Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide (Sung et al., 2021). Triple-negative breast cancer (TNBC) represents 15–20% of all breast cancer cases and exhibits a more aggressive phenotype (with a poorer prognosis) than non-TNBC (Foulkes et al., 2010; Li et al., 2017; Malorni et al., 2012). Due to the absence of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), or progesterone receptor (PR) expression, TNBC lacks effective targeted therapies and treatment regimens. Patients with mTNBC have fewer available treatment options and exhibit worse survival than early-stage TNBC patients. Furthermore, TNBC is a highly heterogeneous disease, resulting in substantial differences in the tumorigenesis, treatment response, and disease progression among patients (Burstein et al., 2015; Jiang et al., 2019; Perou, 2011), which undoubtedly poses significant challenges in prognostic prediction of the mTNBC and efficacy assessment for already limited treatment options. Unfortunately, reliable and tailored biomarkers to predict the prognosis and monitor the treatment response of patients with TNBC are yet to be established.

It has been a long-standing clinical management model to predict the prognosis of patients with mTNBC and guide treatment decision-making through imaging examination and mutational features obtained by tumor biopsy. Radiological examination usually only provides the external characterization of the tumor but can not reveal the tumor internal molecular characteristics. Given the heterogeneity of TNBC, it is impossible to obtain an accurate and comprehensive picture of the mutational landscape using tissue biopsies unless repeated multiple biopsies (Diaz and Bardelli, 2014), and most patients are refractory to repetitive punctures.

Compared with tissue biopsies, ‘liquid biopsies’ collect and analyze tumor-derived substances, such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomes (e.g. from the blood, cerebrospinal fluid, and urine) of cancer patients in a minimally invasive fashion (Alix-Panabières and Pantel, 2016; Palmirotta et al., 2018; Poulet et al., 2019). It can be used for early diagnosis of tumor patients, predicting tumor recurrence and metastasis, and evaluating the characteristics and clonal evolution of tumor genomes. ctDNA is a specific fraction of cell-free DNA (cfDNA) present in the plasma of apoptotic and necrotic tumor cells (Swarup and Rajeswari, 2007). Owing to particular biological origin and the potential for multiple repeat sampling, ctDNA is independent of tumor spatial and temporal heterogeneity, conveys more valuable information than a conventional tumor biopsy, and enables the dynamic monitoring of tumor burden and treatment response (Campos-Carrillo et al., 2020; Chae and Oh, 2019; Dawson et al., 2013; Gerratana et al., 2021). The value of ctDNA in accurately predicting drug resistance and clinical outcomes has also been noted (Asante et al., 2020; Murtaza et al., 2013).

Several studies have demonstrated the prognostic and predictive value of ctDNA for non-mTNBC during or after (neo)adjuvant therapy (Cavallone et al., 2020; Kim et al., 2021; Lin et al., 2021; Ortolan et al., 2021; Riva et al., 2017). Previously, researchers have also been relatively circumscribed, concentrated on evaluating specific copy number variants (CNVs) or ctDNA-based single mutation or clonal evolution or the ctDNA level to predict the prognosis of mTNBC patients and the efficacy of particular treatment regimens (Barroso-Sousa et al., 2022; Chopra et al., 2020; Collier et al., 2021; Stover et al., 2018; Weber et al., 2021; Wongchenko et al., 2020). Even a study showed that the ctDNA level had no prognostic impact on the survival of patients with mTNBC (Madic et al., 2015). A more comprehensive study of the mutational information and related markers embodied in ctDNA as well as a consensus on the predictive role of ctDNA in mTNBC are needed to apply ctDNA in clinical practice.

Hence, in this study, we investigated the mutational characteristics of ctDNA and ctDNA-related markers in mTNBC using targeted, capture-based, next-generation sequencing (NGS), which offers rapid identification and high coverage from a small blood sample. We aimed to dynamically and more comprehensively evaluate the value of ctDNA in predicting the prognosis and monitoring the treatment response of patients with mTNBC.

Since the advent of precision medicine, liquid biopsies have become more widely utilized in the clinical management of cancer. In recent years, ctDNA has become the focus of extensive research as a predictive biomarker of tumor progression and treatment response. Several studies have explored the promising applications of ctDNA in breast cancer. For example, some researchers have investigated the longitudinal dynamics of ctDNA in the treatment monitoring of metastatic breast cancer, while others have studied the prognostic and predictive value of ctDNA during neoadjuvant chemotherapy for TNBC (Cavallone et al., 2020; Gerratana et al., 2021; Ortolan et al., 2021; Riva et al., 2017). However, the application of ctDNA in monitoring mTNBC is rare in clinical practice.

