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Figure 1.  Survival of Study Patients With vs Without Circulating Tumor DNA (ctDNA)
Survival of Study Patients With vs Without Circulating Tumor DNA (ctDNA)

A, Distant disease–free survival (DDFS) (median, 32.5 months vs not reached; hazard ratio [HR], 2.99; 95% CI, 1.38-6.48; P = .006). B, Disease-free survival (DFS) (median, 22.8 months vs not reached; HR, 2.67; 95% CI, 1.28-5.57; P = .009). C, Overall survival (OS) (median, not reached vs not reached; HR, 4.16; 95% CI, 1.66-10.42; P = .002).

Figure 2.  Survival of Study Patients With vs Without Circulating Tumor DNA (ctDNA) and Circulating Tumor Cells (CTCs)
Survival of Study Patients With vs Without Circulating Tumor DNA (ctDNA) and Circulating Tumor Cells (CTCs)

A, Distant disease–free survival (DDFS) (median, 32.5 months vs not reached; hazard ratio [HR], 5.29; 95% CI, 1.50-18.62; P = .009). B, Disease-free survival (DFS) (median, 20.8 months vs not reached; HR, 3.15; 95% CI, 1.07-9.27; P = .04). C, Overall survival (OS) (median, not reached vs not reached; HR, 8.60; 95% CI, 1.78-41.47; P = .007).

1.
Liedtke  C, Mazouni  C, Hess  KR,  et al.  Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer.   J Clin Oncol. 2008;26(8):1275-1281. doi:10.1200/JCO.2007.14.4147 PubMedGoogle ScholarCrossref
2.
Chen  YH, Hancock  BA, Solzak  JP,  et al.  Next-generation sequencing of circulating tumor DNA to predict recurrence in triple-negative breast cancer patients with residual disease after neoadjuvant chemotherapy.   NPJ Breast Cancer. 2017;3:24. doi:10.1038/s41523-017-0028-4 PubMedGoogle ScholarCrossref
3.
Garcia-Murillas  I, Chopra  N, Comino-Méndez  I,  et al.  Assessment of molecular relapse detection in early-stage breast cancer.   JAMA Oncol. 2019. doi:10.1001/jamaoncol.2019.1838 PubMedGoogle Scholar
4.
Racila  E, Euhus  D, Weiss  AJ,  et al.  Detection and characterization of carcinoma cells in the blood.   Proc Natl Acad Sci U S A. 1998;95(8):4589-4594. doi:10.1073/pnas.95.8.4589 PubMedGoogle ScholarCrossref
5.
Cristofanilli  M.  Circulating tumor cells, disease progression, and survival in metastatic breast cancer.   Semin Oncol. 2006;33(3)(suppl 9):S9-S14. doi:10.1053/j.seminoncol.2006.03.016 PubMedGoogle ScholarCrossref
6.
Bidard  FC, Peeters  DJ, Fehm  T,  et al.  Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data.   Lancet Oncol. 2014;15(4):406-414. doi:10.1016/S1470-2045(14)70069-5 PubMedGoogle ScholarCrossref
7.
Smerage  JB, Barlow  WE, Hortobagyi  GN,  et al.  Circulating tumor cells and response to chemotherapy in metastatic breast cancer: SWOG S0500.   J Clin Oncol. 2014;32(31):3483-3489. doi:10.1200/JCO.2014.56.2561 PubMedGoogle ScholarCrossref
8.
Bidard  FC, Michiels  S, Riethdorf  S,  et al.  Circulating tumor cells in breast cancer patients treated by neoadjuvant chemotherapy: a meta-analysis.   J Natl Cancer Inst. 2018;110(6):560-567. doi:10.1093/jnci/djy018 PubMedGoogle ScholarCrossref
9.
Madic  J, Kiialainen  A, Bidard  FC,  et al.  Circulating tumor DNA and circulating tumor cells in metastatic triple negative breast cancer patients.   Int J Cancer. 2015;136(9):2158-2165. doi:10.1002/ijc.29265 PubMedGoogle ScholarCrossref
10.
Clark  TA, Chung  JH, Kennedy  M,  et al.  Analytical validation of a hybrid capture–based next-generation sequencing clinical assay for genomic profiling of cell-free circulating tumor DNA.   J Mol Diagn. 2018;20(5):686-702. doi:10.1016/j.jmoldx.2018.05.004 PubMedGoogle ScholarCrossref
11.
Chang  CL, Huang  W, Jalal  SI,  et al.  Circulating tumor cell detection using a parallel flow micro-aperture chip system.   Lab Chip. 2015;15(7):1677-1688. doi:10.1039/C5LC00100E PubMedGoogle ScholarCrossref
12.
Chang  CL, Jalal  SI, Huang  WF, Mahmood  A, Matei  DE, Savran  CA.  High-throughput immunomagnetic cell detection using a microaperture chip system.   IEEE Sens J. 2014;14(9):3008-3013. doi:10.1109/JSEN.2014.2321167 Google ScholarCrossref
13.
Huang  W, Chang  CL, Brault  ND,  et al.  Separation and dual detection of prostate cancer cells and protein biomarkers using a microchip device.   Lab Chip. 2017;17(3):415-428. doi:10.1039/C6LC01279E PubMedGoogle ScholarCrossref
14.
Huang  W, Chang  CL, Chan  BD,  et al.  Concurrent detection of cellular and molecular cancer markers using an immunomagnetic flow system.   Anal Chem. 2015;87(20):10205-10212. doi:10.1021/acs.analchem.5b02215 PubMedGoogle ScholarCrossref
15.
Garcia-Murillas  I, Schiavon  G, Weigelt  B,  et al.  Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer.   Sci Transl Med. 2015;7(302):302ra133. doi:10.1126/scitranslmed.aab0021 PubMedGoogle Scholar
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    Brief Report
    July 9, 2020

