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Cancer Drug Resist 2020;3:[Online First].10.20517/cdr.2019.79© The Author(s) 2020.
Open AccessOriginal Article

Circulating tumor cells and drug history in primary breast cancer patients

12nd Department of Oncology, Faculty of Medicine, Comenius University, Bratislava 83310, Slovakia.

2National Cancer Institute, Bratislava 83310, Slovakia.

3Slovak Medical University, Bratislava 83101, Slovakia.

4Institute of Molecular Biomedicine, Faculty of Medicine, Comenius University, Bratislava 83172, Slovakia.

5Department of Medicine, St. Elizabeth University, Bratislava 81250, Slovakia.

Correspondence Address: Prof. Michal Mego, 2nd Department of Medical Oncology, Comenius University, Faculty of Medicine, National Cancer Institute, Klenova 1, Bratislava 83310, Slovak Republic. E-mail: misomego@gmail.com

    Science Editor: Dario Marchetti | Copy Editor: Jing-Wen Zhang | Production Editor: Tian Zhang
    ...

    © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

    Abstract

    Aim: Different types of chronic medication may affect breast cancer prognosis. Circulating tumor cells (CTCs) play an important role in cancer metastasis formation. There is no evidence of how chronic medication affects CTCs and breast cancer prognosis. The aim of this study was to evaluate association between chronic medication and CTCs in patients with primary breast cancer.

    Methods: This study involved 414 patients with stage I-III primary breast cancer. Chronic drug history was collected from patients’ medical records and included all drugs that were prescribed for patients over at least the last 6 months prior to CTCs evaluation. CTCs were detected using a quantitative real-time polymerase chain reaction (qRT-PCR)-based method at the time of breast surgery.

    Results: There was no association between CTCs, including their different subpopulations and chronic medication. Chronic medication using angiotensin-converting-enzyme inhibitors (ACEi), metformin, and insulin were associated with inferior disease-free survival (HR = 0.49, 95%CI 0.26-0.94, P = 0.007 for ACEi; HR = 0.27, 95%CI 0.08-0.91, P < 0.001 for metformin; and HR = 0.12, 95%CI 0.01-2.91, P < 0.001 for insulin) and this was most pronounced in patients with epithelial to mesenchymal transition (CTC_EMT) phenotype. In multivariate analysis, chronic administration of metformin and/or insulin was an independent predictor of inferior outcome.

    Conclusion: Our findings show that there was no association between chronically used medication and CTCs in primary breast cancer patients. However, administration of ACEi, metformin, and/or insulin could negatively affect prognosis of patients with CTC_EMT.

    Introduction

    According to global cancer statistics, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among females, accounting for 23% of total cancer cases and 14% of cancer deaths[1].

    Circulating tumor cells (CTCs) are cancer cells intravasated into the blood stream after their separation from the primary tumor, which directly contribute to metastasis development[2,3] and are also established as an independent predictor of progression-free and overall survival in patients with primary and metastatic breast cancer[4-7]. Although the connection between CTCs and bad prognosis is well described in breast cancer, CTCs are detected only in a subset of patients.

    Comorbidities such as obesity, diabetes mellitus, hypertension, alcohol consumption, and non-cancer related drug exposure as well as regular physical activity may affect outcomes of breast cancer. Body mass index (BMI) ≥ 30 kg/m2 is a known factor responsible for increase in overall morbidity and mortality and is associated with breast cancer risk especially in postmenopausal women[8-10].

    Chronically used medications including non-steroidal anti-inflammatory drugs (NSAIDs), metformin, statins, and insulin may influence the progression of cancer[11-14]. However, there is limited evidence about how chronic medication can affect CTCs. In a previous study, it was shown that patients who were treated with statins before the diagnosis of inflammatory breast cancer (IBC) had significantly lower baseline CTC counts than patients not taking statins[15]. This observation was most pronounced in patients taking H-statins and was associated with improved progression-free and overall survival compared with non-statin users[15]. This could be attributed to the fact that certain types of statin may block a step involved in metastasis formation, including invasion, extravasation, epithelial-mesenchymal transition, and angiogenesis, and therefore may block pathways associated with cancer stem cells[16]. However, while clinical data support this observation in IBC, there are no data about the association between CTCs and statin use in non-IBC patients[16].

