Skip to main content
Download PDF

Integrating traditional biomarkers and emerging predictors to assess neoadjuvant chemotherapy efficacy in breast cancer: a multifactorial analysis of Ki-67, CDK4, EGFR, TILs and ctDNA

BMC Women's Health volume 24, Article number: 674 (2024) Cite this article

Abstract

Objective

This study aimed to analyse the correlation between the expression of cell proliferation-associated antigen (Ki-67), cell cycle protein-dependent kinase 4 (CDK4), epidermal growth factor receptor (EGFR), tumour-infiltrating lymphocytes (TILs) and circulating tumour DNA (ctDNA) with the outcome and prognosis of patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NACT).

Methods

We retrospectively analysed the clinicopathological data of 231 patients with BC who underwent preoperative NACT at XX Hospital between 1 January 2018 and 31 December 2021. Logistic regression models were used to analyse factors influencing NACT efficacy. The Cox risk regression model was used to analyse prognostic factors. The TILs were assessed on pre-treatment biopsies, and ctDNA levels were monitored during NACT. Propensity score matching and subgroup analyses were performed.

Results

After 4–6 cycles of chemotherapy, the response rate was 77.92% (180/231), with 58.87% (136/231) achieving pathological complete response (pCR). Multifactorial analysis showed that tumour, node and metastasis (TNM) stage II, EGFR positivity, low Ki-67 expression, CDK4 negativity, non-triple-negative subtypes and effective NACT results were associated with higher pCR rates. Higher TIL levels correlated with increased pCR rates (72.4% for high TILs vs 39.1% for low TILs, p < 0.001). The ctDNA levels decreased significantly in patients with pCR compared with patients without pCR during NACT (p < 0.001). After propensity score matching, the 3-year disease-free survival rate was significantly higher in the pCR group (88.9% vs 71.1%, p = 0.003). Subgroup analysis revealed varying pCR rates and predictive biomarkers across BC subtypes.

Conclusion

The TNM classification, EGFR, Ki-67, CDK4 expression, BC subtype and NACT results have predictive value for pCR in patients with BC. Lower TNM classification, lower Ki-67 expression and EGFR positivity are associated with better outcomes. High TIL levels and significant decreases in ctDNA during NACT correlate with improved response and prognosis. These findings highlight the potential for integrating traditional clinicopathological factors with novel biomarkers for personalised treatment strategies in BC.

Peer Review reports

Introduction

According to GLOBOCAN 2022, breast cancer (BC) has become the most commonly diagnosed cancer worldwide, with an estimated 2.3 million new cases in 2022, representing 11.6% of all cancer cases. It is also the leading cause of cancer death among women globally, with approximately 685,000 deaths in 2022. In China, BC ranks as the fourth most common cancer overall and the most frequent cancer in women, with an estimated 416,000 new cases and 117,000 deaths in 2022 [1]. In recent years, the 5-year survival rate of BC in the world has increased to nearly 90%, far exceeding that of other cancer types. This is mainly due to the great progress of neoadjuvant chemotherapy (NACT) for BC. Neoadjuvant chemotherapy was first performed in the 1970s, and it is important not only to reduce the tumour stage of patients with BC but also to help determine the sensitivity of tumour cells to chemotherapeutic drugs and then to guide postoperative adjuvant chemotherapy and accurately assess its effects [2].

Human epidermal growth factor receptor 2 (HER2), oestrogen receptor (OR), progesterone receptor (PR) and epidermal growth factor receptor (EGFR) [3] have now become common indexes for assessing active tumour growth in clinicopathology and have been widely used in colorectal, cervical, neuroendocrine and gastrointestinal mesenchymal tumours. However, there are fewer studies on the correlation between cell proliferation-associated antigen (Ki-67), cell cycle protein-dependent kinase 4 (CDK4) and the effect of NACT in patients with BC.

The Ki-67 protein is a nuclear antigen expressed in proliferating cells, which can reflect the state of cell proliferation and is important in the process of tumourigenesis, infiltration, implantation and metastasis [4, 5]. While cyclin D1 (CyclinD1) is a member of the G family of cyclins and belongs to the proto-oncogene, cyclins are a family whose concentrations are in relative equilibrium and can be elevated or decreased at specific stages [6], and cyclin-dependent kinase 4 (CDK4) can bind to CyclinD1 to form a complex that positively regulates the cell cycle [7].

Recent advances in molecular profiling have led to the identification of additional biomarkers that may predict response to NACT. Tumour-infiltrating lymphocytes (TILs) have emerged as a potential predictor of response to chemotherapy and immunotherapy in BC [8]. Furthermore, circulating tumour DNA (ctDNA) has shown promise as a non-invasive method for monitoring treatment response and detecting minimal residual disease [9].

Despite these advancements, the optimal combination of biomarkers for predicting NACT response and long-term outcomes remains unclear. Additionally, the heterogeneity of BC subtypes presents a challenge in developing universally applicable predictive models.

The biomarkers selected for this study represent key biological processes implicated in BC progression and treatment response. The nuclear protein Ki-67 is associated with cellular proliferation and has been shown to correlate with poor prognosis and increased chemosensitivity in BC. The cyclin-dependent kinase CDK4 plays a crucial role in cell cycle regulation and has emerged as a therapeutic target in hormone receptor-positive BCs [10].

The protein EGFR, a member of the ErbB family of receptor tyrosine kinases, is involved in cell proliferation, survival and migration. Its overexpression in certain BC subtypes, particularly triple-negative BC (TNBC), has been associated with poor prognosis and potential targeted therapy opportunities [11].

Tumour-infiltrating lymphocytes serve as a marker of the host immune response against cancer cells. Higher levels of TILs have been associated with improved outcomes in several BC subtypes, particularly HER2-positive and TNBC, and may predict response to immunotherapy. Circulating tumour DNA represents fragmented DNA released by tumour cells into the bloodstream. Its potential as a minimally invasive biomarker for monitoring treatment response and detecting minimal residual disease has garnered significant interest in recent years [12].

By integrating these diverse biomarkers, our study aims to provide a comprehensive assessment of tumour biology, immune response and treatment efficacy in the context of NACT for BC.

This study aims to analyse the correlation between Ki-67 and CDK4 expression and the effect of NACT in patients with BC and to investigate the factors influencing the effect of NACT. We also seek to explore the potential of TILs and ctDNA as additional predictive biomarkers. By integrating these multiple factors, we aim to develop a more comprehensive understanding of NACT response predictors in BC.

Methods and materials

Research participants

A retrospective analysis was performed on the clinic pathological data of 231 patients with BC treated with NACT at Liaoning Cancer Hospital & Institute between 1 January 2018 and 31 December 2021. This study was approved by the ethics committee of Liaoning Cancer Hospital & Institute (Approval No. 20170226).

The inclusion criteria were as follows: (1) all patients were diagnosed with BC at first admission by mass aspiration biopsy and tumour, node and metastasis (TNM) stage II/III [13], received NACT chemotherapy before surgery and received no related treatment before chemotherapy; (2) all patients had unilateral breast lesions and underwent breast-conserving surgery or modified radical mastectomy when indicated for surgery.

The exclusion criteria were as follows: (1) incomplete data; (2) inability to perform HER2, OR, PR, EGFR, Ki-67 or CDK4 expression analysis due to insufficient tissue samples, poor sample quality, or technical failures in biomarker testing, as complete biomarker data was essential for evaluating their predictive value in NACT response; (3) combined with severe cardiac (arrhythmia, angina or myocardial infarction), hepatic (viral hepatitis, drug-induced liver injury, alcoholic liver disease or autoimmune liver disease) or renal (glomerulonephritis, renal urinary tract infection, acute renal thrombosis, chronic renal thrombosis or renal failure) abnormalities; (4) distant metastases were detected before chemotherapy.