    Association of Circulating Tumor DNA and Circulating Tumor Cells After Neoadjuvant Chemotherapy With Disease Recurrence in Patients With Triple-Negative Breast Cancer: Preplanned Secondary Analysis of the BRE12-158 Randomized Clinical Trial

    Author Affiliations
    • 1Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis
    • 2Medical College of Wisconsin, Milwaukee
    • 3University of Chicago, Chicago, Illinois
    • 4University of Alabama at Birmingham, Birmingham
    • 5Georgetown University, Washington, DC
    • 6Memorial Healthcare System, Hollywood, Florida
    • 7Winship Cancer Institute of Emory University, Atlanta, Georgia
    • 8Community Regional Cancer Care, Indianapolis, Indiana
    • 9University of Florida, Gainesville
    • 10Sylvester Comprehensive Cancer Center, Deerfield Beach, Florida
    • 11Advocate Aurora Health Care, Milwaukee, Wisconsin
    • 12Erlanger Health System, Chattanooga, Tennessee
    • 13Foundation Medicine Inc, Cambridge, Massachusetts
    • 14University of California at Davis, Davis
    • 15Purdue University School of Mechanical Engineering, West Lafayette, Indiana
    JAMA Oncol. 2020;6(9):1410-1415. doi:10.1001/jamaoncol.2020.2295
    Key Points

    Question  Is the presence of circulating tumor DNA and circulating tumor cells after surgery associated with inferior outcomes for patients with early-stage triple-negative breast cancer?

    Findings  This large preplanned secondary analysis of 196 female patients from a recently completed randomized clinical trial found that the presence of circulating tumor DNA and circulating tumor cells after neoadjuvant chemotherapy in patients with early-stage triple-negative breast cancer was associated with significantly inferior distant disease–free survival, disease-free survival, and overall survival.

    Meaning  Detection of circulating tumor DNA and circulating tumor cells after neoadjuvant chemotherapy in patients with early-stage triple-negative breast cancer is independently associated with disease recurrence, above and beyond standard clinical parameters, and represents an important novel stratification factor for future postneoadjuvant trials.

    Abstract

    Importance  A significant proportion of patients with early-stage triple-negative breast cancer (TNBC) are treated with neoadjuvant chemotherapy. Sequencing of circulating tumor DNA (ctDNA) after surgery, along with enumeration of circulating tumor cells (CTCs), may be used to detect minimal residual disease and assess which patients may experience disease recurrence.

