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.

1. 1. Li H

   (2022) China National Genomics Data Center

   ID HRA002598. HRA002598.

   https://ngdc.cncb.ac.cn/gsa-human/browse/HRA002598

1. 1. Alix-Panabières C
   2. Pantel K

   (2016) Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy

   Cancer Discovery 6:479–491.

   https://doi.org/10.1158/2159-8290.CD-15-1483

   • PubMed
   • Google Scholar
2. 1. Anonymous

   (1996) Clinical practice guidelines for the use of tumor markers in breast and colorectal cancer: adopted on May 17, 1996 by the American Society of Clinical Oncology

   Journal of Clinical Oncology 14:2843–2877.

   https://doi.org/10.1200/JCO.1996.14.10.2843

   • PubMed
   • Google Scholar
3. 1. Asante DB
   2. Calapre L
   3. Ziman M
   4. Meniawy TM
   5. Gray ES

   (2020) Liquid biopsy in ovarian cancer using circulating tumor DNA and cells: Ready for prime time?

   Cancer Letters 468:59–71.

   https://doi.org/10.1016/j.canlet.2019.10.014

   • PubMed
   • Google Scholar
4. 1. Barroso-Sousa R
   2. Forman J
   3. Collier K
   4. Weber ZT
   5. Jammihal TR
   6. Kao KZ
   7. Richardson ET
   8. Keenan T
   9. Cohen O
   10. Manos MP
   11. Brennick RC
   12. Ott PA
   13. Hodi FS
   14. Dillon DA
   15. Attaya V
   16. O’Meara T
   17. Lin NU
   18. Van Allen EM
   19. Rodig S
   20. Winer EP
   21. Mittendorf EA
   22. Wu CJ
   23. Wagle N
   24. Stover DG
   25. Shukla SA
   26. Tolaney SM

   (2022) Multidimensional molecular profiling of metastatic triple-negative breast cancer and immune checkpoint inhibitor benefit

   JCO Precision Oncology 6:e2100413.

   https://doi.org/10.1200/PO.21.00413

   • PubMed
   • Google Scholar
5. 1. Burstein MD
   2. Tsimelzon A
   3. Poage GM
   4. Covington KR
   5. Contreras A
   6. Fuqua SAW
   7. Savage MI
   8. Osborne CK
   9. Hilsenbeck SG
   10. Chang JC
   11. Mills GB
   12. Lau CC
   13. Brown PH

   (2015) Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer

   Clinical Cancer Research 21:1688–1698.

   https://doi.org/10.1158/1078-0432.CCR-14-0432

   • PubMed
   • Google Scholar
6. 1. Campos-Carrillo A
   2. Weitzel JN
   3. Sahoo P
   4. Rockne R
   5. Mokhnatkin JV
   6. Murtaza M
   7. Gray SW
   8. Goetz L
   9. Goel A
   10. Schork N
   11. Slavin TP

   (2020) Circulating tumor DNA as an early cancer detection tool

   Pharmacology & Therapeutics 207:107458.

   https://doi.org/10.1016/j.pharmthera.2019.107458

   • PubMed
   • Google Scholar
7. 1. Cavallone L
   2. Aguilar-Mahecha A
   3. Lafleur J
   4. Brousse S
   5. Aldamry M
   6. Roseshter T
   7. Lan C
   8. Alirezaie N
   9. Bareke E
   10. Majewski J
   11. Ferrario C
   12. Hassan S
   13. Discepola F
   14. Seguin C
   15. Mihalcioiu C
   16. Marcus EA
   17. Robidoux A
   18. Roy J-A
   19. Pelmus M
   20. Basik M

   (2020) Prognostic and predictive value of circulating tumor DNA during neoadjuvant chemotherapy for triple negative breast cancer

   Scientific Reports 10:14704.

   https://doi.org/10.1038/s41598-020-71236-y

   • PubMed
   • Google Scholar
8. 1. Chae YK
   2. Davis AA
   3. Jain S
   4. Santa-Maria C
   5. Flaum L
   6. Beaubier N
   7. Platanias LC
   8. Gradishar W
   9. Giles FJ
   10. Cristofanilli M