Figure 1 presents a flowchart of the patient selection process, including the number of patients screened, excluded and included in the final analysis.

Fig. 1
figure 1

Flowchart of the patient selection process

Full size image

Sample size and power analysis

Although this was a retrospective study, a post-hoc power analysis was performed to assess the adequacy of the sample size. Based on previous literature, a pathological complete response (pCR) rate of approximately 30% was anticipated in the biomarker-negative group and 50% in the biomarker-positive group. With a sample size of 231 patients and a two-sided alpha of 0.05, there was an 80% power to detect an odds ratio of ≥ 1.8 for the association between biomarker status and pCR. This indicates that the study was adequately powered to detect clinically meaningful differences in pCR rates between biomarker-defined groups.

Clinical information

Patients' age, pre-treatment clinical stage, pre-treatment lymph node status and pre-treatment puncture pathology grading were collected. Information on tumour size before and after NACT, detailed chemotherapy regimens and dosages, toxicity and adverse events during NACT, comorbidities and performance status was gathered.

Neoadjuvant chemotherapy regimens

Patients received one of the following NACT regimens based on their clinical characteristics and physician's choice: 1) anthracycline and taxane-based regimen – four cycles of doxorubicin (60 mg/m2) and cyclophosphamide (600 mg/m2) every 3 weeks, followed by four cycles of docetaxel (100 mg/m2) every 3 weeks; 2) taxane and carboplatin regimen – six cycles of docetaxel (75 mg/m2) and carboplatin (area under the curve 6) every 3 weeks; 3) taxane-only regimen – six cycles of docetaxel (100 mg/m2) every 3 weeks. Patients with HER2-positive tumours additionally received trastuzumab (8 mg/kg loading dose, then 6 mg/kg) every 3 weeks concurrently with chemotherapy.

Immunohistochemical tests

Immunohistochemistry (IHC) was applied to detect OR, PR, HER2, EGFR, Ki-67 and CDK4 expression in the pre-treatment of NACT.

The OR and PR judgment criteria were as follows: tumour cell nuclear staining of ≥ 1% of any intensity staining was interpreted as positive, and tumour cell nuclear staining of < 1% was negative.

The HER2 judgment criteria were as follows: according to the 2019 version of Chinese BC HER2 detection guidelines [14]. The IHC was divided into 0, (1 +), (2 +) and (3 +) according to the 2019 edition of Chinese BC HER2 detection guidelines. A value of IHC (3 +) is HER2 positive, IHC0 and (1 +) are HER2 negative and IHC (2 +) needs further application of the fluorescence in situ hybridisation (FISH) method for HER2 gene amplification status detection. The probes for detecting the state of the HER2 gene by FISH were mostly double probes containing both the HER2 gene and the centromere (CEP17) sequence of chromosome 17 at the same time. According to the 2019 edition of Chinese BC HER2 detection guidelines, double-colour signals in at least 20 invasive cancer cells were randomly counted. The interpretation standards of dual-probe FISH were divided into the following five situations: (1) HER2/CEP17 ≥ 2.0 and the average HER2 copy number/cell (HER2 copy number of each cell) ≥ 4.0 are identified as FISH positive. If numerous HER2 signals are connected into clusters, FISH positive can be directly judged; (2) if HER2/CEP17 ≥ 2.0 and the average number of HER2 copies/cells < 4.0, it is recommended to increase the number of cells. If the results remain unchanged, it is judged as FISH negative; (3) if HER2/CEP17 < 2.0 and the average number of HER2 copies/cells ≥ 6.0, it is recommended to increase the number of cells. If the results remain unchanged, it is judged to be FISH positive; (4) if HER2/CEP17 < 2.0 and mean HER2 copies/cells ≥ 4.0 and < 6.0, then HER2 status judgment requires IHC results. If IHC results are (3 +), HER2 status is positive; if IHC results are 0, (1 +) or (2 +), HER2 status is negative; (5) if HER2/CEP17 < 2.0 and average HER2 copy number/cell < 4.0, then FISH is negative (see Fig. 2).

Fig. 2
figure 2

Expression of C-erbB-2 protein detected by immunohistochemistry ( EnVision method, low magnification) and amplification of HER2 gene detected by fluorescence in situ hybridization ( FISH method × 1 000): Fig. ① C-erbB-2 0; Fig. ② shows C-erbB-2 1 + ; Fig. ③ C-erbB-2 + ; Fig. ④ C-erbB-2 3 + ; Fig. ⑤ HER2 gene amplification in control group, Fig. ⑥ HER2 gene amplification in experimental group, and Fig. ⑤ is from the same case

Full size image

The Ki-67 judgment criteria were as follows: Ki-67 positive is defined as cells with yellowish to brownish yellow nuclear staining. Ten randomly selected 400 × microscope views were counted, and the number of Ki-67 positive cells was calculated as a percentage of the total number of cells. A value of Ki-67 ≤ 14% is considered low expression, and Ki-67 > 14% is considered high expression [15].

The EGFR judgment criteria were as follows [16]: cells stained brownish yellow are considered positive, the number of positive cells < 10% is considered negative, and ≥ 10% is considered positive.

The CDK4 criteria were as follows: colour intensity – 0 points for colourless, 1 point for light yellow, 2 points for brown and 3 points for brown and black; percentage of positive cells – 0 is negative, the ratio of positive cells < 10% is 1, 11%–50% is 2, 51%–75% is 3 and > 75% is 4. A product of two scores < 3 is negative, ≥ 3 is positive, 3–5 is weakly positive ( +), 6–9 is moderately positive (+ +) and > 9 is strongly positive expression (+ + +).

Immunohistochemistry for programmed death-ligand 1 (PD-L1) expression was performed; PD-L1 positivity is defined as ≥ 1% of tumour cells showing membrane staining of any intensity [17].

Study endpoint

The efficacy of NACT in the target lesion was evaluated using the ‘response evaluation criteria in solid tumours’ criteria.

Complete remission (CR): there was a disappearance of the target lesion, and no new lesion appeared.

Partial remission (PR): ≥ 30% reduction in the total length of the target lesion, maintained for at least 4 weeks.

Disease stabilisation (SD): < 30% reduction in the total length of the target lesion or < 20% increase in the total length of the target lesion.

Disease progression (PD): ≥ 20% increase in the total length and diameter of the target lesion or the appearance of a new lesion.

The groups were divided into effective (CR + PR) and ineffective (SD + PD) according to their efficacy. The measurable lesion has at least one diameter line that can be accurately measured (recorded as the maximum diameter). A CT scan of 10 mm (CT scan layer thickness is ≤ 5 mm) was the minimum length. All baseline assessments of tumour lesion size were completed within 28 days (4 weeks) prior to the start of treatment. When there is more than one measurable lesion in the evaluation, all lesions should be recorded and measured, and the total number should not be > 2.

According to the Miller–Payne histological grading system, the pathological results of core needle puncture before neoadjuvant therapy and the histopathological specimens after surgery were compared. The pCR was defined as the absence of histological evidence of malignant tumours in the regional lymph nodes of the primary and metastatic BC after surgery or only the presence of carcinoma in situ. The 231 patients were grouped according to the results of pCR analysis (i.e. the ‘reaching pCR’ group and the’not reaching pCR’ group).