    Objective  To determine whether the presence of ctDNA and CTCs after neoadjuvant chemotherapy in patients with early-stage TNBC is independently associated with recurrence and clinical outcomes.

    Design, Setting, and Participants  A preplanned secondary analysis was conducted from March 26, 2014, to December 18, 2018, using data from 196 female patients in BRE12-158, a phase 2 multicenter randomized clinical trial that randomized patients with early-stage TNBC who had residual disease after neoadjuvant chemotherapy to receive postneoadjuvant genomically directed therapy vs treatment of physician choice. Patients had blood samples collected for ctDNA and CTCs at time of treatment assignment; ctDNA analysis with survival was performed for 142 patients, and CTC analysis with survival was performed for 123 patients. Median clinical follow-up was 17.2 months (range, 0.3-58.3 months).

    Interventions  Circulating tumor DNA was sequenced using the FoundationACT or FoundationOneLiquid Assay, and CTCs were enumerated using an epithelial cell adhesion molecule–based, positive-selection microfluidic device.

    Main Outcomes and Measures  Primary outcomes were distant disease–free survival (DDFS), disease-free survival (DFS), and overall survival (OS).

    Results  Among 196 female patients (mean [SD] age, 49.6 [11.1] years), detection of ctDNA was significantly associated with inferior DDFS (median DDFS, 32.5 months vs not reached; hazard ratio [HR], 2.99; 95% CI, 1.38-6.48; P = .006). At 24 months, DDFS probability was 56% for ctDNA-positive patients compared with 81% for ctDNA-negative patients. Detection of ctDNA was similarly associated with inferior DFS (HR, 2.67; 95% CI, 1.28-5.57; P = .009) and inferior OS (HR, 4.16; 95% CI,1.66-10.42; P = .002). The combination of ctDNA and CTCs provided additional information for increased sensitivity and discriminatory capacity. Patients who were ctDNA positive and CTC positive had significantly inferior DDFS compared with those who were ctDNA negative and CTC negative (median DDFS, 32.5 months vs not reached; HR, 5.29; 95% CI, 1.50-18.62; P = .009). At 24 months, DDFS probability was 52% for patients who were ctDNA positive and CTC positive compared with 89% for those who were ctDNA negative and CTC negative. Similar trends were observed for DFS (HR, 3.15; 95% CI, 1.07-9.27; P = .04) and OS (HR, 8.60; 95% CI, 1.78-41.47; P = .007).

    Conclusions and Relevance  In this preplanned secondary analysis of a randomized clinical trial, detection of ctDNA and CTCs in patients with early-stage TNBC after neoadjuvant chemotherapy was independently associated with disease recurrence, which represents an important stratification factor for future postneoadjuvant trials.

    Trial Registration  ClinicalTrials.gov Identifier: NCT02101385

    Introduction

    A large proportion of patients with triple-negative breast cancer (TNBC) are treated with neoadjuvant chemotherapy. Approximately one-third of patients will achieve a pathologic complete response with neoadjuvant chemotherapy and have favorable outcomes. In contrast, two-thirds of patients will have residual disease and are at high risk of relapse.1 Methods that can detect the presence of minimal residual disease (MRD) in the circulation after surgery may be used to determine in which patients disease will recur.

    An established method for detection of MRD is the analysis of circulating tumor DNA (ctDNA). Because somatic mutations provide intrinsic specificity for nucleic acid material derived from tumor tissue, the presence of ctDNA implies the presence of disease. Our group and others have demonstrated that ctDNA detected after neoadjuvant chemotherapy and surgery in the plasma of patients with TNBC is associated with rapid relapse.2,3 Another commonly used analyte from liquid biopsies are circulating tumor cells (CTCs).4 These cells are frequently detected in both early-stage and late-stage breast cancers; enumeration of these cells is associated with prognosis in breast cancer.5-8 Under certain circumstances, CTCs can be isolated from the circulation in the absence of detectable ctDNA. This occurs primarily when the index mutations are not covered by the ctDNA assay or owing to very low concentrations or shedding of ctDNA.9 Herein, using ctDNA and CTCs prospectively collected after neoadjuvant chemotherapy and surgery from patients with TNBC, we analyzed the association of liquid biopsy–based MRD with clinical outcomes.