    In this study, we hypothesized that certain types of chronic medication utilized before diagnosis of primary breast cancer could correlate with presence of CTCs in peripheral blood. The aim of our study was to evaluate an association between different CTC subpopulations and chronic medication and/or whether these drugs could be linked to patients’ outcomes in primary breast cancer.

    Methods

    Study patients

    This was a prospective translational study that evaluated prognostic value of CTCs in stage I-III primary breast cancer, including 427 patients, as described previously[17]. For this sub-study, 414 patients for whom complete medical history, including drug history, was available were eligible. Chronic drug history was collected from the medical records and included all drugs that were prescribed for patient over at least the last six months before date of surgery, when CTCs were evaluated. Chronic medication was categorized into several classes including NSAIDs, L-thyroxin, angiotensin-converting-enzyme inhibitors (ACEi), sartans, anticoagulants (low molecular weight heparin and/or warfarin), betablockers, statins, metformin, and insulin. BMI was calculated at the time of surgery.

    All study participants provided signed informed consent before study enrollment. The study was approved by the Institutional Review Board (IRB) of the National Cancer Institute of Slovakia and was conducted between March 2012 and February 2015. Healthy donors (n = 60) were age-matched women without breast cancer who were enrolled according to the IRB-approved protocol and all of them signed informed consent, as described previously[17].

    Detection of CTC in peripheral blood

    Quantitative real-time polymerase chain reaction (qRT-PCR) assay was used for CTCs detection in peripheral blood that was previously depleted of CD45+ cells for CTCs enrichment, as described previously[17-20].

    CTC definition

    Patient samples with higher KRT19 gene transcripts than those of healthy donors were scored as epithelial CTCs positive (CTC_EP), while patient samples with higher Epithelial-mesenchymal transition transcription factor (TWIST1, SNAIL1, SLUG, and ZEB1) gene transcripts than those of healthy donors were scored as CTC_EMT positive. Expression of at least one of the markers (either epithelial or mesenchymal) at levels above the defined cut-off was sufficient to define a sample as CTC positive[18,20].

    Statistical analysis

    The patients’ characteristics were summarized using the median (range) for continuous variables and frequency (percentage) for categorical variables. The median follow-up period was calculated as a median observation time among all patients and among those still alive at the time of their last follow-up. Disease-free survival (DFS) was calculated from the date of CTC measurement to the date of disease recurrence (locoregional or distant), secondary cancer, death, or last follow-up. DFS was estimated using Kaplan-Meier product limit method and compared between groups by log-rank test. Cox-Mantel hazard ratio and 95%CI for Cox-Mantel hazard ratio were calculated as well. Univariate analyses with Chi squared or by the Fisher’s exact test were performed to find association between drug history and CTC status.

    A multivariate Cox proportional hazards model for DFS was used to assess differences in outcome on the basis chronic medication, CTC_EMT status (present vs. absent), hormone receptor status (positive for either vs. negative for both), HER-2 status (positive or negative), tumor size (T1 vs. T2 and higher), axillary lymph node involvement (N0 vs. N+), and Ki67 status (< 20% vs. > 20%). Step-wise regression techniques were used to build multivariate models using a significance level of 0.10 to remain in the model. All P values presented are two-sided, and associations were considered significant if the P value is less than or equal to 0.05. Statistical analyses were performed using NCSS 11 Statistical Software (2016). NCSS, LLC. Kaysville, Utah, USA, ncss.com/software/ncss.

    Results

    Overall, 414 patients with primary breast cancer were included in this analysis. Patients’ characteristics are shown in Table 1. Median age of patients in this cohort was 60 years (range: 25-83 years). The majority of patients were of good prognosis, with tumor size less than 2 cm (69.3%), without axillary lymph nodes involvement (65.0%), and with low/intermediate grade (65.5%). CTC_EP was detected in 48 patients (11.6%), while CTC_EMT in 73 patients (17.6%); any type of CTC was present in 113 patients (27.3%).