   (2017) Concordance of genomic alterations by next-generation sequencing in tumor tissue versus circulating tumor DNA in breast cancer

   Molecular Cancer Therapeutics 16:1412–1420.

   https://doi.org/10.1158/1535-7163.MCT-17-0061

   • PubMed
   • Google Scholar
9. 1. Chae YK
   2. Oh MS

   (2019) Detection of minimal residual disease using ctDNA in lung cancer: current evidence and future directions

   Journal of Thoracic Oncology 14:16–24.

   https://doi.org/10.1016/j.jtho.2018.09.022

   • PubMed
   • Google Scholar
10. 1. Chalmers ZR
    2. Connelly CF
    3. Fabrizio D
    4. Gay L
    5. Ali SM
    6. Ennis R
    7. Schrock A
    8. Campbell B
    9. Shlien A
    10. Chmielecki J
    11. Huang F
    12. He Y
    13. Sun J
    14. Tabori U
    15. Kennedy M
    16. Lieber DS
    17. Roels S
    18. White J
    19. Otto GA
    20. Ross JS
    21. Garraway L
    22. Miller VA
    23. Stephens PJ
    24. Frampton GM

    (2017) Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden

    Genome Medicine 9:34.

    https://doi.org/10.1186/s13073-017-0424-2

    • PubMed
    • Google Scholar
11. 1. Chen S
    2. Zhou Y
    3. Chen Y
    4. Gu J

    (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor

    Bioinformatics 34:i884–i890.

    https://doi.org/10.1093/bioinformatics/bty560

    • PubMed
    • Google Scholar
12. 1. Chen S
    2. Zhou Y
    3. Chen Y
    4. Huang T
    5. Liao W
    6. Xu Y
    7. Li Z
    8. Gu J

    (2019) Gencore: an efficient tool to generate consensus reads for error suppressing and duplicate removing of NGS data

    BMC Bioinformatics 20:606.

    https://doi.org/10.1186/s12859-019-3280-9

    • PubMed
    • Google Scholar
13. 1. Chopra N
    2. Tovey H
    3. Pearson A
    4. Cutts R
    5. Toms C
    6. Proszek P
    7. Hubank M
    8. Dowsett M
    9. Dodson A
    10. Daley F
    11. Kriplani D
    12. Gevensleben H
    13. Davies HR
    14. Degasperi A
    15. Roylance R
    16. Chan S
    17. Tutt A
    18. Skene A
    19. Evans A
    20. Bliss JM
    21. Nik-Zainal S
    22. Turner NC

    (2020) Homologous recombination DNA repair deficiency and PARP inhibition activity in primary triple negative breast cancer

    Nature Communications 11:2662.

    https://doi.org/10.1038/s41467-020-16142-7

    • PubMed
    • Google Scholar
14. 1. Collier KA
    2. Asad S
    3. Tallman D
    4. Jenison J
    5. Rajkovic A
    6. Mardis ER
    7. Parsons HA
    8. Tolaney SM
    9. Winer EP
    10. Lin NU
    11. Ha G
    12. Adalsteinsson VA
    13. Stover DG

    (2021) Association of 17q22 Amplicon Via Cell-Free DNA with platinum chemotherapy response in metastatic triple-negative breast cancer

    JCO Precision Oncology 5:PO.21.00104.

    https://doi.org/10.1200/PO.21.00104

    • PubMed
    • Google Scholar
15. 1. Concha A
    2. Esteban F
    3. Cabrera T
    4. Ruiz-Cabello F
    5. Garrido F

    (1991)

    Tumor aggressiveness and MHC class I and II antigens in laryngeal and breast cancer

    Seminars in Cancer Biology 2:47–54.