    Methods

    The BRE12-158 study was a phase 2 randomized clinical trial of genomically directed therapy after preoperative chemotherapy for patients with TNBC (NCT02101385) (trial protocol in Supplement 1; eFigure 1A in Supplement 2). This multicenter trial enrolled patients with TNBC treated with neoadjuvant chemotherapy who had residual disease at the time of surgery. Blood samples for the possible detection of ctDNA and CTCs were obtained either prior to treatment on day 1 of chemotherapy treatment cycle 1 for arm A or at the first routine visit for arm B. A CONSORT diagram of patient selection is outlined in eFigure 1B in Supplement 2. Patient characteristics are detailed in eTable 1 in Supplement 2. All patients provided written informed consent, and the study was approved by the institutional review boards of Indiana University Melvin and Bren Simon Cancer Center, Froedtert & The Medical College of Wisconsin, Georgetown University, University of Chicago, University of Alabama at Birmingham, University of Florida, Virginia Oncology Associates, Meritus Center for Clinical Research, Community Regional Cancer Care, Memorial Cancer Institute, Erlanger Health System, University of Miami, University of Cincinnati Cancer Institute, Washington University School of Medicine, IU Health Goshen Center for Cancer Care, Nebraska Methodist Hospital, Winship Cancer Institute of Emory University, Joe Arrington Cancer Research and Treatment Center, Aurora Health Care, PinnacleHealth Cancer Center, Fort Wayne Medical Oncology and Hematology, IU Health Arnett, Mercy Clinic Oklahoma Communities, Tufts Medical Center, and Community Hospital of Anderson and Madison County Inc. Sequencing of ctDNA was performed using the FoundationACT or FoundationOne Liquid assays (Foundation Medicine Inc), as previously described.10 Circulating tumor cells were detected using an epithelial cell adhesion molecule–based positive-selection microfluidic device.11-14 All survival analyses are the product of multivariate analyses. Detailed methods are provided in the eMethods in Supplement 2.

    Results
    Association of ctDNA With Clinical Outcomes

    Circulating tumor DNA samples were sequenced, and mutations were filtered to identify those that had the highest likelihood to be somatic (eFigure 2 in Supplement 2). Circulating tumor DNA positivity was consistent across both groups, with 65% of patients (37 of 57) positive for ctDNA in arm A and 62% of patients (53 of 85) positive for ctDNA in arm B. Median clinical follow-up was 17.2 months (range, 0.1-58.3 months). Detection of ctDNA was significantly associated with an inferior DDFS (median DDFS, 32.5 months vs not reached; hazard ratio [HR], 2.99; 95% CI, 1.38-6.48; P = .006) (Figure 1A). At 24 months, the DDFS probability was 56% for ctDNA-positive patients compared with 81% for ctDNA-negative patients. Similarly, detection of ctDNA was significantly associated with an inferior DFS (median DFS, 22.8 months vs not reached; HR, 2.67; 95% CI, 1.28-5.57; P = .009) (Figure 1B). At 24 months, the DFS probability was 50% for ctDNA-positive patients compared with 76% for ctDNA-negative patients. Last, detection of ctDNA was significantly associated with an inferior OS (median OS, not reached vs not reached; HR, 4.16; 95% CI, 1.66-10.42; P = .002) (Figure 1C). At 24 months, the OS probability was 57% for ctDNA-positive patients compared with 80% for ctDNA-negative patients.