    Table 1

    Patients’ characteristics

    n  %
    All patients414100.0
    T-stage
      T128769.3
      > T112730.7
    Histology
      IDC35285.0
      Other6215.0
    Grade
      Low and intermediate27165.5
      High grade13231.9
      Unknown112.7
    Lymph nodes
      N026965.0
      N+14033.8
      Unknown51.2
    Hormone receptor status (cut-off 1%)
      Negative for both35485.5
      Positive for either6014.5
    HER2 status
      Negative35285.0
      Positive6215.0
    Ki67 status (cut-off 20%)
      < 20%24960.1
      > 20%16339.4
      Unknown20.5
    Molecular subtype
      Luminal A21151.0
      Luminal B9723.4
      HER2+6215.0
      TN4210.1
      Unknown20.5
      CTC_EP4811.6
      CTC_EMT7317.6
      CTC_Any11327.3

    Association between chronic medication and CTC status

    Associations between CTC and chronic medication are shown in Tables 2-4. There was no association between CTC, including different subpopulations and chronic medication, except the trend for association between CTC_EP and ACEi/sartans, where patients on ACEi/sartans had lower prevalence of CTC_EP compared to no ACEi/sartan (7.9% vs. 16.1%, P = 0.06). Association between BMI status and CTC was not detected [Table 5].

    Table 2

    Association between drug history and CTC_EP

    DrugCTC_EP negativeCTC_EP positiveP-value
    nn%n%
    NSAIDNo34029386.24713.8  1.00
    Yes9888.9111.1
    L-thyroxinNo30726385.74414.3  0.48
    Yes423890.549.5
    ACEiNo29024684.84415.2  0.10
    Yes595593.246.8
    SartansNo30526185.64414.4  0.48
    Yes444090.949.1
    ACEi/SartanNo24820883.94016.1  0.06
    Yes1019392.187.9
    BetablockersNo25021887.23212.8  0.40
    Yes998383.81616.2
    StatinsNo29525185.14414.9  0.20
    Yes545092.647.4
    MetforminNo33228585.84714.2  0.49
    Yes171694.115.9
    InsulinNo34529786.14813.9  1.00
    Yes44100.000.0
    Insulin/MetforminNo32928285.74714.3  0.33
    Yes201995.015.0
    LMWH/WarfarinNo33528886.04714.0  0.70
    Yes141392.917.1
    Table 3

    Association between drug history and CTC_EMT

    DrugCTC_EMT negativeCTC_EMT positiveP-value
        n    n  %  n  %
    NSAIDNo36629380.17319.9   0.36
    Yes88100.000.0
    L-thyroxinNo32526380.96219.1   0.57
    Yes493877.61122.4
    ACEiNo30624680.46019.6   1.00
    Yes685580.91319.1
    SartansNo31926181.85818.2   0.14
    Yes554072.71527.3
    ACEi/SartanNo25420881.94618.1   0.33
    Yes1209377.52722.5
    BetablockersNo26721881.64918.4   0.39
    Yes1078377.62422.4
    StatinsNo31225180.46119.6   1.00
    Yes625080.61219.4
    MetforminNo35228581.06719.0   0.40
    Yes221672.7627.3
    InsulinNo37029780.37319.7   1.00
    Yes44100.000.0
    Insulin/MetforminNo34928280.86719.2   0.60
    Yes251976.0624.0
    LMWH/WarfarinNo35828880.47019.6   1.00
    Yes161381.3318.8
    Table 4

    Association between drug history and CTC

    DrugCTC_Any negativeCTC_Any positiveP-value
        n    n  %n  %
    NSAIDNo40529372.311227.7  0.45
    Yes9888.9111.1
    L-thyroxinNo36226372.79927.3  1.00
    Yes523873.11426.9
    ACEiNo34324671.79728.3  0.38
    Yes715577.51622.5
    SartansNo35526173.59426.5  0.35
    Yes594067.81932.2
    ACEi/SartanNo28720872.57927.5  0.91
    Yes1279373.23426.8
    BetablockersNo29621873.67826.4  0.54
    Yes1188370.33529.7
    StatinsNo35025171.79928.3  0.36
    Yes645078.11421.9
    MetforminNo39228572.710727.3  1.00
    Yes221672.7627.3
    InsulinNo41029772.411327.6  0.58
    Yes44100.000.0
    Insulin/MetforminNo38928272.510727.5  0.82
    Yes251976.0624.0
    LMWH/WarfarinNo39728872.510927.5  1.00
    Yes171376.5423.5
    Table 5