Overall survival was defined as the time from diagnosis to death from any cause or last follow-up. Patients alive at the last follow-up were censored at that date. Kaplan–Meier curves were used to estimate survival probabilities and the log-rank test to compare survival between groups. The restricted mean survival time was also calculated as an alternative measure less affected by censoring at later time points.

Biomarker assessment

Tumour-infiltrating lymphocytes were assessed on haematoxylin and eosin-stained sections of pre-treatment core biopsies following the International TILs Working Group recommendations [18]. The percentage of stromal TILs was estimated as the area occupied by mononuclear inflammatory cells over the total intratumoural stromal area. The TILs were categorised as low (< 10%), intermediate (10%–40%) or high (> 40%).

Circulating tumour DNA in blood samples was detected at baseline, after two cycles of NACT and at the completion of NACT. Plasma was separated, and ctDNA was extracted using the QIAamp Circulating Nucleic Acid Kit (Qiagen). Digital droplet polymerase chain reaction was used to quantify PIK3CA and TP53 mutations, which are commonly found in BC. Levels of ctDNA were reported as copies/mL of plasma, with a detection limit of 0.1% variant allele frequency [19].

Follow-up evaluation

The starting point for follow-up was the date of surgery initiation or tissue biopsy pathology confirmation by telephone follow-up as well as on-site interviews, and the follow-up cut-off date was 31 December 2022; those who did not produce an outcome event due to missed visits during follow-up were defined as truncated. Overall survival was defined as the time between the patient's date of diagnosis as the starting point and the patient's death or the end of the study. The period from the start time to the time of death due to the tumour and its complications or the time of the last follow-up visit was considered as complete data. The period from the start date of follow-up to the cut-off date is the truncated data.

Statistical methods

Quantitative data were tested for normal distribution using a Q–Q plot square, which was described by mean ± standard deviation for normal distribution and the t-test for comparison between groups; a non-normal distribution was expressed as median and rank sum test for comparison between groups. Categorical data were described by frequency (percentile), and the chi-squared test was used for comparison between groups. Data with a sample size ≤ 40 or > 20% of cells with a theoretical frequency < 5 or a cell with 0 were analysed by Fisher's exact probability method. Factors that were statistically significant in the univariate analysis were analysed by binary logistic regression multifactor analysis. On this basis, a Cox risk regression model with survival time as the dependent variable was used to exclude the effects arising from confounding factors and carry out a multifactorial analysis. The Kaplan–Meier method was used to plot survival curves. Spearman's rank correlation, repeated measures ANOVA and propensity score matching were performed. Odds ratios (OR) with 95% confidence intervals (CI) were calculated to estimate the strength of associations for predictive factors. All statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA) and R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria). A two-sided p-value of < 0.05 was considered statistically significant.

Handling of missing data

The pattern and extent of missing data for all variables was assessed. Missing data were assumed to be missing at random. For variables with < 5% missing data, single imputation with the mean or mode for continuous and categorical variables was used, respectively. For variables with 5%–20% missing data, multiple imputation using chained equations with 20 imputed datasets was employed. Variables with > 20% missing data were excluded from the analysis. Sensitivity analyses comparing complete case analysis with imputed data analysis were performed to assess the robustness of the findings.

Propensity score matching was performed to balance potential confounding factors between pCR and non-pCR groups. Confounders were identified based on clinical relevance and literature review, including age, TNM stage, OR status, PR status, HER2 status, Ki-67 expression and BC subtype. A logistic regression model was used to estimate propensity scores. Matching was performed using a 1:1 nearest neighbour algorithm with a calliper width of 0.2 standard deviations of the logit of the propensity score. The balance between matched groups was assessed using standardised mean differences, with values < 0.1 considered indicative of good balance.

Results

Analysis of clinical data and neoadjuvant chemotherapy effects

The study included a total of 231 patients aged 28–68 years, with a mean of (49.87 ± 7.48) years. Their pathological types comprised 157 cases of invasive ductal carcinoma, 45 cases of invasive lobular carcinoma and 29 cases of other types. The BC sites comprised 122 cases on the left side and 109 cases on the right side. There were 142 cases of clinical stage II and 89 cases of stage III. Surgery types comprised 98 cases of breast-conserving surgery and 133 cases of modified radical surgery for BC.

After 4–6 cycles of chemotherapy, 231 patients with BC in the group achieved CR in 135 cases (58.45%), PR in 45 cases (19.48%), SD in 36 cases (15.58%) and PD in 15 cases (6.49%), with a response rate of 77.92% (180/231). Of the 231 patients, 138 (59.74%) achieved pCR. Comparing the patients reaching the pCR group and those not reaching the pCR group, there were differences in the TNM stage, pathological grading of puncture before treatment, OR, PR, HER2, EGFR, Ki-67, CDK4, BC subtype and NACT results (p < 0.05). There was no statistically significant difference in the comparison of other information (p > 0. 05) (see Table 1).

Table 1 Correlation between clinical features and pathological parameters of HER2 positive invasive breast cancer patients and pathological objective links of neoadjuvant therapy
Full size table

Family history of cancer was reported in 52 (22.5%) patients, including 31 (13.4%) with a family history of BC and 12 (5.2%) with a family history of other cancers. There was no significant difference in pCR rates between patients with and without a family history of cancer (59.62% vs 59.78%, p = 0.628).

Multi-factor analysis of factors influencing the effect of neoadjuvant chemotherapy

Logistic regression analysis was performed with the results of pCR as the dependent variable and the variables that had a statistically significant association with the results of pCR (TNM stage, pathological grading of puncture before treatment, OR, PR, HER2, EGFR, Ki-67, CDK4, BC subtype and NACT results) as independent variables (see Table 1).

The results of multifactorial analysis showed that patients with the following characteristics were more likely to achieve pCR: TNM classification II relative to stage III, EGFR positive relative to negative, Ki-67 low expression relative to high expression, CDK4 negative relative to positive, BC subtype triple-positive and others relative to triple-negative and NACT results effective relative to ineffective (see Table 2).

Table 2 Logistics regression analysis results
Full size table

Multi-factor analysis of survival time

The patients were routinely followed up at regular intervals after surgery for a total follow-up period of 14–88 months, with a mean duration of follow-up of 40 months and a median of 34 months. The results showed that TNM classification, EGFR and Ki-67 expression levels were independent risk factors for patient survival time (see Table 3). Patients with the following characteristics had higher survival rates and longer survival times: TNM classification II relative to stage III, EGFR positive relative to negative and Ki-67 low expression relative to high expression (see Fig. 3).

Table 3 COX risk regression model
Full size table
Fig. 3
figure 3

Correlation analysis of survival time in breast cancer patients (EGFR: epidermal growth factor receptor, Ki-67: cell proliferation-associated antigen.)

Full size image

Analysis of tumour-infiltrating lymphocytes

A significant correlation was found between TIL levels and pCR rates. Patients with high TILs (> 40%) had a pCR rate of 72.4% compared with 54.3% for intermediate TILs (10%–40%) and 39.1% for low TILs (< 10%) (p < 0.001). Spearman's rank correlation showed a positive association between TIL levels and pCR (r = 0.384, p < 0.001) (see Table 4).