    Association of the Combination of CTCs and ctDNA With Clinical Outcomes

    Circulating tumor cell positivity was detected in 43% of patients (21 of 49) in group A and 39% of patients (29 of 74) in group B. Although patients who were CTC positive had inferior outcomes, results did not reach statistical significance (eFigure 3 in Supplement 2). Increasing CTC count, however, was significantly associated with inferior DDFS (HR, 1.07; 95% CI, 1.01-1.13; P = .02), DFS (HR, 1.11; 95% CI, 1.03-1.19; P = .004), and OS (HR, 1.09; 95% CI, 1.02-1.17; P = .01), suggesting that the quantitative burden of CTCs is associated with outcomes. Circulating tumor cells may provide additional information about the presence of MRD. Specifically, for the 112 patients for whom both ctDNA and CTC results were available, we did not find a significant association between CTC positivity (defined as ≥1 CTC detected) and ctDNA positivity (P = .19). A proportion of patients were positive for 1 marker and not the other, such that the sensitivity to detect recurrences went from 79% (23 of 29) with ctDNA alone and 62% (18 of 29) with CTC alone to 90% (26 of 29) when combined (eFigure 4 in Supplement 2).

    We combined the data on ctDNA and CTCs to compare the DDFS curves for the following 4 groups of patients (1) ctDNA positive and CTC positive, (2) ctDNA positive and CTC negative, (3) ctDNA negative and CTC positive, and (4) ctDNA negative and CTC negative. The DDFS curves demonstrated a stepwise gradation in which patients who were positive for both ctDNA and CTCs had inferior DDFS compared with those who were positive for ctDNA alone or CTC alone, and patients who were negative for both ctDNA and CTCs had the best outcomes (Figure 2A). Patients who were ctDNA positive and CTC positive had a significantly inferior DDFS compared with those who were ctDNA negative and CTC negative (median DDFS, 32.5 months vs not reached; HR, 5.29; 95% CI, 1.50-18.62; P = .009) (Figure 2A). At 24 months, the DDFS probability was 52% for patients who were ctDNA positive and CTC positive compared with 89% for those who were ctDNA negative and CTC negative. We observed similar trends when analyzing DFS (median DFS, 20.8 months vs not reached; HR, 3.15; 95% CI, 1.07-9.27; P = .04) (Figure 2B) and OS (median OS, not reached vs not reached; HR, 8.60; 95% CI, 1.78-41.47; P = .007) (Figure 2C) among patients who were ctDNA positive and CTC positive compared with those who were ctDNA negative and CTC negative. Risk of recurrence was similar for patients who were ctDNA positive and CTC negative vs those who were ctDNA negative and CTC positive.

    Taken together, the combination of ctDNA and CTCs was associated with increased sensitivity and discriminatory capacity; however, statistically adding CTCs into multivariate models of ctDNA was not associated with improved goodness of fit. This outcome possibly comes from the limited sample size and short duration of follow-up.

    Discussion

    These results demonstrate that patients with TNBC at high risk of relapse due to an incomplete pathologic response after neoadjuvant chemotherapy can be risk stratified with MRD. The results here are significant from an effect-size standpoint and remain highly significant after consideration of multiple clinical variables. These results add substantially to the prior body of literature because this study provides one of the largest data sets to date, to our knowledge, and is a preplanned secondary analysis of a prospective randomized clinical trial.

    The postneoadjuvant setting is one that is in need of marked improvements, especially for the subgroup of patients with residual disease. Our findings now support using MRD as a major stratification variable in all clinical trials to be conducted in this setting. In addition, the ability to sequence ctDNA broadly for important gene variations affords the possibility of not only uncovering an ultra–high-risk population for relapse but also revealing drug targets. Perhaps equally important, if the results from the group of patients who are ctDNA negative and CTC negative hold, this may be a subgroup in which the patients do not benefit from additional therapy, and this may be an ideal place to study novel de-escalation strategies. At the present time, we would discourage the use of MRD as a marker for relapse or to guide therapy in routine clinical practice because there is no evidence that early detection improves outcomes.

    Limitations

    This study has some limitations, including the potential interaction with the type of therapy delivered in the postneoadjuvant setting. Although not controlled for in this analysis, there was an equal distribution of testing across both groups and an equal distribution of ctDNA and CTC positivity and negativity across both groups. Another limitation is the relatively short duration of follow-up. This concern is minimized by the well-established early relapse and the infrequent late relapse seen in this population.

    Conclusions

    The strength of these findings, along with the prior body of literature,2,3,15 now supports the routine use of this technology for proper risk stratification across clinical trials in the curative setting. Future trials will determine if genomically guided therapeutic interventions in patients who have molecular MRD can improve outcomes. This concept will be the centerpiece of our planned successor trial to BRE12-158: the PERSEVERE trial, whereby patients with TNBC with ctDNA positivity after surgery will be assigned to receive a targeted agent matched to the patients’ plasma sequencing results.