    Association between BMI and CTC

    BMICTC_EP negativeCTC_EP positiveP-value
    nn%n%
    < 2518015787.22312.8  0.47
    26-3011810790.7119.3
    31-35928087.01213.0
    > 36232295.714.3
    CTC_EMT negativeCTC_EMT positiveP-value
    < 2518015083.33016.7  0.46
    26-301189983.91916.1
    31-35927581.51718.5
    > 36231669.6730.4
    CTC_Any negativeCTC_Any positiveP-value
    < 2518013273.34826.7  0.60
    26-301189076.32823.7
    31-35926469.62830.4
    > 36231565.2834.8

    Disease outcome according to chronic medication

    At a median follow-up time of 55.0 months (range: 4.9-76.7 months), 74 patients (17.3%) had experienced a DFS event, and 36 patients (8.4%) had died. In univariate analysis, chronic administration of ACEi, metformin, and/or insulin was associated with inferior DFS [Table 6 and Figures 1-3] This correlation was most pronounced in patients with CTC_EMT phenotype. The negative prognostic impact of chronic medication was especially observed in patients with CTC_EMT that were on ACEi compared to patients with CTC_EP and/or no CTCs, where administration of ACEi had no impact on patient’s prognosis [Table 7 and Figure 4].

    Table 6

    Impact of drug history on disease-free survival in primary breast cancer

    DrugnHR*95% Low**95% High**P-value***
    No NSAID
    NSAID
    405
    9
    0.00    0.00  0.00    0.200
    No L-thyroxin
    L-thyroxin
    362
    52
    0.93    0.45  1.91    0.840
    No ACEi
    ACEi
    343
    71
    0.49    0.26  0.94    0.007
    No sartans
    Sartans
    355
    59
    0.69    0.35  1.37    0.230
    No ACEi/sartan
    ACEi/sartan
    287
    127
    0.53    0.31  0.89    0.008
    No betablockers
    Betablockers
    296
    118
    0.82    0.49  1.40    0.450
    No statins
    Statins
    350
    64
    0.63    0.32  1.22    0.110
    No metformin
    Metformin
    392
    22
    0.27    0.08  0.91    < 0.001
    No insulin
    Insulin
    410
    4
    0.12    0.01  2.91    < 0.001
    No insulin/metformin
    Insulin/metformin
    389
    25
    0.24    0.08  0.77    < 0.001
    No LMWH/warfarin
    LMWH/warfarin
    397
    17
    1.43    0.43  4.71    0.620

    Figure 1. Kaplan-Meier estimates of probabilities of disease-free survival according to chronic administration of ACEi (n = 414), HR = 0.49, 95%CI 0.26-0.94, P = 0.007. HR: hazard ratio

    Figure 2. Kaplan-Meier estimates of probabilities of disease-free survival according to chronic administration of metformin (n = 414), HR = 0.27, 95%CI 0.08-0.91, P < 0.001. HR: hazard ratio

    Figure 3. Kaplan-Meier estimates of probabilities of disease-free survival according to chronic administration of insulin (n = 427), HR = 0.12, 95%CI 0.01-2.91, P < 0.001. HR: hazard ratio

    Table 7

    Impact of drug history on disease-free survival in primary breast cancer according to CTC subgroups