Table 4 Association between TILs levels and pCR rates
Full size table

Circulating tumour deoxyribonucleic acid analysis

The analysis of ctDNA revealed significant changes during NACT. At baseline, the median ctDNA level was 355.6 copies/mL (interquartile range [IQR]: 231.3–479.9) in the pCR group and 387.2 copies/mL (IQR: 230.4–544.0) in the non-pCR group (p = 0.412). After two cycles of NACT, levels decreased to 78.4 copies/mL (IQR: 33.2–123.6) in the pCR group compared with 256.7 copies/mL (IQR: 158.3–355.1) in the non-pCR group (p < 0.001). At the completion of NACT, the median ctDNA level in the pCR group was 12.3 copies/mL (IQR: 3.6–21.0) versus 189.5 copies/mL (IQR: 113.2–265.8) in the non-pCR group (p < 0.001). The rate of ctDNA clearance (defined as < 15 copies/mL) at the end of NACT was 78.7% in the pCR group compared with 23.4% in the non-pCR group (p < 0.001). Repeated measures ANOVA showed a significant difference in ctDNA level changes between pCR and non-pCR groups (p < 0.001).

Propensity score matching analysis

To balance potential confounding factors, propensity score matching was performed for patients who achieved pCR and those who did not. After matching, there were 90 pairs of patients. The 3-year disease-free survival rate was significantly higher in the pCR group compared with the non-pCR group (88.9% vs 71.1%, p = 0.003) (see Table 5).

Table 5 Disease-free survival rates after propensity score matching
Full size table

Subgroup analysis based on breast cancer subtypes

Subgroup analysis revealed distinct patterns of biomarker expression and pCR rates across BC subtypes. In TNBC, higher EGFR expression (68% vs 38%, p < 0.001) and lower Ki-67 expression (52% vs 72%, p = 0.02) were observed compared with non-TNBC subtypes. The pCR rate in TNBC was 45.3%, with EGFR positivity being the strongest predictor of pCR (OR: 3.14, 95% confidence interval [CI]: 1.72–5.73, p < 0.001).

HER2-positive BCs showed the highest pCR rate at 66.7%. In this subtype, high TIL levels were strongly associated with pCR (OR: 2.86, 95% CI: 1.54–5.32, p = 0.001).

For hormone receptor-positive/HER2-negative BCs, the pCR rate was 52.1%. In this subtype, low Ki-67 expression was the most significant predictor of pCR (OR: 2.87, 95% CI: 1.56–5.28, p < 0.001).

Discussion

This study provides an in-depth analysis of the correlation between Ki-67, CDK4 and EGFR expression with the efficacy of NACT and prognosis in patients with BC. Additionally, we explored the role of TILs and ctDNA in predicting NACT efficacy. Our findings complement recent studies in BC research. Wang et al. demonstrated the potential of gelsolin and tubulin alpha-1b chain as prognostic, diagnostic and immune indicators in pan-cancer analyses, including BC. Their observations on immune cell infiltration parallel our findings on TILs [20, 21]. Ji et al. highlighted the importance of cytoskeletal dynamics in BC metastasis, supporting our focus on cellular markers, such as Ki-67 and CDK4 [22]. Szebényi et al. showed the effectiveness of targeting drug-tolerant persister cells in BC, underscoring the complexity of chemotherapy resistance [23]. These studies collectively emphasise the multifaceted nature of BC biology and the importance of integrating multiple biomarkers and cellular processes in developing predictive models and treatment strategies, aligning with our comprehensive approach to assessing NACT efficacy.

Studies have shown that Ki-67 is closely related to tumour development [24, 25]. A previous study by Li Xue [26] found that the difference in clinical efficacy of NACT between the strongly positive Ki-67 group and the weakly positive group before NACT was significant, and the univariate and multifactorial analyses of Wei Xiaoxia et al. [27] also found that Ki-67 positivity was associated with clinical complete remission and overall clinical effectiveness. The present study showed that the high expression of Ki-67 before chemotherapy was higher in the effective group of NACT than in the ineffective group, which is consistent with the results reported above. These suggest that Ki-67 is highly expressed before NACT in patients with BC. Therefore, Ki-67 is considered to be a valid predictor of drug sensitivity and resistance to NACT chemotherapy in patients with BC.

In the present study, CDK4 was also highly expressed in the effective group. It is one of the seven cell cycle protein-dependent kinases identified so far, which may be a transforming growth factor-β-mediated target protein in some cells. Cyclin D1 can bind to CDK4 and activate protein kinase activity to drive cell transformation to cancer cells. The positive expression rate of CDK4 in patients with BC was 56.3%, and it was closely related to the histological grade of patients [28]. The CDK4 gene is located in the long arm 13–14 region of human chromosome 12, which is a G1 → S phase cell cycle regulatory centre, and its amplified overexpression has been found to exist in many human tumours and their cell lines, indicating that abnormal CDK4 gene is closely related to tumour development. This suggests that CDK4 gene abnormalities are closely related to tumour development, especially the peak expression in the middle and late G1 phase [29]; therefore, CDK4 can also be used as a useful indicator to assess the sensitivity of NACT chemotherapy in patients with BC.

Cox risk regression models showed that TNM stage, EGFR and Ki-67 expression levels were independent risk factors for patient survival time. Previous studies have repeatedly verified that the high proliferative state of BC is correlated with the infiltrative growth and metastasis of tumours [30], indicating a very close association between high Ki-67 expression and poor prognosis of patients with BC. Studies have shown [31] that the Ki-67 positivity rate in patients with BC is approximately 60%–90%, the degree of Ki-67 expression is positively correlated with clinical stage and the higher the degree of Ki-67 expression in BC is, the more likely it is to metastasise and the worse the prognosis. The EGFR is the protein expression product encoded by the proto-oncogene CerbB⁃1, and Liu Yanyan et al. [32] showed that EGFR expression was higher in BC tissues compared with paraneoplastic tissues and was associated with clinical stage, tumour size and lymph node metastasis. In this study, we speculate that the mechanism, abnormal activation of the proto-oncogene CerbB-1 and EGFR overexpression can promote the differentiation of normal breast ductal epithelial cells into malignantly proliferating cancer cells. There is a correlation between the occurrence of EGFR overexpression, mutation, accelerated tumour process generation and drug resistance [33]. The present study also indicated that the cumulative survival rate of EGFR-positive patients was lower than that of EGFR-negative patients, suggesting a poorer prognosis for EGFR-positive patients.

Our analysis of TILs provides new insights into the immune microenvironment's role in NACT response. The significant correlation between high TIL levels and increased pCR rates supports the growing body of evidence suggesting that the immune system plays a crucial role in chemotherapy response [34]. This finding has important implications for immunotherapy in BC and suggests that combining immunotherapeutic approaches with NACT could potentially improve outcomes for selected patients.

The analysis of ctDNA levels during NACT treatment represents a novel aspect of our study. The significant decrease in ctDNA levels in patients achieving pCR, compared with patients who do not, suggests that ctDNA monitoring could serve as a real-time, non-invasive method for assessing treatment response [35]. This approach could potentially allow for early identification of non-responders and timely adjustment of treatment strategies.

Our subgroup analysis based on BC subtypes reveals important differences in pCR rates and predictive biomarkers across different subtypes. The highest pCR rate was observed in HER2-positive BCs (66.7%), likely due to the effectiveness of HER2-targeted therapies in combination with chemotherapy [36]. The finding that EGFR expression was most predictive of pCR in the triple-negative subtype suggests that EGFR-targeted therapies may be particularly beneficial for this difficult-to-treat subgroup [37].