    Back to top
    Article Information

    Accepted for Publication: April 21, 2020.

    Corresponding Author: Bryan Schneider, MD, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, 1030 W Michigan St, Ste 3307, Indianapolis, IN 46202 (bpschnei@iu.edu).

    Published Online: July 9, 2020. doi:10.1001/jamaoncol.2020.2295

    Author Contributions: Drs Radovich and Schneider had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Radovich and Mr Jiang contributed equally to this work.

    Concept and design: Radovich, Cantor, Badve, Schneider.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Radovich, Thompson, Albacker, Solzak, Bales, Shen, Chang, Zhong, Savran, Schneider.

    Critical revision of the manuscript for important intellectual content: Radovich, Jiang, Hancock, Chitambar, Nanda, Falkson, Lynce, Gallagher, Isaacs, Blaya, Paplomata, Walling, Daily, Mahtani, Thompson, Graham, Cooper, Pavlick, Albacker, Gregg, Solzak, Chen, Cantor, Shen, Storniolo, Badve, Ballinger, Chang, Zhong, Savran, Miller, Schneider.

    Statistical analysis: Radovich, Jiang, Hancock, Gallagher, Pavlick, Solzak, Shen, Badve.

    Obtained funding: Radovich, Savran, Schneider.

    Administrative, technical, or material support: Radovich, Hancock, Nanda, Falkson, Paplomata, Walling, Thompson, Pavlick, Gregg, Solzak, Chen, Bales, Cantor, Badve, Chang, Zhong, Savran, Miller.

    Supervision: Radovich, Nanda, Lynce, Mahtani, Albacker, Gregg, Badve, Savran, Schneider.

    Conflict of Interest Disclosures: Dr Radovich reported serving as an advisor for LifeOmic; and having stock ownership in LifeOmic, Macrogenics, Immunomedics, Arqule, and Tyme Technologies outside the submitted work. Dr Falkson reported receiving grants from TBCRC during the conduct of the study. Dr Lynce reported receiving grants from Indiana University during the conduct of the study; and grants from Inivata, Tesaro, Regeneron, Immunomedics, Calithera, and Chugai; grants and personal fees from Pfizer; receiving grants from and serving as a consultant/advisor for BMS; serving as a consultant/advisor for AstraZeneca; and receiving personal fees from ASCO, outside the submitted work. Dr Isaacs reported receiving grants from the National Cancer Institute during the conduct of the study; and personal fees from Genentech, Pfizer, Novartis, AstraZeneca, PUMA, and Seattle Genetics; and grants from Tesaro outside the submitted work. Dr Paplomata reported receiving grants from Hoosier Cancer Research Network during the conduct of the study; and grants and personal fees from Novartis; grants and meals from Genentech and Merck; personal fees from Pfizer, R-Pharm, and Mylan; meals from Tesaro and Amgen; grants from Corcept, Seattle Genetics, and Cascadian outside the submitted work. Dr Mahtani reported receiving personal fees for serving as a consultant/advisor for Agendia, Biotheranostics, Daiichi, Eisai, Genentech, Pfizer, Lilly, Novartis, Seattle Genetics, and Puma outside the submitted work; and reported research support from Genentech to her institution. Dr Thompson reported serving as a consultant/advisor for Syapse, UpToDate, and Doximity; and personal fees from VIA Oncology, Adaptive, GSK, Takeda, and Celgene outside the submitted work. Mr Pavlick reported receiving personal fees from Foundation Medicine and F. Hoffmann-La Roche AG outside the submitted work. Dr Albacker reported receiving personal fees from Foundation Medicine Inc and Roche Holding AG outside the submitted work. Dr Gregg reported receiving personal fees from Foundation Medicine, AstraZeneca, BMS, Novartis, and Roche; and grants from AstraZeneca outside the submitted work. Dr Chen reported being currently employed at Foundation Medicine Inc. Dr Ballinger reported receiving personal fees from Novartis and Medscape outside the submitted work. Dr Chang reported receiving grants from Tom Hurvisand the McKinley Educational Foundation; and personal fees from Savran Technologies Inc during the conduct of the study; stock ownership from Savran Technologies Inc outside the submitted work; and having a patent to 10,335,790 issued, a patent to 10,207,267 issued, a patent to EP2694965B1 issued, a patent to 9,494,557 issued, and a patent to 9,500,625 issued. Mr Zhong reported receiving personal fees from Savran Technologies Inc during the conduct of the study. Dr Savran reported receiving grants from McKinley Educational Foundation and Tom Hurvis during the conduct of the study; stock ownership from Savran Technologies Inc outside the submitted work; and having a patent to 10,335,790 issued, a patent to 10,207,267 issued, a patent to EP2694965B1 issued, a patent to 9,494,557 issued, and a patent to 9,500,625 issued. No other disclosures were reported.