    Sample size 0/1HR*95% Low**95% High**P-value***
    No NSAID vs. NSAID
      CTC negative293/80.00  0.00  0.00    0.253
      CTC_EP positive47/10.00  0.00  0.00    0.739
      CTC_EMT positiveNANA  NA  NA    NA
      CTC_any112/10.00  0.00  0.00    0.673
    No L-thyroxin vs. t-thyroxin
      CTC negative263/380.89  0.36  2.18    0.782
      CTC_EP positive44/40.30  0.01  9.38    0.251
      CTC_EMT positive62/111.18  0.37  3.77    0.785
      CTC_any99/141.10  0.34  3.54    0.874
    No ACEi vs. ACEi
      CTC negative246/550.58  0.27  1.26    0.104
      CTC_EP positive44/40.36  0.01  8.7    0.339
      CTC_EMT positive60/130.36  0.1  1.31    0.029
      CTC_any97/160.27  0.07  1.03    0.002
    No sartan vs. sartan
      CTC negative261/400.67  0.28  1.62    0.308
      CTC_EP positive44/40.00  0.00  0.00    0.490
      CTC_EMT positive58/150.78  0.27  2.31    0.636
      CTC_any94/190.79  0.27  2.28    0.642
    No ACEi/sartan vs. ACEi/sartan
      CTC negative208/930.57  0.3  1.09    0.063
      CTC_EP positive40/80.82  0.08  8.44    0.856
      CTC_EMT positive46/270.50  0.20  1.27    0.114
      CTC_any79/340.43  0.18  1.07    0.036
    No blockers vs. blockers
      CTC negative218/830.93  0.48  1.79    0.817
      CTC_EP positive32/161.98  0.31  12.73    0.535
      CTC_EMT positive49/240.51  0.2  1.34    0.131
      CTC_any78/350.72  0.3  1.72    0.431
    No statin vs. statin
      CTC negative251/500.53  0.24  1.17    0.054
      CTC_EP positive44/40.00  0.00  0.00    0.490
      CTC_EMT positive61/120.92  0.26  3.26    0.892
      CTC_any99/140.85  0.23  3.07    0.787
    No metformin vs. metformin
      CTC negative285/160.37  0.09  1.53    0.027
      CTC_EP positive47/10.00  0.00  0.00    0.739
      CTC_EMT positive67/60.13  0.01  1.6    < 0.001
      CTC_any107/60.10  0.01  1.78    < 0.001
    No Insulin vs. Insulin
      CTC negative297/40.11  0.00  3.00    < 0.001
      CTC_EP positiveNANA  NA  NA    NA
      CTC_EMT positiveNANA  NA  NA    NA
      CTC_anyNANA  NA  NA    NA
    No Insulin/metformin vs. Insulin/metformin
      CTC negative282/190.29  0.08  1.1    0.002
      CTC_EP positive47/10.00  0.00  0.00    0.739
      CTC_EMT positive67/60.13  0.01  1.6    < 0.001
      CTC_any107/60.1  0.01  1.78    < 0.001
    No LMWH vs. LMWH
      CTC negative288/130.96  0.23  4.08    0.960
      CTC_EP positive47/10.00  0.00  0.00    0.739
      CTC_EMT positive70/30.00  0.00  0.00    0.312
      CTC_any109/40.00  0.00  0.00    0.348

    Figure 4. Kaplan-Meier estimates of probabilities of disease-free survival according to CTC status A, CTC negative B, CTC_EP positive C, CTC_EMT positive D, CTC_any. CTC: circulating tumor cell; CTC_EP: circulating tumor cell with epithelial phenotype; CTC_EMT: circulating tumor cell with epithelial-mesenchymal transition phenotype

    In multivariate analysis, presence of CTC_EMT, axillary nodal involvement, hormone receptor status, Ki67 status, and chronic prescription of insulin/metformin were independently associated with DFS [Table 8].

    Table 8

    Multivariate analysis of factors associated with disease-free survival

    VariableHR95% Low95% HighP-value
    N stage N+ vs. N02.43  1.50   3.940.0003
    ER/PR status positive for either vs. negative for both0.51  0.28   0.940.0315
    Ki 67 > 20% vs. < 20%2.33  1.36   4.000.0021
    ACEi yes vs. no1.71  0.94   3.080.0766
    Insulin/metformin yes vs. no3.97  1.95   8.080.0001
    CTC_EMT present vs. absent2.44  1.43   4.150.0010

    Discussion

    In this translational study, we observed no association between CTCs, including their different subpopulations and chronic medication, except the trend for association between CTC_EP and ACEi/sartans, where patients on ACEi/sartans had lower prevalence of CTC_EP compared to patients without ACEi/sartans. We did not noticed association between BMI status and CTCs as well. These data suggest that chronic medication for general co-morbid conditions have only a slight impact on metastatic cascade. Moreover, we observed that chronic administration of ACEi, metformin, and/or insulin was associated with inferior DFS, while, in multivariate analysis, only insulin/metformin remained independently associated with clinical prognosis. CTC status had no effect on patient’s outcome according to chronic medication; however, due to small size of several subgroups, e.g., NSAID, low molecular weight heparin, and others, the statistical power for analysis is limited.