    • PubMed
    • Google Scholar
16. 1. Coussy F
    2. Lallemand F
    3. Vacher S
    4. Schnitzler A
    5. Chemlali W
    6. Caly M
    7. Nicolas A
    8. Richon S
    9. Meseure D
    10. El Botty R
    11. De-Plater L
    12. Fuhrmann L
    13. Dubois T
    14. Roman-Roman S
    15. Dangles-Marie V
    16. Marangoni E
    17. Bièche I

    (2017) Clinical value of R-spondins in triple-negative and metaplastic breast cancers

    British Journal of Cancer 116:1595–1603.

    https://doi.org/10.1038/bjc.2017.131

    • PubMed
    • Google Scholar
17. 1. Darrigues L
    2. Pierga JY
    3. Bernard-Tessier A
    4. Bièche I
    5. Silveira AB
    6. Michel M
    7. Loirat D
    8. Cottu P
    9. Cabel L
    10. Dubot C
    11. Geiss R
    12. Ricci F
    13. Vincent-Salomon A
    14. Proudhon C
    15. Bidard FC

    (2021) Circulating tumor DNA as a dynamic biomarker of response to palbociclib and fulvestrant in metastatic breast cancer patients

    Breast Cancer Research 23:31.

    https://doi.org/10.1186/s13058-021-01411-0

    • PubMed
    • Google Scholar
18. 1. Davis AA
    2. Jacob S
    3. Gerratana L
    4. Shah AN
    5. Wehbe F
    6. Katam N
    7. Zhang Q
    8. Flaum L
    9. Siziopikou KP
    10. Platanias LC
    11. Gradishar WJ
    12. Behdad A
    13. Cristofanilli M

    (2020) Landscape of circulating tumour DNA in metastatic breast cancer

    EBioMedicine 58:102914.

    https://doi.org/10.1016/j.ebiom.2020.102914

    • PubMed
    • Google Scholar
19. 1. Dawson S-J
    2. Rosenfeld N
    3. Caldas C

    (2013) Circulating tumor DNA to monitor metastatic breast cancer

    The New England Journal of Medicine 369:93–94.

    https://doi.org/10.1056/NEJMc1306040

    • PubMed
    • Google Scholar
20. 1. Diaz LA
    2. Bardelli A

    (2014) Liquid biopsies: genotyping circulating tumor DNA

    Journal of Clinical Oncology 32:579–586.

    https://doi.org/10.1200/JCO.2012.45.2011

    • PubMed
    • Google Scholar
21. 1. Eisenhauer EA
    2. Therasse P
    3. Bogaerts J
    4. Schwartz LH
    5. Sargent D
    6. Ford R
    7. Dancey J
    8. Arbuck S
    9. Gwyther S
    10. Mooney M
    11. Rubinstein L
    12. Shankar L
    13. Dodd L
    14. Kaplan R
    15. Lacombe D
    16. Verweij J

    (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)

    European Journal of Cancer 45:228–247.

    https://doi.org/10.1016/j.ejca.2008.10.026

    • PubMed
    • Google Scholar
22. 1. Felker J
    2. Broniscer A

    (2020) Improving long-term survival in diffuse intrinsic pontine glioma

    Expert Review of Neurotherapeutics 20:647–658.

    https://doi.org/10.1080/14737175.2020.1775584

    • PubMed
    • Google Scholar
23. 1. Foulkes WD
    2. Smith IE
    3. Reis-Filho JS

    (2010) Triple-negative breast cancer

    The New England Journal of Medicine 363:1938–1948.

    https://doi.org/10.1056/NEJMra1001389

    • PubMed
    • Google Scholar
24. 1. Garcia-Murillas I
    2. Schiavon G
    3. Weigelt B
    4. Ng C
    5. Hrebien S
    6. Cutts RJ
    7. Cheang M
    8. Osin P
    9. Nerurkar A
    10. Kozarewa I
    11. Garrido JA
    12. Dowsett M
    13. Reis-Filho JS
    14. Smith IE
    15. Turner NC

    (2015) Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer

    Science Translational Medicine 7:302ra133.