The observed differences in biomarker expression and pCR rates across BC subtypes reflect the underlying biological heterogeneity of these tumours. In TNBC, the high EGFR expression and its strong association with pCR suggest a potential role for EGFR-mediated signalling in chemosensitivity. This finding aligns with previous studies showing that EGFR inhibition can enhance chemotherapy efficacy in TNBC.

The high pCR rate in HER2-positive BCs is consistent with the known efficacy of HER2-targeted therapies in combination with chemotherapy. The strong association between high TIL levels and pCR in this subtype underscores the importance of the immune microenvironment in treatment response, possibly due to antibody-dependent cell-mediated cytotoxicity induced by anti-HER2 therapies.

In hormone receptor-positive/HER2-negative BCs, the association between low Ki-67 expression and higher pCR rates may seem counterintuitive, as Ki-67 is typically considered a marker of proliferation. However, this finding suggests that in this subtype, highly proliferative tumours may have alternative survival pathways that confer chemoresistance, while lower proliferation may indicate a more chemo-sensitive luminal A-like phenotype [38,39,40].

The strong correlation between high TIL levels and increased pCR rates observed in our study has important implications for immunotherapy in BC. High TIL levels indicate an immunologically 'hot' tumour microenvironment, which may be more responsive to immune checkpoint inhibitors. Our findings suggest that TIL assessment could serve as a biomarker for patient selection in immunotherapy trials, particularly in TNBC and HER2-positive subtypes where immune checkpoint inhibitors have shown promise. Furthermore, the combination of NACT with immunotherapy may be particularly effective in patients with high TILs, as chemotherapy-induced immunogenic cell death could synergise with checkpoint blockade to enhance anti-tumour immunity. Future studies should investigate whether TIL-guided treatment intensification with immunotherapy in the neoadjuvant setting can further improve pCR rates and long-term outcomes [41,42,43].

The promising results of ctDNA analysis in our study highlight its potential as a powerful tool for real-time monitoring of treatment response and early detection of disease recurrence in patients with BC undergoing NACT. The significant decrease in ctDNA levels observed in patients achieving pCR suggests that ctDNA dynamics could serve as an early predictor of treatment efficacy, potentially allowing for adaptive treatment strategies. In clinical practice, this could translate to more personalised treatment approaches, where patients with persistent ctDNA elevation during NACT may benefit from treatment intensification or alternative regimens. Moreover, post-treatment ctDNA monitoring could help identify patients at high risk of recurrence who may benefit from additional adjuvant therapy or more intensive surveillance. However, the implementation of ctDNA testing in routine clinical practice faces several challenges, including standardisation of assays, determination of clinically relevant thresholds and integration with existing prognostic tools. Future prospective studies are needed to validate ctDNA-guided treatment algorithms and assess their impact on long-term patient outcomes [9, 35, 44].

Our study emphasises pCR as a measure of NACT efficacy; however, it is important to acknowledge the limitations of this endpoint. Although pCR has been associated with improved long-term outcomes in many studies, this correlation is not uniform across all BC subtypes. For instance, the prognostic value of pCR may be less pronounced in hormone receptor-positive/HER2-negative BCs compared with TNBC or HER2-positive subtypes. Furthermore, some patients who do not achieve pCR may still derive significant benefits from NACT in terms of improved surgical outcomes or long-term survival. Therefore, while pCR remains a valuable surrogate endpoint, especially in the context of neoadjuvant trials, it should be interpreted cautiously and in conjunction with other clinical and pathological factors when assessing individual patient prognosis or making treatment decisions [45, 46].

In addition to the biomarkers explored in our study, emerging imaging techniques, particularly advanced ultrasound methods, show promise in predicting BC prognosis and treatment response. Doppler techniques, such as superb microvascular imaging and contrast-enhanced ultrasound, can provide detailed information on tumour vascularity, which is closely linked to tumour aggressiveness and treatment response. These non-invasive imaging biomarkers could complement molecular and cellular markers in developing more comprehensive predictive models. For instance, changes in tumour vascularity during NACT, as assessed by advanced ultrasound techniques, could be integrated with ctDNA dynamics and TIL levels to provide a multi-dimensional assessment of treatment response. Future studies should explore the potential of combining these imaging biomarkers with the molecular and cellular markers investigated in our study to enhance the accuracy of prediction models for NACT efficacy and long-term outcomes in BC [47, 48].

Our study has several limitations that should be considered. First, the retrospective nature of the study introduces potential biases, including selection bias and information bias. Second, while our post-hoc power analysis suggests adequate power for our primary outcomes, some subgroup analyses may be underpowered. Future prospective studies with larger sample sizes are needed to validate our findings, particularly for less common BC subtypes. Third, it is a retrospective study with a relatively short follow-up period. Future prospective studies with longer follow-ups are needed to validate our findings and fully assess the impact of biomarker status on long-term survival outcomes. Fourth, the single-centre design may limit the generalisability of our results to other populations or healthcare settings. Finally, although we examined several biomarkers, the rapidly evolving field of molecular profiling suggests that a more comprehensive genomic analysis could provide further insights into NACT response and prognosis. Competing risks, such as non-cancer-related deaths, may influence the interpretation of our results, particularly in older patients or those with significant comorbidities. Future research directions should explore additional biomarkers and genomic factors to further refine our ability to predict NACT response and long-term outcomes in breast cancer. The neutrophil-to-lymphocyte ratio (NLR), an indicator of systemic inflammation, has shown promise as a prognostic marker in various cancers, including breast cancer [49]. Integrating NLR with the biomarkers studied here could provide a more comprehensive assessment of both local and systemic immune responses. Additionally, comprehensive genomic profiling could reveal germline-somatic mutation interactions that mediate therapeutic vulnerabilities [50]. Such genomic analyses could identify novel predictive biomarkers and potential therapeutic targets, further personalizing treatment approaches in breast cancer. Future studies should aim to combine these diverse biomarkers including traditional clinicopathological factors, immune markers, ctDNA dynamics, and genomic profiles to develop more accurate and robust predictive models for NACT response and long-term outcomes in breast cancer.

Our study has several strengths. First, we integrated multiple biomarkers, including both traditional clinicopathological factors and emerging molecular markers, providing a comprehensive assessment of NACT response predictors. Second, our use of propensity score matching helped minimise potential confounding factors. Third, the inclusion of ctDNA analysis offers insights into the potential of this emerging biomarker for real-time monitoring of treatment response. Last, our subgroup analysis by BC subtypes provides clinically relevant information for tailoring treatment strategies.

In conclusion, TNM classification, EGFR, Ki-67, CDK4 expression, BC subtype and NACT results have a predictive value for the outcome of patients with BC with pCR. Patients with BC with a lower TNM classification, lower expression of Ki-67 and EGFR-positive have a better outcome. The integration of traditional clinicopathological factors with novel biomarkers, such as TILs and ctDNA, offers a more nuanced approach to predicting treatment response and patient outcomes. These findings have important implications for personalised treatment strategies in BC and highlight several areas for future research.

Data availability

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.

References

  1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–263. https://doiorg.publicaciones.saludcastillayleon.es/10.3322/caac.21834. Epub 2024 Apr 4. PMID: 38572751.

  2. Lu YB, Li XJ, Zhang GQ. Analysis of clinical factors influencing pCR of neoadjuvant chemotherapy for breast cancer. J Mod Oncol. 2020;28(21):3737–40. https://doiorg.publicaciones.saludcastillayleon.es/10.3969/j.issn.1672-4992.2020.21.021.