In the current study, we performed targeted, capture-based NGS (with a 457-gene panel) on 139 plasma samples obtained by liquid biopsy from 70 patients with mTNBC. Thirteen paired tumor tissues were also analyzed to verify if ctDNA could be a feasible alternative to tumor-tissue-derived tDNA. This study demonstrated how a ctDNA-based platform could reliably reveal mutational profiles, stably predict the prognosis, and consistently monitor the treatment response of patients with mTNBC. This study also resolved the uncertainty of some current studies regarding the value of ctDNA and provided clear ctDNA-related predictive markers for mTNBC patients. By evaluating the mutational landscape of mTNBC in the Chinese population using ctDNA analysis, we showed that the most frequently mutated genes were TP53, PIK3CA, ARID1A, and KMT2C, and the most frequent CNV was detected in HLA-C, HLA-A, HLA-B, and HLA-DRB1 (Figure 2A). These genes play significant roles in breast cancer tumorigenesis, progression, invasion, and metastasis. In the homogeneous population, the most common mutations detected in tumor tissue were TP53, followed by PIK3CA, KMT2C, and PTEN; however, the top-ranking CNVs were found in different genes to those identified in a previous report: E2F3, IRS2, CCNE1, EGFR, NFIB, CCND1, and MYB (Jiang et al., 2019). This difference may be due to the fact that the plasma-derived ctDNA contains smaller fragments than those that are typically found in tissue-derived tDNA. Meanwhile, compared with the results of other ctDNA identification studies, the distribution of frequent variants in the TNBC cohort was overall consistent (Davis et al., 2020; Rong et al., 2020; Wang et al., 2021). In the study by Chae et al., the concordance between all ctDNA- and tDNA-derived genes was also similar to that in our study (91.0%–94.2% vs. 98.75%; Chae et al., 2017; Figure 2B). The authors reported that the ctDNA-based assay had a high specificity, with a diagnostic accuracy of up to 80%. Although the mutation frequency of tDNA was higher than that of ctDNA in our study, the number of mutations detected in both types of DNA was similar. In addition, we detected 37 specific mutations in each of the ctDNA and tDNA groups, demonstrating the complementarity of blood-derived ctDNA and tissue-derived tDNA. Moreover, compared with tumor tissue analysis, ctDNA assays only require a small blood sample, can capture a variety of mutations (including SNVs and CNVs), and provide information on spatial tumor heterogeneity.

Using NGS, we identified 12 prognosis-relevant mutated genes associated with the shorter PFS of patients with mTNBC (Figure 3—figure supplement 1). Most of the 12 genes have been linked to breast cancer by previous studies. For instance, the aberrant expression of KMT2C (low expression) and ARID4B (high expression) contribute to the poor prognosis of patients with ER-positive breast cancer (Sato and Akimoto, 2017; Zhang et al., 2021). However, previous studies have shown that the upregulated expression of BTG2 and CD22 were associated with improved survival, which is not in agreement with our current findings (Mascia et al., 2022; Zhang et al., 2013); this may be due to the low number of patients included in this study. TGFB1, SGK1, RSPO2, and MCL1 are implicated in the invasion, migration, growth, autophagy, and progression of TNBC, while RSPO2 and MCL1 overexpression is associated with shorter survival rates in patients with TNBC (Coussy et al., 2017; Kim et al., 2015; Yang et al., 2014; Zhu et al., 2020).

As a common driver of breast cancer, MYC amplification plays a role in emerging or acquired chemotherapy resistance during neoadjuvant treatment of TNBC. It can also synergize with MCL1 amplification to maintain chemoresistance (Lee et al., 2017). HLA-B, a major histocompatibility complex (MHC) class I molecule, is involved in immunosurveillance against tumors, and its expression is correlated with the invasiveness and prognosis of breast cancer (Concha et al., 1991). To date, there have been no reports of an association between H3F3A or KYAT3 and breast cancer. Previously, H3F3A was identified as a driver gene in glioma, and its overexpression is linked to shorter survival rates and disease progression in lung cancer (Felker and Broniscer, 2020; Park et al., 2016). Thus, although the associations between some of the 12 mutated genes identified in our study and cancer are known, their roles in the prognosis of mTNBC need to be further defined.

We also explored the value of ctDNA status as a biomarker for predicting the prognosis and monitoring the treatment response of patients with mTNBC. We found that a ctDNA+ status was associated with a worse treatment response (Figure 3B and C). In addition, we showed that the ctDNA status at baseline could potentially discriminate between mTNBC patients with a high or low lesion load (Figure 3D). Moreover, ctDNA, as an independent prognostic factor, directly predicted the prognosis of patients without being influenced by other clinical factors of the patients (Figure 3E, Table 3). This indicates that the ctDNA status, associated with the presence of the 12 prognosis-relevant mutated genes, maybe a good guide to the prediction of clinical outcomes and the clinical management of TNBC. We further explored the optimal cut-off values for the ctDNA-based TMB, MATH score, and ctDNA% parameters at baseline using Kaplan–Meier analysis. A high MATH score (≥6.316) and high ctDNA% (≥0.05) were associated with a significantly shorter PFS (Figure 4B and C) and may therefore be related to the tumor burden of mTNBC. A study reported that most ctDNA fragments originate from metastases and not early-stage cancer, suggesting that ctDNA-based NGS may be more suitable for analyzing metastatic tumors (Vandekerkhove et al., 2017). Unlike ctDNA%, the MATH score represents tumor heterogeneity. A previous study found that TNBC was associated with a higher MATH score (Ma et al., 2017). Moreover, patients with higher MATH scores tend to have more diverse tumor cell clones and may be more prone to drug resistance and progression (McDonald et al., 2019; Mroz and Rocco, 2013). Thus, the MATH score could also potentially be used as a biomarker for mTNBC prognosis.