    https://doi.org/10.1126/scitranslmed.aab0021

    • PubMed
    • Google Scholar
25. 1. Gerratana L
    2. Davis AA
    3. Zhang Q
    4. Basile D
    5. Rossi G
    6. Strickland K
    7. Franzoni A
    8. Allegri L
    9. Mu Z
    10. Zhang Y
    11. Flaum LE
    12. Damante G
    13. Gradishar WJ
    14. Platanias LC
    15. Behdad A
    16. Yang H
    17. Puglisi F
    18. Cristofanilli M

    (2021) Longitudinal dynamics of circulating tumor cells and circulating tumor dna for treatment monitoring in metastatic breast cancer

    JCO Precision Oncology 5:943–952.

    https://doi.org/10.1200/PO.20.00345

    • PubMed
    • Google Scholar
26. 1. Jiang Y-Z
    2. Ma D
    3. Suo C
    4. Shi J
    5. Xue M
    6. Hu X
    7. Xiao Y
    8. Yu K-D
    9. Liu Y-R
    10. Yu Y
    11. Zheng Y
    12. Li X
    13. Zhang C
    14. Hu P
    15. Zhang J
    16. Hua Q
    17. Zhang J
    18. Hou W
    19. Ren L
    20. Bao D
    21. Li B
    22. Yang J
    23. Yao L
    24. Zuo W-J
    25. Zhao S
    26. Gong Y
    27. Ren Y-X
    28. Zhao Y-X
    29. Yang Y-S
    30. Niu Z
    31. Cao Z-G
    32. Stover DG
    33. Verschraegen C
    34. Kaklamani V
    35. Daemen A
    36. Benson JR
    37. Takabe K
    38. Bai F
    39. Li D-Q
    40. Wang P
    41. Shi L
    42. Huang W
    43. Shao Z-M

    (2019) Genomic and transcriptomic landscape of triple-negative breast cancers: subtypes and treatment strategies

    Cancer Cell 35:428–440.

    https://doi.org/10.1016/j.ccell.2019.02.001

    • PubMed
    • Google Scholar
27. 1. Kim S
    2. Lee J
    3. Jeon M
    4. Nam SJ
    5. Lee JE

    (2015) Elevated TGF-β1 and -β2 expression accelerates the epithelial to mesenchymal transition in triple-negative breast cancer cells

    Cytokine 75:151–158.

    https://doi.org/10.1016/j.cyto.2015.05.020

    • Google Scholar
28. 1. Kim H
    2. Kim YJ
    3. Park D
    4. Park W-Y
    5. Choi DH
    6. Park W
    7. Cho WK
    8. Kim N

    (2021) Dynamics of circulating tumor DNA during postoperative radiotherapy in patients with residual triple-negative breast cancer following neoadjuvant chemotherapy: a prospective observational study

    Breast Cancer Research and Treatment 189:167–175.

    https://doi.org/10.1007/s10549-021-06296-3

    • PubMed
    • Google Scholar
29. 1. Lee K-M
    2. Giltnane JM
    3. Balko JM
    4. Schwarz LJ
    5. Guerrero-Zotano AL
    6. Hutchinson KE
    7. Nixon MJ
    8. Estrada MV
    9. Sánchez V
    10. Sanders ME
    11. Lee T
    12. Gómez H
    13. Lluch A
    14. Pérez-Fidalgo JA
    15. Wolf MM
    16. Andrejeva G
    17. Rathmell JC
    18. Fesik SW
    19. Arteaga CL

    (2017) MYC and MCL1 cooperatively promote chemotherapy-resistant breast cancer stem cells via regulation of mitochondrial oxidative phosphorylation

    Cell Metabolism 26:633–647.

    https://doi.org/10.1016/j.cmet.2017.09.009

    • PubMed
    • Google Scholar
30. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform

    Bioinformatics 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • PubMed
    • Google Scholar
31. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The Sequence Alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
32. 1. Li J
    2. Lupat R
    3. Amarasinghe KC
    4. Thompson ER
    5. Doyle MA
    6. Ryland GL
    7. Tothill RW
    8. Halgamuge SK
    9. Campbell IG
    10. Gorringe KL

    (2012) CONTRA: copy number analysis for targeted resequencing

    Bioinformatics 28:1307–1313.

    https://doi.org/10.1093/bioinformatics/bts146

    • Google Scholar
33. 1. Li X
    2. Yang J
    3. Peng L
    4. Sahin AA
    5. Huo L
    6. Ward KC
    7. O’Regan R
    8. Torres MA
    9. Meisel JL

    (2017) Triple-negative breast cancer has worse overall survival and cause-specific survival than non-triple-negative breast cancer

    Breast Cancer Research and Treatment 161:279–287.

    https://doi.org/10.1007/s10549-016-4059-6

    • PubMed
    • Google Scholar
34. 1. Lin P-H
    2. Wang M-Y
    3. Lo C
    4. Tsai L-W
    5. Yen T-C
    6. Huang TY
    7. Huang W-C
    8. Yang K
    9. Chen C-K
    10. Fan S-C
    11. Kuo S-H
    12. Huang C-S

    (2021) Circulating tumor DNA as a predictive marker of recurrence for patients with stage ii-iii breast cancer treated with neoadjuvant therapy

    Frontiers in Oncology 11:736769.

    https://doi.org/10.3389/fonc.2021.736769

    • PubMed
    • Google Scholar
35. 1. Ma D
    2. Jiang YZ
    3. Liu XY
    4. Liu YR
    5. Shao ZM

    (2017) Clinical and molecular relevance of mutant-allele tumor heterogeneity in breast cancer

    Breast Cancer Research and Treatment 162:39–48.

    https://doi.org/10.1007/s10549-017-4113-z

    • PubMed
    • Google Scholar
36. 1. Madic J
    2. Kiialainen A
    3. Bidard F-C
    4. Birzele F
    5. Ramey G
    6. Leroy Q
    7. Rio Frio T
    8. Vaucher I
    9. Raynal V
    10. Bernard V
    11. Lermine A
    12. Clausen I
    13. Giroud N
    14. Schmucki R
    15. Milder M
    16. Horn C
    17. Spleiss O
    18. Lantz O
    19. Stern M-H
    20. Pierga J-Y
    21. Weisser M
    22. Lebofsky R

    (2015) Circulating tumor DNA and circulating tumor cells in metastatic triple negative breast cancer patients

    International Journal of Cancer 136:2158–2165.

    https://doi.org/10.1002/ijc.29265

    • PubMed
    • Google Scholar
37. 1. Malorni L
    2. Shetty PB
    3. De Angelis C
    4. Hilsenbeck S
    5. Rimawi MF
    6. Elledge R
    7. Osborne CK
    8. De Placido S
    9. Arpino G

    (2012) Clinical and biologic features of triple-negative breast cancers in a large cohort of patients with long-term follow-up

    Breast Cancer Research and Treatment 136:795–804.

    https://doi.org/10.1007/s10549-012-2315-y

    • PubMed
    • Google Scholar
38. 1. Maron SB
    2. Chase LM
    3. Lomnicki S
    4. Kochanny S
    5. Moore KL
    6. Joshi SS
    7. Landron S
    8. Johnson J
    9. Kiedrowski LA
    10. Nagy RJ
    11. Lanman RB
    12. Kim ST
    13. Lee J
    14. Catenacci DVT

    (2019) Circulating tumor DNA sequencing analysis of gastroesophageal adenocarcinoma

    Clinical Cancer Research 25:7098–7112.

    https://doi.org/10.1158/1078-0432.CCR-19-1704

    • PubMed
    • Google Scholar
39. 1. Mascia F
    2. Mazo I
    3. Alterovitz W-L
    4. Karagiannis K
    5. Wu WW
    6. Shen R-F
    7. Beaver JA
    8. Rao VA