    Article  Google Scholar 

  3. Nagaraj G, Ma CX. Clinical Challenges in the Management of Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Metastatic Breast Cancer: A Literature Review. Adv Ther. 2021;38(1):109–136. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12325-020-01552-2. Epub 2020 Nov 15. PMID: 33190190; PMCID: PMC7854469.

  4. Chen X, He C, Han D, Zhou M, Wang Q, Tian J, Li L, Xu F, Zhou E, Yang K. The predictive value of Ki-67 before neoadjuvant chemotherapy for breast cancer: a systematic review and meta-analysis. Future Oncol. 2017;13(9):843–57. https://doiorg.publicaciones.saludcastillayleon.es/10.2217/fon-2016-0420. (Epub 2017 Jan 11 PMID: 28075166).

    Article  CAS  PubMed  Google Scholar 

  5. Hong J, Wang DQ, Yin RG, Zhao T, Zhu Y. The correlation between diffusion-weighted magnetic resonance imaging and Ki-67 in invasive ductal carcinoma of breast. Journal of Jiangsu University(Medicine Edition). 2019;29(05):441–443. https://doiorg.publicaciones.saludcastillayleon.es/10.13312/j.issn.1671-7783.y190096.

  6. Ye J, Li L, Feng P, Wan J, Li J. Downregulation of miR-34a contributes to the proliferation and migration of laryngeal carcinoma cells by targeting cyclin D1. Oncol Rep. 2016;36(1):390–8. https://doiorg.publicaciones.saludcastillayleon.es/10.3892/or.2016.4823. (Epub 2016 May 19 PMID: 27220728).

    Article  CAS  PubMed  Google Scholar 

  7. Sherr CJ, Beach D, Shapiro GI. Targeting CDK4 and CDK6: From Discovery to Therapy. Cancer Discov. 2016;6(4):353–67. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/2159-8290.CD-15-0894. Epub 2015 Dec 11. PMID: 26658964; PMCID: PMC4821753.

  8. Denkert C, von Minckwitz G, Darb-Esfahani S, Lederer B, Heppner BI, Weber KE, Budczies J, Huober J, Klauschen F, Furlanetto J, Schmitt WD, Blohmer JU, Karn T, Pfitzner BM, Kümmel S, Engels K, Schneeweiss A, Hartmann A, Noske A, Fasching PA, Jackisch C, van Mackelenbergh M, Sinn P, Schem C, Hanusch C, Untch M, Loibl S. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 2018;19(1):40–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S1470-2045(17)30904-X. (Epub 2017 Dec 7 PMID: 29233559).

    Article  PubMed  Google Scholar 

  9. Cullinane C, Fleming C, O’Leary DP, Hassan F, Kelly L, O’Sullivan MJ, Corrigan MA, Redmond HP. Association of Circulating Tumor DNA With Disease-Free Survival in Breast Cancer: A Systematic Review and Meta-analysis. JAMA Netw Open. 2020;3(11):e2026921. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamanetworkopen.2020.26921. (PMID: 33211112).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gerratana L, Basile D, Buono G, De Placido S, Giuliano M, Minichillo S, Coinu A, Martorana F, De Santo I, Del Mastro L, De Laurentiis M, Puglisi F, Arpino G. Androgen receptor in triple negative breast cancer: A potential target for the targetless subtype. Cancer Treat Rev. 2018;68:102–10. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ctrv.2018.06.005. (Epub 2018 Jun 11 PMID: 29940524).

    Article  CAS  PubMed  Google Scholar 

  11. Stanton SE, Disis ML. Clinical significance of tumor-infiltrating lymphocytes in breast cancer. J Immunother Cancer. 2016;18(4):59.

    Article  Google Scholar 

  12. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer. 2017;17(4):223–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nrc.2017.7. (Epub 2017 Feb 24 PMID: 28233803).

    Article  CAS  PubMed  Google Scholar 

  13. Zhang J, Zhao B, Jin F. The assessment of 8th edition AJCC prognostic staging system and a simplified staging system for breast cancer: The analytic results from the SEER database. Breast J. 2019;25(5):838–847. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/tbj.13347. Epub 2019 Jun 13. PMID: 31192530.

  14. Recommended by Breast Cancer Expert Panel. [Guideline for HER2 detection in breast cancer, the 2019 version]. Chin J Pathol. 2019;48(3):169–175. Chinese. https://doiorg.publicaciones.saludcastillayleon.es/10.3760/cma.j.issn.0529-5807.2019.03.001. PMID: 30831640.

  15. Aman NA, Doukoure B, Koffi KD, Koui BS, Traore ZC, Kouyate M, Toure I, Effi AB. Immunohistochemical Evaluation of Ki-67 and Comparison with Clinicopathologic Factors in Breast Carcinomas. Asian Pac J Cancer Prev. 2019;20(1):73–9. https://doiorg.publicaciones.saludcastillayleon.es/10.31557/APJCP.2019.20.1.73 PMID:30678383;PMCID:PMC6485584.

  16. Sukswai N, Khoury JD. Immunohistochemistry Innovations for Diagnosis and Tissue-Based Biomarker Detection. Curr Hematol Malig Rep. 2019;14(5):368–75. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11899-019-00533-9. (PMID: 31338668).

    Article  PubMed  Google Scholar 

  17. Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, Diéras V, Hegg R, Im SA, Shaw Wright G, Henschel V, Molinero L, Chui SY, Funke R, Husain A, Winer EP, Loi S, Emens LA; IMpassion130 Trial Investigators. Atezolizumab and Nab-Paclitaxel in Advanced Triple-Negative Breast Cancer. N Engl J Med. 2018;379(22):2108–2121. https://doiorg.publicaciones.saludcastillayleon.es/10.1056/NEJMoa1809615. Epub 2018 Oct 20. PMID: 30345906.

  18. Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, Wienert S, Van den Eynden G, Baehner FL, Penault-Llorca F, Perez EA, Thompson EA, Symmans WF, Richardson AL, Brock J, Criscitiello C, Bailey H, Ignatiadis M, Floris G, Sparano J, Kos Z, Nielsen T, Rimm DL, Allison KH, Reis-Filho JS, Loibl S, Sotiriou C, Viale G, Badve S, Adams S, Willard-Gallo K, Loi S; International TILs Working Group 2014. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol. 2015;26(2):259–71. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/annonc/mdu450. Epub 2014 Sep 11. PMID: 25214542; PMCID: PMC6267863.

  19. Dawson SJ, Tsui DW, Murtaza M, Biggs H, Rueda OM, Chin SF, Dunning MJ, Gale D, Forshew T, Mahler-Araujo B, Rajan S, Humphray S, Becq J, Halsall D, Wallis M, Bentley D, Caldas C, Rosenfeld N. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med. 2013;368(13):1199–209. https://doiorg.publicaciones.saludcastillayleon.es/10.1056/NEJMoa1213261. (Epub 2013 Mar 13 PMID: 23484797).

    Article  CAS  PubMed  Google Scholar 

  20. Wang Y, Bi X, Luo Z, Wang H, Ismtula D, Guo C. Gelsolin: A comprehensive pan-cancer analysis of potential prognosis, diagnostic, and immune biomarkers. Front Genet. 2023;23(14):1093163. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fgene.2023.1093163. PMID:37035750;PMCID:PMC10076574.

  21. Wang Y, Li Y, Jing Y, Yang Y, Wang H, Ismtula D, Guo C. Tubulin alpha-1b chain was identified as a prognosis and immune biomarker in pan-cancer combing with experimental validation in breast cancer. Sci Rep. 2024;14(1):8201. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-024-58982-z. PMID:38589634;PMCID:PMC11001892.