    Approximately 16% of breast cancer patients have diabetes[21]. Diabetes mellitus not only increases the risk of breast cancer, but might also worsen breast cancer prognosis[21]. Insulin resistance and hyperinsulinemia state may be a potential mediator of this effect[22]. In our study, chronic use of metformin and insulin was associated with inferior outcome. Previously, it was shown that continual administration of insulin over ≥ 3 years was associated with an increased risk of mortality in breast cancer[23]. Moreover, fasting hyperinsulinemia was reported to be an independent predictor for higher risk of breast cancer distant recurrence and death in women without known diabetes[24]. Insulin is known as an enhancer of cancer cell proliferation, inhibiting apoptosis by its receptor and insulin-like growth factor through the PI3K/Akt and MAPK pathways[22]. The other reason contributing to progression of breast cancer is decrease of plasma levels of sex hormone binding globulin related to insulin, which results in increase of endogenous estrogen and androgen levels[22]. Contrary to our study, metformin, front-line therapy for the treatment of type 2 diabetes, especially in overweight and obese patients, may reduce breast cancer incidence and improve prognosis by several potential mechanisms according to some preclinical data[24,25]. We suppose that, in our trial, worse prognosis associated to metformin and/or insulin administration could be related to diabetes as comorbid condition. Data related to glycemic control and insulinemia were not available. Therefore, we cannot exclude that poor glycemic control and/or hyperinsulinemia might influence this observation. The most negative impact on DFS was observed in CTC_EMT positive subtype. However, our data suggest that worse prognosis related to these drugs might not be related to more efficient metastatic cascade, as there were no differences between CTC and this antidiabetic medication. Due to limited sample size, however, we cannot exclude limited statistical power to definitively answer this question, while the multiple testing approach could affect study results as well. Therefore, our results are only hypothesis generating and validation studies are needed.

    Certain classes of antihypertensive drugs are associated with shorter survival in several types of cancers. However, the connection between antihypertensive agents and cancer patient survival remains unclear[26]. ACEis/angiontensin receptor antagonists are the most active drugs approved for treatment of hypertension, heart failure, and diabetic nephropathy. Several epidemiological studies have investigated relationship between ACEi use and cancer-specific mortality in patients with breast cancer[26-30]. In some of them, there was a small increase in cancer recurrence with ACEi use, while others suggest that this drug could be safely administered to breast cancer patients, without affecting breast cancer outcome. In our study, chronic administration of ACEi was associated with inferior DFS, and this was most pronounced in patients with CTC_EMT phenotype. Contrary to this observation, there was no association between ACEi and CTC count.

    In conclusion, our findings show that there was no association between chronically used medication and/or CTCs in patients with primary breast cancer, while chronic administration of ACEi, metformin, and insulin could negatively affect prognosis. These data suggest that evaluated chronic medications are not able to favorably affect biology of primary breast cancer.

    Declarations

    Authors’ contributions

    Conception and design of this study: Mardiak J, Mego M

    Participated in selection of patients, collection of samples, informed consent obtaining, clinico-pathological data collection: Jurisova S, Karaba M, Benca J, Pindak D

    CTCs detection and analysis: Minarik G, Manasova D, Kalavska K, Sedlackova T

    Performed statistical analysis: Mego M

    Participatedin manuscript preparation: Jurisova S, Mego M

    All authors read and approved the final manuscript.

    Availability of data and materials

    All data source could be available to readers on request.

    Financial support and sponsorship

    This publication is the result of the implementation of projects no. APVV-16-0010 and APVV-14-0327 funded by the Slovak Research and Development Agency.

    Conflicts of interest

    All authors declared that there are no conflicts of interest.

    Ethical approval and consent to participate

    The study was approved by the Institutional Review Board (IRB) of the National Cancer Institute of Slovakia and all patients signed informed consent.

    Consent for publication

    Not applicable.

    Copyright

    © The Author(s) 2020.

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