0087).

Acknowledgements

We gratefully acknowledged Dr. Haihua Shi and Dr. You Meng for their valuable contributions to this study.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Suzhou Municipal Hospital, and was performed in accordance with the Declaration of Helsinki, with all participants providing their written informed consent.

Consent to publication

Not applicable.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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You are reading this latest preprint version

The value of inflammation-related indicators in chemotherapy efficacy and DFS of triple-negative breast cancer

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Abstract

Figures

Introduction

Materials&methods

Subjects

Construction of the prognostic model and validation model

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

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