  22. Ji X, Tian X, Feng S, Zhang L, Wang J, Guo R, Zhu Y, Yu X, Zhang Y, Du H, Zablotskii V, Zhang X. Intermittent F-actin Perturbations by Magnetic Fields Inhibit Breast Cancer Metastasis. Research (Wash D C). 2023;6:0080. https://doiorg.publicaciones.saludcastillayleon.es/10.34133/research.0080. Epub 2023 Mar 15. PMID: 36939445; PMCID: PMC10017101.

  23. Szebényi K, Füredi A, Bajtai E, Sama SN, Csiszar A, Gombos B, Szabó P, Grusch M, Szakács G. Effective targeting of breast cancer by the inhibition of P-glycoprotein mediated removal of toxic lipid peroxidation byproducts from drug tolerant persister cells. Drug Resist Updat. 2023;71:101007. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.drup.2023.101007. (Epub 2023 Sep 17 PMID: 37741091).

    Article  CAS  PubMed  Google Scholar 

  24. Peng JH, Zhang X, Song JL, Ran L, Luo R, Li HY, Wang YH. Neoadjuvant chemotherapy reduces the expression rates of ER, PR, HER2, Ki-67, and P53 of invasive ductal carcinoma. Medicine (Baltimore). 2019;98(2):e13554. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/MD.0000000000013554. Erratum.In:Medicine(Baltimore).2022Jan28;101(4):e28714.PMID:30633152;PMCID:PMC6336639

  25. Xu YT. Research on the prediction of neoadjuvant chemotherapy for breast cancer with ER, PR, HER-2, Ki-67 and molecular subtype. Fujian: Fujian Medical University; 2017.

    Google Scholar 

  26. Li X. The expressions and significance of MIF, IL-8, Ki-67 and cyclinD1 in cervical cancer tissues of neoadjuvant chemotherapy. Shanxi: Shanxi Medical University; 2018.

    Google Scholar 

  27. Wei XX, Zhang R, Pu TJ, Yang LB, Ye F, Bu H. Ki-67 expression and its effect on response to neo-adjuvant chemotherapy in invasive breast cancer. Chin J Pathol. 2017;46(12):817–21. https://doiorg.publicaciones.saludcastillayleon.es/10.3760/cma.j.issn.0529-5807.2017.12.001.

    Article  CAS  Google Scholar 

  28. Shu N, Li LH, Sun ZG, Wang B, Li J. Expressions of P16, CDK4, cyclinD1 and their clinical significance in breast carcinomas. Journal of Dalian Medical University. 2009;31(4):255–9. https://doiorg.publicaciones.saludcastillayleon.es/10.11724/jdmu.2009.04.05.

    Article  CAS  Google Scholar 

  29. VanArsdale T, Boshoff C, Arndt KT, Abraham RT. Molecular Pathways: Targeting the Cyclin D-CDK4/6 Axis for Cancer Treatment. Clin Cancer Res. 2015;21(13):2905–10. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/1078-0432.CCR-14-0816. (Epub 2015 May 4 PMID: 25941111).

    Article  CAS  PubMed  Google Scholar 

  30. Suo S, Cheng F, Cao M, Kang J, Wang M, Hua J, Hua X, Li L, Lu Q, Liu J, Xu J. Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging. 2017;46(3):740–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/jmri.25612. (Epub 2017 Jan 31 PMID: 28139036).

    Article  PubMed  Google Scholar 

  31. Meermira D, Swain M, Gowrishankar S. Study of Ki-67 index in the molecular subtypes of breast cancer: Inter-observer variability and automated scoring. Indian J Cancer. 2020;57(3):289–295. https://doiorg.publicaciones.saludcastillayleon.es/10.4103/ijc.IJC_719_18. PMID: 32769300.

  32. Liu YY, Tong C, Liang H, Wang JG. Expression and clinical significance of Ki-67, HER-2, VEGF and EGFR proteins in breast cancer tissues. Journal of Xinjiang Medical University. 2019;42(11):1419–1422,1429. https://doiorg.publicaciones.saludcastillayleon.es/10.3969/j.issn.1009-5551.2019.11.008.

  33. Crozier JA, Advani PP, LaPlant B, Hobday T, Jaslowski AJ, Moreno-Aspitia A, Perez EA. N0436 (Alliance): A Phase II Trial of Irinotecan With Cetuximab in Patients With Metastatic Breast Cancer Previously Exposed to Anthracycline and/or Taxane-Containing Therapy. Clin Breast Cancer. 2016;16(1):23–30. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clbc.2015.08.002. Epub 2015 Aug 19. PMID: 26381420; PMCID: PMC4698217.

  34. Caparica R, Bruzzone M, Agostinetto E, Franzoi MA, Ceppi M, Radosevic-Robin N, Penault-Llorca F, Willard-Gallo K, Loi S, Salgado R, de Azambuja E. Tumour-infiltrating lymphocytes in non-invasive breast cancer: A systematic review and meta-analysis. Breast. 2021;59:183–192. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.breast.2021.07.007. Epub 2021 Jul 9. PMID: 34273666; PMCID: PMC8319525.

  35. Chen JH, Addanki S, Roy D, Bassett R, Kalashnikova E, Spickard E, Kuerer HM, Meas S, Sarli VN, Korkut A, White JB, Rauch GM, Tripathy D, Arun BK, Barcenas CH, Yam C, Sethi H, Rodriguez AA, Liu MC, Moulder SL, Lucci A. Monitoring response to neoadjuvant chemotherapy in triple negative breast cancer using circulating tumor DNA. BMC Cancer. 2024;24(1):1016. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-024-12689-6. (PMID: 39148033).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gianni L, Pienkowski T, Im YH, Tseng LM, Liu MC, Lluch A, Starosławska E, de la Haba-Rodriguez J, Im SA, Pedrini JL, Poirier B, Morandi P, Semiglazov V, Srimuninnimit V, Bianchi GV, Magazzù D, McNally V, Douthwaite H, Ross G, Valagussa P. 5-year analysis of neoadjuvant pertuzumab and trastuzumab in patients with locally advanced, inflammatory, or early-stage HER2-positive breast cancer (NeoSphere): a multicentre, open-label, phase 2 randomised trial. Lancet Oncol. 2016;17(6):791–800. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S1470-2045(16)00163-7. (Epub 2016 May 11 PMID: 27179402).

    Article  CAS  PubMed  Google Scholar 

  37. Nakai K, Hung MC, Yamaguchi H. A perspective on anti-EGFR therapies targeting triple-negative breast cancer. Am J Cancer Res. 2016;6(8):1609–23 PMID: 27648353; PMCID: PMC5004067.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, Bonnefoi H, Cameron D, Gianni L, Valagussa P, Swain SM, Prowell T, Loibl S, Wickerham DL, Bogaerts J, Baselga J, Perou C, Blumenthal G, Blohmer J, Mamounas EP, Bergh J, Semiglazov V, Justice R, Eidtmann H, Paik S, Piccart M, Sridhara R, Fasching PA, Slaets L, Tang S, Gerber B, Geyer CE Jr, Pazdur R, Ditsch N, Rastogi P, Eiermann W, von Minckwitz G. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet. 2014;384(9938):164–72. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(13)62422-8. Epub 2014 Feb 14. Erratum in: Lancet. 2019;393(10175):986. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(18)32772-7. PMID: 24529560.