    (2022) In search of autophagy biomarkers in breast cancer: Receptor status and drug agnostic transcriptional changes during autophagy flux in cell lines

    PLOS ONE 17:e0262134.

    https://doi.org/10.1371/journal.pone.0262134

    • PubMed
    • Google Scholar
40. 1. McDonald K-A
    2. Kawaguchi T
    3. Qi Q
    4. Peng X
    5. Asaoka M
    6. Young J
    7. Opyrchal M
    8. Yan L
    9. Patnaik S
    10. Otsuji E
    11. Takabe K

    (2019) Tumor heterogeneity correlates with less immune response and worse survival in breast cancer patients

    Annals of Surgical Oncology 26:2191–2199.

    https://doi.org/10.1245/s10434-019-07338-3

    • PubMed
    • Google Scholar
41. 1. Mroz EA
    2. Rocco JW

    (2013) MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma

    Oral Oncology 49:211–215.

    https://doi.org/10.1016/j.oraloncology.2012.09.007

    • PubMed
    • Google Scholar
42. 1. Murtaza M
    2. Dawson S-J
    3. Tsui DWY
    4. Gale D
    5. Forshew T
    6. Piskorz AM
    7. Parkinson C
    8. Chin S-F
    9. Kingsbury Z
    10. Wong ASC
    11. Marass F
    12. Humphray S
    13. Hadfield J
    14. Bentley D
    15. Chin TM
    16. Brenton JD
    17. Caldas C
    18. Rosenfeld N

    (2013) Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA

    Nature 497:108–112.

    https://doi.org/10.1038/nature12065

    • PubMed
    • Google Scholar
43. 1. Ortolan E
    2. Appierto V
    3. Silvestri M
    4. Miceli R
    5. Veneroni S
    6. Folli S
    7. Pruneri G
    8. Vingiani A
    9. Belfiore A
    10. Cappelletti V
    11. Vismara M
    12. Dell’Angelo F
    13. De Cecco L
    14. Bianchi GV
    15. de Braud FG
    16. Daidone MG
    17. Di Cosimo S

    (2021) Blood-based genomics of triple-negative breast cancer progression in patients treated with neoadjuvant chemotherapy

    ESMO Open 6:100086.

    https://doi.org/10.1016/j.esmoop.2021.100086

    • PubMed
    • Google Scholar
44. 1. Palmirotta R
    2. Lovero D
    3. Cafforio P
    4. Felici C
    5. Mannavola F
    6. Pellè E
    7. Quaresmini D
    8. Tucci M
    9. Silvestris F

    (2018) Liquid biopsy of cancer: a multimodal diagnostic tool in clinical oncology

    Therapeutic Advances in Medical Oncology 10:1758835918794630.

    https://doi.org/10.1177/1758835918794630

    • PubMed
    • Google Scholar
45. 1. Park S-M
    2. Choi E-Y
    3. Bae M
    4. Kim S
    5. Park JB
    6. Yoo H
    7. Choi JK
    8. Kim Y-J
    9. Lee S-H
    10. Kim I-H

    (2016) Histone variant H3F3A promotes lung cancer cell migration through intronic regulation

    Nature Communications 7:12914.

    https://doi.org/10.1038/ncomms12914

    • PubMed
    • Google Scholar
46. 1. Perou CM

    (2011) Molecular stratification of triple-negative breast cancers

    The Oncologist 16 Suppl 1:61–70.

    https://doi.org/10.1634/theoncologist.2011-S1-61

    • PubMed
    • Google Scholar
47. 1. Poulet G
    2. Massias J
    3. Taly V

    (2019) Liquid Biopsy: General Concepts

    Acta Cytologica 63:449–455.

    https://doi.org/10.1159/000499337

    • PubMed
    • Google Scholar
48. 1. Riva F
    2. Bidard F-C
    3. Houy A
    4. Saliou A
    5. Madic J
    6. Rampanou A
    7. Hego C
    8. Milder M
    9. Cottu P
    10. Sablin M-P
    11. Vincent-Salomon A
    12. Lantz O
    13. Stern M-H
    14. Proudhon C
    15. Pierga J-Y