Breast cancer is a highly heterogeneous and dynamic disease; therefore, longitudinal monitoring and management are necessary (Garcia-Murillas et al., 2015). The predictive value of ctDNA has prompted further exploration of its feasibility in the dynamic monitoring of the efficacy of neoadjuvant therapy for breast cancer and in predicting the occurrence of distal metastasis and drug resistance (Cavallone et al., 2020; Darrigues et al., 2021; Wang et al., 2021). Here, we monitored ctDNA to track the dynamic changes in the 12 identified prognosis-related genes during the treatment of patients with mTNBC. The results showed that the elimination of these mutations or the reduction in the mutation rate of these genes was often associated with a better treatment response. Conversely, the reappearance of these mutations at a later time or an increase in their mutation rate signaled disease progression. Thus, we showed that ctDNA was sensitive and accurately reflected the treatment response and disease status of patients with mTNBC promptly. Conventional tumor markers have been widely used in clinical cancer management for a long time (Anonymous, 1996). We found that the serum CEA and CA153 levels contradicted the treatment response of Patient 31 at the mid-treatment time point (Figure 5B). The elevation of conventional tumor markers could indicate tumor changes and be interpreted as an early warning of disease progression or pseudo-progression. However, this pseudo-progression or ‘tumor marker spike’ is caused by extensive neoplastic cell necrosis induced by anti-tumor therapy. This phenomenon is observed in up to 30% of patients who respond to treatment (Seregni et al., 2004). NGS-mediated ctDNA detection identifies hundreds or thousands of mutations. Even if individual mutations were the result of a ‘ctDNA spike’ (similar to a ‘tumor marker spike’), other mutations could be relied upon to mirror treatment response accurately. Moreover, we observed positive correlations between treatment response, MATH score, and ctDNA%, but not the CEA, CA125, and CA153 levels (Figure 5E). Hence, compared with conventional tumor markers, ctDNA dynamics may better reflect treatment response and progression in mTNBC.

Several limitations exist in our study. First, this was a single-center study with a small sample size. Second, the relatively short median follow-up duration was insufficient for capturing a clinically significant association between ctDNA mutations and overall survival. Third, several patients were lost to follow-up, which may have biased the results. Fourth, the potential influence of different treatment lines and regimens was not evaluated; nevertheless, no differences were found in survival based on these factors. Finally, compared with whole-exome sequencing or whole-genome sequencing, NGS with a panel of 457 selected genes, as used in our study, provided limited mutation data.

The raw sequence data generated during the current study have been deposited in the China National Genomics Data Center-Genome Sequence Archive (GSA) (https://ngdc.cncb.ac.cn/gsa-human/) and are available with the accession code HRA002598.

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Author details

1. Yajing Chi

   1. Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
   2. School of Medicine, Nankai University, Tianjin, China

   Contribution

   Conceptualization, Formal analysis, Supervision, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

2. Mu Su

   Department of Bioinformatics, Berry Oncology Corporation, Beijing, China

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft

   Competing interests

   Affiliated with Berry Oncology Corporation. No financial interests to declare

3. Dongdong Zhou

   Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

   Contribution

   Resources, Data curation, Formal analysis, Validation, Visualization, Methodology

   Competing interests

   No competing interests declared

4. Fangchao Zheng

   Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

5. Baoxuan Zhang

   Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

6. Ling Qiang

   Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

7. Guohua Ren

   Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

8. Lihua Song

   Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

9. Bing Bu

   Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

10. Shu Fang

    Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

    Contribution

    Resources, Data curation

    Competing interests

    No competing interests declared

11. Bo Yu

    Department of Bioinformatics, Berry Oncology Corporation, Beijing, China

    Contribution

    Resources, Data curation, Formal analysis, Investigation

    Competing interests

    Affiliated with Berry Oncology Corporation. No financial interests to declare

12. Jinxing Zhou

    Department of Bioinformatics, Berry Oncology Corporation, Beijing, China

    Contribution

    Formal analysis, Investigation

    Competing interests

    Affiliated with Berry Oncology Corporation. No financial interests to declare

13. Jinming Yu

    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Project administration

    Competing interests

    No competing interests declared

14. Huihui Li

    Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China

    Contribution

    Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

    For correspondence

    huihuili82@163.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6040-3424

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