  39. Spring LM, Gupta A, Reynolds KL, Gadd MA, Ellisen LW, Isakoff SJ, Moy B, Bardia A. Neoadjuvant Endocrine Therapy for Estrogen Receptor-Positive Breast Cancer: A Systematic Review and Meta-analysis. JAMA Oncol. 2016;2(11):1477–86. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamaoncol.2016.1897. PMID:27367583;PMCID:PMC5738656.

  40. Schmid P, Cortes J, Pusztai L, McArthur H, Kümmel S, Bergh J, Denkert C, Park YH, Hui R, Harbeck N, Takahashi M, Foukakis T, Fasching PA, Cardoso F, Untch M, Jia L, Karantza V, Zhao J, Aktan G, Dent R, O'Shaughnessy J; KEYNOTE-522 Investigators. Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med. 2020;382(9):810–821. https://doiorg.publicaciones.saludcastillayleon.es/10.1056/NEJMoa1910549. PMID: 32101663.

  41. Loi S, Drubay D, Adams S, Pruneri G, Francis PA, Lacroix-Triki M, Joensuu H, Dieci MV, Badve S, Demaria S, Gray R, Munzone E, Lemonnier J, Sotiriou C, Piccart MJ, Kellokumpu-Lehtinen PL, Vingiani A, Gray K, Andre F, Denkert C, Salgado R, Michiels S. Tumor-Infiltrating Lymphocytes and Prognosis: A Pooled Individual Patient Analysis of Early-Stage Triple-Negative Breast Cancers. J Clin Oncol. 2019;37(7):559–569. https://doiorg.publicaciones.saludcastillayleon.es/10.1200/JCO.18.01010. Epub 2019 Jan 16. PMID: 30650045; PMCID: PMC7010425.

  42. Asano Y, Kashiwagi S, Goto W, Takada K, Takahashi K, Hatano T, Takashima T, Tomita S, Motomura H, Ohsawa M, Hirakawa K, Ohira M. Prediction of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer by Subtype Using Tumor-infiltrating Lymphocytes. Anticancer Res. 2018;38(4):2311–21. https://doiorg.publicaciones.saludcastillayleon.es/10.21873/anticanres.12476. (PMID: 29599354).

    Article  PubMed  Google Scholar 

  43. Schmid P, Rugo HS, Adams S, Schneeweiss A, Barrios CH, Iwata H, Diéras V, Henschel V, Molinero L, Chui SY, Maiya V, Husain A, Winer EP, Loi S, Emens LA; IMpassion130 Investigators. Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2020;21(1):44–59. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S1470-2045(19)30689-8. Epub 2019 Nov 27. PMID: 31786121.

  44. Garcia-Murillas I, Chopra N, Comino-Méndez I, Beaney M, Tovey H, Cutts RJ, Swift C, Kriplani D, Afentakis M, Hrebien S, Walsh-Crestani G, Barry P, Johnston SRD, Ring A, Bliss J, Russell S, Evans A, Skene A, Wheatley D, Dowsett M, Smith IE, Turner NC. Assessment of Molecular Relapse Detection in Early-Stage Breast Cancer. JAMA Oncol. 2019;5(10):1473–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamaoncol.2019.1838. Erratum.In:JAMAOncol.2020Jan1;6(1):162.doi:10.1001/jamaoncol.2019.6325.PMID:31369045;PMCID:PMC6681568.

  45. von Minckwitz G, Untch M, Blohmer JU, Costa SD, Eidtmann H, Fasching PA, Gerber B, Eiermann W, Hilfrich J, Huober J, Jackisch C, Kaufmann M, Konecny GE, Denkert C, Nekljudova V, Mehta K, Loibl S. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol. 2012;30(15):1796–804. https://doiorg.publicaciones.saludcastillayleon.es/10.1200/JCO.2011.38.8595. (Epub 2012 Apr 16 PMID: 22508812).

    Article  Google Scholar 

  46. Symmans WF, Wei C, Gould R, Yu X, Zhang Y, Liu M, Walls A, Bousamra A, Ramineni M, Sinn B, Hunt K, Buchholz TA, Valero V, Buzdar AU, Yang W, Brewster AM, Moulder S, Pusztai L, Hatzis C, Hortobagyi GN. Long-Term Prognostic Risk After Neoadjuvant Chemotherapy Associated With Residual Cancer Burden and Breast Cancer Subtype. J Clin Oncol. 2017;35(10):1049–1060. https://doiorg.publicaciones.saludcastillayleon.es/10.1200/JCO.2015.63.1010. Epub 2017 Jan 30. PMID: 28135148; PMCID: PMC5455352.

  47. Park AY, Seo BK. Up-to-date Doppler techniques for breast tumor vascularity: superb microvascular imaging and contrast-enhanced ultrasound. Ultrasonography. 2018;37(2):98–106. https://doiorg.publicaciones.saludcastillayleon.es/10.14366/usg.17043. (Epub 2017 Aug 19 PMID: 29025210).

    Article  PubMed  Google Scholar 

  48. Nguyen PN, Nguyen VT. Endometrial thickness and uterine artery Doppler parameters as soft markers for prediction of endometrial cancer in postmenopausal bleeding women: a cross-sectional study at tertiary referral hospitals from Vietnam. Obstet Gynecol Sci. 2022;65(5):430–40. https://doiorg.publicaciones.saludcastillayleon.es/10.5468/ogs.22053.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Gago-Dominguez M, Matabuena M, Redondo CM, Patel SP, Carracedo A, Ponte SM, Martínez ME, Castelao JE. Neutrophil to lymphocyte ratio and breast cancer risk: analysis by subtype and potential interactions. Sci Rep. 2020;10(1):13203. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-020-70077-z. Erratum.In:SciRep.2020Nov20;10(1):20641.doi: https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-020-75458-y. (PMID: 32764699).

  50. Chen C, Lin CJ, Pei YC, Ma D, Liao L, Li SY, Fan L, Di GH, Wu SY, Liu XY, Wang YJ, Hong Q, Zhang GL, Xu LL, Li BB, Huang W, Shi JX, Jiang YZ, Hu X, Shao ZM. Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities. Cell Discov. 2023;9(1):125. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41421-023-00614-3. (PMID: 38114467).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Outstanding Youth Foundation of Liaoning (No. 2022-YQ-08 to SL. S).

Author information

Author notes

  1. Tianzhao Du and Ye Yuan contributed equally to this study.

Authors and Affiliations

  1. Central Laboratory, Cancer Hospital of China Medical University, Dalian Medical University Clinical Oncology College, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, 110042, People’s Republic of China

    Tianzhao Du, Ye Yuan, Shulan Sun & Zhichao Gao

  2. Department of Blood Transfusion, Shengjing Hospital of China Medical University, Shenyang, 110004, China

    Xiaoshuai Li

Authors
  1. Tianzhao Du
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ye Yuan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shulan Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhichao Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiaoshuai Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

DTZ and YY conceived of the study, and GZC and LXS participated in its design and data analysis and statistics and DTZ, YY and SSL helped to draft the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shulan Sun.

Ethics declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of Liaoning Cancer Hospital & Institute (Approval No. 20170226). We obtained signed informed consent from the participants in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, T., Yuan, Y., Sun, S. et al. Integrating traditional biomarkers and emerging predictors to assess neoadjuvant chemotherapy efficacy in breast cancer: a multifactorial analysis of Ki-67, CDK4, EGFR, TILs and ctDNA. BMC Women's Health 24, 674 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12905-024-03486-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12905-024-03486-1

Keywords

BMC Women's Health

ISSN: 1472-6874

Contact us