    Funding/Support: Financial support for this study was provided by the Vera Bradley Foundation for Breast Cancer Research, the Walther Cancer Foundation, the Indiana University Grand Challenge Precision Health Initiative, and the Thomas Hurvis and McKinley Educational Foundation.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Meeting Presentation: This article was presented at the San Antonio Breast Cancer Symposium; December 13, 2019; San Antonio, Texas.

    Additional Contributions: We would like to thank the patients and their families for participating in the BRE12-158 clinical trial.

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    Liedtke  C, Mazouni  C, Hess  KR,  et al.  Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer.   J Clin Oncol. 2008;26(8):1275-1281. doi:10.1200/JCO.2007.14.4147 PubMedGoogle ScholarCrossref
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    Chen  YH, Hancock  BA, Solzak  JP,  et al.  Next-generation sequencing of circulating tumor DNA to predict recurrence in triple-negative breast cancer patients with residual disease after neoadjuvant chemotherapy.   NPJ Breast Cancer. 2017;3:24. doi:10.1038/s41523-017-0028-4 PubMedGoogle ScholarCrossref
    3.
    Garcia-Murillas  I, Chopra  N, Comino-Méndez  I,  et al.  Assessment of molecular relapse detection in early-stage breast cancer.   JAMA Oncol. 2019. doi:10.1001/jamaoncol.2019.1838 PubMedGoogle Scholar
    4.
    Racila  E, Euhus  D, Weiss  AJ,  et al.  Detection and characterization of carcinoma cells in the blood.   Proc Natl Acad Sci U S A. 1998;95(8):4589-4594. doi:10.1073/pnas.95.8.4589 PubMedGoogle ScholarCrossref
    5.
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    6.
    Bidard  FC, Peeters  DJ, Fehm  T,  et al.  Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data.   Lancet Oncol. 2014;15(4):406-414. doi:10.1016/S1470-2045(14)70069-5 PubMedGoogle ScholarCrossref
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    Bidard  FC, Michiels  S, Riethdorf  S,  et al.  Circulating tumor cells in breast cancer patients treated by neoadjuvant chemotherapy: a meta-analysis.   J Natl Cancer Inst. 2018;110(6):560-567. doi:10.1093/jnci/djy018 PubMedGoogle ScholarCrossref
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    Madic  J, Kiialainen  A, Bidard  FC,  et al.  Circulating tumor DNA and circulating tumor cells in metastatic triple negative breast cancer patients.   Int J Cancer. 2015;136(9):2158-2165. doi:10.1002/ijc.29265 PubMedGoogle ScholarCrossref
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    12.
    Chang  CL, Jalal  SI, Huang  WF, Mahmood  A, Matei  DE, Savran  CA.  High-throughput immunomagnetic cell detection using a microaperture chip system.   IEEE Sens J. 2014;14(9):3008-3013. doi:10.1109/JSEN.2014.2321167 Google ScholarCrossref
    13.
    Huang  W, Chang  CL, Brault  ND,  et al.  Separation and dual detection of prostate cancer cells and protein biomarkers using a microchip device.   Lab Chip. 2017;17(3):415-428. doi:10.1039/C6LC01279E PubMedGoogle ScholarCrossref
    14.
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