    (2017) Patient-specific circulating tumor DNA detection during neoadjuvant chemotherapy in triple-negative breast cancer

    Clinical Chemistry 63:691–699.

    https://doi.org/10.1373/clinchem.2016.262337

    • PubMed
    • Google Scholar
49. 1. Rong G
    2. Yi Z
    3. Ma F
    4. Guan Y
    5. Xu Y
    6. Li L
    7. Xu B

    (2020) Mutational characteristics determined using circulating tumor DNA analysis in triple-negative breast cancer patients with distant metastasis

    Cancer Communications 40:738–742.

    https://doi.org/10.1002/cac2.12102

    • PubMed
    • Google Scholar
50. 1. Sato K
    2. Akimoto K

    (2017) Expression Levels of KMT2C and SLC20A1 identified by information-theoretical analysis are powerful prognostic biomarkers in estrogen receptor-positive breast cancer

    Clinical Breast Cancer 17:e135–e142.

    https://doi.org/10.1016/j.clbc.2016.11.005

    • PubMed
    • Google Scholar
51. 1. Seregni E
    2. Coli A
    3. Mazzucca N
    4. Italian Group RIA-IRMA Test, Italian Association of Nuclear Medicine

    (2004) Circulating tumour markers in breast cancer

    European Journal of Nuclear Medicine and Molecular Imaging 31 Suppl 1:S15–S22.

    https://doi.org/10.1007/s00259-004-1523-z

    • PubMed
    • Google Scholar
52. 1. Stover DG
    2. Parsons HA
    3. Ha G
    4. Freeman SS
    5. Barry WT
    6. Guo H
    7. Choudhury AD
    8. Gydush G
    9. Reed SC
    10. Rhoades J
    11. Rotem D
    12. Hughes ME
    13. Dillon DA
    14. Partridge AH
    15. Wagle N
    16. Krop IE
    17. Getz G
    18. Golub TR
    19. Love JC
    20. Winer EP
    21. Tolaney SM
    22. Lin NU
    23. Adalsteinsson VA

    (2018) Association of cell-free DNA tumor fraction and somatic copy number alterations with survival in metastatic triple-negative breast cancer

    Journal of Clinical Oncology 36:543–553.

    https://doi.org/10.1200/JCO.2017.76.0033

    • PubMed
    • Google Scholar
53. 1. Sung H
    2. Ferlay J
    3. Siegel RL
    4. Laversanne M
    5. Soerjomataram I
    6. Jemal A
    7. Bray F

    (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

    CA 71:209–249.

    https://doi.org/10.3322/caac.21660

    • PubMed
    • Google Scholar
54. 1. Swarup V
    2. Rajeswari MR

    (2007) Circulating (cell-free) nucleic acids--a promising, non-invasive tool for early detection of several human diseases

    FEBS Letters 581:795–799.

    https://doi.org/10.1016/j.febslet.2007.01.051

    • PubMed
    • Google Scholar
55. 1. Vandekerkhove G
    2. Todenhöfer T
    3. Annala M
    4. Struss WJ
    5. Wong A
    6. Beja K
    7. Ritch E
    8. Brahmbhatt S
    9. Volik SV
    10. Hennenlotter J
    11. Nykter M
    12. Chi KN
    13. North S
    14. Stenzl A
    15. Collins CC
    16. Eigl BJ
    17. Black PC
    18. Wyatt AW

    (2017) Circulating tumor DNA reveals clinically actionable somatic genome of metastatic bladder cancer

    Clinical Cancer Research 23:6487–6497.

    https://doi.org/10.1158/1078-0432.CCR-17-1140

    • PubMed
    • Google Scholar
56. 1. Wang Y
    2. Lin L
    3. Li L
    4. Wen J
    5. Chi Y
    6. Hao R
    7. Dai X
    8. Chen Y
    9. Huang D
    10. Zhou Y
    11. You J
    12. Ye Z
    13. Chen H
    14. Jin L
    15. Chen D
    16. Yang F
    17. Xia E
    18. Ma X
    19. Guo F
    20. Tong Y
    21. Zheng M
    22. Wang O

    (2021) Genetic landscape of breast cancer and mutation tracking with circulating tumor DNA in Chinese women

    Aging 13:11860–11876.

    https://doi.org/10.18632/aging.202888

    • PubMed
    • Google Scholar
57. 1. Weber ZT
    2. Collier KA
    3. Tallman D
    4. Forman J
    5. Shukla S
    6. Asad S
    7. Rhoades J
    8. Freeman S
    9. Parsons HA
    10. Williams NO
    11. Barroso-Sousa R
    12. Stover EH
    13. Mahdi H
    14. Cibulskis C
    15. Lennon NJ
    16. Ha G
    17. Adalsteinsson VA
    18. Tolaney SM
    19. Stover DG

    (2021) Modeling clonal structure over narrow time frames via circulating tumor DNA in metastatic breast cancer

    Genome Medicine 13:89.

    https://doi.org/10.1186/s13073-021-00895-x

    • PubMed
    • Google Scholar
58. 1. Wongchenko MJ
    2. Kim S-B
    3. Saura C
    4. Oliveira M
    5. Lipson D
    6. Kennedy M
    7. Greene M
    8. Breese V
    9. Mani A
    10. Xu N
    11. Dent R

    (2020) Circulating Tumor DNA and biomarker analyses from the LOTUS randomized trial of first-line ipatasertib and paclitaxel for metastatic triple-negative breast cancer

    JCO Precision Oncology 4:1012–1024.

    https://doi.org/10.1200/PO.19.00396

    • PubMed
    • Google Scholar
59. 1. Yang L
    2. Perez AA
    3. Fujie S
    4. Warden C
    5. Li J
    6. Wang Y
    7. Yung B
    8. Chen Y-R
    9. Liu X
    10. Zhang H
    11. Zheng S
    12. Liu Z
    13. Ann D
    14. Yen Y

    (2014) Wnt modulates MCL1 to control cell survival in triple negative breast cancer

    BMC Cancer 14:124.

    https://doi.org/10.1186/1471-2407-14-124

    • PubMed
    • Google Scholar
60. 1. Zhang Y
    2. Wei L
    3. Liu M
    4. Li J
    5. Zheng Y
    6. Gao Y
    7. Li X

    (2013) BTG2 inhibits the proliferation, invasion, and apoptosis of MDA-MB-231 triple-negative breast cancer cells

    Tumour Biology 34:1605–1613.

    https://doi.org/10.1007/s13277-013-0691-5

    • PubMed
    • Google Scholar
61. 1. Zhang J
    2. Hou S
    3. You Z
    4. Li G
    5. Xu S
    6. Li X
    7. Zhang X
    8. Lei B
    9. Pang D

    (2021) Expression and prognostic values of ARID family members in breast cancer

    Aging 13:5621–5637.

    https://doi.org/10.18632/aging.202489

    • PubMed
    • Google Scholar
62. 1. Zhu R
    2. Yang G
    3. Cao Z
    4. Shen K
    5. Zheng L
    6. Xiao J
    7. You L
    8. Zhang T

    (2020) The prospect of serum and glucocorticoid-inducible kinase 1 (SGK1) in cancer therapy: a rising star

    Therapeutic Advances in Medical Oncology 12:1758835920940946.

    https://doi.org/10.1177/1758835920940946

    • PubMed
    • Google Scholar

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, Funding acquisition, 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. The author has 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. The author has no financial interests to declare

12. Jinxing Zhou

    Department of Bioinformatics, Berry Oncology Corporation, Beijing, China

    Contribution

    Formal analysis, Investigation

    Competing interests

    Jinxing Zhou is affiliated with Berry Oncology Corporation. The author has 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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